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Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in the brain. Using the speed of modern computers and large datasets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience.

Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artificial intelligence algorithms—based on the very slow brain dynamics—which outperform learning rates achieved to date by state-of-the-art learning algorithms.

In an article published today in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years.

Just five months ago at the RSA conference, the NSA released Ghidra, a piece of open source software for reverse-engineering malware. It was an unusual move for the spy agency, and it’s sticking to its plan for regular updates — including some based on requests from the public.

In the coming months, Ghidra will get support for Android binaries, according to Brian Knighton, a senior researcher for the NSA, and Chris Delikat, a cyber team lead in its Research Directorate, who previewed details of the upcoming release with CyberScoop. Knighton and Delikat are discussing their plans at a session of the Black Hat security conference in Las Vegas Thursday.

Before the Android support arrives, a version 9.1 will include new features intended to save time for users and boost accuracy in reverse-engineering malware — enhancements that will come from features such as processor modules, new support for system calls and the ability to conduct additional editing, known as sleigh editing, in the Eclipse development environment.

The Defense Advanced Research Projects Agency (DARPA) is experimenting with using a swarm of autonomous drones and ground robots to assist with military missions. In a video of a recent test, DARPA showed how its robots analyzed two city blocks to find, surround, and secure a mock city building.

DARPA conducted its test back in June in Georgia, featuring both drones and ground-based robots. The demonstration was part of DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, which is designed to eventually accompany small infantry units as they work in dense urban environments, and could eventually scale up to 250 drones and ground robots. The test back in June was the second of six planned tests, which DARPA says will increase in complexity as they happen over the next couple of years.

This robot sees and analyzes trash the same way a person does, but sorts it a lot faster 🤖.

This video is the ninth in a multi-part series discussing computing and the second discussing non-classical computing. In this video, we’ll be discussing what quantum computing is, how it works and the impact it will have on the field of computing.

[0:28–6:14] Starting off we’ll discuss, what quantum computing is, more specifically — the basics of quantum mechanics and how quantum algorithms will run on quantum computers.

Continue reading “What Is Quantum Computing (Quantum Computers Explained)” »

Driverless cars seem straight out of the future, but thanks to developments in autonomous vehicle technology, that future could be right around the corner. Similar to the incremental adoption of electric cars — with hybrid models hitting the road first—car manufacturers have been introducing driverless features to conventional vehicles for some time now. Already on the road today we have partially autonomous vehicles, cars and trucks with cruise control, braking assistance and self-parking technology. And industry specialists predict that fully autonomous vehicles could be on the market in a matter of a few years.

Of the 1,500 active volcanoes worldwide, about 6 percent of them erupt each year, or 50 to 85. Less than half of all volcanoes have sensors, and even fewer are considered well-monitored, the result of high costs and difficulty in maintaining equipment in such unforgiving environments. Volcanoes that are considered dormant rarely have any monitoring, despite surprises like the 2008 eruption of the Chaitén volcano in Chile after 8,000 years of inactivity.

Now, volcanologists are turning to satellite imagery and artificial intelligence to keep a closer eye on more volcanoes and, eventually, forecast eruptions. MOUNTS (Monitoring Unrest from Space), currently tracks 18 volcanoes, including Mount Fuego in Guatemala and Mount Etna in Italy.

With 800 million people living within 62 miles of an active volcano, there are plenty of reasons to increase monitoring.

When it comes to detecting whether an image or video is fake, it’s the little mistakes that matter, and to help with the sleuthing, the Defense Advanced Research Projects Agency wants to improve what it calls “semantic forensics.”

The agency announced this week that it plans to hold a proposers day on Aug. 28 to give more information on an anticipated Semantic Forensics (SemaFor) Broad Agency Announcement. It’s the latest expression of DARPA’s interest in countering the chaos-inducing potential of “deepfakes” — the practice of using artificial intelligence to manipulate audio, video, text or photo files.

The SemaFor program, DARPA says, will explore ways to get around some of the weaknesses of current deepfake detection tools. The statistical detection techniques used in the past have been successful to date, but those tools won’t always have the upper hand.