Part 1 of the Series where I interview my Mentors I have very recently started making some progress with my But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. Self-Taught ML Journey. In this Series of Blog Posts, I talk with People that have really inspired me. People that I really look up to and that I learn and work with. The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of from. learning Today we’re talking with . Dominic Monn A Great Leader, Founder, Community Leader, Self Driving Car Engineer and currently a DL Engineer working Remotely. **Sanyam:**​ Hey Dominic! Thank you for doing this interview. I’m thrilled to be interviewing you. Hey Sanyam, always glad to talk to you! Dominic: You’re one of the best Practitioners I have met in 2018.Can you tell the readers about yourself? Sanyam: Oh, what an honour :) My name is Dominic, I am 20 years old and working as a Deep Learning Engineer for Loom.ai. The company is SF-based and I am working remotely from Switzerland. Aside from other things, Loom.ai is the company behind Samsung’s AR Emoji. Dominic: Before that, I was a Deep Learning Intern at NVIDIA in Zurich. I got there after doing the Udacity Nanodegree for Self Driving Car Engineers. Awesome. Sanyam: You’re also working on a lot of platforms outside of your job. Can you share more about these? I like to spend my weekends and my free time building sideprojects. The two taking up most of my time are , a marketplace to connect students with experienced mentors in Tech — and , a global community and job board, all around working remotely in Machine Learning. Dominic: MentorCruise RemoteML I’m always looking for the next problems to solve and the next product to work on, it’s a lot of fun. You’re an experienced practitioner today. But what was the first thing that got you interested in AI? Sanyam: I got started in Software Engineering with a 4-year apprenticeship where I worked at a software and web agency. After doing webapps and websites for 2–3 years, I just got bored of it and started branching out a little. I started dabbling a little with Unreal Engine and Game Development, then had a small interest in UX and ended up in AI/Machine Learning. Dominic: Was there a point where you decided that you want to take this up as your profession? Sanyam: My interest in Machine Learning started as just another sideproject. I got my fingers dirty in and ultimately took on a Udacity Nanodegree. I always enjoyed experimenting and doing that as my profession (working in Research & Development) seemed like the obvious path. When I got the chance to go to NVIDIA (despite more lucrative offers as Software Engineer), I took it. Dominic: Kaggle You’ve mentioned some of the great platforms that you’re working on, and I personally know that you ship code very fast. Sanyam: How do you manage to work on multiple task? What’s your secret? When I wake up, I always know what I’ll work on for the day. I plan out my whole day the evening before, using Todoist. I work full-time, which takes up most of my day, so between meals, going for walks, working out and sleeping, I need to know what I can get done for the day. The key is to move fast and not being afraid of shipping things. Dominic: I usually have a large list of to-dos for each of my projects. Some are more important and usually get done quickly, some others have been on that list for months. It’s all about managing expectations, splitting up work in chunks and shipping fast. We were chatting offline about your upcoming ventures. What can we expect from logits.co? Sanyam: RemoteML started out as a small job board. Nowadays we are over 750 members worldwide, most of which are burning to get some work in Machine Learning. Companies and startups can use Logits.co to tap into that community to hire freelancers for their projects. I am excited to see how it’s going to work out. Dominic: Before we conclude, What best advice would you give to someone starting out in the field? Sanyam: Don’t be afraid to get your name out and your hands dirty as early as possible — both in business and Machine Learning. Ship fast and get internships, be determined and the rest will come by itself. Dominic: What would be the best place to get in touch with you or follow your work? Sanyam: I’m fairly active on my (@dqmonn) and my DMs are open. I’m also almost always online in the , so be sure to visit me there. Dominic: Twitter RemoteML Machine Learning Chat Thank you so much for talking doing this interview. Sanyam: If you found this interesting and have any suggestions for interviewing someone, you can find me on Twitter here .

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Nominated for 2022 - HackerNoon Contributor of the Year - Deep Learning

Nominated for 2022 - HackerNoon Contributor of the Year - Machine Learning

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Dominic Monn: DL Practitioner Interview #1

We are proud to release “Supervisely Country Roads” dataset, share our research as tutorial on how to build semantic segmentation model for drivable area and provide , so you will understand how to build, download and use custom models with . So you also can reproduce all steps below easily. source codes Supervisely Motivation Self-driving industry is huge today, companies invest in hardware, software, and labeled data. And it’s not only about self-driving cars, but it’s also about robots-couriers to deliver stuff, road quality inspection and other apps. The main purpose of this post is to give the quickest possible guide to the most common task in self-driving industry — drivable area recognition. What we want is to … Take training data, spend as little time as possible on data conversion / processing routines Pick a model, something Unet-like, train & run it Visualize the results in jupyter notebook Homework: try to improve our initial solutions by leveraging public datasets and more complex neural network architectures What we don’t want is to … Spend hours to download gigabytes of annotated images and to convert them from one format to another one Know anything about data formats Try various github repos until we find the right one — implementation of semantic segmentation model that actually works Now that our preferences are defined, let’s think about overall approach to address the task. There are two important challenges to deal with: It’s highly desirable to perform the entire research within single environment. In this case, there will be no need to do unnecessary data conversion and to switch between different tools and github repos Though there are public datasets (cityscapes or mapillary) that we can use to extract drivable area segmentation masks, our dataset is better to be lightweight, trivial and easy to use. So, doing all the work inside platform solves the first challenge. To address the second — we have released “Supervisely Country Roads” dataset. Supervise.ly All steps of this tutorial will be done inside Supervisely without any coding. The high level plan is the following: you will add “Supervisely Country Roads” dataset to your account, use DTL (Data Transformation Language) to create augmented training dataset, train NN and apply it to test images, then you can download NN weights and run inference right in . jupyter notebook Let’s get it started! Step 1. Add “Supervisely Country Roads” dataset to your account. Cityscapes and Mapillary datasets are mostly about city environment. But there are a lot of cases when autonomous vehicles have to drive on country roads: autonomous harvesters, trucks and agriculture robots. Other reason why you can not use mentioned datasets for this task directly is that many classes (e.g. “road shoulder”) are marked as neutral. It leads to inaccurate prediction of drivable area on country roads. We at had a project on this topic and we are happy to share our internal dataset and few experiments with community. We believe this contribution will be useful for many researchers. We release 1000 labeled images and 500 test images. We tried to keep the variety of labeled images. DeepSystems To get this dataset just go to “Import” -> “Datasets Library” page, choose “Supervisely country roads” dataset and define the name of the resulting project as “country_roads_labeled”. That’s all. Here are the few examples of labeled images: Step 2. Prepare training dataset Inside Supervisely we have special module called DTL (Data Transformation Language). It allows to merge datasets, make class mappings, filter objects and images, and of course apply various augmentations to your data. You just have to stack the data operations together. So you can build custom computational graph (analogy is the graph of neural networks layers), each your image and its annotations will go through this graph and the results will be saved to new project. In most cases automatic augmentations significantly increase the accuracy. We prepared DTL query for you. Just go to “DTL” page, copy&paste json file ( ), and press “Start” button. As a result of DTL query new project with 12k images will be created. It will contain original images, flipped version, and number of another augmentations: random crops, brightness, contrast and color transformations, and blurring. This example illustrates how it is easy and quick to prepare training dataset with our platform. link to github You will find the detailed description of applied transformations below. Step 3. Train neural network for semantic segmentation Before train NN you have to connect computer with GPU to your account. Go to “Cluster” page and click “Add node” button. Then execute following command on you computer. Docker image will be pulled, and Supervisely Agent (our task manager) will be started on your computer. It will automatically connect your PC to your account and you will be able to use your GPU for all NN related tasks (train/inference/deploy). Source codes also available in our . public git repo Let’s train segmentation neural network to recognize drivable area. Just go to the “Neural Networks” -> “Model Zoo” page, choose “UNet V2 (VGG weights)” and click “Add model” button. This model will be added to your account. Then click “Train” button near the model, choose training project “training_01”, define the name of resulting NN (latest checkpoint) as “nn_roads”, choose training configuration from template (it will be automatically added after you add model from Model Zoo) and press “Start training” button. Find example in our docs with tons of screenshots . here After training model “nn_roads” will appear in your models list. During training you can monitor metrics in real time: Step 4. Visualize predictions Let’s add test images to your account from “Import” -> “Datasets Library” -> “Supervisely country roads [test]” as project “country_roads_test”. Now you can press “Test” button near your model (that you have just trained), choose “country_roads_test” project, define the name of resulting project that will contain test images and neural network predictions and press “Start inference” button. Here are few examples of NN predictions on test images: Step 5. Download final model and use it the way you want (e.g. jupyter notebook) The idea is that you can use Supervisely to perform a lot of research experiments without any coding. But how to use the model in production? Option1. Deploy as API (we described the entire process in our ). In this tutorial we would like to show you another way — option 2: you can download NN weights and then use our source codes to run it right in . Notebook contains the example of inference. previous post jupyter notebook By default model is stored on your machine. Just click “Upload to Supervisely” button and then “Download” button ( ). As a result the tar archive will be downloaded. link to docs Here is the with example for this tutorial. Just follow instructions in (just unpack the model to certain directory and run docker container). link to our github readme.md Conclusion In this post we have shown how to build drivable area segmentation model from scratch. Although, the task gives you a taste of what self-driving companies are working on, production ready solutions require more challenges to overcome. In a more complicated scenarios we have to build a model capable of segmenting multiple classes of objects like cars, pedestrians, bikes, road landmarks and so on. In practice, constructing “multi class datasets” requires time & efforts due to the need to label / merge / filter / clean the data from various sources. Multiple classes of objects from various datasets. In a multi class settings, some of the objects are underrepresented. For example, on a road scenes, bikes are far less frequent object than cars. This makes it challenging to train a neural network that achieves required level of accuracy for the rare classes of objects. Class imbalance problem. Production level solutions should be stable in way that “edge case” images are recognized in a proper way. So we need to identify these “edge case” images first, then label them and finally, retrain the model. The process described is continuous and, sometimes, called Active Learning. It’s highly desirable to automate it as much as possible. Continuous model improvement. If you have any questions, feel free to ask in our public . We are happy to help! Slack We will address the challenges above in the next posts. Meanwhile, If you found this article interesting, give it some 👏, so that more people could see it!

The Graph

Slack

🚘 Traveling the ML. Next hop: step by step guide to recognize drivable area

Part 2 of the Series where I interview my Mentors I have very recently started making some progress with my But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. Self-Taught ML Journey. In this Series of Blog Posts, I talk with People that have really inspired me. People that I really look up to and that I learn and work with. The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of from. learning Today we’re talking with Tuatini Godard. My Great Friend and an Awesome Deep Learning Freelancer from France. Hey Tuatini! Thank you for doing this interview. It’s a pleasure to have you on the blog Sanyam:​ Hey Sanyam, thank you for having me! Tuatini: You’re one of the great Freelancers I have met. Can you tell us about yourself? How do you work and what jobs do you usually take up? Sanyam: Thanks for the compliment but I started freelancing few months ago just like you. I was an Android developer 2 years ago and I jumped in the ML/DL boat since then, learned everything I could in that field and started freelancing few months ago when I felt confident about it. I only work remotely, I can’t work next to a client looking for your every move to see if he invested his money in the right guy, it stresses me aha. I mainly target jobs which have a real purpose or which passionates me. For instance, nowadays I’m using deep learning on pathological data to automatically diagnose skin cancer. Tuatini: Very Cool! How do you usually find work? Via Networking or Platforms? Sanyam: Via Upwork mainly, it’s not the best place to get a lot of money but it’s ok if you’re not very good at marketing yourself like me. Tuatini: I know you’ve been a Machine Learning Practitioner for a few months now. What got you interested? Sanyam: I was mainly interested by AI at first. It was taught at my school when I was doing my MS but I didn’t really focus on it as opportunity in that field were… “scarce”. Then came the deep learning hype and suddenly everyone needed a deep learning expert so I jumped on the occasion as resources to learn it and opportunities were abundant. Along the way I learned about the traditional way of doing real “data science” and I kinda liked it so I’ve put aside the hype on what people call “AI” and started focusing more seriously on that very interesting field. Tuatini: At what point did you decide to start freelancing as a ML Practitioner? What made you take the decision? Sanyam: I had a deadline and a roadmap before I left my job as an Android developer. Here in France you can have unemployment benefits for approx 2 years so this is what I did. For a year and half / 2 years I was only focused on studying and on my roadmap. Recently I had to shift that focus to doing freelancing as I was confident enough on my skills and my unemployment benefits were about to come to an end. Tuatini: During our offline Chats we have often discussed the difficulties of freelance work. What are some of the difficulties you face working remotely? Any downsides you would mention to the people trying to break into this space? Sanyam: Well the first one is loneliness. When you work at home you’re on your own. Fortunately for me I have my gf around to whom I can talk to and for the current client I have I actually work with someone that I met back when I started studying deep learning so I can exchange ideas and all kind of stuff about the project with him. Most freelancer platforms put the accent on individual freelancing like freelancer.com/upwork/fiverr etc… While it’s great for an individual to get started I think it’s even better if you could do freelance mission in a team, or more like a squad of 2 or 3 people so when you’re not around your colleagues can maintain the link with your clients and you can exchange with them etc etc… Tuatini: What would you recommend to someone starting out in the field? Sanyam: You have to show your skill to the world, one way or another, to justify your prices. If you charge a lot of money but have no presence on the internet (no blogs/no github/no review on your profile/no kaggle profile) etc…. It will be hard for you to justify those prices. Every person is different but for me what worked was writing a blog/doing some open source projects. Kaggle is good also to show your skills, I think it’s even better than blogging/open source as everyone can see and especially understand your skills based on your kaggle profile. I mean, take a non tech potential client and give him a github vs a kaggle profile, what stands out the most to him? A bunch of line of code/projects he doesn’t understand or a bunch of straightforward badges on today’s most used platform for data science? Tuatini: Anything else you want to share with the readers? Sanyam: Don’t give up, data science is a multidisciplinary field and it’s hard to be good on every front. Choose a specialization at first and put all your energy on it. For instance I chose deep learning for computer vision instead of say, time series or . Don’t get fooled by the AI hype like me when I started 2 years ago, there is no AI (or AGI, call it the way you want), if you choose the computer vision path, then you become a computer vision expert, for NLP you become a NLP expert. Deep learning only helps to solve a part of the big picture, it’s not the big picture itself, only a tool to add to your toolbelt. As an freelancer or an “expert” (call it the way you want) you will be asked to do more than just playing with deep learning models most of the time. Learn about data preparation, software engineering skills, some devops and co, basically the whole stack to create and ship a deep learning project in production, not just the fun part. But most importantly, learn to learn and to adapt, keep an eye on the latest trends and always stay up to date on what is going on as things moves really fast in the field of data science. Tuatini: NLP Thank you for the valuable insights. What would be the best place to get in touch with you or follow your activities? Sanyam: I have a blog . I don’t find a lot of time to blog these days but you can find everything about me in there. Tuatini: https://tuatini.me/ Thank you so much for talking doing this interview. Sanyam: If you found this interesting and have any suggestions for interviewing someone, you can find me on Twitter here . This interview was done via the RemoteML Community

BUNCH

Target

Interview with Deep Learning Freelancer Tuatini Godard

The biggest hurdle for blockchain adoption is scalability. Mobile and desktop applications spoiled consumer expectations for performance and responsiveness. The latency of 1994 is unacceptable in 2018. The slow grind towards production-ready scalability solutions kills the pace of development and embrace of decentralized applications (Dapps). Dapps . Dapps are crippled by the performance of smart contract cryptocurrency platforms like Ethereum. High gas costs, low-throughput capacity, and slow confirmation speeds are major problems facing blockchain platforms. aren’t able to sustain users Despite the scalability problems at hand, there have been some noteworthy and dynamic developments over the last couple of years to address the scalability of decentralized blockchain networks. Problems Facing Smart Contract Platforms and Dapp Usage The general limitation that blockchain networks face at inception and beginning phases is best outlined by Ethereum founder Vitalik Buterin as the . This is the necessary trade-off between security, decentralization, and scalability. Scalability Trilemma Ethereum sacrificed scalability to ensure robust security and decentralization. As a Turing-complete smart contract platform, Ethereum’s scalability problem is compounded by complexity required to scale to production thresholds. However, Ethereum still stands as the most popular platform for dapps despite problems plaguing dapps (specifically games) such as extraordinarily high gas costs during periods of network congestion. Network effects are a powerful motivator for dapp developers. The majority of dapp developers continue to build on Ethereum despite its current limitations because of large audience (ie — customers).. As a result, even if developers begin to wander away from building on Ethereum, it will take a substantially improved platform with a forward-thinking approach and immense gravity to attract a migration of developers over the long-term. Ethereum’s limitations highlighted the pressing needs for scalable dapps to retain users. Dapp games represent the best example as they are too expensive to play and actual gameplay leaves much to be desired. Just ask anybody who played Etherbots, or take a look at the top dapps for Ethereum on . You will notice the top dapps are mostly DEXs, gambling platforms, or games focused on crypto collectibles rather than gameplay (i.e., CryptoKitties). DappRadar So how exactly do dapps begin to retain substantial numbers of users? Well, for widespread adoption, they need to match the scalability and responsiveness of traditional apps that run on centralized servers. Some platforms like take a hybrid approach with the game on a centralized server and the assets on the blockchain. Gods Unchained Building on centralized servers runs contrary to the whole notion of building dapps in the first place. That is why there are appealing solutions racing to produce the first generation of scalable dapps. Promising Solutions Scalability solutions come in all shapes and sizes, particularly platform-oriented or platform-agnostic. Many emerging platform-oriented solutions that focus on scalability include EOS, Zilliqa, and DFINITY. Regarding dapps, scalability solutions that have achieved the most focus at the application-specific level are . These consist of innovations such as and . layer 2 solutions State Channels Sidechains The most well-known state channel is Bitcoin’s (LN), and it is an excellent method for enabling Bitcoin — a value-focused and payment solution network — to scale to the necessary levels for mainstream adoption.Despite state channels’ ability to facilitate bidirectional digital payments off-chain, limitations include large communication overhead and needing to be written in smart contract-specific languages. Lightning Network Moreover, are necessary to implement arbitrary state machines that are optimal for dapps to scale, and those types of state channels are still under heavy engineering and theoretical development. generalized state channels Promising dapp scalability innovations that recently made headlines emerged from dapp gaming. Specifically, the use of sidechains — Dappchains as they call them — have some real potential to launch a new wave of dapps. The Dappchains effectively function as sidechains pegged to Ethereum with their own DPoS consensus to scale to levels of mainstream, centralized apps. These are a form of and are worth your attention moving forward. Loom Network’s application-specific sidechains Ethereum’s implementation is also an off-chain sidechain implementation focusing on “child chains” connected to the Ethereum primary “root chain” for offloading computation and alleviating network congestion. Notably, Plasma at this stage only handles the secure transfer of crypto assets rather than general state changes. Plasma A compelling case of production-ready scalability of dapps comes in the form of the recently announced from startup . The Tenfold Protocol consists primarily of 3 components: a state registry, validator network, and liquidity market. Tenfold Protocol Binary Mint Each dapp is a single state registry which fundamentally is a (TCR). definition of bounded vs. unbounded is warranted. Unbounded transfers would be asset transfers, such as sending ETH. A bounded transaction might be a social media post, a dragon levelling up, or a micro-transaction. Token-Curated Registry Tenfold makes a distinction between bounded and unbounded transactions by processing unbounded transactions on-chain for maximal security and transitioning bounded transactions off-chain using a crypto-economically secure model. Notably, this allows Tenfold to vastly improve performance while reducing gas costs, be both blockchain and language-agnostic, compatible with Plasma, and available for developers to use now. Conclusion The race for scalable dapps is accompanied bynovel technologies and fascinating developments in decentralized scalability. Whether more complex platform-oriented scalability solutions or application-specific innovations will attract the next generation of dapp users is set to unfold.

Zilliqa

Chain

ConsenSys

Dapp

The Amazing Race For Scalable dApps

Photo by on Clark Tibbs Unsplash Being an entrepreneur isn’t that easy, you have a vision and put yourself to it and started moving towards that vision. You have to be stay focused for Here we bring some important tools for entrepreneurs and for their teams to bring their work handy and to easily manage their projects along with their team. reaching your goals . Read Full Story Here These are the productive tools that allows you to take control on your tasks and allows you to stay focus on your goals.

Productivity Tools for Entrepreneurs and Startup Teams

The Next Big Thing . Software is universally less expensive than human labour. Plus, PLEs can legally operate with crypto tokens which reduces operational friction and costs. PLEs will also allow automatic taxation through methods of triple-entry bookkeeping. Opening a fully operational legal shell will be as easy and cheap as opening a bank account today. PLEs are very affordable and convenient so more people can do more business, legally _The two very different strata share similar technological foundations — but their goals, principles, and prospects are…_medium.com Some Use Cases Risks To move, sharks and dolphins create body waves in orthogonal planes. Sharks move sideways; their tails are vertical. Dolphins move up and down; their tails are horizontal. However, in a practical sense, this technical difference does not mean anything. A horizontal tail neither increases speed nor improves maneuverability. Technically, both the relevant regulation and the software frameworks that support programmable legal entities have emerged as the result of the interest in cryptocurrencies and blockchains. Enduring the odour of paradox all over, the broader business community has given the benefit of the doubt to those narratives. Ten years in, not a single distributed application represents a significant threat to an incumbent rival. By now, the hype is mostly . Blockchain is a “horizontal tail” feature. Despite being part of its DNA, blockchains are not critical for the innovation in consideration. Blockchains are not conspicuous in how PLEs will complete against ordinary forms of legal entities. over Dependence on existing public blockchain platforms is dangerous, as they were created in conditions of extremely facilitated financing through mining of overvalued cryptocurrencies and, not too uncommonly, dishonest token sales to undereducated gamblers. There will hardly be easy money. Tokens are very important, but they should be introduced organically, not as dummies primarily designed to run unregulated fundraising campaigns. The Gartner adoption cycle is very often cited in connection to crypto. Supposedly, now we just passed the disillusionment stage and are starting to go up the slope of enlightenment. That is hardly true. There are thousands of different crypto applications that have nothing in common from the consumer’s point of view. There is just no single object or idea of adoption. The situation is rather described by and right now it’s not pretty. Unfortunately, the current poor “reputation” of crypto affects both developers and legislators. We can only hope that the dirty bequest of the historically short ICO season dies off without destructing important structural elements. more reputation cycles _Summary:_hackernoon.com Pony up, Folks! Programmable Legal Entities are Tectonically Reshaping Capital Markets Great Future Benefits Sharks and dolphins radically differ in the way they breathe. Dolphins’ lungs are fundamentally alien to water, which may seem odd. However, the ultrasound language is creatively complemented by the ability to create meaningful patterns of air bubbles, so lungs add a lot to the “core property” of being a dolphin — communication and cognition. In this Internet age, ordinary entities always use a third party to pass payment or release cargo. There’s always “someone of trust” to provide an online coordination between the two, for a fee. PLEs transfer value and managing signals in a very different way; their “breath” is alien. They exchange authentic operational tokens, hand-to-hand, free of charge. This convenient and intuitive reality is the heritage of the trustlessness concept that inspired much of the underlying blockchain technology. Notably, while PLEs exploit the idea in a healthy way, to cut costs and reduce pressure of monopolies, the original decentralisation aficionados seem to have monstrously , all the way down to the denial of the principle of trust between business parties. PLEs don’t question natural ways of creating relations in business, they use the technology to create the new based on the “token calculus”. misinterpreted things advanced business fabric PLEs may also induce new governance models and free us from hierarchies of primates and morale of wolves that still dominate all our management systems, despite the fact we all know they are heavily influenced by instincts and hormonal background. Well-designed PLEs will allow coordination of people and machines in a more civilized way by cutting out pointless middlemen, simplifying contracts, and reducing political friction. PS: Join the Movement The focus on price of publicly listed tokens is now to be misguided as a proxy for ecosystem health. Simply speaking, crypto trading is irrelevant. Returns of crypto assets mainly correlate with two factors specific to its markets — momentum and speculator attention. There’s little sense to invest efforts into crowds of clueless speculators who don’t create any economic foundation for anything, being simply gamblers-at-heart (pachinko effect). statistically proven The last several months have proven that institutional capital, enterprise and regulators are all coming round to leverage the technology. To increase the likelihood of a sustainable business, they choose a real representation of things, not virtual tokens that create no responsibility for anyone. Venture capital and M&A activities have both hit new all time highs. Hundreds of deals have collectively raised around two billion dollars this year in venture capital and there were at least . 30 M&A deals Well-crafted PLEs will facilitate the trend of normalizing the crypto space. We are working on a number of business cases with large, real physical assets but comparatively simple human organisational structures (real estate, wind farms and alike), which are convenient firms to test the concept. If you are a marketing professional who has been involved with token sales, we offer a solid chance to put yourself to good use. Please contact us here: _1. DISCOURAGE COMPETITORS WITH EXTRAORDINARY UPGRADES 2. GET FUNDED WITHOUT WORRIES OF EQUITY DILUTION 3. SINGULARLY…_ate.business Programmable Legal Entities

Waves

Programmable Legal Entities

I am not talking about my job, I am talking about the founder and CEO of HackerNoon David Smooke’s. The writer-editor loop can be explained in two steps: 1. Writers submit or edit their story 2. Editors return a story for additional editingThese two steps are repeated until the story is up to standard and ready to be published. The best way that David’s job can be made obsolete is to automate the tedious parts of the syndication process and give more incentives to editors and writers. I‘ll try to create a step by step guide how could this be achieved while keeping in mind a few pointers of the bigger vision. Hacker Noon 2.0 The Vision of Hacker Noon 2.0 taken from . Hacker Noon startengine campaign HackerNoon started a , and it explains that HackerNoon will be free to read forever without tracking the users or forcing them to log in. Moreover, HackerNoon will not run intrusive ads and considering all that, it’s easy to see why it would be a perfect place for anyone to read up on the latest tech-related stories. crowdfunding campaign Without further ado, let me walk you through a possible solution that could automate the syndication process and allows us to keep all wonderful aspects of this great publication. Let’s nerd out. Smart contracts as a writer-editor loop solution The writer-editor loop problem could be solved by having a transparent CMS which enforces the same rules for each participant. One way to implement this kind of a solution would be by using smart contracts. For those who haven’t been reading Hacker Noon articles very thoroughly a smart contract is a computer protocol intended to digitally facilitate, verify and enforce the negotiation or performance of a contract without third parties. This technology allows us to have traceable and irreversible interactions between writers and editors because we will use transactions on the blockchain to implement them. Interactions such as submitting, publishing, editing or versioning a story will all be represented as a transaction in the blockchain. Subsequently, all this data will be immutable and forever stored inside the blockchain. By using this technology the writer is no more condemned to either blindly trust the closed system or to write countless emails just to get the response where the story got stuck. By using smart contracts the writer can now as an equal in the system see all interactions that have happened with his story and in real time see it’s progress. Moreover, anyone regardless of being a member of this system can see what has happened with our writer’s story. The same goes for any story and any writer/editor interaction. Observability, verifiability, and enforceability are important properties of smart contracts which are gained by design (check out article by ). this Taylor Pearson Observability means the ability for the stakeholders to see the state of the system. In our case, that means that all stakeholders get to see all writer-editor interactions that make up our system. Verifiability is having the means to verify whether or not something truly happened. This would be the verification of an interaction inside of our system. Last but not least, enforceability means having the contract enforced in the same way for all stakeholders. For HackerNoon 2.0, all writers and all editors comply with the same rules as stated in the smart contract and participate in the exact same process. These key smart contract properties are the ones that enable us to have a fully transparent system. To implement such a system, in my example I have used Ethereum smart contracts. However, only using that technology would not suffice, because we have a high transaction rate and each transaction carries a lot of data. For those reasons using only Ethereum smart contract to implement HackerNoon 2.0 would result in a slow and expensive system. Using Gas Limit, minimal transaction cost and average block time we get a maximum of 28 transactions per second, which is way below what we need. (Data gathered from .) ethstats To solve this implementation problem I’ ve transferred the smart contracts to a Layer 2 solution: . Any scaling mechanism that isn’t programmed into the Ethereum protocol is called a Layer 2 solution. Loom network is one of many such solutions out there and I’ve chosen it because it relies on smart contracts and allows it’s clients to create a more centralized and localized Ethereum chain that has a higher transaction rate than the original. We call these centralized and localized Ethereum chains sidechains. By sacrificing the total decentralization of the system we are in fact gaining the speed of transaction fulfillment. Everything else in the system stays the same — including all the before mentioned positive aspects of smart contracts. Even the used programming language frameworks stay the same. Loom Network Creating a sample solution using Loom Network Loom Network is a Layer 2 scaling solution for Ethereum that is ready for use in the production environment. You can use it to build highly scalable applications and games while still using all the positive aspects of smart contracts. So let’s write a smart contract that will implement an interaction: writer submitting a story. Implementation of writer submitting a story. link to the gist The code above is a simple implementation of such interaction written in solidity. Every Hacker Noon story must have a title, content and the author. This data is represented in a struct called Story and stored in an array called stories. In addition, we have defined a function for submitting stories ( ) which basically creates, saves the submitted story and is followed by an emission of an event ( ). Anyone who is subscribed to this event via WebSocket protocol will be informed of this change in the system. Contract events are important for solving our writer-editor problem because they provide a channel for notifying editors that a new story has been submitted for review. submitStory StoryAdded For additional info about , you can check out their or this . Loom Network homepage article For anyone wishing to run this simple example here is the link to the . The code contains a simple HTML form which can be used to simulate submitting and publishing a story. With that being said, I do not recommend the new site to look like that. 😄 repo Indexing Layer Indexing can be thought as a way of organizing data according to a specific schema or plan for the purpose of streamlining data retrieval. In this solution, we can use it to store data from the blockchain into a database that is even more performant and optimized for information retrieval. This way we can query that database opposed to retrieving the data from the blockchain. A good example of such usage is ’s website . They even have an awesome explaining it in detail. Loom Network DelegateCall blog post The number one go-to solution for implementing this is the .Easy enough, we can pipe the events emitted from the contract to the Elasticsearch instance, as written in the following code. Elasticsearch With such implementation, you get a fully functional search updated every time a story is published to HackerNoon 2.0’s CMS. Tokens as rewards David and his team mentioned that they would introduce tokens to the system like this for the purpose of rewarding writers and publishers for their devoted work. The proposed solution has the ability to create ERC-20 and ERC-721 tokens directly to the Ethereum main-chain. What this essentially means is that you can fully automate the token creation as well as their distribution. Tokens can be distributed by sidechain smart contracts and upon triggering a defined action in the system. As this is not a fully implemented solution, my example does not contain code for that, but be sure to check out some of these articles: Plasma on Loom Network DAppChains: Scalable DApps With Ethereum-Secured Assets Plasma Cash Initial Release — Plasma-Backed NFTs Now Available on Loom Network Sidechains Conclusion To sum it up, our new system consists of a CMS connected to the Loom.js sidechain. Every interaction that happens in the CMS gets written in the sidechain and passed to the indexer via emitted events. The indexer makes sure that everything is stored in Elasticsearch and that data is displayed to the user via the new website. The diagram below shows a bird-eye view of the elements within the system and the users that interact with them. The overall architecture of the system HackerNoon is taking its first steps to creating a fascinating solution, both for its stakeholders and for technology nerds. I definitely believe that it’s going to be a game changer in the tech media field and I can’t wait to see HackerNoon 2.0 MVP. I hope you have enjoyed this small glimpse into a possible implementation of such a system. If you think there is an even better approach, be sure to let me know, I would like to hear the thoughts of fellow HackerNoon enthusiasts.

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This is republished from my newsletter at inside.com/ai . For the past few years as AI has risen in stature, we have witnessed regular takedowns of the tech and posts about why this is just another temporary boom preceding another AI winter. Filip Piekniewski back in May arguing that an AI winter was well on it’s way. Other takes on the subject include by Ron Schmelzer and a in Popular Science. But I don’t buy it. wrote a great piece an analysis very recent piece The biggest problem with AI is that it’s different tech, and the world has changed a lot since previous AI winters. But soooo many people in tech constantly use the frameworks of the last technology wave to evaluate the current one. Several of my own experiences have reinforced this. I remember keying in to the topic when Josh Kopelman from First Round Capital hosted a small dinner in Boston many years ago. Josh sold Half.com to Ebay so was known as an ecommerce guy. But that was 1999, and this dinner was probably 2011 or 2012. Josh said ecommerce entrepreneurs come to him because of his experience but, then he noted that the three biggest customer acquisition channels for most of them: Social, Mobile, SEO, were not even around when he built Half.com. He wasn’t sure his personal experiences were relevant to modern ecommerce companies. (side note — all the best investors I’ve seen have this kind of intellectual humility and self awareness). My last company was a SaaS company started in December 2008. Many of our early investors did not understand SaaS metrics. I constantly had to fight the battle of being judged by the metrics of the packaged software industry rather than as a recurring revenue business. While the laggards came around, it was a lot of education on my part, and from the more progressive investors on my Board, to get everyone comfortable with MRR, CAC, and LTV. AI is where SaaS was in 2009. Most people don’t understand that it requires different frameworks to evaluate it. So when I read about an AI winter, I’m very skeptical that people are looking at the right signals. Consider this — most of the AI news that has driven hype cycles over the past 3 years is just research. Very little of it has been news about really great AI applied in products. That research news is stalling not because AI is stalling, but because AI is working it’s way from the research labs into more applications. The problems to solve there are real, and aren’t very sexy to write about. I see this at . Our team recently worked to put a model into the product. While it’s a model that can perform really well in isolation on a synthetic data set, getting it to work in the real world was messy. We had to solve a bunch of very pragmatic implementation problems, and some conceptual problems (the model always returns an answer, and we had to train it not to return something if the answer isn’t good or relevant). They weren’t sexy or newsworthy, but we believe we pushed practical implementations of Bidaf forward pretty far. Talla Bidaf Machine Comprehension Enterprise adoption of AI is still in the very early days. Again, I see this first hand in Talla’s sales pipeline. Two years ago, no one had budget for an AI project. One year ago, a few really forward thinking companies had budget for prototypes and paid pilots. Now, those more progressive companies have budgets and projects and requirements all around AI. It’s still small but, the difference is noticeable. It will be 5–7 years before all enterprises are educated enough on AI to start buying and implementing it effectively. Previous AI winters have happened because things never really made it out of the research lab into real adoption, outside of very targeted use cases. Now, while we definitely aren’t seeing the amazing we-are-close-to-GAI types of applications that the research sometimes suggests should be here, we are seeing real applications that have real value in the world. The framework I use for thinking about it is actually a consumer adoption framework that I first learned about from BIjan Sabet of Spark Capital, when he spoke at a conference. He said too many investors who don’t invest in consumer companies regularly freak out when growth of new users stalls, but that, it happens all the time. Consumer companies go through waves of viral adoption vs deeper user engagement, and so, he said that the right way to think about it is as this oscillating wave. You get a bunch of new users, and then user growth slows, so you focus on getting the existing user base more engaged. This eventually leads to a new viral growth trend, etc. I think a similar framework will drive AI adoption for the next decade. Companies will deploy the latest and greatest out of research labs, but it will be slow. They have to find (and cleanse) data sets that work. They have to solve pragmatic problems like whether inference should happen on the edge, or centralized in the cloud (which has both performance and privacy issues), how to shard large models in production systems, how to effectively QA models that have probabilistic outputs, and much more. These things take time. So I think what you will see in AI going forward is an oscillation between smarter algorithms that do cooler things, then what seems like stalled innovation but is really just the practical time period of everyone being more concerned with applying the new algorithms rather than inventing new ones. Once the new stuff gets deployed into production systems effectively, we will shift back to focusing on algorithm performance problems (rather than those due to implementation challenges) and need better algorithmic approaches. I don’t think we are headed into another AI winter. I think we are just in a different part of the oscillation wave — laying the groundwork for the next research boom beyond DNNs. On top of that, you have massive innovation in neuromorphic chips and tool sets to make deep learning more easily applied by non-experts. Both of these will take a few years to really take hold, but they will break the frameworks people currently use to think about what products and services can be built with AI. I’m particularly excited about the innovation that will come from breaking the x86 chip architecture framework we’ve relied on for so long. In summary, I think this talk of an AI winter is wrong. It’s focused on a slow down in the research, but that’s a combination of over-hyped research news along with the slow arduous work of really getting these systems running in enterprises. Don’t underinvest in AI because of this AI winter scare. Now is the time to push forward and lay the groundwork for the next waves.

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Mobile Apps are an important chunk of our lives. Barely an hour passes without us our smartphones to check out regular notifications from our favorite apps. While we use these applications to satisfy our needs, we hardly acknowledge the people behind the scenes. Yes, I am talking about top app development companies, the corporations, and enterprises which work to get the app you exactly want. These app development companies in New York cater to your specific requirements in the most efficient manner. Let us discuss some of these important App Development Companies in New york: Blue Label Labs Blue Label Labs is a 40-person mobile, tablet, watch, TV, IoT and AR/VR app design, development and marketing agency based in New York City and Seattle. Over the last 6 years, we have partnered with enterprises, entrepreneurs and digital/full-service agencies to build over 150 iOS and Android-based apps. We can help with everything from initial app design and development through app marketing/PR and post-launch maintenance. Pricing: $100 — $149/hr Location: new york Employees: 10–49 Founded: 2009 2. Hopinfirst Hopinfist is a web and mobile app development company delivering the best-in-class solutions. We’re a team of design-oriented entrepreneurs building award winning mobile applications. We specialize in the beautiful craft of branding, designing, developing, and launching , , and web apps. iOS Android Pricing: $25 — $49/hr Location: new york Employees: 10–49 Founded: 2016 3. Utility Utility specializes in crafting custom mobile applications and web software with innovative partners, from startup ventures to Fortune 500 brands. Founded by digital leaders from companies like Major League Baseball and McCann Erickson, Utility is built by folks that have been on the other side of the table. We’re made up of digital product strategists, user experience designers, and top-tier software engineers. Pricing: $150 — $199/hr Location: new york Employees: 50–249 Founded: 2013 4. iTechArt Group iTechArt is a top-tier, one-stop custom software development company with a talent pool of 1000+ experienced engineers. We help VC-backed startups and fast-growing tech companies build successful, scalable products that users love. Our forte is agile dedicated teams of brilliant minds who rock in Web, Mobile, Big Data, QA & Testing, and DevOps. Pricing: $50 — $99/hr Location: new york Employees: 1,000–9,999 Founded: 2002 5. Five Founded in 2008, Five builds award-winning mobile products that deliver measurable value to our clients, including Rosetta Stone, Squarespace, Napster, Choice Hotels, and Marriott International. Our product teams offer world-class development, design, QA, customer service and support across offices in Brooklyn, New York and Zagreb, Croatia. Pricing: $100 — $149/hr Location: new york Employees: 50–249 Founded: 2008 6. Prolific Interactive Prolific Interactive is a mobile-focused product agency based in Brooklyn and San Francisco. Through a strategy-driven, user-centric approach to creating products, Prolific delivers mobile experiences for leading brands Pricing: $150 — $199/hr Location: new york Employees: 50–249 Founded: 2009 7.Sneakers Agency Sneakers Agency is a, NYC based mobile-first product studio with a ‘work hard, laugh hard’ ethos. We provide start-ups, enterprises, non-profits and other agencies with solutions for digital product strategy, roadmapping, design, development, and QA. Pricing: $100 — $149 / hr Location: new york Employees: 10–50 Founded: 2014 8. Dom & Tom Dom & Tom is a product agency that helps Fortune 500 companies and next generation startups build the future of digital. We support our client’s strategic initiatives first and foremost through open collaboration, forward-thinking user experience, engaging design, and cross-platform development for web, mobile, and emerging technologies. Dom & Tom has launched 400+ web projects and 120 native mobile applications with teams located in New York, Chicago, and Los Angeles. Pricing: $150 — $199/hr Location: new york Employees: 50–249 Founded: 2009 9. Fueled Fueled specializes in designing and developing award-winning mobile apps and websites that are fast, attractive, responsive, and easy to use. But we’re not here to work on just any apps or for any client. We come to work in the morning to work on the most interesting projects with the best clients, whether for startups or big brands, because we’re united by an unwavering passion for quality. It’s what makes us tick. Pricing: $150 — $199/hr Location: new york Employees: 50–249 Founded: 2007 10. App Partner App Partner is a NYC-based app development firm that works with Fortune 500 brands, leading retailers, funded startups and more to craft digital products and strategies that solve business problems and drive measurable results. Pricing: $100 — $149 / hr Location: new york Employees: 10–49 Founded: 2011 These are the Top app development companies in New york that you can look out. You can also give me your suggestions in the comment box below. PS: This list is in progress and I update it every fortnight. Would you Like to list your App Development Company? Please do comment with your company name.

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This AI is hungry - Part I - Deep Learning Tutorial: Classification of Cooking Dishes and Recipes…

Welcome to my NLP core Tutorial Today, we’re going to be looking at NLTK. Before moving forward I’ll highly recommend to go and take some familiarity with Regular Expression Python Complete Tutorial Numpy Tutorial Pandas Beginner Tutorial Python Data Science Handbook What you’ll learn Kaggle Script: Installing NLTK in Python Tokenizing Words and Sentences How tokenization works? — Text Introduction to Stemming and Lemmatization Stemming using NLTK Lemmatization using NLTK Stop word removal using NLTK Parts Of Speech Tagging POS Tag Meanings Named Entity Recognition Text Modelling using Bag of Words Model Text Modelling using TF-IDF Model Understanding the N-Gram Model Understanding Latent Semantic Analysis What is NLP? ( ) is an area of and concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of data. Refer Natural language processing NLP computer science artificial intelligence natural language Wikipedia Installing NLTK in Python Natural Langauge ToolKit NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries. Installing NLTK NLTK requires Python versions 2.7, 3.4, 3.5, or 3.6 Mac/Unix Install NLTK: run sudo pip install -U nltk Install Numpy (optional): run sudo pip install -U numpy Test installation: run python then type import nltk For older versions of Python, it might be necessary to install setuptools (see ) and to install pip (sudo easy_install pip). http://pypi.python.org/pypi/setuptools Windows These instructions assume that you do not already have Python installed on your machine. 32-bit binary installation Install Python 3.6: (avoid the 64-bit versions) http://www.python.org/downloads/ Install Numpy (optional): (the version that specifies python3.5) http://sourceforge.net/projects/numpy/files/NumPy/ Install NLTK: http://pypi.python.org/pypi/nltk Test installation: Start>Python35, then type import nltk Installing Third-Party Software Please see: https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software This is a Leonardo DiCaprio Speech taken from here Tokenizing Words and Sentences Now, we are going to use to NLTK function which is used to break down this large paragraph into a small sentence and another function is for calculating different words in the sentence. sent_tokenize() word_tokenize() How tokenization works? — Text Tokenization is a process in which a sequence is broken down into pieces such as words, sentences, phrases etc. Just above, you have learned how to tokenize a paragraph into sentences or words. The way these tokenizations work are explained as follows. Word Tokenization: In this process, a sequence like a sentence or a paragraph is broken down into words. These tokenizations are carried out based on the delimiter “space” (” “). Say, we have a sentence, “the sky is blue”. Then here the sentence consists of 4 words with spaces between them. Word tokenization tracks these spaces and returns the list of words in the sentence. [“the”,”sky”,”is”,”blue”] Sentence Tokenization: In this process instead of tokenizing a paragraph based on “space”, we tokenize it based on “.” and “,”. Therefore, we get all the different sentences consisting of the paragraph. We can perform these tokenizations even without the use of nltk library. We can use the split() function for this as follows, str = “I love NLP” words = str.split(” “) print(words) Out: [“I”,”love”,”NLP”] For sentence tokenization just replace str.split(” “) with str.split(“.”) . Introduction to Stemming and Lemmatization So what are Stemming and Lemmatization? So Let’s get started !! Corpus ( means a collection of written texts, especially the entire works of a particular author or a body of writing on a particular subject ) Sentences – ” Putrid does his work intelligently ” ” Putrid is an intelligent man ” ” Putrid is always working on “ What we have to do is to generate words as a feature from sentences and for that, we need stem the different words I will first tokenize each of these sentences You can notice sentence 1 and sentence 2 have one word common that is ” intelligently ” and “intelligent”, but they have a different form. Similarly, the case happened in sentence1 and sentence3 have one word common that is “work”. So, Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form — generally a written word form. intelligence — — — -| intelligent — — — — -| — — — — intelligen intelligently — — — -| All these words are reduced to intermediate root form which is called ” intelligen “ So we are gonna convert these words with intelligen for example ” Putrid does his work intelligen “ Problem with Stemming Produced intermediate representation of the word may not have any meaning. For Example — intelligen Solution: Lemmatization Lemmatization is same as stemming but a representation/root form has a meaning intelligence — — — -| intelligent — — — — -| — — — — intelligent intelligently — — — -| All these words are reduced to intermediate root form which is called ” intelligent “ Difference between Stemming and Lemmatization Stemming using NLTK Lemmatization using NLTK Stop word removal using NLTK Now, look at the paragraph you can see there are some words that are so common like — to, of, in etc. In computer search engines, a stop word is a commonly used word (such as “the”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query. Parts Of Speech Tagging We will be going to learn how you can find out the different parts of speech associated with the word table or word. You can see the above paragraph it contains a lot of words. And each of the different words has a specific part of speech because some words are nouns, verbs, adjectives etc. You can see the output somewhere thank is a proper noun and somewhere it is acting as the verb. So we can easily get part of speech, but there is one problem that is output we get is a tuple And we really can’t use them for formulating models so what we are going to do now is we will generate a whole new paragraph and there each of the words will be appended with their corresponding parts of speech. POS Tag Meanings Here are the meanings of the Parts-Of-Speech tags used in NLTK CC — Coordinating conjunction CD — Cardinal number DT — Determiner EX — Existential there FW — Foreign word IN — Preposition or subordinating conjunction JJ — Adjective JJR — Adjective, comparative JJS — Adjective, superlative LS — List item marker MD — Modal NN — Noun, singular or mass NNS — Noun, plural NNP — Proper noun, singular NNPS — Proper noun, plural PDT — Predeterminer POS — Possessive ending PRP — Personal pronoun PRP$ — Possessive pronoun RB — Adverb RBR — Adverb, comparative RBS — Adverb, superlative RP — Particle SYM — Symbol TO — to UH — Interjection VB — Verb, base form VBD — Verb, past tense VBG — Verb, gerund or present participle VBN — Verb, past participle VBP — Verb, non-3rd person singular present VBZ — Verb, 3rd person singular present WDT — Wh-determiner WP — Wh-pronoun WP$ — Possessive wh-pronoun WRB — Wh-adverb Named Entity Recognition Now, we are going to extract the entities out of the sentences sentence3 = "The Taj Mahal was built by Emperor Shah Jahan" This is our sentence and out of this sentence we are going to find out different entities Step 1: Break the sentence using word_tokenize() into words Step 2: We need to do POS tagging because to do Named Entity Recognition you need POS tagging Step 3: Use ne_chunk() function and pass the argument # step 1words2 =  nltk.word_tokenize(sentence3) words2 ['The', 'Taj', 'Mahal', 'was', 'built', 'by', 'Emperor', 'Shah', 'Jahan'] # step 2tagged_words2 =  nltk.pos_tag(words2) tagged_words2 [('The', 'DT'),('Taj', 'NNP'),('Mahal', 'NNP'),('was', 'VBD'),('built', 'VBN'),('by', 'IN'),('Emperor', 'NNP'),('Shah', 'NNP'),('Jahan', 'NNP')] # step 3named_entity = nltk.ne_chunk(tagged_words2) named_entity.draw#to see the graph you can use named_entity.draw() in your local Machine and the below output occur Output : <bound method Tree.draw of Tree('S', [('The', 'DT'), Tree('ORGANIZATION', [('Taj', 'NNP'), ('Mahal', 'NNP')]), ('was', 'VBD'), ('built', 'VBN'), ('by', 'IN'), Tree('PERSON', [('Emperor', 'NNP'), ('Shah', 'NNP'), ('Jahan', 'NNP')])])> Text Modelling using Bag of Words Model What is Bag of Words Model? The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). Also known as the vector space model. In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. Refer to Wikipedia We are going to remove all the punctuation, errors as well as break down into sentences Text Modelling using TF-IDF Model Bag of Words Problem All the words have the same importance No semantic information preserved Solution : TF-IDF Model TF = Term Frequency IDF = Inverse Document Frequency TF-IDF = TF*IDF The formula of TF = Number of occurrences of a Word in a sentence / Number of the word in that document Example: ” I know okay I don’t know “ TF for I = 2/6 TF for know = 2/6 etc. The formula of IDF = log ( Number of documents / Number of documents containing word ) Make sure you take log base e Example : sentence 1 = ” to be or not to be”sentence 2 = ” I have to be “sentence 3 = ” you got to be “ IDF for to = log(3/3) log with base eIDF for be = log (3/3)IDF for or = log(3/1) etc. TFIDF (Word) = TF(Document, Word) * IDF ( Word) Just for you, I’ll be going to showing you manually how this output comes I already provide you the formula above The formula of TF = Number of occurrences of a Word in a sentence / Number of the word in that document Example: ” I know okay I don’t know “ TF for I = 2/6 TF for know = 2/6 etc. Let’s take the first sentence (taken from dataset1) sentence = barbara rae venter s genealogical sleuthing acumen has inspired others to help law enforcement with unsolved cases as well as an ethics and privacy debate Tf for ‘the’ = 0/8 = 0 now you wonder how 0.10714285714285714 value comes from Actually, Tf value for ‘the’ is checked in all the 8 sentences which we got above take a look at sentence 3 sentence 3 = none of this is a planned event creditcreditbrian l frank for the new york times by heather murphy aug 29 2018 much to the frustration of her proud son the genetic genealogist who helped crack the unsolved case of the golden state killer decided early in the investigation that she did not want to be named Calculating Tf value for ‘the’ in sentence 3 which is = 6/56 = 0.1071428571 Understanding the N-Gram Model What is the N-Gram Model? N-gram is a contiguous sequence of n items from a given sample of text or speech. The items can be phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus. When the items are words, n-grams may also be called shingles. Used in your phone for example — when you type any keyword in your phone and your cell phone automatically tells you did you mean this … Example- When you type intell its show you Intelligence, Intelligent, Intell etc. Understanding Latent Semantic Analysis Latent Semantic Analysis is a technique of analyzing relationships between a set of documents and the term they contain by producing a set of concepts related to the documents and terms. So that was all about the NLP core tutorial. IF you have any feedback on the article or questions on any of the above concepts, connect with me in the comment section below. Fork this notebook on Kaggle Please share it, like it, subscribe to it my themenyouwanttobe ! Share Some Love too ❤

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I’m sure every one of you must have heard of this . Wondering what it is. Let’s jump into its basics. buzzword Machine Learning : This is just an overview to have quick knowledge about machine learning and how it is helpful. Note M is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. focuses on the of computer programs that can access data and use it learn for themselves. achine learning Machine learning development Why Machine learning Matters? Alpha Go is a computer program that plays the board game Go. It was developed by Alphabet Inc.’s Google DeepMind in London. AlphaGo AlphaGo defeated one of the best human players at Go — an extraordinary achievement in a game dominated by humans for two decades after machines first conquered chess. Dota 2 is a multiplayer online battle arena (MOBA) video game developed and published by Valve Corporation Dota OpenAI reached yet another incredible milestone by defeating the world’s top professionals in 1v1 matches of the online multiplayer game Dota 2 is a machine website from that can text between different languages. It can words, sentences, documents and web pages between any combination of more than 100 supported languages. It was launched on April 28, 2006, and has always been free to use. Google Translate translation Google translate translate Artificial intelligence will shape our future more powerfully than any other innovation this century. Machine Learning Types Supervised Learning Let’s look at the two task of supervised Learning Regression Classification SUPERVISED LEARNING = DATA SET CONTAINING TRAINING EXAMPLES WITH ASSOCIATED CORRECT LABELS Supervised Learning is a function that maps an input to an output based on example input-output pairs. It infers a function from labeled data consisting of a set of examples. training training For example, learning to classify handwritten digits. Train your data set to such extent that it becomes easy to find out the result of unknown inputs. How supervised Learning Works? let’s examine the problem of predicting annual income of person based on the number of years of higher education he/she has completed. Inshort,Build a model that approximates the relationship between the number of years of higher education X and corresponding annual incomeY f Solution There are two ways you can find a solution **Engineering Solution**This solution would go by simple formula income = ($5,000 * years_of_education) + baseline_income **Learning Solution**Get information about some basic parameters like Degree Type, Years of work experience, School tiers etc. This will helps use to find the income easily. For example: “If they completed a Bachelor’s degree or higher, give the income estimate a 1.5x multiplier.” Goal of supervised Learning Learns the relationship between income & education from , by running labeled training data through a learning algorithm. scratch This learned function can be used to estimate the income of people whose income Y is unknown, as long as we have years of education X as inputs. Inshort, we can apply our model to the test data to estimate unlabelled . Predict Y as accurately as possible when given new examples where X is known and Y is unknown Regression — predicting continuous values — Predicts a continuous target variable Y. — Estimate a value, such as housing prices or human lifespan, based on input data X. — Continuous means there aren’t gaps (discontinuities) in the value that Y can take on. Eg. A person’s weight and height are continuous values. — Discrete Variables means it can only take on a finite number of values Eg. Number of kids somebody has is a discrete variable. Predicting income is a classic regression problem. Your input data X includes all relevant information about individuals in the data set that can be used to predict income, such as years of education, years of work experience, job title, or zip code. — These attributes are called , which can be numerical(e.g. years of work experience) or categorical (e.g. job title or field of study). Features You’ll want as many training observations as possible relating these features to the target output Y, so that your model can learn the relationship between X and Y. f Linear regression(ordinary least squares) We have our data set X_,_ and corresponding target values Y. The goal of ordinary least squares (OLS) regression is to learn a linear model that we can use to predict a new given a previously unseen with as little error as possible. We want to guess how much income someone earns based on how many years of education they received. y x is the y-intercept and is the slope of our line, i.e. how much income increases (or decreases) with one additional year of education. β0 β1 Our goal is to learn the model parameters (in this case, and ) that minimize error in the model’s predictions. β0 β1 To find the best parameters: Define a cost function, or loss function, that measures how inaccurate our model’s predictions are. 2. Find the parameters that minimize loss, i.e. make our model as accurate as possible. Classification Classification predicts a discrete target label Y. Classification is the problem of assigning new observations to the class to which they most likely belong, based on a classification model built from labeled training data. Logistic regression: 0 or 1? Deals with binary type of classification — example, when assigning handwritten digits a label between 0 and 9, or using facial recognition to detect which friends are in a Facebook picture. SVM (Support vector Machines) — The algorithm is in nature, rather than being driven by probabilistic thinking. — SVMs use a separating line (or, in more than two dimensions, a multi-dimensional hyperplane) to split the space into a red zone and a blue zone geometrically motivated — The distance to the nearest point on either side of the line is called the margin, and SVM tries to maximize the margin. — Think about it like a safety space: the bigger that space, the less likely that noisy points get misclassified. Unsupervised Learning is a type of algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. Unsupervised learning machine learning unsupervised learning Clustering Items inside a should be very similar to each other, but very different from those outside. cluster Does grouping of data without being told ahead of time Used for knowledge discovery rather than prediction — clustering is useful whenever there is diverse and varied data Famous clustering Method k-means Now you know about the basics types in Machine Learning let’s have quick look at the difference between supervised and unsupervised learning. Main Difference That’s it you are all good with the basics of Machine Learning. Happy Learning! 😆 🖥

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Hello, my Dear Crypto Reader! If you interested in Mythology, please, skip this article and read my favorite stories: is a novel written by Neil Gaiman. For the television adaptation of the novel, see American Gods American Gods (TV series) by Homer The Iliad But if you are a technical person interested in EOS, you can continue with the current article. Some numbers before Myths EOS vs. Ethereum Market Cap: 700 000 BTC vs. 3 000 000 BTC Transaction per day: vs. 600 000 400 000 -600 000 But a lot of EOS transactions have not transfer type ( etc.) promote, buy resources, new account, Accounts with non zero balance: (?) vs. (as all EOS accounts are not free) ~ 15 400 000 310 000 Nodes: 18 000 vs. 21(total 400 for standby) We can see that Ethereum still the king for DApp world but EOS is going ahead. Myths list We will follow five simple myths about EOS: M1. Real Decentralization M2. The Fastest blockchain M3. User-friendly M4. free transaction M5. Command lines for geeks tool If you want to see favorite Myth just note it in the comments M1. Decentralization The main core of the myth that EOS is more decentralized than Ethereum. Adepts of Myth provide diagrams from Etherscan: To control Ethereum Network, you require an alliance with 4–5 main mining pools compare to 21 (or active 14) producers for EOS. Is it true? No, because you can’t compare Producers with Miners. Ethereum Miners is just slaves of the Network. Miner pool is a temporary structure which can TRY to do only limited things: transaction double spending ignore transaction queue create a new fork But as the pool is a temporary alliance: people start a migration to another pool, after any leeks informing manipulations.But if we are talking about the community of Ethereum, it is nobody (including Vitalik Buterin or Gavin Wood) cannot provide any fast changes. For example, Bitman is controlling near half or Bitcoin market, but nobody worries as Bitmain just miner and can only create a new fork or destroy network reputation but can’t take control of the blockchain itself. Or look at the situation at Decentralized Exchange Order Tracker But for EOS situation is dramatically differ: Developing changes is the authority and curator of the EOS network. Within the DPoS system, Block Producers run the underlying network layer to process all transactions on EOS. There will be a total of 21 block producers along with another 400 Backup nodes overlooking the activities of the 21 Block Producer EOS producer can: write block (mining role) validate transactions (mining role) EOS awards producers every time they produce a block suggest and vote for Proposals is the most significant player and controlling 10% of tokens. Block.one Actually, top100 accounts including exchanges are controlling near 75% of tokens. Another 25% of users are inactive and delegate mostly formally. As we have Delegated-proof-of-Stake model, I can imagine that it is enough to stake near 30% of tokens with media support for full control of the network. As another 70% of users can be inactive or understand the real situation. Controlling cash After ICO Daniel Larimer has obtained a tremendous amount of money.But do you know any information about using this cash or ICO Ethereum? For example, do you remember the decision to attack the Ethereum Network? Do you think Network holders or Producers approved it? Exactly, No. If a small group of people controls more than 1 billion dollars without any reporting can we say that it is a decentralized community? If Daniel will require 5–10% of tokens to change the balance? He can spend cash to buy it. Compare this with Ethereum. The funds from ethereum’s initial $18m crowdsale and project development are now managed by the Ethereum Foundation, a non-profit entity based in Switzerland. DPoS: Risk of attack or shut down by governments The number of full nodes in EOS is going to be smaller. So Dan Larimer has this concept called DPOS where he says that in DPOS you only need about 100 nodes in the network and consensus happens among these full nodes and everyone else is a thin client. The other reason it (EOS) claims it can process more transactions is because the requirements (bandwidth, computing) for each of these full nodes is much higher. This is one way to achieve scalability. The problem is that if you have 100 nodes the system is much more centralized. You can denial of service them. Because you have to vote for them, the nodes that win are going to be the nodes that everyone knows. So it is much easier to attack the people running the nodes. ISPs can shut them down, companies can shut them down, governments can shut it down fairly easily. Vitalik’s basic argument is that there are fewer full nodes and that they are easily identified and can be shut down by governments. But try to simulate a situation when hat any structure will close Ethereum? Exactly no, because it is more decentralized. Summary And it looks like that EOS a toy for picture Daniel Larimer and his friends. But now it’s favorite toy and developing extra intensive. But if I can image Ethereum without Vitalik, but can you EOS future without Daniel? M2. Fastest blockchain EOS is abnormally quicker than Ethereum. Just now expected real speed near ~4000 transactions per second. And it can be increased after fixes of code and adding more powerful producers hardware. But Why EOS is faster than Ethereum? The main reason that EOS has another consensus, security and logic model. Not better, just another! Main speed key factors: Consensus: Delegated-Proof-of-Stake, or dPoS, is a consortium blockchain that is validated by a set of master nodes known as ranked delegates. And to confirm transaction you need 14 nodes only. EOS is designed to allow nodes that validate partial state. This means that not all nodes need to run every contract. This in turn means it is possible to know the state of the applications and contracts you care about without having a highly resource demanding full node. Ethereum is still on PoW and requires colossal calculation and for all network with 18 000 nodes. For example, Ethereum with PoA consensus can support 80 or more transactions per second versus 15 for PoW. Security: EOS is public blockchain with users registration. Private blockchain expects to minimize security validations. For example, private Hyperledger Fabric can support up to 7500 transactions per second. As EOS trust own users and Producers development team cut two critical things: s are the main calculation bottleneck of Ethereum and were kicked from EOS. Merkle tree The reason why EOS says that they can process more transactions comes from two sources. One of them is there are a lot of protocol features that Ethereum has EOS doesn’t have. One of them, for example, was Merkle trees. So this if you want to prove anything about a transaction then anyone in the network can prove it. And you actually don’t need that much computing power to prove that the state of that account is some number and that the transaction was included. EOS does not have this. (с) Vitalik Buterin _At 8:20 minutes into the above video Vitalik Buteran says: The reason why EOS says that they can process more…_steemit.com Response to Vitalik Buterin on EOS - Steemit In order to support the efficient generation of merkle proofs and validation thereof, it is useful to have a merkle tree comprising all blocks. This tree can be calculated on an incremental basis by tracking the leading edge of the tree. _In order to support the efficient generation of merkle proofs and validation thereof, it is useful to have a merkle…_github.com Continuous Growing Merkel of All Block IDs · Issue #8 · EOSIO/eos Blockchain platform EOS found containing critical security vulnerabilities See Life Cycle of an Ethereum Transaction EOS is not fastest blockchain, but for DApp environment speed 4000 tx per second is enough for the actual requirements. M3. User-friendly blockchain Correct. It is a true myth. EOS is a user friendly-blockchain. Username EOS supports username which is more flexible than the public address detonator212 vs. EOS7epiMwQShVAYS6py3SrrJJbuCnbmhN9C6USYnMKBxJPYkydBcZ Messages inside transaction EOS support readable messages inside of the transaction. You can send details like in bank transaction! In Ethereum you have you convert it from HEX before reading. Read next article to compare it with Ethereum _Blockchain becomes more and more commodity and usable not only by geeks. You may try it just now!Today we will spend…_hackernoon.com Blockchain Art. Images including Kitties in Blockchain. Step by step instruction. Plugins and UX tools EOS has own MetaMask — Scatter and all basic tools _EOSToolkit is the premier free, open source interface for managing EOS accounts. Create, transfer, stake, vote and more…_eostoolkit.io EOSToolkit.io | All in one EOS web wallet M4. free transaction fee We cannot honestly say that a transaction fee is really free. Users need to stake (lock to be non-transferable for a certain period of time) their tokens in order to use the blockchain. More details on by Daniel Larimer. How to build a Decentralized Application without Fees But for most of operations users don't need to pay (micropayments hold included in basic account creation). Usually, expected that the owner of DAapp create Smart Contract and pay for operations. It’s principal difference between EOS and Ethereum because in Ethereum payment is provided by the user who runs the function in the Smart Contract. As the owner of application you have to provide the primary resources: requires the stake. Unstake at any time to reclaim your EOS tokens CPU CPU Bandwidth is measured as your average consumption in microseconds over the last 3 days. CPU bandwidth is temporarily consumed when you send an action or transaction but decreases over time returning to 0. The longer your transaction runs, the more CPU bandwidth it will consume. has to be purchased RAM RAM is required to store data on the blockchain and must be purchased. You get a set number of bytes of storage based on the current RAM market price. The price automatically adjusts up and down with buying and selling action. When you release storage you can sell RAM back to recover EOS at the current ram market price. requires the stake. Unstake at any time to reclaim your EOS tokens NET Network Bandwidth is measured as your average consumption in bytes over the last 3 days. Net bandwidth is temporarily consumed every time you send an action or transaction but decreases over time returning to 0 Check the current price for EOS.IO network storage and bandwidth resources with online EOS Resource Planner: _ERP is a free and open-source tool to display the current price for EOSIO network storage and bandwidth resources._www.eosrp.io EOS Resource Planner M5. Only Command lines tool for geeks It’s not truth myth! Actually, you can provide all necessary actions from GUI: creation of account creation key pare transferring EOS or tokens simple payment from a website page If you want to click for the test network, kindly link: _OS is young, and most of the environments are just developing, but some necessary things are available and … work!_hackernoon.com EOS first baby steps: account, wallet, keypair, testnet, transfer tokens, validate transactions. For mainnet link: _EOSToolkit is the premier free, open source interface for managing EOS accounts. Create, transfer, stake, vote and more…_eostoolkit.io EOSToolkit.io | All in one EOS web wallet Summary EOS is an innovative blockchain with impressive differentiators: fast performance near free transactions developing environment and tools strong community But my private opinion that Ethereum is still a King of the DApps blockchains.

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Five key Myths about EOS — next Ethereum killer?

Orchestration on cloud (k8 on Azure) It is probably not the first time you heard the Cloud Native term. It is fashionable these days and it is propagated by many consultants and technology companies. Of course they are all saying you have to get aboard this train, the benefits are huge and you surely can't afford to lose it. But what does it exactly means and why wasn't cloud computing enough? To answer this let me shortly explained what I understood a couple of years ago by cloud computing and what I learned after it. Autoscaling I deployed a first production app on AWS back in 2012. It wasn't designed for the cloud, we were spinning up a vm, connecting to it, copying the binaries and starting the web server. But version after version we learned and understood what the power of the cloud meant in a word: . autoscaling In order to support both scaling up and down, your application had to be stateless: you can't save things to the local disk or the session details in process because that vm can always be terminated as a result of a scale down event. The same was with logs: if before writing logs on the disk was enough as you could always retrieve them, now that luxury was gone. But this was great because this is how many teams adopted tools like Kibana or Graylog. Another thing autoscaling brought to the table was that you didn't have to over provision, you started with a processing power and then based on metrics you were able to reduce or increase that power. And the good part of the cloud was that our bill was for exactly what we used, if a vm only ran for 2 hours, we only paid those hours and nothing else. Autoscaling on AWS The true gain wasn't the money. I worked with some companies that didn’t move to the cloud just because they had a big infrastructure investment already done and when they calculated their monthly cloud bill even with all the discounts the received, it was just too much. The cloud meant no upfront investment, but on the longer run you actually were paying more, so for companies already with a big investment in infrastructure it was far from feasible. But that was a very simplistic view. The main gain came from the flexibility we gained by being able to handle the spikes we had from our clients and reduce costs when there was less traffic. And more important was that we outsourced completely our infrastructure to pretty good engineers that were providing us a realiable API for it. We all know good developers are hard to find. Automation Soon we realized it was pretty repetitive work what we were doing: copy the binaries on the existing vm, create a new image for the vm and create a new auto scaling group. Also the term immutable infrastructure was getting more attention, so the next natural step for us was to start using a configuration management tool like Ansible or Chef and later adding Terraform for infrastructure. This allowed us to always create a new vm for a new version and just replace the older vm with the new one in the load balancer. Which brought us really close to no downtime deployments, we just had to separate the database deployments than the code ones (or the db to support 2 different code versions). devops.com What happened since I started automating application installation until today? Well now I see everything in pipelines. Do you need a completely new environment to test your new features. A pipeline should do that, no matter if the output is a new kubernetes cluster or a new set of vms for your app and db or just a new s3 bucket and a route53 entry for a static site hosting. Need to run upgrade scripts for your database? Sure, there should be a pipeline to handle that (and totally separated from the code one). And so on for application deployment. Just remember to separate CI from CD. Move to microservices Even from the first monolith that I worked with we had discussions about separating the product verticals. If you sell on your website both hotel rooms and car rentals, why a deploy to one of the product should affect the other? And back then they always did. We could just disable car rentals from the website, do an upgrade to it and then enable it back. So our move to microservices was the normal path, even though when we started with it we were actually building a distributed monolith. people.groupon.com And while using microservices you will soon have to start with containers because you simply realize running one microservice per vm is just too much, especially when you end up with a big number of them. So we started with autoscaling, used some automation and this brought us to the path of microservices and later containerisation. And there is still one last step to reach cloud native and that is to use a container orchestration engine. Orchestration Running microservices and containers becomes pretty hard when you reach a big number of microservices. New hosts have to be added, while some will need maintenance. New versions of microservices are being added constantly and we need different ways to deploy those new versions. So the next natural step is to start using a container orchestration engine like ECS, Docker Swarm or Kubernetes. Actually first you probably end up writing many scripts for orchestration, but you should soon realise that such a solution just doesn't scale. And in the last years Kubernetes is by far the default choice for orchestration and somehow it became the starting point when taking the cloud native way. Cloud native So at the beginning I said cloud computing for me is equivalent of doing autoscaling and I tried to explain how I came to the concusion that autoscaling means using the power of the cloud versus the classic hosting solutions. pivotal.io And now if you are taking the cloud native way you are most likely doing kubernetes or some other solution of container orchestration. Because that means you are running containers, you have an microservices architecture in place, automation and pipelines are in use for deployments and your hosts and microserices are autoscaling based on monitoring that you do for your application. And these are the pillars for cloud native computing. I remember going from a classic application to one that was able to autoscale was a pretty hard transformation that lasted more than 1 year (almost 2 for us). Now going from cloud computing up to container orchestration with having all the pipelines in place is also a journey that I think it will take a little more, while some even say this road is never ending. And of course it is totally worth it, you can't miss this train. References: AWS: What is Cloud Computing? https://aws.amazon.com/what-is-cloud-computing/ Pivotal.io: What are Cloud-Native Applications? https://pivotal.io/cloud-native Container-Solutions: Six steps to Successful Cloud Native migration https://container-solutions.com/six-steps-to-successful-cloud-native-migration-part-1/

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For a first-time entrepreneur who is eager to turn his idea into a Minimum Viable Product (MVP), a path to a quick and successful launch is not very clear. There are many aspects that need attention. In parallel to developing MVP, the founding team has to think about user acquisition and operational strategies. Founders typically fall into the trap of “ ” and underestimate many aspects outside mere product development. Any successful venture needs as much focus on product distribution strategies and operations as developing a software for an App or a website. During the initial stages of a startup, most of the founder’s time should be devoted to talking to potential users while building MVP. First-time founders, especially coming from a tech background, sometimes get carried away with the nitty-gritty of product development and forget interacting with users till the MVP is completely ready. The MVP should be considered as a way to quickly validate your thesis with the focus on your users while minimizing your costs. If you build, they will come There is a tendency to plunge head on into coding without any initial preparation. A few weeks of preparation can save precious time in the long run and creates a solid foundation for your product. A concise product specification, a nicely designed user interface, well separated service-oriented architecture, scalable software stack, well-defined and automated development process, skilled team, and well-thought out marketing strategies during MVP cycle build a solid foundation for a long-term success of your product. Having worked with a lot of entrepreneurs to bring their ideas to life, I have come up with a 5 step guideline for building a successful software MVP. 1. Product Definition Product definition provides a common view of the background and what you are building to all the stakeholders. Specification This document describes a high-level business idea or an investment thesis and how it is supported by the product. It should also describe what you know about the market that is contrarian view from majority, in short, your differentiation. It should briefly describe target segment, user channels (mobile app, website, voice interface, etc.), major features, product distribution, engagement and monetization strategies. It should also clearly state the goal of your MVP which can range from a validation of assumed user behavior to target number of paying users after 6 months of release of MVP. Use specification as a common guideline throughout the MVP cycle for users, team and investors. Use cases Use cases describe MVP functionality from the point of view of your users. Each use case is a short description of a user action with respect to their roles and corresponding response: “As an end user, I can send ‘Hello’ to my chat bot and receive greeting back”. Each use case should be short enough to be implemented quickly and should preferably define functionality that is visible to the user. You can also write non-functional use cases that define characteristics like response time, scalability, costs etc. Define use cases to clarify MVP behavior, break down features in small chunks, tighten the scope to focus on and simplify. _Use Cases with labels in Git issues (_Source: 47Billion ) 2. User Interface Design MVP is a first impression of your product to customers and potential investors. So it is important to develop simple, functional and good-looking user interface. A professional user interface designer or a team can help set the tone of the UI which can then be extended to subsequent versions of the product. Though the following steps describe traditional graphical user interfaces design, they apply equally to the speech and chat bot based conversational interface design. For a simple user interface design, interactive high fidelity mockups can be developed directly while keeping initial steps in mind. Information Architecture Information architecture definition phase is optional but can help put proper structure to user interface before diving deep into flows and interactions. During this phase, a product manager or a user experience designer can help define user personas, roles, information affinity, various operations and flows. This phase can be used to make decisions such as category-based vs stream-based information presentation. Use Information architecture to develop a simple structure around the information to be presented to users. Flow Diagram Visualizing entire user flow in a static flow diagram helps in organization and optimizing user interactions early in the process. For conversational interfaces with natural language, you can design user conversations for most frequently used interactions. Simplify information organization and user transitions with a static flow diagram. User Interface Flow (Source: 47Billion ) Wireframes Wireframes are used to layout information on a screen. A good wireframe positions content based on type and its importance. If there is a variety of content types to be displayed on one page, wire framing allows focussing on designing clean, uncluttered arrangement before designing high-fidelity mockups. Arrange content layout on a page using wireframes. Wireframe (Source: 47Billion ) High Fidelity Mockups High fidelity mockups closely mimic the final look of user interface. Here decisions such as branding guidelines, colors, fonts, iconography, micro-interactions are introduced in wireframes to show final rich user interface. Context and personas defined during information architecture phase and wireframes should be used as input for high fidelity mockups. Mockups need to be developed with grid layout if UI design needs to be responsive to show properly on mobile browsers. Design high-fidelity mockups to visualize detailed look-and-feel of user interface. High Fidelity Mockups (Source: 47Billion ) Redlined high fidelity mockup (Source: 47Billion ) Interactive Mockups Interactive mockups combine user flow and high fidelity static mockups to provide early user experience of navigation with a canned data without waiting for an entire development to be over. You can use these mockups for demo to potential users and investors to gauge their reaction, interest and gain a valuable feedback. Use interactive mockups to gain early feedback on your service from your users and investors. Interactive Mockups using Adobe XD CC (Source: 47Billion ) In part 2 of this series, I will talk about defining architecture, choosing software stack, setting up agile development process and marketing practices for your MVP. What other techniques have you used during MVP development cycle? If you need help bringing your ideas to life, contact us at info@47billion.com.

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We’ve lost our way in Product Management. When is setting roadmap priorities based on market feedback, it’s symptomatic of the underlying practices and tools. the biggest problem Product Managers face Simply put, Products are Conversations. How you orchestrate conversations with customers and cross-functionally in your organization is what makes great products. To set the right strategy, align the team, and execute to the delight and fulfillment of customers. Agile was supposed to bring product creation closer to the customer. But there is too much distance, data, organizational complexity. Great products are made of conversations. With customers. With leadership and PM direct reports. And with stakeholders. The conversations that form the strategy. The conversations that align the strategy. The conversations drive and iterate execution. Here’s 3 reasons we’ve lost our way in Product Management. Reason #1: The Path to PM Tech Product Managers who develop their skills as Engineering Leaders. This path to PM is all about the How, and many have good people skills to relate requests to the How. A second path to PM is people who develop their skills as Project Managers. This is again about the How, but with less CS education and typically from marketing. And in marketing they found great success in the quantitative practices of lead generation and growth hacking. There’s a third path to PM, rising in reflection of what has made great products in more competitive markets, which comes from Design. Less about the How, more about the Why, with deeply rooted user empathy. Problem #2: Data-driven Using data to drive product decisions is particularly great for finding things that don’t work. But usage data is just one factor for decision making, and because in PM meetings it’s something you can point to as evidence to support an argument, it’s often overweight (particularly when the presenter has good persuasion skills). The Lean Startup movement put particular emphasis on throwing out hypothesis to test with data. Thank goodness Steve Blank evolved it with Customer Development. Which involved actually taking that hypothesis out in the world talking directly to customers about problems and solutions. And there’s wisdom in talking to customers as a , and that can love you. So more startups are ramping up the . But too many tech founders prefer to cast out artifacts and measure reactions at a distance. non-scalable activity focusing on a few sales learning curve Reason #3: Alignment is Hard Even in a well-oiled machine with clear strategic direction, awesome talent and culture, aligning PM stakeholders is hard. The PM as the CEO of Product is dysfunctional myth. The PM may have the decision rights for product, but her stakeholders have decision rights for organizational functions, and their goals, objectives and resources may run counter. Some product leads like have found greater success sharing control to create value. By sharing decision rights with your CTO you may find yourself making plans based on more realistic effort estimates and a collaborative vision set with both the Why and How. Matthew Mahoney This isn’t to say that the CPO shouldn’t be the Directly Responsible Individual for product decisions, but don’t mythologize Steve Jobs for either vision unbound from users (the first version of the iPhone was visionary, but sucked, and iteration with user and market feedback made it a success under his stewardship) or might that’s right. Consider . Sharing access to information does not have to mean sharing control. The challenge is that information needs to be provided in-context so it is not misconstrued. decoupling information rights from decision rights The PM function has more stakeholders than any other in today’s organization. Customers, Leadership, direct reports, design, development, QA, DevOps, Support, Customer Success, Sales, Marketing, BizDev and Backoffice. And I’m probably missing some. Because stakeholders enable the product, and the product enables stakeholders. How you gain customer and market insight, plan your strategy and roadmap and execute all depends upon alignment. Products are Conversations The clueful will recall that . Customers are empowered and authentic voice cuts through all the noise in today’s marketplace. Your customers want to talk with you, the PM. They want to be heard and want you to understand their needs. It’s your job to enable these conversations, and figure out how to have them at scale. Markets are Conversations The right customers for you will understand your tradeoffs, validate and support your decisions, and align. They will value the experience you are creating for them, and progress in a longer term relationship, not a transaction. Rather than see the Head of Product as the CEO of Product, the PM is put as close to the customer as possible to make decisions on their behalf, and plays the role of Information Hub to align stakeholders through effective conversations. Your stakeholders want to have these conversations, and it’s your job to make sure they happen. George Bernard Shaw “The single biggest problem in communication is the illusion that it has taken place. “ In today’s product companies, the team is more connected and engaged through Slack and other group messaging apps. Conversations are happening, but it takes additional practices and for them to be effective. Across the company you need practices for what should be read and replied to, how decisions are documented, knowledge is captured and tasks are tracked. tools And for Product Management, how to: Turn conversations into insight Turn conversations into plans Turn conversations into actions Turn conversations into continuous improvement Product Managers, if you takeaway one thing from this post — get out of your spreadsheet and make time for more customer conversations, and think through the cadence of conversations with your stakeholders. Update: I’m humbled… body[data-twttr-rendered="true"] {background-color: transparent;}.twitter-tweet {margin: auto !important;} ' Products are Conversations is closer to what meant by "Markets are conversations" than anything marketing has done with it in the nearly 2 decades since it was first uttered (at ). @Ross https://t.co/xyNSOWhaYy @cluetrain https://t.co/fpPZ8uX73w — @dsearls function notifyResize(height) {height = height ? height : document.documentElement.offsetHeight; var resized = false; if (window.donkey && donkey.resize) {donkey.resize(height); resized = true;}if (parent && parent._resizeIframe) {var obj = {iframe: window.frameElement, height: height}; parent._resizeIframe(obj); resized = true;}if (window.location && window.location.hash === "#amp=1" && window.parent && window.parent.postMessage) {window.parent.postMessage({sentinel: "amp", type: "embed-size", height: height}, "*");}if (window.webkit && window.webkit.messageHandlers && window.webkit.messageHandlers.resize) {window.webkit.messageHandlers.resize.postMessage(height); resized = true;}return resized;}twttr.events.bind('rendered', function (event) {notifyResize();}); twttr.events.bind('resize', function (event) {notifyResize();});if (parent && parent._resizeIframe) {var maxWidth = parseInt(window.frameElement.getAttribute("width")); if ( 500 < maxWidth) {window.frameElement.setAttribute("width", "500");}} See also: The Product Manager Problem and Product Managers — Here is how to leverage Slack to automate your workflow, get closer to your customer and build breakthrough products . And Pingpad for Slack is modern collaboration for Product Management.

Products Are Conversations

An already existing platform (500k+ downloads on Google Play) is creating a token So far in 2018, all we have known in the crypto market is the bearish trend. A lot of people have likened this trend to the bear market in 2014. I wasn’t in crypto in 2014, but I have read quite a bit about the bear market that rocked the crypto-verse then. Unlike today, people thought bitcoin was actually going to die off. Though Bitcoin clearly prevailed, the fact remains that a lot of crypto coins died, leaving only the genuine ones that survived. Cryptocurrencies that have a genuine use case will stand the test of time. There will always be bear trends and volatility, but use case will trump all in the survival and growth of the altcoin. There is a cogent problem when it comes to some blockchain projects. There are companies developing tokens for the sake of capital raise and market dominance when there are existing tokens that provide the solutions they require. An example of the industry that this affects is the gaming industry. There are quite a number of gaming tokens. Each one is different from the other in distinctive features. We have examples like ENJ, FUN and many others. But you know the problem; it will be extremely difficult for just one coin to rule the industry. Online gambling is said to generate about $56B per annum, which makes room for a lot of competition. Hence, the goal here is not to get to the moon but to have a token that will . An already existing product makes me score a crypto project quite high and this crypto project has that. The already existing platform is and the crypto project is . always be in demand PlayUp PlayChip Now, this is not a financial or an investment advice. This is an informative review and overview of a crypto project for enthusiasts who would be interested in it or something like it. PlayChip is a crypto token created by the team behind PlayUp to incentivize their existing sports community and gaming ecosystem. PlayChip has the vision to become the universal gaming token for sports betting, gaming, fantasy sports, and eSports. . The token is connected to an already operating business with over 1 million users on 7 platforms in 70 countries PlayChip plans to disrupt the unregulated gambling industry (said to be worth $3T) through fairness, transparency, ease of use and lower fees. . Players convert the rewards to vouchers with affiliates (which includes Mastercard and Amazon). Currently, PlayChip is a reward token on PlayUp with a fixed value of $0.01 PlayChip will be an ERC-20 Ethereum token which will be the exclusive token of the PlayChip ecosystem currently comprising of 7 platforms. The PlayChip Foundation will be responsible for the operations of PlayChip and the technologies alongside namely; the PlayWallet, PlayXchange, and PlayChain. . The PlayXchange is the medium through which PlayChip tokens are converted into fiat and other cryptocurrencies. Direct conversion of PlayChips to fiat and other cryptos is possible within the wallet, making both the PlayChip and cryptocurrency as a whole more accessible to their vast user base and new users to crypto. The PlayWallet is the wallet interface (and base) of the PlayChip ecosystem where players hold and use their PlayChip tokens The first is . There is no minimum amount for withdrawal and there is instant access to funds. The blockchain technology also , dispelling any rumors of insider trading or preferential terms for players in the ecosystem. The blockchain also creates a platform for global growth for the ecosystem. Why is a blockchain solution necessary for the platform? What problem would it solve? fast and easy withdrawal with low fees offers transparency, visibility, and security There are a lot of benefits regarding the use of PlayChip. The blockchain use provides increased trust for the gaming platform. PlayChip Foundation can audit transactions and remain compliant with regulatory bodies while maintaining user privacy. Regulatory authorities can monitor the platform to ensure compliance. . (However, changes in PlayChip value will affect the value of winnings). Transaction fees are also reduced based on the removal of the middlemen. There will be KYC/AML to participate in the ecosystem, but it will only be done once to participate on all platforms. Players are also incentivized for social network growth activities like referrals, sharing, bet-posting, gifting and so on. The accessibility brought by the PlayChip increases global liquidity which leads to larger prize pools and better odds, which in turn increases player participation Now, the moment of truth; the Ethereum scaling problem. Since the token will be an Ethereum token, there is a need to address how it will cope with the slow Ethereum network. The solution created for this is the PlayChain. . The blockchain architecture of PlayChip is also being designed in such a way that it will be able to accommodate any blockchain solution that solves Ethereum’s inefficiencies. As technology improves, PlayChip will be developed to match the recent developments or could be migrated. PlayChain is PlayChip’s own off-chain network to achieve consistency in pricing and odds (across the PlayChip ecosystem), high transaction speed, secure and reliable transactions, and a real-time ledger transaction within the platform to be batch-transferred to the Ethereum blockchain To tackle market volatility and avoid pumps and dumps, . Team members, affiliates and advisors are also subject escrows with performance-based release metrics for a minimum period of 12 months. Overall, the PlayChip price would be determined by how much people use the platform and the demand for the token. escrows have been implemented on accounts with a contribution of over $50k The next question then is, Quite a lot to say here, but let’s just make the figure below do the talking. how does PlayChip stand out from its competitors especially those in the blockchain space? The PlayChip Advantage Yes, PlayChip is having a token sale but participating in it should be your own decision after further careful research. The token sale has already raise double its soft cap of $10M. The crypto market is currently in a bear trend and it’s a wake-up call for everyone. Buying tokens just for the purpose of selling it later at a hopefully “higher” price is not a wise thing to do. However, if fantasy sports, online sports betting are your thing, then this is a platform (and could also be an opportunity) for you. P.S. This article is for informative purposes only and not intended as any form of financial advice. Participating in ICOs carries considerable risk. . This is just to learn about an innovative project in the crypto world. Do Your Own Research

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is instant messaging and voice service with a focus on privacy. It also just so happens that they also have a pretty slick interface for creating bots. It’s all done from within the app, by talking to another bot called the BotFather. Telegram This tutorial will walk you through creating a Telegram bot that can translate text into a few different languages. The translation will be handled by IBM Watson’s . The and Watson will be brought together by . Language Translator Telegram bot API Standard Library If you’re not familiar with , we combine a scaleable compute layer, along with automatically generated documentation, authentication, billing, and more. We strive to enable anybody to turn simple functions into production-ready APIs, in seconds. And with the help of our online editor , you can ship to Standard Library with zero setup or installation on your local machine. Standard Library JavaScript Code.xyz What You’ll Need Beforehand 1x Telegram Account 5x Minutes ≈ Step 1: Consult the BotFather It’s the day of the BotFather’s daughter’s wedding. You’ve got the opportunity to ask for one gift, which of course he cannot refuse. You choose to ask for Telegram bot of your very own. Whether you use the Telegram or your phone’s respective client, go to the search bar and enter “botfather”. There might be a few options, make sure you pick the one with the “verified” check-mark. web app, Imposters! You want the one with the blue check. With your conversation started, type and the BotFather will ask for a name. This is not the username of the bot, so it doesn’t have to be unique. I went with . Next, the BotFather will ask for a username (i.e., ). This is how people will call your bot, so it must be unique. I, again, went with , but you’ll have to pick something different. After this step, the BotFather will return a token used to control your bot. Copy that down, we’ll need it in just a moment. /newbot notoriaga_test_bot @BotFather notoriaga_test_bot Now we need to enable for the bot. This lets people type in any chat to use your bot, rather than having to open a chat as we did with the BotFather. Type and then click on the bot you just created. The BotFather will ask for some placeholder text. I went with and that gets displayed like this — inline mode @<bot-name> [/setinline](about:invalid#zSoyz) Some text to translate… Step 2: Add Some Basic Functionality with Standard Library — Standard Library’s free online editor for APIs Code.xyz Before we get to the translating, let’s just get a basic “hello world” example working. Head over to Standard Library’s online code editor. It comes preloaded with (almost) everything you need to get your bot going. You can find the code by following that link or by clicking on the “Featured API Sources” tab on the landing page. Code.xyz, Once loaded, open up the file. You’ll see four different variables. For now, we are only concerned with . That is that token from earlier. If you can’t find it for some reason, you can make a new one with while talking to the BotFather. Either way, put that token in your . env.json TELEGRAM_BOT_TOKEN /token env.json Now head over to . This function, when run, registers the file in your project as the recipient of all your bot’s inline queries. After hitting run, you should get a response from Telegram that everything is good — function/setup.js functions/__main__.js Now head back to Telegram to give your bot a try. Type , and after a moment a popup should appear that says — @<bot_name> hello world echo Click the popup and — Success! With the basic functionality set up, let’s make the bot do something a little more useful. Step 3: Translate Inline Queries with IBM Watson Head over your (or create an account if you don’t have one). From your dashboard, click near the top right corner. Click from the categories sidebar on the left and then . Give your service a name and click . Now your service page should have two credentials. The first is . Simply copy that into your as . Now for the , you are only interested in the host, that is, the part between and . Copy the host into your as . IBM Bluemix Dashboard Create resource AI Language Translator Create API Key env.json WATSON_TRANSLATE_KEY Url https:// /language-translator/api env.json WATSON_TRANSLATE_HOST The complete env.json With those new environment variables, go back to and click run again. Now, the moment of truth. Go back to Telegram and enter again. This time you should see — functions/setup.js @<bot_name> hello-world And of course, clicking on one enters that translation into the chat — That’s All! I hope this tutorial has been helpful in showing you how easy it is to combine disparate services using Standard Library. For more inspiration as to how you can better use Standard Library, you can check out more guides written by the team . If you have a neat idea you’d like to share, reach out to me directly by e-mail: , or follow and the team on Twitter. here steve@stdlib.com me Standard Library Steve Meyer is a recent graduate of Oberlin College and Software Engineer at Standard Library. When he’s not programming , you can find him baking bread, or playing Spider-Man.

Build a Telegram Bot that Translates Text in 3 Steps with Node.js and Code.xyz

Interview with Jeana Alayaay, Director of Internal Products and Services at Pivotal To build a product, you need a team of engineers, designers, and the glue that keeps them together: product managers! The role of the product manager has dramatically changed over the past decade, and because it’s still a relatively new field that’s in flux, companies often struggle to find candidates, which in turn makes it hard for candidates to understand what companies are looking for. So all this month, we’re going to focus on a number of best practices for sourcing, hiring, interviewing, and retaining product managers. In today’s episode, we’ll focus on giving you a lay of the land, starting with how product management is evolving and how to go about sourcing candidates for a product manager position at your company. To help us out, I’ve invited Jeana Alayaay, the Director of Internal Products and Services at Pivotal. This episode is chock full of helpful best practices for both product managers and those looking to hire them. As you watch, you’ll learn the following: How product management has evolved over the years Why you need to think about the type of product manager you are looking for (hint: there is more than one persona!) How to communicate the key criteria you are looking for in a candidate How to build a pipeline of candidates How to train recruiters to help screen candidates How to stop hiring out of desperation Why it’s actually helpful to give candidates a quick no Listen to the episode on iTunes! . You can listen to this episode of on iTunes Build Enjoyed this post? Help others enjoy it too by hitting the 👏🏽! Build is produced as a partnership between Femgineer and Pivotal Tracker . San Francisco video production by StartMotionMEDIA .

Apple

Glue

How To Hire Product Managers

Interview with Jeana Alayaay, Director of Internal Products and Services at Pivotal Last week, we dug into the various product manager personas, and how to go about sourcing candidates. If you missed the episode, . you can check it out here This week, we’re going to talk about another critical step when it comes to hiring product managers: interviewing. Unlike interviewing engineers and designers, where you can test their ability to code and design and their responses provide tangible results, it’s harder to formulate questions related to one’s skill and abilities as a product manager that will produce concrete responses. Let’s face it — product management is more nuanced because it often requires people to have experience analyzing data and making decisions related to the goals of the business, in addition to some technical skills. Exposing the spectrum and depth of these capabilities can make interviewing product managers a challenge. For example, it maybe easy to evaluate if someone can organize a backlog of user stories, but harder to evaluate whether they are really capable of creating and prioritizing a product roadmap that balances out various business goals and milestones to an acceptable level of quality and depth for your team. Plus a product team usually has one product manager who interfaces with many engineers and designers, so hiring the first product manager who is going to gel with all those people puts them under a lot of scrutiny. To handle all these challenges, many companies end up crafting their interview process to resemble a standardized test that candidates end up studying for, rather than demonstrating key skills that they will be using to support the team and product. It’s no wonder some of the best candidates fall through, and companies feel stuck with a product manager who underperforms. This episode is a must watch if you’re a hiring manager who is concerned about losing a talented product manager, or you’re a product manager who is trying to assess a company’s interviewing process. In this episode, we’ll share some best practices around interviewing and coming up with objective criteria to use when screening candidates. Jeana Alayaay, Director of Internal Products and Services at Pivotal, is back to help us out. As you watch today’s episode, you’ll learn the following: Why there is such a thing as being a bad interviewer How to prepare the people who will be interviewing candidates How to expose skill sets during the interview How to regroup after the interview Why candidates that don’t meet a checklist are sometimes still hired How to set expectations with candidates when your company is going through a period of change How expectations and the role are different when you are the first product manager on a team Listen to the episode on iTunes! . You can listen to this episode of on iTunes Build Enjoyed this post? Help others enjoy it too by hitting the 👏🏽! Build is produced as a partnership between Femgineer and Pivotal Tracker . San Francisco video production by StartMotionMEDIA .

How To Interview Product Managers

Jeana Alayaay, Director of Internal Products and Services at Pivotal (right) and Poornima Vijayashanker, founder of (left) Femgineer Interview with Jeana Alayaay, Director of Internal Products and Services at Pivotal All this month, we’ve been sharing best practices around and . If you checked out both episodes, you might be thinking: hiring interviewing product managers “This is a lot of work! How can we be sure we’ll end up with a stellar product manager, and that they won’t quit in three days or three months?” We get that hiring and interviewing are just two pieces of a larger puzzle around talent management. And of course it’s not enough to just attract top talent; there’s more that needs to be done to make sure they stay motivated and productive. So to quell your concerns and help you figure it out, we’re going to do a deep dive in today’s episode around what to do after you hire a product manager. We’ll be sharing why current practices often fall short of meeting a new employee’s expectations and some alternate best practices for onboarding, training, retaining, and evaluating the performance of product managers. Jeana Alayaay, Director of Internal Products and Services at Pivotal, is back this week. Here’s what you’ll learn in this meaty episode: How to onboard a new product manager and set expectations Why you need to have a development plan ready for your new product manager and how to walk them through it Why an annual performance review is too late to check in and provide feedback, and what to do instead Why even a seasoned product manager will benefit from coaching and guidance as part of their onboarding process What success metrics look like for a new product manager How to evaluate your product manager’s performance in the midst of changes that are beyond their control Why it’s good to set granular expectations around deliverables and milestones What to do when your product manager stops performing or suddenly quits Listen to the episode on iTunes! . You can listen to this episode of on iTunes Build Enjoyed this post? Help others enjoy it too by hitting the 👏🏽! Build is produced as a partnership between Femgineer and Pivotal Tracker . San Francisco video production by StartMotionMEDIA .

Stellar

How To Train and Retain Top Product Managers

Stop oversharing personal data by logging into platforms with social media credentials If your identity is going to be monetized by third parties, you should be explicitly aware of how, with whom and to what extent the information is utilized. The , where social networks and commercial services monetize your personal digital information, is that: typical defense of the current status quo i) Users volunteer to use the service(s) ii) Certain use-cases streamline use of additional services (i.e. Login with Facebook functionality) iii) People have come to expect free and discounted services To address each of the primary issues above: Users volunteer to use the service(s) i) : Once certain platforms reach a critical mass, choosing to not use the platform or service can exclude individuals from participating in our digital society. For example, if someone was uncomfortable with Facebook’s data retention, tracking, monetization and ad targeting system, they could in theory opt out of the service. However, the social downside for many individuals is too extensive to justify. Few people are willing to sever convenient and expected social interactions due to a service’s ad targeting systems and user data sharing policies. Moreover, once we examine the holdings of the largest technology companies, Facebook owns Instagram, Alphabet owns Google, Google products include gmail, google docs and calendar, Apple controls the content on users’ iPhones and access to both the App Store and iTunes, Amazon owns everything from your digital reading library (i.e. Kindle), to grocery stores and pharmacies, resisting authorizing the use of one’s personal digital information begins to look at best eccentric and worst, entirely impractical. XKCD: Opinions on Internet Privacy 1269 ii) Certain use-cases streamline use of additional services (i.e. Login with Facebook functionality) Break the Chain — Login with SelfKey While we may not want to forgo participating in the 21st century, that doesn’t mean we can’t reverse the trend of oversharing personal data. For example, , which provides one-click OAuth login, is part of the product suite. Currently, the enables individuals to . Soon, users will be able to not only safely send and receive digital assets, but securing login to third party services, via . Login with SelfKey SelfKey Identity Wallet SelfKey Identity Wallet s_ecurely store personal data and digital assets on their local machine_ SelfKey Wallet Login with SelfKey “Convenience of one-click login, must not come at the price of unnecessarily oversharing personal data” Perhaps most critically, users will have complete control as to the scope of what personal information they share and with whom. . Perhaps a user doesn’t want to share their social network details or browser history signing up for and using a new service. Likewise, there’s no need to send your browser and application history back to social media platforms, furthering the problem of unnecessarily oversharing personal information — which get repackaged, targeted or utilized as a digital commodity. SelfKey Wallet Convenience of one-click login, must not come at the price of unnecessarily oversharing personal data Login with SelfKey iii) People have come to expect free and discounted services Stop feeding the beast! Altering industry expectations of who should be in control of individual personal information is the best way to destroy the pernicious, loss-of-privacy, feedback loop. While we can’t prevent Facebook, Twitter or Google from tracking what you do while utilizing their services, you can start to control what is shared with them while privately using other services. and are altering the norm. Login with SelfKey other Digital Identity Wallets There’s an overall shift in the digital ecosystem, with a focus on user privacy, security and requesting user consent before using their personal information. Another example of the growing shift is , a privacy focused search engine, which has made tremendous technical strides, providing additional privacy-focused consumer choices. Brave , a privacy focused web browser, compensates users for viewing ads. Brave is a massive step in the right direction, that deliberately involves and expressly compensates individuals for participating in their ad targeting system. DuckDuckGo With enhanced consumer choice, the future is bright indeed! Clap, like and share the article if you want to further consumer protection, enhance individual privacy and eliminate the oversharing of personal digital information! Thanks for reading!

HackerNoon Tech Stories Archive September 9th, 2018

@init_27Sanyam Bhutani

@deepsystemsSupervise.ly

@init_27Sanyam Bhutani

@YoavVilnerYoav Vilner

@vinogeoVG

@alexoshАlех Оsh

@devabramDavid Abram

@robmayRob May

@jamestredwell47James Tredwell

@Muriz.SerifovicMuriz Serifovic

@thems18Mohit Sharma

@MakadiaHarshHarsh Makadia

@AndriiAndrii.ryzhenko

@clm160Liviu Costea

@anaminguyenAnami Nguyen

@dixiterkRajeev Dixit

@rossRoss Mayfield

@davidolarinoyeDavid O.

@notoriagaSteve Meyer

@poornimaPoornima Vijayashanker

@poornimaPoornima Vijayashanker

@poornimaPoornima Vijayashanker

@ChaseTheTruthChase Perkins

@init_27
Sanyam Bhutani

@deepsystems
Supervise.ly

@init_27
Sanyam Bhutani

@YoavVilner
Yoav Vilner

@vinogeo
VG

@alexosh
Аlех Оsh

@devabram
David Abram

@robmay
Rob May

@jamestredwell47
James Tredwell

@Muriz.Serifovic
Muriz Serifovic

@thems18
Mohit Sharma

@MakadiaHarsh
Harsh Makadia

@Andrii
Andrii.ryzhenko

@clm160
Liviu Costea

@anaminguyen
Anami Nguyen

@dixiterk
Rajeev Dixit

@ross
Ross Mayfield

@davidolarinoye
David O.

@notoriaga
Steve Meyer

@poornima
Poornima Vijayashanker

@poornima
Poornima Vijayashanker

@poornima
Poornima Vijayashanker

@ChaseTheTruth
Chase Perkins