The reality is that the concept of Artificial General Intelligence (AGI), which refers to highly autonomous systems that outperform humans at most economically valuable work, has evolved so much in 2023, that it can even perform leadership tasks as well. The tech world is highly divided on AI’s future potential. While Google boss Sundar Pichai has predicted that the tool will enhance professions, Stability AI CEO Emad Mostaque and OpenAI chief Sam Altman have pitched for the tech to be regulated.

Are we going to see ‘technological singularity’?

Yes, it is difficult to predict the future with certainty; with experts holding differing views on the timeline and likelihood of technology surpassing human intelligence. Proponents of the idea, such as futurist Ray Kurzweil, believe in the concept of a technological singularity, a hypothetical point in the future when AI and technology become so advanced that they surpass human intelligence.

In a 2005 non-fiction book, Kurzweil described his law of accelerating returns, where there will be a massive increase in technologies like computers, genetics, nanotechnology, robotics and artificial intelligence. Once the singularity has been reached, machine intelligence will be infinitely more powerful than human intelligence. Is ChatGPT the first step towards the fulfilment of the ‘technological singularity’? No one can answer that, at least in 2023.

However, experts are also cautioning against making definitive predictions about when or if this will occur. Developing AGI involves complex challenges, including understanding and replicating human-level intelligence, achieving common-sense reasoning, and most importantly, navigating through the tricky alleys of ethical aspects of the tech. In short, developing AI tools involve a significant amount of technical, philosophical, and societal obstacles to overcome.

Also, let’s not forget another reality. While technology has made remarkable advancements in specific domains such as chess and language translation, human intelligence encompasses a wide range of capabilities, including creativity, emotional intelligence, and intuition, which are yet to be fully replicated in machines and will take a few decades more (to be realistic), if we are talking about the timeline of a ‘technological singularity’ kind of scenario arriving.

What about a symbiotic relationship?

In all likelihood, we will witness a symbiotic relationship between humans and technology, where AI and advanced technologies augment human intelligence rather than replace it entirely. This perspective aligns with the concept of Artificial Narrow Intelligence (ANI), a specific type of artificial intelligence in which a learning algorithm is designed to perform a single task, and any knowledge gained from performing that task will not automatically be applied to other tasks. We already have instances in the banking sector where AI and robotics have been deployed to do tasks like processing huge data sets in a very short span, thus spotting fraudulent information in a matter of seconds, using intelligent algorithms to find anomalies from a vast amount of data, an activity which would have been unfathomable for a human agent.

This is helping the banks to reduce operational costs, increase productivity and take quick decisions. A Business Insider report suggests that by 2023, banks will save $447 billion by using AI apps. These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, productivity, and reduce costs. So the sector is on the way to establishing a workable model of the symbiotic relationship between humans and technology

The trajectory of technological development and its impact on human intelligence will depend on various factors, including societal choices, regulatory frameworks, ethical considerations, and ongoing research and development efforts.

Also, before jumping the gun, one needs to consider some additional points regarding the potential relationship between technology and human intelligence.

One of them is ‘Enhanced Cognitive Abilities.’ Technology can enhance human cognitive abilities. Wearable devices, brain-computer interfaces, and neurofeedback systems can improve our memory, attention, and learning capabilities. Such technologies can act as cognitive tools that amplify human intelligence and help individuals perform tasks more efficiently.

Recently, the University of Waterloo came up with ‘Companion Robots’, which will help people with dementia. These devices will have episodic memories of their own, powered by object-detection algorithms, thus helping the robots to detect, track and keep a memory log of specific objects in its camera view through stored video. Here, you have a perfect example where technology is symbiotic towards humans with medical conditions.

Collaborative intelligence: The road ahead?

Rather than technology outpacing human intelligence, a more possible way ahead will be the development of collaborative intelligence. It will be a multi-agent, distributed system where each agent, human/machine, will be autonomously contributing to a problem-solving network, with each contributing with their unique strengths. Machines will process vast amounts of data, analyze patterns, and perform repetitive tasks, and humans will provide creativity, critical thinking, and ethical decision-making.

Also, we need to address the ethical questions, when it comes to technology’s potential impact on human society, especially risks like job displacement, inequality, privacy, and algorithmic bias. Striking a balance between technological progress and ethical values is essential to ensure that human intelligence is respected and protected. With systems becoming more autonomous and complex, there is a need for robust oversight, accountability, and safeguards against unintended consequences. Close attention must be paid to AI safety, control, and the potential for misuse.

One solution can be in the form of ensuring that the technology becomes human-centric, and considers the needs, values, and well-being of individuals, apart from empowering and enhancing human intelligence. Remember that human intelligence, apart from having cognitive abilities, is also guided by elements like consciousness, subjective experience, emotions, and social interactions. These aspects are currently not fully understood or replicated by machines. Neither will they be able to encompass the entirety of human intelligence.

The impact of technology influences various aspects of society, including education, healthcare, governance, and communication. Integrating technology in a way that fosters equitable access, empowers individuals, and addresses societal challenges can lead to a more inclusive and intelligent society. So in the long run, we will see a collaborative relationship between humans and machines. Technology will augment human intelligence, address societal issues, and amplify human potential.

Striking a balance crucial here

Finding a balance between technological progress, ethical considerations, and human-centric design will be the key, to realise the dream of a future where technology and human intelligence will coexist harmoniously. Remember, human intelligence is deeply rooted in contextual understanding and the ability to make sense of complex and ambiguous situations. While machines can process vast amounts of data, they often struggle with understanding nuances, cultural context, and abstract concepts that humans effortlessly grasp, and it’s a proven fact.

Human intelligence encompasses creative and innovative thinking, which involves generating novel ideas, making connections between ‘unrelated concepts’, and thinking outside the box. While machines can assist in generating possibilities and performing certain creative tasks, their thought process lacks the elements called ‘originality’ and ‘ingenuity’. Neither do they have the adaptability and the capacity of learning new skills and knowledge (from a wide range of experiences), like their human counterparts. For meeting these scenarios, AI, robotics or any other technology requires explicit programming/training.

Programmed rules or algorithms lack human empathy

Humans possess empathy, intuition, and the ability to navigate complex social dynamics. Machines do face limitations, when it comes to understanding and responding to human emotions, building relationships, and demonstrating ethical behaviour. Things like moral reasoning and decision-making based on values and ethics, determining ethical priorities, resolving ethical dilemmas, and making value-based judgments are complex aspects of human intelligence that are challenging to replicate in machines. All the above elements come under the domain called self-awareness, which is very much alien to technology.

Also, despite technology making significant strides, there are inherent limitations to its development. Power consumption, computational constraints, data availability, and algorithmic limitations pose hurdles to achieving Artificial General Intelligence. Overcoming these limitations will require ground-breaking scientific discoveries, and most importantly, an unlimited capital flow.

Conclusion

Humans have a remarkable ability to adapt to technological advancements and incorporate them into their lives. Humans have displayed the trait of adapting and leveraging technology to enhance their own intelligence, along with fulfilling professional and personal requirements. Collaboration and co-evolution between humans and technology are more probable than technology surpassing human intelligence entirely. And expect the trend to continue in future as well.

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In order to understand what’s going on, let’s step back and examine what exactly computers are doing. We will start with today’s digital technology. At its core, the digital computer is a calculating machine. It made mathematical calculations inexpensive to perform and had a tremendous impact on society. Advances in both hardware and software have enabled all types of computing to be applied to products and services. Today’s days, cars, dishwashers, and boilers are all equipped with some kind of computer, and that’s before we even get to smartphones and the internet. Without computers, we would never have reached the moon or put satellites into orbit.

These computers use binary signals (the famous ones and zeros of the code) measured in bits or bytes. The more complicated the code, the more computing power is required and the longer it takes to process. This means that for all their advancements, from self-driving cars to defeating grandmasters at Chess and Go, there are still tasks that traditional computing devices struggle with, even when the task is spread across millions of machines.

A particular problem they struggle with is a category of computation called combinatorics. These calculations are about finding an arrangement of elements that optimizes a specific goal. As the number of items increases, the number of possible arrangements grows exponentially.

In order to find the best arrangement, today’s digital computers basically have to go through each permutation to find a result and then figure out which one achieves the goal best. In many cases, this can require an enormous amount of calculations (for example, think about cracking passwords). The challenge of combinatorial computation, as you will see shortly, applies to many important fields, from finance to pharmaceuticals. It also represents a critical bottleneck in the development of artificial intelligence, and this is where quantum computing comes in. Just as traditional computers have reduced the cost of arithmetic, quantum computers represent a similar cost reduction in computing massive combinatorial problems.

The value of quantum

Quantum computers (and quantum software) are based on a completely different model of how the world works. In classical physics, an object exists in a well-defined state. In the world of quantum mechanics, objects do not appear in a well-defined state until the user observes them. Before observation, the states of two objects and their relationship are a matter of probability. Computationally, this means that data is recorded and stored in a different way by non-binary information qubits than by binary bits, reflecting the multitude of states in the quantum world. This variety can allow faster and cheaper computation of combinatorial arithmetic.

It has been noted that even particle physicists have difficulty becoming familiar with quantum mechanics and the many extraordinary properties of the subatomic world it describes. However, it is said that quantum mechanics can explain many aspects of the natural world better than classical physics and that it takes into account almost all the theories that classical physics has produced. Quantum, in the world of commercial computing, means machines and software that can, in principle, do many of the things that traditional digital computers can do, plus one great thing that traditional or classic computers can’t: perform combinatorial computations quickly.

In a paper called ‘Commercial Applications of Quantum Computing’, experts described, this is going to be a big deal in a few important areas. In some cases, it is already known that the importance of combinatorics is central to the subject. The paper has described various aspects that go with quantum which includes chemical and biological engineering sciences, cybersecurity, artificial intelligence, financial services, and complex manufacturing.

Chemical and biological engineering is about the discovery and manipulation of molecules. This requires the movement and interaction of subatomic particles. In other words, it’s quantum mechanics. The simulation of quantum mechanics was a central motivation in Richard Feynman’s original proposal to build a quantum computer. The more complex the molecules become, the greater the number of possible configurations. The result is a combinatorial calculation that is suitable for a quantum computer. For example, programmable quantum computers have already demonstrated successful simulations of simple chemical reactions, paving the way for increasingly complex chemistry simulations in the near future. With the increasing feasibility of quantum simulations, which help predict the properties of new molecules, engineers will be able to account for molecular configurations that would otherwise be difficult to model. This capability means that quantum computing will play an important role in accelerating current efforts in materials research and drug development.

Another aspect is cybersecurity in quantum computing. Combinatorics has been central to encryption for over a thousand years. Al-Khalil’s 8th-century book of cryptographic messages dealt with permutations and combinations of words. Today’s encryptions are still based on combinatorics, which underlines the assumption that combinatorial calculations are fundamentally unmanageable. However, with quantum computers, cracking encryption becomes much easier, which poses a threat to data security. A new industry is emerging to help organizations prepare for upcoming cybersecurity vulnerabilities.

As more and more people turn to the potential of quantum computing, applications are emerging that go beyond quantum simulation and encryption. Quantum computing potentially opens up new possibilities in artificial intelligence, which often involves the combinatorial processing of very large amounts of data to make better predictions and decisions (think facial recognition or fraud detection). A growing field of research in quantum machine learning is identifying ways in which quantum algorithms can enable faster AI. Due to the current limitations of technology and software, the possibility of quantum artificial general intelligence is slim, but it certainly makes thinking machines more than just a science fiction topic.

Another area where quantum computing can potentially open up is Financial services. Finance was one of the first areas to use big data. And much of the science behind the pricing of complex assets like stock options is based on combinatorial calculations. For example, when Goldman Sachs prices derivatives, it applies a very computationally intensive calculation called Monte Carlo simulation, which creates forecasts based on simulated market movements. Computational speed has long been a source of advantage in financial markets (where hedge funds compete for millisecond advantages in obtaining price information). Quantum algorithms can increase the speed of important financial calculations.

Also, with quantum computing, large manufacturing datasets on operational failures can be captured and turned into combinatorial challenges that are combined with a quantum-inspired algorithm and can determine which part of a complex manufacturing process contributed to product failures. For products like microchips, where this production process can involve thousands of steps, quantum technology can help reduce costly failures.

Quantum computing’s ability to solve large-scale combinatorial problems faster and cheaper has led to billions of dollars in investment in recent years. Perhaps the biggest opportunity is finding more new applications that benefit from the solutions offered by Quanta. As professor and entrepreneur Alan Aspuru-Guzik said, “Imagination, intuition and adventure come into play. Maybe it’s not about how many qubits we have; Maybe it’s about how many hackers we have.”

The quantum race is underway

Governments around the world have allocated more than $25 billion to quantum research and development. Tech giants like IBM, Google, Alibaba, Microsoft, Amazon and others are competing to mainstream quantum computing as an everyday tool for businesses. For example, IBM announced its plans to build a 1,000-qubit quantum computer by 2024, a first in the tech industry, and broke new ground in November 2022 with the unveiling of Eagle, a cutting-edge processor that appears to be the most remarkable of quantum computing. The processor developed by IBM could thus embark on a remarkable new path in IT. The developers assembled a 54-qubit Sycamore processor and demonstrated its quantum quality by performing the task of generating an irregular number in 200 seconds, which would take the supercomputer 10,000 years to complete.

The company also unveiled its latest 72-qubit quantum computer, the Bristlecone. Alibaba’s cloud management service provider Aliyun and the Chinese Academy of Sciences have jointly developed an 11-qubit quantum program available to the general public on its quantum computing cloud platform. Not only big technology companies, but also well-funded startups have developed the quantum computing room to develop hardware, algorithms and security applications. Some of these are Rigetti, Xanadu, 1Qbit, IonQ, ISARA, Q-CTRL and QxBranch.

One of the main goals that companies are currently advancing towards is the supposed quantum incomparability, when a quantum computer applies the estimate that no conventional computer can function in a reasonable time frame. In October 2019, Google also claimed it had achieved unprecedented quantum quality, but this case has been disputed. Some experts, including Intel’s head of quantum hardware, Jim Clarke, believe the ultimate goal should be quantum practicality, he told IEEE, alluding to the moment when quantum computers can truly achieve something new and unique. Additionally, researchers accept that quantum computing will gradually enter the enterprise space and do things that would have been generally unthinkable.

An ocean of opportunities for enterprises

The varied analysis and advances in quantum computing by major technology companies and other organizations are opening an ocean of open doors for CIOs and IT offices to transform the innovation into the current reality. As Prashanth Kaddi, Partner at Deloitte India, notes, quantum computing is undeniably suited to handling complex optimizations and searching unstructured data quickly. They can bring about potentially problematic changes in all areas, including research, drug discovery, the distributed branch network and traffic flow, energy optimization, and more. In addition, quantum computing significantly reduces time to market and helps improve customer satisfaction.

For example, a drug organization may significantly limit the ability to introduce new drugs. In finance, it could enable faster and more complicated Monte Carlo stimuli such as trading, price optimization, market instability, value appreciation techniques, and more. Once again, JP Morgan Chase and co., together with IBM, are developing advanced philosophies for financial demonstration, including decision estimation and risk analysis. Another company, ExxonMobil, plans to work with IBM Quantum to solve the strategic challenge of shipping the world’s cleanest fuel, LPG, around the world.

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NVIDIA established itself as a prominent player in the computer gaming sector, with the RIVA series of graphics processors in 1997. The business rose to prominence two years later, with the advent of the GeForce 256 GPU, which provided improved three-dimensional graphics quality.

The company battled with prominent video card maker 3dfx Interactive, pitting the GeForce against 3dfx Interactive’s popular Voodoo technologies. NVIDIA’s GeForce proved to be more powerful and later in the year 2000, the company acquired assets of 3dfx Interactive. NVIDIA was chosen by Microsoft Corporation to create graphics cards for its eagerly anticipated Xbox video game console that same year. The company received the Forbes Magazine’s 2007 Business of the Year award in recognition of its explosive growth and achievement.

NVIDIA offers a wide range of computing technologies, the Quadro series of video cards for professional graphics, the Tegra series for mobile phone processors, the nForce series for multimedia functions, and the Tesla series for high-end business and scientific-research computers. These products are in addition to those for the computer gaming industry.

NVIDIA, which is considered the best in the graphics segment to date, was co-founded by a 60-year-old, Taiwanese American electrical engineer Jensen Huang on his 30th birthday in 1993. He is currently the President and CEO of the company.

Who is Jensen Huang?
• Jensen Huang was born in Taiwan and when he was nine years old his family immigrated to the United States
• He completed his graduation degree in electrical engineering from Oregon State University in 1984 and his master’s from Stanford University in 1992
• In 1993, Jensen Huang co-founded NVIDIA with Chris Malachowsky and Curtis Priem
• His company went public in 1999, where he owned 3.6% of NVIDIA stock
• In 1999, Jensen Huang was named ‘Entrepreneur of the Year’ in ‘High Technology’ by Ernst & Young
• In 2003, he received the Dr. Morris Chang Exemplary Leadership Award, a pioneer recognition from the field of the semiconductor industry, given by the Fabless Semiconductor Association
• Jensen Huang was also a National Finalist for the EY Entrepreneur of the Year Award in 2003 and was an Award Recipient for the Northern California region in 1999
• In 2007, Forbes ranked him as the 61st highest-paid US CEO, he earned USD 24.6 million that year
• In the same year, Jensen Huang also won the Silicon Valley Education Foundation’s Pioneer Business Leader Award for his work in both the corporate and philanthropic worlds
• In 2018, he was listed in the inaugural Edge 50, naming the world’s top 50 influencers in edge computing
• In October 2019, Harvard Business Review named him the best-performing CEO in the world
• In 2020, Jensen Huang was named ‘Supplier CEO of the year’ by Eurostars AutomotiveNewsEurope
• He was included in the Time 100, Time’s annual list of the 100 most influential people in the world in 2021
• In August of that year, the Semiconductor Industry Association (SIA) announced Huang as the 2021 recipient of the industry’s highest honour, the Robert N. Noyce Award
• According to the Bloomberg Billionaires Index, as of January 2023, Jensen Huang’s net worth is USD 16.3 billion
• Jensen Huang is also known for his charities. In 2022, he donated USD 50 million to his alma mater, Oregon State University, as a part of a USD 200 million donation towards the creation of a supercomputing institute on campus
• He also gave his other alma mater Stanford University USD 30 million to build the Jen-Hsun Huang School of Engineering Center

NVIDIA’s Financials

In November 2022, NVIDIA released its financial data for Q3 of its 2022 fiscal year (FY), the net income for the quarter was USD 2.5 billion, increasing 84.4% over the same period in 2021. Sales reached a record high of USD 7.1 billion, up by 50.3%. Operating income, which NVIDIA uses as a performance indicator for each of its business sectors, also increased by 91.1% to USD 2.7 billion.

The semiconductor firm publishes a revenue breakdown for each of its five key markets, which include gaming, data centres, professional visualisation, automotive, original equipment manufacturers (OEM), and others: Gaming revenue, which made up 45% of total revenue, increased by 41.8% in the third quarter. Data centre revenue, which made up 41% of total revenue, increased by 54.5% and its professional visualisation revenue also jumped by 144.5%.

The COVID-19 pandemic has improved the financial performance of NVIDIA, with the company now facing massive demand for its gaming, data centre, and professional visualisation market platforms.

Jensen Huang stated that as more businesses adopt the technology and boost their use of hyperscale and cloud computing, the demand for its artificial intelligence solutions will grow.

NVIDIA’s Recent Developments

In February 2022, NVIDIA cancelled its agreement to acquire UK-based semiconductor firm Arm Limited, backed by SoftBank Group. The USD 40 billion arrangement, first announced in September 2020, faced significant regulatory challenges in the United Kingdom.

In January 2022, the company stated that Meta Platforms, formerly Facebook, would use its technology to create its artificial intelligence Research SuperCluster (RSC). This artificial intelligence supercomputer is expected to be the largest customer installation of NVIDIA DGX A100 systems.

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