Machine Learning has been around for a few decades now. Most companies today, IT or not, have adapted to Machine Learning to solve their business problems. It has worked wonders in predicting prices, classifying customers, and a myriad of other business goals. However, ML has its own set of limitations, starting with the inability to handle a large amount of data. Hence, there is an ever-increasing need for technologies that can overcome the challenges traditional ML faces today.

In the 21st century, "learn to code" has become a mantra of sorts for a certain kind of person. And yes, for many people, coding is a great first or even second career choice after attending universities, coding bootcamps, or one of the best online coding courses. But the related terms you see online are confusing. What is coding compared with programming or even terms like software engineering? The differences are big, and the terms are often muddled together.

On TikTok, for instance, AI sorts the posts so that the first ones you see are likely to be those you'd prefer. AI serves up the useful results of every Google search. When you ask Siri to play Taylor Swift, AI turns your speech into a command to start her songs. But before an AI can do any of that, developers must train it. In fact, that training's appetite for energy could soon become a huge problem, researchers now worry.

Neural networks are the crown jewel of the AI boom. They gorge on data and do things like transcribe speech or describe images with near-perfect accuracy (see "10 breakthrough technologies 2013: Deep learning"). The catch is that neural nets, which are modeled loosely on the structure of the human brain, are typically constructed in software rather than hardware, and the software runs on conventional computer chips. IBM has now shown that building key features of a neural net directly in silicon can make it 100 times more efficient. Chips built this way might turbocharge machine learning in coming years.

Neural networks are the crown jewel of the AI boom. They gorge on data and do things like transcribe speech or describe images with near-perfect accuracy (see "10 breakthrough technologies 2013: Deep learning"). The catch is that neural nets, which are modeled loosely on the structure of the human brain, are typically constructed in software rather than hardware, and the software runs on conventional computer chips. IBM has now shown that building key features of a neural net directly in silicon can make it 100 times more efficient. Chips built this way might turbocharge machine learning in coming years.

Software companies make headlines but research on computer hardware could bring bigger rewards.Credit: Morris MacMatzen/Getty Advances in computing tend to focus on software: the flashy apps and programs that can track the health of people and ecosystems, analyse big data and beat human champions at Go. Meanwhile, efforts to introduce sweeping changes to the hardware that underlies all that innovation have gone relatively unnoticed. Since the start of the year, the Semiconductor Research Corporation (SRC) -- a consortium of companies, academia and government agencies that helps to shape the future of semiconductors -- has announced six new university centres. Having watched the software giant Google expand into hardware research on artificial intelligence (AI), the main chip manufacturers are moving to reclaim the territory. As they do so, they are eyeing the start of a significant transformation -- arguably the first major shift in architectures since the birth of computing.

It's the Monday morning following the opening weekend of the movie Blade Runner 2049, and Eric C. Leuthardt is standing in the center of a floodlit operating room clad in scrubs and a mask, hunched over an unconscious patient. "I thought he was human, but I wasn't sure," Leuthardt says to the surgical resident standing next to him, as he draws a line on the area of the patient's shaved scalp where he intends to make his initial incisions for brain surgery. "Did you think he was a replicant?" "I definitely thought he was a replicant," the resident responds, using the movie's term for the eerily realistic-looking bioengineered androids. "What I think is so interesting is that the future is always flying cars," Leuthardt says, handing the resident his Sharpie and picking up a scalpel. "They captured the dystopian component: they talk about biology, the replicants. But they missed big chunks of the future. Where were the neural prosthetics?"

By our estimate, today's very biggest supercomputers are within a factor of a hundred of having the power to mimic a human mind. Their successors a decade hence will be more than powerful enough. Yet, it is unlikely that machines costing tens of millions of dollars will be wasted doing what any human can do, when they could instead be solving urgent physical and mathematical problems nothing else can touch. Machines with human-like performance will make economic sense only when they cost less than humans, say when their "brains" cost about $1,000. When will that day arrive?

Artificial Intelligence Artificial intelligence and neuroscience researchers have taken inspiration from the human brain in creating a new deep learning system that enables computers to learn about the visual world largely on their own, just like human babies do. Artificial intelligence and neuroscience experts from Rice University and Baylor College of Medicine using inspiration from the human brain have developed a new deep learning method that lets computers learn about the visual world largely on their own, much the same way human babies do. In tests, the group's "deep rendering mixture model" (DRMM) largely taught itself how to distinguish handwritten digits using a standard dataset of 10,000 digits written by federal employees and high school students. The results which were presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona,the researchers described how they trained their algorithm by giving it just 10 correct examples of each handwritten digit between zero and nine and then presenting it with several thousand more examples that it used to further teach itself. The algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms that were trained with thousands of correct examples of each digit.

GPUs are highly specialized computer chips that were originally designed to accelerate the processing of video information in a computer. The conversation we had led to a discussion about the future of computer hardware, and I became more excited than ever. Our new friend had us over to the Silicon Valley headquarters of his company, where we walked into a room that had the very latest demonstrations for their GPU processors. We saw video games with graphics so realistic and impressive that it made everything I had seen previously look simplistic and cartoonish. Just as impressively, the games reacted to my control button pushes instantaneously.