Elon Musk is recruiting for his AI team at Tesla, and he says education is “irrelevant.” The team members will report “directly” to Musk and “meet/email/text” with Musk “almost every day.” Musk will also throw a “super fun” party at his house with the Tesla artificial intelligence and autopilot teams.
Amid mounting concern about a novel coronavirus spreading from China, Lawrence Livermore National Laboratory (LLNL) researchers have developed a preliminary set of predictive 3D protein structures of the virus to aid research efforts to combat the disease.
The models are based on the genomic sequence of the novel coronavirus and a protein found in the virus that causes Severe Acute Respiratory Syndrome (SARS), which closely resembles the new virus.
The researchers plan to use the models to accelerate countermeasure design, using a combination of machine learning, biological experiments and simulation on supercomputers.
Roboticists at the California Institute of Technology launched an initiative called RoAMS, which uses the latest research in robotic walking to create a new kind of medical exoskeleton. With the ability to move dynamically, using neurocontrol interfaces, these exoskeletons allow users to balance and walk without the crutches. Learn more in the latest IEEE Spectrum article! https://ieeexplore.ieee.org/document/8946313 #RoAMS #exoskeletons
Bipedal robots have long struggled to walk as humans do-balancing on two legs and moving with that almost-but-not-quite falling forward motion that most of us have mastered by the time we’re a year or two old. It’s taken decades of work, but robots are starting to get comfortable with walking, putting them in a position to help people in need.
In the future of urban warfare, swarms of autonomous drones and ground vehicles will win battles before they even begin.
DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program recently tested a swarm of 250 unmanned vehicles in a mock city at Camp Shelby Joint Forces Training Center in Mississippi.
The US Air Force (USAF) is expected to declare an initial operating capability (IOC) with the Raytheon Missile Systems GBU-53/B StormBreaker glide munition on the Boeing F-15E Strike Eagle multirole combat aircraft in the first half of this year, following compliance with a Required Assets Available (RAA) milestone, which is anticipated in the next few weeks.
The StormBreaker RAA is the pre-IOC benchmark capability to arm 12 USAF F-15Es with two, fully-loaded (four weapons) BRU-61/A carriage systems each for 1.5 sorties (144 assets total).
Optimised to address moving battlefield targets, StormBreaker — formerly designated ‘Small Diameter Bomb II’ — is a 250 lb-class, air-launched unpowered glide weapon system furnished with a unique tri-mode seeker, which combines millimetre wave (MMW) radar, imaging infrared (IIR), and semi-active laser (SAL) sensors with a GPS/inertial navigation system (INS) autopilot (the provider for which is undisclosed) for precision accuracy in adverse weather conditions. The seeker’s optical dome is protected by a clamshell shroud, which is jettisoned before the seeker is activated. A Rockwell Collins TacNet bi-directional dual-band datalink enables Joint Tactical Information Distribution System (JTIDS) connectivity with aircraft and an ultra-high frequency (UHF) link with a ground designator.
The drug, known as DSP-1181, was created by using algorithms to sift through potential compounds, checking them against a huge database of parameters, including a patient’s genetic factors. Speaking to the BBC, Exscientia chief executive Professor Andrew Hopkins described the trials as a “key milestone in drug discovery” and noted that there are “billions” of decisions needed to find the right molecules for a drug, making their eventual creation a “huge decision.” With AI, however, “the beauty of the algorithm is that they are agnostic, so can be applied to any disease.”
We’ve already seen multiple examples of AI being used to diagnose illness and analyze patient data, so using it to engineer drug treatment is an obvious progression of its place in medicine. But the AI-created drugs do pose some pertinent questions. Will patients be comfortable taking medication designed by a machine? How will these drugs differ from those developed by humans alone? Who will make the rules for the use of AI in drug research? Hopkins and his team hope that these and myriad other questions will be explored in the trials, which will begin in March.
In a recent IEEE Spectrum article, read how autonomous fighter jets will accompany human-piloted planes. This self-piloted airplane may be the first experiment to truly portend the end of the era of crewed warplanes. #autonomousplane #autonomousfighterjet
If you drive along the main northern road through South Australia with a good set of binoculars, you may soon be able to catch a glimpse of a strange, windowless jet, one that is about to embark on its maiden flight. It’s a prototype of the next big thing in aerial combat: a self-piloted warplane designed to work together with human-piloted aircraft.
Do you think Xenobots is the early stage of nanobots, which could repair our body to achieve longevity escape velocity?
Scientists have created the world’s first living, self-healing robots using stem cells from frogs.
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The hidden secret of artificial intelligence is that much of it is actually powered by humans. Well, to be specific, the supervised learning algorithms that have gained much of the attention recently are dependent on humans to provide well-labeled training data that can be used to train machine learning algorithms. Since machines have to first be taught, they can’t teach themselves (yet), so it falls upon the capabilities of humans to do this training. This is the secret achilles heel of AI: the need for humans to teach machines the things that they are not yet able to do on their own.
Machine learning is what powers today’s AI systems. Organizations are implementing one or more of the seven patterns of AI, including computer vision, natural language processing, predictive analytics, autonomous systems, pattern and anomaly detection, goal-driven systems, and hyperpersonalization across a wide range of applications. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. It goes without saying then, that the machine learning data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.
According to analyst firm Cognilytica, over 80% of AI project time is spent preparing and labeling data for use in machine learning projects:
