This article was written, edited and designed on laptop computers. Such foldable, transportable devices would have astounded computer scientists just a few decades ago, and seemed like sheer magic before that. The machines contain billions of tiny computing elements, running millions of lines of software instructions, collectively written by countless people across the globe. You click or tap or type or speak, and the result seamlessly appears on the screen. Computers were once so large they filled rooms. Now they're everywhere and invisible, embedded in watches, car engines, cameras, televisions and toys. They manage electrical grids, analyze scientific data and predict the weather. The modern world would be impossible without them. Scientists aim to make computers faster and programs more intelligent, while deploying technology in an ethical manner. Their efforts build on more than a century of innovation. In 1833, English mathematician Charles Babbage conceived a programmable machine that presaged today's computing architecture, featuring a "store" for holding numbers, a "mill" for operating on them, an instruction reader and a printer. This Analytical Engine also had logical functions like branching (if X, then Y).

Bias in law enforcement has long been a problem in America. The killing of George Floyd, an unarmed Black man, by Minneapolis police officers in May 2020 most recently brought attention to this fact--sparking waves of protest across the country, and highlighting the ways in which those who are meant to "serve and protect" us do not serve all members of society equally. With the dawn of artificial intelligence (AI), a slew of new machine learning tools promise to help protect us--quickly and precisely tracking those who may commit a crime before it happens--through data. Past information about crime can be used as material for machine learning algorithms to make predictions about future crimes, and police departments are allocating resources towards prevention based on these predictions. The tools themselves, however, present a problem: The data being used to "teach" the software systems is embedded with bias, and only serves to reinforce inequality.

Apple has secured another patent related to autonomous vehicles even as it remains tight-lipped about its AV plans. Patent number 11,243,532 from the U.S. Patent and Trademark Office relates to machine learning systems and algorithms for reasoning, decision-making and motion-planning for controlling the motion of autonomous or partially autonomous vehicles. First unearthed by Patently Apple, the patent, titled "evaluating varying-sized action spaces using reinforcement learning," details a system that evaluates actions using a reinforcement learning model to help direct the movements of a vehicle. "A set of actions corresponding to a particular state of the environment of a vehicle is identified. A respective encoding is generated for different actions of the set, using elements such as distinct colors to distinguish attributes such as target lane segments," the abstract reads.

Robots and self-driving cars have one very large challenge in common, how to navigate the world. Typically, that task is approached by artificial intelligence as a problem of how to map the surroundings, to construct a precise overview of the geometry of a scene before a robot or a car moves across that terrain. There may be a simpler way.

Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is "healthy" or requires maintenance. Whether railway wheels or generators in a power plant, whether pumps or valves--they all make sounds. For trained ears, these noises even have a meaning: devices, machines, equipment or rolling stock sound differently when they are functioning properly compared to when they have a defect or fault. The sounds they make, thus, give professionals useful clues as to whether a machine is in a good--or "healthy"--condition, or whether it will soon require maintenance or urgent repair.

Machine learning algorithms enhanced technical performance and learning outcomes during simulated brain tumor removal. The COVID-19 pandemic has presented both challenges and opportunities for medical training. Remote learning technology has become increasingly important in several fields. A new study finds that in a remote environment, an artificial intelligence (AI) tutoring system can outperform expert human instructors. The Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro (Montreal Neurological Institute-Hospital) recruited seventy medical students to perform virtual brain tumor removals on a neurosurgical simulator.

They can be too opaque, and to raise the signal of what is most important, they end up hiding too much. "The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise."--

George Orwell's novel 1984 got one thing wrong. A surveillance state will not have people watching people, as the Stasi did in East Germany. Computers will be the ones watching people. Technology lets you perform surveillance at an industrial scale. This is already happening in China, where facial recognition software is being used by law enforcement for catching relatively minor offenders such as jaywalkers to enabling much more disturbing activities such as tracking Uyghurs.

Is there a line connecting machine learning observability to explainability, leading to responsible AI? Aporia, an observability platform for machine learning, thinks so. After launching its platform in 2021, and seeing good traction, Aporia today announced a $25 million Series A funding round. Aporia CEO and co-founder Liran Hason met with VentureBeat to discuss Aporia's vision, its inner workings and its growth. Hason, who founded Aporia in 2019, has a background in software engineering. After a five-year stint in the elite technological unit of the Israeli intelligence forces, he joined Adallom, a cloud security startup that was later acquired by Microsoft.

The software, which uses an AI technique called deep learning, can predict the shape of proteins to the nearest atom, the first time a computer has matched the slow but accurate techniques used in the lab. Scientific teams around the world have started using it for research on cancer, antibiotic resistance, and covid-19. DeepMind has also set up a public database that it's filling with protein structures as AlphaFold2 predicts them. It currently has around 800,000 entries, and DeepMind says it will add more than 100 million--nearly every protein known to science--in the next year. DeepMind has spun off this work into a company called Isomorphic Labs, which it says will collaborate with existing biotech and pharma companies.