The world of work is changing radically. Each of us, to a greater or lesser extent, has to come to terms with new forms of interaction, business and work flows. New actors have appeared on the scene, clad not in flesh and blood, but in circuits and transistors: Artificial Intelligence systems, which are increasingly present in the management of a company's personnel. The recent work, elaborated by the Global Legal Group Ltd. of London, entitled "AI, automatic learning & Big Data -- Third Edition", in which various situations are analyzed, in which this new cybernetic actor enters by force into the global market, appears very interesting. As stated in the introduction, more and more employers are relying on these automated systems to decide on recruitment, select curricula, issue disciplinary measures or make dismissals!

The pandemic taught us a lesson that we needed to learn again, says Alondra Nelson: Science and technology have everything to do with issues of society, inequality, and social life. After a year in which science became politicized amid a pandemic and a presidential campaign, in January president-elect Joe Biden appointed Nelson deputy director of science and society in the White House Office of Science and Technology Policy, a new position. Nelson will build a science and society division within the OSTP aimed at addressing issues ranging from data and democracy to STEM education. In another first, Biden made his science adviser Eric Lander, who is also director of OSTP, part of his cabinet. Nelson has spent her career at the intersection of race, tech, and society, writing about topics like how Afrofuturism can make the world better and how the Black Panthers used health care as a form of activism, leading the organization to develop an early interest in genetics.

Melanie Mitchell, a professor of complexity at the Santa Fe Institute and a professor of computer science at Portland State University, acknowledges the powerful accomplishments of "black box" deep learning neural networks. But she also thinks that artificial intelligence research would benefit most from getting back to its roots and exchanging more ideas with research into cognition in living brains. This week, she speaks with host Steven Strogatz about the challenges of building a general intelligence, why we should think about the road rage of self-driving cars, and why AIs might need good parents. Listen on Apple Podcasts, Spotify, Android, TuneIn, Stitcher, Google Podcasts, or your favorite podcasting app, or you can stream it from Quanta. Melanie Mitchell: You know, you give it a new face, say, and it gives you an answer: "Oh, this is Melanie." And you say, "Why did you think that?" "Well, because of these billions of numbers that I just computed." Steve Strogatz [narration]: From Quanta Magazine, this is The Joy of x. Mitchell: And I'm like, "Well, I can't under-- Can you say more?" And they were like, "No, we can't say more." Steve Strogatz: Isn't that unnerving, that it's this great virtuoso at these narrow tasks, but it has no ability to explain itself? Strogatz: Melanie Mitchell is a computer scientist who is particularly interested in artificial intelligence. Her take on the subject, though, is quite a bit different from a lot of her colleagues' nowadays. She actually thinks that the subject may be adrift and asking the wrong questions. And in particular, she thinks that it would be better if artificial intelligence could get back to its roots in making stronger ties with fields like cognitive science and psychology, because these artificially intelligent computers, while they're smart, they are smart in a way that is so different from human intelligence. Melanie's been intrigued by these questions for really quite a long time, but her journey got started in earnest when she stumbled across a really big and really important book that was published in 1979.

Up until recently, the methods wineries used to decide what to produce and what to sell have been almost entirely subjective. Without data, it was really hard to figure out how to make a great wine that would sell well in the market. But in an extremely competitive market, where many wineries only have one shot per season to make a great wine, more and more winemakers are looking for ways to predict what consumers will like, so they can create better products and increase their profits. Now Tastry, a sensory sciences company based in California, is using machine learning and advanced chemistry to teach artificial intelligence to "taste" – and that technology is shaking up the wine industry by providing valuable information to winemakers and retailers about the wines that customers enjoy. I interviewed Katerina Axelsson, Founder and CEO of Tastry, about how her company uses artificial intelligence to recommend wines to consumers and advise wineries about what products to make.

Editor's note: This year What If?, our annual collection of scenarios, considers the future of health. Each of these stories is fiction, but grounded in historical fact, current speculation and real science. IT WAS A scene that the Nobel committee had dearly hoped to avoid. As the recipients of this year's prizes filed into the Stockholm Concert Hall to take their seats, dozens of protesters, including several former laureates, clashed with police in the streets outside. They had gathered to express their opposition to the unprecedented decision to award the Nobel prize in physiology or medicine to an artificial intelligence.

I have got a new doorbell. It should be; it cost £89. It's a Ring video doorbell; you'll have seen them around. There are others available, made by other companies, with other four-letter names such as Nest and Arlo. When someone rings my doorbell, I'm alerted on my smartphone. I can see who is there, and speak to them. C major first inversion chord, arpeggiated, repeated, for the musically trained – you'll recognise it if you've heard it. Amazon, as it happens; Amazon acquired Ring in 2018, reportedly for more than $1bn.

Marc Andreessen should need no introduction, but I'll do one anyway. He helped code the first widely used graphical web browser, Mosaic, which as I see it makes him one of the inventors of the internet. He co-founded Netscape and various other companies. He also co-founded the venture capital firm Andreessen Horowitz (with Ben Horowitz), also known as A16Z, one of the country's largest VC firms. Recently he has launched a media publication called Future, where he occasionally writes his thoughts. Marc has been a sort of hero of mine ever since I was a teenager, when Netscape Navigator felt like it opened up the world. I came out to California in part to meet people like him. Now we know each other well, and he's a subscriber to my blog! The thing I always like about talking to Marc is how he combines relentless optimism with the concrete knowledge to back up that optimism -- both knowledge of specific details and a broad understanding of various schools of thought. Lots of people will tell you the future holds amazing possibilities; Marc will tell you exactly what those possibilities are, and why they're possible.

If you drive in the United States, chances are you can't remember the last time you bought a paper map, printed out a digital map, or even stopped to ask for directions. Thanks to Global Positioning System (GPS) and the mobile mapping apps on our smartphones and their real-time routing advice, navigation is a solved problem. If you live in a place like Doha, Qatar, where the length of the road network has tripled over the last five years, commercial mapping services from Google, Apple, Bing, or other providers simply can't keep up with the pace of infrastructure change. "Each one of us who grew up in Europe or the US probably cannot understand the scale at which these cities grow," says Rade Stanojevic, a senior scientist at the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, a Qatar Foundation university, in Doha. "Pretty much every neighborhood sees a new underpass, new overpass, new large highway being added every couple of months." As Qatar copes with this rapid growth--and especially as it prepares to host the FIFA World Cup in 2022--the bad routing advice and accumulating travel delays from outdated digital maps is increasingly costly. That's why Stanojevic and colleagues at QCRI decided to try applying machine learning to the problem. A road network can be interpreted as a giant graph in which every intersection is a node and every road is an edge, says Stanojevic, whose specialty is network economics. Road segments can have both static characteristics, such as the designated speed limit, and dynamic characteristics, such as rush-hour congestion. To see where traffic really is going--rather than where an old map says it should go--and then predict the best routes through an ever-changing maze, all a machine-learning model would need is lots of up-to-data data on both the static and dynamic factors. "Fortunately enough, modern vehicle fleets have these monitoring systems that produce quite a lot of data," says Stanojevic.

We all know that there are troves of data that exist online about us and our browsing, clicking, and spending habits. However, given all that information and the people that spend their lives on the internet, how do those who tailor the ads we see parse that information? As with many things these days, it's useful to have machines to help. RJ Talyor is the CEO and founder of Pattern89, an Indianapolis-based marketing firm using the power of artificial intelligence (AI) to help advertisers figure out what works and what doesn't when it comes to the ads we see everyday. I spoke with him about how AI is helping marketers figure out not only who to target, but what elements to include in those ads.

A project using artificial intelligence to develop digital triage tools for mental health clinicians (CHRONOS) has been successful in the latest round of the Artificial Intelligence in Health and Care Award. Oxford Health BRC was one of a handful of organisations to receive the funding, which was announced by the Health Secretary Matt Hancock on June 16. The AI Award is making £140 million available over four years to accelerate the testing and evaluation of artificial intelligence technologies which meet the aims set out in the NHS Long Term Plan. The Award means that Oxford Health BRC will be able to develop digital tools to make it easier for clinicians in secondary mental health care to rapidly identify the most appropriate treatments for their patients. This is the first time a mental health project has received the AI Award.