Rad AI is on a mission to improve patient outcomes and access to quality healthcare by applying state-of-the-art artificial intelligence to radiology, a critical part of medical diagnosis and treatment

Rad AI

With the technological evolution of artificial intelligence (AI), many companies have explored ways to incorporate this new capability into their products and services to serve their customers better. Many industries, such as manufacturing, retail, transportation, and logistics, etc., have already successfully incorporated this technology into their business. The medical field has also seen AI aiding in discovering new drugs and improving diagnostic capabilities to help physicians and medical staff. Android app development services have been working on these apps for some time. The potential for using this technology in personal goods and services is limitless. Some of today's "smart" apps, such as personal assistants, show how AI could improve people's lives on many levels. Artificial intelligence is now taking over our personal lives by integrating personal relationship technologies. From finding a partner on a dating app to psychology and communication aids for better relationships, artificial intelligence can now occupy a central place as a technology allowing access to new possibilities. No longer just a human domain, relationships and love are now approached from a technological point of view. There are many possibilities for improving people's lives using AI-assisted applications. For example, you could date a custom avatar in the future with AI, the new matchmaker! Dating Industry and AI - Current Scenario In a constantly expanding industry, online dating is now big business. The multi-billion dollar industry is dominated by Match Group, which owns more than 40 dating companies, including the famous "Tinder" service. However, unlike traditional apps and programs, AI-assisted dating apps can predict, analyze and offer a more exciting product. The Match Group and its competitors have accumulated vast amounts of personal data, which technology like AI can now take full advantage of. Predictions like how people decide on a mate are just one way AI could use data. React Native app developers can easily leverage this data. With the expansion of the dating industry, examples of AI can already be used. For example, a female AI chatbot can guide users through finding love and romance by offering suggestions for analyzing personal data. After asking the user their preferences, the program separates the profiles that best match your personality. When a profile is selected, the app can then offer more details about the particular choice. Some of the AI ​​applications so far in the industry are based on a novelty approach. An exciting app using AI can be seen with the Loveflutters app feature, which suggests restaurant recommendations to give a date a little boost. Now, there are plenty of dating apps that provide users with suggestions for first date ideas, whether it's a club, restaurant, cafe, or bar. The Badoo service has fun technology that matches your partner's face with that of a similar celebrity. This feature uses the integration of AI technology and facial recognition to match the user with the celebrity. By uploading an image, the app can search a massive database for lookalikes. On a slightly more sophisticated level in terms of relationships, the well-known eHarmony website uses technology to analyze discussions to develop suggestions to users on how to approach their next "move." Another example of technology is helping users by ranking profiles and predicting user preferences. Simply put, the basic premise of AI in the dating world was explained by Tinder CEO Sean Rad - AI will act as a 'smart filter' and predict a person's interest. Therefore, custom mobile application development services should take this aspect very seriously. Today's dating services leverage AI to filter big data to create a larger pool of potential matches for users. The possibilities are also found in websites and dating apps, such as public social media posts, providing insight into personal preferences. This analysis could avoid potential biases based on more deliberate or artificial responses provided on dating questionnaires. Inaccurate or misleading answers are one of the main reasons people are unsuccessful with online dating. Data and Meetings AI can process vast volumes of data. As more and more data is generated on the internet through social media, AI-assisted applications will generate very accurate predictions. Complementary technology to virtual reality (VR) is evolving with AI. The potential of these two apps to work together in the dating world is only a matter of time. There is a growing demand for products like "virtual girlfriends." Ever-evolving technology aims to provide users with the ability to practice dating in a virtual environment to aid them in their attempts in the real world. What all of this will lead to in the future could be your very own personalized VR partner! The volume of data that AI-assisted technology can process could lead to creating the "perfect" partner. By integrating with other advanced technologies, the concept could take it further by allowing users to experience their personalized avatar physically. Disrupting human relationships would be the creation of an interactive robot or companion. Although this idea can be confronted at first glance, it harbors several possibilities to improve people's lives. For example, an AI-assisted virtual reality companion could be used by the elderly or widowers, the disabled, or anyone facing a challenge to find an intimate partner or companion. The Fate of Dating Apps With advancements in technology and AI and growing interest in online dating services, businesses in this space need to keep abreast of the latest developments to stay relevant and meet customer demand. One area that has yet to be touched on is where technology can resolve some of the negatives encountered when using dating apps. One of these negative experiences for some users is meeting users operating under false identities, also known as "catfishing." This behavior can drastically change the user's trust in the service. One solution that businesses are exploring is for users to confirm their identities through facial mapping technology. Problems remain, however, regarding the need to verify details against the implementation of requirements that infringe on privacy and discourage the adoption of the app. A new feature seen in some dating apps is a concierge service where the program helps users create an ideal online profile. Using a combination of AI, machine learning, and human intervention, the feature tries to maximize your profile and improve appeal. While the feature often comes at an additional cost, it does provide the ability to develop a profile for full matches. Future AI developments may explore other elements that could lead to more successful matchmaking on dating apps. The level of sophistication will increase as AI-based programs "learn" what works with improved data and feedback. Features such as psychological profiles may be developed in the future to delve into more than just personal preferences. Physical characteristics are another crucial area of ​​unique attraction that can be further explored with the help of technology. Another possible direction is the use of DNA and collaboration with existing ancestry services. DNA-based people matching still has a long way to go before it can be considered a viable concept for people matching; However, with AI, the scope of these possibilities is increasing in the dating app industry. A React Native app development company can take advantage of these opportunities. Money Matters There is enormous potential for dating apps to generate income through several innovative feeds that complement their service. One way is to leverage partnerships with appropriate third parties to increase growth and generate revenue for mutual benefit. As previously stated, the Loveflutter dating app uses data from chats to match people and recommend places to eat or meet. In addition, it can suggest suitable places for both users based on their location using another specially designed app to find nearby services such as bars and restaurants. It could be extended by redirecting the suggestions made by the app to third-party apps. For example, this can be direct to a restaurant or even to an aggregation app like Uber Eats. Different income options could then be explored between the dating app and the third-party provider. For example, one possibility could be that the third-party provider receives a lead from the dating app; various fees could apply, including a fee based on the average cost of acquiring a new customer if known. Or a commission or fee based on a percentage agreed upon when the user orders. The discount could also be incorporated into a model to influence user choice of a restaurant or location. Opportunities may also arise using similar revenue models based on leads from a wide range of applications or third parties. These can be directly related to the dating industry or completely separate but want to attract users. But, again, it is up to the application companies to decide whether the involvement of third parties would add value or compromise their core product. Growing Opportunities One challenge for dating apps is to continue to get user engagement once a date has been successful. Many dating app users indicate an interest in finding long-term relationships. If later dates arise, AI could suggest different options to users than previously tried. In this way, the app can analyze the newly introduced date suggestions or extend the idea to kitchen choices or even gifts. At this point, the application may be showing real creativity. The user might see the benefit of providing information to users on specific preferences. For example, a comment on a taste for jewelry or clothing could be associated with a color preference mentioned. This information was suggested to one of the users as a great gift idea. A third-party provider could then be suggested and an order seamlessly placed through the dating app. What AI-assisted apps offer is the ability to understand users through their chats on the app and to be able to learn and therefore suggest other ideas continuously. All of this can add to the app's value outside of the core function or matchmaking and ensure that the experience attracts a loyal following of users. As mentioned, these features can add value and produce leads for third parties in the dating industry and other opportunities outside of this space. Innovation and AI - Challenges As with any innovation, the implications must be considered, and the impact on the user must be at the forefront. Especially when considering human relationships and emotions, which can be unpredictable and spontaneous, any suggestion given by the AI ​​function should be taken into account as to whether it will add value and support the primary function of the application or if it can disable a user or confuse their decisions and interactions. The conversation and rapport built on a dating app chat is an integral part of the success of the overall experience. Too many suggestions could interrupt the report; however, suitable program interventions could reveal users' new understanding. Ideally, the concept of innovation and income should help the user in their quest and not distract their minds by too many options that seem intrusive. A Blockchain application development company should incorporate the above points. AI-enabled Developers and Dating Apps AI is already being used in innovative ways in the dating industry. Many early adopters have managed to find the balance between technology and improved user experience. Fun features can keep people coming back as well. Developers can still focus on using technology to iron out problems, solve user problems, and generate insight from data. In addition, it is possible to take advantage of the growing volume of data generated through apps and online. Conclusion The world of AI and dating apps continues to evolve at a rapid pace. Matchmaking innovations go beyond traditional old-fashioned ways and evolve towards a better personal understanding of people and relationships. AI-assisted solutions in the dating world leverage large volumes of new and old data to analyze user behavior like never before. Working hard behind the scenes, algorithms increase the chances of finding their life partner, or just a great friend, even a casual one, if that is their criteria. If the ideas in this article are interesting, a developer's experience in AI applications could help harness the basic capabilities of artificial intelligence in dating applications. In addition, it can make sure you keep in touch with the future of high-tech human relations.

The Next Revolution in the Dating Industry: Will AI Help Me Find a Boyfriend?

In the first two months of the pandemic, we saw two years’ worth of digital transformation. And it’s just one of the examples of how fast the world around us is moving. To keep up with competition and ever-changing user needs, businesses and their IT teams need to rethink the way they’ve been creating software for decades. This is where rapid application development (RAD) comes into play. In this article, we’ll take a closer look at this concept, assess rapid application development advantages and disadvantages, and investigate RAD’s connection to low-code and no-code app development. Let’s dive in! What is Rapid Application Development Exactly? The rapid application development concept was invented in the 80s and has since become one of the most popular Agile approaches. Essentially, RAD aims to create applications faster. This is achieved through frequent interactions and continuous feedback analysis. Unlike the Waterfall methodology, which dictates that software teams need a detailed roadmap for their project, RAD does not rely on heavy planning. On the contrary, its goal is to release a working application (or a small piece thereof!) faster, present it to stakeholders, test the application on real-world data and tasks, and tweak the software based on what’s been discovered so far. Another core difference from the traditional software development methodologies is that RAD is driven by interface rather than coding requirements, which helps create applications with a particular user in mind. Rapid Application Development Stages The rapid application development model incorporates four phases: Requirements analysis. At this stage, a business analyst sits down to talk with stakeholders to loosely define software requirements. This is a sharp contrast with Waterfall projects, which rely on thorough requirements elicitation and leave little room for adjustments and change requests once the project scope has been agreed upon. Iterative prototyping. A software development team creates a proof of concept (POC) version of an application containing its essential features. The working prototype is then demonstrated to stakeholders, and critical changes are made. The iterative prototyping approach helps software engineers evaluate the feasibility of a complex app or functional component early on and rule out possible technology roadblocks. Actual development. The development (aka “construction”) stage encompasses coding, application testing, and feedback collection. Since most of the user problems and change requests are addressed during the prototyping stage, software engineers can create applications faster. Product finalization. The RAD paradigm revolves around faster development cycles, meaning developers might have to cut corners to put up an app in a tight timeframe. As a result, technology debt may accumulate — and that’s one of the issues RAD teams address during the product finalization phase. Additionally, they conduct full-scale testing, write technical documentation, and onboard users. How RAD and Low-Code App Development Complement Each Other As we mentioned earlier, rapid application development is interface-driven. Since no-code and low-code app development platforms incorporate well as complete back-end infrastructures, RAD advocates often use them to prototype and build applications. Additionally, most low-code and no-code platforms now offer pre-packaged templates to fast-track app development. builders as drag-and-drop app To choose the optimum technology stack for your RAD project, you should compare the advantages of low-code and no-code application development tools and study the on product review websites like G2. platforms’ ratings Rapid Application Development: Pros and Cons Different approaches to software development have their advantages and downsides, and RAD is no exception. Advantages of Rapid Application Development** The major advantage of rapid application development is that software teams can change the key features and functionality of an application at any given moment. Other reasons to embrace RAD include: Stakeholders communicate frequently, solving issues together and creating a product that best meets the business/technology needs of a company With RAD, you can manage your resources appropriately and avoid running over budget without seeing a glimpse of real value from your new technology RAD helps reduce your time to market, which is key for innovative technology startups and companies undergoing ad-hoc digital transformation In RAD projects, user feedback takes center stage, increasing customer satisfaction levels Disadvantages of Rapid Application Development The core disadvantage of the RAD concept stems from software projects’ shorter lifespan and user-centricity. The larger and more complex your project is, the harder it becomes to collect stakeholder feedback and coordinate developer activities. ** Here’s what you should consider before opting for RAD methodology: To sail through the rapid application development stages, you need a strong, inherently Agile software team whose members collaborated on similar projects in the past and have direct contact with each other. This makes RAD hard to implement in enterprise-grade settings and outsourced projects with many developers scattered around the globe RAD is interface-driven, meaning developers often cut corners during the prototype stage to get the green light from the stakeholders. These workarounds can lead to a huge technical debt on the back-end level, which needs to be addressed later in the development process and may eventually affect release deadlines Unlike Waterfall, RAD requires stakeholder commitment, which seldom suits executives and senior project/product managers at large enterprises due to scheduling conflicts To reap RAD’s benefits, your team needs to choose suitable app building software that combines the power of low-code development with the simplicity of no-code tools When to Use Rapid Application Development Now that we’ve assessed rapid application development’s advantages and disadvantages, it’s time to define instances when RAD should be the go-to methodology to coordinate your project. Creating an MVP to Raise Funding After a turbulent 2020, startup funding is on the rise again, with it’s 1999 all over again. To avoid missing out on the investment frenzy, a software startup must develop a prototype or minimum viable product (MVP) version of their applications faster  — and that’s where RAD comes in handy. checks like investors writing huge In addition to speeding up the development cycle, the RAD approach helps put up a decent-looking, full-featured prototype on a shoestring budget and refine the product once you raise funding. Getting Buy-In from a Company’s C-Suite Although pandemic has given a second wind to their digital transformation initiatives, very few companies splurge on a new technology solution with only vague ROI potential. believe the 77% of executives might seem like a waste of money if you go the traditional route, building separate apps for iOS and Android users and sucking the company’s annual IT budget dry. But if you develop a cross-platform prototype using a no-code or low-code app builder, test the software among sales representatives working in the field, and present the results of your experiment to your CEO, your chances of getting a budget will increase exponentially. Creating a mobile version of an agricultural company’s CRM system Prototyping Applications at Hackathons Hackathons are where bright tech ideas take shape and rising stars are discovered. If you don’t believe me, just ask the founders of EasyTaxi and Appetas. What makes hackathons challenging is that you only get a couple of days to __ __or asset management system.** create a novel meditation app With RAD and the underlying technology stack, including visual development tools and website/application templates, it is totally possible to turn your idea into a prototype in mere days. Rapid application development is a powerful approach that helps exercise greater risk control and ensure greater user satisfaction with the final product. When used appropriately and backed up by a suitable technology stack, RAD can work wonders for your business — especially if you’re working on a novel product or toying with the idea of implementing new software in the workplace.

Advantages and Disadvantages of Rapid Application Development 

[Photo by on ] freestocks.org Unsplash CVS is offering to pay $66 billion for Aetna — a blending that could turn local drugstores into health insurers. Assessing the impacts of the recent rash of mergers and takeovers in the healthcare field. The mega-marketplace revolution is in full swing across all industries right now. Amazon shelled out $13.7 billion for Whole Foods in an effort to gain a stronger foothold in the grocery industry, AT&T wants to buy Time Warner for $85 billion, UnitedHealth Group is acquiring DaVita Medical Group and the latter’s 300 doctors’ clinics for $4.9 billion, and CVS is offering to pay $66 billion for Aetna — a blending that could turn local drugstores into health insurers. These deals collectively paint a picture of a business environment that’s ripe for consolidation and ready to merge. Of particular interest to the healthcare industry — already grappling with changes and turmoil on many different fronts — the proposed CVS-Aetna deal could find the acquirer creating a unified, seamless benefits offering to customers (such as large employers). “The combined behemoth would also have a large and diversified store of health data, an important asset in today’s competitive environment,” the reports, noting that Amazon’s intention to “move into the pharmacy business helped spur CVS’ move on Aetna.” Wall Street Journal From this recent spate of mega-deals, the entities involved expect benefits like improved economies of scale (the “power of one” versus various, scattered entities), better profits (which, in turn, can fund R&D and other initiatives); and new faces on the newly-combined firm’s management and leadership teams. The disadvantages include, but aren’t limited to, difficulties coordinating and communicating across the combined entity and the length of time it takes to physically “merge” the two companies into a single, efficient organization. The question is, how will these combined superpowers impact their customers and their respective industries as a whole? Here are four ways things could play out: It could spawn a groundswell of mega-mergers and, subsequently, less competition. If the merger goes through, it could trigger another wave of takeovers. In fact, analysts feel it could cause a wave of industry consolidation, with Walgreens Boots Alliance (WBA) and Rite-Aid (RAD) currently seeking mergers or acquisitions with other major health insurers. This could give a smaller concentration of companies a bigger stronghold on the nation’s healthcare industry. Consumer options could be reduced and customers could be subjected to higher co-pays (i.e., if they select a non-CVS drugstore to fill an Aeta plan prescription). Healthcare is a fundamental service that’s comparable to cable, electricity, and telephones, all of which have been consolidated under mega brands and via mega-mergers. And while healthcare is a highly-regulated industry, the idea of only having one drugstore or one insurance provider to choose from could push prices up while whittling down choices. Better economies of scale could be in sight for healthcare insurers and other providers. Under pressure to address losses suffered from former President Barack Obama’s Affordable Care Act, and then navigate the murky waters of Obamacare eradication, major health insurers have been grappling with reductions in program offerings. Health care mergers and acquisitions have been rampant over the last few years, making the savings associated with economies of scale that much more critical as margins tighten. In the CVS-Aetna deal, for example, the combined entity would also have more firepower against a giant e-tailer like Amazon — an advantage that companies across many industries are trying to figure out right now. In fact, the “Amazon Effect” is one of the drivers of healthcare mergers and the quest to provide a single point of service and information for customers. This, in turn, helps to reduce the cost of running the healthcare supply chain, the cost of customer acquisition, and the cost of healthcare delivery. Amazon is already leveraging these advantages in numerous marketplaces, and healthcare firms want to be able to replicate that in their own industry. Premiums may not get any lower. Seeking relief from rising insurance and healthcare bills, consumers are waiting for a miracle to come and lower their bills. The CVS-Aetna deal probably isn’t their knight in shining armor. In fact, there’s been no real impact on lowering premiums among beneficiaries due to health insurance mergers, according to a study. In fact, statistics show that less competition in the health payer market can result in higher monthly premiums for beneficiaries, and particularly for those in employer-sponsored, commercial health plans. But just because you merge an insurance company with the drugstore doesn’t mean consumers will be privy to lower prices. At a basic level, premiums really only go down if fewer people visit the doctor or hospital, and a mega-merger isn’t going to make that happen. Harvard Business Review Organizations may stop innovating. When was the last time your cable company introduced a groundbreaking, innovative product? Or your electric company rolled out a pioneering new service? The answer is probably “never,” and that’s because competition spawns innovation. In the absence of a competitor knocking on your customers’ doors, trying to sell them a newer, better, and more efficient product or service, the drive to innovate dwindles to zero. This could happen if we start to see more healthcare companies merging and creating less competition for themselves. Anytime you have a captive consumer, companies don’t think too hard about innovation. As more companies look to merge with one another in an effort to gain operational savings, reduce overhead, eliminate redundancies, and gain a foothold in new verticals, the underlying tenets of healthcare probably won’t change much — namely because transformation can’t happen unless we take a holistic view of the problem and fix all of the pain points within that larger picture. Because all of the systems that make up the bigger healthcare picture are interdependent, you can’t just fix one part and expect transformation to occur. In the end, preventative medicine is what’s going to reduce the cost of healthcare, and that’s both an individual and a social responsibility. Copyright © 2017 by Faisal Hoque. All rights reserved. I am an . Founder of and other companies. Shadoka enables aspirations to lead, innovate, and transform. Shadoka’s accelerators and solutions bring together the management frameworks, digital platforms, and thought leadership to enable innovation, transformation, entrepreneurship, growth and social impact. entrepreneur and author SHADOKA Author of “ ” (McGraw Hill) and “ ” (Motivational Press). Follow me on Twitter . Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability Survive to Thrive: 27 Practices of Resilient Entrepreneurs, Innovators, and Leaders Faisal Hoque Use the Everything Connects leadership app and Suvvive to Thrive resiliancy app for free.

The Rise of the Healthcare Mega-Marketplaces: What Does It Mean For You?

05: PushInterview The journey of how a Solo, Non-Technical Founder learned to code and built EDM Hunters In the 21st century that we live in, whenever we talk about a tech , the first thing that comes to mind is often “coding”. startup So is it even possible for a non-technical founder to build a successful tech startup? Don’t be disheartened if you are a non-technical founder. History says, Yes of course! Credits: iPrice (Founder of AirBnB), (Founder of ) and Sean Rad (Founder of ) all being non-technical founders built some of the most successful tech startups. Brian Chesky Ryan Hoover Product Hunt Tinder But how did they succeed? Read through this interview to know: “How a Solo, Non-Technical Founder, , learned to code and built a successful Tech Startup” Jaskaran This is the fifth interview of the series “ powered by . PushInterview : Interviews that helps you Pushstart ” Pushstart Check out our previous interview on “ ” if you happen to miss it. How I built one of the most active startup communities of India _My journey of building a startup community from zero to one_hackernoon.com How I built one of the most active startup community of India 🕴Who are you? I am the one wearing the black t-shirt Hi ! I’m , a 25-year-old enthusiast, and a non-techie turned coder from India. I graduated from Manipal Institute of Technology in the year 2014. Pushstarters Jaskaran Rana music I built , an electronic music discovery startup back in my final year at college all by myself. EDM Hunters Post college, I worked with and for around 3 years before finally quitting in February 2017 and working full time on . BuyingIQ CollegeDekho EDM Hunters Working with these startups immensely helped me hone my technical skills and gain critical insights into what goes behind building a tech startup from scratch. 🤔 What’s the deal with EDM Hunters ? was a passion project born out of my love for electronic music. EDM Hunters The current version of EDM Hunters was built for enthusiasts like me to get the daily fix of electronic music. is currently developed and run solely by me. EDM Hunters dance music EDM Hunters Being extremely passionate about , I started building back in college in the year 2014. Electronic Dance Music EDM Hunters It was started with the aim of helping both new and seasoned listeners of the genre discover the best and latest songs of electronic music. I believe that community wins over algorithms and experts when it comes to supporting new and undiscovered talent. That’s why on it is the community which decides the top songs. EDM Hunters You can browse through various music charts to discover top songs by artists, genre, DJs, month and year, all compiled based on likes, play count, and downloads. 💯 What motivated you to start ? EDM Hunters My motivation for building was to consolidate all my learnings of “How to Code” into a real-life project, while at the same time build a product that I would personally love to use. EDM Hunters Back in college, I started listening to electronic music and got instantly hooked. had just started gaining popularity and entering the mainstream music scene. As an ardent electronic music fan, I was disgruntled by the lack of mediums on which one could discover electronic music of his/her choice. EDM There wasn’t a one-stop solution to help me discover this kind of music at one place. Thus, was born to solve this problem. EDM Hunters 🛠 What all went into building the initial product? To give you a bit of a background, I wasn’t a bright student academically in my college. Though my major was Computer Science, I didn’t know how to code until my final year. I had a 5.0 GPA and was barely able to pass my exams. The main reason for this was the way the course was taught. The education system here in India focuses more on Rote learning than Practical learning. After a point, It becomes difficult to pay attention. To learn to code, I decided to give online courses a shot. helped me immensely at the initial stage. Codecademy I learned , , , and in a span of 6 months. Following that, I decided to learn , a web framework based on . Python JavaScript jQuery HTML/CSS Django Python I started building in my final year at college. I used to work on it after my classes and on the weekends. EDM Hunters I was done with the basic prototype in 3 months and launched the initial version in January 2014. This is how EDM Hunters looked at the initial stage: The initial version of EDM Hunters Post-launch I kept on iterating with the design and features of the website. It didn’t take a lot of money to launch the site. Primarily, the only cost associated was with hosting it. Since I decided to use a , that turned out to be pretty cheap. VPS How do you source your songs? I get this question a lot: I use 3 different sources for the songs. My primary source is , which is a music store for electronic music. I use their to get all the songs of a specific artist. They also provide me with a 2-minute preview of each song to stream. Beatport API The other source is , where artists upload their own songs. I use their to stream the songs. I use the to stream the videos. SoundCloud API YouTube API Do remember that I learned to code because I was really passionate about solving the problem. 🤝 How did you get your initial users? The growth in traffic has been pretty phenomenal. To be honest, when I first launched , I didn’t expect it to grow as big as it is right now. EDM Hunters For the first 6 months, the traffic was pretty much non-existent with less than 1k visitors/month. However, after I got my first job, I started learning new things every day. Whenever I learned something new which was relevant and could be integrated into my own site, I did so the same night. I also started doing a lot of research on . SEO Counter to what many people believe, there isn’t a secret recipe for SEO. It is just about building a website with good user experience while making it easily indexable and searchable for the bots. Some SEO tricks that helped me: Try to get a backlink from other relevant and high-quality websites. Ensure a faster load time. Make sure that your website is mobile friendly and test this exhaustively. Keep content unique and relevant to your website. Give each page a relevant and unique title. Add in a meta description to each page describing the purpose. Mark up your site using , if applicable, to display rich snippets. schema.org Build a sitemap for your website and submit it using the . Google Webmaster Tools Use HTML correctly . ( tags, titles, and alt attributes on images ) Try to use relevant keywords in the URL. Growth in traffic from 2014 to 2016 Currently, the website has over 500k+ sessions every month, with 85% of the traffic being organic and it is increasing at the rate of 20% month-on-month. SEO has been the major driving force behind the growth of . For me, it’s been more about identifying the right keywords with sufficient traffic and building products around them. EDM Hunters If I had to attribute another reason for growth, it would be good media coverage. I tried to get the word out for by attempting to get it covered by various blogs. EDM Hunters I was lucky enough to get featured in some of the prominent blogs like . Recently, it also got featured in a promotional video for . YourStory , IndieHackers , Homegrown , and MyVenture Codecademy Growth has all been organic till now with SEO contributing to the majority of it. 💰 What is your business model? I didn’t start EDM Hunters with a “business model” in mind. I started it because of my passion and love for electronic music. Currently, the major source of income is Advertisement. I started off with one of the most famous Ad network. Google AdSense , After using it for some time, I realised that it didn’t have potential to offer substantial revenue, as the Ads were not really relevant to my users. I decided to integrate a few niche Ad networks that primarily focussed on music. I am currently using and on my website. These offer better value to the users and are now a major chunk of my Ad revenue. Wavo Boost Ad network Feature.fm The current average revenue from these Ad networks: Around Rs. 80k — Rs. 100k per month The revenue is directly proportional to the traffic. So an increase in traffic leads to an increase in revenue. I have also started promoting upcoming artists and labels using sponsored listings on my website. There are various plans available to choose from, ranging from $10 to $300. The money made from these ranges anywhere between: Rs. 15k to Rs. 50k per month I’ll be working towards building a more stable and concrete sponsored listing network in the time to come. 💪 Being a Solo Founder, how do you keep yourself motivated? Being a Solo Founder is definitely not the easiest thing out there. It’s easy to lose motivation and keep on procrastinating. But I guess what really motivated me was the cause. Since I was passionate about the product I was building, I wanted to build it in the best possible way. Taken in the right sense, Obsession can be a good motivator. There are a bunch of other things Solo Founders can try though: Try surrounding yourself with other entrepreneurs, programmers, or designers. You could also try working in a co-working space where you can find like-minded people, especially those with whom you can bounce off ideas. 🏁 What are your future goals and how do you plan to achieve them? I just launched a revamped UI for . Creating a mobile app for has been on my mind for quite some time now. I have been learning and would be using it to build the app for both and . EDM Hunters EDM Hunters React Native iOS Android I am planning to build a concrete sponsored listing network which would help budding artists gain visibility alongside the mainstream artists. Also, I will be focussing on building a strong social presence alongside steady organic growth. I have a bunch of other projects I’m planning to work on. I will be building a product which is aimed at music producers and creators. I also have plans to launch a project around live gigs in India. Some exciting times ahead I guess, keep a look out guys! 🌋 The biggest challenges you have faced till now? Like any other non-technical founder, the initial phase when I started to learn to code was the toughest for me. The decision to build an entire website on my own, from the frontend to the backend and being able to visualise how all the things connect and work together as a whole was quite challenging at the start. I had help from a couple of coder friends which was a big plus. For anything else, I always had the internet at my disposal. All you need from your end is total dedication. If you are ready to put in the efforts, they will definitely bear fruits in the long run. 🗒 What is your advice to starting out? Pushstarters Things might seem complex at the beginning, just don’t be intimidated. Everyone started off at the same spot. Start with a small project and try to get your fundamentals clear. Especially for non-technical founders: It gets intimidating at times but the key is not to give up. There is nothing more satisfying than watching something you wanted to build come to life. Try to find an internship/job at a startup to start with. Startups are always on the lookout for enthusiastic people with relevant skills. This is the best possible way to gain some on-field experience. Be prepared to fail. Not all Startup Ideas turn into the Next Big Thing. In order to succeed in a business, you must be prepared to fail. It is all about how passionate you are to solve the problem and not about knowing “How to Code”. ✉️ How can we get in touch with you? You can reach out to me on Linkedin , Facebook or info@edmhunters.com . Thanks a lot for reading! If you liked it, please support by clapping 👏🏻 and sharing the post. Feel free to leave a comment 💬 below. Want to become part of India’s most active startup community? Send a request on Pushstart and become part of our ever-growing family. Hey! Neeraj here, the founder of Pushstart , India’s most active entrepreneur community. I release interviews with successful entrepreneurs every week. Feel free to reach out to me on Facebook | Linkedin | neeraj.joshi@pushstart.in | Twitter .

How a Solo, Non-Technical Founder learned to code and built a successful  Tech Startup

It's lowering barriers needed to compete asymmetrically against better funded competition. If you're not already using AI to power your marketing efforts, you're falling behind. Here are the 7 AI marketing categories I'll be discussing that may benefit your B2B: It's no secret that artificial intelligence (AI) is revolutionizing the SME and B2B marketing landscape. From content creation to email marketing, AI is changing the way businesses market their products and services. Video Marketing AI Content Writing AI Social Media AI Buyer Persona AI Email Marketing AI Competitor Analysis AI Advertising AI “Be sure that you're targeting early wins first so that you can quickly gain momentum, motivation and organizational buy-in.” Sound interesting? Then let’s get started! Video Marketing AI and with video marketing AI, it's easier than ever to create high-quality videos that capture your audience's attention. Video marketing AI software like , n, or makes it easy to create professional-looking videos without any prior video editing experience. Just select a template, add your logo and text, and let the software do the rest. Once the video marketing AI software has made the first draft, just jump into the editor to change text, stock video, or transition effects. If you're not using video to market your business, you're missing out. Video is now one of the most effective marketing tools available, Splasheo Wave Animatro Lumen5 You can easily create blog summary videos, advertising assets, listicles, and social media content, or enhance a company video podcast or live stream in a matter of minutes or hours. There's no need to hire a costly video production company—you can create stunning videos in-house with ease. Save that money for retargeting your website visitors to buy that product or request a quote! Content Writing AI I tried Jasper on this article and was pleased with my starting point. Webpages, blogs, case studies, social media posting, video scripts for that neat AI software you're about to try... but creating quality content can be time-consuming and expensive. That's where content writing AI comes in. Content writing AI software like , I, r or allows businesses to quickly and easily create quality content at scale. Businesses need to produce a lot of content to stay competitive. Quill Persado, RAD A Jaspe Anyword These content writing AIs use algorithms to analyze your topic and then generate a completely original article or webpage copy in minutes. I tried Jasper on this article and was pleased with my starting point. So long as you know who your target audience is and the tone for your brand, the content description or content brief you provide will come to life in a matter of moments. Natural Language Processing It's not just long-form content or blogs you can generate with these AI writers; you can produce ad copy, social media text, captions, or even video script drafts that do much of the initial heavy lifting. Now there's no need to spend hours writing articles completely by hand—let the software do the work so your team can do the human stuff. Social Media AI —especially if you're posting on multiple platforms. Social media is a powerful marketing outreach and awareness tool, but crafting engaging social media posts can be challenging Social media writing AI software like Emplifi.io and our content writing AIs mentioned earlier can help take the guesswork out of creating social media, optimal timing, and scheduling it by automatically generating posts that are optimized for each platform, while still based on your target customer audience. Social media AIs make it easy to schedule and publish your posts at the best times so you can free up your team's time for the more important tasks AIs can't do. Before spending on social AIs, consider where the bulk of your online leads and customers have historically come from. If your analytics show that social media channels currently drive more than 13-15% of your marketing qualified leads (MQL), an investment or trialing of a social AI should be on your shortlist as a potential low-hanging fruit opportunity. Segmentation and Buyer Persona AI Unfortunately, doing research well using a combination of tools for surveys, interviewing and publicly available information is not convenient, nor is it easy for every marketing professional to do. Developing buyer personas for your business is a critical marketing activity that influences not just marketing, but your sales team and the effectiveness of your customer service & retention. Executive leadership knows it's essential to understand who the best kinds of customers are and what their pain points and aspirations may be. But doing this effectively is highly nuanced. Yet it needs to be the core of every marketing strategy your business does. The good news is that I have a few AIs that can jumpstart the work if it's not done yet, or further enhance the definition and quality of established buyer personas your sales and marketing teams share. I'll explain briefly the way AI does this for those who are curious like me. AI uses found from social media platforms, job descriptions, and publicly available user-generated content (USG) from review sites of various kinds as a starting point, along with whatever inputs you initially provided. or can gauge and digest this data to uncover details about your target audience you didn't know exist so that you can execute strong marketing persona intel. Big Data Linkfluence, Pulsar Platform NewtonX In a similar vein but for B2B teams targeting business accounts software like can help your sales team identify and prioritize target accounts, get buying team specifics, optimize for timely engagements with those accounts along with keeping it relevant and adding value without being pushy, all while keeping the ball rolling until prospects are sale-ready. 6sense.com Email Marketing AI When leads unsubscribe, you lose that digital doorway that leads directly to their attention. And if you don’t know your audience well enough? When leads unsubscribe, you lose that digital doorway that leads directly to their attention. And with the volume of email spam, we’re all getting today, the stakes have never been higher with crafting the right emails at the right time. Email remains one of the most effective marketing channels available, but manually crafting individual emails can be time-consuming and tedious. Email marketing AI software like or makes it easy to create personalized emails at scale. These SaaS machine learning algorithms analyze customer data and then generate custom emails that are designed to convert. Plus, real-time reporting capabilities allow you to track the performance of your email campaigns so you can optimize results over a period of time. AdRoll Hubspot Regardless of the specific email software, almost all email marketing SaaS’ these days have varying levels of machine learning or AI built-in because it has quickly become the standard. But don't forget our content writing AIs mentioned earlier because they too can draft emails! Either way, now you know you can take the guesswork out of effective email marketing campaigns and focus more on what matters most—growing your business. Competitor Analysis AI budgeting for a CI program comes down to how similar your product or service is to your competition, and how profitable the margins are with the product. Similarly to the way sales persona AI works, competitor analysis AI takes the big data found from job sites, reviews, and social media posts and then synthesizes the unstructured data into an API that can meaningfully structure the information into competitor intelligence (CI). I'm a fan of because of their but there are other highly regarded options at varying price points like and , to name a few. Crayon blog Deeperinsights.com Klue.com My recommendation for budgeting for a CI program comes down to how similar your product or service is to your competition, and how profitable the margins are with the product. For instance, if you sell a product or service that's nearly identical to your competitor and the margins make it quite profitable, explore the CI options to defend against loss of market share or simply grow your share of the addressable market. And if you believe you’re on the other end of the spectrum, differentiation strategies via regular content creation and awareness campaigns should be prioritized, instead. Digital Advertising AI We already considered Ads and other major advertising platforms AI applications today. Creating a lookalike audience based on customer lists or similar interests meets the criteria and standards. Even being able to anonymously retarget your audience after they visit your website is AI. What isn't standard is creating display ads, video ads, and search ads that get results. Google When applying a growth marketing perspective, we can just apply our AI content and copywriter to write scripts that can be copied and pasted into an AI video maker or formatted into design software like Canva's templates. But will be up for the job for most ad copy needs. Just feed it into Lumen5 or another video SaaS, and do those final tweaks, and your set! Rad AI, Persado, Jasper or Anyword Closing Advice “Be sure that you're targeting early wins first so that you can quickly gain momentum, motivation and organizational buy-in.” Everyone's going to have a unique starting point for integrating marketing AI into their business or job. So start with what's most time-consuming, expensive, or what is a glaringly obvious deficiency for your business right now. Some may want to trim back their reliance on advertising agencies, and for others, video marketing could be almost nonexistent or take up a massive amount of marketing budget by outsourcing. Whatever it is, be sure that you're targeting early wins first so that you can quickly gain the momentum, motivation, and organizational buy-in needed to make your marketing AI transformation a success.

18 AI Marketing Softwares Your B2B Needs to Try Today

I write a newsletter called Above Average, where I talk about the second-order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, . subscribe to it A lot of people want podcasts transcribed and read instead of listening to them. We can go one level up and even extract insights from the podcasts as well using Open AI API. Here’s my tweet exchange, which provoked this experiment. So here is what we are going to do. And it will be slightly different from what I suggested in the tweet. The goal is to pick a YouTube video and get a transcription of that video, and then using prompt engineering, we extract insights, ideas, book quotes & summaries, etc., To Summarize, we achieve our goal in three steps: Step 1: Select a YouTube podcast video Step 2: Transcribe the video Step 3: Get Insights from the transcription Step 1: Select a YouTube podcast video A recent podcast conversation that broke YouTube was Jeff Bezos on Lex Friedman's podcast. So, for this exercise, I will pick this . video Step 2: Transcribe the video I used langchain along with Open AI’s audio-to-text model whisper to transcribe the youtube video. As usual you would need your OpenAI secret key to use the following script. YouTubeAudioLoader import os
import sys
import openai

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key  = os.environ['OPENAI_API_KEY']


## youtube video's audio loader - langchain 
from langchain_community.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import OpenAIWhisperParser #, OpenAIWhisperParserLocal

url="https://www.youtube.com/watch?v=DcWqzZ3I2cY&ab_channel=LexFridman"
save_dir="outputs/youtube/"
loader = GenericLoader(
     YoutubeAudioLoader([url],save_dir),
     OpenAIWhisperParser()
 )
docs = loader.load()
print(docs[0].page_content[0:500])

# Specify the file path where you want to save the text
file_path = "audio-transcript.txt"
try:
    with open(file_path, 'a', encoding='utf-8') as file:
        for doc in docs:
            file.write(doc.page_content)
    print(f'Large text saved to {file_path}')
except FileNotFoundError:
    print(f"Error: Input file '{file_path}' not found.")
except Exception as e:
    print(f"An error occurred: {e}") You might see the following error while running this script, and I pasted the solution that works in case you are using a Windows system. Postprocessing: and not found. Please install or provide the path using –ffmpeg-location. ERROR: ffprobe ffmpeg Running this script will generate the transcript and store it in a text file audio-transcript.txt. Step 3: Extract insights from the conversation To extract insights, I am using Open AI API, and here is the script. The code loads the transcript text and passes it along with a prompt designed to extract insights, people & books. To get more interesting things out of this conversation, you can come up with a more interesting prompt. Note that the file name is slightly different because I had to cut the transcript to a short length since my completion query to Open AI API exceeded my TPM limits. import os
import sys
import openai
import shutil
from pprint import pprint

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key  = os.environ['OPENAI_API_KEY']

client = openai.OpenAI()

file_path = "audio-transcript-copy.txt"
try:
    with open(file_path, 'r', encoding='utf-8') as file:
        long_text = file.read()
    print(f'{file_path} is rad')
except FileNotFoundError:
    print(f"Error: Input file '{file_path}' not found.")
except Exception as e:
    print(f"An error occurred: {e}")

prompt2 = f"""
You will be provided with text deilimited by triple quotes.
The given text is a podcast transcript.

Provide the host and guest name.
Summarize the transcript in to 10 points.

If there are any people referred in the transcript. Extract the people mentioned and list them along with some info about them in the following format
1. Person 1's Name: Person 1's profession or what he or she is known for or the context in which he or she was referred to.
2. Person 2's Name: Person 2's profession or what he or she is known for or the context in which he or she was referred to.
...
2. Person N's Name: Person N's profession or what he or she is known for or the context in which he or she was referred to.
If the transcript doesnt contain refereces to any people then simply write \"No people referred to in the conversation.\"

Extract the books mentioned and list them in the following format.
1. Book 1's Title: Context in which the book was referred to.
2. Book 2's Title: Context in which the book was referred to.
...
N. Book N's Title: Context in which the book was referred to.
If the transcript doesnt contain refereces to any books then simply write \"No books referred to in the conversation.\"

IF you find any inspiration quotoes complie them in to a list.

\"\"\"{long_text}\"\"\"
"""

response = client.chat.completions.create(
  model="gpt-4",
  messages=[
    {
      "role": "user",
      "content": prompt2
    }
  ],
  temperature=0.7,
  #max_tokens=64,
  #top_p=1
)

print(response.choices[0].message.content) Here is what the output I got: Result: Lex Freidman & Jeff Bezos Podcast Summary Can provide a service to podcasters to generate smart transcripts with insights. This would be a B2B play. AI PRODUCT IDEA ALERT 1: Instead of a service to podcast creators, it could be B2C customers who listen to podcasts and want to read through podcasts and create their own library of insights. AI PRODUCT IDEA ALERT 2: Expect both these ideas to be used by existing podcast hosting companies like Spotify & launch these ideas as new features. If I was a Product Manager in any such companies I would be pitching them by now. That’s it for day 5 of 100 Days of AI. Follow me on , for latest updates on 100 days of AI or & Check out Twitter LinkedIn bookmark this page Day 4: How to Use ChatGPT to be More Productive? Also published . here

100 Days of AI Day 5: Transcription and Extracting Insight from Podcasts with OpenAI

We’ve heard of , we’ve heard of NeRFs, and we’ve seen these kinds of applications allowing you to recreate someone’s face and pretty much make him or her say whatever you want. deepfakes What you might not know is how inefficient those methods are and how much computing and time they require. Plus, we only see the best results. Keep in mind that what we see online is the results associated with the faces we could find most examples of, so basically, internet personalities and the models producing those results are trained using lots of computing, meaning expensive resources like many graphics cards. Still, the results are really impressive and only getting better. Fortunately, some people like Jiaxian Tang and colleagues are working on making those methods more available and effective with a new model called RAD-NeRF. From a single video, they can synthesize the person talking for pretty much any word or sentence in real time with better quality. You can animate a talking head following any audio track in real-time. This is both so cool and so scary at the same time... Learn more in the video References ►Tang, J., Wang, K., Zhou, H., Chen, X., He, D., Hu, T., Liu, J., Zeng, G. and Wang, J., 2022. Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition. . ►Results/project page: ​​ arXiv preprint arXiv:2211.12368 https://me.kiui.moe/radnerf/ Video Transcript 0:02 [Music] 0:07 we've heard of deep fakes we've heard of 0:09 Nerfs and we've seen these kinds of 0:11 applications allowing you to recreate 0:13 someone's face and pretty much make him 0:15 say whatever you want what you might not 0:17 know is how inefficient those methods 0:20 are and how much Computing and time they 0:22 require plus we only see the best 0:24 results keep in mind that what we see 0:26 online is the results associated with 0:29 the faces we could find most examples of 0:31 so basically internet personalities and 0:34 the models producing those results are 0:36 trained using lots of computing meaning 0:38 expensive resources like many graphics 0:41 cards still the results are really 0:43 impressive and only getting better 0:45 fortunately some people like Jackson 0:47 tang and colleagues are working on 0:49 making those methods more available and 0:52 effective with a new model called red 0:54 Nerf but let's hear that from their own 0:57 model hello thanks for watching the 0:59 supplementary video for our paper 1:00 real-time neural Radiance talking head 1:03 synthesis via decomposed audio spatial 1:05 encoding 1:06 our method is person-specific and only 1:08 needs a three to five minutes monocular 1:10 video for training 1:11 after training the model can synthesize 1:14 realistic Talking Heads driven by 1:15 arbitrary audio in real time while 1:17 keeping comparable or better rendering 1:19 quality compared to previous methods so 1:21 you heard that right from a single video 1:23 they can synthesize the person talking 1:26 for pretty much any word or sentence in 1:28 real time with better quality you can 1:30 animate a talking head following any 1:33 audio track in real time this is both so 1:36 cool and so scary at the same time just 1:39 imagine what could be done if we could 1:40 make you say anything at least they 1:43 still need access to a video of you 1:45 speaking in front of the camera for 5 1:47 minutes so it's hard to achieve that 1:48 without you knowing still as soon as you 1:51 appear online anyone will be able to use 1:53 such a model and create infinite videos 1:56 of you talking about anything they want 1:58 they can even host live streams with 2:00 this method which is even more dangerous 2:03 and makes it even harder to say wetsuit 2:05 or not anyways even though this is 2:08 interesting and I'd love to hear your 2:10 thoughts in the comments and keep the 2:11 discussion question going here I wanted 2:13 to cover something that is only positive 2:15 and exciting science more precisely how 2:19 did they achieve to animate Talking 2:20 Heads in real time from any audio using 2:23 only a video of the face as they State 2:26 their red Nerf model can run 500 times 2:29 faster than the previous works with 2:31 better rendering quality and more 2:33 control you may ask how is that possible 2:36 we usually trade quality for efficiency 2:39 yet they achieve to improve both 2:41 incredibly these immense improvements 2:43 are possible thanks to three main points 2:46 the first two are related to the 2:48 architecture of the model more 2:50 specifically how they adapted the Nerf 2:52 approach to make it more efficient and 2:54 with improved motions of the Torso and 2:57 head the first step is to make nerves 2:59 more efficient I won't dive into how 3:02 Nerfs work since we covered it numerous 3:04 time basically it's an approach based on 3:06 neural networks to reconstruct 3D 3:09 volumetric scenes from a bunch of 2D in 3:11 images which means regular images this 3:14 is why they will take a video as input 3:17 as it basically gives you a lot of 3:19 images of a person from many different 3:21 angles so it usually uses a network to 3:24 predict all pixels colors and densities 3:26 from the camera Viewpoint you are 3:28 visualizing and does that for all 3:31 viewpoints you want to show when 3:32 rotating around the subject which is 3:34 extremely computation hungry as you are 3:37 predicting multiple parameters for each 3:39 coordinate in the image every time and 3:41 you are learning to predict all of them 3:43 Plus in their case it isn't only a Nerf 3:46 producing or 3D scene it also has to 3:49 match an audio input and fit the lips 3:51 mouth eyes and movements with what the 3:53 person says instead of predicting all 3:56 pixels densities and colors matching the 3:58 audio for a specific frame they will 4:00 work with two separate new and condensed 4:03 spaces called grid spaces or grid-based 4:06 Nerf they will translate their 4:08 coordinates into a smaller 3D grid space 4:11 trans laid their audio into a smaller 2D 4:13 grid space and then send them to render 4:16 the head this means they never merge the 4:19 audio data with the spatial data which 4:22 will increase the size exponentially 4:23 adding two dimensional inputs to each 4:26 coordinate so reducing the size of the 4:29 audio features along with keeping the 4:31 audio and spatial features separate is 4:34 what makes the approach so much more 4:36 efficient but how can the results be 4:38 better if they use condensed spaces that 4:40 have less information adding a few 4:42 controllable features like an eye 4:44 blinking control to our grid Nerf the 4:47 model will learn more realistic 4:48 behaviors for the eyes compared to 4:51 previous approaches something really 4:53 important for realism the second 4:55 Improvement they've done is to model the 4:57 Torso with another Nerf using the same 5:00 approach instead of trying to model it 5:02 with the same Nerf used further head 5:04 which will require much fewer parameters 5:07 and different needs as the goal here is 5:09 to animate moving heads and not whole 5:12 bodies since the Torso is pretty much 5:14 static in these cases they use a much 5:16 simpler and more efficient Nerf based 5:18 module that only works in 2D working in 5:21 the image space directly instead of 5:24 using camera arrays as we usually do 5:26 with Nerf to generate many different 5:28 angles which aren't needed for a torso 5:30 so it is basically much more efficient 5:32 because they modified the approach for 5:35 this very specific use case of the rigid 5:37 torso and moving head videos they then 5:40 recompose the head with the Torso to 5:42 produce the final video and voila this 5:45 is how you produce talking head videos 5:47 over any audio input super efficiently 5:50 of course this was just an overview of 5:53 this new exciting research publication 5:55 and they do other modifications during 5:57 the training of their algorithm to make 5:59 it more efficient which is the third 6:01 point I mentioned at the beginning of 6:03 the video if you were wondering I invite 6:05 you to read their paper for more 6:07 information the link is in the 6:09 description below before you leave I 6:10 just wanted to thank the people who 6:12 recently supported this channel through 6:14 patreon this is not necessary and 6:16 strictly to support the work I do here 6:18 huge thanks to artem vladiken Leopoldo 6:22 Alta Murano J Cole Michael carichao 6:25 Daniel gimness and a few Anonymous 6:28 generous donors it will be greatly 6:30 appreciated if you also want and can 6:33 afford to support my work financially 6:35 the link to my patreon page is in the 6:37 description below as well but no worries 6:39 if not a sincere comment below this 6:42 video is all I need to be happier I hope 6:45 you've enjoyed this video and I will see 6:47 you next week with another amazing paper 6:51 [Music]

Efficient NeRFs for Real-Time Portrait Synthesis (RAD-NeRF)

NeRF eficientes para la síntesis de retratos en tiempo real (RAD-NeRF)

रीयल-टाइम पोर्ट्रेट सिंथेसिस (आरएडी-एनईआरएफ) के लिए कुशल एनईआरएफ

リアルタイム ポートレート合成 (RAD-NeRF) のための効率的な NeRF

NeRFs eficientes para síntese de retrato em tempo real (RAD-NeRF)

NeRF hiệu quả để tổng hợp chân dung thời gian thực (RAD-NeRF)

用于实时人像合成的高效 NeRF (RAD-NeRF)

DALL-E Homebrew In this post, we’ll look at getting setup with running your own A.I. image generator. You need a Linux system with a CUDA enabled card to get this working through to the end. I did get the basic txt-2img script working in Windows. Unfortunately, for the July released , you need in order to index the openimages dataset, which is only available on Linux. worked fine for me. retrieval-augmented diffusion models scann WSL Here’s roughly what I gather you’ll need: Linux (WSL fine) 11.3 CUDA graphics = 32GB RAM 50GB disk space (~30GB without openimages) 3xxx series card (I am just guessing here. This is all being done with a 3090 FE) Latent-Diffusion I randomly stumbled upon the while I was browsing AI subreddits. A few posts mentioned it and I didn’t know what it was so I went googling. latent-diffusion repo I am not a computer scientist, mathematician, or any of the things that would be required to really understand what’s going on here. The paper about the new RAD models are understandable and interesting, but I haven’t done much research yet beyond that. Text-to-image vs. RAD There are 2 image generation techniques possible with Latent Diffusion. We will install and take a look at both. I believe the txt2-img model that we’ll setup first is what we are used to with other image generation tools online – it makes a super low res image clip thinks is a good prompt match and denoises and upscales it. The RAD model uses a configurable database of images as a reference AND does the diffusion like we are used to. This seems to mean it’s less good as a general purpose generator, but could be used with a specific training set you could feasibly create. I will try this in the future. RAD also generates 768X768 images which is pretty impressive. Install Instructions Text-to-Image Make sure you have installed and works in your shell. You can get CUDA 11.3 conda Conda here , lest ye end up in dependency hell later. I did this via but conda may work too. (Re)install Pytorch now pip Clone the repo to a drive with a lot of free space into the repo and run: cd conda env create -f environment.yaml
conda activate ldm This will setup the initial environment. If you get errors with Pytorch later and need to reinstall it, come back to environment.yaml and reinstall using the correct package versions. Download the model and you’re all ready to go so long as your torch dependencies are all setup correct. mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt You should be able to sample images now using: python scripts/txt2img.py --prompt "my cool image" Make sure to see the options you’re able to set in the README. You can use this bash script to make running everything from a shell easier. You can include this in your file and reopen your shell. Make sure to change the 3 paths to suit your needs. We’ll look at PLMS later. ~/.bashrc function txt2img() {
  if [ $# -eq 0 ]; then
    echo "Usage: diffuse '[prompt]' [--plms]"
    exit 1
  fi
  cd /mnt/c/diffusion/latent-diffusion;
  conda activate ldm;
  if [ $2 = '--plms' ]; then
    python scripts/txt2img.py --prompt $1 --outdir '/mnt/g/My Drive/AI Image Tests/text2img' --plms --n_iter 4 --ddim_eta 0.0
  else
    python scripts/txt2img.py --prompt $1 --outdir '/mnt/g/My Drive/AI Image Tests/text2img' --n_iter 4 --ddim_eta 0.0
  fi
} The images are 512X512 by default. You can use to upscale the images with pretty good results. This Jupiter Notebook Troubleshooting If you get CUDA memory errors running commands, try decreasing the batch size. Use to troubleshoot/view memory usage. htop If using WSL, make sure all your RAM is available to linux. If you get torch related errors, you probably need to reinstall Pytorch. This will likely cause other issues with packages. Check environment.yaml and manually install the correct package versions with and/or (I did both and am not sure which made everything work) pip conda RDM You can read more about RDM . As said before, it essentially combines a database of images with Clip descriptions with the existing diffusion process, from what I understand. This part takes a lot longer to get running. here Note, this model, especially when using the openimages training is best at recreating real things and doesn’t seem very good (yet) and creating the weird images we’re used to from the diffusion models. Get the Text-to-Image model working first Install new packages and download training model. pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0
pip install git+https://github.com/arogozhnikov/einops.git

mkdir -p models/rdm/rdm768x768/
wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/model.ckpt Test everything is working so far with python scripts/knn2img.py  --prompt "I'm a computer" If everything went well, you should see a success text in your shell. Now we need to download all the image indexes/models. The openimages zip is 11GB. The ArtBench data is pretty small. mkdir -p data/rdm/retrieval_databases
wget -O data/rdm/retrieval_databases/artbench.zip https://ommer-lab.com/files/rdm/artbench_databases.zip
wget -O data/rdm/retrieval_databases/openimages.zip https://ommer-lab.com/files/rdm/openimages_database.zip
unzip data/rdm/retrieval_databases/artbench.zip -d data/rdm/retrieval_databases/
unzip data/rdm/retrieval_databases/openimages.zip -d data/rdm/retrieval_databases/

mkdir -p data/rdm/searchers
wget -O data/rdm/searchers/artbench.zip https://ommer-lab.com/files/rdm/artbench_searchers.zip
unzip data/rdm/searchers/artbench.zip -d data/rdm/searchers We’re ready to use the Artbench models now (which work pretty well in my limited testing), but what we really want is to use the massive openimages model as our reference. We downloaded the data, but we need to create the index. If you want to test the Artbench database, run python scripts/knn2img.py --prompt "A blue pig" --use_neighbors --knn 20 Openimages Index Unless you have a super computer, the terrible memory management in python multiprocessing (from what I can tell) will stop us from using the 4 files we unzipped with the script from the repo to index them. Everything else I tried, the processes would run out of memory. Luckily, we just need to concatenate the files. You can delete the old part files after if you would like. We also need to move the part files out of the openimages folder. cd data/rdm/retrieval_databases/openimages/
cat 2000000x768_part1.npz 2000000x768_part2.npz 2000000x768_part3.npz 2000000x768_part4.npz > 2000000x768.npz
mv 2000000x768_* ~/temp
cd ../../../../ Now when we run the training script, we shouldn’t run out of memory. I think this is some issue in their multi file function in the training script. python scripts/train_searcher.py We need to change the batch size in so we don’t run out of GPU memory. You may need to lower to 1, but I was able to set to 2 on a 3090. scripts/knn2img.py Open in an editor scripts/knn2img.py Go to line 243 or find “n_samples” Change the default value to 1 or 2 and save You can also do this using the command line parameter, but since it will never work for me, I wanted to change the default value. Note if you update the repo in the future this will likely get overwritten. Now we can generate RDM images using the openimages database: python scripts/knn2img.py --prompt "A blue pig" --database openimages --use_neighbors --knn 20 Here is a script to run it conveniently. Again, make sure to change the 3 paths to suit your needs: ~/.bashrc function diffuse() {
    if [ $# -eq 0 ]; then
       echo "Usage: diffuse '[prompt]' [--plms]"
       exit 1
    fi
    cd /mnt/c/diffusion/latent-diffusion;
    conda activate ldm;
    if [ $2 = '--plms' ]; then
        python scripts/knn2img.py  --database openimages --prompt $1 --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20 --plms
    else
        python scripts/knn2img.py  --database openimages --prompt $1 --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20
    fi
} Speeding Things Up You can also run prompts in batches by creating a file (in this case ) and pointing the script to it with the parameter like this prompts.txt –from-file python scripts/knn2img.py  --database openimages --from-file ./prompts.txt --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20 sample prompts.txt prompt 1
prompt 2
prompt 3 This should save a lot of time if you’re running a lot of queries since the model only gets loaded once. The text-to-image model does not have this option. Initial Testing, & PLMS Below are some inital results from both the models with PLMS on and off. PLMS should make things run faster. For now, we won’t measure the speed increase but will look subjectively at image quality/results. Since the RDM model will only make 2 photos at a time for my system, I included both photos instead of a grid for that model. I will almost certainly do more specific testing in the future and these models will be included in future comparison posts. A wood house on a hill, landscape photography Text-to-image without plms with plms RDM without plms with plms A purple stop sign Text-to-image without plms with plms RDM without plms with plms fox den, digital art Text-to-image without plms with plms RDM without plms with plms winning the big game, award winning photography Text-to-image without plms with plms RDM without plms with plms a sports car driving in sand dunes Text-to-image without plms with plms RDM without plms with plms An Image Generator to Call Your Own And there you have it – no more credits or monthly limits, just pure AI image generation all your own. Once I figure out some tuning settings and create my own image training database, we’ll likely talk about this again. Hopefully with some tuning and new releases/models this will get closer to the WOW factor DALL-E has. Also published . here

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