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David Godes remembers his first year as a Harvard Business School professor, when young graduate students started dropping out like flies. It was 2000, the dawn of the modern internet, and would-be Harvard MBA grads thought theyd be better off starting and joining nascent dot-com companies.

They didnt know it was all a bubble of historic proportions.

It was crazy, recalled Godes, whose class of more than 100 quickly shrunk to about 80 that year. Even faculty left academia to get in on the early internet frenzy. It was a FOMO thing, he said. You know, Ive got to be part of this. All my friends from undergrad are part of startups.

Investors ultimately threw too much money at risky startups like Pets.com pushing their stocks far above levels justified by their underlying businesses. Eventually it all came crashing down, with the bubble burst leading to trillions in lost market cap before the early-2000s recession.

Todays craze over generative artificial intelligence is different, Godes said. He now teaches at Johns Hopkins, and his students arent leaving for Silicon Valley any time soon. Theyve got a healthy skepticism of the emerging technology, he said. Thats just one reason why he sees excitement about AI as entirely unlike the early internet era.

Generative AI has enthralled investors to the tune of many billions of dollars over the last year. Companies that make AI hardware and software, especially the chip giant Nvidia, have seen their stocks skyrocket. Its led skeptics to warn of another tech bubble that will inevitably burst. One economist said earlier this year that the AI craze has companies even more overvalued than in the late-90s.

But to those on the side of the debate, the sense of alarm is short-sighted.

When we had the internet bubble the first time around that was hype. This is not hype, JPMorgan Chase CEO Jamie Dimon told CNBC in February. Its real.

Generative AI is the most disruptive technology since the internet, said Gil Luria, an analyst with D.A. Davidson.

But theres a skepticism about AI thats unlike the dot-com era, Godes said. Much of the political and cultural conversation about AI is doom and gloom: State-sponsored groups using it to meddle in elections, chatbots sending disturbing messages, AI making music that imitates real artists and of course, the ongoing debate over whether AI will take peoples jobs. (Its complicated.)

Its sort of more of a sense of dread than a sense of wonder, Godes said.

People were skeptical about the internet, too. But now, with the evolution of the internet as a cultural frame of reference, fears about AIs downsides are more defined. Governments across the globe, academic institutions, and even companies making AI software are studying its potential risks with a level of scrutiny that wasnt present in the 1990s.

The dot-com hype was so big that by the spring of 1999, one in 12 Americans surveyed said they were in the process of starting a business. The bubble started to form in the mid-1990s and burst in 2000. There was a massive influx of cash for internet-related tech companies as global interest in personal computers and the World Wide Web exploded. It all happened as the U.S. was experiencing its longest period of economic expansion since the post-World War II era.

Read more: What bubbles are and why they happen

Internet companies including Priceline, Pets.com, and eToys went public, captivating investors who sent their market values to soaring heights all while ignoring their shaky business fundamentals. Banks had a lot of cash as the Fed kept printing money in 1999, and they shoved that money into those same dot-coms. That fall, the market caps of 199 internet stocks tracked by Morgan Stanley were valued at a collective $450 billion even as their actual businesses lost a combined $6.2 billion. Pets.com went bankrupt less than a year after it went public.

There were websites in the late 90s that just made no sense, Godes said. There was nothing complicated about the technology.

AI startups are different, he said, because the technology is quite complicated.

Its harder for an MBA student without technical training to put together a business plan and go out there and start [an AI] business, he said.

D.A. Davidsons Gil Luria takes issue with even using the word bubble for AI. Assets can become inflated and enter a bubble, he said, while their underlying technology goes through cycles. Like all new technology, AI may be in a hype phase. But that doesnt mean all AI-related companies values are over-inflated, Luria said.

Theres an important difference between stock rallies for AI hardware companies and those of AI software producers, Luria said. While the share prices for a handful of companies, especially Microsoft, have gotten big boosts from AI, thats because AI software actually boosted their profits unlike the websites of the dot-com boom. Todays AI software stocks are still trading reasonably within range of their historical [price] multiples, Luria said. (In other words, while Big Techs stock prices are a lot higher than they used to be, their price to earnings, sales, and free-cash-flow ratios arent radically different.) And the software those companies make will continue to boost sales for years.

But hardware is a one-time sale, he said, so the bigger disappointment could be in the hardware stocks. Luria likened the AI chipmaking giant Nvidia to Cisco Systems, a company whose products helped build the early infrastructure of the internet and whose burst came to define the dot-com era. Nvidias chips are to AI what Ciscos networking hardware was to the early internet, Luria said.

We had enough tools by 1999 and 2000. We had enough equipment and fiber and routers to support the growth of the internet for years to come, Luria said. And thats what we believe is the point in time were at now. By the end of this year, Microsoft, Amazon, Google, and the like will have enough [AI chips].

Read more: Google and Intel are challenging Nvidias AI chip dominance. It wont be easy

Cisco stock plummeted 80% between 2001 and 2002 when revenues fell short of expectations, as demand for its networking hardware sunk from record heights. Like with Cisco, Luria said, demand for AI hardware wont continue at its breakneck pace.

If investors are counting on the current growth rates for equipment hardware that supports the growth of AI to continue, he said, they may be disappointed.

He pointed to Nvidias own biggest customers making their own AI chips. Just this month, Google and Meta, two of Nvidias top five buyers, released the latest iterations of their own custom AI chips. While Metas isnt powering its AI applications just yet, Googles AI chatbot Gemini is being run on its new chip. Because Nvidias top five customers make up two-thirds of its revenues, Big Tech shifting its AI hardware in-house could seriously hurt Nvidias bottom line, Luria said.

Even among the experts who see an AI bubble forming, many say it wont end as bad as the dot-com burst. Richard Windsor, founder of the research firm Radio Free Mobile, said people are using convoluted and untested methods to justify very high valuations for [AI] companies, like they did during the dot-com era.

But, he said, the internet bubble bursting [was] worse than the AI bubble bursting will be. Thats partly because even in its immature form today, AI is capable of generating substantially greater revenues than the internet was in the 1990s and early 2000s. The internet in the 1990s was super slow, he said, and it took a long time to realize its full potential. Meanwhile, Windsor said he sees AIs full potential as ultimately limited. Even if the AI bubble bursts, what the internet became will be bigger than what AI will become in its current form, Windsor said.

Windsor said one of the reasons he sees AI models as ultimately limited is that machines cant tell the difference between causality and correlation.

Because of that, they will never really get to the point where they can be super intelligent, because they cannot reason, Windsor said.

Read more: Is Nvidia stock in a bubble that will burst? Wall Street cant make up its mind

Windsor said he doesnt know when the AI bubble will burst, but there are signs to look for, including price erosion or when the price of a product falls over time due to customer demand and competition. Windsor said he is already seeing indications of price erosion starting to take hold. Those signs include OpenAI letting people use its products without an account, which Windsor said looks like the company trying to get more users, and the search engine Perplexity AI starting to sell advertisements despite previously saying search should be free from the influence of advertising-driven models which Windsor sees as a sign its monetization hasnt gone well. He also pointed to surveys indicating large companies are wary about the deployment of generative AI, due largely to safety and security fears.

The general expectation out there in the market at the moment is artificial general intelligence is on the way, Windsor said. I respectfully disagree with that statement.

Luria sees Nvidia stock coming back to Earth in 12 to 18 months.

We may not see the top of the hype until maybe even next year, he said. But when we do, theres going to be a lot of people that are going to be very disappointed.

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AI stocks aren't like the dot-com bubble. Here's why - Quartz

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The 80's movie Terminator depicts a bleak world where a cyborg assassin is sent to the past to eliminate the mother of an unborn child who holds the key to humanity's salvation.

The frightening prospect of machines that can teach themselves and, in the worst-case scenario, machines that are more intelligent than human beings has led senior scientists to raise the alarm about the ethics of Artificial Intelligence and its potential destructive force.

For Muslims, AI raises many ethical questions about human society, the economy and, indeed, the potential for AI-inspired ijtihad, machines that tell us how to live our Islamic lives. Are scholars and orators about to go out of business as might taxi drivers and couriers in the coming technological age.

To help us understand the world of AI, we have invited Riaz Hassan onto The Thinking Muslim. Riaz is works in the field of innovation for many years and has had direct and extensive experience in the use of AI and the commercial use of ChatGPT. He has worked on using AI and robotics on one of the largest infrastructure projects in this country. He has responsibility for looking at the wider dimensions of innovations with its associated impacts on our economy.

Timestamps

00:00 - Introduction 02:40 - Should we be afraid of AI? 03:38 - What is AI? 05:53 - What differentiates AI from other technology? 07:15 - Types of AI 08:15 - What is generative AI? 10:08 - What is AGI? (Artificial General Intelligence) 11:57 - What to expect after ChatGPT? 14:13 - The issue with driverless cars 15:40 - Imam Ghazali's version of the trolley problem 18:02 - Islamic values in AI 22:08 - Dystopian features of AI 26:50 - America vs. China 29:29 - What professions are going to be replaced? 34:34 - Labour and economy 38:48 - Redefining work 44:26 - Concerns around AI 49:08 - AI in the wrong hands? 51:04 - AI in warfare 53:04 - Can AI do ijtihad? 57:33 - Can acquire consciousness? 1:01:25 - Why AI cannot replace human intelligence 1:06:10 - Tackling crises in faith 1:07:01 - Can a supercomputer become Khalifa?

Published on Sep 29, 2023

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Silicon Scholars: AI and The Muslim Ummah - IslamiCity

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Artificial intelligence (AI) has become ubiquitous, transforming industries and powering everything from facial recognition to self-driving cars. However, the dream of Artificial General Intelligence (AGI) machines with human-level intelligence and understanding remains elusive. Let's delve into the key differences between AI and AGI, particularly regarding their performance and the immense computational cost that hinders AGI development.

AI: The Specialized Powerhouse

Current AI excels in specific tasks. Deep learning algorithms trained on massive datasets can identify objects in images with superhuman accuracy, translate languages with remarkable fluency, or play games at a level surpassing even the most skilled humans. This specialization, however, comes at a cost. AI systems often struggle with tasks outside their narrowly defined domain. For example, an image recognition AI trained on cat pictures may misidentify a dog as a cat due to a lack of broader understanding.

Computationally, AI can be quite efficient. While training complex models requires significant resources, once trained, they can run on relatively inexpensive hardware. This efficiency is crucial for real-world applications where cost is a major factor.

AGI: The Elusive Generalist

AGI represents the holy grail of AI research a machine that can learn, reason, and adapt to new situations just like a human. Such an intelligence would have applications beyond our wildest dreams, revolutionizing every aspect of society. However, achieving AGI presents a significant challenge.

The human brain, with its intricate network of neurons and complex processes, is a marvel of biological engineering. Replicating this level of intelligence artificially requires immense computational power. Training AGI models on the vast amount of data needed for general knowledge would require massive computing clusters, consuming enormous amounts of energy. This not only raises practical concerns about cost but also environmental ones.

The Road Ahead

The quest for AGI continues, with researchers exploring various avenues. Neuromorphic computing, which attempts to mimic the structure and function of the brain, holds promise for more efficient learning algorithms. Additionally, advancements in hardware, such as specialized AI chips, could help reduce the computational burden.

While the development of true AGI might still be far off, the ongoing research paves the way for more powerful and versatile AI. By optimizing existing algorithms and developing new computational architectures, we can bridge the gap between specialized AI and the dream of a general artificial intelligence. This journey will require innovation not just in AI research but also in sustainable energy solutions to power these future advancements.

1Current AI vs. Non-existent AGI: By definition, there is no true AGI (Artificial General Intelligence) yet. So, in that sense, current AI excels in its specific field because AGI wouldn't have a "field" in the same way.

Specialized AI vs. Hypothetical General AGI: If an AGI ever emerges, it's unlikely to directly compete with specialized AI in their narrow domains. Here's why:

Specialization is Key: Current AI thrives because it's laser-focused on specific tasks. An AGI, with its broader intelligence, might not be as efficient for these tasks. Different Tools for Different Jobs: Imagine needing to hammer a nail. You wouldn't use a Swiss Army knife (the AGI) when a simple hammer (the specialized AI) is perfect for the job.

Outperform in Unfamiliar Situations: While a specialized AI might struggle with anything outside its training data, an AGI could potentially adapt and learn new tasks more readily. Revolutionize the Field: An AGI might not directly "beat" a specialized AI, but it could completely redefine how a task is approached, leading to even more powerful AI solutions.

DeepMind, a leading AI research lab owned by Google, is tackling a wide range of ambitious projects. Here are some highlights:

Healthcare: DeepMind Health is applying AI to medical challenges. They've collaborated with hospitals to develop algorithms for analyzing eye scans for early signs of blindness and differentiating healthy from cancerous tissues. Scientific Discovery: DeepMind's AlphaFold project has made significant strides in protein folding prediction, a critical step in understanding diseases and developing new drugs. Efficiency and Sustainability: A collaboration with Google AI led to WaveRNN, a method for improving audio call quality, even with dropped packets. Their AlphaFold project itself has the potential to accelerate discoveries in clean energy and materials science. Gaming and Robotics: DeepMind's AI agents have achieved superhuman performance in complex games like StarCraft II. Their AlphaFold project demonstrates the potential for AI-powered robotics in scientific experimentation and materials creation (Project A-Lab). AI for the Future: DeepMind's efforts extend beyond specific applications. Their Visualising AI program commissions artists to create thought-provoking pieces that challenge how we perceive AI. Additionally, their recent release of Gemma, a state-of-the-art open model, promotes responsible AI development by making research tools more accessible. These are just a few examples. DeepMind is constantly pushing the boundaries of AI research, aiming to use this technology for positive impact across various fields. You can find more details on their latest projects on their website

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AI vs. AGI: The Race for Performance, Battling the Cost? for NASDAQ:GOOG by Moshkelgosha - TradingView

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ByDanielle Williams, Arts & Sciences at Washington University in St. Louis

A roomsize computer equipped with a new type of circuitry, the Perceptron, was introduced to the world in 1958 in a brief news story buried deep in The New York Times. The story cited the U.S. Navy as saying that the Perceptron would lead to machines that will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

More than six decades later, similar claims are being made about current artificial intelligence. So, whats changed in the intervening years? In some ways, not much.

The field of artificial intelligence has been running through a boom-and-bust cycle since its early days. Now, as the field is in yet another boom, many proponents of the technology seem to have forgotten the failures of the past and the reasons for them. While optimism drives progress, its worth paying attention to the history.

The Perceptron, invented by Frank Rosenblatt, arguably laid the foundations for AI. The electronic analog computer was a learning machine designed to predict whether an image belonged in one of two categories. This revolutionary machine was filled with wires that physically connected different components together. Modern day artificial neural networks that underpin familiar AI like ChatGPT and DALL-E are software versions of the Perceptron, except with substantially more layers, nodes and connections.

Much like modern-day machine learning, if the Perceptron returned the wrong answer, it would alter its connections so that it could make a better prediction of what comes next the next time around. Familiar modern AI systems work in much the same way. Using a prediction-based format, large language models, or LLMs, are able to produce impressive long-form text-based responses and associate images with text to produce new images based on prompts. These systems get better and better as they interact more with users.

In the decade or so after Rosenblatt unveiled the Mark I Perceptron, experts like Marvin Minsky claimed that the world would have a machine with the general intelligence of an average human being by the mid- to late-1970s. But despite some success, humanlike intelligence was nowhere to be found.

It quickly became apparent that the AI systems knew nothing about their subject matter. Without the appropriate background and contextual knowledge, its nearly impossible to accurately resolve ambiguities present in everyday language a task humans perform effortlessly. The first AI winter, or period of disillusionment, hit in 1974 following the perceived failure of the Perceptron.

However, by 1980, AI was back in business, and the first official AI boom was in full swing. There were new expert systems, AIs designed to solve problems in specific areas of knowledge, that could identify objects and diagnose diseases from observable data. There were programs that could make complex inferences from simple stories, the first driverless car was ready to hit the road, and robots that could read and play music were playing for live audiences.

But it wasnt long before the same problems stifled excitement once again. In 1987, the second AI winter hit. Expert systems were failing because they couldnt handle novel information.

The 1990s changed the way experts approached problems in AI. Although the eventual thaw of the second winter didnt lead to an official boom, AI underwent substantial changes. Researchers were tackling the problem of knowledge acquisition with data-driven approaches to machine learning that changed how AI acquired knowledge.

This time also marked a return to the neural-network-style perceptron, but this version was far more complex, dynamic and, most importantly, digital. The return to the neural network, along with the invention of the web browser and an increase in computing power, made it easier to collect images, mine for data and distribute datasets for machine learning tasks.

Fast forward to today and confidence in AI progress has begun once again to echo promises made nearly 60 years ago. The term artificial general intelligence is used to describe the activities of LLMs like those powering AI chatbots like ChatGPT. Artificial general intelligence, or AGI, describes a machine that has intelligence equal to humans, meaning the machine would be self-aware, able to solve problems, learn, plan for the future and possibly be conscious.

Just as Rosenblatt thought his Perceptron was a foundation for a conscious, humanlike machine, so do some contemporary AI theorists about todays artificial neural networks. In 2023, Microsoft published a paper saying that GPT-4s performance is strikingly close to human-level performance.

But before claiming that LLMs are exhibiting human-level intelligence, it might help to reflect on the cyclical nature of AI progress. Many of the same problems that haunted earlier iterations of AI are still present today. The difference is how those problems manifest.

For example, the knowledge problem persists to this day. ChatGPT continually struggles to respond to idioms, metaphors, rhetorical questions and sarcasm unique forms of language that go beyond grammatical connections and instead require inferring the meaning of the words based on context.

Artificial neural networks can, with impressive accuracy, pick out objects in complex scenes. But give an AI a picture of a school bus lying on its side and it will very confidently say its a snowplow 97% of the time.

In fact, it turns out that AI is quite easy to fool in ways that humans would immediately identify. I think its a consideration worth taking seriously in light of how things have gone in the past.

The AI of today looks quite different than AI once did, but the problems of the past remain. As the saying goes: History may not repeat itself, but it often rhymes.

Danielle Williams, Postdoctoral Fellow in Philosophy of Science, Arts & Sciences at Washington University in St. Louis

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Previously Published on theconversation.com with Creative Commons License

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We've Been Here Before: AI Promised Humanlike Machines In 1958 - The Good Men Project

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After comparing the billions of dollars going into AI development to crypto hype, Googles AI chief executive said Monday the company will spend over $100 billion over time to develop AI technology.

TSMC beat on Q2 sales expectations driven by AI boom, Nvidia, and Apple

Demis Hassabis, chief executive of Google DeepMind, talked about the tech giants investment into AI during a TED conference in Vancouver on Monday, where he was asked about OpenAIs and Microsofts reported plans for a U.S.-based data center referred to as Stargate, Bloomberg reported. The data center would house a supercomputer made up of millions of AI chips, and could cost up to $100 billion, The Information reported, citing unnamed sources.

We dont talk about our specific numbers, but I think were investing more than that over time, Hassabis said in response to the question about Stargate. He didnt offer further details on Googles spending plans, Bloomberg reported. Hassabis, who co-founded AI startup DeepMind in 2010 before it was acquired by Google in 2014, reportedly added that Google parent Alphabet has better computing power than its rivals, including Microsoft.

Thats one of the reasons we teamed up with Google back in 2014, is we knew that in order to get to AGI we would need a lot of compute, Hassabis said. Artificial general intelligence (AGI) is the point at which AI reaches human-level knowledge across a range of tasks. Google had and still has the most computers, Hassabis said.

In March, Hassabis told the Financial Times that the billions of dollars being poured into AI is reminiscent of crypto hype, and is taking attention away from the phenomenal science and research behind its development.

The investment into AI brings with it a whole attendant bunch of hype and maybe some grifting, he said, comparing it to crypto and similar areas, adding that the sentiment has now spilled over into AI, which I think is a bit unfortunate.

However, Hassabis said he thinks the industry is only scratching the surface of what is possible. Were at the beginning, maybe, of a new golden era of scientific discovery, a new Renaissance, he said.

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Google will spend more than $100 billion on AI, exec says - Quartz

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