As we know, electrons are sub-atomic particles and are considered indivisible and fundamental particles. Recent research reveals there could be split-electrons — a feature of quantum mechanics — and these mimic the behavior of half an electron. It is an important milestone in quantum computing. (Physical Review Letters, Adrew Mitchell and Sudeshna Sen, from Dublin School of Physics and IIT, Dhanbad respectively).

Already there is miniaturization of electronics. A circuit components are nanometers across. The rules here are governed by quantum mechanics. An electric current blowing through a wire is made up of lots of electrons. If the wire is made smaller and smaller, the electrons will pass one-by-one. We can make transistors which work just with a single electron.

If the nano-electric circuit is designed to give electrons the choice of two pathways, there is quantum interference — similar to that which is observed in Double-Slit Experiment.

Double-slit Experiment

The double-slit experiment demonstrates the wave like properties of quantum particles (such as an electron). This led to the development of quantum mechanics in the 1920s.

Individual electrons are fired at a screen through two tiny apertures. The place they hit is recorded on a photographic plate on the other side. Electrons can pass through either slit. Therefore, they interfere with each other. In fact, a single electron can interfere with itself. It is similar to what a wave does (as it passes through both slits at the same time).

The result is an inference pattern of alternating high and law intensity stripes on the backscreen.

The probability of finding an electron in certain places can be zero due to destructive interference. (Two waves colliding ( peaks and troughs — cancelling each other).

Majorana Fermions

A nonelectric circuit is similar — electrons going down different paths in the circuit can destructively interfere and block current from flowing. It is a phenomenon observed in quantum devices.

The new thing detected was that when electrons are forced close enough together, they strongly repel each other. The quantum interference changes. Collectively, they behave as if electron has been split into two.

The result is Majorana fermion. It is a particle first theorized by mathematicians in 1937. It has not yet been isolated experimentally.

This finding is useful in building new quantum technologies. If Majorana fermion can be created in an electric device, the device can be manipulated.

Research continues on this as these are key ingredients for proposed topological quantum computers.

Nvidia CEO states that quantum computing is still 15-30 years away. It raised misgivings about the readiness of this technology, resulting into the price decline of quantum computing stocks. The remark from the CEO is applicable to fully scalable, general-purpose quantum systems. It ignores the fact that quantum computing already delivers results today.

Quantum has not remained a preserve of physicists and futurists. It has already started helping industries to solve problems which classical systems cannot tackle. Quantum systems help in predictive analytics and decision-making. Quantum era is unfolding right now.

Quantum computing scores over the classical computing by processing data differently taking advantage of qubits and superposition and uses an entire spectrum of possibilities. Entanglement provides interconnected qubits, and changes in one affect others, no matter how far apart they are. A complex problem is tackled by quantum systems more effectively. The challenges of optimization are solved.

Quantum plus AI is a real game changer. Quantum excels at optimization whereas AI unlocks the potential even more. AI is good at pattern recognition and predictive modelling. However, AI suffers from computational bottlenecks.

Though quantum computing offers unparalleled speed and efficiency, it is only as good as the data it processes. Quantum applications require clean, structured and actionable data. Quantum requires data preparation. If businesses invest in better data pipelines, quantum’s transformative potential could be fully realized.

Quantum at present requires certain hardware conditions — cryogenic cooling nearing absolute zero. In addition, the systems are not general-purpose. They are suitable for optimization and simulations. However, the technology is evolving a rapid pace. In the meantime, there could be hybrid models of quantum-classical systems to bridge the gap.

Quantum and us are not 30 years apart. Quantum is already solving problems which classical system cannot. The quantum era has begun.

It is claimed by OpenAI that there are glimmers of AGI in its latest reasoning models. Still the different models currently available in the market such o1 from OpenAI, Anthropic from Google and Amazon and Microsoft’s model could not solve the Connections puzzle of the New York Times. The puzzle is solved by countless people everyday.

Connections refer to a word game which is deceptively simple. You are given 16 terms, and you have to figure out what terms have in common, within groups of four. The commonality could be as simple as the ‘titles of the book’ or as the words that start with ‘fire’. In fact, it is a challenging puzzle.

All the models failed to solve the puzzle despite the hype created around them.

At least o1 could get some of the groupings right but the other groupings were bizarre.

It was clear that LLMs work well while regurgitating already well-documented information but struggle while facing novel queries.

OpenAI claims that it has reached close to AGI or has achieved the start of it. Perhaps the company is keeping it wrapped, because this is not AGI manifestation at all.

We are transiting to the second quarter of the century, and by now are aware of the potential of AI. Since then, we have shifted attention from AI to AI agents. These agents will follow a pattern of evolution, passing through different levels.

The very beginning of AI agents is through Reactive Agents. These do not rely on memory or learn from their past. They follow predefined rules to respond to inputs. A basic chatbot is a Reactive Agent. Later comes Task-specialized Agents who excel in specific tasks and outperform humans in these tasks. A recommendation engine of an e-commerce site is an example. Domain experts train these systems. Next, we shall examine Context-Aware Agents. They analyze complex scenario, historical data, real-time streams and unstructured information. They adapt to all these and then respond. Neural network initiated by Geoffrey Hinton and Yam LeCun are examples. Beyond this are Socially Savvy Agents lying at the intersection of AI and emotional intelligence. They can deal with customer service. Self-reflecting Agents try to improve themselves. They are aware of the philosophical discussions about consciousness. They refine algos governing them. AGI-powered Agents are integrators and coordinators. They can handle data from multiple spheres. Last, we can envisage Superintelligent Agents who will surpass human intelligence.

Sam Altman on a personal blog writes that OpenAI knows how to build AGI and is focusing its attention now on superintelligence.

He says the current products they market are highly satisfactory but the futuristic superintelligent tools would accelerate scientific discovery well beyond the humans are capable of doing on their own. It will lead to greater abundance and prosperity.

Previously, Altman speculated that super intelligence could be ‘a few thousand days’ away. He also emphasized that its arrival would be ‘more intense than people think.’

AGI or artificial general intelligence is a hazy term, but OpenAI has formulated its own definition. AGI represents highly autonomous systems that outperform humans at most economically valuable work. Microsoft that backs up OpenAI has given its own definition of AGI. AGI systems generate at least $100 in profits. When OpenAI achieves this target, Microsoft will lose access to its technology. This is in accordance with the agreement between the two companies.

Altman has not specified which definition he has in mind. However, the former definition is the likeliest.

AI agents may soon join the workforce by working autonomously, and may materially change the output of the companies.

Progressively, they believe, in putting great tools in the hands of people.

At present, the AI technology has limitations. These hallucinate and make mistakes.

Altman is confident that these limitations can be overcome. They have also learnt that the timelines could shift.

In the next few years, they would be able to put more effective systems. It is humbling to be able to play a role in this work.

As OpenAI shifts its focus on superintelligence, it is hoped that the company would allocate sufficient resources to ensure the safety of such superintelligent systems. At present they do not have a solution for steering or controlling a potentially superintelligent AI and prevent it from going rogue (blog July 2023).

One more mode of transport could be added to urban transport soon — electric vertical take-off and landing vehicle (eVTOL). It is an aircraft which can lift off the ground like a helicopter and can land also easily. It can fly at speeds of 322 kms per hour with a range of 161 kms. The aircraft does not produce excessive noise.

Private companies in the US have raised billions of dollars in funding and are working hard to convert their dream of flying taxis into reality. Some of these companies are being backed by Big Tech such as Google and Boeing. Some have tied up with existing airlines.

They are working closely with regulatory authorities. They have created a new aircraft category called ‘powered lift’. Such a lift has been introduced when helicopters were introduced for civilian use in the 1940s. There are still some regulatory hurdles to be cleared.

Dubai is most likely to be the place where eVTOLs will take commercial flight, perhaps by the end of 2025.

It is going to be like a crawl, walk and run situation. They are still crawling at present.

China is also pursuing the idea of flying cars. This could motivate the US administration to make such vehicles a priority.

Such taxis can serve the passengers at airports of New York and Los Angelas. Electric taxis can fly four passengers from say New York airport, to say Manhattan in about 10 minutes or less.

In the beginning, they could charge a higher price. They would be costlier than the cab rides. The difference between air taxi and cab ride could narrow over time, as they would be able to transport a higher volume of passengers than ground vehicles stuck in city traffic.

Surely, eVTOLs are going to transform the way we move. It is better to see the world from the air than being stuck in the traffic on the roads.

AGI is not just one new tool. It is a new phase of civilization. OpenAI is introducing progressively powerful reasoning models. These ferret out knowledge and information from a vast base of accumulated reservoir of knowledge. They have an additional ability to think further and solve complex problems. The implications of this leap have not yet fully sunk in by penetrating human consciousness. However, this has profound implications.

GPT o1 model scored 83 per cent on IMO. GPT o3 model achieved an unprecedented score of 87.5 per cent on the ARC-AGI benchmark. It measures a model’s ability to solve completely novel problems without depending on pre-trained knowledge. ARC-AGI tests conceptual reasoning and adaptive intelligence. These areas are traditionally the forte of human beings.

Till now, AI systems demonstrated narrow intelligence — writing copy, diagnosing diseases from symptoms. optimizing logistics. These have narrow limits. General intelligence is fundamentally different. It has the ability to adapt, reason and solve problems across domains.

LLMs and muti-modal models have already shown proto-AGI traits — generalization across tasks, multi-modal reasoning and adaptability. Let us call these glimmers of AGI. These capabilities are improving iteratively — better architecture, larger datasets and improved training methods.

OpenAI has redefined AGI. It is first of all an autonomous system. It secondly outperforms humans at most economically valuable work. That creates a shifting endpoint. Microsoft and OpenAI has created a linkage between AGI and profits — an AI system that generates $ 100 billion in profits.

AGI challenges a basic human trait — intelligence. It is no longer exclusive to humans. There is an issue of integrating AGI to our lives.

AGI puts us on the road to ASI — artificial superintelligence. AGI systems will become self-learning and will surpass collective human intelligence.

The goal is ambitious. There should be machines that not only think but evolve.

The emergence of AGI will not be sudden. It will unfold gradually. There will be journey from AGI to ASI.

Microsoft proposes to invest $ 3 billion in India in cloud and AI in frastructure and skilling over the next two years. This also includes the establishment of new data centers.

The company will also help train 10 million people over the next five years with AI skills.

India is emerging as a leader in AI innovation. Microsoft wants to make India AI-first.

Microsoft has 3 data center regions and the fourth will go live in 2026. The investment aims to develop a scalable AI computing ecosystem to meet the growing demands of India’s rapidly expanding AI startups and research community.

Indian professionals are early adopters of a new technology, and many professionals are adding AI skills to their profiles.

Nvidia has unveiled its first desktop computer — called Project DIGITS. It is a computer designed for programmers, rather than regular consumers. It costs $3000 and runs Nvidia operating system based on Linux.

Nvidia’s data center AI chips will now power PCs and laptops. Nvidia has also introduced Cosmos foundational models that generate photo-realistic video. It can be used to train robots and self-driving cars at a much lower cost than using conventional data.

Cosmos will be made available on open license, similar to Meta’s Llama3 language models. Nvidia’s Cosmos will do for the world of robotics and industrial AI what Llama3 has done for enterprise AI.

Nvidia also unveils its gaming chips that use its Blackwell AI technology. Nvidia calls them RTX 50 series. It will give video games movie-like graphics.

The new chips will help the game developers to generate more accurate human faces.

Sam Altman writes in a blog post that his team knows ‘how to build AGI’ and is hopeful that by the end of 2025 the world is likely to witness ‘the first AGI agents.’ It will have an impact on every facet of human life — from economy to education.

The effect will be felt in every industry and every home.

AGI is highly autonomous system that outperforms humans at most economically valuable work. In his blog, Altman calls this the ‘most impactful technology in human history.’ It is both promising and disruptive. Though AGI integration will take time, AGI will ‘materially change the output of companies.’

AGI will accelerate scientific discovery and innovation. It will redefine what it means to ‘learn’ and ‘teach’. Students will have to foster critical thinking and resilience. Education must prepare students for ethical challenges posed by AGI. AI is evolving very fast. It shows accelerating pace of innovation. There should be now a culture of lifelong learning. There should be commitment to ensure the benefits of AGI are broadly shared.

Altman looks beyond AGI to the promise of superintelligence.

There is intersection of technology and education. AGI is not just a technological advance but a societal transformation. It involves engagement of educators, policy makers and citizens.

Educators should welcome AI as a partner in learning.