WENDELL WALLACH: It gives me great pleasure to welcome my longtime colleague Shannon Vallor to this Artificial Intelligence & Equality podcast. Shannon and I have both expressed concerns that ethics and ethical philosophy is inadequate for addressing the issues posed by artificial intelligence (AI) and other emerging technologies, so I have been looking forward to our having a conversation about why that is the case and ideas for reenvisioning ethics and empowering it for the information age. Before we get to that conversation, let me introduce Shannon to our listeners, provide a very cursory overview of how ethical theories are understood within academic circles, and provide Shannon with the opportunity to introduce you to the research and insights for which she is best known. Again, before turning to Shannon, let me make sure that listeners have at least a cursory understanding of the field of ethics. Ethical theories are often said to all fall into two big tents, and one of those tents--the determination of what is right, good, or just--derives from following the rules or doing your duty. Often these rules are captured in high-level principles, that the rules can be the Ten Commandments or the four principles of biomedical ethics. In India they might be Yama and Niyama. Each culture has its own set of rules. Even Asimov's "Three Laws of Robotics" do count as rules meant to direct the behavior of robots. All of these theories are said to be deontological, a term going back to the Greeks, referring to duties, and it is basically saying that rules and duties define ethics--but of course there are outstanding questions about whose rules, what to do when rules conflict, and how you deal with situations when people prioritize the rules very differently. At the end of the 18th and beginning of the 19th centuries, Jeremy Bentham, a British philosopher, came up with a totally different approach to ethics, which is sometimes called utilitarianism or consequentialism.

Chien Lu received a runner up award for best student paper at ACML 2021. In this interview, he tells us about the implications of this research, the methodology, and plans for future work. Our paper is entitled "Cross-structural factor-topic model: document analysis with sophisticated covariates." This paper proposes a novel topic model to analyze text documents with sophisticated covariates. Text data are usually accompanied by various numerical covariates in many real-world situations.

David Silver thinks games are the key to creativity. After competing in national Scrabble competitions as a kid, he went on to study at Cambridge and co-found a video game company. Later, after earning his PhD in artificial intelligence, he led the DeepMind team that developed AlphaGo--the first program to beat a world champion at the ancient Chinese game of go. But he isn't driven by competitiveness. That's because for Silver, now a principal research scientist at DeepMind and computer science professor at University College London, games are playgrounds in which to understand how minds--human and artificial--learn on their own to achieve goals. Silver's programs use deep neural networks--machine learning algorithms inspired by the brain's structure and function--to achieve results that resemble human intuition and creativity.

Did you know that Bangladeshi girls and boys won four gold medals at the International Robot Olympiad (IRO) last month? Since 1999, girls and boys from around the world have been competing here every year with their amazing ideas and the robots they build. In 2021, Team Bangladesh won four gold, two silver, five bronze, and four technical medals at the IRO. This is an achievement that should encourage many more youngsters to enter the amazing world of robotics. As a nation, we should be proud and assured that amid myriad problems, we are producing intelligent youngsters--our first robot builders.

Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as a human-computer interface. For many applications, KWS is the entry point for our interactions with the device and, thus, an always-on workload. Many smart devices are mobile and their battery lifetime is heavily impacted by continuously running services. KWS and similar always-on services are thus the focus when optimizing the overall power consumption. This work addresses KWS energy-efficiency on low-cost microcontroller units (MCUs). We combine analog binary feature extraction with binary neural networks. By replacing the digital preprocessing with the proposed analog front-end, we show that the energy required for data acquisition and preprocessing can be reduced by 29x, cutting its share from a dominating 85% to a mere 16% of the overall energy consumption for our reference KWS application. Experimental evaluations on the Speech Commands Dataset show that the proposed system outperforms state-of-the-art accuracy and energy efficiency, respectively, by 1% and 4.3x on a 10-class dataset while providing a compelling accuracy-energy trade-off including a 2% accuracy drop for a 71x energy reduction.

On your homepage at Artsted.com you say "to use AI to help identify tomorrow's blue-chip artists". Could you please expand on the use of AI? How does your technology work? So, it would be fair to say that our AI model is currently being tested as we are having more users join the platform and therefore more data to perfect it. The data to train the initial evaluations algorithm was taken from open-source auction house records (openly available auction results published directly on major auction house websites so we could match a market performance to a database of 400.000 artists. As a result, our proprietary algorithm (a unique algorithm, built entirely by our development team – based on the publicly sourced data) works the same way, reacting to the "trigger" data input, that artists provide, that the algorithm goes on to consider as significant for their career: an article in Artforum, a solo show in a mid-tier gallery, a selection for a biennial, an art prize shortlist are a few examples of the categories of data that influence the end result.

After I published my conversation on AI Ethics with GPT-3, my colleague Jorge Lesmes and I thought that it would be fun to try the same kind of conversation on Blockchain. He kindly prepared the questions you will find below, so I want to thank him very much for his contribution! Let's see what GPT-3 replied to his questions. I am sure that you have heard about Blockchain before so, can you elaborate on what the main benefits of this technology are when compared to traditional solutions? A: The main benefit is that the blockchain is decentralized and transparent, which means that there is no central authority that can control the data and perform fraudulent actions.

It must be CES time! A few years ago, the only robots at CES were toys. And as the robot toy makers at Ologic can attest, having your robot featured as the leading image for CES was still no guarantee that your robot would make it into production (AMP is pictured above). Luckily Ologic have transferred their consumer electronics experience into building robots of every other kind. The 2022 CES Innovation Awards recognize a range of robotics technologies as Honorees, and feature three in the "Best of Innovation" category as well. See & Spray is a technologically advanced, huge robot for the agriculture industry that leverages computer vision and machine learning to detect the difference between plants and weeds, and precisely spray herbicide only on the weeds. This groundbreaking plant-level management technology gives a machine the gift of vision and reduces the use of herbicide by up to 80 percent, benefiting the farmer, the surrounding community and the environment.

In 2017, Google DeepMind developed an artificial intelligence (AI) program called AlphaZero. It was programmed to play chess against an earlier developed program called Stockfish. The difference between the two, Stockfish, the then dominant program in chess, was programmed with all the moves that could be made in chess matches and it made choices from this database. AlphaZero was different, it used logic of its own informed by the ability to recognize patterns of moves across a vast series of possibilities, many not conceived by human minds. It learned from these patterns of possibilities, actually playing against itself to build this knowledge base.

Ada Palmer is a professor of European history at the University of Chicago. Her four-volume science fiction series, Terra Ignota, was inspired by 18th-century philosophers such as Voltaire and Diderot. "I wanted to write a story that Voltaire might have written if Voltaire had been able to read the last 70 years' worth of science fiction and have all of those tools at his disposal," Palmer says in Episode 495 of the Geek's Guide to the Galaxy podcast. Palmer says that Voltaire could actually be considered the first science fiction writer, thanks to a piece he wrote in 1752. "Voltaire has a short story called'Micromégas,' in which an alien from Saturn and an alien from a star near Sirius come to Earth, and they are enormous, and they explore the Earth and have trouble finding life-forms because to them a whale is the size of a flea," she says.