India will be in near future making at AI chip sets — (GPUs) and other computing hardware. These are essential to create large scale computing. Currently, the US and China lead in computing infrastructure necessary for the development of AI technology. Nvidia, US-based, is the market leader with 88 per cent market share. And there is a lag of 12-18 months in getting GPUs from the company due to its high demand across the globe.

Several other organizations want to make GPUs, but their processing capacity is around 60 per cent of Nvidia’s. These organizations are also interested in scaling up. India has to start from ground zero. If the other organizations partner with India, together we will be able to make chips with processing capacity close to that of Nvidia’s over a period of time. It will be a win-win game for both sides.

The alliance with other firms could be on the basis of public-private partnership.

India will also start domestic manufacturing of printed circuit boards, production of telecom equipment and consumer electronics. Micron has decided to set up a local assembly, testing, marking and packaging plant. Going ahead, many more players will set up manufacturing facilities in India.

Faiss or Facebook AI Similarity Search is an open-source library (from Facebook AI). It accomplishes fast similarity search, thus making it suitable for image retrieval, recommendation systems, and anomaly detection. It supports various vector types, indexing and GPU acceleration for large datasets.

Weaviate is also an open-source vector database. It makes you store and query the data objects (along with their vector embeddings). It offers GraphQL, supports semantic search and integrates various ML frameworks.

Pinecone is designed for ML apps. It offers fast and scalable vector search. It leverages Faiss under the hood. It provides use-friendly API for integration with different tools and frameworks.

Milvus is too an open-source similarity search engine and excels in search across different cloud environments. It supports different similarity metrics, indexing and filtering capabilities for large datasets.

Qdrant is cloud-based vector database for vector similarity search. It is user-friendly, does real-time search. It integrates various cloud platforms.

Elastisearch is used for full-text search. It also-supports storing and searching vector data. Its version 7.0 has been introduced. It is for indexing vectors. It enables efficient k-nearest neighbours search.

Apache Spark is distributed computing framework for large scale vector processing and similarity search.

The MLib includes algos such as kMeans and cosine similarity. It enables vector analysis on big data.

Ne04j is graph database which can be extended to vector similarity search capabilities using plugins. ArangoDB is NOSQL document database supporting storage and querying vectors. DolphinDB is a time-series database. It is suitable for tasks involving high-dimensional vectors in generative models.

Facebook makes use of AI to transform its advertising business. There is increasing use of AI and automation tools, particularly its Advantage+ suite of solutions. It facilitates automation across every step of the creation process of the ads. It drives better outcomes too.

Generative AI is going to be game-changing for brands. It changes the way ads are created and the way marketers reach their audience. Creatives are to be tested for their performance. Generative AI allows advertisers to launch and test ad creatives faster. It enables them to reach their audience more accurately.

Generative AI-powered ad creative features have been introduced; one example is Image Expansion. It adjusts creative assets to fit different aspect ratios across multiple surfaces.

Background generation is another feature that creates multiple backgrounds to complement and enhance product image. Text variation feature creates multiple versions of an ad copy based on the original, highlighting different angles and selling points.

Generative AI has the potential to unlock a new era of advertising.

A few years back, we heard a lot about Web 3.0 based on peer proof through blockchain technology. Currently, ICANN registers domain names. Instead, in Web 3.0, this function was to be managed by a decentralized community. It would verify domain transactions and the traffic passing through the network using proof-of-work (PoW) blockchains.

What was promised was user sovereignty. Still, Web 3.0 has witnessed a pause. It is not being talked about. It has taken a back seat.

There are some issues. There are technological hurdles. There are regulatory uncertainties. The technology is complex and is not scalable. It leads to poor user experience. It is not easy to deal with decentralized apps.

Setting up a domain is a daunting task. It requires outsourcing. Web 2.0 is user friendly.

Security and decentralization are talked about, but these come at a cost. There are slow transactions. There is intensive energy consumption. Decentralized nature causes regulatory issues. Despite promises of security, there are hacks. DeFi is vulnerable due to smart contracts.

Crypto boom and NFTS led to an interest in Web 3.0. However, the speculative bubble has burst.

It is too early to write off web.3.0. The principles on which it is based cannot be overlooked. It requires some key developments to move further — advances in blockchain technology, more efficient consensus mechanism, user friendly apps, lowering barrier to entry for non-technical users.

Web 3.0 can overcome current stagnation by suitable regulation and technological innovation.

During Covid times, everyone was fond of using Zoom. However, after things have settled down, it has been realized that Zoom should be more than just a video call service and becomes an end-to-end communications platform with emphasis on AI assistance.

Zoom’s traction has increased by AI powering — it has garnered 7-million consumers worldwide for its Zoom Phone. There are other products such as Zoom Contact Centre and AI Companion.

Zoom Phone is a cloud business communication platform. It enables Wi-Fi phones, cellular data calls. One can switch from video to voice calls and from desktop to handphone. There are various other chat and call management tools.

AI Companion is generative AI assistant. It can summarize chats, give meeting transcripts, do language translations, compose emails, send calendar invites, generate post-call tasks for follow-up among other things. More than 5 lacs plus Zoom accounts have enabled AI Assistant since its September 2023 launch. In five months, it has generated 7.2 million meeting summaries.

India has two data centers of Zoom — Mumbai and Hyderabad. It has two technology centers — Bangalore and Chennai.

AI features are offered free to the customers. It empowers everyone.

LLMs are being trained by vast amounts of data and operate with lot of programming. In fact, human programmers create AI machines. Consequently, human errors and innate biases are seamlessly transferred to the machines and get manifested through AI actions and behaviour.

AI’s life blood is data. Data is the driving force behind its development and learning. ChatGPT was trained using 570 GB of text data, or around 300 billion words. DALL-E and Midjourney, two image-generating apps, employ a stable diffusion algorithm that is trained on 5.8 billion image-text pairs. We do not know what training data has been used by Google for Gemini. It could include trillions of pieces of text, images, videos and audio clips.

The data used by AI developers is sourced from high-quality academic papers, books, news articles, Wikipedia and filtered internet content to train the LLMs. The available high-quality data is not enough. Consequently, low-quality data from user generated texts (blog posts, social media posts, online comments) is also used. The low-quality data is perhaps more biased than the high-quality data. It might have illegal content as well. Apart from this, AI systems use simulated or synthetic data that is created by an AI model. Almost some 60 per cent data is likely to be synthetic by 2024, as against 1 per cent in 2021. The underlying programming for these simulations may introduce bias into the data.

If the data is not regularly updated, it becomes dated. Initially, ChatGPT’s data’s cut off was until 2021.

There has been research on aging and longevity in bits and pieces, but of late, the world’s billionaires are pouring in money and deploying talent to extend human life.

Sam Altman of OpenAI is taking moonshot on reprogramming human body. He runs a side project called Retro Biosciences. His startup has an investment of $180 million. It has a goal of extending human life by 10 healthy years. He has teamed up with Betts-LaCroix who promotes hard science and deep biology through his non-profit organization called Health Extension Foundation.

Life extension may seem to be a quirky project but it is a part of Altman’s futuristic world view.

Is Silicon Valley over-reaching by trying to fix everything? Or is it that spirit of rugged and toughened fend-for-yourself attitude that motivates Altman to take both fusion energy and human longevity into his stride.

The live-longer time frame suggested by him is plausible.

Open AI’s headquarters are in San Francisco. Retro Biosciences is located 30 miles south of it. It is closer to Meta, Apple and Stanford than the golden city. Its ethos is ‘more pirate than navy.’ It has a warehouse like office desks are perched on a platform. The employees can peek through narrow windows up there. The ambiance stimulates people to move fast and break things. The labs at Retro are converted shipping containers with high end ventilation.

The whole project is divided into three buckets.

1. Autophagy. Our cells are being recycled to keep them healthy. This has the potential to become a quick fix for aging. Theoretically, the recycling can be tweaked with a pill. Rapamycin and Metformin (for kidney transplants and diabetes respectively) are existing medications closest to a pill approach. They can boost longevity. It has yet no direct drug that specifically addreses this cellular housekeeping.

2. Cellular reprogramming. This is a trendy idea. Old cells can be reprogrammed to a slightly younger state. Japanese stem cell researcher and Nobel laureate Shinya Yamanaka’s research talks of Yamanaka Factors that can do this reprogramming, but experimentally it is difficult to accomplish this. Actually, it is remodeling of cells — this can cause cancer and there could be other health issues. Betts-Lacroix advises a gradual approach. Extract cells from ears or knee joints. Reprogramme them partially to de-age. Put them back into people when they are safe enough for treatment. One can avoid a person to start with — just do cell extraction to do programming. If that helps hearing loss or joint mobility, Retro can venture further –try something more advanced.

3.Plasma therapy. No doubt, this sounds like a Dracula-like concept since it promises rejuvenation. Mice have been used for experimentation. Their blood plasma is diluted with Saline. (It is better than young blood transfusion). In aged mice it improves may age-related issues — reduction in inflammation, improvement of liver and muscle health and improvement in the formation of brain cells.

Some research at Retro suggests that the technique can work in non-rodents too.

The life extension mission is a gargantuan mission. Both co-founders meet once a week. They are the Board now. In this longevity race, other competitors of Altman are Bezos (Amazon), Thiel and other billionaires. Bezo’s lab is San Diego. Thiel wants his body to be cryogenically preserved when he dies. It can be brought back to life later. He also believes that we can escape the velocity of death someday soon. German billionaire Angermayer is developing pills for improving aging. The pills include those that can keep ovaries younger so as to prolong the fertility window. Betts-LaCroix believes Retro is taking a moral call, rather than developing just a business model.

In India and elsewhere, there is a growing non-music audio market consisting of audiobooks, podcasts and audio series. Here two startups Pocket FM and KuKu FM are in audio entertainment segment.

Pocket FM offers bite-sized episodes of audio stories and novels in regional languages. It wants to deepen push into the US and expand into Europe and LATAM market in 2024. It follows a freemium model where the revenues are generated through both subscriptions and advertising. There are micro-transactions — a user can unlock a chapter of a novel for as low as Rs.9. In 2023, there were 20 million transactions and 75 billion of minutes of streaming worldwide.

KuKu FM largely focuses on audiobooks and podcasts in regional languages. It has signed an exclusive contract licensing deal with Storytel (Stockholm-based). It will offer translations of English books in regional languages.

Mustafa Suleyman, as we know, was the co-founder of AI research lab DeepMind. DeepMind since then has been acquired by Google in 2014. He continued to work in DeepMind until 2022. It wanted to create AI that will not veer into racist, sexist or violent behaviour. He left DeepMind in 2022 to cofound Inflection AI, an ML and generative AI company. Reid Hoffman (Greylock’s) too was a co-founder of Inflection AI. In 2023, Inflection AI launched a chatbot named ‘Pi’.

Microsoft has now appointed Suleyman (2024) as EVP and CEO of its newly created consumer AI unit, Microsoft AI. Several members of Inflection AI’s team have been appointed to the division, including co-founder Karen Simonyan.

He has co-written a book, The Coming Wave, that examines AI’s promise and the need to limit its potential perils.

The companies in AI space, namely Google, Microsoft and Apple are forming alliances so as to capitalize on generative AI. The idea is to generate revenue by marketing suitable consumer products and capture the market share. Each company does not have all the ingredients which can be assembled together to capitalize on generative AI. The ingredients are computing power, top-of-the-line AI models, trustworthy products and ways of getting them to people. The companies continue to search worldwide for talent and promising startups.

Google’s products have serious errors and biases. Microsoft has not been skilled at building exciting consumer products apart from video games. Apple is years behind in AI.

Big Tech is incapable of innovating the entire generative AI ecosystem single handedly.

Microsoft has infused AI into Bing search engine, Windows, Office and other products such as digital assistants under the Copilot brand.

This is the reason why Suleyman has been roped in. They went to craft in a true end-to-end product experience. Suleyman compares this to sculpting — recognizing when a piece of technology is ready and how to dress the experience that it becomes accessible and trusted.

Is Suleyman the right person to do this? He has developed a chatbot called Pi, which attracted a million active users. However, his startup Inflection never found a business case.

Bitcoin prices reached a high $73000 mark. The market anticipates a high demand, while the supply is limited. It is all because of the halving event which occurs once every four years, The event is steeped into the design and philosophy of scarcity. Satoshi Nakamoto, the creator of Bitcoin, designed the crypto to have a finite supply of 21 million coins. The issuance of new coins is reduced. Halving is significant as it cuts by half the rewards the miners receive while validating the transactions. It reduces the rate at which Bitcoins are created, suppressing the supply of new Bitcoins. In 2009, miners received 50 Bitcoins for every 10 minutes. There were three halving. They now receive 6.25 Bitcoins every 10 minutes.

Halving occurs after mining 2,10,000 blocks. Mining is the process of making computer hardware do mathematical calculations for Bitcoin network to confirm transactions and increase security. In April 24. there will be next halving. The block reward will be restricted to 3.12 Bitcoins for every 10 minutes of mining.

Bitcoin pricing, among other things, is scarcity driven. By the year end (2024), the prices may hit $150,000 and $250,000 in 2025.

As value increases, despite reduction in rewards, profitability of miners may not get affected. Miners may have to reduce power consumption and may have to improve hardware efficiency.