Lifeboat Foundation

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.”

The notion that some computational problems in math and computer science can be hard should come as no surprise. There is, in fact, an entire class of problems deemed impossible to solve algorithmically. Just below this class lie slightly “easier” problems that are less well-understood—and may be impossible, too.

David Gamarnik, professor of operations research at the MIT Sloan School of Management and the Institute for Data, Systems, and Society, is focusing his attention on the latter, less-studied category of problems, which are more relevant to the everyday world because they involve randomness —an integral feature of natural systems. He and his colleagues have developed a potent tool for analyzing these problems called the overlap gap property (or OGP). Gamarnik described the new methodology in a recent paper in the Proceedings of the National Academy of Sciences.

The Pentagon, the CIA, and the State Department are already using the technology.

Who can forget the attack on Capital last January 6th? For those who do remember it well, there is an urgency to do something to avoid it ever happening again. One way to do that is to predict these events before they happen just like you can predict weather patterns.

Some data scientists believe they can achieve exactly that, according to The Washington Post. “We now have the data — and opportunity — to pursue a very different path than we did before,” said Clayton Besaw, who helps run CoupCast, a machine-learning-driven program based at the University of Central Florida that predicts coups for a variety of countries.

Continue reading “Can Algorithms Predict Political Unrest? These Data Scientists Believe So” »

Today’s Google Doodle honors the late physicist Stephen Hawking on his 80th birthday. Hawking was a renowned cosmologist, and he spent his career theorizing about the origins of the universe, the underlying structure of reality, and the nature of black holes. But he became a household name for the way he communicated those ideas to the public through books and TV appearances.

“My goal is simple,” he once said. “It is a complete understanding of the universe, why it is as it is and why it exists at all.”

One of Hawking’s best-known ideas is that black holes slowly regurgitate information about all the matter they’ve swallowed — but it comes out in a jumbled form called Hawking radiation. In 1974, Hawking proposed that the event horizon of a black hole emits energy. Because energy can be converted into mass, and vice versa (that’s what Albert Einstein’s famous equation E=mc² tells us), emitting all that energy into space will shrink the black hole. Eventually, it will run out of mass and disappear.

Continue reading “Saturday’s Google Doodle Celebrates Physicist Stephen Hawking” »

A review of The Age of AI and Our Human Future by Henry A. Kissinger, Eric Schmidt, and Daniel Huttenlocher. Little, Brown and Company, 272 pages (November, 2021).

Potential bridges across the menacing chasm of incompatible ideas are being demolished by a generation of wannabe autocrats presenting alternative facts as objective knowledge. This is not new. The twist is that modern network-delivery platforms can insert, at scale, absurd information into national discourse. In fact, sovereign countries intent on political mischief and social disruption already do this to their adversaries by manipulating the stories they see on the Internet.

Continue reading “The Case Against the Case Against AI” »

About a decade ago, MIT researchers discovered a technique that speeds physics modeling by 1000X. They spun this out into a new company, called Akselos, which has been helping enterprises to weave the tech into various kinds of digital twins used to improve shipping, refining, and wind power generation.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making. Connected sensors on the physical asset collect data that can be mapped onto the virtual model.

The specific innovation improves the performance of finite element analysis (FEA) algorithms which underpin most types of physics simulations. Akselos experience over the last decade can help executives explore the implications of the million-fold improvements in physics simulation that Nvidia is now demonstrating thanks to improvement in hardware, scalability, and new algorithms.

Smart factories will be very useful in metaverse.workers can operated machines in factories using Internet.

As the idea of interconnected and intelligent manufacturing is gaining ground, competing in the world of Industry 4.0 can be challenging if you’re not on the very cusp of innovation.

Seeing the growing economic impact of IIoT around the globe, many professionals and investors have been asking themselves if the industry is on the verge of a technological revolution. But judging from the numbers and predictions, there is tangible and concrete evidence that the idea of smart manufacturing has already burst into corporate consciousness. According to IDC, global spending on the Internet of Things in 2020 is projected to top $840 billion if it maintains the 12.6% year-over-year compound annual growth rate. There is no doubt that a huge part of this expenditure will be devoted to the introduction of IoT into all types of industry, especially including manufacturing.

Continue reading “What is the smart factory? The impact of factory 4.0 on manufacturing” »

AI algorithm interprets brain EEG signals to guide robot arms, deep learning’s overinterpretation problem and how ensembles can help.