Ask Stefan Jockusch what a factory might look like in 10 or 20 years, and the answer might leave you at a crossroads between fascination and bewilderment. Jockusch is vice president for strategy at Siemens Digital Industries Software, which develops applications that simulate the conception, design, and manufacture of products like cell phones or smart watches. His vision of a smart factory is abuzz with "independent, moving" robots. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "Depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product," Jockusch says. "It will self-organize itself to do something different." Behind this factory of future is artificial intelligence (AI), Jockusch says in this episode of Business Lab. But AI starts much, much smaller, with the chip.

More and more is happening…. More connectivity occurs now in a calendar year than occurred in a million years a billion years ago. So somehow as we approach the present, we find ourselves in an ever denser realm of activity, interrelationship, connectivity, and the result of this is more of the same: producing a shrinking globe, ever more immersive technologies, dissolution of political, social, gender, class boundaries, of all sorts…. We're about to become unrecognizable to ourselves as a species. And then there's that new Netflix docudrama, The Social Dilemma, that, in the words of one review, "examines the various ways social media and social networking companies have manipulated human psychology to rewire the human brain and what it means for society in general."

How does Amazon help Alexa understand what people mean and not just what they say? And, we couldn't be talking about Alexa, smart home tech, and AI at a better time. During this week's Amazon Devices event, the company made a host of smart home announcements, including a new batch of Echo smart speakers, which will include Amazon's new custom AZ1 Neural Edge processor. In August this year, I had a chance to speak with Evan Welbourne, senior manager of applied science for Alexa Smart Home at Amazon, about everything from how the company is using AI and ML to improve Alexa's understanding of what people say, Amazon's approach to data privacy, the unique ways people are interacting with Alexa around COVID-19, and where he sees the future of voice and smart tech going in the future. The following is an transcript of our conversation edited for readability. Bill Detwiler: So before we talk about maybe IoT, we talk about Alexa, and kind of what's happening with the COVID pandemic, as people are working more from home, and as they may have questions that they're asking about Alexa, about the pandemic, let's talk about kind of just your role there at Amazon, and what you're doing with Alexa, especially with AI and ML. So I lead machine learning for Alexa Smart Home. And what that sort of means generally is that we try to find ways to use machine learning to make Smart Home more useful and easier to use for everybody that uses smart home. It's always a challenge because we've got the early adopters who are tech savvy, they've been using smart home for years, and that's kind of one customer segment. But we've also got the people who are brand new to smart home these days, people who have no background in smart home, they're just unboxing their first light, they may not be that tech savvy.

Imtiaz Adam, Twitter @Deeplearn007 Updated a few sections in Sep 2020 Artificial Intelligence (AI) is increasingly affecting the world around us. It is increasingly making an impact in retail, financial services, along with other sectors of the economy.

I'm Vegard, and I currently work as the Lead Data Scientist in a software company called Axbit. In addition to that I also have a part-time position as an Associate Professor in machine learning at Molde university college. Today I am happy to answer a couple of questions related to data science, what data science is all about and how working within this field is like. Transcript: How did I become a data scientist? First of all, I think my background is probably a bit different compared to a lot of other data scientists.

Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.

If you've taken a look at the state of the art benchmarks/leaderboards for ImageNet sometime in the recent past, you've probably seen a whole lot of this thing called "EfficientNet." Now, considering that we're talking about a dataset of 14 million images, which is probably a bit more than you took on your last family vacation, take the prefix "Efficient" with a fat pinch of salt. But what makes the EfficientNet family special is that they easily outperform other architectures that have a similar computational cost. In this article, we'll discuss the core principles that govern the EfficientNet family. Primarily, we'll explore an idea called compound scaling which is a technique that efficiently scales neural networks to accommodate more computational resources that you might have/gain. In this report, I'll present the results I got from attempting to try the various EfficientNet scales on a dataset much smaller than ImageNet which is much more representative of the real world.

The housing market continues to defy gravity. Sales of existing homes rose more than 10% last month compared to a year ago, hitting their highest level since December 2006, according to the National Association of Realtors. And now, more than ever, people are relying on online platforms to search for -- and even buy -- houses. And that opens the door for artificial intelligence to play a bigger role, like using computer vision to create real estate listings based on photos. I spoke with Christopher Geczy, a professor at the Wharton School of the University of Pennsylvania who teaches about real estate and insurance technology.

How is AI Ethics and Responsible AI currently being taught in Computer Science and Engineering Curriculums across Africa? What issues related to this topic are relevant to students and faculty? And what roadblocks or challenges are instructors facing to bring more discussion of AI ethics to classrooms? The goal of this workshop is to foster a discussion on how to effectively integrate AI Ethics into Computer Science/Engineering programs at African Universities. This is an initial step to gather perspectives on the current situation at representative universities in different countries in Africa, and to initiate a discussion on how we can better support each other with lessons learned and share materials/curriculums to further develop AI ethics programs in higher education. After identifying the current state, the interests of students and faculty and the needs of departments in this workshop session, the goal is to continue the series with more in-depth workshops on specific topics.

Natural Language Processing (NLP) is one of the most exciting fields of artificial intelligence that enables computers to understand human languages. NLP techniques are constantly evolving and promising applications are increasingly implemented by organizations to solve a wide range of problems. What exactly are companies using NLP for? What are exciting NLP techniques in a practical context and what are the challenges when applying them? We talked to thought leaders applying NLP in different industries about their favorite NLP techniques, the biggest trends, as well as opportunities and challenges of NLP in 2020.