In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. A research team led by the University of Washington has developed new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. Optical computing noise essentially comes from stray light particles, or photons, that originate from the operation of lasers within the device and background thermal radiation. Of course the optical computer didn't have a human hand for writing, so its form of "handwriting" was to generate digital images that had a style similar to the samples it had studied, but were not identical to them.

A giant 18-wheel transport truck is barreling down a multi-lane Texas highway, and there is no one behind the wheel. The futuristic idea may seem surreal, but it is being tested in this vast southern US state, which has become the epicenter of a rapidly developing self-driving vehicle industry. Before driverless trucks are allowed onto roads and highways, however, multiple tests must still be conducted to ensure they are safe. Self-driving lorries are operated using radars, laser scanners, cameras and GPS antennas that communicate with piloting software. "Each time we drive a mile or a kilometer in real life, we re-simulate a thousand more times on the computer by changing hundreds of parameters," explains Pierre-François Le Faou, trucking partner development manager at Waymo, the self-driving unit at Google's parent company Alphabet.

Assistant Professor Wim van Rees and his team have developed simulations of self-propelled undulatory swimmers to better understand how fish-like deformable fins could improve propulsion in underwater devices, seen here in a top-down view. MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet."

In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.

Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify. In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months.

When my business partner and I launched our company in the mid-1990s, we debated whether to install an automated phone system. It seemed to be the wave of the future, and we were intrigued by the prospect of it saving us the expense of hiring a receptionist. We eventually decided against it because of the frustrations we'd experienced on the other end of such systems. As a startup, the last thing we wanted to do is frustrate our clients and vendors by making them use a complicated telephone tree. In some circumstances, there's just nothing that can replace a human being.

Many believe that Wall Street is always an arm and a leg ahead of us. That it's impossible for the little guys to find amazing companies before it is too late. Well, with a little bit of help, I can steer you into mega trends that Paul and I think will flourish for years to come. Paul has a way of looking at the market, finding trends and investing in the future like no one else. America 2.0 and the Fourth Industrial Revolution is the biggest wealth-building era we'll ever see.

Machine learning has been enjoying prominence across the business landscape for quite some time now. It has paved the way for technological advancements like never before. Simply put, there are tons of applications that machine learning caters to, thereby, simplifying our lives. On that note, have a look at the top 10 most interesting machine learning applications of 2022. Almost all the e-commerce websites that one can think of relying on product recommendation systems heavily.

More Chinese automakers collaborating on EVs -- The automotive industry has entered into an intense era of collaboration among carmakers, technology giants, and even software start-ups, among others. This trend comes as countries, including China, accelerate into increased usage of EVs and AVs. Numerous partnerships have sprouted up in the past year, adding density and life to this ecosystem. Among Chinese automakers themselves, a handful of significant partnerships were made to accelerate the developments of EVs and AVs within the country. In fact, China is shaping up to be the first real test of Big Tech's ambitions in the world of car making.

The purpose of Computer Vision (CV) is to allow machines to obtain valuable information from their surroundings, by analyzing visual data that can be provided by different sources such as digital images and videos. The nature of such information depends on the final goal of the machine. Think, for example, of self-driving cars. A CV module that is capable of detecting in real-time objects that appear in front of the car is essential to avoid accidents. On the other hand, a robot that has to give directions to people inside a railway station can change the way of speaking based on whether the listener is a child or an adult.