Domestic shipments of personal computers surged 37.4 percent in 2019, sparked by demand created from an increase in teleworking and people and companies replacing their Windows 7 operating systems, which Microsoft stopped supporting last week, an industry body said Wednesday. PC shipments totaled 9.74 million last year, up from 7.08 million in 2018, and were also aided by last-minute buying ahead of the consumption tax hike on Oct. 1, the Japan Electronics and Information Technology Industries Association said. The increase compares with a 4.5 percent rise in 2018, and followed a drop for five straight years through 2017 as consumer demand shifted to smartphones. "It will be difficult to surpass the figure of 2019, but we're hoping demand for teleworking and that related to education will support overall demand," an association spokesman said. Shipments of desktop computers jumped 49.4 percent to 2.58 million in 2019 while those of laptop computers were up 33.6 percent to 7.15 million.

The massive pace at which companies are adopting automation and artificial intelligence may lead to elimination of middle management roles over the next decade. Smart machines will soon become co-workers -- changing the modus operandi of how employees work. According to Gartner, "People managers will focus on people-related activities that require intuition, empathy and interpersonal skills." A recent McKinsey report also said that nearly half of the activities undertaken by the workers could be automated -- including managing expertise. A lot of offices have already replaced secretaries with virtual assistants.

Artificial intelligence routinely produces startling achievements, but those advances require staggering amounts of computing power and electricity. Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months. The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.

More and improved use of artificial intelligence, and an overhaul of medical education to include advances in machine learning, could cut down significantly the time it takes to develop and bring new drugs to market, according to a new joint report by the National Academy of Medicine and the Government Accountability Office. Before that can happen, however, the United States must address legal and policy impediments that inhibit the collection and sharing of high-quality medical data among researchers, the report said. "Machine learning holds tremendous potential in drug development," according to the two-part report released Tuesday, which said such technologies could cut down the current time of about 10 to 15 years it takes to develop and bring a new drug to market. "In drug discovery, researchers are using [machine learning] to identify new drug targets, screen known compounds for new therapeutic applications, and design new drug candidates, among other applications." Researchers involved in drug discovery said infusion of machine learning technologies at the early stage of drug development could result in savings of between $300 million and $400 million per successful drug, the GAO said.

Podcasts might not be as popular as videos but are surely gaining prominence and helping in educating people all around the world. As per reports, 36% of people listened to podcasts in 2019, a rise of 2% from 2018. New technologies like AI, big data, and more are one of the prime reasons that are driving the podcast industry. Often people learn about the latest technologies over the internet, and podcasts have become a go-to channel for staying abreast of the AI trends and gaining insights into its implementations. Analytics India Magazine brings to you some of the best AI podcast that you should follow.

The use of artificial intelligence and machine learning algorithms in healthcare is poised to expand significantly over the next few years, but beyond the investment strategies and technological foundations lie serious questions around the ethical and responsible use of AI. In an effort to clarify its own position and add to the debate, the executive vice president and chief technology officer for Royal Philips, Henk van Houten, has published a list of five guiding principles for the design and responsible use of AI in healthcare and personal health applications. The five principles – well-being, oversight, robustness, fairness, and transparency – all stem from the basic viewpoint that AI-enabled solutions should complement and benefit customers, patients, and society as a whole. First and foremost, well-being should be front of mind when developing healthcare AI solutions, van Houten argues, helping to alleviate overstretched healthcare systems, but more importantly to act as a means of supplying proactive care, informing and supporting healthy living over the course of a person's entire life. When it comes to oversight, van Houten called for proper validation and interpretation of AI-generated insights through the participation and collaboration of AI engineers, data scientists, and clinical experts.

Artificial intelligence must be regulated to save humanity from being hit by its dangers, Google's boss has said. The potential damage the technology could do means it is "too important" not to be constrained, according to Sundar Pichai. While it has the potential to save and improve lives, it could also cause damage through misleading videos and the "nefarious uses of facial recognition", he wrote in the New York Times, calling on the world to work together to define what the future of AI should look like. Regulation would be required to prevent AI being influenced by bias, as well as protect public safety and privacy, he said. "Growing up in India, I was fascinated by technology. Each new invention changed my family's life in meaningful ways. The telephone saved us long trips to the hospital for test results. The refrigerator meant we could spend less time preparing meals, and television allowed us to see the world news and cricket matches we had only imagined while listening to the short-wave radio," he said.

Neural architecture search(NAS) is one of the hottest trends in modern deep learning technologies. Conceptually, NAS methods focus on finding a suitable neural network architecture for a given problem and dataset. Think about it as making machine learning architecture a machine learning problem by itself. In recent years, there have been an explosion in the number of NAS techniques that are making inroads into mainstream deep learning frameworks and platforms. However, the first generation of NAS models have encountered plenty of challenges adapting neural networks that were tested on one domain to another domain.

Over the years that I have spent with startups, I've come across both genuine and fake AI products. I'll start with the ones that truly solved problems using AI. A few years ago, one of the co-founders of Liv.ai, a Bengaluru-based AI startup, met me and demonstrated their product that used natural language processing to convert speech to text in multiple Indian languages. I had always known that text to speech was easy, but converting speech to text in multiple languages was a hard problem to solve. I was a bit sceptical at first, but when I saw the product, I was quite blown away.

You may think that artificial intelligence (AI) will make doctors obsolete soon but that day is still far off. In fact, computers are not that intelligent just yet. Most computer solutions emerging in healthcare rely on algorithms written to analyse data and recommend treatments. They do not rely on computers thinking independently. The computers in question are fed with large amounts of known data and use rules or algorithms set by experts to extract information and apply it to a health issue or problem.