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#10549. What does the term in Biology Chloroplast mean?

#10550. What does the term in Biology Cholesterol mean?

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#2461. What does the medical term Premolar mean?

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#6234.

2234) David Baker (biochemist)

Gist:

Work

Proteins control and drive all the chemical reactions that together are the basis of life. Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Baker used computer-based methods, which he had developed. This created opportunities to develop an enormous diversity of new proteins. New proteins can be used in, for example, drugs, vaccines and materials and sensors.

Summary

David Baker (born 1962, Seattle, Washington, U.S.) is an American biochemist and computational biologist who developed computerized methods for the de novo (from scratch) design of proteins with entirely new functions. Baker’s work on protein structure prediction and design fueled advances in synthetic biology and in the development of novel drugs and proteins for industrial applications. He was awarded the 2024 Nobel Prize in Chemistry (shared with English computer scientist Demis Hassabis and American researcher John M. Jumper) for his breakthroughs in computational protein design.

Education and early career

Baker grew up in Seattle. As an undergraduate, he attended Harvard University, where he studied philosophy and social science until his last year, when he transitioned to biology. After earning a bachelor’s degree from Harvard in 1984, he went to the University of California, Berkeley, to study biochemistry. There he carried out research on systems of protein transport in yeast. He earned a Ph.D. in 1989 and subsequently became a postdoctoral researcher at the University of California, San Francisco, where his focus shifted to structural biology. In 1993 he joined the faculty at the University of Washington and began to more deeply investigate protein folding and the ways in which sequences of amino acids form the wide variety of protein shapes observed in cells.

Rosetta

In the early 1990s Baker’s research team employed standard approaches available for protein analysis, including mass spectrometry and nuclear magnetic resonance spectroscopy. Eventually, however, they broadened their investigations to include computer modeling, which provided critical insight into the three-dimensional shapes that result from protein folding and the relevance of these shapes to protein function. This work led to his development of Rosetta, a revolutionary computational platform for predicting and designing protein structures that is based on a Monte Carlo sampling algorithm. Rosetta allows researchers to model protein folding and to design new proteins with desired functions.

In 1998 Baker used Rosetta in a competition known as Critical Assessment of protein Structure Prediction (CASP), a biennial scientific experiment in which participants attempt to predict the structures of proteins from known amino acids sequences. Rosetta performed well in the competition, successfully predicting how a given sequence would fold. After a series of refinements, Baker’s team began using Rosetta in reverse, obtaining information on amino acid sequences based on a given protein structure. In this way, researchers gained the ability to design proteins according to desired shapes.

Baker’s major breakthrough came in 2003, when Rosetta was fed an entirely new protein struc­ture and computed a corresponding sequence consisting of 93 amino acids. The researchers verified Rosetta’s output by inserting the gene for the novel sequence into bacterial cells, which then produced the artificial protein, known as Top7. Top7 was the first protein to be designed and produced de novo. Baker subsequently released Rosetta’s code, making it available to other researchers. The technology has enabled Baker and others to create novel proteins with valuable functions, such as enzymes that specialize in breaking down toxic chemicals. Baker has also developed mini-proteins for therapeutic applications, including a mini-protein of about 56 amino acids capable of inhibiting SARS-CoV-2, the virus responsible for the COVID-19 pandemic.

Awards and honors

Baker has received various awards throughout his career, in addition to the Nobel Prize. In 2002 he was awarded the Overton Prize by the International Society for Computational Biology, and in 2004 he received the Feynman Prize in nanotechnology from the Foresight Institute (shared with Brian Kuhlman). In 2021 he was recognized with the prestigious Breakthrough Prize in Life Sciences and the following year received the Wiley Prize in Biomedical Sciences (shared with Hassabis and Jumper). He is a Howard Hughes Medical Institute investigator (2000) and a member of the National Academy of Sciences (2006).

Details

David Baker (born October 6, 1962) is an American biochemist and computational biologist who has pioneered methods to design proteins and predict their three-dimensional structures. He is the Henrietta and Aubrey Davis Endowed Professor in Biochemistry, an investigator with the Howard Hughes Medical Institute, and an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics at the University of Washington. He was awarded the shared 2024 Nobel Prize in Chemistry for his work on computational protein design.

Baker is a member of the United States National Academy of Sciences and the director of the University of Washington's Institute for Protein Design. He has co-founded more than a dozen biotechnology companies and was included in Time magazine's inaugural list of the 100 Most Influential People in health in 2024.

Biography:

Early life and education

Baker was born into a Jewish family in Seattle, Washington on October 6, 1962, the son of physicist Marshall Baker and geophysicist Marcia (née Bourgin) Baker. He graduated from Seattle's Garfield High School.

Baker received a Bachelor of Arts degree with a major in biology from Harvard University in 1984. He then joined the laboratory of Randy Schekman, where he worked primarily on protein transport and trafficking in yeast, and obtained a Doctor of Philosophy in biochemistry from the University of California, Berkeley in 1989. In 1993, he completed his postdoctoral training in biophysics with David Agard at the University of California, San Francisco.

Career

Baker joined the Department of Biochemistry at the University of Washington School of Medicine as a faculty member in 1993. He became a Howard Hughes Medical Institute investigator in 2000. Baker was elected a Fellow of the American Academy of Arts and Sciences in 2009.

Personal life

Baker is married to Hannele Ruohola-Baker, another biochemist at the University of Washington. They have two children.

Research

Although primarily known for the development of computational methods for predicting and designing the structures and functions of proteins, Baker maintains an active experimental biochemistry group. He has authored over 600 scientific papers.

Baker's group developed the Rosetta algorithm for ab initio protein structure prediction, which has been extended into a tool for protein design, a distributed computing project called Rosetta@home, and the computer game Foldit. Baker served as the director of the Rosetta Commons, a consortium of labs and researchers that develop biomolecular structure prediction and design software. His group has regularly competed in the CASP structure prediction competition, specializing in ab initio methods, including both manually assisted and automated variants of the Rosetta protocol. Using artificial intelligence, his group has developed later a newer version of the program known as RoseTTAFold.

Baker's group is also active in the field of protein design; they are noted for designing Top7, the first artificial protein with a novel fold.

In 2017, Baker's Institute for Protein Design received over $11 million from Open Philanthropy, followed by an additional $3 million donation in 2021.

In April 2019, Baker gave a TED talk titled "5 challenges we could solve by designing new proteins" at TED2019 in Vancouver, Canada.

Baker has co-founded several biotechnology companies, including Prospect Genomics which was acquired by an Eli Lilly subsidiary in 2001, Icosavax which was acquired by AstraZeneca in 2023, Sana Biotechnology, Lyell Immunotherapeutics, and Xaira Therapeutics.

Awards

For his work on protein folding, Baker has received numerous awards, including the Overton Prize (2002), the Sackler International Prize in Biophysics (2008), the Wiley Prize (2022) and the BBVA Foundation Frontiers of Knowledge Award in the category "Biology and Biomedicine" (2022).

For his work on protein design, Baker has received the Newcomb Cleveland Prize (2004),  the Feynman Prize in Nanotechnology (2004), and the Breakthrough Prize in Life Sciences (2021).

In 2024, Baker was awarded half of the Nobel Prize in Chemistry for his work on protein design; the other half went to John M. Jumper and Demis Hassabis for development of AlphaFold, a program for protein structure prediction.

2381) Niagra Falls

Gist

What makes Niagara Falls a natural wonder? Statistically speaking, Niagara Falls is recognized as having the greatest flow rate of any waterfall throughout the globe. The falls have more than six million cubic feet (168,000 cubic meters) flow over the top of the falls every minute during the peak season.

Niagara Falls is a group of three waterfalls—American, Bridal Veil, and Horseshoe Falls—located on the Niagara River at the border between Ontario, Canada, and New York, USA. Known for the immense flow of water and spectacular views, the falls have the highest flow rate of any waterfall globally. The site is a popular tourist attraction, a source of hydroelectric power, and home to Niagara Falls State Park, the oldest state park in the United States. 

The falls consist of the American Falls, the Bridal Veil Falls, and the much larger Horseshoe Falls.

Summary

Niagara Falls[a] is a group of three waterfalls at the southern end of Niagara Gorge, spanning the border between the province of Ontario in Canada and the state of New York in the United States. The largest of the three is Horseshoe Falls, which straddles the international border of the two countries. It is also known as the Canadian Falls. The smaller American Falls and Bridal Veil Falls lie within the United States. Bridal Veil Falls is separated from Horseshoe Falls by Goat Island and from American Falls by Luna Island, with both islands situated in New York.

Formed by the Niagara River, which drains Lake Erie into Lake Ontario, the combined falls have the highest flow rate of any waterfall in North America that has a vertical drop of more than 50 m (164 ft). During peak daytime tourist hours, more than 168,000 {m}^{3} (5.9 million cu ft) of water goes over the crest of the falls every minute. Horseshoe Falls is the most powerful waterfall in North America, as measured by flow rate. Niagara Falls is famed for its beauty and is a valuable source of hydroelectric power. Balancing recreational, commercial, and industrial uses has been a challenge for the stewards of the falls since the 19th century.

Niagara Falls is 27 km (17 mi) northwest of Buffalo, New York, and 69 km (43 mi) southeast of Toronto, between the twin cities of Niagara Falls, Ontario, and Niagara Falls, New York. Niagara Falls was formed when glaciers receded at the end of the Wisconsin glaciation (the last ice age), and water from the newly formed Great Lakes carved a path over and through the Niagara Escarpment en route to the Atlantic Ocean.

Details

Niagara Falls, waterfall on the Niagara River in northeastern North America, one of the continent’s most famous spectacles. The falls lie on the border between Ontario, Canada, and New York state, U.S. For many decades the falls were an attraction for honeymooners and for such stunts as walking over the falls on a tightrope or going over them in a barrel. Increasingly, however, the appeal of the site has become its beauty and uniqueness as a physical phenomenon.

The falls are in two principal parts, separated by Goat Island. The larger division, adjoining the left, or Canadian, bank, is Horseshoe Falls; its height is 188 feet (57 metres), and the length of its curving crest line is about 2,200 feet (670 metres). The American Falls, adjoining the right bank, are 190 feet (58 metres) high and 1,060 feet (320 metres) across.

The formation of the Niagara gorge (downriver) and the maintenance of the falls as a cataract depend upon peculiar geologic conditions. The rock strata from the Silurian Period (about 444 to 419 million years ago) in the Niagara gorge are nearly horizontal, dipping southward only about 20 feet per mile (almost 4 metres per km). An upper layer of hard dolomite is underlain by softer layers of shale. Water exerts hydrostatic pressure and only slowly dissolves the dolomite after infiltrating its joints. Dolomite blocks fall away as water from above infiltrates and rapidly erodes the shale at the falls itself. The disposition of the rock strata provides the conditions for keeping the water constantly falling vertically from an overhanging ledge during a long period of recession (movement upstream) of the cataract. As blocks of dolomite are undercut, they fall off and are rapidly destroyed by the falling water, further facilitating the retreat of the falls and the maintenance of a vertical cataract.

The water flowing over the falls is free of sediment, and its clearness contributes to the beauty of the cataract. In recognition of the importance of the waterfall as a great natural spectacle, the province of Ontario and the state of New York retained or acquired title to the adjacent lands and converted them into public parks.

The very large diversion of water above the falls for hydroelectric power purposes has lessened the rate of erosion. Elaborate control works upstream from the falls have maintained an even distribution of flow across both the U.S. and Canadian cataracts, thereby preserving the curtains of the waterfalls. A large part of the great river above the falls is diverted and disappears into four great tunnels for use in the power plants downstream. Because of concern over the possibility of major rockfalls, water was diverted from the American Falls in 1969, and some cementing of the bedrock was done; an extensive boring and sampling program was also carried out. River flow was returned to the American Falls in November of that year, and it was decided that safety measures for the viewing public should be implemented and that measures to stem natural processes were both too expensive and undesirable.

Excellent views of the falls are obtained from Queen Victoria Park on the Canadian side; from Prospect Point on the U.S. side at the edge of the American Falls; and from Rainbow Bridge, which spans the Niagara gorge about 1,000 feet (300 metres) downstream from Prospect Point. Visitors may cross from the U.S. shore to Goat Island by footbridge and may take an elevator to the foot of the falls and visit the Cave of the Winds behind the curtain of falling water. The Horseshoe Falls, which carry about 90 percent of the river’s discharge, receded upstream at an average rate of about 5.5 feet (1.7 metres) per year in 1842–1905. Thereafter, control works and the diversion of water decreased the erosion rate, which is presently so slow at the American Falls that large blocks of dolomite accumulate at the base of the falls, threatening to turn it into rapids.

Additional Information

Situated between the state of New York and the province of Ontario, Niagara Falls is one of the most spectacular natural wonders in North America. This series of waterfallsis on the Niagara River, which flows between the United States and Canada from Lake Erie to Lake Ontario. The falls are about 17 miles (27 kilometers) northwest of Buffalo, New York. The industrial city and tourist center of Niagara Falls, New York, is adjacent to the American side of the falls. Niagara Falls, Ontario, Canada, is across the river.

The falls are divided into two parts by Goat Island. The larger portion, on the southwest side, is the Canadian falls, known as the Horseshoe Falls. It measures 2,600 feet (790 meters) along its curve and drops 162 feet (49 meters). The smaller American Falls is northeast of Goat Island. It is 1,000 feet (305 meters) across and drops about 167 feet (51 meters). Between the American Falls and Goat Island are the small Luna Island and the small Luna, or Bridal Veil, Falls.

Just before flowing over the ledge, the American stream is only about 3 1/2 feet (1 meter) deep. The Canadian stream is about 20 feet (6 meters) deep and carries some 95 percent of the Niagara River’s water.

Every minute about 12,000,000 cubic feet (340,000 cubic meters), or 379,000 tons, of water pours in torrents over the cliffs of the falls of Niagara. As the water plunges from the brink of the falls, it fills the air with a silvery mist, which under the sunlight displays many rainbows. The plunging water also sends out a never-ending roar as it strikes the bottom. For this reason the Haudenosaunee (Iroquois) people called the falls Niagara, meaning “thunder of waters.”

The plunging water has worn the lower rocks away so that there are caves behind the sheets of water of both falls. Sightseers may enter the Cave of the Winds at the foot of the American Falls and get an unusual view. The Horseshoe Falls have carved a plunge basin 192 feet (59 meters) deep.

Both the United States and Canadian governments have built parks, viewing platforms, paths, and highways. The Niagara Reservation State Park was established in 1885 and is New York’s oldest state park. It includes an observation tower, elevators that descend into the gorge at the base of the American Falls, and boat trips into the waters at the base of the Horseshoe Falls.

The park area has long been a tourist site and a favorite spot for couples to spend their honeymoons. At night colored lights illuminate the falls.

Almost all the drainage from four of the Great Lakes pours over the crest of Niagara Falls. This tremendous volume of water is used to generate power in six hydroelectric plants. They develop a maximum of about 5 3/4 million horsepower, some 55 percent on the American side and about 45 percent on the Canadian. The plants draw water from the river above the falls through canals. Near each plant the water drops through penstocks to powerhouses on the Niagara River below the falls. There it turns turbine generators.

The control of Niagara Falls between the United States and Canada has long offered the world an example of international cooperation. A treaty in 1910 and later agreements fixed the amounts of water that could be diverted. An international Niagara Control Board was established in 1923.

In 1950 the two countries signed a new treaty that specified the minimum flow to be maintained over the falls. This treaty made possible greater hydroelectric development. It provides that 100,000 cubic feet (2,830 cubic meters) per second of water must flow over the falls during the tourist season in the daytime and 50,000 (1,415) at night and during the off-tourist season in the daytime. The remainder is equally divided between Canada and the United States. An average of 202,300 cubic feet (5,729 cubic meters) per second flows over the falls.

Between 1954 and 1958 the United States and Canada completed the Niagara Remedial Works Project. This enormous operation checked erosion with a gated control structure, excavations, and fills.

The Hydro-Electric Power Commission of Ontario completed the Sir Adam Beck-Niagara Generating Station No. 1 in 1925 and No. 2 in 1958. The combined capacity of the plants is 1,443,000 kilowatts.

In 1957 the United States Congress approved the construction of the Niagara Power Project by the Power Authority of the State of New York. It has a capacity of 2,190,000 kilowatts. The first electric current from the project was delivered in 1961.

The falls of Niagara are about 25,000 years old. The hard rock (Lockport dolomite) at the brink of the falls is much older. It was made on the bed of an inland sea in the Silurian period (about 443 million to 419 million years ago). Gradually the limy sediment hardened to stone—either limestone or dolomite, a limestone with magnesium.

Later the Niagara region was raised in a widespread uplift centered in Michigan. Streams wore down the land. The layer of tough rock, however, resisted erosion. The edge of the deposit formed a great cliff—the Niagara escarpment. It runs west from Rochester, New York, between Lakes Erie and Ontario, then swings northward through the province of Ontario. It is capped by hard Niagara limestone or Lockport dolomite.

Glaciers covered the Niagara region during the Ice Age that took place during the Pleistocene Epoch (about 2.6 million to 11,700 years ago). As the last glacier retreated, it left Lake Erie at its southern edge. Water from the lake began to spill over the Niagara escarpment into the Ontario basin below, just south of where Queenston and Lewiston now stand.

The new falls did not wear away the dolomite caprock as fast as it churned away the softer rock below. From time to time blocks of the undermined caprock broke off. The falls worked back toward Lake Erie, forming a steep-walled gorge.

Niagara’s rate of cutting has changed many times. It started slowly, for at first the river drained Lake Erie only. Lakes Superior, Michigan, and Huron had a northerly outlet. The drainage changed as glaciers retreated. Water from all four lakes then poured over the falls. When the river spread to the point where the famous whirlpool now is, it reached an ancient valley that had cut into the dolomite from the west. Later the valley filled with glacial debris. The river wore away the soft material, forming the 60-acre (24- hectare) basin.

Louis Hennepin, a priest who accompanied the French explorer René-Robert Cavelier, sieur de La Salle, was the first European to view Niagara Falls, in 1678. The site was of strategic use to the British and French in the struggle to control the Great Lakes. The British built Fort Schlosser there in 1761.

Hi,

#5737. What does the verb (used with object) exert mean?

#5738. What does the verb (used with without object) exhale mean?

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#9727.

2233) Geoffrey Hinton

Gist:

Work

When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. In 1983–1985, Geoffrey Hinton used tools from statistical physics to create the Boltzmann machine, which can learn to recognise characteristic elements in a set of data. The invention became significant, for example, for classifying and creating images.

Summary

Geoffrey Hinton (born December 6, 1947, London, England) is a British-Canadian cognitive psychologist and computer scientist known as the “godfather of AI.” He revolutionized the field of artificial intelligence with his work on neural network models. He contributed significantly to AI research with novel insights and key discoveries in the areas of backpropagation, Boltzmann machines, distributed representations, and time-delay neural networks. Although he spent the majority of his career advancing AI, Hinton became an outspoken critic of the technology in 2023 and highlighted its potential harms. He was awarded the 2024 Nobel Prize in Physics and shared the prize with American physicist John J. Hopfield.

Hinton was born into a family with a rich intellectual history. His father, Howard Everest Hinton, was a distinguished entomologist, and all three of his siblings conducted scholarly work. His family includes multiple mathematicians, among them Mary Everest Boole and her husband, George Boole, whose algebra of logic (known as Boolean logic) became the basis for modern computing. Other notable relatives include Joan Hinton, one of the few women to work on the Manhattan Project; Charles Howard Hinton, the mathematician famous for visualizing higher dimensions; and George Everest, the surveyor Mount Everest is named for.

Hinton attended the University of Cambridge, where he switched his studies between physiology, philosophy, and physics before earning a degree in experimental psychology in 1970. He then attended the University of Edinburgh, where he received a Ph.D. in AI in 1978. Although discouraged by his professors, Hinton embraced unconventional computer networks modeled after neural nodes and the structure of the human brain. He began researching systems known as neural networks and completed postdoctoral research at the University of California, San Diego.

In 1982 Hinton joined the faculty of Carnegie Mellon University, where he worked with psychologist David Rumelhart and computer scientist Ronald J. Williams to develop an algorithm to work backward from output to input when measuring error. The process, called “backpropagation,” was discussed by the trio in 1986 in an influential paper that laid the groundwork for neural network development.

Hinton left the United States for Canada in 1987, a decision fueled by disdain for the U.S. military and the Reagan administration. The majority of American AI research at the time was funded by the U.S. Department of Defense, and Hinton opposed using AI for combat. He continued his research, this time as a professor at the University of Toronto, for the next 11 years. In 1998 Hinton left Toronto to found and direct the Gatsby Computational Neuroscience Unit at University College London. While a researcher there, he studied neural networks and their applications.

Hinton returned to the University of Toronto in 2001 and continued to make advances in neural network models. His research group developed and began to apply practical means for deep-learning technology in the 2000s. In 2012 Hinton and two of his graduate students, Alex Krizhevsky and Ilya Sutskever, developed an eight-layer neural network program, which they named AlexNet, to identify images on ImageNet, a massive online dataset of images. AlexNet outperformed the next most accurate program by more than 40 percent. The trio created a company, DDNresearch, for AlexNet. In 2013 Google acquired the company for $44 million. That same year Hinton joined Google Brain, the company’s AI research team, and he was eventually named a vice president and engineering fellow.

In May 2023 Hinton quit his job at Google, because he wanted to be able to speak freely about the risks of commercial AI use. He expressed concerns particularly about its power to create fake content and its potential to upend the job market. Hinton has stated that he does not fully regret his life’s work but fears that AI will become uncontrollable in the long run.

Hinton has received extensive recognition for his role in revolutionizing AI. Among his numerous awards are the Cognitive Science Society’s first-ever David E. Rumelhart Prize (2001) and the Gerhard Herzberg Canada Gold Medal (2010), the country’s highest award for science and engineering. In 2018 Hinton was named a joint recipient of the Turing Award, often described as the “Nobel Prize of Computing,” for his breakthrough research on neural networks, and four years later he received the Royal Society’s Royal Medal for his pioneering work on deep learning.

Details

Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian computer scientist, cognitive scientist, and cognitive psychologist known for his work on artificial neural networks, which earned him the title "the Godfather of AI".

Hinton is University Professor Emeritus at the University of Toronto. From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto before publicly announcing his departure from Google in May 2023, citing concerns about the many risks of artificial intelligence (AI) technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.

With David Rumelhart and Ronald J. Williams, Hinton was co-author of a highly cited paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks, although they were not the first to propose the approach. Hinton is viewed as a leading figure in the deep learning community. The image-recognition milestone of the AlexNet designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever for the ImageNet challenge 2012 was a breakthrough in the field of computer vision.

Hinton received the 2018 Turing Award, together with Yoshua Bengio and Yann LeCun for their work on deep learning. They are sometimes referred to as the "Godfathers of Deep Learning" and have continued to give public talks together. He was also awarded, along with John Hopfield, the 2024 Nobel Prize in Physics for foundational discoveries and inventions that enable machine learning with artificial neural networks.

In May 2023, Hinton announced his resignation from Google to be able to "freely speak out about the risks of A.I." He has voiced concerns about deliberate misuse by malicious actors, technological unemployment, and existential risk from artificial general intelligence. He noted that establishing safety guidelines will require cooperation among those competing in use of AI in order to avoid the worst outcomes. After receiving the Nobel Prize, he called for urgent research into AI safety to figure out how to control AI systems smarter than humans.

Education

Hinton was born on 6 December 1947 in Wimbledon, England, and was educated at Clifton College in Bristol. In 1967, he enrolled as an undergraduate student at King's College, Cambridge, and after repeatedly switching between different fields, like natural sciences, history of art, and philosophy, he eventually graduated with a Bachelor of Arts degree in experimental psychology at the University of Cambridge in 1970. He spent a year apprenticing carpentry before returning to academic studies. From 1972 to 1975, he continued his study at the University of Edinburgh, where he was awarded a PhD in artificial intelligence in 1978 for research supervised by Christopher Longuet-Higgins, who favored the symbolic AI approach over the neural network approach.

Career and research

After his PhD, Hinton initially worked at the University of Sussex and at the MRC Applied Psychology Unit. After having difficulty getting funding in Britain, he worked in the US at the University of California, San Diego and Carnegie Mellon University.[38] He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London. He is currently University Professor Emeritus in the Department of Computer Science at the University of Toronto, where he has been affiliated since 1987.

Upon arrival in Canada, Geoffrey Hinton was appointed at the Canadian Institute for Advanced Research (CIFAR) in 1987 as a Fellow in CIFAR's first research program, Artificial Intelligence, Robotics & Society. In 2004, Hinton and collaborators successfully proposed the launch of a new program at CIFAR, "Neural Computation and Adaptive Perception" (NCAP), which today is named "Learning in Machines & Brains". Hinton would go on to lead NCAP for ten years. Among the members of the program are Yoshua Bengio and Yann LeCun, with whom Hinton would go on to win the ACM A.M. Turing Award in 2018. All three Turing winners continue to be members of the CIFAR Learning in Machines & Brains program.

Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012. He co-founded DNNresearch Inc. in 2012 with his two graduate students Alex Krizhevsky and Ilya Sutskever at the University of Toronto’s department of computer science. In March 2013, Google acquired DNNresearch Inc. for $44 million, and Hinton planned to "divide his time between his university research and his work at Google".

Hinton's research concerns ways of using neural networks for machine learning, memory, perception, and symbol processing. He has written or co-written more than 200 peer-reviewed publications.

While Hinton was a postdoc at UC San Diego, David E. Rumelhart and Hinton and Ronald J. Williams applied the backpropagation algorithm to multi-layer neural networks. Their experiments showed that such networks can learn useful internal representations of data. In a 2018 interview, Hinton said that "David E. Rumelhart came up with the basic idea of backpropagation, so it's his invention". Although this work was important in popularising backpropagation, it was not the first to suggest the approach. Reverse-mode automatic differentiation, of which backpropagation is a special case, was proposed by Seppo Linnainmaa in 1970, and Paul Werbos proposed to use it to train neural networks in 1974.

In 1985, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski. His other contributions to neural network research include distributed representations, time delay neural network, mixtures of experts, Helmholtz machines and product of experts. An accessible introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993. In 2007, Hinton coauthored an unsupervised learning paper titled Unsupervised learning of image transformations. In 2008, he developed the visualization method t-SNE with Laurens van der Maaten.

In October and November 2017, Hinton published two open access research papers on the theme of capsule neural networks, which, according to Hinton, are "finally something that works well".

At the 2022 Conference on Neural Information Processing Systems (NeurIPS), Hinton introduced a new learning algorithm for neural networks that he calls the "Forward-Forward" algorithm. The idea of the new algorithm is to replace the traditional forward-backward passes of backpropagation with two forward passes, one with positive (i.e. real) data and the other with negative data that could be generated solely by the network.

In May 2023, Hinton publicly announced his resignation from Google. He explained his decision by saying that he wanted to "freely speak out about the risks of A.I." and added that a part of him now regrets his life's work.

Notable former PhD students and postdoctoral researchers from his group include Peter Dayan, Sam Roweis, Max Welling, Richard Zemel, Brendan Frey, Radford M. Neal, Yee Whye Teh, Ruslan Salakhutdinov, Ilya Sutskever, Yann LeCun, Alex Graves, Zoubin Ghahramani, and Peter Fitzhugh Brown.