Part of the implicit promise and marketing hype around Machine Learning is that these technologies will make life easier for humans by reading and processing information with us. The basic premise of NLP is the ability -- after training -- to recognise and classify the intent in natural text such as a blog article or speech. Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between humans and computers using natural language. NLP is used to build applications that can understand human languages and respond in a way that is natural for humans. NLP is used to create chatbots, analyse sentiment, extract information, translate text, and more.

Some of today's tech giants believe that artificial intelligence (AI) should be more widely utilized. If Tesla's Elon Musk delivers on his promise of offering true self-driving cars (and by extension, delivery trucks) and they become widely available within the next decade, then what's going to happen to those millions of people? A major breakthrough on this front occurred in 2015 when a bot named Eugene Goostman became the first computer to pass the Turing test. Human dominance is not due to strong muscles and sharp teeth but rather intelligence and ingenuity. We can defeat stronger, bigger and faster animals because we're able to create and use physical and cognitive tools to control them.

After years of research and development, Uniquify, a Silicon Valley neural network and AI edge computing company, is ready to unveil neural network 2.0 technology at the CES 2022 event. Currently, neural network technology is used in creating visual, audio, data, and natural language processing (NLP) models with the multiply-accumulate (MAC)-based operations. But with Uniquify's second-generation neural network 2.0 technology, neural networks shrink neurons by using proprietary AI processing elements (AIPEs) in place of MAC operations. AIPE technology shrinks the neurons in neural networks to enable the creation of the most advanced and complex AI visual, audio, and NLP models. In the past, MAC hardware was used to implement advanced but bulky neural network models, which severely hindered the possibilities of edge computing.

This year, new technologies will enable more drivers to take their hands off the wheel while on the road. But that doesn't mean their cars will be fully self-driving -- that day still remains far in the future. Automakers like General Motors (GM), Ford (F) and Stellantis (the company formed in the recent merger of Fiat Chrysler and Groupe PSA) are introducing -- or upgrading existing -- technologies that allow drivers to completely take their hands off the steering wheel and pull their feet away from the pedals for long stretches of time. But these systems will still be limited in their capabilities. Drivers will still be required to pay constant attention to the road, for instance.

Artificial Intelligence (AI) is one of the hottest topics today. Recent advances literally talk for themselves -- say hi to GPT-3, and it will greet you back. AI-discovered pharmaceutics is around the corner. Companies are hiring more Ph. Ds than ever while policy-makers are trying to make sense of this year tech with centuries-old laws.

"[AI] is going to change the world more than anything in the history of mankind. AI oracle and venture capitalist Dr. Kai-Fu Lee, 2018 In a nondescript building close to downtown Chicago, Marc Gyongyosi and the small but growing crew of IFM/Onetrack.AI have one rule that rules them all: think simple. The words are written in simple font on a simple sheet of paper that's stuck to a rear upstairs wall of their industrial two-story workspace. Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse. It was captured from overhead courtesy of a Onetrack.AI "forklift vision system." Employing machine learning and computer vision for detection and classification of various "safety events," the shoebox-sized device doesn't see all, but it sees plenty. Like which way the driver is looking as he operates the vehicle, how fast he's driving, where he's driving, locations of the people around him and how other forklift operators are maneuvering their vehicles. IFM's software automatically detects safety violations (for example, cell phone use) and notifies warehouse managers so they can take immediate action. The main goals are to prevent accidents and increase efficiency. The mere knowledge that one of IFM's devices is watching, Gyongyosi claims, has had "a huge effect." "If you think about a camera, it really is the richest sensor available to us today at a very interesting price point," he says. "Because of smartphones, camera and image sensors have become incredibly inexpensive, yet we capture a lot of information.

Scientists have been able to create an army of tiny, walking robots in a new breakthrough. The objects are the first microscopic robots that are made out of semiconductor components. That allows them to be controlled and forced to walk with standard electronic signals, allowing them to be integrated into more traditional circuits. The researchers behind the discovery now hope that they can be built into even more complex versions. That could allow for future robots to be able to be controlled by computer chips, produced en masse – and built in such a way that they could travel through human tissue and blood, acting like surgeons, the researchers say.

While the knowledge and skills of your hires can drive individual project success, good processes drive the success of a portfolio of projects and the team overall. When creating processes, work with your team and focus on taking small steps and quick iteration of ideas, not major breakthroughs. You'll also need to select the best tech stack to support your objectives. Technology should not be a substitute for good processes and the right team. Instead, as you put processes in place, look for technology to help drive efficiency.

Google's processor, Sycamore, performed a truly random-number generation in 200 seconds. The achievement marks a major breakthrough in the decadeslong quest to use quantum mechanics to solve computational problems. Google's processor, Sycamore, performed a truly random-number generation in 200 seconds. The achievement marks a major breakthrough in the decadeslong quest to use quantum mechanics to solve computational problems. Google says it has built a computer that is capable of solving problems that classical computers practically cannot.

In a world first, Insilico Medicine, a Hong Kong-based startup developing deep neural networks for drug discovery, has successfully synthesized and pre-clinically validate a drug candidate in just 25 days, making the drug discovery process, including the designing stage, take about 46 days. According to Insilico's research team and its collaborators at the University of Toronto, the method of designing new kinds of molecules by using a deep generative artificial intelligence (AI) model – called generative tensorial reinforcement learning (GENTRL) – not only set a record time compared to traditional methods but also proved to be 15 times faster than a typical pharma corporation's efficient R&D process. It's worth pointing out, especially for readers unfamiliar with the big pharmaceutical industry, that it takes more than a decade and millions of dollars to discover and develop a drug candidate. What's even more depressing about this inefficient industry that keeps passing off the illusion of innovation for real innovation, is that in the last twenty-plus years the success rate for a drug candidate entering Phase I trials have stagnated at under 10%. Meanwhile, in pre-clinical phases the failure rates for new compounds is over 99%.