If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. Despite what I tell my son, I really don't have eyes in the back of my head. But I do have Wi-Fi security cameras with smartphone apps, which allow me to keep tabs on him, as well as my dog, my car, the front door, and the yard. Picking the right one (or two, or three) depends on what you want to do with it.
Earlier this month the University of Nottingham published a study in PloSOne about a new artificial intelligence model that uses machine learning to predict the risk of premature death, using banked health data (on age and lifestyle factors) from Brits aged 40 to 69. This study comes months after a joint study between UC San Francisco, Stanford, and Google, which reported results of machine-learning-based data mining of electronic health records to assess the likelihood that a patient would die in hospital. One goal of both studies was to assess how this information might help clinicians decide which patients might most benefit from intervention. Amitha Kalaichandran, M.H.S., M.D., is a resident physician based in Ottawa, Canada. Follow her on Twitter at @DrAmithaMD.
On the floor of the New York Auto Show this week, Genesis showed off its sweet little Mint concept, an electric two-seater with a very abbreviated sedan body. The Hyundai luxury arm does not, however, have any plans to put the adorable thing into production--perhaps because, as we learned this week, getting world-changing tech into the market takes a fair amount of elbow grease. Elon Musk's Boring Company is slowly making its way through the necessary paperwork to make its DC to Baltimore Loop concept a real, live thing. Uber is rounding up the oodles of cash it needs to develop self-driving vehicles. "Flying taxi" engineers are trying to get their concepts past now-nervous aviation regulators.
It's so disappointing to see a great game come along that falls shorts because it keeps getting in its own way. I don't know what went wrong with Heaven's Vault. Developer Inkle Studios has done enough strong work over the years for me to go into any of their games with confidence and curiosity. I still feel that way even now. But I'm disappointed with this latest game.
Scientists are constantly figuring out how to expand the field of use of this incredible invention, which enables computer software to progressively improve its actions by adopting knowledge gained from previous experience. Machine learning, also referred to as artificial intelligence due to its ability to perform tasks using its own judgment, has been the subject of both praise and controversy. However, the sophisticated algorithms that have served in providing you ads on social networks might have a grand future in philology, archaeology, and linguistics. According to Émilie Pagé-Perron, a Ph.D. candidate in Assyriology at the University of Toronto, we might be closer than we thought to deciphering numerous Middle-Eastern cuneiform tablets written in Sumerian and Akkadian languages, all of which are several thousand years old. Pagé-Perron is in charge of the project officially titled Machine Translation and Automated Analysis of Cuneiform Languages, which currently operates in Frankfurt, Toronto, and Los Angeles, using combined efforts to create a program capable of translating the clay tablets.
The current approach to AI and machine learning is great for big companies that can afford to hire data scientists. But questions remain as to how smaller companies, which often lack the hiring budgets to bring in high-priced data scientists, can tap into the potential of AI. One potential solution may lie in doing machine learning on edge devices. Gadi Singer, vice president of the Artificial Intelligence Products Group and general manager of architecture at Intel, said in an interview at the O'Reilly AI Conference in New York that even one or two data scientists are enough to manage AI integration at most enterprises. But will the labor force supply adequate amounts of trained data scientists to cover all enterprises' AI ambitions?
As artificial intelligence algorithms get applied to more and more domains, a question that often arises is whether to somehow build structure into the algorithm itself to mimic the structure of the problem. There's usually some amount of knowledge we already have of each domain, an understanding of how it usually works, but it's not clear how (or even if) to lend this knowledge to an AI algorithm to help it get started. Sure, it may get the algorithm caught up to where we already were on solving that problem, but will it eventually become a limitation where the structure and assumptions prevent the algorithm from surpassing human performance? This week, we'll talk about the question in general, and especially recommend a recent discussion between Christopher Manning and Yann LeCun, two AI researchers who hold different opinions on whether structure is a necessary good or a necessary evil.
Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.
The fear of robots coming for your job is one of the many challenges confronting 21st-century workers, but the machines aren't ready to take on every industry just yet. Bridgewater Associates, the massive hedge fund founded by legendary investor Ray Dalio, just released a report on the changing relationship between labour and capital in the US. One of the big factors the Bridgewater authors highlighted was the ongoing rise in automation across industries, which they noted could be a support for corporate profits in the years to come as more efficient robots and software potentially replace slower and error-prone human labour. Bridgewater cited a 2016 report from consulting firm McKinsey & Company that looked at which industries in the US were most susceptible to being automated. The McKinsey report used data from the Department of Labour to estimate how much time workers in various industry sectors spent doing different types of tasks, and which of those tasks could, theoretically, be automated using present technology.