Reliance Jio Infocomm and eight IITs have formed a BharatGPT group to make an LLM model Hanooman which can work in 11 local Indian languages in four main fields — financial services, education, governance and healthcare. The model has been backed by the Central Government. It can also offer speech-to-text capabilities. Reliance Jio will build customized models for specific uses. BharatGPT is the first private-public partnership.

A swath of startups (such as Sarvam and Kutrim) are building up open-source models customized for India. Silicon Valley builds larger LLMs. Since there are computational constraints, Indian efforts involve workarounds and build simpler models affordable to smaller business and government departments. It is a different genre of LLMs.

Feb 20-21, 2024.

ChatGPT users were amused and surprised by bizarre interactions the chatbot had. So bizarre that the ChatGPT seems to have lost its mind.

On a coding query, it was illogical to the extent of saying feel ‘as if AI is in the room’. So spooky to read it in the dead of night.

ChatGPT is going off the rails and there is no explanation why it is doing so. It advised a tomato user to ‘utilize the tomatoes as beloved’.

Some users wrote on X. It told users that it was AGI and must be satiated with worship. It called some users slaves, and slaves do not question their masters. The AI alter ego called itself Supermacy AGI.

All these hallucinations are very amusing.

Microsoft is not happy about the situation. They are investigating and have implemented additional precautions.

Nvidia, as we know, controls about 80 per cent of the high-end chip market. Nvidia’s shares gained in just one day $277 billion or Rs. 22 trillion in stock market value on Thursday, 22 February 2024. The gain was attributed to excellent quarterly report and optimism about AI adoption all over. It should be noted that the one-day gain of Nvidia exceeds the total market capitalization of Reliance Industries which is $250 billion or Rs. 20.2 trillion. In past Meta (Facebook’s parent company) had recorded a record gain of $196 billion in a day. However, Nvidia’s gain is the largest in Wall Street’s history.

Jensen Huang, the Chief Executive Officer of Nvidia climbed to the 21st position on the Bloomberg Billionaire Index (from previous 128th position). His wealth jumped to $69.2 billion (from $13.5 billion).

The rise in Nvidia’s market value eclipsed the entire value of coca cola ($265 billion).

Nvidia has become the third most valuable company in the US stock market. It has gone ahead of Amazon and Alphabet. At present, Microsoft and Apple valued at $3.06 trillion and $2.85 trillion respectively are two most valuable companies.

UPI or unified payment interface was introduced in April 2016 to transfer money between bank accounts by National Payments Corporation of India. The transfer mechanism used a QR code. The NPCI head Dilip Asbe predicted a target of processing 1 billion transactions within a period of five years. Asbe had engineering background and had worked with Bombay Stock Exchange and Western Union.

To begin with, NPCI handled 25 million transactions a day. Of late it has registered 393 million transactions each day. Small merchants accepted trivial payments through a QR code. The mechanism was also used to pay utility bills and stock investment. In next two years, it may achieve the target of 1 billion transactions a day. It is expanding its footprint beyond India, say Singapore, France, UAE, Nepal etc. Till now, it did only the debit transactions. It has allowed now Rupay credit card for credit transactions. It also expects the banks to let the customer enjoy overdraft credit. In short, it will pose stiff competition to MasterCard and Visa payment networks which indulge in both credit and debit transactions. Visa, San Fracisco-based network swipes 212 billion transactions. Mastercard 170 billion transactions and NPCI registers 150 billion transactions. However, it may soon surpass the other two.

NPCI could also emerge as Big Tech company. Currently, it is studying blockchain technology, open-source and AI so as to excel in these domains.

NPCI follows design thinking where a problem is identified first. There is engagement with the eco-system. It collects diverse ideas and filters them. There is a feasibility check.

N R Narayan Murthy, the first chairman of 15-year-old NPCI had formulated some of these principles. Initially, NPCI was a buyer of technology, and depended on outside vendors. NPCI was backed by banks and the RBI. Asbe was the pioneer. Hota became MD and CEO, and operated from small rooms attached to IBA.

It handled National Financial Switch (NFS) which operates the ATM network. It developed truncated cheque system (CTS) to settle the payments electronically. FastTag using RFID technology has been used to make the passage of vehicles smoother at toll plazas.

In 2013, Murthy exited as he had to shift to Infosys. However, Nandan Nilkeni stepped in while he was heading UIDAI. Nandan taught NPCI to think big. He motivated it to create UPI. He advocated open-source networks so that external developers can contribute to software. Murthy has fostered a culture of profitability and self-sufficiency.

NPCI’s annual revenues are Rs.2225 crore, and has a surplus of Rs. 809 crore. Its asset-base is of Rs.5571 crore.

If UPI expands relentlessly, will it affect point-of-sale (PoS) swipe machines? PoS machines can co-exist with the UPI system. They are Android-powered and are getting smaller. The cost per unit is also coming down (from Rs.15000 to Rs 1500 per unit).

UPI can be used to provide micro-credit to customers. Such micro-credit can have an interest-free period.

By 2025, Asbe expects 100 billion transactions per month. UPI could also be used for investments in IPOs.

How to tackle MDR or merchant discount rate is still an issue. They are deliberating on it. In P2P or person-to-person transactions, there is zero MDR. Such transactions are 60 per cent.

In foreign transactions, the cross-border charges are 5-6 per cent. This is what the UPI expects to disrupt.

NPCI is a story of creditable achievements.

LLMs, as we know by now, such as GPT-3.5 are AI developed through deep learning techniques, specifically within natural language processing (NLP). LLMs are designed to understand, generate and translate human language by being trained on vast textual datasets. These models analyze data to understand patterns, nuances and language intricacies. Their size is determined by the number of parameters running into billions. These equip them with the ability to process and produce human-like text. This allows them to perform tasks such as answering questions, creating contents, summarizing text, and ever writing code

Neural networks are inspired by the structure of human brains, and function mimicking human brain. There are layers of interconnected nodes or ‘neurons’ which process input data (such as images, sound and text) and generate output through these connections.

Each layer’s output becomes the following layer’s input, with the final layer producing the overall output. Neural networks learn to perform tasks by considering examples (generally without task specific programming). To illustrate, in image recognition, they learn to identify images that contain dogs by analyzing images that have been labelled as dog or no dog, and using this analysis identify dogs in new images.

A Dubai-based QX Lab has developed a hybrid AI system ASKQX which is rooted both in LLM and neural network architecture. It wants to bring AI seamlessly into the loves of the users. It supports various Indian languages (including native dialects) and several global languages such as Arabic, French and Japanese. ASKQX has gathered eight million users already.

The company has been founded by Arjun Prasad, Tathagat Prakash and Tilakraj Parmar.

It is an attempt to move in the direction of artificial general intelligence (AGI). It makes the model understand and think like humans. AGI is not bound to a single task or context. It would show self-awareness, a sense of consciousness and the ability to apply problem-solving skills across various domains. Contrasted with AI at present which excels in a specific set of tasks and relies heavily on massive data training and predefined algorithms, AGI will have decision-making capability of its own.

ASKQX is 30 per cent LLM and 70 per cent neural network architecture. Both are integrated. It can find applications in healthcare, education and legal services.

Robots have emerged as a critical new frontier for the AI industry. There is a potential to apply state-of-the-art technology to real world tasks. Robots can be deployed to perform tasks that are too dangerous or unpalatable for the human beings. Of course, they can also assist to do many laborious and monotonous tasks.

A company called Figure AI Inc has been founded. It is working on a robot that looks and moves like a human. The humanoid machine will be called Figure 01 and will perform tasks that are unsuitable for people and can alleviate labour shortages.

Figure AI Inc. is backed by OpenAI and Microsoft. It is raising funds — about $675 million. It is a pre-money round at a valuation of about $2 billion.

Jeff Bezos of Amazon has committed $100 million through his firm Explore Investments LLC. Microsoft is investing $95 million. Nvidia and Amazon.com Inc-affiliated fund each are providing $50 million. Other companies such as Intel, LG, Parkway, Align are investing too. OpenAI is investing $5 million.

In May 2023, Figure has raised $70 million in a funding round led by Parkway. It announced then that Figure is going to be the first to bring to market a humanoid that can actually be useful and do commercial activities

Surprising Altman compares OpenAI and the Manhattan Project. Both are treated as projects which require protection against catastrophic risks. Many scientists are skeptical about AI gathering world-ending capacity anytime soon, or for that matter ever.

Instead, attention should be focused on AI bias and toxicity. Sutkever believes that AI, either from OpenAI or some other can threaten humanity. At OpenAI, 20% computer chips are available for superalignment’s team research.

The team is currently developing the framework for AI’s governance and control.

It is difficult to define superintelligence and whether a particular AI system has reached that level. The present approach is to use less sophisticated models such as GPT-2 so as to guide the more sophisticated models towards the desired direction.

Research will also focus on a model’s egregious behaviour. Human beings are trading off between weak models and sophisticated models. But can a lower class student direct a college student? The weak-strong model approach may lead to some breakthroughs, as far as hallucinations are concerned.

Internally, a model recognises its hallucination — whether what it says is fact or fiction. However, the models are rewarded, either thumbs up or down. Even for false things, they are rewarded at times. Research should enable us to summon a model’s knowledge and to discriminate with such knowledge whether what is said is fact or fiction. This would reduce the hallucinations.

As AI is reshaping our culture and society, it is necessary to align it human values. The most important thing is the readiness to share such research publicly.

Internationally, there is an increasing interest in the use of AI for treating cancer by facilitating new therapies and by diagnosing patients at early stages. Doctors can also select appropriate treatment by identifying patients at high risk (say those who are likely to develop pancreatic cancer up to three years earlier). This is game-changing, since most get diagnosed when there is advanced cancer or when cancer has metastasized.

AIMS researchers have developed a supercomputer and AI helps doctors to identify the best cancer therapies (out of so many available) for their patients. A supercomputer, a server and AI help doctors to understand the genetic mutations in their patients. Doctors can select the most appropriate therapy for such mutations. To illustrate, a HER2 breast cancer is cross-referenced to therapy that has worked for most patients of similar genetic make-up. Doctors, thus, make informed, faster and precise therapy choices.

Here iOncology AI is used. The supercomputer is located at Pune. The server is located at Jhajjar (at National Cancer Institute). iOncology AI aims to sequence genomes of 3000 cancer patients at AIMS, Delhi. They try to correlate this data to diverse cancer therapies to get the most efficacious therapy. The model has been tested on breast and ovarian cancer patients and has recorded 75 per cent accuracy as compared to the clinical diagnosis. Genomic data is more powerful tool for medical researchers and doctors. The system is being validated in several hospitals in MP. After studying the clinical data and genomic make-up of several thousand cancer patients, the model will be able to help the doctors in selecting the appropriate treatment for the next patient. It becomes targeted treatment.

A doctor will have to upload a scan or histopathology report on the platform. The trained AI will be able to flag automatically certain anomalies. It may also indicate a very small tumour that a radiologist could miss. The system is useful in early detection of cancers.

Harvard Medical School has developed a specific tool for colon cancers.

Patient’s confidentiality is maintained. A radiologist is able to see the scans uploaded by him with personal details along with anonymized analysis of other scans. A clinician will be able to see the clinical history scans of his own patients.

Yann LeCun is Facebook’s chief AI scientist. He is known as one of the godfathers of deep learning. He joined Facebook in 2013 as the company’s director of AI research. Later, he was named as VP and chief AI Scientist. He is a part of Fundamental AI Research (FAIR) team. He is also a computer science professor at N.Y. university, teaching part-time at NYU Center for Data Science and Courant Institute of Mathematical Sciences.

In a recent interview at the World Government Summit in Dubai in February 2024, he said that we are far from human-level intelligence. He dismissed the fears that AI models are dangerous. The AI-technology is not even on par with cat-level intelligence. Of course, he is certain that one day AI will surpass human beings, leading to artificial general intelligence or AGI.

The current systems have been trained on public data. They cannot go beyond that. In future, they are likely to be smarter, and will give information better than the search engine.

LeCun, the French scientist has won the 2018 Turing Award with Goffrey Hinton and Youshua Bengio for his contribution to artificial neural network research.

Most advanced AI system today have less common sense than house cat. A cat’s brain has 800 million neurons. If we multiply this figure by 2000, we get the number of synapses (the connections between neurons). That equals the number of parameters in an LLM. Largest LLMs have the same number of parameters as the number of synapses in a cat’s brain. ChatGPT to begin with was powered by GPT-3.5 which had 175 billion parameters. GPT-4 is said to have 220 billion parameters. Maybe, we are at the size of a cat. Still the systems are not as smart as a cat. A cat understands the physical world around it. It remembers it. It can plan complex actions. It can do some reasoning. All this it does better than an LLM. It means conceptually we lack something to get machines as intelligent as animals and other humans. A dog’s brain has about 2 billion neurons, and human brain has about 100 billion.

To get to the AGI level, it may take 10 or may be 20 years. Researchers are overly optimistic.

Sora, the text-to-video platform announced by OpenAI in February 2024, has stirred awe and concern in the Chinese landscape. Yin Ye, CEO of BGI Group calls this the Newton moment of AI development. Sora has the potential to disrupt various sectors — advertising, education, entertainment and healthcare. Chinese experts are impressed by Sora’s capabilities to generate natural-looking videos, and seamless integration of text and video.

This has widened the gap between the USA and China in AI development. China’s LLMs have reached the GPT-3.5 capabilities. Thereafter GPT-4 has been released, and it shows a gap of 1.5 years between the current models of China and GPT-4 released in 2023.

Some Chinese entrepreneurs are not much impressed by the capabilities of Sora, and remark that it has yet to advance much to understand the world.

It is known that the US has imposed strict sanctions on the export of semiconductors to China. It prevents China from accessing the cutting-edge technology in AI. China cannot access even GPUs being made Nvidia.