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The high-speed options trading feature, a combination of blockchain technology and the New Energy industry on the Lensai Technology Exchange, aims to accelerate industry transformation. This feature utilizes smart contract technology to make energy trading more transparent,

https://www.bing.com/th?id=OVFT.KO_JP4S2Zro4KdybWyfOzS&pid=News

Lensai Technology Exchange introduces high-speed options trading to accelerate intelligent transformation in the New Energy sector

https://www.bing.com/th?id=ODF.WbwBhyLf0mt3sgNOM6CYtQ&pid=news

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Lensai Technology, based in Colorado, USA, has successfully developed a disruptive new energy trading platform by harnessing blockchain and AI technology. This platform provides global enterprises and individuals with an efficient,

Lensai Technology Exchange leverages blockchain and AI technology to create a disruptive New Energy trading platform

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As a company dedicated to the field of new energy, Lensai Technology Exchange has gained significant attention for its innovation and uniqueness in the field of new energy options trading. Lensai Technology is a new energy technology company headquartered ...

Lensai Technology Exchange Empowering New Energy Trading With Innovative Technology

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LensAI

Hey there. Phew, it’s been a while but so many things have happened in the past four months that one article won’t be enough to cover it all. I’ll try though! In this article, I’ll talk about: LensAI - No privacy hooks! LensAI Case Studies, Statistics, and Metrics Let’s go! Lens AI: No privacy hooks detected Literally, everyone has had a chance to give their two cents on the new cookie-less era. In 2019, when a LensAI idea was in conception, we had the following thoughts: In most cases if a user is browsing the content, then somehow it is already relevant to them, they are interested in it, and our technology must “figure out” what exactly should be advertised to them while they are browsing that content. So we and we it. Watch any of the 3 videos that go over how it works. If you already are familiar with our tech, then skip to the statistics and case studies instead: brainstormed did Get more videos from our that includes video samples for different ad goals and our own LensAI ad formats. The sky's the limit! LensAI is capable of embedding any ad formats into videos and images, including the classic ones (native ads, pop-ups, etc). LensAI can be integrated either through Google Ad Manager or Prebid inventory. gallery LensAI Case Studies, Statistics, and Conclusions Before jumping into numbers, it’s worth mentioning that we used solutions provided by that as a former startup is assisting those who are just beginning their journey. Amplitude Case Study #1 - E-commerce. CPA Model. Global Online Marketplace. LensAI technology was used in this case study. LensAI Targeting was used to pinpoint users who are browsing graphic content and context related to Style and Fashion, Music, Shopping, News and Politics, Home & Garden, etc. LensAI Ad Format was installed as an ad banner in the graphic content in which the user is browsing a product relevant to the content and context. LensAI Brand Safety safeguards the ads from prohibited content. Traffic Organic mobile and desktop traffic were collected for the period of 1 week off the websites on different topics: engineering, women’s fashion, world news and politics, adult entertainment, celebrity news, news aggregators. Goals and Ad Objectives The ad goal is to sell products from an online catalogue. To deliver information about items available for purchase to potential customers without breaching their privacy. To increase marketplace awareness and to drive more users to it through different channels. LensAI Solution LensAI was targeting potential key users via LensAI Ad Format, LensAI Targeting, LensAI Brand Safety in order to draw the attention of those who take a particular interest in online shopping in different product categories offered by the marketplace. was used to target those users who are browsing graphic content and context related to Style & Fashion, Music, Shopping, News and Politics, Home & Garden, and so on. LensAI Targeting was installed as an ad banner into the graphic content in which the user is browsing a product relevant to the content and context. LensAI Ad Format safeguards the ads from prohibited content. LensAI Brand Safety A targeted ad is being delivered to the user as soon as its image or frame gets into the browsing area. This is the safest way to reach the rightest audience and draw their attention while they are focused on studying the content. Results This ad campaign produced high CTR metrics and Conversion Rates(Payments Orders). CTR numbers fluctuated between 0,4 to 6,3% from website to website. LensAI has achieved desired CTR and sales numbers and has finalized this ad campaign as efficiently. Case Study #2- Brand Awareness. CPM Model. Local Clothing Retailer. E-commerce. LensAI technology was used in this case. LensAI Targeting was used to pinpoint users who are browsing graphic content and context related to Style and Fashion, Music, Shopping, News and Politics, Home & Garden, etc. LensAI Ad Format was installed as an ad banner in the graphic content in which the user is browsing a product relevant to the content and context. LensAI Brand Safety safeguards the ads from prohibited content. Traffic Organic mobile and desktop traffic were collected for the period of 1 week off the websites on different topics: women’s fashion, world news, and politics, celebrity news. Goals and Ad Objectives The ad goal is to increase brand awareness and draw new audiences. To deliver information about a new brand to potential customers without breaching their privacy. To increase brand awareness and to drive more users to it through different channels. LensAI Solution LensAI was targeting potential key users via , , in order to draw the attention of those who take a particular interest in online shopping of fashion clothing. LensAI Ad Format LensAI Targeting LensAI Brand Safety was used to target those users who are browsing graphic content and context related to Style & Fashion, Music, Shopping, Women’s Blogs, Dating, Home and Garden, Luxury Life. LensAI Targeting was installed as an ad banner into the graphic content in which the user is browsing a product relevant to the content and context. LensAI Ad Format safeguards the ads from prohibited content. LensAI Brand Safety A targeted ad is being delivered to the user as soon as its image or frame gets into the browsing area. This is the safest way to reach the rightest audience and draw their attention while they are focused on studying the content. Results This ad campaign produced high CTR metrics and Conversion Rates(New Users Sign Up). CTR numbers fluctuated between 0,7 to 5,7% from publisher to publisher. LensAI has achieved desired CTR and new users sign-up numbers and has finalized this ad campaign as efficient. Miscellaneous Data We tried to analyze inside and outside during these case studies. While receiving the first statistics, we thought about optimizing our calculation expenses and how this may affect our flexibility and scaling. How will the user react to the novel ad format?  Which algorithms should be applied to filter the nonessential graphic content and context for analysis? How does the tuning of our algorithms affect the impressions and their numbers? How many ad slots can we sell within one frame? What is the threshold for unit economics? The data we collected will be enough to discuss over the next couple of articles, for sure. Obviously, I won’t make all the data public. But some are worth sharing, as they reflect the results of the engineering scope. Case Number #1 и #2 Data Heavy Load Testing LensAI technology was tested on the web traffic provided by QT. Media. Goals of this testing: Workload and resilience capacity of LensAI technology when mass launched on different websites. Errors detection in data analysis with heavy loads Metrics collection: impressions, conversions, CTR, etc. Ad campaign efficacy analysis on the available web traffic Received Data As a result of the heavy load testing, we have come up with the following tech conclusions: Workload and resilience under heavy load has successfully satisfied our expectations Errors related to unessential content (porn, banners, logos, backgrounds, etc.) were detected. This enabled us to introduce new filters and to avoid unnecessary processing of the graphic content that doesn’t need to be analyzed. This has been successfully implemented. Unit economics data were measured in terms of processing huge volumes of data. Cost reduction optimization has been performed. Basic metrics have been collected; they are within the norm. Ad campaign efficacy with LensAI technology and “money dot” format  - an animated dot - can be improved through introducing new algorithms which take into account the position of the graphic content on the page. This solution will enable webmasters to select the position of the ad slot by default. This is to be implemented in the near future. The click to impression ratio has been increasing from day to day due to the increasing number of processed images as well as new traffic that is served the ad right away without having to overcome “a minimum threshold of page impressions to display ads. Oh yeah, while I was busy writing this article, we received this email from : Plug & Play Hi Pavel and Tanya,  After sitting down with our corporate partners, we are really excited to inform you that LensAI has been selected to participate in Plug and Play's Media & Ad Cohort 4! Our corporate representatives were impressed with your presentation and product. We are looking forward to having you in the program and helping you foster connections with our corporates. We are humbly happy that what we do causes a stir and caters to the needs of the current and future market needs. To sum it up, I’m including pitch speech of prepared for investors: founders

LensAI: Cookie-Busting Ads, Statistics, & Case Studies

HackerNoon Reporter: Please tell us briefly about your  background. Pavel is an experienced IT entrepreneur with a background in aviation and economics; he has been developing cybersecurity software since 1996 and has led various venture teams. Prior to LensAI, Pavel launched four start-ups with one successful exit. What's your startup called? And in a sentence or two, what does it do? LensAI. AI-powered contextual digital ad platform It all started with a sofa. Precisely, with me sitting on that sofa and clicking buttons on the remote control. After going through two hundred channels or so, surprisingly, I paused on the Shopping Channel. First of all, I was shocked that it even existed. Secondly, I was even more shocked after I realized it existed because that means someone still buys cheap crap like deluxe knives for $50, rare diamonds, and magic steamers. I kept switching channels until I ran into the "Casino Royale" movie with Daniel Craig. As I was watching, I could not stop wondering why they never sell items that they show in the movies? I liked the suit James Bond was wearing in one scene, and if given a chance right now, I would probably order it instantly. Although I did not need the suit, I liked how it looked on the actor and simply the idea of having such a suit gave me a good feeling. After I personally proved to myself that purchases are often an impulsive event, I began to grow this idea in my mind. The first question arose: what if the concept of the shopping channel was implemented right into the movie giving me, a potential consumer, an opportunity to buy things that were shown at that moment? Then, the second question followed: what if I could instantly buy all the things that I see in the digital space without spending so much time searching for them online or remembering the exact source I first saw them on? We wrote before. more about that What do you love about your team, and why are you the ones to solve this problem? After six months of intense work on the previous project, I finally got a free week or two. I was finally able to unload my brain, but my irresistible craving “to create” was too intense to ignore.
It's been a while since thoughts about the world's overproduction of goods and how it created the problem of selling rather than producing occupied my mind. I kept thinking of how quickly Instagram turned into a vast marketplace, where every homemaker sells a luxury anti-wrinkle cream. It doesn't take a brain surgeon to figure out that these led to such an oversupply of information that an average consumer can't really focus on one specific item for a long time, let alone focus on the text. Even though I’ve always enjoyed the process of solving problems, it felt like I’d had enough of it. Anyway, a solution to specific issues is a niche market, and I was ready to solve universal truths. It was time to shift my focus to creating new opportunities. I have always strived to think big. My team focuses on the solution. My team solves product problems. This complements us. I love it. It makes us strong. If you weren’t building your startup, what would you be doing? I would build another startup or enter politics :) At the moment, how do you measure success? What are your core metrics? At the moment, we are measuring conversion rates and testing the technology on 10,000+ websites. Checking the users’ behavior. We are also measuring the capacity of advertising inventory and generating metrics that will show us how to improve the technology for its smooth release into the market. What’s most exciting about your traction to date? Creation. Recognition. :)  Winning over competitors. What technologies are you currently most excited about, and most worried about? And why? Augmented reality. Neural Interfaces. These opportunities will open up the possibility for building new models of human interaction with the world as well as cognition processes. What drew you to get published on HackerNoon? What do you like most about our platform? There is an old-school spirit. Highly intelligent people get together on this platform. There are different points of view. It allows me to keep track of the direction modern IT entrepreneurship is heading in. What advice would you give to the 21-year-old version of yourself? Don't waste time on making money. Instead, create IT products. Technology is the new gold. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Happiness is when you find and accept yourself. This is the most important thing in everyone’s life. It may be difficult to find your real self, but it's worth it. LensAI was nominated as in Startups of the Year hosted by HackerNoon. one of the best startups in Ridgefield

Startup Interview with Pavel Shkliaev, CEO Of LensAI

Our hearts demand changes, Our eyes demand changes, The voice in our laughter, in our tears, and the pulse in our veins cry: "Changes! We are waiting for changes." Band: KINO Song: (aka "Changes") "Peremen!/Перемен!" Besides telling you about my Russian roots, these lines are a pure expression of upcoming inevitable changes and revolution. For ages, the song has been lifting my spirit in the toughest times, and the LensAI part of my life is no exception! So yeah, come hell or high water, LensAI is going to make it work! 💪 We are coming close to our "It's a wrap!" moment and waiting with bated breath when LensAI goes live to disturb an old-school cookie-friendly advertising era. With LensAI, there will be no tracking users and eroding their trust but only relevant contextual advertising! It's a long-awaited change that every stakeholder of the digital advertising ecosystem deserves! While tirelessly working on the product development, we made a decision to slow the pace on all external communications to invest that energy into creating a . It took us months, but it was a must, as being judged by the cover is still a thing! new website We enjoy sharing every side of LensAI and what our tireless team had accomplished during these last three months but (there is always a "BUT") not this time. Even though there is a final draft of a very piquant story ready to be posted, the Hacker Noon world will never see it: I got a soft no-no from our , who are the top industry players by the day. advisors "It is a quite questionable way to build a unicorn and make it history," they said. (Yep, these are our ambitions!) Anyway, I could not leave my people with no peek behind a curtain, so I am giving you a hint or two about the article that was scheduled to be posted today instead of this one. Firstly, try to recall where digital advertising took its roots. (We all kinda came to this world that way.) If it does not help, go back to the header image and look closely. That's the most you can get from me. #sorrynotsorry It's time to drum up some excitement about LensAI, so we are unveiling two short but informative videos for you to watch! Meet our LensAI spokesperson! She tells an easy-to-digest story on what kind of a product we are building here. Here's another one that expands on how LensAI technology turns any pics (and videos, of course) into shoppable ones within a second. To sum it up (for those who don't have time to watch it), LensAI is an AI-powered contextual computer vision ad solution that monetizes any visual content and fine-tunes targeting through identifying objects, logos, actions, and context and matching them with relevant ads. LensAI automatically embeds relevant ads straight into visual content as an overlay snippet with direct reference to the object or entire scene. The ad embedding process is instant and happening while the user is browsing the content. Those who want to dive into a cookie-less world of contextually relevant advertising, visit our new website! Come closer to a . novel advertising format What else is there to add? Maybe, some juicy details? We are happy there is zero indifference when talking about LensAI: you either love us or hate us! 😈 So, here are the top-five quotes regarding LensAI from top industry players: "A go-to website to educate yourself on contextual advertising!" "Expect copycatting of your technology from big and small fishes!" "The LensAI Era is #MoneyDots all over the Internet!" "I would pay a premium to stop seeing it! Bad for my wallet, but so much $ for publishers!" "I do not know about others, but this particular group of users will hate you a lot!" (That's coming from the person who knows more about our internal stuff than anyone else, and we kind of agree with this statement. Another hint for all those who stayed till the end of my monologue: the title of that never-to-be-released article would be "Oopsie... The Ad at That Very Moment." It tells it all, doesn't it?! 😉) PS If you are a newbie in the LensAI community, I welcome you! Here are the links to my for your convenience. previous stories

Introducing LensAI: Our Hearts Demand Changes

"Association in psychology refers to a mental connection between concepts, events, or mental states that usually stems from specific experiences." [1] Once the associative link between events A and B has been built, the appearance of event A naturally entails the appearance of event B. [2] Cool, huh? Now, imagine a scenario in which a machine (AI) understands what is happening on the user's screen (while he/she is surfing through online content) and then uses that information to show brand awareness ads designed to implant an apparent associative connection into the user's brain. Hey everyone, I'm Pavel, the CEO & Founder of LensAI. We build AI-powered advertising technology that performs in-depth aggregate analysis of visual, textual, and audio contents to enable our unique algorithms to select and then embed relevant ads into visual content automatically. Just a quick but crucial note, there is no-preprocessing needed, and everything is happening in under a sec once the user comes across the visual content. Let's start with the questions that bother me the most: How vital is non-stop complex content analysis for Advertisers? How valuable is an opportunity for ads to promote brand awareness while simultaneously being converted into real $ by facilitating impulse purchasing? How necessary to provide Advertisers with a tool/service that allows careful curation and filtration of circumstances under which to show ads for each brand/product? I am a tech guy, so maybe it will sound not very clear to you, but I will try my best to explain. Right now, our technology extracts separate frames from the video to analyze. For example, while the user is watching a reality show, the technology counts specific objects, environments, events, plus other important elements, and then it tracks the duration of their presence in the video along with the appearance of particular classes or IAB categorization. The data we get helps us design formulas that bring value to any brand as they allow the machine to get the highest level of understanding of video content and how it satisfies the needs of, let's say, the Veet brand (for those who are not familiar with a brand, it is hair removal products). We have created a technology that does a superior valuation of content based on made-to-order AI formulas: an exclusive way of setting circumstances (e.g., type of entities, environments, words pronounced, and other various parameters) under which to show ads for a specific brand. We ask our Advertisers to express their "wants" for each brand or even individual product. Then, we put them into action by enabling our algorithm to monitor particular objects, actions, events, and every critical piece of information. LensAI will never stop to enhance the machine's understanding of how different video content fits each brand/product. See the results of implementing an exclusive formula for the Enfamil brand below. From now on, LensAI can build a valuable connection between any brand/product and the relevant content. Again, no manual work is involved as a machine does everything within milliseconds once it sees that the user has landed on the page of our Publishers. The speed of the whole process is mind-blowing and one of the gems of LensAI. The formula incorporates multiple pieces of data that have to be present to initiate advertising. Let's take a look at a few examples that demonstrate how LensAI works from an end-user perspective. We used visual content currently available online for demo purposes: Due to the short length of the videos, we set the Ad Display Setting to "Highest Frequency" to demonstrate technology possibilities. Now let's watch the same video to observe how the machine inserted completely different ads into it. Why is that? What made it change its mind? That's what happens when we make our machine respect each viewer's individual characteristics based on the IAB Audience. These two examples prove our machine's ability to select various objects and use them for building associative connections with different brands and their products. Demography, interests, previous purchases, and other additional data about users influence the machine's decisions about selecting the most relevant ad, its placement, and advertising purposes it should serve. I have mentioned above that LensAI can build a valuable connection between any brand/product and the relevant content while the user is surfing on the page or when he/she hits the play button. We didn't stop there and added another precious piece of information - specific data about each user - to allow AI to enhance targeted advertising. Your Questions Here are the top three fair comments/questions/concerns that our followers expressed: "I saw a total of five ads for Lysol in your videos. The ads were shown in places where no Lysol disinfectant spray was present. That's exactly what other ad networks do: show ads of anything that has to be sold everywhere and anywhere. So, what differentiates you from them? If a beautiful cover does not lead directly to sales, it has close to zero value. So, why should I choose your product? Why should I use your software?" "I was hoping you could explain the benefits for me as a potential client rather than a consumer who is the last link in the advertising chain. Make it simple. Suppose I have pictures and videos on the site and participate in the affiliate programs (ranging from marketplaces like Amazon to affiliate marketing service providers like Rakuten Marketing). Why do I switch to you? What are the distinctive advantages you have over your competitors?" "I have a question from the end-user perspective. What if I see some item of interest in the video, but the Publisher who partnered with you does not market that product (let's say, it's a dining table). Suppose I keep seeing Lysol ads embedded into that item and, even worse, other ads embedded into other objects according to the same scenario. In that case, I will stop paying attention to your ads due to seeing "wrong" ads. If users like me were able to build an immune system for banner ads, we could build one for LensAI ads too." My Answers I love questions like these and take any criticism with appreciation. So, here are my answers to those who reached out to me personally and all of you who might have similar concerns. Everything you currently see is a basic framework. We currently limit inventory that we use for analysis to cut computational resources expenses and choose short videos for demos for the same reasons. The high density of ads that you witness now is not pleasant even for me. The final product will enable Publishers to control it and adjust what is needed based on Analytical Data we make available to them (e.g., % of users who watched the video to the end). Let me speak the truth. Yes, we work hellishly hard to ensure the most accurate matching of identified objects to products available to advertise. We do want users to enjoy the interactive advertising experience with the eCommerce touch we offer. Even though we consider all stakeholders' interests, the software is being developed to precisely satisfy the needs of Advertisers and Publishers. Regarding the question about our competitors, please study the slide below. Participation in various affiliate programs does not allow Publishers to market products through image and video content available on their sites. No one but LensAI can enable them to do so. None of our competitors is technologically capable of selecting the fittest product to advertise from numerous different product lines and categories among several marketplaces. That's why our competitors by technology cannot sufficiently monetize traffic; they cannot cover a large volume of goods and, therefore, cannot find the ideal goods from advertising inventory to display (feel free to read about it ). here Impressions equal money; that's clearly why they are important to Publishers. Each of our technological competitors takes away an opportunity for Publishers to dramatically increase their revenue by their inability to match advertising inventory to the graphic content that Publishers posted, post, and will post. Suppose our machine cannot find a perfect product to advertise among products available on the marketplaces we affiliate with. In that case, we will promote the relevant item that satisfies either cross-sell or up-sell requirements (we wrote about it ). Meaning, Publishers will never lose an opportunity to make $ while users will never question why this particular ad pops up. Relevancy and similarity are the keys to success. Don't forget about our brand associations formulas we do for brand awareness ads. here Here's quite an upsetting message for the users. The dot format is simply one of the numerous designs and UX options. We take ad blindness seriously, so we made plans A, B, and Z to fight any derivative form of ad blindness syndrome. Maybe I should write another article about the advertising standard that we developed as it allows superimposing any UI/UX as easy as one-two-three. Imagination is the only thing that can stop us, and we all know it is limitless. Long story short, no technologies and products that are known today can operate according to this scheme: the installation of the script on the website/app within minutes-> the extraction of the content in real-time (once the user lands on the page) -> the detection of objects and matching them to any possible product available for sale ->  embedding the fittest ad to re-sell the product for a commission -> if no similar product is found, either a relevant brand ad or a relevant product ad pops up. Easy-peasy! Nothing is more upsetting than seeing someone do something you thought was impossible! Create brand formulas with us! Invest in us! We are open to any collaborations, so let's do something novel together! For those who want to dive in, read the I wrote. The truth is somewhere near. previous articles

LensAI: Associative Advertising as An Inevitable Evil

Have you heard or perhaps even tried new ways to purchase stuff you see on TV? You know, the ones that invite you to buy things you see while your favorite show is being aired? They offer you to shop through various user interaction mechanics that range from scanning a QR code shown at the corner of the TV screen to pressing a set of navigation buttons on the remote control to receive a text message with a link to the product. What a maze, I have to say. Come on, people! Do you really think the user can enjoy such an effortful shopping experience? I would change my mind to purchase something a number of times before completing the "hell of navigations" like this... Let me spill the tea here. We are often compared to such products, and when it happens, it devastates us. We Are Different If you missed the previous articles about our technology, I recommend you familiarize yourself with what we've been working on and how it all started before reading this one (it will make more sense to you then). How We Taught Artificial Intelligence to Sell Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines Online Advertising in The Time of The Consumer Attention Deficit Hyperactivity Syndrome (ADHD) Different by Concept Inspired by computer games, cyberpunk, and HUD interfaces, we imagined a wholly digitalized and full of AR-based interactions world where every object would allow you to get information about itself and there were no boundaries to purchase it. That's how we started to develop our internally called "seamless sale" concept that would create an opportunity for the user to buy goods directly from the open-world when interacting with the object or viewing the content where the object was. No visit to the store's website or another place is necessary. Effortless! For us, LensAI, the fundamental quest was to solve inefficient web traffic monetization by installing our ads in any graphic and video content regardless of its release date. In "techy" words, we had to train the machine not only to detect all types of objects in the content (that vary from cats and dogs to a spaceship) but detect these objects instantly. To execute it instantly was essential criteria as we wanted the machine to show only relevant to the content ads at the very same moment when the user is viewing that content. To solve the before-mentioned problems, we first had to become conceptually different by technology, and everything else was second. Our conceptual difference is not recognizing objects and then searching for similar ones, but the machine's ability to begin to understand the cumulative meaning of all three types of content: visual, textual, and audio. Feel free to explore more details on the challenges we faced in the article . Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines Different by Technology The foundation of the world's digitalization lies in the classification of all possible entities, objects, events, actions, and relationships between all of them. It reminds me of creating a game engine where particle interaction (physics) is replaced with a comprehensive advertising graph with a detailed description of how every entity relates to and influences each other. When creating our relevance model of advertising, we combined various taxonomies (taxonomy "is the practice and science of classification of things or concepts, including the principles that underlie such classification"). [1] In the model, our AI detects every entity and how entities influence each other. This approach gave us a clear understanding of how everything found in the image correlates to its context. The understanding we’ve gained opens up almost endless possibilities for both displaying ads and accomplishing more specific tasks. Different by Capabilities Our technological differences get us the access to previously inaccessible opportunities. Advertising Selection for Any Object Case #1: The advertising goal is to sell products from the eCommerce catalog by selecting the fittest ones for ad display from 5,500 advertising categories from Google Products and advertising feeds/APIs of top US marketplaces. Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- First, each object that was determined suitable display advertising, select relevant advertising e-commerce categories, marketplaces, and then specific products (that range anything like pet goods to aircraft parts).
- Lastly, select the fittest products ad impressions based on:
       -- the product availabilities, the rotation impressions, and other parameters that affect the decision to display a particular product,
       -- the context analysis,
       -- the similarity analysis. in of for for as for from for of Product Display Ads: Cross-Sell & Upsell Case #2 The advertising goal is to sell products from the eCommerce catalog. Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- Select the fittest eCommerce advertising creatives to render on a user in of for 's screen depending on the product availability, rotation of ad impressions, and other parameters that influence the decision of ads being fetched. Case #3 The advertising goal is to sell products from the eCommerce catalog. Relevant Ad Selection Regardless of The Initial Content Release Date Tasks: - Detect objects images or video frames.
- Determine which the objects are suitable display advertising.
- Select the fittest eCommerce advertising creatives based on associative thinking algorithms. in of for Associative Display Advertising Selection Case #4 The advertising goal is to boost brand awareness, traffic, and conversions. Tasks: - Detect objects images on the site.
- Determine which the objects are associatively suitable to use displaying advertisements various plans the internet, cable TV, or telephone.
- Select the fittest advertising creatives among others provided by an advertiser (there are many such creatives).
- Embed advertising creatives. in of for of for of New Opportunities for Advertisers New Opportunities for Publishers Different by Ad Format: Visualization & User Interaction At the early "birth-of-an-idea" stage, we dreamt of AR smart glasses. We were inspired by interaction with the outside world and the instant "getting-to-know-it" process: the possibility to obtain all information about the object as soon as we focus our attention on it. The cost of consumer attention rises every day. Rapid embedding of our ad formats straight into the graphic/video content that had caught the user's eyes seemed to be the most promising. Alongside, we enlivened the picture with animation, added bindings, and tried to cause no significant interruptions to the main content. Initially, we craved to implement a spatial positioning algorithm that would consider objects' perspective in the frame to make our ads shift relative to other objects while the video was being watched. The old geek wisdom saved me from troubles (at least for now): "Do not develop new functions until you have not sold the old ones." Having developed a completely new #MoneyDot format, we endowed it with great flexibility and settings for various uses based on advertising goals and advertising creatives currently available on the market. #MoneyDot Ad Formats Our ad formats support the following advertising goals: Brand Awareness Traffic Mobile App Downloads Video Views Conversions & Message Displays eCommerce Catalog Sales We work with Publishers according to the following scenario: Publishers install LensAI scripts to their websites. The installation process is similar to Google AdSense. Publishers may configure or use default settings. Scripts extract visual content once user traffic is recorded. LensAI servers analyze the content of the web pages, identify images and videos, and detect objects in them (e.g., a dress, chairs, a microwave, etc.) LensAI algorithms find matching product categories for the detected objects and then products themselves. A user comes to the website. (*Optional Step) The LensAI scripts that were pre-installed (see #1) look up the user's interests through cookies. Based on that particular user's interests, content and context analysis, ads of the most relevant products are embedded into the objects of visual (video and image) content. LensAI receives a commission from marketplaces/advertisers for ad impressions and purchases made by users on Publishing Partners' websites. LensAI shares its earned commission with its Publishing Partners. The Cases Demonstration of Technology on The Verizon FIOS TV Platform (In-Video Ads) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone.
- Show other advertisements for products found in video content. The demo shows the performance of our advertising format for both Verizon plans and products available from various U.S. marketplaces. Desktop version Mobile version Demonstration of Technology on The TechCrunch Platform (In-Image Ads on Website) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone. 
- Show advertisements for other products found in the content. The demo shows the performance of our advertising format for Verizon plans only. The demo shows the performance of our advertising format for Verizon and other advertisers. Demonstration of Technology on The AOL Platform (In-Video Ads on Website) For this demonstration, the following tasks were set: - Show advertisements various Verizon for 's services, including the Internet, cable TV, and phone. 
- Media advertising for other advertisers.
- Show advertisements for other products found in the content. Desktop version Mobile version All advertising goals and various display advertising options can be combined and customized based on the . specification Brief Information About Settings Our ad format has a flexible system of settings that enable publishers to fine-tune their advertisements. 1. Platforms Mobile Desktop 2. Ad Placement Area.  The setting enables publishers to choose if they want to embed ads all over the entire site, on an individual page only, or even in a particular area of that page. There are two ways of how ad slots can be created: Ad slots that are generated by our system automatically. Ad slots that are hand-picked by publishers. 3. Content for Ad Placement. For ad placements, we use visual (graphic) content that our system detects on sites. After content analysis, AI selects the most relevant ads to show in it. The following formats are supported to use for embedding our ads: Images: jpeg, jpg, png, webp, gif Videos 4. Context for Ad Placement (IAB Standard). Along with graphic content, our system analyzes any accompanying textual content. This additional analysis is performed to help AI to make the rightest selection for ad display. 5. Placement Position. The setting enables publishers to choose the position of content (in which ads will be embedded) on the site.  It relates to visibility/significance. Here is a list of ad placement positions to choose from: above the fold, below the fold, header, footer. 6. Ad Placement Density. The setting enables publishers to choose the frequency of ads to be embedded: For images, publishers can set the frequency of ad displays per page. For videos,  publishers can set the frequency of ad displays per video stream. 7. The Number of Ad Placements. The setting enables publishers to set the number for simultaneous ad placements in the content. For images,  publishers can set the number of ads to embed per one image. For videos,  publishers can set the number of ads to embed per one video frame. 8. The Order for Ad Placements. The setting sets the rules and describes specific placement parameters for advertisements on the platform as a whole, its part, or its content. 9. Ad Placement Categorization. The setting specifies what categories of Google Product Taxonomy as well as brands can be advertised and what advertising purposes can be executed on the platform as a whole, its part, or its content. Advertising Purposes Categories Brands 10. Ad Placement Classes. The setting enables publishers to choose specific classes of objects among detected ones to use for ad display on the platform as a whole, its part, or its content. 11. User Data Parameters. The setting allows publishers to choose the characteristics of users to target for ad displays. Browser Data Device User Data (IAB):Interests &Purchases 12. Types of Ad Format. We developed various ad formats to be placed straight into the graphic content. The setting allows publishers to choose which ad formats to use for display. Here is a list of currently available ones: #MoneyDot #MoneyBox #MoneyLens Abar classic Vbar 13. Display Methods. The setting allows publishers to choose various ways to display ads: Animation Elements Interaction Dimensions Display Logic 14. Other Settings Is there a need to implement more "meanings" into technology? Is there a need to implement more "meanings" into technology? If you read to the end of this article, you are a rock, and we share the same blood! :) Join us, invest in us, do business with us.

Our Quest To Solve Inefficient Web Traffic Monetization

Wikipedia characterizes ADHD symptoms as "difficulty paying attention, excessive activity, and acting without regards to consequences." [1] As we all live in times when not only new generations, but older ones are prone to the syndrome, how to promote and make money on online advertising? How to gain consumer attention with ad blindness on the rise? How to respond to this consumer trend that negatively affects every online business? Hi everyone! For those of you who don't know me already, I'm Pavel Shkliaev, a CEO & Founder of LensAI. LensAI is a tech startup that offers a business-to-business service to monetize both image and video content. The LensAI Technology allows our Publishing Partners to earn money for ad impressions that are displayed in an innovative format using PiP (picture-in-picture) placement. If you have not read my previous articles about LensAI, here are the links for your convenience: How We Taught Artificial Intelligence to Sell Where Can Dreams Take You? The Art of Teaching Associative Thinking to Machines The Fight for Attention: The Most Recent LensAI Technology Results I hasten to share the results of LensAI Technology that we've got through our combat testing. I call it "combat" as we did it without including the last module, which focuses on the similarity and relevance of the products. (We are currently testing it and optimizing products from the most significant marketplaces such as Amazon, eBay, Walmart, etc.) The videos below show how LensAI Technology analyzes graphic content on TechCrunch and Mashable sites. Notice how the new "money dot" advertising format is placed straight into detected objects. Both videos are fresh from the oven. The first video shows the processing of images and placement advertising dots in images, and the second one shows the processing of video and placing of advertising dots in it. Here’s a quick recap of how LensAI technology works. Firstly, it identifies an object, and then it moves to identify either a product category or an advertising category if the object is not associated with e-commerce. After that, it identifies a specific product that is available among various marketplaces. It might look as simple as one-two-three to you, but a whole bunch of analyses takes place, starting from the context analysis and ending with the analysis of the scene in the frame/image. Also, the influence each piece has over each other piece is incredible — what a thick layer of AI we've built. #proudofmyteam Finally, we are approaching the moment of transition from technology to product and first . investments Money Dot: As You Name the Boat, So Shall It Float In developing LensAI ad format and testing it among our followers, we decided to stick with the "Money Dot" name for it. We came across it in the dialog with one of our followers. Here's how he convinced us: The end-user who clicks on the ad is not interested in, how you name your advertising format. Also, as Advertisers are not likely to use LensAI Technology to sell their products directly, the name "Sales Dot" is not quite suitable. I see that LensAI Technology's primary users include Affiliate Networks, Content Delivery Networks, Webmasters, and Arbitrageurs. They are the ones that will sell those products for Advertisers through their sites, and they care about two things mostly. The first one is how much money your advertising dots will help them to earn. The second one is whether, and how, the advertising dots will affect the quality of their sites. Therefore, go with "Money Dot Join Us. If You Do Not, Won't Somebody Else? If you recognize yourself within these lines, you are welcomed to join the closed beta testing group at LensAI benefits its Publishing Partners by: Producthunt placing ads in previously unused places that have the highest audience response, selecting advertising based on both content analysis and analysis of objects found in that content, based on similarity and relevance. Also, all early joiners will get the most out of it. Why? Because nothing catches the eye better than something you have never seen before. It is a well-known fact that Advertisers want to get a higher return on investment. For this reason, LensAI Technology allows Advertisers to choose not only the site where its products will be displayed, but also the specific type of content and objects in the selected content. That is to say the likelihood of purchasing those products, by users who view the content, rises dramatically. Publishers, in turn, get an innovative way to monetize the content that they have not monetized before. I want to stress that by using LensAI Technology, Publishers do not lose their existing earnings by using other types of online advertising. LensAI "Money Dots" do not occupy any existing ad slots and can co-exist with other types of ads. It's time to wrap up. Don't hesitate to and follow us. As we have moved to the stage of optimizations and technology improvements, it's time for us to attract the attention of end-users. I and thank you for your interest. subscribe to our updates LensAI Team Works Cited Wikipedia contributors. "Attention deficit hyperactivity disorder." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 14 Jun. 2020. Web. 15 Jun. 2020.

Online Advertising in The Time of The Consumer Attention Deficit Hyperactivity Syndrome (ADHD)

The idea of LensAI was born last year sometime in May. If you've missed Part I of my story, you can read it here -> . How We Taught Artificial Intelligence to Sell At the early stage of our product development, we were passionate about it and full of energy. We desired to change the world now and forever. We felt that we were the innovators the industry had never seen, and massive changes would come with us! We wanted to fly, but the experience we gained from previous startups kept us pulled to the ground. "You are dreaming again. Stop it! It is time to build a product that can make you money, preferably quickly! Enough thinking about changing the world, at least until you see the first AI-earned $1k in your pocket," my mind kept repeating to me all the time. After negotiating with my inner-critics and my partner, we decided to press the stop button. A well-known and proven business model was chosen for our startup. Since that moment, there have been no more thoughts about the integration of our technology into lenses and glasses. We shifted our focus to recognizing objects and selling ads. Competitors as a Source of Inspiration We were doing our research on competitors, but our social media followers were a great help too as they were continually sending us tons of information on various companies that were somehow engaged in the object recognition in images or videos. The Cutting Board Being the restless minds, Konstantin and I were tormented by endless doubts. We had so many unanswered questions. "Why all these competing companies are worth millions and hundreds of millions of dollars, but there are no unicorns among them? Why are their technologies so rare to come across on the Internet? Why? Why? Why?" I was asking myself so often that it drove me nuts. To proceed further with our idea, we had to find the answers. Using "visual search" and "advertising" as the keywords, we were able to find the companies that were more or less related to our idea. We gather all of them on our "cutting board," took out a "knife," and began the messy examining process. Through analyzing companies one after the other, we noticed two major similarities: limited advertising inventory and limited ad displays. All those projects were sharpened to work with a narrow circle of products. More than that, some of them worked only within a single category of products or one brand. Also, to display an advertisement, the identical match of the product had to be available. This prerequisite seemed too strict for us. Many projects had limitations due to the business model they used. Either they were B2B (business-to-business) and very complex or B2C (business-to-consumer) and, in our opinion, not viable. For example, some projects required interaction with product placement agencies to show ads in a video that was streamed on Smart TV. All projects had the same "chicken and egg" problem in common.  It occurred due to an insufficient amount of data about products that were being sold at that moment and could be displayed in relevant visual content even though the advertiser was ready to pay to advertise these products. Our "cutting board" helped us to identify three main problems that we needed to solve more efficiently than our competitors if we wanted to succeed: The ads of products/services had to be hyper-relevant to the detected objects of the visual content regardless of the date when the content was first released. We had to place our ads straight into detected objects and show them in a way that ad placement decisions would not depend strictly on similarity algorithms. Simultaneously, we didn’t want the user to question our ad placements or ad choices. We had to provide a machine with a broad and relevant advertising inventory that we would have to receive regularly. This way, the machine could apply various display advertising algorithms. Eureka! We had to make the machine to "think" exactly like a person in the moment of browsing. For example: "I want this red bag. It goes perfect with my shoes!" "What a cute shirt this guy is wearing, I know that a men's watch would match it perfectly!" "Great hotel, I should book it for my next vacation!" As we know, a human brain thinking process is hard to duplicate by the computer due to its emotional inability. But we decided to give it a try and train the machine in associative decision-making. Teaching Associative Thinking to Machine Our first try to formulate all the requirements and link relationships between detectable objects, entities, people, and other characteristics ended up looking like some unknown monster that demanded us to analyze all possible and impossible data. Through multiple tests and pain in the brain, we were able to collect all the data we possessed that could be analyzed.  The following picture cleared up: We systematized the entire "zoo" of data into an influence map that navigated how every entity influenced every other entity in that particular scene. Let me show you how it worked within a frame. For example, once "Environment" was identified as "Kitchen," the influence of objects from such advertising categories as "Kitchen Furniture," "Kitchen Appliance," "Spoons," "Kitchen Care Products," "Cups," "Tableware," and others, was increased. As a result, we built an influence map, a version of which you can see below.  (We distorted it on purpose as it includes parameters of the algorithm that are of value to us.) In this image, you can see what entities we operate and how all of them influence each other while the process of analyzing images and context is running to display the most relevant advertisement. The task was successfully defined: determine the relationships of objects and entities to advertising categories according to characterizing features. Once accomplished, the next one was to establish a connection between two entities: a category and an object. Object relationships are ways of one object to have an obvious connection with some Taxonomy category. In other words, each object relation can be described as some influence of the object on the products from Taxonomy. Meaning, when we see an object in the image, we can assume that this object is a central one, and we need to find relevant to this object products according to some characterizing feature. When working on the task, it was necessary to apply the following relationships (characterizing features): Taxonomy products are often located on the object, Taxonomy products are often used for an object, Taxonomy products cannot be used without an object, Taxonomy products are used with the object. We needed two elements to work with large volumes of data and various types of relationships between entities, they were: The Tool people with arms and heads, a tool that would help these people to transfer their knowledge to a machine, in our case, the particular knowledge to identify relationships between objects. Guess which one was much harder to find? (Spoiler Alert! It was the first one.) We searched all over the Internet but could not find a tool that would satisfy our requirements. We had no choice but to create our own. We brought together the capabilities of working with massive amounts of data and types of connections. Notice that each possible connection had to have its own weight of influence. Also, all these connections had to be established by various people who could catch machine-made errors and then correct them. As a result, we got a two-panel data manager that had a broad set of functions and the ability to work with both relational and graph databases. Handwork: Hell Exists This stage of product development was the one that almost burnt us out. Neither Konstantin nor I experienced the euphoria we had at the beginning of the project. It seemed to us that to process and link such volume of data is beyond the bounds of possibility. Our mission to determine the relationships between the objects was about to fail. To feel our pain, imagine a few of the processes we had to deal with, such as linking detectable objects to advertising categories, collecting all possible variations of detectable objects, connecting both industries and context themes with advertising categories... We didn't count all these processes, but there were hundreds of thousands of them. It felt like HELL. We knew it was time to delegate, so two data since groups of five people were created, both had the only purpose of linking all entities together. Here is the picture we got. You can see the relationships of various entities in the images above. The last one shows a "scene," aka the influence of multiple entities within a single image. We were able to achieve the scenario where the detected in the frame objects always fight for the right to be displayed, and the algorithm's job is to determine which ad to display in each frame. For example, the final decision on which one of three advertisements to show (a teapot, a coffee maker, or a furniture store brand) is totally on the algorithm's shoulders. Nowadays, you cannot surprise anyone with object recognition software, but with the high accuracy of recognition, you can. The next step was to identify how accurately our models worked and give Fs to machines if they were wrong. In parallel with this work, we also manually tested some of the relationships that we established when we were completing the task to determine relationships between entities. As a result, we got a whole bunch of classification errors that were done by machine algorithms. We found them in thousands of images and video frames of movies. To correct the errors, both our data since groups had to mark them manually and then classify them one by one. There were nearly thirty different classes of errors. I will list some of the largest ones below. Please know that each of these error classes includes up to ten subclasses. Here is a list of errors: Wrong Class Wrong Product Wrong Taxonomy For Classes Without Coordinates: Wrong Surrounding We continue to work and are getting closer to our goal. The next step is to release a public demo so that you can evaluate our work or point out our shortcomings that require a fix. Continue to follow LensAI and join us on . PH We promise to delight you with quality content, update on the progress of our project and various entertaining stories about machine learning. LensAI is an Affiliate Marketing Platform that automatically delivers the right ads in the right place at the right time! 🎯 We are building an ad format that turns visual content into a seamless shopping experience — no more gap between browsing and sale. And yes, we know how to catch a fleeting glance of a customer! 😉 LensAI is about to bring some changes to digital advertising. Be the first one to witness them! 👀 -> https://www.producthunt.com/upcoming/lensai

Where Can Dreams Take You? 
The Art of Teaching Associative Thinking to Machines

Introduction I am Pavel Shkliaev, a co-founder of , and this is my story. LensAI After six months of work on the previous project, I finally got a free week or two. I was finally able to unload my brain, but my irresistible craving to create was too intense to ignore. It's been a while since thoughts about the world's overproduction of goods and how it created the problem to sell rather than produce occupied my mind. I kept thinking of how Instagram turned into a vast marketplace, where every homemaker sells another luxury wrinkle cream. It doesn't take a brain surgeon to figure out that all these led to such an oversupply of information that an average consumer can't focus on one specific thing for a long time, forget about focusing on the text. Even though I always enjoy the process of solving the problems, it felt like I had enough of it. Anyway, a solution to specific issues is a niche market, and I was ready to solve universal truths. It was time to shift my focus to creating new opportunities. The Birth of An Idea It all started with a sofa. Precisely, with me sitting on that sofa and clicking remote control. After changing over two hundred channels, surprisingly, I reached the Shopping Channel. First of all, I was shocked it existed. Secondly, I was more shocked to realize it existed because someone still buys such crap as super-duper knives for $50 , questionable diamonds, and magic steamers. Channel switching continued until the "Casino Royale" movie with Daniel Craig caught my attention. While watching, I could not stop wondering why would they not sell items that they show in the films? I liked the suit James Bond was wearing in that scene, and if given a chance right now, I would probably place an order for it. Although I did not need the suit, I liked how it suited the actor and just an idea of having one gave me a good feeling. As I proved to myself the known truth that the purchase of consumer goods is often a spontaneous event, I began to grow this idea in my mind. The first question arose: what if the concept of the shopping channel would be implemented right into the movie giving me, a potential consumer, an opportunity to buy things that were shown in that same movie? And then, the second question followed: what if I could instantly buy all the things that I see in the digital space without spending time searching for them or remembering the exact source where I saw them? The idea tempted me. I did not think about AI and computer vision at that time, but some visuals began to emerge in my head. I kept imagining some object selection tool that would enable me to pick a product from the movie and buy it straight from that movie in real-time. The idea's global nature and potential caused me sleepless nights. I started to gather information and research other similar projects. It looked promising. Once I finished the online research part, it became evident that similar ideas and projects exist in one way or another. However, all of them are niche projects that use computer vision to solve only one specific problem, such as video search for weapons, smartphone applications for searching for particular goods, etc. While researching, I crossed only one American project that was based on a similar idea and had a similar implementation, but it specifically concentrated on media advertising and stopped there. Undoubtedly, Internet giants such as Google, MS, and Amazon were ahead of all. I had to consider new inputs to make sure my idea could work, but everything else was pretty stable. During the development process, the main points of my idea had to shift more than once since I knew how essential it was to enter the market as quickly as possible. "Hi, Konstantin! I have an idea!" I called the partner from the previous projects. Before making a call, knowing Konstantin and his tiredness from the cut-and-dried work, I knew for a fact that he would get involved as it was not another application for Apple but a damn artificial intelligence and machine learning! You know, some good food for an analytical mind. "Yes, you've got some interesting idea, but I have to check the technical feasibility before giving you an answer. I will need a week," Konstantin replied. That's when the technical feasibility study began.  We quickly concluded that the project was technically feasible, and we expected no unusual restrictions. The one thing we didn't foresee is a crazy amount of manual work that we disliked the most but teaching associative thinking to the machine was too tempting. It took us three weeks to create a prototype, and that's what we got: The prototype was able to identify both key objects and positions of them and calculate the percent accuracy of the object detection algorithm. We moved to the brainstorming the ways to develop the project. Among all unbridled creativity and non-stop idea-generation, we highlighted four main components: Development of an advertising platform for Publishers and Webmasters to join. A computer vision technology that processes both images and videos found on the website and then, depending on the detected objects and other data obtained through the analysis, automatically embeds advertising straight into visual content. A pay that comes from Advertisers for the traffic that such advertisements bring. A commission that we take through our platform. The output should be: A user clicks on an advertisement and finds himself/herself on the website of the store where that particular product is available for sale. Yes, everything is that simple. The technical implementation was a fascinating process that generated lots of obstacles that we overcame during the development stage. We wrote another post regarding some of them but be aware of a lot of incomprehensible words we used to write it. "Hello, Max! We are ending the work on the "Bread Card," I called my guy to bring some news that it was time to put our previous project on the shelf. "Oh, why? Damn, so much work has been done on it," Max sounded upset. "Yeah, no one pays for it... Anyway, I've got a better idea, and it is far away from being as banal as this "Bread Card," I cheered Max up. I knew Max well, so his immediate desire to engage was not a surprise… My team of engineers has been building software for the past ten months that hopefully, will bring a change to the digital advertising industry in the nearest future. I am looking for some feedback on using AI-powered ads that would turn the visual content of any website into a shoppable one. Last summer, I got that crazy idea to create a tool that would allow users to shop items that they see in the video/image straight from that video/image. , that's how I named it, is B2B software that would analyze any visual content found on your website and then automatically deliver hyper-relevant ads of the products that were detected and available for sale on leading marketplaces like Amazon, Walmart, eBay, etc. Also, I choose the design of the ads to be as native as possible, and the ads would look more like helpful hints rather than irrelevant ads. What do you think about the idea in general? Can you see yourself using it if it doesn't cost you anything but unlocks new income for you? has already joined the first round of various Affiliate Programs, and we would share generated revenue from those programs with our Publishing Partners/Websites, so it's a win-win. We are making our baby steps to bring some innovative changes to the digital advertising industry. Here's what we got for you! We worked hard 🤓(from home, of course 😉) and finally finished Slide #2 on our website at Yay! 💥Make sure to check it out to see how LensAI Advertising Technology works on the famous "Diana" movie trailer. 🎥 Grab popcorn! 🍿 OR Today LensAI LensAI https://lens-ai.com Subscribe on PH: https://www.producthunt.com/upcoming/lensai Stay tuned for more cool AI-Powered stuff to come!

How We Taught Artificial Intelligence to Sell

AI and crypto: What happens when they meet? Against the backdrop buzz of all things AI, there is a new trend emerging that feels full of possibility: What will happen when crypto is combined with AI? So far, the trend seems to be positive and basically goes something like “AI+crypto=endless possibilities.” But what, exactly, are those possibilities? And is some kind of financial super intelligence even possible? And maybe even most important, are the two things even really compatible at any kind of meaningful level? The 30,000-foot way of thinking about this is that AI represents digital abundance while crypto represents digital scarcity. Through another lens, AI represents highly centralized systems (information passed through language models). In some ways, this is like consolidation. Crypto, on the other hand, represents the ideal of or developing distributed networks based on the fundamentals of and immutability, all controlled by . This is more like fragmentation. complete digital decentralization permissionless access private keys held by individual users In a revised intro to the book, , The Sovereign Individual, venture capitalist Peter Thiel wrote In truth, the great conflict over our megapolitical future is only just beginning. On the dimension of technology, the conflict has two poles: Al and crypto. Artificial Intelligence holds out the prospect of finally solving what economists call the "calculation problem": Al could theoretically make it possible to centrally control an entire economy. It is no coincidence that Al is the favorite technology of the Communist Party of China. Strong cryptography, at the other pole, holds out the prospect of a decentralized and individualized world. If Al is communist, crypto is libertarian. The future may lie somewhere between these two extreme poles. But we know the actions we take today will determine the overall outcome. The basic premise of the Sovereign Individual is that massive shifts in technology during our lifetime will give rise to more individual freedom — especially for the people that learn how to leverage the tools of the information age. At the same time, the shift toward more individual freedom will come at the cost of major societal disruptions as geographical borders start to become less significant, and global technology platforms start to replace the need and utility of more localized government and civic groups. And in this zoomed-out context, we see the emergence of crypto, which is built around the idea of individual control and custody, and AI, which is about exporting tasks and production to massive models. So what happens when these two ideals or visions for the future of the internet intersect? Where does it lead, and how will it impact us? Judging by early opinions across the internet, there seems to be a collective sense that the pairing of AI and crypto will be infinitely powerful and next-level everything by reducing the friction imposed by human-centered technology. And in a lot of ways, it’s the human-centered design aspects of crypto that make it so challenging, things like UX, ease of interoperability, understanding the computer science problems crypto solves in the first place, etc. So looking at it from that angle, maybe there is some synergy. But it feels like a larger question looms: How well will these two major technological and philosophical movements mesh with each other? Or, do they actually represent competing ideas and visions? Or, put a different way, what will happen when we give robots bank accounts with no off-switches? Is blind greed a component of general artificial intelligence, or is there something innate about human psychology that enables us to corner the market on wanting more and doing whatever we need to to get it? AI and Crypto Potential Use Cases One of the biggest promises of AI is that it will help humans throw off the yoke of digital drudgery by automating all kinds of routine tasks. Finding issues or bugs in software, which in the past required hours of investigating code by someone with very specialized knowledge or skills, is one example. Bugs in smart contracts and other crypto-related systems can cause havoc and present all kinds of security concerns. and elsewhere when there is a heavy reliance on smart contracts. This is a real-world problem in DeFi Small vulnerabilities written into code can be exploited and cost millions (or billions) of dollars if a hacker is able to access the contract’s funds. So what if there was some kind of AI bug bounty bots that were constantly testing and double-checking smart contract codes and other systems that are used to transfer and store digital assets? It could be like an automated security force that could work at scale and easily work across jurisdictions and projects by leveraging the openness of decentralized systems. Another example of incorporating AI into crypto and DeFi is that using AI’s language models and interfaces might help remove some of the complexity of crypto and make it more user-friendly and intuitive. Rather than having to navigate the complexity of different kinds of offerings, or finding a product that matches a user’s exact specifications, what if people looking for financial alternatives could navigate DeFi products via chat? Or have a simple way to compare and contrast all of the emerging DeFi products in a way that makes understanding their unique value propositions? By removing friction, things will move faster and make seamless automation possible. After all, automation is great — when it works right. Where the combo of crypto and AI gets a little bit more complex is when it comes to the governance of DeFi protocols and decentralized systems. In some cases, making decisions about how protocols are governed — everything from underlying tokenomics and software upgrades to new kinds of products and services — is a challenge because it requires near-constant human attention. According to the , there is something within the decentralized autonomous organization (DAO) called “governance fatigue.” Binance paper about AI and crypto AI can help with this emerging grind of governance by becoming deputized to make decisions that fit within the scope of an individual user’s preferences. On one hand, this is a little bit like having a self-driving car for governance. In many ways, it could be safer and more convenient. But, having deputized AI agents to start making decisions that have real impacts on the DAO could also have unintended consequences. This leads to an even more complex territory: How do we manage autonomous artificial intelligence? Privacy in a Time of Digital Abundance Practicality and philosophical conundrums aside, combining crypto with artificial intelligence should raise privacy concerns. Already, the dynamics of DeFi — the combo of openness and transparency enabled by distributed digital ledgers combined with enough details at the level of individual addresses to provide digital fingerprints (low resolution, maybe, but enough to establish patterns of behaviors for individual wallet holders) — is surfacing the need for better ways of protecting people using new tech stacks to conduct financial transactions. Within the broader digital asset community, we are seeing the privacy protection conversation play out on numerous fronts, such as in the custodial/non-custodial wallet wars, the potential for government surveillance or interference if/when central bank digital currencies (CBDCs) happen, and with things like the use/need/legitimacy of tools like third party mixers. At the same time, there are near-constant calls (in the United States, especially) to figure out how to regulate these new technologies independently. On the crypto side, developing a regulatory framework is falling to agencies that typically develop and enforce financial rules and regs. On the AI side, who will be responsible for regulating a new kind of intelligence is less clear. Regardless of which entities ultimately get tasked with regulating crypto and AI or the combo of crypto and AI, protecting individual user privacy should be a major point for everyone working with these frontier technologies to rally around.

What Happens When AI and Crypto Meet?

¿Qué sucede cuando la IA y las criptomonedas se encuentran?

Que se passe-t-il lorsque l'IA et la crypto se rencontrent ?

क्या होता है जब एआई और क्रिप्टो मिलते हैं?

AIと暗号通貨が出会うと何が起こるのか?

O que acontece quando IA e criptomoeda se encontram?

Điều gì xảy ra khi AI và tiền điện tử gặp nhau?

当 AI 和加密货币相遇时会发生什么？

183 Stories To Learn About Engineering

HackerNoon is an independent publishing platform that makes it to get published. This means that everyone and their grandma wants to promote themselves and their brand on our website — often, for free (lol). possible for anyone It takes to build a distribution channel that brings value to millions of technologists around the world, and we did it by listening to feedback and to work on actionable insights while simultaneously to to meet operational expenses. 🙈🙉🙊 years hiring the right talent fighting the urge shove paywalls and intrusive ads down our readers’ throats When technologists open HackerNoon, they know they’re reading content that has been curated and reviewed by professional to ensure that it’s objective, valuable, and that SEO experts and digital marketers are oh so fond of. Over the years, this approach has yielded HackerNoon that returns to us for . But most of all, it has earned HackerNoon something invaluable: trust™. at least one editor free of gimmicks a strong domain authority and a dedicated community thought leadership and quality tech stories “I love the passion, energy, honesty, and raw talent that is celebrated on HackerNoon. The power belongs in the hands of the people.” - . I-Ally, Inc 🖖🛰️📡 HackerNoon values this trust. We understand the importance of it. And we respect our readers’ time and attention. While there’s nothing inherently wrong with promoting a brand, we often wonder where to draw the line between providing value to our readers (for free) and turning them into leads for Salesforce gurus. We believe we have an answer. Enter, . 🤯 🤯 🤯 Brand-as-Author (‘BAA’) program “Hackernoon is a hub of genuine tech insights and a community of talented developers. We want to support this community and provide advice and information for those interested in building DApps and getting started with blockchain.” - CENNZnet When you serve an audience that prides itself on its tech-savviness, you understand the value of transparency. After all, nobody likes the idea of being a or having their data mined because we live in an era of . product surveillance capitalism Instead, we offer brands a pathway to partner with us on an equal footing, in a manner that remains true to our objective of providing value to our readers, and which continues to foster and nurture the trust we have developed throughout the years. In other words: Brands that want to be a part of this journey find that the is an excellent way to create a long and meaningful relationship with our readers. When they publish and/or republish their blog posts, press releases, and technology updates, they do so by owning their brand’s voice and utilizing the coveted tag that technologists love and trust 👀. But most of all, they signal to our readers that they’re not here to distract them from what they really care about, and are certainly not trying to mine their individual data. BAA program #good-company “There is an old-school spirit. Highly intelligent people get together on this platform. There are different points of view. It allows me to keep track of the direction modern IT entrepreneurship is heading in.” - LensAI The result is the true gold standard in publishing, combining editorial excellence with earned media that no amount of marketing budget in the world can buy. 👏👏👏 “Readers’ trust means everything for us. In publishing tens of thousands of stories, we’ve learned this: the best stories, branded or not, need to provide value for readers, and teach them something they don’t already know. In being transparent with the readers about the inherent bias that’s baked in to some of the content they read, we sustain that hard-earned trust, and in the long run, promote healthy growth for the sponsors and partners we care about,” Linh Dao Smooke, COO Hackernoon. If you want to join like , and to showcase your brand to a monthly 3M+ tech audience, visit sponsor.hackernoon.com or email us at . HackerNoon reserves the right to choose which brand it partners with. top tech companies Heroku AngelList Indeed sponsor@hackernoon.com

Readers Trust Is Everything: Why Brands Publish via Brand As Author Account on HackerNoon

La confianza de los lectores lo es todo: por qué las marcas publican a través de una cuenta de marca como autor en HackerNoon

La confiance des lecteurs est primordiale : pourquoi les marques publient via le compte Brand As Author sur HackerNoon

रीडर्स ट्रस्ट इज़ एवरीथिंग: क्यों ब्रांड हैकरनून पर लेखक खाते के रूप में ब्रांड के माध्यम से प्रकाशित होते हैं

読者の信頼がすべてです: ブランドが HackerNoon の作成者アカウントとしてブランドを介して公開する理由

A confiança dos leitores é tudo: por que as marcas publicam por meio da conta de marca como autor no HackerNoon

Độc giả Tin tưởng Là Tất cả: Tại sao Thương hiệu Xuất bản qua Tài khoản Thương hiệu Là Tác giả trên HackerNoon

读者信任就是一切：为什么品牌在 HackerNoon 上通过品牌作为作者帐户发布

Meet the Winners of Startups in North and Central America

“Storytelling with ChatGPT & Lensa AI: 6 Enchanting Tales of Avatar Girls Crafted by Artificial Intelligence and Stable Diffusion Technology “ As an intrepid content creator, I recently combined the powers of ChatGPT and Lensa AI’s magic to create sophisticated stories for my avatars. I’m happy to report that I’ve monetized my Instagram use for the first time ($50)! In this article, I’ll share examples of how fellow creators can harness the power of AI for social media, campaigns, or to tell captivating stories. And don’t worry; my blood isn’t purple, but who wouldn’t want that as a kid? While my primary goal wasn’t monetary, this method could potentially speed up the process of generating passive income through serial content. However, let’s hope we don’t reach a point where we can’t distinguish between real and AI-generated influencers, thanks to GPT. Only time will tell! I had a blast combining these two AI applications to create content and added personal touches from my history, favorite movies, and shows. The magic avatars, generated from my real photos, provided an intimate and memorable experience. One night, I was thrilled to conceptualize and combine two AI applications, ChatGPT and Lensa AI, to create content. While doing so, I drew from my history, giving ChatGPT commands based on my favorite movies, books, documentaries, and TV series. Lensa AI’s magic avatars were generated from my real photos, so I mentioned some of my personal characteristics, thinking it would create a more intimate and memorable experience. Of course, as you’ll soon read in these captivating, adventurous stories, my blood doesn’t run purple, but I always wished it did, especially as a child. I was particularly fascinated with purple and am still enamored with all its shades. No other color combines such contrasting shades of black or blue. But our focus here isn’t my admiration for purple; it’s my excitement for the advanced level that artificial intelligence technology has reached. In the past, I wrote various articles about AI developments, and two years ago, I even created a podcast discussing AI history while integrating two AI platforms. I’ve always closely followed AI technology; resources are available in the sources section below. At the beginning of my writing, I’d like to mention an important point from Harari expressed his belief that AI would become increasingly sophisticated in the book I read two years ago — and should be re-read every year. Yuval Harari’s book “21 Lessons for the 21st Century” that he penned after his work, Homo Deus. He added that our future would include AI and a single global civilization, with the critical challenge being to find out how we can solve problems together with AI. According to Harari, the future offers opportunities for new story types involving humans, superhumans, and AI. He also wrote that he believed superhumans, which combine AI and humans, would soon be created (Elon Musk is working on a superhuman project and is a co-founder of OPENAI with Sam Altman). People generally worry that AI will completely take over our jobs and leave us unemployed. However, Harari disagrees, believing AI will actually help create new jobs for humans. Those willing to collaborate with AI will be the ones to rise in the workplaces of the future. Harari also believed that AI would enhance automation and our creativity. His predictions in this regard have come true two years later. The most interesting insight he shared was: “The lesson to be drawn from all this is that the future is bright. We should not worry about AI’s potential in the workplace and should see it as an opportunity. These tools can help us move away from mundane tasks we don’t want to do and allow us to focus on more important or fun tasks. This, in turn, can enable us to spend more time improving society.” An incredible foresight and problem-solving observation! The freedom to do what I consider more important, more fun, and a better society — what more could I want? Will the future be brighter than we think? I hope so, and we all do. With these thoughts in mind, let’s continue with our stories featuring six adventurous, enchanting girls with superhuman abilities who live on different planets and times, and talk to animals or flowers. These tales span familiar elements from Star Wars and prehistoric eras to our curiosity about the cosmos, Game of Thrones, and more. I don’t need to explain how delightful it was to immerse myself in these stories, feeling connected to each one. I must thank the original story creators, such as G.R.R. Martin, George Lucas, Carl Sagan, Ann Druyan, and Neil deGrasse Tyson, for providing me with this knowledge and curiosity and helping me dream. While writing the stories, I’ll also include the commands I gave to ChatGPT, as it’s super creative, but so are we! I’ll also add the Reels links because I selected a specific piece of music for each story. (These music pieces were found and added without AI assistance.) Enjoy the stories! AI Avatar Girls Stories 1- * Victoriax Has Purple Blood 💜 https://www.instagram.com/reel/Cl7pQo4uHzQ AI Stories 1 ✨ ⚡️🔮 visual by .ai -story of content by Victoriax @lensa ChatGPT — concept & context by me @vickivale Victoriax is a being from a distant planet vastly different from Earth. She is a member of a highly advanced civilization that has mastered the art of intergalactic travel and has a deep understanding of the universe. Because of her purple blood, she is likely from a planet with a different atmosphere and chemical composition than Earth, and her physical appearance may be quite different from humans. Despite her otherworldly appearance, Victoriax is kind and compassionate, and she is dedicated to sharing her knowledge with other species in the galaxy. She is a skilled communicator and can impart her wisdom in a way that is easily understandable to others. She also has a love for music, which is a common form of expression among her people. As a social being, Victoriax is likely to be quite outgoing and friendly. She may have a wide circle of friends and acquaintances among the different species she encounters on her travels. However, despite her friendly demeanor, she is also quite independent and is not afraid to strike out on her own if the situation calls for it. She is a strong and capable being who is not easily deterred from her mission to spread knowledge and understanding throughout the galaxy. ✨✨ Do you like Victoriax? 🔮 AI Avatar Girls Stories 2-* Victoria From a Baroque Planet https://www.instagram.com/reel/Cl7uyaHL4M7 AI Stories II- from baroque planet 🌸🌷🌸 Visual by — story content by — concept & context by me Victoria @lensa.ai @chatgpt @vickivale Victoria, the princess of a distant planet with purple-blooded inhabitants, was known for her love of flowers and her ability to communicate with them. She had the unique ability to change her body’s color to match the blooming plants’ hues. As a young princess, Victoria was fascinated by the beauty and wisdom of the flowers, and she spent many hours listening to their stories and learning from them. She soon discovered that she could use her ability to change her body color to blend in with the flowers and become one with them. As she grew older, Victoria’s love for flowers and knowledge only grew stronger. She became known as a wise and kind princess who used her abilities to help others and spread love and kindness throughout the universe. Despite her royal status, Victoria was always humble and unassuming, never letting her position go to her head. She spent her days exploring the universe and learning from the various species she encountered, always seeking to expand her knowledge and understanding. One of Victoria’s greatest passions was music, and she loved nothing more than to listen to the beautiful melodies of the baroque and Satie II genres. She often found herself humming along to the soothing tunes, letting them wash over her and transport her to a different world. Victoria used her unique experiences and knowledge to craft incredible stories that captivated readers and transported them to distant worlds. She wrote of her adventures among the stars, her encounters with otherworldly beings, and her deep connection with the flowers. Through her stories, Victoria continued to spread love, knowledge, and kindness, sharing her wisdom and experiences with others and inspiring them to seek their adventures among the stars. ✨ AI Avatar Girls Stories 3- Vi 🌈 🏹 From the Most Colorful Planet, “Color Cosmos.” Visual by — story of content by — concept & context by me ✨ @lensa.ai @chatgpt @vickivale https://www.instagram.com/reel/Cl7yJR7gcTs Vi was a young princess from a colorful planet in the depths of space. She had been born with a face and hair that were a riot of colors, each hue representing a different element, feeling, thought or experience. As she grew older, her colors shifted and changed, reflecting her experiences and the lessons she had learned. role in her community was to keep the balance. She did this using the power of color, harnessing the different energies and abilities of each hue to maintain harmony and stability. Vi’s She was especially attuned to the colors blue and green, representing collaboration, ease, movement, trust, ambition, and love. Through her connection with the colors, Vi was able to sense the moods and emotions of those around her. She used this ability to help others, offering support and guidance when needed. She was always ready to listen and offer words of comfort or encouragement, using her power of color to bring a sense of peace and tranquility to those in need. Vi’s love for color was boundless, and she spent much of her time exploring the different hues and shades that existed in her world. She discovered that each color had its unique properties and powers, and she was constantly amazed by the beauty and wonder of the spectrum. As she grew older and wiser, Vi continued to use her power of color to help others and keep the balance in her community. She was known as a kind and compassionate princess who always sought to bring peace and harmony to those around her. She was loved and respected by all who knew her, and her legacy as a champion of color and balance lived on long after she was gone. Color Cosmos — Vi’s colors vocabulary 🌈 🔴 Red: The color red is associated with passion, energy, and strength. In Vi’s story, she learns that red has the power to ignite and inspire. She uses this power to motivate others and help them overcome their fears and doubts. She also discovers that red represents love and romance, and she uses this power to help people connect and form deeper bonds. 💜 Purple: The color purple is associated with royalty, spirituality, and mystery. In Vi’s story, she learns that purple has the power to connect people with the divine and the mystical. She uses this ability to help others seeking guidance or direction, offering insight and wisdom. She also discovers that purple represents intuition and insight, and she uses this power to help people trust their instincts and follow their inner voice. 🌝 Yellow: The color yellow is associated with happiness, joy, and positivity. In Vi’s story, she learns that yellow has the power to uplift and energize. She uses this ability to help others when feeling down or discouraged, offering a ray of sunshine and hope. She also discovers that yellow represents intelligence and creativity, and she uses this power to help people tap into their innate wisdom and imagination. 🍀 Green: Green is associated with nature, growth, and harmony. In Vi’s story, she learns that green has the power to nourish and heal. She uses this ability to help others when they feel sick or injured, offering comfort and care. She also discovers that green represents balance and stability, and she uses this power to help people find harmony and peace in their lives. AI Avatar Girls Stories 4 — VICTORIAS 🐉🎠⚔️ From the Seven Kingdoms Visual by — Story of content from — Concept&Context by me 🔮 @lensa.ai @chatgpt @vickivale https://www.instagram.com/reel/Cl71hSJANOC Victorias, was a young woman from the North in the Seven Kingdoms. She was a skilled rider and a fierce knight, with a grey dragon and a beautiful horse by her side. She had always been curious about her heritage and her family, but she did not know her true house. As she set out on a journey to discover her roots, Victoria encountered many challenges and dangers. She rode her dragon and horse across the vast landscape of the North, battling wildlings and other threats along the way. She was a skilled fighter, wielding her sword with deadly precision and courage. Despite the challenges she faced, Victoria remained determined and resourceful. She used her knowledge of the land and her ability to ride a dragon to her advantage, outwitting and outmaneuvering her foes. She also drew on her inner strength and resolve, refusing to give up or be discouraged. As she continued her journey, Victoria encountered many other characters from the world of the Seven Kingdoms. Some were friends and allies who helped her on her quest. Others were enemies who sought to hinder or harm her. But Victoria remained steadfast and true to herself, always seeking the truth and her path. Eventually, Victoria discovered her true house and her true identity. She learned that she was a member of House Stark, one of the great houses of the North. She was welcomed into the fold by her kin, who was impressed by her bravery and skill. She was also reunited with a long-lost love who had been searching for her all along. Victoria’s dragon was named Storm, and her horse was called Snow. Her sword was named Ice, reflecting her heritage and the frozen lands of the North. She was loved and admired by many, including Jon Snow and Sansa Stark, who recognized her strength and courage. AI Avatar Girls Stories 5-AI Story V- Vixtorian ✴️🪄☄️ https://www.instagram.com/reel/Cl9zEkzAZF3/?utm_source=ig_web_copy_link Visual by — Story of content by — Concept & Context by me 🌷 @lensa.ai @chatgpt @vickivale Vixtoria was born five years after the Return of the Jedi, during a time when the galaxy was still reeling from the effects of the Galactic Civil War. She grew up among the Mandalorians, a proud and fiercely independent people known for their martial prowess and their unique culture. Despite her upbringing, Vixtoria was always drawn to the ways of the Jedi, and she spent much of her young life training in the ancient arts of the Force. As she grew older, Vixtoria felt a strong calling to undertake a journey of self-discovery. She left her home world and set out into the galaxy, determined to learn all she could about the ways of the Jedi and to hone her skills as a warrior and a diplomat. Along the way, she encountered many different species and cultures, and she quickly gained a reputation as a powerful and skilled Jedi. As a Jedi, Vixtoria knew that there were three important dimensions to becoming a true master of the Force. The first was to master the physical skills necessary to wield a lightsaber and fight with grace and precision. The second was to master the mental skills necessary to control one’s emotions and to use Force for good. And the third was to master the spiritual skills necessary to understand the will of the Force and to act in accordance with its principles. 🔮 Despite her many accomplishments, Vixtoria never forgot her humble beginnings. She continued to travel the galaxy, always seeking out new challenges and opportunities to help others. She lived a life of adventure and excitement, and she remained a powerful force for good in the galaxy. Vixtorian! ✨ AI Avatar Girls Stories 6- Vv 🦚🦕🏹 10,000 BC ✨✨ Visual by @lensa.ai — Story of content @chatgpt — Concept & Context by me @vickivale 🔮 https://www.instagram.com/reel/Cl91aQIuT0o/?utm_source=ig_web_copy_link Story of Vv, a woman who was born in 10000 BC and lived in the prehistoric era. She was one of the first humans to walk the earth, and she learned how to survive in a world filled with danger and uncertainty. Vv was a skilled hunter, and she spent her days tracking and killing the animals that she needed to survive. She was also able to communicate with the wolves and birds that she encountered on her travels, and she could even fly with them if she wanted to. One of Vv’s most remarkable abilities was her ability to change her appearance at will. She could take on the guise of any creature she wanted to, which made her a master of disguise. Her god was Many Face God, and she worshipped him with all her heart. ❤️‍🔥 Despite her wild and unpredictable nature, Vv was also a kind and compassionate woman. She understood the importance of living in harmony with the world around her, and she worked hard to protect the animals and plants she depended on for survival. One of the things that Vv loved most was running. She had to be fast to catch her prey, and she became an expert at outrunning even the fastest animals. She also loved looking up at the stars, the sun, and the sky, and she spent hours thinking about the mysteries of the universe. When it came to surviving in the prehistoric world, Vv knew exactly what she needed to do to stay alive. Here are the five best survival tips that she used to thrive in the ancient world: 🏹 S tay alert and aware of your surroundings. The prehistoric world was full of dangers, so you had to be constantly on the lookout for threats. 🏹 Learn how to hunt and gather food. Vv was an expert at tracking and killing her prey, and she knew how to find the plants and berries that she needed to eat. 🏹 Stay flexible and adaptable. The prehistoric world was constantly changing, so you had to be ready to adapt to new situations and challenges. 🏹 Stay connected to the natural world. Vv was able to speak with the animals and understand the rhythms of the earth, which helped her to thrive in the wild. The End. Follow me : | | | Twitter LinkedIn Instagram Telegram Peace 🌻 Also published here

Storytelling With ChatGPT & Lensa AI: 6 Enchanting Tales

Let's learn about via these 70 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Mongodb /Learn Repo 1. Let's Make An API And Sell It APIs are in high demand. Making an API that companies want to use would be even better if you could sell it, too. This is how to do it with every step inside. 2. MongoDB 101: A Free Course By Academy 3T MongoDB is a document-based NoSQL database, typically used for its ability to quickly scale, especially large amounts of data. MongoDB has two clear main benefits over SQL when it comes to data organization: 3. Get Started Building your First MERN Application - Step by Step I am sure that when you first begin learning full-stack web development using the MERN stack, you will have questions. 4. MongoDB vs. DynamoDB: Choosing the Best Database for Your Business All about MongoDB vs DynamoDB. Explore benefits, and in-depth comparison to find out the best choice for your business app. 5. MongoDB vs. DocumentDB: Which Is A Better Choice for You? Are you trying to decide if you should use MongoDB or DocumentDB? With the recent controversy surrounding licensing with MongoDB, it can be confusing to decide which option is right for your company or project. Amazon decided the core MongoDB code is challenging to scale while remaining highly available. Amazon wrote their implementation, which is compatible with the Apache 2.0 open source MongoDB 3.6 API. MongoDB, Inc. has also recently changed their license to make future imitations more difficult. To do this, they created an entirely new license called the Server Side Public License. 6. I made AWS Lose Money - Here's How! This is a short series that I wanted to share for a long time about the basics of “Cost Optimization” on AWS. 7. 8 Tools You Can Use to Analyze Big Data An essential part of modern business, no matter what the industry, is Big Data - sets of copious amounts of data that reveal much in terms of trends and patterns regarding human behavior and interaction. 8. Homebrew - Explained in Simple English So, there are tonnes of jargon, commands, processes that we follow blindly after surfing from the internet without knowing the actual reasons and under the hood processes, that just remain in our short term memory and get purged eventually, thus we don't feel comfortable using or even about discussing in a group about them. 9. Enabling Virtuals In Mongoose Lean Mode (in a FeathersJs service) Originally published at melvinkoh.me 10. How to Erase Expired Docs Automatically with MongoDB (TTL index) Recently I needed to delete some documents that I saved in MongoDB after some time. I can think of a few examples of why we would want to delete data after some time: 11. How I Built a Warehouse Management App Called Cardus A web app that allows users to create and manage their warehouse of items. 12. How to Use .populate() With mongoDB How to use populate() method with mongoDB to itch data across referenced collections 13. Mastering MongoDB - one tip a day series 14. Uploading a 1 Million Row CSV File to the Backend in 10 Seconds Uploading 1 million row size large CSV to mongoDB using nodejs stream 15. How to Find Insecure MongoDB Connection Strings in Public GitHub Repositories A guide on how to protect yourself and your projects while on Github. 16. MongoDB: Exploring Data Visualization Tools and Techniques Looking for MongoDB data visualization tool? There are plenty of options but firstly its better to explore what kinds of solutions there are on the market. 17. Efficient Server Side Pagination using Mongodb How to implement server-side pagination in MongoDB using aggregation pipeline instead of client-side pagination and why you should do it server-side instead. 18. Best Tools For Building Your First MERN Application Top tools to use when building your next awesome web application using the MERN stack 19. Learning the basics of MongoDB by Writing a User Registration API Learning MongoDB has been one of the things in my checklist for the past 6 months but hasn’t gotten around actually learning it until now. In order to understand writing queries and all I figured it’ll be better to make something instead of just reading and watching some tutorials. So, I’ve decided to make a user registration API i.e, a simple registration form kinda thing with only sign up and sign in functions as of now. As learning Mongo was the main intention, I’ve decided just to make the core app which is the backend functionality and no front end as it’s not necessary and I hate to do it. 20. When ORMs Disobey: A Mongoose Story In hindsight, it seems like mongoose was doing the sensible thing by simply ensuring that the document existed in the database. 21. How To Create a URL Shortener using MongoDB And Node Let us learn about MongoDB, Mongoose, Node, and other tech by building a simple URL shortener project. 22. InfluxDB: From Open Source Time Series Database to Millions in Revenue The InfluxBD Origin Story: How Paul Dix took a failing SaaS product and turned it into a successful commercial open source time series database company. 23. Instant GraphQL + MongoDB Backend With Mongoke Implementing a good GraphQL backend to serve your database data is not an easy task, you have to implement a lot of resolvers, add authorization, pagination of the fields and use a DataLoader to not repeat your database queries during relations. 24. A Guide to Using MongoDB Database in a ASP NET Core Web API Application When you think about database providers for ASP NET Core apps, you probably think about Entity Framework Core (EF Core), which handles interacting with SQL databases. But what about the NoSQL options? A popular option for NoSQL is MongoDB. So in this article we're going to learn how to create a simple ASP NET Core CRUD API using MongoDB as the database provider. 25. How to Change Parameters of a Server at Runtime Implementation of business configuration provider tips. Java, Spring and MongoDB in action 26. Building a REST API in Go with MongoDB Integration: A Step-by-Step Guide Code or Write a REST API with GO internal libraries and MongoDB as database 27. Is MEAN Tech Stack the Best Choice for Enterprise Solutions? Get to know why the MEAN tech stack for enterprise solutions is the way to go about scalable, secure, and sustainable software for organizations. 28. Mastering MongoDB - currentOp “Dashboard in the pilot's cockpit” by Mitchel Boot on Unsplash 29. MEAN Stack Development: All That You Need To Know Web development is not the same it was in the early internet age. It is a fast-paced and diverse environment. With the continuous advancements in frameworks, programming languages and tools, it becomes difficult to choose the “right” tech to ensure rapid, accurate and secured web application development. 30. How to Create a Serverless API Using Azure Cosmos DB In this tutorial, we’ll build a Web API using Azure Functions that stores data in Azure Cosmos DB with MongoDB API in C# 31. Design for E-Commerce Apps Using a NoSQL Database Working with a lot of data like products, orders, categories, users and payments is a very important topic when building e-commerce applications. In this post you'll learn the very basics of structuring your noSQL schema so it's fast and scalable for e-commerce scenarios. 32. 2019 Database Trends – SQL vs. NoSQL, Top Databases, Single vs. Multiple Database Use Wondering which databases are trending in 2019? We asked hundreds of developers, engineers, software architects, dev teams, and IT leaders at DeveloperWeek to discover the current NoSQL vs. SQL usage, most popular databases, important metrics to track, and their most time-consuming database management tasks. Get the latest insights on MySQL, MongoDB, PostgreSQL, Redis, and many others to see which database management systems are most favored this year. 33. AvionDB: A MongoDB-like Distributed Database In the past few months we have been getting this question a lot: 34. A Brief Introduction to MongoDB MongoDB is a document-oriented NoSQL database for storing large amounts of data. MongoDB uses collections and documents instead of tables and rows 35. A DIY Guide to Building an Analytics Dashboard with Node, Express, and Cube.js In the following tutorial, I’ll show you how to create a basic analytics dashboard with Node, Express, and Cube.js. As a database, we’re going to use MongoDB with MongoDB BI Connector. If you’re not familiar with it, I highly recommend you go through the Building a MongoDB Dashboard tutorial. It covers the basics of setting up Mongo and its BI Connector. 36. Learnings From The Meow Bot Attack on Our MongoDB Databases and ever since then we never look at a kitty the same way again. meow! 37. How I build Coronavirus Tracking Application What is Coronavirus (COVID-19)? Coronavirus (COVID-19) which is an infectious disease caused by respiratory illness and symptoms like flu, cough, fever, difficulty breathing, on December 31, 2019, the first case was recorded in Wuhan, China and later the virus got spread around the world and as of now, around 1 million+ confirmed cases are being recorded since then. You can protect yourself from this virus by following basic precautions like washing your hand frequently, avoid touching your face, maintaining a distance of (1 meter or 3 feet) between people and most importantly if you are sick then immediately call for medical help at your nearest hospital. 38. AvionDB Introduction: A MongoDB-like Distributed Database In the past few months we have been getting this question a lot: 39. Which Database Is Right For You? HarperDB vs. MongoDB vs. PostgreSQL The purpose of this article is not to determine which database is the best but to help determine which is a fit for your specific project. 40. Top 3 Databases Every New Developer Should Know Most new developers wonder which database is suitable for their project since they don't want to get off on the wrong foot. So, first and foremost, let's define a database. A database is a type of storage facility that electronically stores and organizes data so that it may be utilized and accessed later. 41. NoSQL: A Brief History and the DynamoDB Challenge Dynamo accelerated the NoSQL revolution that’s driving the database industry. 42. The Pros and Cons of NoSQL Learn how NoSQL differs from SQL and how it's built for high performance and availability. And no, it doesn't stand for "No SQL". 43. SQL Databases Vs. NOSQL Databases The decision to choose a database  for project is not that simple. But when it comes to choosing a database, the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure. 44. “I Want to Make my Own Netflix!” — So I Become a Software Developer Growing up, I had an interest in learning and always dreamed of creating my own software. 45. The Ultimate Guide To Custom Role Based Access Without Third Party Libraries Here, we have four roles: Sme, Sponsor, Admin, Operations.Initially, we had only 3 roles.Operations role was added later and Operations user has permissions similar to the Admin user.In the code, we had to replace every instance of if (user.type == USER_TYPES.ADMIN) with if (user.type == USER_TYPES.ADMIN || user.type == USER_TYPES.OPERATIONS).As this is time consuming and we can also miss many instances, we have created a roles module. In the roles module,the roles are defined along with their respective permissions as seen in Code (Part-III). Based on the permissions for each role, we will evaluate the authorization for the user in each of our controller methods.If the user has access, only then he will be granted the resources. 46. How to Reset Password with Node.js and Angular [Part 1] There are lots of tutorials about user authentication but all of them are just covering the topics like how to sign up and how to sign in. Even if you find a tutorial about reset password then you will see that it covers old school method without APIs and using jQuery based front-end. In this tutorial, we are going to use a Node.js based API which will connect to Angular front-end. Let’s get started: 47. How to Stream XML messages from IBM MQ into Kafka into MongoDB Let’s imagine we have XML data on a queue in IBM MQ, and we want to ingest it into Kafka to then use downstream, perhaps in an application or maybe to stream to a NoSQL store like MongoDB. 48. Improving Our MongoDB Write Throughput with SQS Deep dive on how we got our MongoDB load at a steady sub 50% CPU load by using an SQS layer between our Node application and the database to save costs. 49. How to Run Node.js with MongoDB [Tutorial] Many of today’s web applications use node.js in conjunction with MongoDB to store and access data on the backend server. Not only does node.js excel in ease of access to non-relational databases like MongoDB, but brings scalability and speed to the server side of your app. 50. How to Connect MongoDB(4.2.10) and MongoDB Compass Locally What is MongoDB? 51. Breaking Down MSON Template Queries Template Queries are dynamic templates constructed with MongoDB-style operators, which allow you to customize MSON components with less code. 52. Augh, Someone deleted production data. “A man stretching out his two hands with careless written on them in black and white” by Mitchel Lensink on Unsplash 53. One-Time Password Generation Using speakeasy, Nest.js And MongoDB Here, we have designed and developed a flow for OTP(One time password) for user registration and also blocking a user's account after the maximum retries for incorrect otp is exceeded. 54. Get a GraphQL server up and running in 5 minutes using graphql-yoga and mongoose THIS ARTICLE IS NO LONGER UP TO DATE. USE APOLLO SERVER INSTEAD. 55. Solving invisible scaling issues with Serverless and MongoDB Ever since software engineering became a profession, we have been trying to serve users all around the globe. With this comes the issue of scaling and how to solve it. Many times these thoughts of scaling up our software to unimaginable extents are premature and unnecessary. 56. Building a MongoDB NoSQL E-Commerce Data Model MongoDB supports ACID transactions, and its cloud offering is PCI DSS compliant. It's one of the best NoSQL options for e-commerce. 57. How To Link Mongoose And Typescript for a Single Source of Truth Without external tools 58. Advanced and Efficient MongoDB Pagination How to make clean pagination with no additional dependencies. Basically for Node.js but can be used in any other languages and platforms. 59. Creating a Object Storage System from scratch I was very much interested in developing distributed systems and the like. But it was very difficult to find related beginner articles. One of my projects was a cloud drive. In order to implement that, i had to go many places i haven't. It had a good steep learning curve.  I wanted to share that knowledge. 60. PostgreSQL vs. MongoDB: Evaluating Database Structure, Speed, and More Choosing a database can be scary as it’s nearly impossible to change once you’ve built on one. So which will you choose? PostgreSQL or MongoDB? 61. Getting started with MongoDB Enterprise Operator for Kubernetes Introduction 62. Building User Service With GRPC, Node.JS, and MongoDB: The Complete Microservice Tutorial — [Part 2] Microservices are about decoupling your system. In a monolith, you build all the components of the software in one large codebase and deploy the software at once. But in the microservice world, we build each component of a large system decoupled from each other. 63. How to Build A Well Structured 3-tier Architecture (MERN Stack - ES6) [Step-by-Step Guide] This project serves as a guide/template for a 3-tier architecture. If you are entirely new to Web Development. I highly suggest you watch this video first. 64. Basic Guide To Building Microservices With MongoDB, NodeJs, And Express Most of us have gone through tough times where you have a large scale application which has a bunch of features, API integrations, database connections, etc. New features and updates get released, and of course you need to fix the bugs and maintain the code. 65. Mastering MongoDB - Introducing multi-document transactions  in v4.0 Photo by Madison Grooms on Unsplash 66. Why We Should Have Different Databases Today there are hundreds of SQL and NoSQL databases. Some of them are popular, some are ignored. Some are user-friendly and well documented and some are hard to use. Some are open sourced and some are proprietary. And, perhaps, the most important - some are scalable, optimized, highly available and some are difficult to scale or maintain. 67. How to Back Up and Restore a MongoDB Database MongoDB. Seven Ways to Back Up and Restore Data 68. Mastering MongoDB - Faster elections during rolling maintenance Photo by annie bolin on Unsplash 69. Configure MongoDB with Kerberos Authentication The MongoDB database administrators in a large enterprise may need to configure MongoDB to support Kerberos Authentication. The configuration of MongoDB with Kerberos authentication is very simple, provided you have some Kerberos knowledge. The MongoDB documentation article, Configure MongoDB with Kerberos Authentication on Linux, is pretty extensive on this topic. However, the article states — “Setting up and configuring a Kerberos deployment is beyond the scope of this document”, resulting in some of the starters/enthusiasts with limited knowledge in Kerberos not proceed any further. This article is geared more towards bridging that gap and to help you understand 70. A Complete Guide to Laravel Sail This post is about what to expect from Laravel Sail, how it works and how to make the most of it; it is also a plea to developers to break away from it. Thank you for checking out the 70 most read stories about Mongodb on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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As we see apps and programs like Lensa and ChatGPT making big waves in the online world, it is clear that AI is here to stay and that it brings incredible innovation with it. Of course, these tools are not without controversy. Many people see AI technology as a threat that will eliminate jobs and destroy the uniquely human creative and emotional perspective. However, for expert product managers like Sophia Xing, the key is in how this technology is implemented to work with humans. When brought to appropriate industries, AI holds immense potential for improving efficiency and maximizing profit margins. Furthermore, when humans and AI can actually work together, the possibilities are endless. A Background Originally from Australia, Xing first encountered the powers of artificial intelligence and machine learning while conducting academic research during her time at Princeton University. She ran topic modeling and sentiment analysis on newspaper articles that covered public equities, and then used the results as a foundation to construct a dynamic and risk-managed investment portfolio. This experience gave her an understanding of the scalability of insights that can be garnered from using machine learning techniques on unstructured data, which is so prevalent nowadays. In 2015, Sophia completed her BA in economics, statistics and machine learning, and applications of computing. She then took a position with Goldman Sachs, as a portfolio manager in quantitative investment strategies, which was a very informative period of her career. In this position, she consistently witnessed individual data vendors supplying sources of verticalized data to quantitative analysts, who would then comb through to determine the robustness of the data source to deduce useful insights. Although there was great potential in the unstructured data, she was also blown away by the sheer quantity of noise that most industry practitioners had to work with before arriving at even the possibility of true insights. Finding Direction After five years with Goldman Sachs, Xing decided it was time to take the next step. She was admitted into the highly-reputable MIT Sloan School of Management, where she excelled and graduated amongst the top 5% of her class. The program provided some incredible opportunities, and Xing’s quantitative background enabled her to leave a lasting impact on many projects. In 2020, she was brought on to MIT’s Covid Analytics Task Force, where she worked to construct a risk calculator in order to optimize hospital ventilator allocation in Massachusetts and neighboring states. Xing also developed TalkAI, a unique product that provided an AI-powered audience in the AR space that reacts to the speaker’s tone and content, in order to help users overcome their fear of public speaking. This project won the Best Tool category of the MIT Reality Hack in 2020 – one of the world’s largest and most reputable mixed reality hackathons. Throughout her coursework at MIT, she mastered advanced natural language processing (NLP) and computer vision, laying the foundation for future aspirations. Outside of the AI space, Sophia’s financial and business background also enabled her to contribute immensely. She served as the Partnership Director of MIT’s Clean Energy Prize of 2020, as well as the Sponsorship Director of MIT’s FinTech Conference of 2020. AI Meets Customer Support With Great Success Sophia had the opportunity to work with some of the biggest and most innovative tech companies in the industry: Google, Bodeswell, and Curated. Her passion for conversational AI helped her to land the role of Lead Product Manager at , an AI SaaS platform for customer service teams. Forethought She has led the product management, from ideation to launch, of the company’s software that allows customer service managers, who may not have technical knowledge about machine learning, to be able to easily construct an AI-powered customer support agent for their company. Xing’s expertise in NLP and software product development have been key to the platform’s success. These AI agents are different from standard chatbots because traditional chatbots are decision tree-based and require extensive behavioral programming, whereas the AI agents that Forethought has developed can learn from a handful of input phrases and can then dynamically learn to improve their capabilities. These AI-powered customer support agents are able to resolve customer needs with lower cost, faster turnaround, and they lead to overall higher customer satisfaction. For Xing, the idea is not to eliminate human customer support agents. Instead, by working hand in hand, AI technology can help to reduce the noise for human agents by resolving the tasks that can be automated and, in turn, allow them to give their time to the cases that require human sensibility. She explains: “In a customer support context, AI is usually good at tasks such as finding the most relevant and recent information, processing tasks are routine and require layers of validation, conducting automation with integrated systems, and gathering feedback at scale to optimize business decisions. On the flipside, humans are usually better at exercising discretion when offering similar solutions, serving as a sounding board to make sure customers feel that their feedback is heard by a representative from the company, as well as processing one-off cases that may not be detailed in company customer policies.” From her experience, there are certain steps that a company can take to provide its unique AI-powered customer support agent with the greatest potential for success. For example, investing time and resources to create strong supporting knowledge articles helps to provide a solid foundation of knowledge for the technology, and can be easily linked without the need for human engagement. Narrowing down inquiries based on the highest ROI can help to focus resources on engagements that will pay off the most. Evaluating key terms, like “password reset” or “address change”, can help to take the easiest tasks off the plates of human agents as quickly as possible. Lastly, by considering the customer persona, companies can help to direct their AI towards the lower-stakes customers first and leave human agents to engage with VIP or repeat customers that would require more personalized attention. At a time when there are such strong emotions on both extremes about technology, it is refreshing to hear from someone finding the middle ground. Sophia Xing is perfectly positioned to utilize the benefits of AI technology to improve the human experience in the customer service industry. In reflecting on her work, she shares that “Customer satisfaction improvements at scale require thoughtful automation.” Of course, the truth is that AI software can only be as thoughtful as the people who design them. Thankfully, with Sophia Xing leading the way, we can look forward to a future with tech tools that make our lives a little bit easier.

Sophia Xing: The Product Manager Crafting AI-Driven Products That Balance Efficiency and Sensibility

Deep Learning Language Models vs. Cognitive Science What Is Thinking? The pioneering goal of Artificial Intelligence has been to understand how humans think. The original idea was to merge intellectual and computer contributions to learn about cognition. In the 1990s, a shift took place from a knowledge-driven AI approach to a data-driven AI approach, replacing the original objectives with a type of Machine Learning called Deep Learning, capable of analyzing large amounts of data and drawing conclusions from the results. Deep Learning is a predictive machine model that operates off of pattern recognition. Some people believe that if you simply feed the model more and more data, then the AI will begin to evolve on its own, eventually reaching AGI (Artificial General Intelligence), the ‘Holy Grail’ of AI. This theory, however, is viewed as being deeply flawed because these AI machines are not capable of “awareness” or the ability to “reason.” With Machine Learning/Deep Learning AI, there is no “thinking taking place.” These predictive machines are void of any actual intelligence. Scaling into bigger models by adding more and more parameters until these models consume the entire internet, will only prove useful to a point. A larger data bank will not be able to solve for recognizing toxicity within the data structures, nor will it enable the ability to navigate sensitive data, permissioned information, protected identities, or intellectual property. A larger data bank does not enable reasoning or abstract thinking. For AI to achieve the ultimate goal of AGI we need to be able to construct cognitive models of the world and map ‘meaning’ onto the data. We need a large database of abstract knowledge that can be interpreted by a machine imparting a level of ‘awareness’. Newton vs. Einstein Model-Based AI for Active Inference is an Artificial Intelligence methodology that possesses all the ingredients required to achieve the breakthrough to AGI by surpassing all of the fundamental limitations of current Machine Learning/Deep Learning AI. The difference between Machine Learning AI and Active Inference AI is as stark as the jump from Newton’s Laws of Universal Gravitation to Einstein’s Theory of Relativity. In the late 1800s, physicists believed that we had already discovered the laws that govern motion and gravity within our physical universe. Little did they know how naïve Isaac Newton’s ideas were until Albert Einstein opened mankind’s eyes to spacetime and the totality of existence and reality. . This is what is happening with AI right now It’s simply not possible to get to AGI (Artificial General Intelligence) with a machine learning model, but AGI is inevitable with Active Inference. So, What ARE the Limitations of Deep Learning? Deep Learning AI is simply machines (software programs/algorithms) made up of neural networks that become smarter (more refined) machines over time through the process of scaling the data parameters in tandem with the number of training tokens. Basically, the more data that is fed into the machine, the better it gets at recognizing patterns, and generating and predicting outcomes that ‘seem’ to be correct. A neural network, what is that and how does it work? An artificial neural network (ANN) is an AI algorithm made up of many layered and interconnected processing “nodes,” intended to mimic the structure and behavior of human brain neurons. A node receives input and processes it against a set of weights and biases, producing an output. That output is then passed along as an input to another node, with the process repeating simultaneously throughout the network of nodes until it achieves a final output. Neural networks are trained using large datasets along with an optimization algorithm to refine the output results and minimize error. methods used to develop today’s most celebrated AI applications are GAN models (MidJourney and DALL.E) and Transformer models (ChatGPT). The two common Deep Learning GAN AI — Generative Adversarial Networks — Primarily used for text-to-image, face generation, 3D object generation, video prediction, and more — Consists of two neural networks (AI algorithms) simultaneously playing off each other. One is the ‘generator’, spitting out random examples of the item searched for. The other is the ‘discriminator’, evaluating the results and checking them against the data bank of pattern data that match the criteria, in order to pass or fail the generative network, which just keeps trying until it generates a passing answer. Transformer AI— Primarily used for NLP (Natural Language Processor) and CV (Computer Vision ) — These models process data input with applications toward tasks like translation or text summarization, and are trained with large language datasets, such as the Corpus and , with the ability to be fine-tuned for specific tasks. Wikipedia Common Crawl By now, we’ve all seen and heard of programs using Deep Learning AI machines through their many various applications: Image Generation: DALL.E, MidJourney, Stable Diffusion, Lensa, and many others 3D Creation: Dream Fusion, Point-E, Anything World Text Generation through Natural Language Processing (NLP) and Large Language Models (LLMs): ChatGPT, LaMDA, PaLM and others. The truth is these are just tools. They don’t have any real contribution to cognitive science. They can be very useful tools, but they are also prone to error, and ultimately limited in the scope of human-like value. The Glaring Problem With Machine Learning AI Current AI programs possess no understanding or meaning regarding tasks they can perform. They are tools with no comprehension, merely predictive machines, trained to do one thing. They don’t understand the data, they just get good at performing tasks based on parameters within the programming of the algorithms. It’s important to understand that these machines are not capable of awareness or perception. An AI machine used to scan MRIs to detect cancer does not know what an MRI is, nor what cancer is. It has merely been trained to recognize image patterns based on having been fed extensive datasets of MRI images labeled ‘with’ and ‘without’ cancer. It gets good at recognizing the patterns within the images, but the contents of the images themselves and the context surrounding the task at hand are meaningless. The results can only be offered in percentages of possible accuracy — never 100%. Some other BIG PROBLEMS: Singular Functions It’s also important to note that each of these machine models is singular in functional objective. Each is only good at one thing. Whatever operation a particular AI model is programmed and created to “do,” whether it be visual art creation, language processing, or scanning MRIs or X-Rays, each is built to stay in its own lane. ChatGPT cannot create a photographic or graphic image, and DALL.E, although it creates images from text descriptions, will not write blog articles for you, or answer a question, or tell you a joke. These are not sentient or all-knowing AI networks, they are predictive model machines, each trained with data to accomplish a pre-determined task. Eventually, these datasets will achieve more and more overlap, but they are still bound by the task requests and parameters they are trained within. Training Data Ages Out Quickly Large datasets are used to train these Machine Learning/Deep Learning systems, but due to the inherent problem of lack of comprehension and awareness, and the threat of exposing the system to toxic or otherwise potentially damaging data, none of this training data is ‘live’. It is done through Supervised Learning, carefully curated by data scientists. GPT-2 was trained using 1.5 billion parameters, and GPT-3 was trained with substantially more — 175 billion parameters. However, this data is historical data. It is not live, real-time, evolving data. In fact, ChatGPT-3 was only trained on data through mid-2021, so users who are relying on it to answer questions about technology, for instance, are receiving outdated answers, given the rate of acceleration in technological advancement. A lot of progress takes place over one year. Inexplicable Path to Results Another huge issue with these predictive machine models is that it is impossible to audit the path to results. These machines are trained on the datasets, yet, the neural networks are set to crunch the data throughout a process that is both random and obscured within multiple overlapping inputs and outputs until a final outcome is produced that has a high probability of success. Meaning, we have no way to know the internal workings of exactly how it got to its results. This doesn’t present a problem if you are asking your AI model to win a chess game. In that case, it doesn’t matter much how it resulted in coming up with the best possible winning move. It simply won. However, if you have a Neural Net AI model that is being used in the field of medicine, and somehow its training data was flawed, or it erred in its conclusion, wrongly suggesting something like an amputated leg or risky unnecessary operation, then that’s a problem. Without understanding how the AI model arrived at its decision, there is no way to correct it and prevent the same mistake from occurring in the future. 'Truth' Doesn't Exist Keep in mind, these Deep Learning machines are also indifferent to truth. They are not concerned with truth. They are not capable of abstract thought. They are not aware of truth. These machines are merely trained to output something that closely resembles ‘accuracy’ based on the input received and the random pattern-matching sequences that filter out the likelihood of a “failed” answer. This means with Large Language Models like ChatGPT, they are prone to spit out nonsense, as long as it ‘sounds’ plausible and follows the rules with which it has been trained. ChatGPT doesn’t understand what it is saying, only the likelihood of what words should come next. The more the machine is trained with larger data sets and interaction, the more likely a proper output becomes, but , and the odds of accuracy are based on a cultivated probability that inherently leaves room for error. an ‘appropriate’ output is very different from an ‘accurate ‘output Deep Learning Has Hit a Fundamental Wall. Through this Machine Learning technology, we have engineered some valuable and helpful tools to enhance performance and creativity, but these tools make no real contribution to cognitive science, and due to their inherent limitations, they will never be able to achieve AGI on their own accord. The Roadmap to AGI According to Dr. Gary Marcus, scientist, and leading voice in artificial intelligence, in his predictions for 2023, one of the fundamental ways forward to AGI is the ability to “construct internal models of how the world works” along with the “ability to understand things at an abstract level.” He goes on to predict that the “critical step on the optimal path to AI that we can trust…we will need genuinely new architectures that incorporate explicit knowledge and world models at their very core.” Introducing: Active Inference, KOSM™, and the Spatial Web Protocol According to VERSES Technologies, a cognitive computing company specializing in the next generation of Artificial Intelligence (AI) systems, “The Spatial Web is “an open, hyper-connected, context-aware, governance-based network of humans, machines, and AI.” The VERSES core platform, KOSM™ Operating System, ‘is a network operating system that streamlines development and deployment of autonomous applications on the Spatial Web.” KOSM™ enables the development and deployment of a new class of hyper-integrated context-aware autonomous applications that support cross-platform networking between disparate hardware (i.e. drones, sensors, smart devices, robots) and software systems (i.e. enterprise services, cloud platforms, mobile applications, artificial intelligence). It is composed of five “Flow Modules” that work in concert to process and synthesize data coming from various IoT sensors and digital information systems into a coherent human-readable and machine-executable structure for purposes of delivering greater intelligence and automation of Human, IoT, AI, and Robotic field activities. “ — VERSES Technologies Through global digital twin simulation, Model-Based AI for Active Inference occupies an interior representation of the world that is continuously updated in real-time, through sensor and camera data, making inferences (decisions) on an ever-improving model. In this next evolution of the internet, the Spatial Web, also known as the Network of Everything, you have the model (digital twin of every object in every space), and you have the context of everything that the object comes in contact with (encoded into the models through HSML, the context graphing programming language of the Spatial Web Protocol). This new network of Web 3.0 becomes the basis for a truly augmented existence and an entirely new generation of AI and data sharing. Through HSTP (Hyperspace Transaction Protocol) and HSML (Hyperspace Modeling Language), context is coded into every object — person, place, or thing, whether physical or digital, to define, describe, and categorize distinguishable and evolving traits of any user, object, activity, or protocol in any environment, whether real-world or virtual. based on defining, recording, and tracking the changing details in physical and digital dimensions, social dimensions, meanings, culture, conditions, circumstances, and situations, whether geometrical, geo-political, or geo-social by nature. In the Spatial Web, HSML enables computable context The Who, What, When, Where, Why, and How of all entities and data within this new network will automatically be knowable and permissionable, enabling AI with a level of adaptive intelligence automation through a knowledge graph based on the HSML modeling classifications, able to distinguish the contingencies and interrelationships between these elements, creating computable context that is easily understood and processed for every object in space and over time. Active Inference ‘decision making’ Artificial Intelligence operates within the entire Spatial Web ecosystem as nested hierarchies based on the Free Energy Principle, developed by renowned theoretical neuroscientist, Dr. Karl Friston, Chief Scientist at Verses Technologies and VERSES Artificial Intelligence Lab and Sensor Fusion Research Facility. Ranked #1 most influential neuroscientist in the world by Semantic Scholar in 2016, Dr. Friston is one of the most acclaimed and cited scientists in the field of Computational Neuroscience and Artificial Intelligence. Additionally, he is the world’s leading expert in the development and application of Active Inference, a biologically inspired approach to AI. Active Inference, applied to Artificial Intelligence, is a method for understanding sentient behavior incorporating the brain, cognition, and behavior, modeled after the design principles from nature for how the brain, nervous system, and body act and react. Active Inference AI within the Spatial Web on the KOSM™ platform operates within a holonic structure of nested Spatial Domains. Holon architecture can be described whereas something that can be a ‘whole’ in and of itself, yet also be a part of something greater. For example, a human heart is made of cells. Those cells are ‘whole’ cells, while still being ‘part’ of the heart, which is a ‘whole’ heart, yet a ‘part’ of the human body. Each component exists as a whole element that is also part of the entire organism, situated as nested entities within each other that are governed by the rules of the greater influencing organism, and similarly inherited and passed to the internal parts nested within it, with each part having its own governmental considerations for its specific intrinsic requirements. Additionally, Active Inference includes the principle of Markov Blankets at each level of analysis, defining boundaries acting as partitions to mediate the interactions between the ‘internal’ and ‘external’ states of a single unit, region, or entire complex network, resulting in a self-organizing system. One example of how this may play out in the Spatial Web, there may be a factory that is part of a supply chain with raw materials feeding into it from various locations, mines, etc.., The factory then produces finished goods that ship to another destination like a distribution warehouse. Just as that factory is an object within a larger ecosystem of the global supply chain, every object within the factory belongs to its internal ecosystem. You have an internal and an external ecosystem that simultaneously operates according to individual internal and external standards, interdependencies, and interrelationships. Every object within the entire Spatial Web network is part of an interrelated ecosystem that feeds a continuous stream of real-time context between all points in space and time. This continual interaction and communication stream takes note of all nuances and changes within the relationship between all objects and the parameters that govern them. Model-Based AI for Active Inference becomes a global Artificial Intelligence organism comprised of nested AIs, referred to as Intelligent Agents, that inhabit the Spatial Web through the KOSM™ Operating System, living on the network and updating in real-time. Due to its very nature, Active Inference AI is fully auditable with the foundations of all its decisions completely knowable and precisely controllable. Additionally, Active Inference AI inside of KOSM™ has access to the entire Spatial Web with IoT sensors and cameras and all context markers attached to all objects within all space and time. This AI becomes an ecosystem of Intelligent Agents that are all interoperable over HSTP. This Collective Intelligence is trained on real-time data and evolves making decisions and updating its interior model based on what is happening NOW, not on historical datasets. This is the missing piece of the data puzzle that gets us to Artificial General Intelligence. We now have the world model, and we now possess the ability for continuous and adaptive context markers enabling a cognitive model of this world and the ability to compute awareness. Dan Mapes, President, and Co-Founder, , and Director of “For the first time in history, starting now, we have the roadmap. AGI is now possible.” — VERSES Technologies The Spatial Web Foundation VERSES Technologies, and the Spatial Web Foundation, is on target to get us there. This is Part 1 of a three-part series entitled, The Spatial Web and the Era of AI. Next up, in Part 2, we gain a more thorough understanding of Active Inference AI and Dr. Karl Friston, then we will dive deep into the intricacies of the KOSM™ Operating System in Part 3 of this series. —  Enjoy the ride! To learn more about the Spatial Web Protocol and the evolution of Web 3.0, visit SpatialWebFoundation.org

The Era of AI and The Spatial Web: What It All Means - Part 1

Spotify's now ubiquitous "Wrapped" annual review isn't the only thing that filled social media feeds everywhere in 2022. Generative AI flooded our feeds on Instagram, TikTok, Twitter, and (even) LinkedIn, with art derived from text via apps like DALL-E and Wonder, and fantastical avatars produced from selfies created by the likes of Prequel and Lensa. One app wants to go further, with a mission to help you become the best version of yourself possible. is an AI-powered body image app that helps you view your body the way it can be with proper diet and exercise. Nufa Nufa allows users to transform their bodies in seconds with just the press of a button and then will deliver personalized training programs to help the user reach their desired outcome. The company says that its individualized training programs - a feature being added in the near future - are being developed with the help of renowned coaches and top-level athletes. Nufa will soon offer motivation and workouts to help users achieve their desired body transformation, and track progress, keeping notes and photos in one place. The startup claims that with a combination of the AI-powered "goal image," a personalized plan, and constant progress notes, users stay motivated and strive for more, while also giving them the mental self-confidence they need to stay the course. When going through my own transformation, many years ago, Tim Ferriss' " " book was a significant and constant source of useful techniques, practices, and plans. One of the things I'll always remember is the suggestion to take a "before" photo as a body transformation motivator. 4-Hour Body One of the main benefits of taking a before photo is that it serves as a visual reminder of the individual's starting point. By taking that “where it began” image, we have a reference point to compare our progress to and can see the physical changes that have occurred over time. In addition to serving as a visual reminder, before photos can also provide psychological benefits. Seeing them can serve as a source of motivation and encouragement, as individuals can see how far they have come and how much progress they have made. It can also provide a sense of accomplishment and help individuals stay committed to their weight loss goals. Now, with Nufa's app, it is possible to add a predictive goal image to enhance the before photo's  benefits. Psychologically, it is healthier to move towards pleasure than run from pain, and the release of endorphins experienced after a user sees their altered body can help them to make positive lifestyle changes, such as exercising, dieting, and sleeping better. Will people use the generative AI aspect of Nufa to look better in dating apps? I can't imagine they won't. There are tens of thousands of words already written on the ethical and moral issues surrounding apps such as FaceTune, Peachy, and more being used in this way, so I won't repeat them here. In any case, no AI was used in my own " " - if people want to lie, they'll lie. world's worst catfishing story But that aspect of the app is just one part of a much bigger story. The inspiration Nufa strives to provide its users is the beginning, but the upcoming customized plan, constant motivation, and ability to track progress are where the real magic happens, along with the user's own commitment to the process.

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