The Future Of Artificial Intelligence

Uploaded on 2023-05-29 in TECHNOLOGY-Key Areas-Artificial Intelligence, Education & Research

The Future Of Artificial Intelligence

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With Artificial Intelligence (AI) Revolution is happening, and companies and society must prepare to adapt to this change. We can expect a significant increase in work and systems improvements using different versions of AI, but also potentially serious humanity problems.

AI’s influence on technology is due in part because of how it impacts computing. Through AI, computers have the ability to harness massive amounts of data and use their trained intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans.

Indeed, AI is shaping the future of humanity across nearly every industry. It is already the main driver of emerging technologies like big data, robotics and IoT and it will continue to act as a technological innovator for the foreseeable future.

Many experts believe AI will make some workers, including doctors, lawyers and computer programmers, more productive than ever, but it is also thought that AI will replace a lot of current work as earlier Industrial Revolutions have done. Roughly 44% of companies are looking to make serious investments in AI and integrate it into their businesses. And of the 9,130 patents received by IBM inventors in 2021, 2,300 of them were AI related.

In the games market AI programs have made a mark and have beaten humans at chess and in the more complicated game of Go.

Programs such as ChatGPT can weave exciting stories and answer complex questions. Training a large network can take months on powerful servers with hundreds of thousands of processors.

Generative AI can already answer questions, write poetry, generate computer code and carry on conversations. As “chatbot” suggests, they are first being rolled out in conversational formats like ChatGPT and Bing.

Background

Although it is difficult to pinpoint the roots of AI it can probably be traced back to the 1940s, specifically 1942, when the American Science Fiction writer Isaac Asimov published his short story Runaround. The plot of Runaround, a story about a robot developed by the engineers Gregory Powell and Mike Donavan, evolves around the Three Laws of Robotics:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given to it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Asimov’s work inspired generations of scientists in the field of robotics, AI, and computer science, among others the American cognitive scientist Marvin Minsky, who later co-founded the MIT AI laboratory. At roughly the same time, but over 3,000 miles away, the English mathematician Alan Turing worked on much less fictional issues and developed a code breaking machine called The Bombe for the British government, with the purpose of deciphering the Enigma code used by the German army in the Second World War.

The Bombe, which was about 7 by 6 by 2 feet large and had a weight of about a ton, is generally considered the first working electro-mechanical computer.

The powerful way in which The Bombe was able to break the Enigma code, a task previously impossible to even the best human mathematicians, made Turing wonder about the intelligence of such machines. In 1950, he published his seminal article “Computing Machinery and Intelligence” where he described how to create intelligent machines and in particular how to test their intelligence. This Turing Test is still considered today as a benchmark to identify intelligence of an artificial system: if a human is interacting with another human and a machine and unable to distinguish the machine from the human, then the machine is said to be intelligent.

The word Artificial Intelligence was then officially coined about six years later, when in 1956 Marvin Minsky and John McCarthy (a computer scientist at Stanford) hosted the approximately eight-week-long Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) at Dartmouth College in New Hampshire. This workshop, which marks the beginning of the AI Spring and was funded by the Rockefeller Foundation, reunited those who would later be considered as the founding fathers of AI. Participants included the computer scientist Nathaniel Rochester, who later designed the IBM 701, the first commercial scientific com- puter, and mathematician Claude Shannon, who founded information theory.

The objective of DSRPAI was to reunite researchers from various fields in order to create a new research area aimed at building machines able to simulate human intelligence. The study of neural networks dominated the history of artificial intelligence from the 1950s to the 1970s; machine learning applications began to emerge in the next three decades, from the 1980s to the 2010s.

Machine learning has given birth to the more nuanced idea of Deep Learning due to constant study, increased interest, and broad application.

Additionally, with new chapters opening up every year, the initial research into AI's leap into the unknown has evolved into more of a leap of faith.

Predictions

One of the most promising new technologies is neuromorphic processing. Neuromorphic means "like the brain." Dedicated circuits are used to mimic the way dynamic cells in the brain operate. They do not run any programs but are capable of learning, and just like actual brain cells, they all work simultaneously rather than sequentially.
Experts predict that AI will be even more intelligent and capable by 2050. Many believe that AI systems will be able to perform tasks that are currently only possible for humans, such as creative tasks like writing novels and composing music.

There is a certain set of ideas that humans can computationally explore and there’s a much bigger set of ideas that humans with computers, plus AI, can successfully tackle. However, some experts predict that AI will become so advanced that it will surpass human intelligence, leading to a technological singularity. “AI is more dangerous than, say, mismanaged aircraft design or production maintenance or bad car production, in the sense that it is, it has the potential, however small one may regard that probability, but it is non-trivial, it has the potential of civilisation destruction,” Musk said in an interview.

Even Geoffrey Hinton, a man widely seen as the godfather of AI, has quit his job warning about the growing dangers from developments in the field.

The 75-year-old announced his resignation from Google in a statement to the New York Times, saying he now regretted his work and he told the BBC some of the dangers of AI chatbots that were "quite scary".
Advanced AI could pose a threat to "kill everyone" and there would be nothing humans could do to prevent it if it’s not regulated like nuclear weapons, experts warn. According to Oxford University experts Michael Osborne, professor of machine learning at Oxford University, and researcher in engineering science Michael Cohen, AI can eliminate humanity when it eventually becomes more intelligent than us.

Warning of a “literal arms race” among nation states and tech firms, Professor Michael Osborne called for global regulation to prevent AI from posing an actual threat to humanity when used for military purposes.

AI neural networks are systems that are similar to the human brain in the way they learn and process information. They enable AI to learn from experience, as a person would. This is called deep learning. The British-Canadian cognitive psychologist and computer scientist has said that chatbots could soon overtake the level of information that a human brain holds. There is a broader set of ideas that humans with computers can address.

AI enables an unprecedented ability to analyse enormous data sets and computationally discover complex relationships and patterns.

AI, augmenting human intelligence, is primed to transform the scientific research process, unleashing a new golden age of scientific discovery in the coming years.

AI Will Transform The Scientific Method

Currently important science, for instance clinical trials or building particle colliders, is expensive and time-consuming. In recent decades there has been considerable, well-deserved concern about scientific progress slowing down. Scientists may no longer be experiencing the golden age of discovery. With AI and machine learning (ML), we can expect to see orders of magnitude of improvement in what can be accomplished. There's a certain set of ideas that humans can computationally explore.

AI Will Become A Pillar Of Foreign Policy

We are likely to see serious government investment in AI. Already the US Secretary of Defense Lloyd J. Austin III has engaged with the importance of partnering with innovative AI technology companies to maintain and strengthen global US competitiveness. The National Security Commission on Artificial Intelligence has created detailed recommendations, concluding that the US government needs to greatly accelerate AI innovation.

There’s little doubt that AI will be imperative to the continuing economic resilience and geopolitical leadership of the United States.

AI Will Enable Next-Gen Consumer Experiences

Next-generation consumer experiences like the Metaverse and crypto currencies have garnered much attention. These experiences and others like them will be critically enabled by AI. The metaverse is inherently an AI problem because humans lack the sort of perception needed to overlay digital objects on physical contexts or to understand the range of human actions and their corresponding effects in a metaverse setting.

More and more of our life takes place at the intersection of the world of bits and the world of atoms. AI algorithms have the potential to learn much more quickly in a digital world.

These are natural catalysts for AI to bridge the feedback loops between the digital and physical realms. For instance, blockchain, cryptocurrency and distributed finance, at their core, are all about integrating frictionless capitalism into the economy. But to make this vision real, distributed applications and smart contracts will require a deeper understanding of how capital activities interact with the real world, which is an AI and ML problem.

Addressing The Climate Crisis Will Require AI

As a society we have much to do in mitigating the socioeconomic threats posed by climate change. Carbon pricing policies which are still in their infancy, are often not effective. Many promising emerging ideas require AI to be feasible. One potential new approach involves prediction markets powered by AI that can tie policy to impact, taking a holistic view of environmental information and interdependence.

This would likely be powered by digital "twin Earth" simulations that would require staggering amounts of real-time data and computation to detect nuanced trends imperceptible to human senses.

Other new technologies such as carbon dioxide sequestration cannot succeed without AI-powered risk modeling, downstream effect prediction and the ability to anticipate unintended consequences.

AI Will Enable Personalised Medicine

Personalised medicine has been an aspiration since the decoding of the human genome. But tragically it remains an aspiration. One compelling emerging application of AI involves synthesising individualised therapies for patients. Moreover, AI has the potential to one day synthesise and predict personalised treatment modalities in near real-time, no clinical trials required.

Simply put, AI is uniquely suited to construct and analyse "digital twin" rubrics of individual biology and is able to do so in the context of the communities an individual lives in.

The human body is mind-boggling in its complexity, and it is shocking how little we know about how drugs work. Without AI, it is impossible to make sense of the massive datasets from an individual’s physiology, let alone the effects on individual health outcomes from environment, lifestyle and diet. AI solutions have the potential not only to improve the state of the art in healthcare, but also to play a major role in reducing persistent health inequities.

Conclusions

The applications of AI are likely to impact critical facets of our economy and society over the coming decade.
The fact that in the near future AI systems will increasingly be part of our day-to-day lives raises the question of whether regulation is needed and, if so, in which form. And we are in the early innings of what many credible experts view as the most promising era in technology innovation and value creation for the foreseeable future.
However, when researchers look at historical patterns, they often find long gestation periods before these apparent accelerations, often three or four decades.

Interchangeable parts production enabled the massive gun manufacturing of the American Civil War, for example, but it was the culmination of four decades of development and experimentation. After that war, four more decades would pass before those manufacturing techniques matured to enable the innovations of assembly-line production. The Wright Brothers first flew in 1903, but despite the military application of World War I, it was the 1930s before aviation saw the beginnings of profitable commercial transport, and another few decades before aviation matured to the point that ordinary people could fly regularly and safely.

Moreover, the expected natural evolution towards supersonic passenger flight hardly materialised, while the technology evolved towards automation, efficiency, and safety at subsonic speeds, dramatic progress, but along other axes than the raw measure of speed. More recently, the basic technologies of the Internet began in the 1960s and 1970s, then exploded into the commercial world in the mid-1990s.

Even so, it is only in the past decade that most businesses have truly embraced networked computing as a transformation of their businesses and processes. While approximate, four decades is a useful time period to keep in mind as we evaluate the relationship of technological change to the future of work.

As the science fiction writer William Gibson famously said, “The future is already here, it’s just not evenly distributed.” Gibson’s idea profoundly links the slow evolution of mass adoption to what we see in the world today.
Rather than simply making predictions, with their inevitable bias and poor results, we can look for places in today’s world that are leading technological change and extrapolate to broader adoption.

Today’s automated warehouses likely offer a good glimpse of the future, though they will take time for widespread adoption, and likely will not be representative of all warehouses. The same can be said for today’s most automated manufacturing lines, and for the advanced production of high-value parts. Autonomous cars are already 15 years into their development cycle, but just beginning to achieve initial deployment. We can look at those initial deployments for clues about their likely adoption at scale.

Nobody yet knows for certain whether AI will allow us to increase our own intelligence, as Raymond Kurzweil from Google thinks, or whether it will eventually lead us into World War III, a concern raised by Elon Musk. However, everyone agrees that it will result in unique ethical, legal, and philosophical challenges that will need to be addressed.

For decades, ethics has dealt with the 'Trolley Problem', a thought experiment in which an imaginary person needs to choose between inactivity which leads to the death of many and activity which leads to the death of few. In a world of self-driving cars, these issues will become actual choices that machines and, by extension, their human programmers will need to make.

References:

MIT Sloan: Image: Black_Kira

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