Halloween is forever if you’re a Japanese black bear.
The Japanese city of Takikawa is using robot wolves to prevent bear attacks. Bear encounters have been on the rise as cities have grown and acorns — a key part of black bears’ pre-hibernation diet — have become harder to find. Since these robots were installed in Takikawa, there have been no attacks in the city’s surrounding area.
The tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behavior have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behavior, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase-separating proteins. Moreover, by combining this insight with neural network-based sequence embeddings, we trained machine-learning classifiers that identified phase-separating sequences with high accuracy, including from independent external test data.
Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed, with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, we established an in silico strategy for understanding on a global level the associations between protein sequence and phase behavior and further constructed machine-learning models for predicting protein liquid–liquid phase separation (LLPS). Our analysis highlighted that LLPS-prone proteins are more disordered, less hydrophobic, and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database and that they show a fine balance in their relative content of polar and hydrophobic residues.
They weren’t scheduled to return to Earth until April 28th at the earliest, so why did NASA astronauts Michael Hopkins, Victor Glover, and Shannon Walker, along with Japan Aerospace Exploration Agency (JAXA) astronaut Soichi Noguchi, suit up and climb aboard the Crew Dragon Resilience on April 5th? Because a previously untested maneuver meant that after they closed the hatch between their spacecraft and the International Space Station, there was a chance they weren’t going to be coming back.
On paper, moving a capsule between docking ports seems simple enough. All Resilience had to do was undock from the International Docking Adapter 2 (IDA-2) located on the front of the Harmony module, itself attached to the Pressurized Mating Adapter 2 (PMA-2) that was once the orbital parking spot for the Space Shuttle, and move over to the PMA-3/IDA-3 on top of Harmony. It was a short trip through open space, and when the crew exited their craft and reentered the Station at the end of it, they’d only be a few meters from where they started out approximately 45 minutes prior.
The maneuver was designed to be performed autonomously, so technically the crew didn’t need to be on Resilience when it switched docking ports. But allowing the astronauts to stay aboard the station while their only ride home undocked and flew away without them was a risk NASA wasn’t willing to take.
Those that have grown up with open source in the past 20 years know that open source is popular. It’s popular because of a number of reasons including that it fosters innovation, speeds up delivery, and helps us all collectively learn from each other.
We ourselves at the AGI Lab have just assumed this was a good thing. We believe that Open Source research helps everyone. Many research groups in AGI research are already open sourcing including Open Cog, Open Nars, and more.
From an ethical standpoint, we use a system called SSIVA Theory to teach ethics to systems we work on such as Uplift and so we assumed we should release some of our code (which we have here on this blog and in papers) and we planned on open sourcing a version of the mASI or collective system that we work on that uses an AGI Cognitive Architecture.
Going forward, Northrop Grumman projects that starting in 2025 they will begin refueling satellites in orbit and removing orbital debris from nearby “high value” satellites, Anderson said.
Satellites could live longer lives thanks to new technology being tested by Northrop Grumman.
On Monday (April 12), Northrop Grumman Corporation and SpaceLogistics LLC (a subsidiary of Northrop Grumman) announced that their satellite servicing spacecraft, called Mission Extension Vehicle 2 (MEV-2), successfully docked to the commercial communications satellite Intelsat 10–02 (IS-10–02).
Altman suggests taxing capital rather than labour. And, these taxes can be used to distribute ownership and wealth to citizens. Altman said his idea is nothing new but is more critical than ever as AI applications outclass their contemporaries. “If everyone owns a slice of American value creation, everyone will want America to do better,” wrote Altman.
“We should therefore focus on taxing capital rather than labor, and we should use these taxes as an opportunity to directly distribute ownership and wealth to citizens.”
Pinning careers and hopes to Moore’s law does sound like utopia, and even Altman admits it. He also believes that the AI revolution will compensate for the disruption by generating new jobs. Jobs, which we haven’t heard of yet (think: urban rodentologist). That’s why the OpenAI co-founder stresses establishing a system that will result in a society that is “less divisive” and enables everyone to participate in its gains. According to him, this technology revolution is an eventuality, and nothing can stop it. The revolution will be further accelerated as machines that make machines get smarter. For example, OpenAI’s GPT-3 was used to generate machine learning code, a million-dollar startup idea in itself. One application can put many developer jobs at risk.
Domino’s and Nuro teamed up for autonomous pizza delivery in Houston. Don’t get your hopes up, though, for a driverless drop-off: Many restrictions apply, and only a handful of hungry people can opt in right now.
Beginning this week, select customers who place a prepaid website order from the lone participating pizza shop in Woodland Heights can opt to have their food delivered by Nuro’s R2 robot. Those lucky patrons receive text alerts highlighting R2’s location, and can track the vehicle via GPS on the order confirmation page. Domino’s also provides a unique personal identification number required to open the bot’s door and reveal that piping hot pizza.
“We’re excited to continue innovating the delivery experience for Domino’s customers by testing autonomous delivery with Nuro in Houston,” Dennis Maloney, Domino’s senior vice president and chief innovation officer, said in a statement. “There is still so much for our brand to learn about the autonomous delivery space.”
In real-world attacks, “a simple scenario… would have an attacker infiltrating a manufacturing network via an RCE on an exposed IoT device then causing a production line to stop by causing a DoS on an industrial controller,” Daniel dos Santos, research manager at Forescout Research Labs, said. “Similarly, the attacker could switch off the lights of a target company by leveraging a vulnerable building automation controller.”
Many of the Name: Wreck vulnerabilities stem from DNS implementations of a protocol feature called message compression. Message compression reduces the size of DNS messages, due to DNS response packets often including the same domain name. This compression mechanism has been problematic to implement on products for 20 years, said researchers, causing issues on DNS servers, enterprise devices and, more recently, TCP/IP stacks. Forescout researchers disclosed three flaws relating to message compression during previous research into TCP/IP vulnerabilities (particularly the Ripple20 and AMNESIA:33 sets of flaws). Consequently, they hunted for other similar types of flaws in other protocol stacks.
As part of the ensuing Name: Wreck research, researchers found DNS message compression vulnerabilities in four popular TCP/IP stacks, including FreeBSD (version 12.1), IPnet (version VxWorks 6.6), NetX (version 6.0.1) and Nucleus Net (version 4.3). The most critical flaws exist in FreeBSD, popular IT software used by high-performance servers in millions of IT networks, including major websites such as Netflix and Yahoo; and in Siemens’ Nucleus NET firmware, which has been used for decades by critical OT and Internet-of-Things (IoT) devices.
MIT Technology Review Insights, in association with AI cybersecurity company Darktrace, surveyed more than 300 C-level executives, directors, and managers worldwide to understand how they’re addressing the cyberthreats they’re up against—and how to use AI to help fight against them.
Cyberattacks continue to grow in prevalence and sophistication. With the ability to disrupt business operations, wipe out critical data, and cause reputational damage, they pose an existential threat to businesses, critical services, and infrastructure. Today’s new wave of attacks is outsmarting and outpacing humans, and even starting to incorporate artificial intelligence (AI). What’s known as “offensive AI” will enable cybercriminals to direct targeted attacks at unprecedented speed and scale while flying under the radar of traditional, rule-based detection tools.
Some of the world’s largest and most trusted organizations have already fallen victim to damaging cyberattacks, undermining their ability to safeguard critical data. With offensive AI on the horizon, organizations need to adopt new defenses to fight back: the battle of algorithms has begun.