Big data, deep learning, AGI (2005–2017)
In the first decades of the 21st century, access to large amounts of data (known as “big data“), cheaper and faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy. A turning point was the success of deep learning around 2012 which improved the performance of machine learning on many tasks, including image and video processing, text analysis, and speech recognition.[262] Investment in AI increased along with its capabilities, and by 2016, the market for AI-related products, hardware, and software reached more than $8 billion, and the New York Times reported that interest in AI had reached a “frenzy”.[263]
In 2002, Ben Goertzel and others became concerned that AI had largely abandoned its original goal of producing versatile, fully intelligent machines, and argued in favor of more direct research into artificial general intelligence. By the mid-2010s several companies and institutions had been founded to pursue Artificial General Intelligence (AGI), such as OpenAI and Google’s DeepMind. During the same period, new insights into superintelligence raised concerns that AI was an existential threat. The risks and unintended consequences of AI technology became an area of serious academic research after 2016.
Big data and big machines
See also: List of datasets for machine-learning research
The success of machine learning in the 2000s depended on the availability of vast amounts of training data and faster computers.[264] Russell and Norvig wrote that the “improvement in performance obtained by increasing the size of the data set by two or three orders of magnitude outweighs any improvement that can be made by tweaking the algorithm.”[206] Geoffrey Hinton recalled that back in the 90s, the problem was that “our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow.”[265] This was no longer true by 2010.
The most useful data in the 2000s came from curated, labeled data sets created specifically for machine learning and AI. In 2007, a group at UMass Amherst released Labeled Faces in the Wild, an annotated set of images of faces that was widely used to train and test face recognition systems for the next several decades.[266] Fei-Fei Li developed ImageNet, a database of three million images captioned by volunteers using the Amazon Mechanical Turk. Released in 2009, it was a useful body of training data and a benchmark for testing for the next generation of image processing systems.[267][206] Google released word2vec in 2013 as an open source resource. It used large amounts of data text scraped from the internet and word embedding to create a numeric vector to represent each word. Users were surprised at how well it was able to capture word meanings, for example, ordinary vector addition would give equivalences like China + River = Yangtze, London-England+France = Paris.[268] This database in particular would be essential for the development of large language models in the late 2010s.
The explosive growth of the internet gave machine learning programs access to billions of pages of text and images that could be scraped. And, for specific problems, large privately held databases contained the relevant data. McKinsey Global Institute reported that “by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data”.[269] This collection of information was known in the 2000s as big data.
In a Jeopardy! exhibition match in February 2011, IBM‘s question answering system Watson defeated the two best Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[270] Watson’s expertise would have been impossible without the information available on the internet.[206]
Deep learning
Main article: Deep learning
In 2012, AlexNet, a deep learning model,[am] developed by Alex Krizhevsky, won the ImageNet Large Scale Visual Recognition Challenge, with significantly fewer errors than the second-place winner.[272][206] Krizhevsky worked with Geoffrey Hinton at the University of Toronto.[an] This was a turning point in machine learning: over the next few years dozens of other approaches to image recognition were abandoned in favor of deep learning.[264]
Deep learning uses a multi-layer perceptron. Although this architecture has been known since the 60s, getting it to work requires powerful hardware and large amounts of training data.[273] Before these became available, improving performance of image processing systems required hand-crafted ad hoc features that were difficult to implement.[273] Deep learning was simpler and more general.[ao]
Deep learning was applied to dozens of problems over the next few years (such as speech recognition, machine translation, medical diagnosis, and game playing). In every case it showed enormous gains in performance.[264] Investment and interest in AI boomed as a result.[264]
The alignment problem
It became fashionable in the 2000s to begin talking about the future of AI again and several popular books considered the possibility of superintelligent machines and what they might mean for human society. Some of this was optimistic (such as Ray Kurzweil‘s The Singularity is Near), but others warned that a sufficiently powerful AI was existential threat to humanity, such as Nick Bostrom and Eliezer Yudkowsky.[274] The topic became widely covered in the press and many leading intellectuals and politicians commented on the issue.
AI programs in the 21st century are defined by their goals – the specific measures that they are designed to optimize. Nick Bostrom‘s influential 2005 book Superintelligence argued that, if one isn’t careful about defining these goals, the machine may cause harm to humanity in the process of achieving a goal. Stuart J. Russell used the example of an intelligent robot that kills its owner to prevent it from being unplugged, reasoning “you can’t fetch the coffee if you’re dead”.[275] (This problem is known by the technical term “instrumental convergence“.) The solution is to align the machine’s goal function with the goals of its owner and humanity in general. Thus, the problem of mitigating the risks and unintended consequences of AI became known as “the value alignment problem” or AI alignment.[276]
At the same time, machine learning systems had begun to have disturbing unintended consequences. Cathy O’Neil explained how statistical algorithms had been among the causes of the 2008 economic crash,[277] Julia Angwin of ProPublica argued that the COMPAS system used by the criminal justice system exhibited racial bias under some measures,[278][ap] others showed that many machine learning systems exhibited some form of racial bias,[280] and there were many other examples of dangerous outcomes that had resulted from machine learning systems.[aq]
In 2016, the election of Donald Trump and the controversy over the COMPAS system illuminated several problems with the current technological infrastructure, including misinformation, social media algorithms designed to maximize engagement, the misuse of personal data and the trustworthiness of predictive models.[281] Issues of fairness and unintended consequences became significantly more popular at AI conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The value alignment problem became a serious field of academic study.[282][ar]
Artificial general intelligence research
In the early 2000s, several researchers became concerned that mainstream AI was too focused on “measurable performance in specific applications”[284] (known as “narrow AI“) and had abandoned AI’s original goal of creating versatile, fully intelligent machines. An early critic was Nils Nilsson in 1995, and similar opinions were published by AI elder statesmen John McCarthy, Marvin Minsky, and Patrick Winston in 2007–2009. Minsky organized a symposium on “human-level AI” in 2004.[284] Ben Goertzel adopted the term “artificial general intelligence” for the new sub-field, founding a journal and holding conferences beginning in 2008.[285] The new field grew rapidly, buoyed by the continuing success of artificial neural networks and the hope that it was the key to AGI.
Several competing companies, laboratories and foundations were founded to develop AGI in the 2010s. DeepMind was founded in 2010 by three English scientists, Demis Hassabis, Shane Legg and Mustafa Suleyman, with funding from Peter Thiel and later Elon Musk. The founders and financiers were deeply concerned about AI safety and the existential risk of AI. DeepMind’s founders had a personal connection with Yudkowsky and Musk was among those who was actively raising the alarm.[286] Hassabis was both worried about the dangers of AGI and optimistic about its power; he hoped they could “solve AI, then solve everything else.”[287] The New York Times wrote in 2023 “At the heart of this competition is a brain-stretching paradox. The people who say they are most worried about AI are among the most determined to create it and enjoy its riches. They have justified their ambition with their strong belief that they alone can keep AI from endangering Earth.”[286]
In 2012, Geoffrey Hinton (who been leading neural network research since the 80s) was approached by Baidu, which wanted to hire him and all his students for an enormous sum. Hinton decided to hold an auction and, at a Lake Tahoe AI conference, they sold themselves to Google for a price of $44 million. Hassabis took notice and sold DeepMind to Google in 2014, on the condition that it would not accept military contracts and would be overseen by an ethics board.[286]
Larry Page of Google, unlike Musk and Hassabis, was an optimist about the future of AI. Musk and Paige became embroiled in an argument about the risk of AGI at Musk’s 2015 birthday party. They had been friends for decades but stopped speaking to each other shortly afterwards. Musk attended the one and only meeting of the DeepMind’s ethics board, where it became clear that Google was uninterested in mitigating the harm of AGI. Frustrated by his lack of influence he founded OpenAI in 2015, enlisting Sam Altman to run it and hiring top scientists. OpenAI began as a non-profit, “free from the economic incentives that were driving Google and other corporations.”[286] Musk became frustrated again and left the company in 2018. OpenAI turned to Microsoft for continued financial support and Altman and OpenAI formed a for-profit version of the company with more than $1 billion in financing.[286]
In 2021, Dario Amodei and 14 other scientists left OpenAI over concerns that the company was putting profits above safety. They formed Anthropic, which soon had $6 billion in financing from Microsoft and Google.[286]
