Lifeboat Foundation

Artificial intelligence has exploded, and perhaps no one knows it more than Harry Shum, the executive vice president in charge of Microsoft’s AI and Research Group, which has been at the center of a major technological shift inside the company.

Delivering the commencement address Friday at the University of Washington’s Paul G. Allen School of Computer Science and Engineering, Shum drew inspiration from three emerging technologies — quantum computing, AI, and mixed reality — to deliver life lessons and point out the future of technology for the class of 2018.

Continue reading “Life lessons from artificial intelligence: What Microsoft’s AI chief wants computer science grads to know about the future” »

IBM and the Department of Energy’s Oak Ridge National Laboratory have revealed the world’s “most powerful and smartest scientific supercomputer.” Known as Summit, IBM says that its new computer will be capable of processing 200,000 quadrillion calculations per second. To put that into perspective, if every person on Earth did a single calculation per second, it would take 305 days to do what Summit does in a single second. Assuming those numbers are accurate, that would make Summit the world’s fastest supercomputer. It would also mark the first time since 2012 that a U.S. computer held that title.

Summit has been in the works for several years now and features some truly impressive specs. According to Tech Crunch, the computer will feature 4,608 compute servers, 22 IBM Power9 chips and six Nvidia Tesla V100 GPUs each. In addition, the machine will feature more than 10 petabytes of memory. As the Nvidia GPUs attest, this machine will be primarily used for the development of artificial intelligence and machine learning. In addition to the work on A.I., Summit will also be used for research into energy and other scientific endeavors at Oak Ridge.

IBM was the Department of Energy’s general contractor for the Summit project, but it also had the help of several other partners within the tech industry. The GPUs were provided by Nvidia, which remains one of the leaders in cutting-edge GPU development. Mellanox and Redhat were also brought on to work on the development of Summit.

The point of the experiment was to show how easy it is to bias any artificial intelligence if you train it on biased data. The team wisely didn’t speculate about whether exposure to graphic content changes the way a human thinks. They’ve done other experiments in the same vein, too, using AI to write horror stories, create terrifying images, judge moral decisions, and even induce empathy. This kind of research is important. We should be asking the same questions of artificial intelligence as we do of any other technology because it is far too easy for unintended consequences to hurt the people the system wasn’t designed to see. Naturally, this is the basis of sci-fi: imagining possible futures and showing what could lead us there. Issac Asimov gave wrote the “Three Laws of Robotics” because he wanted to imagine what might happen if they were contravened.

Even though artificial intelligence isn’t a new field, we’re a long, long way from producing something that, as Gideon Lewis-Kraus wrote in The New York Times Magazine, can “demonstrate a facility with the implicit, the interpretive.” But it still hasn’t undergone the kind of reckoning that causes a discipline to grow up. Physics, you recall, gave us the atom bomb, and every person who becomes a physicist knows they might be called on to help create something that could fundamentally alter the world. Computer scientists are beginning to realize this, too. At Google this year, 5,000 employees protested and a host of employees resigned from the company because of its involvement with Project Maven, a Pentagon initiative that uses machine learning to improve the accuracy of drone strikes.

Norman is just a thought experiment, but the questions it raises about machine learning algorithms making judgments and decisions based on biased data are urgent and necessary. Those systems, for example, are already used in credit underwriting, deciding whether or not loans are worth guaranteeing. What if an algorithm decides you shouldn’t buy a house or a car? To whom do you appeal? What if you’re not white and a piece of software predicts you’ll commit a crime because of that? There are many, many open questions. Norman’s role is to help us figure out their answers.

Continue reading “MIT fed an AI data from Reddit, and now it thinks of nothing but murder” »

A new technique developed by MIT physicists could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices. It may also help physicists tackle a variety of thorny research problems, in ways that could in some cases be orders of magnitude faster than existing methods.

The innovation uses computational neural networks, a form of artificial intelligence, to “learn” how a nanoparticle’s structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples. Then, having learned the relationship, the program can essentially be run backward to design a particle with a desired set of light-scattering properties—a process called inverse design.

The findings are being reported in the journal Science Advances, in a paper by MIT senior John Peurifoy, research affiliate Yichen Shen, graduate student Li Jing, professor of physics Marin Soljacic, and five others.

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Recently we saw a new “Master algorithm” that could be used to create the first generation of super intelligent machines, and now a team of researchers from Maryland, USA, announced this week that they’ve invented a general Artificial Intelligence (AI) way for machines to identify and process 3D images that doesn’t require humans to go through the tedium of inputting specific information that accounts for each and every instance, scenario, difference, change and category that could crop up, and they claim it’s a world first, even though it follows on from a not too dissimilar breakthrough from Google DeepMind whose own platform, Alpha Zero, recently taught itself a mix of board games including chess to a grand master level, in just four hours.

Cutting the grass with this heavy duty lawn mower won’t feel like a chore anymore.

For the first time, Volkswagen experts have succeeded in simulating industrially relevant molecules using a quantum computer. This is especially important for the development of high-performance electric vehicle batteries. The experts have successfully simulated molecules such as lithium-hydrogen and carbon chains. Now they are working on more complex chemical compounds. In the long term, they want to simulate the chemical structure of a complete electric vehicle battery on a quantum computer. Their objective is to develop a “tailor-made battery”, a configurable chemical blueprint that is ready for production. Volkswagen is presenting its research work connected with quantum computing at the CEBIT technology show (Hanover, June 12–15).

Martin Hofmann, CIO of the Volkswagen Group, says: “We are focusing on the modernization of IT systems throughout the Group. The objective is to intensify the digitalization of work processes – to make them simpler, more secure and more efficient and to support new business models. This is why we are combining our core task with the introduction of specific key technologies for Volkswagen. These include the Internet of Things and artificial intelligence, as well as quantum computing.”

The objective is a “tailor-made battery”, a configurable blueprint Using newly developed algorithms, the Volkswagen experts have laid the foundation for simulating and optimizing the chemical structure of high-performance electric vehicle batteries on a quantum computer. In the long term, such a quantum algorithm could simulate the chemical composition of a battery on the basis of different criteria such as weight reduction, maximum power density or cell assembly and provide a design which could be used directly for production. This would significantly accelerate the battery development process, which has been time-consuming and resource-intensive to date.

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Abstract: Can machines design? Can they come up with creative solutions to problems and build tools and artifacts across a wide range of domains? Recent advances in the field of computational creativity and formal Artificial General Intelligence (AGI) provide frameworks for machines with the general ability to design. In this paper we propose to integrate a formal computational creativity framework into the G” odel machine framework. We call this machine a design G” odel machine. Such a machine could solve a variety of design problems by generating novel concepts. In addition, it could change the way these concepts are generated by modifying itself. The design G” odel machine is able to improve its initial design program, once it has proven that a modification would increase its return on the utility function. Finally, we sketch out a specific version of the design G” odel machine which specifically aims at the design of complex software and hardware systems. Future work could be the development of a more formal version of the Design G” odel machine and a potential implementation.

Here’s my speech and panel participation from yesterday at the Annual Danish Top Executive Annual Summit via VL-dognet 2018: It was covered by Danish Jyskebank TV. My panel included a minister, an ambassador, a country company head, a television host, and Sophia the Robot. I had a great time at the wonderful event (hosted at a castle), and the Danish people are very nice. The Vice President of the EU spoke a few sessions before me. My solo speech that started my afternoon session was on the future of politics and transhumanism. It’s the first 10 minutes of the video: