Arquivo da tag: Inteligência artificial

With little training, machine-learning algorithms can uncover hidden scientific knowledge (Science Daily)

Date: July 3, 2019 Source: DOE/Lawrence Berkeley National Laboratory

Summary: Researchers have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. They collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.


Sure, computers can be used to play grandmaster-level chess (chess_computer), but can they make scientific discoveries? Researchers at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge.

A team led by Anubhav Jain, a scientist in Berkeley Lab’s Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.

“Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals,” said Jain. “That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven’t studied so far.”

The findings were published July 3 in the journal Nature. The lead author of the study, “Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature,” is Vahe Tshitoyan, a Berkeley Lab postdoctoral fellow now working at Google. Along with Jain, Berkeley Lab scientists Kristin Persson and Gerbrand Ceder helped lead the study.

“The paper establishes that text mining of scientific literature can uncover hidden knowledge, and that pure text-based extraction can establish basic scientific knowledge,” said Ceder, who also has an appointment at UC Berkeley’s Department of Materials Science and Engineering.

Tshitoyan said the project was motivated by the difficulty making sense of the overwhelming amount of published studies. “In every research field there’s 100 years of past research literature, and every week dozens more studies come out,” he said. “A researcher can access only fraction of that. We thought, can machine learning do something to make use of all this collective knowledge in an unsupervised manner — without needing guidance from human researchers?”

‘King — queen + man = ?’

The team collected the 3.3 million abstracts from papers published in more than 1,000 journals between 1922 and 2018. Word2vec took each of the approximately 500,000 distinct words in those abstracts and turned each into a 200-dimensional vector, or an array of 200 numbers.

“What’s important is not each number, but using the numbers to see how words are related to one another,” said Jain, who leads a group working on discovery and design of new materials for energy applications using a mix of theory, computation, and data mining. “For example you can subtract vectors using standard vector math. Other researchers have shown that if you train the algorithm on nonscientific text sources and take the vector that results from ‘king minus queen,’ you get the same result as ‘man minus woman.’ It figures out the relationship without you telling it anything.”

Similarly, when trained on materials science text, the algorithm was able to learn the meaning of scientific terms and concepts such as the crystal structure of metals based simply on the positions of the words in the abstracts and their co-occurrence with other words. For example, just as it could solve the equation “king — queen + man,” it could figure out that for the equation “ferromagnetic — NiFe + IrMn” the answer would be “antiferromagnetic.”

Word2vec was even able to learn the relationships between elements on the periodic table when the vector for each chemical element was projected onto two dimensions.

Predicting discoveries years in advance

So if Word2vec is so smart, could it predict novel thermoelectric materials? A good thermoelectric material can efficiently convert heat to electricity and is made of materials that are safe, abundant and easy to produce.

The Berkeley Lab team took the top thermoelectric candidates suggested by the algorithm, which ranked each compound by the similarity of its word vector to that of the word “thermoelectric.” Then they ran calculations to verify the algorithm’s predictions.

Of the top 10 predictions, they found all had computed power factors slightly higher than the average of known thermoelectrics; the top three candidates had power factors at above the 95th percentile of known thermoelectrics.

Next they tested if the algorithm could perform experiments “in the past” by giving it abstracts only up to, say, the year 2000. Again, of the top predictions, a significant number turned up in later studies — four times more than if materials had just been chosen at random. For example, three of the top five predictions trained using data up to the year 2008 have since been discovered and the remaining two contain rare or toxic elements.

The results were surprising. “I honestly didn’t expect the algorithm to be so predictive of future results,” Jain said. “I had thought maybe the algorithm could be descriptive of what people had done before but not come up with these different connections. I was pretty surprised when I saw not only the predictions but also the reasoning behind the predictions, things like the half-Heusler structure, which is a really hot crystal structure for thermoelectrics these days.”

He added: “This study shows that if this algorithm were in place earlier, some materials could have conceivably been discovered years in advance.” Along with the study the researchers are releasing the top 50 thermoelectric materials predicted by the algorithm. They’ll also be releasing the word embeddings needed for people to make their own applications if they want to search on, say, a better topological insulator material.

Up next, Jain said the team is working on a smarter, more powerful search engine, allowing researchers to search abstracts in a more useful way.

The study was funded by Toyota Research Institute. Other study co-authors are Berkeley Lab researchers John Dagdelen, Leigh Weston, Alexander Dunn, and Ziqin Rong, and UC Berkeley researcher Olga Kononova.


Story Source:

Materials provided by DOE/Lawrence Berkeley National Laboratory. Note: Content may be edited for style and length.


Journal Reference:

  1. Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A. Persson, Gerbrand Ceder, Anubhav Jain. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 2019; 571 (7763): 95 DOI: 10.1038/s41586-019-1335-8

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Animal training techniques teach robots new tricks (Science Daily)

Virtual dogs take place of programming

Date:
May 16, 2016
Source:
Washington State University
Summary:
Researchers are using ideas from animal training to help non-expert users teach robots how to do desired tasks.

Virtual environments in which trainers gave directions to robot dog. Credit: Image courtesy of Washington State University

Researchers at Washington State University are using ideas from animal training to help non-expert users teach robots how to do desired tasks.

The researchers recently presented their work at the international Autonomous Agents and Multiagent Systems conference.

As robots become more pervasive in society, humans will want them to do chores like cleaning house or cooking. But to get a robot started on a task, people who aren’t computer programmers will have to give it instructions.

“We want everyone to be able to program, but that’s probably not going to happen,” said Matthew Taylor, Allred Distinguished Professor in the WSU School of Electrical Engineering and Computer Science. “So we needed to provide a way for everyone to train robots — without programming.”

User feedback improves robot performance

With Bei Peng, a doctoral student in computer science, and collaborators at Brown University and North Carolina State University, Taylor designed a computer program that lets humans teach a virtual robot that looks like a computerized pooch. Non-computer programmers worked with and trained the robot in WSU’s Intelligent Robot Learning Laboratory.

For the study, the researchers varied the speed at which their virtual dog reacted. As when somebody is teaching a new skill to a real animal, the slower movements let the user know that the virtual dog was unsure of how to behave. The user could then provide clearer guidance to help the robot learn better.

“At the beginning, the virtual dog moves slowly. But as it receives more feedback and becomes more confident in what to do, it speeds up,” Peng said.

The user taught tasks by either reinforcing good behavior or punishing incorrect behavior. The more feedback the virtual dog received from the human, the more adept the robot became at predicting the correct course of action.

Applications for animal training

The researchers’ algorithm allowed the virtual dog to understand the tricky meanings behind a lack of feedback — called implicit feedback.

“When you’re training a dog, you may withhold a treat when it does something wrong,” Taylor explained. “So no feedback means it did something wrong. On the other hand, when professors are grading tests, they may only mark wrong answers, so no feedback means you did something right.”

The researchers have begun working with physical robots as well as virtual ones. They also hope to eventually use the program to help people learn to be more effective animal trainers.

Artificial intelligence replaces physicists (Science Daily)

Date:
May 16, 2016
Source:
Australian National University
Summary:
Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize.

The experiment, featuring the small red glow of a BEC trapped in infrared laser beams. Credit: Stuart Hay, ANU

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment.

The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize.

“I didn’t expect the machine could learn to do the experiment itself, from scratch, in under an hour,” said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering.

“A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out.”

Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

They could be used for mineral exploration or navigation systems as they are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth’s magnetic field or gravity.

The artificial intelligence system’s ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA.

“You could make a working device to measure gravity that you could take in the back of a car, and the artificial intelligence would recalibrate and fix itself no matter what,” he said.

“It’s cheaper than taking a physicist everywhere with you.”

The team cooled the gas to around 1 microkelvin, and then handed control of the three laser beams over to the artificial intelligence to cool the trapped gas down to nanokelvin.

Researchers were surprised by the methods the system came up with to ramp down the power of the lasers.

“It did things a person wouldn’t guess, such as changing one laser’s power up and down, and compensating with another,” said Mr Wigley.

“It may be able to come up with complicated ways humans haven’t thought of to get experiments colder and make measurements more precise.

The new technique will lead to bigger and better experiments, said Dr Hush.

“Next we plan to employ the artificial intelligence to build an even larger Bose-Einstein condensate faster than we’ve seen ever before,” he said.

The research is published in the Nature group journal Scientific Reports.


Journal Reference:

  1. P. B. Wigley, P. J. Everitt, A. van den Hengel, J. W. Bastian, M. A. Sooriyabandara, G. D. McDonald, K. S. Hardman, C. D. Quinlivan, P. Manju, C. C. N. Kuhn, I. R. Petersen, A. N. Luiten, J. J. Hope, N. P. Robins, M. R. Hush. Fast machine-learning online optimization of ultra-cold-atom experimentsScientific Reports, 2016; 6: 25890 DOI: 10.1038/srep25890

Robôs inteligentes podem levar ao fim da raça humana, diz Stephen Hawking (Folha de S.Paulo)

SALVADOR NOGUEIRA

COLABORAÇÃO PARA FOLHA

16/12/2014 02h03

O físico britânico Stephen Hawking está causando novamente. Em entrevista à rede BBC, ele alertou para os perigos do desenvolvimento de máquinas superinteligentes.

“As formas primitivas de inteligência artificial que temos agora se mostraram muito úteis. Mas acho que o desenvolvimento de inteligência artificial completa pode significar o fim da raça humana”, disse o cientista.

Ele ecoa um número crescente de especialistas –de filósofos a tecnologistas– que aponta as incertezas trazidas pelo desenvolvimento de máquinas pensantes.

Alex Argozino/Editoria de Arte/Folhapress
Robô

Recentemente, outro luminar a se pronunciar foi Elon Musk, sul-africano que fez fortuna ao criar um sistema de pagamentos para internet e agora desenvolve foguetes e naves para o programa espacial americano.

Em outubro, falando a alunos do MIT (Instituto de Tecnologia de Massachusetts), lançou um alerta parecido.

“Acho que temos de ser muito cuidadosos com inteligência artificial. Se eu tivesse que adivinhar qual é a nossa maior ameaça existencial, seria provavelmente essa.”

Para Musk, a coisa é tão grave que ele acredita na necessidade de desenvolver mecanismos de controle, talvez em nível internacional, “só para garantir que não vamos fazer algo bem idiota”.

SUPERINTELIGÊNCIA

A preocupação vem de longe. Em 1965, Gordon Moore, co-fundador da Intel, notou que a capacidade dos computadores dobrava a cada dois anos, aproximadamente.

Como o efeito é exponencial, em pouco tempo conseguimos sair de modestas máquinas de calcular a supercomputadores capazes de simular a evolução do Universo. Não é pouca coisa.

Os computadores ainda não ultrapassaram a capacidade de processamento do cérebro humano. Por pouco.

“O cérebro como um todo executa cerca de 10 mil trilhões de operações por segundo”, diz o físico Paul Davies, da Universidade Estadual do Arizona. “O computador mais rápido atinge 360 trilhões, então a natureza segue na frente. Mas não por muito tempo.”

Alguns tecnologistas comemoram essa ultrapassagem iminente, como o inventor americano Ray Kurzweil, que atualmente tem trabalhado em parceria com o Google para desenvolver o campo da IA (inteligência artificial).

Ele estima que máquinas com capacidade intelectual similar à humana surgirão em 2029. É mais ou menos o de tempo imaginado por Musk para o surgimento da ameaça.

“A inteligência artificial passará a voar por seus próprios meios, se reprojetando a um ritmo cada vez maior”, sugeriu Hawking.

O resultado: não só as máquinas seriam mais inteligentes que nós, como estariam em constante aprimoramento. Caso desenvolvam a consciência, o que farão conosco?

Kurzweil prefere pensar que nos ajudarão a resolver problemas sociais e se integrarão à civilização. Mas até ele admite que não há garantias. “Acho que a melhor defesa é cultivar valores como democracia, tolerância, liberdade”, disse à Folha.

Para ele, máquinas criadas nesse ambiente aprenderiam os mesmos valores. “Não é uma estratégia infalível”, diz Kurzweil. “Mas é o melhor que podemos fazer.”

Enquanto Musk sugere um controle sobre a tecnologia, Kurzweil acredita que já passamos o ponto de não-retorno –estamos a caminho da “singularidade tecnológica”, quando a IA alterará radicalmente a civilização.