In 2015, OpenAI was set up as no-profit company with an objective of building safe AI. In 2018, it became a ‘capped profit’ company called OpenAI LP so as to bring compute power and capital backing. There was imbalance in this formation — a non profit Board was set up to keep the company on track — keep benefit of mankind above the commercial motives.

Sam Altman was a successful CEO of OpenAI. He was terminated by this Board. Surprisingly, he comes back in just five days as CEO again. On his return, he becomes more powerful.

The reasoning for his termination-not so candid termination — not so candid communication with the Board, disregard for the safety issues or some internal politics did not sound sensible enough to keep him out.

There are other founder CEOs — Facebook’s Zuckerberg — who holds a substantial stake in the company. However, Altman is not a shareholder in OpenAI. He has won on the basis of his charisma and smartness. The manpower stood firmly behind him — X witnessed a wave of emojis through the weekend to express the solidarity of the staff of OpenAI with Altman. It also expressed the outrage of the manpower.

Any organization is what its people are, and the manpower felt strongly that OpenAI is nothing without its people. They wrote a letter addressed to the Board asking for Altman’s reinstatement, or else they would quit.

Microsoft extended support to the ousted CEO and president at lightning speed. In the process, Microsoft shares shot upwards.

Altman-like CEOs come ‘once in a generation’, and they exhibit a missionary spirit rather than a mercenary one. He is a good communicator at the same time. He is incredibly curious about things. He has set a higher objective before the manpower — achieving AGI. He provides the suitable environment in the organization to goad the manpower towards this objective. They do their work diligently. They are there for a mission.

The launch of ChatGPT in late November, 2022 was transformative step. The manpower now can ‘do and die’ for him. They are all praise for him.

OpenAI has been valued at almost $90 billion, and employees cannot be denied the big cash their stock option could bring them. Can such a lucrative deal in future be frustrated? The manpower in fact was acting in its own interest.

The Board that outsted Altman has been abolished by and large. Ilaya, the chief scientist, was full of regret for being a party to the episode. Ilaya was into research. His difference with Altman pertained to the direction the company is taking — ethical AI should be a priority rather than crass commercialization. As it is AI as a technology is coming of age, and it is working. This could have driven the wedge between the two.

On his return, Altman expressed his love for OpenAI and OpenAI too reciprocated loud and clear.

Mathematics is extensively used computer programing. The mathematical concepts make you a better programmer.

Computers use binary numeral system where there are two symbols — 0 and 1. Hexadecimal system (base-16) and base-64 are essential in programming, and understanding these is facilitated by our understanding of the binary system.

Floating point numbers are not precise, resulting into tiny errors in calculations. These numbers use scientific notations.

Logarithmic functions are used in algorithms such as binary research.

Set theory is important for working with databases.

Boolean algebra uses three operators to work with Boolean values — AND, OR and NOT.

Combinatorics facilitates the calculation all possible permulations and combinations. It is a valuable skill in programming.

Graph theory is used for network routing and optimizing various scenarios.

Complexity theory or Big O notation make us aware about the efficiency of algorithms — time and memory complexity of algorithms.

Statistics is used in ML and AI. Measures of central tendency makes you understand predictions.

Linear algebra is useful in computer graphics, neural networks. It makes us understand scalars, vectors and matrices. These are used to represent data. In cryptography, 3D graphics and ML, you have to use linear algebra.

As we know, pay TV has two major segments — cable TV, where a cable operator takes a connection from a wholesale signal distributor firm or MSM and takes the signal to individual TV sets at homes through cable. Another pay TV is DTH or direct to home through a satellite dish. The total pay TV market was 160 million homes in 2020, which has now reduced to 100 million homes.

There is leakage at both the ends. Viewers are going to streaming platforms through broadband internet (OTT channels). These channels are offered by Jio and Airtel. At the other end, there is free DTH of Prasar Bharati through DD Freedish.

It means TV watching is done more through streaming platforms received through broadband connection or through freedish. The DTH or cable connection suffer in the process.

You should have a smart TV connected to broadband to watch both OTT channels and regular TV.

Subscriptions of DTH channels are falling down. To counter this, Tata Play in 2020 launched Binge offering Netflix and 20 other OTT channels in bundles. They provide a special set-top box for this . Airtel Xstream also provides 18 OTT channels.

Aggregation of OTT services is a natural transition to improve average revenue per user.

There are wired broadband homes in India. Many of them have smart TVs. A bulk of viewing happens on smart phones. These consumers do not bother about smart TVs and wired broadband.

TV screens ad rates are four to six times than those of the mobile screens. TV screen could be of ordinary linear TV or smart TV. Tata Play used Tata Broadband to operate in the last mile connectivity to reap the benefits, but the big players have realised that fibre laying costs are very high, and the payback period is long. That is the reason why cable operators are used for last mile pipe. Still in bigger cities operators do provide last mile connectivity, as the quality of fibre is good.

DTH industry has stabilised and would recover, but it may not have double digit growth.

AI requires careful handling to benefit humanity. At its worst, it can become an existential threat for humanity. AI can facilitate autonomous weapons systems which can identify targets with precision. AI can generate surveillance systems to help authoritarian regimes. AI can enable cloning abilities among criminals to carry out scams.

At the same time, AI can do a lot of good. It has the potential to be a game changer. It can perform hard tasks such drug discovery, management of nuclear power plants and telecom networks, management of power grids and road and air traffic, management of mining.

In a couple of years, NASSCOM puts the benefit to India due to AI-related activity at $450-500 billion to India’s output.

Thus, both the boardrooms and regulators will have to make numerous adjustments while dealing with AI.

The employees of OpenAI were overwhelmingly in favour of the outsted CEO, since he was seen to be driving the commercialization of ChatGPT which is not much ahead of Bard, Llama or Grok of competitors. The employees want to encash their skills, and get stock options if the subsidiary gets listed. It is normal to expect this in Silicon Valley.

Altman, after his return from a few days exile, had a hero’s return. Before the turmoil, the six member Board had three staff members and three independent directors, and was commited to effective altruism. After the turmoil, the Board with nine members may have at least one seat (say as an observer) for Microsoft. It is not yet clear how the independent Board members responsible for Altman’s ouster will be dealt with. They have agreed to step down. Though Quora’s D’Angelo voted against Altman, he continues after change of heart. OpenAI’s structure as a non-profit organisation and a for-profit company is difficult to balance. And the Board answered to the moral instincts only. This set-up was problematic for Microsoft, with 49% stake in OpenAI with no Board seat. The CEO will be more accountable to Microsoft henceforth. The new Board will neutralise the overarching influence of the Board on for-profit activities of the company.

Microsoft’s creating an OpenAI team would not have worked so well. As an independent unit OpenAI so far takes all the flak for corporate reputation and legality while deploying ChatGPT and DALL-E2 in the market. OpenAI remains a startup, and can fiddle with cutting-edge AI technology.

Microsoft keeps intact all its glow. and with none of its liability. Besides, it can exercise more control. It is a better deal for Microsoft to have OpenAI as a free unit than to have OpenAI inhouse.

A story has been floating around that some employees wrote a letter to the Board pointing out the development of an algorithm that could be a breakthrough in AGI. It solved certain math problems. It makes them optimistic about future success. It solves the problems for which it has not been trained. It means it has certain reasoning abilities. The project was code-named Q* or Q Star. In fact, the company was pushing the veil of ignorance back and and the frontier of discovery forward. However, this triggered Altman’s ouster.

There are unfair trade practices in travel industry. They are the patterns that lure consumers to opt for something that involves additional expenditure. Travel websites and airlines can show all the seats in a flight ‘paid’ when passengers are asked to do online checking before the travel. The design interface is so made. The consumers do not know ‘paid’ seats are not mandatory and there could be ‘free’ seats. It makes a consumer opt for a ‘paid’ seat and pay the seat allocation fees.

In addition, travel websites do not disclose that booking through them attract ‘convenience fee’.

There are complaints that though tickets are cancelled but refunds do not come from the airlines. There is delayed compensation for the lost or damaged luggage. There are unfair charges for the aisle or window seats. There is the dark practice of forcing the passengers to insure their trip.

It is necessary to protect consumers from misleading or coercive online tactics. In 2010, a phrase ‘dark patterns’ was coined by Harry Bringall, a user experience (UX) expert. Dark patterns refer to user interfaces (UIs) designed intentionally. The aim is to deceive, manipulate or compel users into specific actions. These run contrary to their preferences.

Mostly manipulative practices, these leverage cognitive biases — perceived scarcity, urgency, the need for validation. Users are thus prone to act hastily. There is fear of being left out.

Dark patterns have invited attention of the authorities. The European Union’s Digital Services Act aims to ban dark patterns. The UK has started investigating such practices. The US is concerned about deceptive design. In India, there are guidelines for prevention and regulation of dark patterns.

When consumers are led to unintended actions, their autonomy is undermined.

Some illustrations of dark patterns are subscription traps, interface interference (manipulation of information presentation), bait and switch tactics, false urgency (only 25 products are left), basket sneaking ( adding items without the knowledge of the user, surreptitious addition ), forced action (imposing additional purchases), use fear or guilt to influence user actions, drip pricing (price is concealed or delayed disclosure of price).

The guidelines are issued in exercise of powers conferred by the Consumer Protection Act, 2019. These are in fact an extension of the concept of ‘unfair trade practices’.

Indian transactions are based on trust, and the dark patterns erode the trust.

Despite the guidelines, what matters is the enforcement. There should be a robust enforcement mechanism. Regulators should stay ahead of new deceptive tactics.

Nagging falls between marketing and undue interference with the user experience. It is a subjective concept. There is litigation on such areas.

Guidelines align with the global efforts to fight deceptive design practices.

OpenAI’s ouster of Sam Altman at the instance of a non-profit Board on Friday had the unpredictable effect on the AI talents the company had — they threatened to resign en masse if the Board refuses to reinstate him.

Ilaya Sutskever, the chief scientist played a bizarre role and he is the one who asked Sam to quit. Later he came into a self-correcting mode and tweeted his intention is ‘not to harm OpenAI.’

In the meanwhile, Microsoft was ready to accommodate the oustees and appointed Sam as the CEO of a new AI structure.

As we know, OpenAI being valued $90billion, has been thrown into a turmoil by its overarching non-profit Board. It seems Sutskever loves being esoteric, scared of AI attaining the god-like status. He would not like to have an unaligned AI contrary to the interests of humanity.

It is a strange case of a company where the state-of-the art technology is being hampered by some untested ideas.

The situation is pretty chaotic. Maybe, the Board imagined that there is rapid hurtling towards singularity. Could it be a locker-room mentality where rival views are expressed by colleagues?

It is a story with many twists and turns.

It is now learnt that the new Board of OpenAI has reinstated both Sam Altman and Greg Brockman in view of the supporting stand taken by the majority of existing employees. The new initial Board has Bret Taylor, Chair, Larry Summers and Adam D’Angelo. Bret Taylor is former, Salesforce co-CEO.Larry Summers, is former US Treasury Secretary. Adam is from Quora.

Though the technology AI is likely to flourish, this saga at OpenAI puts doubts in our minds about the future of OpenAI.

The race is on to achieve AGI or artificial general intelligence. There are various views among the research workers about the level of AGI achieved. Some say we are very far away from it, whereas some say that there are ‘sparks of AGI’ visible in the present-day LLMs.

Shane Legg along with other research scientists at Google’s DeepMind throw new light on the concept of AGI.

It is necessary to be clear about what we call AGI, and its attributes. These attributes could be performance, generality and autonomy.

The scientists defined AGI in nine different ways, ranging from Turing Test, Coffee Test, levels of consciousness, capabilities with regard to tasks to economic measures. Each such definition is not perfect. The present-day LLMs do pass Turing Test, but generating elegant text is not enough for AGI. It is a moot point whether machines possess awareness and consciousness. Machiness cannot do everything, e.g. making good tea.

Researchers have suggested six criteria for measuring AGI.

Capabilities : AGI must have capabilities. There should not be focus on sentience and consciousness.

Performance and Generality : Both performance and generality must be confirmed so that these systems can perform a range of tasks as well as are good at execution too.

Cognitive and Metacognitive Levels : These traits must be present. However, there should not unnecessary focus on embodiment and physical tasks.

AGI-level Tasks : The system should have this potential. It is not necessary for this trait to be deployable. Deployment has legal and social issues.

AGI – Not End-point But a Path : AGI is not an end-point. It is a path. There are different levels of AGI along the path.

Five Levels of Performance and Generality

DeepMind researchers have made a matrix to measure ‘performance’ and ‘generality’ across five levels.

At level O, there is no AI. At level 1, there is emerging AI. At level 2, there is competent system. At level 3, there is expert system. At level 4, the system is virtuoso. At level 5, we are dealing with a superhuman system that outperforms 100 per cent of humans.

At each level, the system could be narrow or general.

ChatGPT, Bard and Llama-2 are competent (level 2) in some narrow tasks — text generation, simple coding and so on. They are emerging ( level 1) in other tasks, e.g. reasoning abilities, planning and mathematical abilities. Mostly, our models represent emerging AGI till they become proficient for a broader set of tasks.

Models are rated according to their performance. On deployment, the system may not show the same level in practice.

AGI should include a broad suite of cogntive and metacognitive tasks. It is not possible to enumerate all such tasks of general intelligence. There are always some new tasks.

Autonomy and Risk

In AI systems, scientists we a seperate matrix of autonomy and risks. At level O, there is no autonomy, say for example, a driver has to drive the car all by himself/herself. At level 1 autonomy, AI is used as a tool. At level 2, AI acts as a consultant. At level 3, AI collaborates. At level 4, AI is an expert. At level 5, AI is an agent. It is fully autonomous, with no need to for human intervention.

Depending on the level of autonomy, we assign risks to the system. DeepMind , thus, has created a framework for AGI.

At intervals of a few decades, the world witnessses a technological wave. When such a wave comes, a few come out to praise it handsomely. The rest are worried about what the whole thing is. Later, the wave gathers force. Thinkers in the society get worried about its impact. Some time later, the benefits are seen. We try to adapt to the changes to avail of the benefits.

The first such wave was the Industrial Revolution at Manchester, England. It was in the 1750s. Its effects were felt by world for the next 100 years. It brought about the machine age for spinning and weaving of cotton fibre and cloth. This was far speedier than the manual spinning and weaving by human beings. The machines were powered by steam engine. It was hailed as a ‘revolution’. Karl Marx and Engels, the two German visitors thought this was the worst thing that happened to the mankind. They organised a movement against it called communism which has still survived.

Industrial Revolution made cotton affordable to all and sundry, and not for only the nobles. In fact, Gandhi’s freedom movement to evict the British is rooted in this revolution.

The Industrial Revolution was followed by chemical (industrial) revolution. First substance that fascinated us was synthetic indigo blue. Later, we were fascinated by the clothes dyed in attractive colours. Later chemistry was used to produce synthetic fibres such as nylon and polyester. It made clothing attractive, affordable and crease-free.

In Bombay, there were some 80 cotton textile mills. Their cotton became unmarketable. It became difficult to pay wages to the workers. They went on a strike. Ultimately, it resulted into the closure of the textile mills, and rendered their 1.5 lac workers jobless. Mill closure can be attributed to chemical revolution, and not to labour movement as some do it.

With ChatGPT which appeared in November 2022, we are witnessing an Artificial Intelligence wave. President Biden has signed an executive order in November 2023 to regulate it. PM Sunak held an AI Summit at UK’s Buckinghamshire. Even godfathers of AI such as Geoffrey Hinton alert us about the risks of AI. AI could be weaponized in the wrong hands. There are issues of fake news and fake images. We have to think about both the oppportunities and threats. Ai is capable of rendering professional services at a fraction of a cost. The earnings and numbers of professionals is likely to shrink. The job markets will have both positive and negative effects.

We should adopt rational policies to deal with AI revolution.

In pharma marketing, there are two major segments — prescription products and over-the-counter (OTC) products. Prescription products are scheduled medicines (covered mostly by Schedule H and L of Drugs and Cosmetics Act, 1940). These medicines are prescribed by doctors to their patients. The doctors are briefed about the products by the sales staff of the pharma companies called medical representatives (MRs). These MRs are either biology or life sciences or pharma graduates trained by the companies about the pharmacology of prescription drugs. They call on the doctors (visits) to promote the prescription drugs. The more a product is prescribed, the better is its sale. This model of selling is called ethical promotion. Non-scheduled drugs such as antipyretics like acetyl salicylic acid (Aspirin), paracetamol (Crocin), external use preparations, multi-vitamins are branded, and these brands are promoted by the general media. Brand building is not an easy exercise, but once a brand gets established, it ensures steady and stable revenues. Some products are both prescription and OTC or OTX products, e.g. Liv 52 as a liver tonic is promoted among public, and is also promoted through doctors.

Mankind and Piramals have focused on OTC brands.

OTC route provides direct access to consumers and makes the company aware of the needs and preferences of the market. Such understanding fosters brand loyalty. Lupin promotes Softovac, an Isabgol-based bowel regulator through OTC route.

OTC products are not price controlled, whereas prescription products are price-controlled. OTC products thus offer better margins.

OTC products do require guidelines from the regulatory authorities. There should be clarity about packaging, labelling, marketing and licensing.