Archive for the ‘Alphago’ Category

A Chatbot Beat the SAT. What Now? – The Atlantic

Last fall, when generative AI abruptly started turning out competent high-school- and college-level writing, some educators saw it as an opportunity. Perhaps it was time, at last, to dispose of the five-paragraph essay, among other bad teaching practices that have lingered for generations. Universities and colleges convened emergency town halls before winter terms began to discuss how large language models might reshape their work, for better and worse.

But just as quickly, most of those efforts evaporated into the reality of normal life. Educators and administrators have so many problems to address even before AI enters the picture; the prospect of utterly redesigning writing education and assessment felt impossible. Worthwhile, but maybe later. Then, with last weeks arrival of GPT-4, came another provocation. OpenAI, the company that created the new software, put out a paper touting its capacities. Among them: taking tests. AIs are no longer just producing passable five-paragraph essays. Now theyre excelling at the SAT, earning a score of 1410. Theyre getting passing grades on more than a dozen different AP exams. Theyre doing well enough on bar exams to be licensed as lawyers.

It would be nice if this news inspired educators, governments, certification agencies, and other groups to rethink what these tests really meanor even to reinvent them altogether. Alas, as was the case for rote-essay writing, whatever appetite for change the shock inspires might prove to be short-lived. GPT-4s achievements help reveal the underlying problem: Americans love standardized tests as much as we hate themand were unlikely to let them go even if doing so would be in our best interest.

Many of the initial responses to GPT-4s exam prowess were predictably immoderate: AI can keep up with human lawyers, or apply to Stanford, or make education useless. But why should it be startling in the slightest that software trained on the entire text of the internet performs well on standardized exams? AI can instantly run what amounts to an open-book test on any subject through statistical analysis and regression. Indeed, that anyone is surprised at all by this success suggests that people tend to get confused about what it means when computers prove effective at human activities.

Read: The college essay is dead

Back in the late 1990s, nobody thought a computer could ever beat a human at Go, the ancient Chinese game played with black and white stones. Chess had been mastered by supercomputers, but Go remainedat least in the hearts of its playersimmune to computation. They were wrong. Two decades later, DeepMinds AlphaGo was regularly beating Go masters. To accomplish this task, AlphaGo initially mimicked human players moves before running innumerable games against itself to find new strategies. The victory was construed by some as evidence that computers could overtake people at complex tasks previously thought to be uniquely human.

By rights, GPT-4s skill at the SAT should be taken as the opposite. Standardized tests feel inhuman from the start: You, a distinct individual, are forced to perform in a manner that can be judged by a machine, and then compared with that of many other individuals. Yet last weeks announcementof the 1410 score, the AP exams, and so ongave rise to an unease similar to that produced by AlphaGo.

Perhaps were anxious not that computers will strip us of humanity, but that machines will reveal the vanity of our human concerns. The experience of reasoning about your next set of moves in Go, as a human player doing so from the vantage point of human culture, cannot be replaced or reproduced by a Go-playing machineunless the only point of Go were to prove that Go can be mastered, rather than played. Such cultural values do exist: The designation of chess grand masters and Go 9-dan professionals suggests expertise in excess of mere performance in a folk game. The best players of chess and Go are sometimes seen as smart in a general sense, because they are good at a game that takes smarts of a certain sort. The same is true for AIs that play (and win) these games.

Read: A machine crushed us at Pokmon

Standardized tests occupy a similar cultural role. They were conceived to assess and communicate general performance on a subject such as math or reading. Whether and how they ever managed to do that is up for debate, but the accuracy and fairness of the exams became less important than their social function. To score a 1410 on the SAT says something about your capacities and prospectsmaybe you can get into Stanford. To pursue and then emerge victorious against a battery of AP tests suggests general ability warranting accelerated progress in college. (That victory doesnt necessarily provide that acceleration only emphasizes the seduction of its symbolism.) The bar exam measuresone hopessomeones subject-matter proficiency, but doesnt promise to ensure lawyerly effectiveness or even competence. To perform well on a standardized test indicates potential to perform well at some real future activity, but it has also come to have some value in itself, as a marker of success at taking tests.

That value was already being questioned, machine intelligence aside. Standardized tests have long been scrutinized for contributing to discrimination against minority and low-income students. The coronavirus pandemic, and its disruptions to educational opportunity, intensified those concerns. Many colleges and universities made the SAT and ACT optional for admissions. Graduate schools are giving up on the GRE, and aspiring law students may no longer have to take the LSAT in a couple of years.

GPT-4s purported prowess at these tests shows how little progress has been made at decoupling appearance from reality in the tests pursuit. Standardized tests might fairly assess human capacity, or they might do so unfairly, but either way, they hold an outsize role in Americans conception of themselves and their communities. Were nervous that tests might turn us into computers, but also that computers might reveal the conceit of valuing tests so much in the first place.

AI-based chess and Go computers didnt obsolesce play by people, but they did change human-training practices. Large language models may do the same for taking the SAT and other standardized exams, and evolve into a fancy form of test prep. In that case, they could end up helping those who would already have done well enough to score even higher. Or perhaps they will become the basis for a low-cost alternative that puts such training in the hands of everyonea reversal of examination inequity, and a democratization of vanity. No matter the case, the standardized tests will persist, only now the chatbots have to take them too.

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A Chatbot Beat the SAT. What Now? - The Atlantic

Exclusive: See the cover for Benjamn Labatut’s new novel, The … – Literary Hub

Literary Hub is pleased to reveal the cover for Benjamn Labatuts new novel, The MANIAC, which will be published by Penguin Press this fall. Heres how the publisher describes the book:

From one of contemporary literatures most exciting new voices, a haunting story centered on the Hungarian polymath John von Neumann, tracing the impact of his singular legacy on the dreams and nightmares of the twentieth century and the nascent age of AI

BenjamnLabatutsWhen We Cease to Understand the Worldelectrified a global readership. A Booker Prize and National Book Award finalist, and one of theNew York Times Ten Best Books of the Year, it explored the life and thought of a clutch of mathematicians and physicists who took science to strange and sometimes dangerous new realms. InThe MANIAC, Labatut has created a tour de force on an even grander scale.

A prodigy whose gifts terrified the people around him, John von Neumann transformed every field he touched, inventing game theory and the first programable computer, and pioneering AI, digital life, and cellular automata. Through a chorus of family members, friends, colleagues, and rivals, Labatut shows us the evolution of a mind unmatched and of a body of work that has unmoored the world in its wake.

The MANIACplaces von Neumann at the center of a literary triptych that begins with Paul Ehrenfest, an Austrian physicist and friend of Einstein, who fell into despair when he saw science and technology become tyrannical forces; it ends a hundred years later, in the showdown between the South Korean Go Master Lee Sedol and the AI program AlphaGo, an encounter embodying the central question of von Neumanns most ambitious unfinished project: the creation of a self-reproducing machine, an intelligence able to evolve beyond human understanding or control.

And heres the cover, which was designed by Darren Haggar:

The image on this cover was created by film director Bennett Miller, using OpenAIs DALL-E 2 software, the publisher told Lit Hub. He arrived at the final product by making extensive edits on variations of an image generated using the following prompt: a vintage photograph of huge plumes of smoke coming from an enormous UFO crashed in the desert.

The MANIAC will be published on October 3, 2023 by Penguin Press. You can preorder it here.

A show of Bennett Millers prints is currently on at the Gagosian Gallery through April 22, 2023.

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Exclusive: See the cover for Benjamn Labatut's new novel, The ... - Literary Hub

These companies are creating ChatGPT alternatives – Tech Monitor

Right now, major tech firms are clamouring to replicate the runaway success of ChatGPT, the generative AI chatbot developed by OpenAI using its GPT-3 large language model. Much like potential game-changers of the past, such as cloud-based Software as a Service (SaaS) platforms or blockchain technology (emphasis on potential), established companies and start-ups alike are going public with LLMs and ChatGPT alternatives in fear of being left behind.

While many of these will succeed some in the mass market and others with niche applications many more will likely fail as the market consolidates.

Who, then, are the companies in good stead to challenge OpenAI?

Googles LaMDA has attracted the most attention from mainstream observers of any LLM outside of GPT-3, but for not quite the same reasons.

Months before ChatGPT exploded into national headlines in late 2022, LaMDA was proving controversial after Google engineer Blake Lemoine was suspended for claiming falsely as became evident that it had developed sentience.

In reality, the LaMDA LLM operates similarly to its main competitor, except that it has fewer parameters at 137 billion compared to 175 billion for GPT-3.5, which was used to train ChatGPT.

LaMDA is also the bedrock of Googles chatbot competitor, named Bard, which the search giant is currently testing for search with select users. Bard had an inauspicious start, however, as it presented a factual error during a launch event.

Israel-based start-up AI21, while less well-known than its rival OpenAI, is a serious challenger in the market. The company created the Jurassic-2 large language model in 2021 with a similar number of parameters to GPT-3.5 178 billion compared to 175 billion and customisation capabilities.

March 2023 then saw the release of Jurassic-2, which focuses on optimised performance as opposed to size. According to AI21, the smallest version of Jurassic-2 outperforms even the largest version of its predecessor. It will also contain a grammatical correction API and text segmentation capabilities.

Users of AI21 studio can train their own versions of the LLM with as few as 50-100 training examples, which then become available for exclusive use.

AI21 also deployed Jurassic-1, and now Jurassic-2 to underpin its WordTune Spices chatbot, which distinguishes itself as a ChatGPT alternative by the use of live data retrieval and the citation of sources in its formulations. Given the risks of factual error and plagiarism associated with LLM chatbots, this is a significant advantage in an increasingly competitive field.

Founded by former OpenAI employees, Anthropic is fast making waves as a rival to its quasi-progenitor.

The generative AI company has launched its own large language model, Claude, whose ChatGPT alternative boasts what it calls constitutional AI. In effect, the model is designed to act according to programmed principles (i.e. its constitution) as opposed to ChatGPT, which is prohibited from answering certain controversial or dangerous queries.

Much like Microsofts investment in OpenAI, Google has invested $300m into Anthropic for a 10% stake in the company.

Baidu Chinas answer to Google is looking to combat its long-term struggles in the face of rival Tencent with its heavy investment in AI.

The team at Baidu has expanded its ERNIE 3.0 large language model into a new version called ERNIE 3.0 Titan. While its predecessor had just 10 billion parameters, Titans PaddlePaddle platform operates on 260 billion.

Titans creators claim that it is the largest dense pre-trained model so far and that it outperforms state-of-the-art models on natural language processing (NLP) tasks.

Hardware and software supplier Nvidia is currently core to the operation of ChatGPT, with an estimated 10,000 of the companys GPUs used to train the chatbot and a predicted 30,000 to be used in future.

This dynamic could be upended, however, as Nvidia CEO Jensen Huang announced in February 2023 that the firm plans to make its DGX AI supercomputer available via the cloud.

Already accessible through Oracle Cloud Infrastructure and Microsoft Azure, the AI supercomputer will have the capacity to allow customers to train their own large language models.

Nvidia has seen a financial boost as companies such as Google and Microsoft look to it for the GPUs necessary for training.

British AI company and Alphabet subsidiary Deepmind, famous for its AlphaGo program, is investing heavily in large language model research and development. Deepmind has iterated on multiple LLMs, including Gopher, Chinchilla and the RETRO system, which combines an LLM with an external database.

This experimentation is leading the way in more targeted and energy-efficient types of LLM Chinchilla has just 70 billion parameters, as opposed to others with double, triple or even more than that, yet outperforms the larger Gopher at certain tasks. Likewise for the 7.5 billion-parameter RETRO, whose external database allows it to outperform vastly larger models.

Not content to invest in the metaverse, Meta has also entered the LLM space with its LLaMA model. Mark Zuckerbergs company does not yet have a publicly available ChatGPT alternative but it is in development.

Unlike many others, the 65-billion parameter LLM has been made open source (upon request, crucially) with the intention of knowledge sharing and crowdsourcing bug fixes.

But just a week after it launched, a torrent for the LLM found its way to the wider internet via a 4Chan leak, prompting fears that such unfettered access could be used for phishing and other cybercrime activities.

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These companies are creating ChatGPT alternatives - Tech Monitor

Google’s AlphaGo AI Beats Human Go Champion | PCMag

A Google artificial intelligence algorithm on Tuesday inched closer to once again claiming the title of world champion of the ancient Chinese game of Go, besting its human opponent in the first match of a best-of-three championship.

The algorithm, called AlphaGo, is the brainchild of DeepMind, the artificial intelligence research arm of Google parent company Alphabet. It faced off against 19-year old Kie Jie, who is the current human world champion of Go, a strategy game similar to chess that requires players to place black or white stones on a board and capture the opponent's pieces or surround empty spaces to build territories.

"Last year, it was still quite humanlike when it played," Mr. Ke told(Opens in a new window) the New York Times after AlphaGo's win on Tuesday. "But this year, it became like a god of Go."

If the algorithm wins a second game, it will be the second time it has stolen the Go crown from a human opponent. Last year, AlphaGo defeated the previous world champ Lee Sedol in Seoul, Korea. That tournament was a five-game series that saw AlphaGo win the first three matches, although the tournament continued just for fun, with Sedol making a comeback in game four only to be defeated again in the final match.

Go is a strategy game, and its playershuman or otherwisemust frequently adapt and adjust to their opponents' moves. That makes it an ideal challenge for artificial intelligence, which can use machine learning techniques to avoid repeating its own past mistakes, as well as those of its human competitors, as DeepMind CEO Demis Hassabis noted during Tuesday's match.

"Ke Jie is using the ideas AlphaGo used in the master series of online games in January against AlphaGo," Hassabis tweeted(Opens in a new window). "Intriguing to see what it will do."

In the end, the algorithm ended up beating Jie by just half a point, which suggests that the outcome of the final two matches is anyone's guess.

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Google's AlphaGo AI Beats Human Go Champion | PCMag

AlphaGo: using machine learning to master the ancient game of Go – Google

The game ofGooriginated in China more than 2,500 years ago.Confuciuswrote about the game, and it is considered one of thefour essential artsrequired of any true Chinese scholar.Played by more than 40 million people worldwide, the rules of the game are simple: Players take turns to place black or white stones on a board, trying to capture the opponent's stones or surround empty space to make points of territory. The game is played primarily through intuition and feel, and because of its beauty, subtlety and intellectual depth it has captured the human imagination for centuries.

But as simple as the rules are, Go is a game of profound complexity. There are 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 possible positionsthats more than the number of atoms in the universe, and more than a googol times larger than chess.

This complexity is what makes Go hard for computers to play, and therefore an irresistible challenge to artificial intelligence (AI) researchers, who use games as a testing ground to invent smart, flexible algorithms that can tackle problems, sometimes in ways similar to humans. The first game mastered by a computer wasnoughts and crosses(also known as tic-tac-toe) in 1952.Then fell checkers in 1994. In 1997Deep Blue famously beat Garry Kasparov atchess. Its not limited to board games eitherIBM'sWatson[PDF] bested two champions at Jeopardy in 2011, andin 2014 our own algorithms learned to play dozens of Atari gamesjust from theraw pixel inputs. But to date, Go has thwarted AI researchers; computers still only play Go as well as amateurs.

Traditional AI methodswhich construct asearch treeover all possible positionsdont have a chance in Go. So when we set out to crack Go, we took a different approach. We built a system, AlphaGo, that combines anadvanced tree searchwithdeep neural networks. These neural networks take a description of the Go board as an input and process it through 12 different network layers containing millions of neuron-like connections. One neural network, the policy network, selects the next move to play. The other neural network, the value network, predicts the winner of the game.

We trained the neural networks on 30 million moves from games played by human experts, until it could predict the human move 57 percent of the time (the previous record before AlphaGo was44 percent). But our goal is to beat the best human players, not just mimic them. To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and adjusting the connections using a trial-and-error process known as reinforcement learning. Of course, all of this requires ahuge amount of computing power, so we made extensive use ofGoogle Cloud Platform.

After all that training it was time to put AlphaGo to the test. First, we held a tournament between AlphaGo and the other top programs at the forefront of computer Go. AlphaGo won all but one of its 500 games against these programs. So the next step was to invite the reigning three-time European Go champion Fan Huian elite professional player who has devoted his life to Go since the age of 12to our London office for a challenge match. In a closed-doors match last October, AlphaGo won by 5 games to 0. It was the first time a computer program has ever beaten a professional Go player. You can find out more in our paper, which was published inNaturetoday.

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AlphaGo: using machine learning to master the ancient game of Go - Google