Javed is a senior editor at TIME, based in the London bureau. Javed is a senior editor at TIME, based in the London bureau. Leaders from Dow Chemical Company, EY, and NTT Data Inc. shared their perspectives on the impact of scaling up new technologies like AI during a TIME100 Talks panel discussion in Davos on Jan. 20. The panel took place on the sidelines of the World Economic Forum's annual meeting, which kicked off on Jan. 19 in Davos, drawing around 3,000 high-level participants from business, government, and beyond, in addition to many more observers, journalists, activists, and others. During the panel, titled "Innovation in a Multipolar Era," the participants discussed the benefits of integrating AI, and its potential in areas such as health care and education, as well as some of the challenges of integrating the technology at scale within businesses.

That lasted about four years. By 2015, 3D TVs were a fading fad, and by 2017, the last few holdouts manufacturing them, LG and Sony, weren't making them at all anymore. By that point, 3D TVs were synonymous with Microsoft Zune and Betamax, decent ideas overruled in the court of public opinion and doomed to be ridiculed as failures. What went wrong with 3D TV? Consumers were still navigating their way toward larger TV sets with 4K-quality resolutions. The ability to display 3D content added cost to those TV sets, and required game consoles or Blu-ray players capable of putting out that content just as physical media was starting to decline in favor of streaming.

From shoes to clothes, vinyl records and the latest smartphone, humans have a seemingly insatiable desire for the latest products. Now, researchers have used computer models to try and explain why we constantly crave more and more material things – even when they make us feel miserable. According to the findings, we pursue more rewards when we become'habituated' to a higher standard of living and compare ourselves to various standards. Do you crave more and more stuff even though it's making you miserable? Even in favorable circumstances, humans often find it hard to remain happy with what they've got.

In the past four years, about 90% of enterprises have experienced a turn that upset normal operations, and organisations with a higher adoption rate of contemporary technologies including artificial intelligence (AI) and robotic process automation (RPA) will have a competitive advantage, a Gartner report has said. Despite analytics including AI being part of "discretionary spend", there has been an increase in demand for such solutions during the current downturn. "This was not the case during the recession of 2008-09," said Srikanth Velamakanni, co-founder and group chief executive, Fractal Analytics. "AI is still discretionary but most of our clients in the last three months have come up to us and said that though it is discretionary, it is mission critical. It is super important to us and we are actually going to expand," said Velamakanni.

Despite transitional growing pains, the promise of artificial intelligence (AI) in innovation and decision-making offers a future with better decisions made at the command of but not by humans. That's what Pradeep Dubey, director of the Parallel Computing Laboratory at Intel, told attendees of a plenary talk at the PEARC18 conference in Pittsburgh, Pa., on July 25. "Humans and machines have had this very nice separation of labor," Dubey said. "Humans make decisions; machines crunch numbers … but humans are terrible decision makers." The annual Practice and Experience in Advanced Research Computing (PEARC) conference--with the theme Seamless Creativity--stresses key objectives for those who manage, develop and use advanced research computing throughout the U.S. and the world. This year's program offered tutorials, plenary and contributed talks, workshops, panels, poster sessions and a visualization showcase.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning are some of the buzzwords swirling around today. You'e more likely to hear about AI and ML when tech companies talk about voice assistants and smart home devices. Now, while Artificial Intelligence and Machine Learning are very much related, they are not the same thing. Let's dive in a little deeper to understand what these terms mean. While tech giants have started talking about AI more recently, it is something that existed decades ago, and you probably didn't even realize it back then.

Last year, Intel Corp. acquired neural-network hardware maker Nervana and built Nervana's chip, integrating it with Intel's own on-processor deep-learning and artificial-intelligence (AI) capabilities. This month, Intel Capital invested in AI startups CognitiveScale, Aeye Inc., and Element AI. Intel fellow Pradeep Dubey outlined the big picture for Intel's growing AI portfolio. Intel is investing in AI startups, acquiring others, and blending the mix with its own AI expertise to ensure a leadership position in machine learning, deep learning, and brainlike neural networks based on its AI hardware and software. The company is aiming at all applicable industries, from drug screening with the Xeon Phi to software-defined visualization with its graphics hardware, Dubey said.

The layoffs in India's IT companies, one of the largest private employers providing jobs to more than 37 lakh people, are worrying. Combined with the backlash the IT industry is facing in the US on the H1-B visa front, and in countries such as Australia, the future of the industry as a beacon of hope for young professionals is dimming, feel many. Add to this a widespread pessimism in the sector that much of its workforce will become redundant very soon (McKinsey in a report puts this number at half the workforce) and it is bad news for job-seekers and the government, which is staring at slack job creation across all segments. It also draws a grim picture of the economy's growth, which was being postured to be picking up smartly despite the demonetisation impact. Every business is going through huge transformation with the rampant use of technology that is making several traditional jobs obsolete.

To solve real-world problems with AI, a deep learning system would need to be trained on a trillion parameters in 20 minutes. Even Intel is willing to admit that computers are great at crunching numbers, but not so great that they also make good decision-makers. Based on a recent webinar about the hardware advancements that have made better artificial intelligence (AI) possible, and what the future holds, that is about to change, and much faster than many would believe. Pradeep Dubey, the director of the Parallel Computing Lab at Intel, explained the difference between traditional AI systems and newer implementations like deep learning--primarily, it's about who is making the rules. In traditional AI, humans have to create rule-based systems for understanding which data should be processed, and how.

Machine learning, which involves programs that get more accurate with experience, is fundamentally different from any kind of computing that's come before. "There's always been a simple division of labor: machines do number crunching, and humans make decisions," says Pradeep Dubey, an Intel Fellow at the company's Intel Labs division. Machine-learning programs--and in particular the high-profile deep-learning subset that can teach themselves--are different. These programs have the potential to discover new drug compounds or identify consumer trends without human intervention. For Dubey and others at Intel, it was clear that they needed to find a way to make machine-learning programs work well on Intel's architecture.