Lifeboat Foundation

It could hardly be more complicated: tiny particles whir around wildly with extremely high energy, countless interactions occur in the tangled mess of quantum particles, and this results in a state of matter known as “quark-gluon plasma”. Immediately after the Big Bang, the entire universe was in this state; today it is produced by high-energy atomic nucleus collisions, for example at CERN.

Such processes can only be studied using high-performance computers and highly complex computer simulations whose results are difficult to evaluate. Therefore, using artificial intelligence or machine learning for this purpose seems like an obvious idea. Ordinary machine-learning algorithms, however, are not suitable for this task. The mathematical properties of particle physics require a very special structure of neural networks. At TU Wien (Vienna), it has now been shown how neural networks can be successfully used for these challenging tasks in particle physics.

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery.

“The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures,” says Wim van Rees, the ABS Career Development Professor at MIT. “At the same time, the ocean holds countless opportunities — from aquaculture to energy harvesting and exploring the many ocean creatures we haven’t discovered yet.”

Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

The more a technology or concept permeates and gets normalized in our day-to-day lives, the more we grow to expect from it. About two decades ago, a sub-56kpbs dial-up internet connection seemed miraculous. Today, with internet speeds as high as 2000Mbps becoming normal, the 56Kbps connection would be considered a failure of sorts—in the developed world, at least. This shift in expectation also applies to AI. Having seen numerous practical AI applications aid human convenience and progress, both the general population and the AI research community now expects every new breakthrough in the field to be more earth-shattering than the previous one. Similarly, what qualifies as AI failure has also seen a massive shift in recent years, especially from a problem owner’s perspective. failure, in most cases, is attributed to technology-centric factors like the quality of data or the capabilities of algorithms and hardware used, ignoring the most crucial aspect of AI success—the end user.

One of the questions that I always get when I talk about credit risk modeling (Loan payment default, credit card payment default) is about the algorithms’ or models’ prediction limitations.

How can we implement a solution if the prediction probability is lower? How can we use the model or algorithm effectively for real-world problems?

Have chalked out what are all the available methods to predict the probability of default, while not getting into them detail since that’s not what this article’s intent is.

The single technique for teaching neural networks multiple skills is a step towards general-purpose AI.

“No AI technology ‘where training or transactional data is known to be of poor quality, carry bias, or where the quality of such data is unknown’ should ever be considered for use, and thus should be deemed Extreme Risk, not High Risk. Any AI technology based on poor quality or biased data is inherently compromised.”

“No AI technology that assists in “identifying, categorizing, prioritizing or otherwise making decisions pertaining to members of the public” should be deemed Low Risk. Automating such actions through technology, even with the inclusion of a human-in-the-loop, is an intrinsically risky activity, and should be categorized as such by the Policy.”

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Hardware company uses neutral atom design while algorithm experts integrate quantum algorithms into existing software platforms.

Pasqal is combining its neutral atom-based hardware with Qu&Co’s algorithm portfolio to launch a combined quantum computing company based in Paris with operations in seven countries. The companies announced the merger Tuesday, Jan. 11.

For enterprises that are looking to bring a zero trust approach as a way to better secure identities and permissions, leveraging advanced AI is now essential in order to achieve accuracy and scalability, ForgeRock CEO Fran Rosch told VentureBeat.

While traditionally, zero trust decision-making has relied mostly upon rules–for instance, rejecting a user request based on an impossible geographic location– ForgeRock adds in AI algorithms that enable far greater accuracy, Rosch said. This accuracy equates to dramatically enhanced security, he said–citing an example of a recent customer that increased its entitlement rejections by 300% after deploying ForgeRock.

“Because it was previously all done by these rules, and people were rubber-stamping these entitlement requests, they were letting these things go that they should never have approved,” Rosch said in a recent interview. “That was increasing the risk to the company. Because there were people who had no business accessing HR data, and no business accessing sales data, that were getting that information. So by leveraging the AI, a 300% increase in request rejections really tightened up the security of the organization.”

A group of scientists at the U.S. Department of Energy’s Ames Laboratory has developed computational quantum algorithms that are capable of efficient and highly accurate simulations of static and dynamic properties of quantum systems. The algorithms are valuable tools to gain greater insight into the physics and chemistry of complex materials, and they are specifically designed to work on existing and near-future quantum computers.

Scientist Yong-Xin Yao and his research partners at Ames Lab use the power of advanced computers to speed discovery in condensed matter physics, modeling incredibly complex quantum mechanics and how they change over ultra-fast timescales. Current high performance computers can model the properties of very simple, small quantum systems, but larger or more complex systems rapidly expand the number of calculations a computer must perform to arrive at an accurate model, slowing the pace not only of computation, but also discovery.

“This is a real challenge given the current early-stage of existing quantum computing capabilities,” said Yao, “but it is also a very promising opportunity, since these calculations overwhelm classical computer systems, or take far too long to provide timely answers.”