Lifeboat Foundation

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player—or much of a poker fan, in fact—but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely—a view shared years later by Sandholm in his research with artificial intelligence.

“Poker is the main benchmark and challenge program for games of imperfect information,” Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.

Tall and thin, with wire-frame glasses and neat brow hair framing a friendly face, Sandholm is behind the creation of three computer programs designed to test their mettle against human poker players: Claudico, Libratus, and most recently, Pluribus. (When we met, Libratus was still a toddler and Pluribus didn’t yet exist.) The goal isn’t to solve poker, as such, but to create algorithms whose decision making prowess in poker’s world of imperfect information and stochastic situations—situations that are randomly determined and unable to be predicted—can then be applied to other stochastic realms, like the military, business, government, cybersecurity, even health care.

By estimating how much time you need.

https://bit.ly/3jwJDYs from Neurozo Innovation

How can we tell whether an idea is truly impossible (so one should not fake it and had better give it up as soon as possible) or just very hard to achieve? In this article, we answer the question on the basis of “time”…

Continue reading “Fake It or Drop It? Answering the Question” »

“What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. Study up now.”

Full Story:

If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

Continue reading “The Next Generation Of Artificial Intelligence” »

KENNEDY SPACE CENTER (FL), October 19, 2020 – The Center for the Advancement of Science in Space (CASIS) and the National Science Foundation (NSF) announced three flight projects that were selected as part of a joint solicitation focused on leveraging the International Space Station (ISS) U.S. National Laboratory to further knowledge in the fields of tissue engineering and mechanobiology. Through this collaboration, CASIS, manager of the ISS National Lab, will facilitate hardware implementation, in-orbit access, and astronaut crew time on the orbiting laboratory. NSF invested $1.2 million in the selected projects, which are seeking to advance fundamental science and engineering knowledge for the benefit of life on Earth.

This is the third collaborative research opportunity between CASIS and NSF focused on tissue engineering. Fundamental science is a major line of business for the ISS National Lab, and by conducting research in the persistent microgravity environment offered by the orbiting laboratory, NSF and the ISS National Lab will drive new advances that will bring value to our nation and spur future inquiries in low Earth orbit.

Continue reading “Three Tissue Engineering Projects Awarded From Joint National Science Foundation and CASIS Solicitation to Leverage the Space Station” »

Featured image: TransAlta / Twitter One of Tesla’s main goals is the deployment of energy generation and storage systems. These microgrids can provide electricity to homes, relieving our dependence on a mine-and-burn hydrocarbon economy. Tesla currently has over 120 operational microgrids around the world, which is an excellent confirmation of the company’s success in achieving its goals.

SOUTHLAKE, Texas, Oct. 22, 2020 /PRNewswire/ — Sabre Corporation (NASDAQ: SABR), the leading software and technology company that powers the global travel industry, today announced that Sabre and Google are developing an Artificial Intelligence (AI)-driven technology platform that is an industry first in travel. The technology, known as Sabre Travel AI™, is infused with Google’s state-of-the-art AI technology and advanced machine-learning capabilities that will help customers to deliver highly relevant and personalized content more quickly, deliver personalized content that better meets the demands of today’s traveler, and create expanded revenue and margin growth opportunities. The Company is integrating Sabre Travel AI into certain products in its existing portfolio, with plans to bring those to market in early 2021.

“Sabre Travel AI is a game-changer. We are proud to be working with Google to build technologies that will seek to re-define the way travel companies do business, and turn the insights derived from analyses into repeatable, scalable operations. The development of Sabre Travel AI marks a milestone in our technology transformation and a significant step toward achieving our 2025 vision of personalized retailing,” said Sundar Narasimhan, president of Sabre Labs. “With the creation of Sabre Travel AI, we are rebuilding our platform on cloud-native, data-driven technology that can be integrated into the existing and future products that Sabre offers. We are combining Google Cloud’s infrastructure, AI and machine-learning capabilities with Sabre’s deep travel domain knowledge to create, not next, but third-generation solutions that we believe are smarter, faster and more cost-effective – a first-of-its kind in travel.”

Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with MITRE, and contributions from 11 organizations including IBM, NVIDIA, Bosch, Microsoft is releasing the Adversarial ML Threat Matrix, an industry-focused open framework, to empower security analysts to detect, respond to, and remediate threats against ML systems.

During the last four years, Microsoft has seen a notable increase in attacks on commercial ML systems. Market reports are also bringing attention to this problem: Gartner’s Top 10 Strategic Technology Trends for 2020, published in October 2019, predicts that “Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems.” Despite these compelling reasons to secure ML systems, Microsoft’s survey spanning 28 businesses found that most industry practitioners have yet to come to terms with adversarial machine learning. Twenty-five out of the 28 businesses indicated that they don’t have the right tools in place to secure their ML systems. What’s more, they are explicitly looking for guidance. We found that preparation is not just limited to smaller organizations. We spoke to Fortune 500 companies, governments, non-profits, and small and mid-sized organizations.

Our survey pointed to marked cognitive dissonance especially among security analysts who generally believe that risk to ML systems is a futuristic concern. This is a problem because cyber attacks on ML systems are now on the uptick. For instance, in 2020 we saw the first CVE for an ML component in a commercial system and SEI/CERT issued the first vuln note bringing to attention how many of the current ML systems can be subjected to arbitrary misclassification attacks assaulting the confidentiality, integrity, and availability of ML systems. The academic community has been sounding the alarm since 2004, and have routinely shown that ML systems, if not mindfully secured, can be compromised.

As the electric vehicle sector grows, the demand for batteries is poised to see a significant increase. This is a big opportunity for companies like LG Chem, which supplies batteries for several EV makers like Tesla. Amidst this surging demand, LG Chem revealed on Wednesday that it is planning on tripling its production capacity for cylindrical batteries, the type used by the Silicon Valley-based electric car maker.

LG Chem currently forecasts a further rise in its battery sales and profits this fourth quarter. This is rather optimistic of the South Korean company considering that the company has already posted record quarterly earnings thanks to its growing EV battery business. “Sales are continuously expected to grow thanks to greater shipments of automotive batteries and cylindrical batteries for EVs,” LG Chem noted.

Global Manufacturing Companies Trust LandingLens to Enhance Their Existing Visual Inspection Systems with AI

PALO ALTO, Calif. – October 21, 2020 – Landing AI, a company that empowers customers to harness the business value of AI by providing enablement tools and transformation programs, today unveiled LandingLens, an end-to-end visual inspection platform specifically designed to help manufacturers build, deploy, and scale AI-powered visual inspection solutions.

Visual inspection is a widely used method in manufacturing for processes like defect identification and assembly verification. While this has generally been performed by human workers and traditional rule-based machine vision, more and more companies are turning to AI to automate and enhance their visual inspection operations given the accuracy, flexibility and low cost that the technology brings.