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.
Named xenobots after the African clawed frog (Xenopus laevis) from which they take their stem cells, the machines are less than a millimeter (0.04 inches) wide — small enough to travel inside human bodies. They can walk and swim, survive for weeks without food, and work together in groups.
These are “entirely new life-forms,” said the University of Vermont, which conducted the research with Tufts University’s Allen Discovery Center.
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:
When doctors in a Washington hospital sought to treat the first confirmed case of the Wuhan coronavirus in the United States on Wednesday, they tapped a device called Vici that allowed them to interact with their patient not in person, but through a screen.
The telehealth device, which looks like a tablet on wheels that doctors can use to talk to patients and perform basic diagnostic functions, like taking their temperature, is one of a handful of high-tech machines that doctors, airport workers, and hotel staff are using to help contain the outbreak that has been sweeping the world since it was discovered in Wuhan, China in late December.
“Caregivers provide care within the isolation unit, but technology is allowing us to reduce the number of up-close interactions,” says Dr. Amy Compton-Phillips, chief clinical officer at Providence Regional Medical Center in Everett, Washington, where the patient is being treated. Vici, made by Santa Barbara, California-based InTouch Health, resembles a tablet on wheels, and can protect caregivers from infection.
If you’re interested in mind uploading, then I have an excellent article to recommend. This wide-ranging article is focused on neuromorphic computing and has sections on memristors. Here is a key excerpt:
“…Perhaps the most exciting emerging AI hardware architectures are the analog crossbar approaches since they achieve parallelism, in-memory computing, and analog computing, as described previously. Among most of the AI hardware chips produced in roughly the last 15 years, an analog memristor crossbar-based chip is yet to hit the market, which we believe will be the next wave of technology to follow. Of course, incorporating all the primitives of neuromorphic computing will likely require hardware solutions even beyond analog memristor crossbars…”
Here’s a web link to the research paper:
Computers have undergone tremendous improvements in performance over the last 60 years, but those improvements have significantly slowed down over the last decade, owing to fundamental limits in the underlying computing primitives. However, the generation of data and demand for computing are increasing exponentially with time. Thus, there is a critical need to invent new computing primitives, both hardware and algorithms, to keep up with the computing demands. The brain is a natural computer that outperforms our best computers in solving certain problems, such as instantly identifying faces or understanding natural language. This realization has led to a flurry of research into neuromorphic or brain-inspired computing that has shown promise for enhanced computing capabilities. This review points to the important primitives of a brain-inspired computer that could drive another decade-long wave of computer engineering.
