GANs are innovative algorithms used in unsupervised ML. It is implemented by two neural networks competing with each other. It is a zero-sum game network.

GANs generate new data with the same statistics as the training set. (they can generate photographs that look authentic).

GANs consist of two main parts — the generator that generates data and the discriminator that evaluates it.

It is used in image generation, photorealistic image modification, art creation, generating realistic human faces.

These neural architectures are the foundation for NLP. It followed the 2017 paper of Vaswani et al titled Attention Is All That You Need. Transformers differ from RNNs and CNNs by avoiding recurrence and processing data in parallel, significantly reducing the training time.

Here attention mechanism is utilized to weigh influence of different words on each other. Transformers have the ability to handle data sequences without the need for sequential processing. It makes them effective for various NLP tasks. They can do translation, text summarization and sentiment analysis.

Transformers have achieved state-of-the art results in various NLP tasks. BERT is a variant. GPT is a variant. Transformer architecture consists of an encoder and decoder each composed of multiple layers of self-attention mechanism. This enables it to capture lang-range dependencies in input sequence.

The encoder processes the input sequence. The decoder generates the output sequence. This architecture does not rely on recurrent connection. It is highly parallelizable. It is more efficient.

The European Union has taken the lead to introduce an Act that regulates AI. Broadly, AI has been analyzed in terms of risks it poses. At one extreme, there is AI that poses an unacceptable risk — say AI that affects people’s rights, biometric systems, facial recognition system, social scoring, predictive policing, system that manipulates human behaviour or exploits people’s vulnerabilities. Such AI is of course, prohibited. At the other extreme, there is minimal-risk AI systems — these could remain unregulated. What is actually regulated is AI that falls in between these two extremes — it is neither in unacceptable risk category nor in minimal-risk category.

The European legislation establishes the EU AI Authority (nodal agency for implementation and enforcement of the AI Act). It has extra-territorial jurisdiction.

Securities Market

Generative AI has sneaked into the securities market. Some thirty years back, this area hardly had any use of technology. Technology entered the scene with some seriousness after the introduction of dematerialized shares. Gradually, we have reached a stage of T+O settlement cycle.

AI is transforming the securities market. The issue is that of data privacy. AI generated algorithms spread into many sectors — including securities market. Regulators have to design laws that govern these technologies.

Algo trading is carried out by automated means. SEBI suggests regulating algo trading. AI can write codes based on instructions fed to it. It bypasses an IT-trained programmer. The algo can violate the securities laws. Who is then accountable — whether the person who allowed AI to create the algo. The principle applied could be the person behind the machine. However, as AI advances, this could require a revisit.

There is robot-advisory which may take center stage in near future. It analyzes vast amounts of data points very quickly. It may require a revision of the regulatory framework.

Judicial Systems

AI can be used for dispensation of justice by integrating it with our judicial system — say resolution of traffic offences or enforcement of securities laws. There is a focus on online dispute resolution. AI can serve as an arbiter or mediator.

Pattern Recognition and Predictive Analysis

AI models can recognize patterns, and by doing so predict the future. It all depends upon the data points AI has access to, and these are coded to think. Algos are deployed to track suspicious activities. SEBI has suggested use of blockchain technology to verify information and to ensure transparency. AI can safeguard investor interest. Such technology should be used with caution. There should be strict safeguards to prevent any misuse.

As we have already observed, Microsoft has released Phi-3 mini, and is on the way to release two more Phi-3 versions. According to Microsoft, these small versions attract a wider client base as they are cost effective options.

As we know, OpenAI has been financially backed by Microsoft so that it can deploy ChatGPT infused software with generative AI to take the lead.

These days Microsoft is training a new model (internally called MAI-1). This AI language model is large enough to compete with other rival models from Google and OpenAI. Its training is being overseen by Mustafa Suleyman, who previously worked for DeepMind and Inflection ÁI.

This model being bigger will be more expensive. Microsoft is setting up a cluster of servers powered by Nvidia’s GPUs and is making available large amount of data to improve the model.

Roughly, this model will have 500 billion parameters. GPT-4 has one trillion parameters. Phi-3 mini has 3.8 billion parameters.

The exact purpose of this model has not been determined yet.

Zoom is a communication technology company. During the pandemic, it facilitated communications within and across the companies. Zoom meetings are the heart of the platform.

Apart from meetings, Zoom is becoming a collaboration platform. Here there are two components — Zoom Workplace (meetings, chats, mail, calendar, productivity, engagement tools) and Business Services (virtual agents, webinars, events, contact centers and integration of these applications). It is an omnichannel experience. AI cuts across all the offerings and functions. They have introduced Zoom AI Companion. It provides meeting summaries, synchronized chats, emails etc. It is available in various languages.

Zoom’s market share is 50 per cent for video conferencing where it competes with Google Meet and Microsoft Teams. They allow users to fire up a Zoom Meeting right from a Teams chat or join a Google Meet from Zoom. It is a differentiator for them. The users can exercise their own choices of technology.

India has two development centers of Zoom — one in Chennai and the other in Bangalore. Chennai center is for R&D. Bangalore center is for global tech support.

They intend to bring Zoom phone to India. Here the users can transfer calls across devices. They can convert a voice call into a video conference without interruption. They also intend to set up a cloud contact center. They also want to bring in an employee engagement center.

They have a varied clientele. LensKart video contact center allows the customers to come in touch with an eye specialist at the store. They are also working with pharma brands such as Glenmark. They have worked with Goa police to maintain law and order on New Year’s Eve.

Zoom Voice, their platform for video, voice and chat, is useful where internet connectivity is an issue.

While dealing with AI, we come across two concepts — generative AI and Artificial General Intelligence (AGI). Both the concepts are revolutionary enough to transform the world, but both are different.

Generative AI is used to generate content. It is akin to a parrot repeating the human language. It understands the complex patterns of language and predicts the next word so as to create content. It does not understand language like a parrot. While dealing with images, it predicts the next stroke.

A poet draws on his emotional reservoir to compose a poem. Generative AI depends on its vast database. Its writing is more mechanical than emotive. Generative AI is good at commercial work, economics and summarization. It fails to grasp complex human experiences and cannot perform those tasks for which it has not been trained.

AGI is a big theoretical leap. Here a machine goes beyond tasks. It understands and initiates cognitive abilities of a human being. It can innovate and adapt. AGI can make a machine drive a car or do a medical diagnosis. Here the human tasks are replicated by understanding the context.

AGI still remains chimerical. It does not exist right now. There is a lot of speculation about it. Some experts see AGI looming large over our shoulders. Some think is a distant dream.

There are insurmountable technical hurdles in achieving AGI. There are issues of context and generalization. AGI has to be intuitive. It should grasp how different pieces of information relate to each other. It is not just processing power that you need. You also need artificial cognition. There should be connection of different disparate ideas and experiences.

Human beings have sensory perception. They interact with the physical world. AGI will have to perceive environment. There should be recognition of things in the environment. The whole thing builds a context.

Even with little information and data, AGI must adapt to different situations. It is called transfer learning.

Current models just regurgitate information learnt. They do not go beyond their programming. There is a limitation to the capability of generative AI models.

Generative AI has no real understanding but depends on algorithms and statistics. By contrast, AGI will have to develop understanding of the world around it.

Generative AI is applied to raise productivity and generate content. AGI, as and when realized, will transform the world by autonomously working for tasks. AGI would be able to reason, learn and understand complex concepts just like humans.

Super AI refers to AI that surpasses human intelligence. It will solve complex problems beyond human capabilities. It would learn and adapt at a rate faster than human intelligence. It is still a hypothetical concept. It is the ultimate goal of AI research.

GPT-4 does not possess self-awareness or introspection abilities, which are essential components of AGI. AGI deals with consciousness and sentience. AGI is able to learn new skills and knowledge on its own just like a human (without explicit programming).

GPT-5 is likely to go as close to AGI as we have ever been. OpenAI has been showcasing demos of GPT-5 to some enterprise customers.

AGI could revolutionize many industries and solve complex problems in medicine, climate change and exploration.

As we know, quantum computing has capabilities beyond the reach of traditional computers. It will enable us to navigate the unchartered territory. It is faster than the available supercomputers in number crunching.

Quantum computing deserves to be mainstream. However, there are serious cybersecurity issues while deploying it. These affect banking, military, power grids, government systems and more.

The backbone of cybersecurity are cryptography and encryption. The existing encryption algorithms cannot withstand an assault of a quantum computer. It could break encryption algorithms in the financial systems.

It is believed that the quantum threat is not imminent, as such computers have not yet been deployed. However, the possibility of quantum computing being used is within sight. Cybersecurity must be upgraded and innovated to take care of the quantum scenarios.

There is a race for quantum supremacy. The US has released National Security Memorandum spelling out policies and initiatives to maintain the competitive edge in quantum computing. It aims to achieve quantum-resistant cryptography.

India has initiated National Quantum Mission (NQM) to boost R&D and take lead in quantum technology.

Incense sticks or agarbattis have been extensively used for religious purposes, and also for filling rooms with aroma. Beyond this, these can be used as insect repellants.

The market is dominated by unorganized players — nearly 75-80 per cent incense stick market is fragmented and is dominated by unorganized players. The challenge is to shift users from unbranded to branded use. There should be greater scrutiny of unbranded products and there should be restriction on cheap imports from China and Vietnam. There should be standardization of this market.

In organized market, there are fast moving consumer goods players (FMCG). Godrej Consumer Private Ltd (GCPL) has introduced mosquito repellant incense sticks. The brand name is Good Knight Agarbatti. Anti-mosquito agarbatti market is worth Rs.1200 crore. The total agarbatti market is Rs.10000 crore. Godrej product must have gained a market share of 5-10 per cent since its launch in December 2023.

ITC’s Mangaldeep, Zed Black from Mysore Deep Perfumery House are big players. They produce over a billion incense sticks per month. They focus on premium fragrances. They do aggressive promotion. Cycle Pure Agarbatti from Karnataka is also a big player. Some brands are lost midway — once upon a time Marath Darbar and Metro Darbar agarbattis were popular in Maharashtra.

This is a competitive market with thin margins.

Lux soap was introduced in India in1925, and so in 2025 it completes a century of its existence here. In 1929, there was a global campaign featuring more than two dozen of the biggest female stars of the day. This created a huge recall among movie-loving audiences. It also initiated the trend of brand endorsements by celebrities. It was in 1941 that Lux began using women stars from Hindi and regional film industries in its advertisements.

Lux has extended the brand to body wash products. However, it has a stare of only 9 per cent. A substantial 90 per cent share is commanded by its cleansing format i.e. Lux Soap. It is available at various price points, for as low as Rs.10 to over Rs. 200 per piece.

The Indian toilet soap market is estimated at about Rs. 25000 crore (2023). The market is growing at a CAGR of 6.8 per cent. Beauty soap segment is around 50 per cent of this market (Lux, Dove, Pears).

Over years, Lux focused on just physical beauty, but has now realized that it should celebrate every facet of femineity. In one of the ad campaigns in 2023, the female athletes were shown wearing QR codes on their bodies. On scanning, the film projected sportswomen calling cameramen to ‘Change the Angle’ of how they were portrayed. They wanted their strengths to be highlighted, rather than physical attributes. The video highlighted sexist sports coverage at international events. It ends with six best practice tips for the media on how female athletes should be portrayed.

Lux has been promoted as ‘filmi sitaron ka saundarya sabun‘. It made you look as beautiful as a film star. These days, apart from beauty, society aspires to have many other expressions of femineity. As society’s aspirations have changed, it is necessary to change the ad campaign of Lux.

These changes are subtle and innovative. In 2000, the change was from skin benefits to an emotional space. ‘Lux brings out the star in you’. Apart from beauty, Lux expanded to confidence. In 2005, Shah Rukh was used as a male protagonist admiring women’s beauty. The brand positioning changed, but within the same overall theme.

Lux could have changed much faster and much earlier. It was stuck as a beauty soap brand. Women have marched ahead of stereotypical beauty standards. Consumers resonate with other brands such as Dove portraying real-lite models. Lux should move beyond film stars ki pasand. It is time to reflect as to how the brand could remain relevant. In its centenary year, it has chosen Suhana Khan as the new brand ambassador for its body washes. The brand has begun talking to younger women.

Lux ad folklore was about favourite film stars using Lux. A female star endorsing Lux meant she has arrived. Some promoted Lux without charging anything. That was the power Lux. There were 100-star endorsements for Lux once upon a time.

Lux is now washing off the stereotypes.

We have already learnt that an LLM is trained on vast amounts of data consisting of mountains of text. It then learns to predict the next word. Each prediction requires small adjustments to improve its chances of getting the prediction right. All this training gives an LLM a statistical understanding of proper language. All this is a part of pre-training. However, an LLM fumbles when asked to crack a joke to elevate the mood. Here reinforcement learning through human feedback (RLHF) helps. OpenAI introduced this technique in March 2022. (As you know, ChatGPT was released in November 2022 eight months later.)

There are three steps in RLHF. To a given prompt, human volunteers are asked to choose two potential LLM responses. This is repeated thousands of times. The data is used to train a second LLM. It is called the reward model. It assigns higher scores to responses a human would like (and lower to everything else). In RLHF, knobs and levers are tweaked of the original LLM to help reinforce the behaviours that earn it a reward.

It takes time and is cumbersome. The same results can be achieved with less effort. It is called Direct Preference Optimization (DPO). Archit Sharma and Eric Mitchell presented DPO in December 2023.

There is an observation. For every reward model, there is a specific theoretical LLM that scores full marks. Each LLM conceals an implicit reward model. Researchers can tinker with it. LLM, instead of learning from another LLM, can learn directly from the data. Thus, the intermediary is removed. It makes the process efficient. DPO is being used extensively by leading LLMs. Facebook has integrated DPO in its model. French model Mistral uses DPO.