Lifeboat Foundation

A team of researchers including Neil Johnson, a professor of physics at the George Washington University, has discovered that decentralized systems work better when the individual parts are less capable.

Dr. Johnson was interested in understanding how systems with many moving parts can reach a desired target or goal without centralized control. This explores a common theory that decentralized systems, those without a central brain, would be more resilient against damage or errors.

This research has the potential to inform everything from how to effectively structure a company, build a better autonomous vehicle, optimize next-generation artificial intelligence algorithms—and could even transform our understanding of evolution. The key lies in understanding how the “sweet spot” between decentralized and centralized systems varies with how clever the pieces are, Dr. Johnson said.

Continue reading “Decentralized systems are more efficient at reaching a target when its components are not overly capable” »

Forensics is on the cusp of a third revolution in its relatively young lifetime. The first revolution, under the brilliant but complicated mind of J. Edgar Hoover, brought science to the field and was largely responsible for the rise of criminal justice as we know it today. The second, half a century later, saw the introduction of computers and related technologies in mainstream forensics and created the subfield of digital forensics.

We are now hurtling headlong into the third revolution with the introduction of Artificial Intelligence (AI) – intelligence exhibited by machines that are trained to learn and solve problems. This is not just an extension of prior technologies. AI holds the potential to dramatically change the field in a variety of ways, from reducing bias in investigations to challenging what evidence is considered admissible.

AI is no longer science fiction. A 2016 survey conducted by the National Business Research Institute (NBRI) found that 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. “The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long winter,” writes Gil Press, contributor to Forbes.com.

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Researchers develop an app that can estimate the biological age of a rodent from its mug shot, and could give a boost to the science of human aging in the process.

AI may quickly point out a corrupt official, but it is not very good at explaining the process it has gone through to reach such a conclusion.

“We just use the machine’s result as reference,” Zhang Yi, an official in a province that’s still using the software, told the SCMP. “We need to check and verify its validity. The machine cannot pick up the phone and call the person with a problem. The final decision is always made by humans.”

Algorithmic Justice

Continue reading “China Built an AI to Detect Corruption and Officials Shut it Down” »

AI farms are well suited to impoverished regions like Guizhou, where land and labor are cheap and the climate temperate enough to enable the running of large machines without expensive cooling systems. It takes only two days to train workers like Yin in basic AI tagging, or a week for the more complicated task of labeling 3D pictures.

A battle for AI supremacy is being fought one algorithm at a time.

A new breed of algorithms has mastered Atari video games 10 times faster than state-of-the-art AI, with a breakthrough approach to problem solving.

Designing AI that can negotiate planning problems, especially those where rewards are not immediately obvious, is one of the most important research challenges in advancing the field.

A famous 2015 study showed Google DeepMind AI learnt to play Atari video games like Video Pinball to human level, but notoriously failed to learn a path to the first key in 1980s video game Montezuma’s Revenge due to the game’s complexity.

Continue reading “Atari master: New AI smashes Google DeepMind in video game challenge” »

Welcome to the future.

In the first experiment of its kind, scientists have been able to translate brain signals directly into intelligible speech. It may sound like wild science fiction at first, but this feat could actually help some people with speech issues.

And yes, we could also get some futuristic computer interfaces out of this.

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Last year, Microsoft Corp.’s Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company’s administrators, who usually logs on from New York, was trying to gain entry from Romania. And no, the admin wasn’t on vacation. A hacker had broken in.

Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far.

Chalk one up to a new generation of artificially intelligent software that adapts to hackers’ constantly evolving tactics. Microsoft, Alphabet Inc.’s Google, Amazon.com Inc. and various startups are moving away from solely using older “rules-based” technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behavior and previous attacks to ferret out and stop hackers.

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Generative adversarial networks are not just good for causing mischief. They can also show us how AI algorithms “think.”