Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we chat about coming back from summer break, and discuss a research paper recently published by Kerry and the AI ethicist and researcher Os Keyes called The Infopolitics of Feeling: How race and disability are configured in Emotion Recognition Technology. We discuss why AI tools that promise to be able to read our emotions from our faces are scientifically and politically suspect. We then explore the ableist foundations of what used to be the most famous Emotion AI firm in the world: Affectiva. Kerry also explains how the Stop Asian Hate and Black Lives Matters protests of 2020 inspired this research project, and why she thinks that emotion recognition technologies have no place in our societies.

Turkey gets all the attention. Cranberry sauce ruffles feathers. But pumpkin pie is the lovable staple many Americans crave on Thanksgiving. And many don't want to make it themselves. That's why Sam's Club, a retail and grocery warehouse owned by Walmart, is using artificial intelligence to predict how much pie each of its nearly 600 stores needs to make for the holidays.Subscribe to The Post Most newsletter for the most important and interesting stories from The Washington Post. Their model use

In April of 2019, Walmart launched an Intelligence Research Laboratory where cameras and sensors are wired into algorithms to monitor how stocked shelves are. In March, Kroger launched an AI lab where technology can keep track of vegetable freshness. Ketchup maker Kraft Heinz now uses machine learning to track demand for its products leading up to events such as the Super Bowl. Amazon opened a fully automated Whole Foods this year that uses deep-learning software to let customers shop and walk out without needing a cashier.

When Peter Diamandis took to the stage at Madrid's Palacio de Cibeles for the Audi Summit for Progress last Tuesday, WhatsApp had crashed and the Wi-Fi wasn't working properly. It was a blow to the audience's faith in technology, but Diamandis, the star speaker at the summit, was ready to counter this. The 61-year-old doctor and engineer from New York has blind faith in the power of innovation and science. Diamandis, who is the founder of Singularity University and a friend of tycoon Elon Musk, has set up a number of technology companies and written several books in which he predicts a future of abundance, longevity, flying cars and an exponential increase in resources. It's a vision that is hard to imagine in times of war, an energy crisis and growing fears of recession.

Armed with mountains of data, artificial intelligence is emerging as an important tool for airlines to find the ideal fares to charge passengers, helping them squeeze out as much revenue as possible as the industry emerges from its biggest crisis. Fed by data on everything from internet searches and COVID-19 outbreaks to weather forecasts and football results, computers are learning how everyday life influences demand for flights. In its most advanced form, AI blows up the arcane airfare codes and pricing bands that have straitjacketed ticket sales for decades. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

We don't have much reason to think that they have an internal monologue, the kind of sense perception humans have, or an awareness that they're a being in the world. Over the weekend, the Washington Post's Nitasha Tiku published a profile of Blake Lemoine, a software engineer assigned to work on the Language Model for Dialogue Applications (LaMDA) project at Google. LaMDA is a chatbot AI, and an example of what machine learning researchers call a "large language model," or even a "foundation model." It's similar to OpenAI's famous GPT-3 system, and has been trained on literally trillions of words compiled from online posts to recognize and reproduce patterns in human language. LaMDA is a really good large language model.

Have you ever gone to the dentist and been unsure if that spot on your tooth the doctor is looking at is really a cavity? Or maybe you've gone to get a second opinion, only to have the new practice tell you that you need a crown on a completely different tooth? The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. Unfortunately, this story is all too common in dentistry -- in fact, there's a well-known story about a Reader Digest reporter who went to see 50 different dentists and received nearly 50 different diagnoses.

When college instructor Angela Dancey wants to decipher whether her first-year English students comprehend what she's trying to get across in class, their facial expressions and body language don't reveal much. "Even in an in-person class, students can be difficult to read. Typically, undergraduates don't communicate much through their faces, especially a lack of understanding," said Dancey, a senior lecturer at the University of Illinois Chicago. Dancey uses tried-and-true methods such as asking students to identify their "muddiest point" -- a concept or idea she said students still struggle with -- following a lecture or discussion. "I ask them to write it down, share it and we address it as a class for everyone's benefit," she said.

Humans reason is based on our knowledge and draw conclusions about some topic. The concept of representing knowledge and drawing conclusion from it also is used in AI. In this context, knowledge is used by knowledge-based agents, where information is represented internally in the computer. With those pieces of information, we can infer that it did rain and that I didn't go to my dad's house. Now let's see how AI can use the logic we did to draw conclusions.

Why is this an important question? Ultimately this is a stepping stone to answering the ethical question: Should we teach Artificial Intelligence to detect your ethnicity? This is an analogous question to If we had complete discretion, would we teach our children to recognize someone's ethnicity? As the creators and users of these tools, it is our responsibility to regulate AI in a similar manner to how we enforce seat belts and speed limits. On the pathway to AI ethics, we can first explore AI's ability to predict your gender simply based on text. We will not use voice nor facial recognition because that would be too easy.