Dr. Susan Schneider is an Associate Professor of Philosophy and Cognitive Science at The University of Connecticut. She writes about issues in philosophy, AI, cognitive science and astrobiology. Within philosophy, she works on both the computational nature of the brain and the metaphysical nature of the mind. The topics she has written about most recently include radical brain enhancement, machine intelligence, consciousness, and the nature of persons. Her new book is Artificial You – AI And The Future Of Your Mind.
In our chat we covered many of the major issues of AI: the computational nature of the mind, consciousness, the question of whether consciousness is restricted to humans, extraterrestrial post-biological intelligence, AI implants in humans, and the ethical and cybersecurity issues of AI. Susan talks to AI issues you may have never thought about before. Join me in this awesome 30 minute virtual seminar on AI.
ImageNet Roulette is part of an art and technology exhibit called Training Humans. Upload a photo and the algorithm will give you a classification. Some of the labels are funny, others are racist.
ImageNet Roulette is meant in part to demonstrate how various kinds of politics propagate through technical systems, often without the creators of those systems even being aware of them.
We did not make the underlying training data responsible for these classifications. We imported the categories and training images from a popular data set called ImageNet, which was created at Princeton and Stanford University and which is a standard benchmark used in image classification and object detection.
I uploaded a photo of me and the label I received was “beard.” Accurate.
A fake AI voice was used to impersonate a CEO’s voice to demand a fraudulent transfer of US$243,000. No suspects have been found yet.
John Martellaro and Charlotte Henry join host Kelly Guimont to talk about the leaked TV+ Pricing and the latest AI hire at Microsoft.
Are robocall-blocking apps on your iPhone trustworthy? It seems some have bypassed Apple’s scrutiny.
Ott Veslberg, Estonia’s chief data officer, wants a government AI to work in every aspect of the country’s public services, and healthcare is next.
The question we must always have for the high tech giants is embedded in this essay at the Internet Health Report:
“Are you going to harm humanity and, specifically, historically marginalized populations, or are you going to sort of get your act together and make some significant structural changes to ensure that what you create is safe and not harmful?”
Given the demonstrated proclivity of many high tech companies to, without adult supervision, create technologies that callously enrich them at our great expense, the above is a great question to ask. Every day. Of every technology.
John Martellaro and Bryan Chaffin join host Kelly Guimont to discuss the effects of machine learning on creativity and artistic pursuits.
The Verge writes about legal issues when an AI composes music.
The word “human” does not appear at all in US copyright law, and there’s not much existing litigation around the word’s absence. This has created a giant gray area and left AI’s place in copyright unclear. It also means the law doesn’t account for AI’s unique abilities, like its potential to work endlessly and mimic the sound of a specific artist.
Not to mention the question of who owns the copyright of this new music. Fascinating discussion here.
Security Week writes:
The threat of a HAL-9000 intelligence directing malware from afar is still the realm of fiction, so too is the prospect of an uber elite hacker collective that has been digitized and shrunken down to an email-sized AI package… However, over the next two to three years, I see six economically viable and “low hanging fruit” uses for AI infused malware – all focused on optimizing efficiency in harvesting valuable data, targeting specific users, and bypassing detection technologies.
Author Gunter Ollmann describes six ways networks will be attacked.
Andrew Orr and Charlotte Henry join host Kelly Guimont to discuss Apple Music/Spotify subscriptions, and new hires in Special Projects.
We hear the first two terms all the time from Apple. They can be confusing. So, in order to help differentiate between the terms, the TechRepublic has written up a short but helpful tutorial for business people.
The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning. There is some argument that AI, ML, and deep learning are each individual technologies. I view AI/ML/deep learning as successive stages of computer automation and analytics that are built on a common platform.
A traffic planning example makes it clear.
In the Animal-AI Olympics, AI will be given tests originally designed to test animal cognition in a US$10,000 competition.
The Animal-AI Olympics is the creation of a team of researchers at the Leverhulme Centre for the Future of Intelligence in Cambridge, England, along with GoodAI, a Prague-based research institute. The competition is part of a bigger project at the Leverhulme Centre called Kinds of Intelligence, which brings together an interdisciplinary team of animal cognition researchers, computer scientists, and philosophers to consider the differences and similarities between human, animal, and mechanical ways of thinking.
Digital Trends writes: “While it’s been clear for quite some time that modern A.I. is getting pretty darn good at generating accurate human faces, it’s a reminder of just how far we’ve come…” The face shown here is just one of many created by an AI, explained in the article. “The results … well, you can see them for yourself by checking out the website. Hitting refresh will iterate an entirely new face.”
Soon there will be artificial people on the internet writing AI created articles. (I am actually one of them.)
John Martellaro and Andrew Orr join Kelly Guimont to discuss webcams and security measures, as well as AI that freaks out even Elon Musk.
The new feature called Enhance Details will be found in Camera Raw, Lightroom Classic CC, and Lightroom CC for macOS and Windows.
Siri is just good enough that it makes us think about where it could go next. John asks the tough questions.
Blockchain technology is sometimes presented as a cure-all – a technology that can improve everything from finance to health, and anything in between. While it may not be able to solve all the world’s ills, there is no doubt that it is a hugely powerful technology that can be used for a large amount of good. One field where the blockchain could have a profound effect is in artificial intelligence, as Yessi Bello Perez outlined on The Next Web.
Unlike cloud-based solutions, the data on a blockchain is broken up into small sections and distributed across the entire computer network. There’s no central authority or control point, and each computer, or node, holds a complete copy of the ledger – meaning that if one or two nodes are compromised, data will not be lost. All that takes place on the blockchain is encrypted and the data cannot be tampered with. Essentially, this means blockchains are the perfect storage facility for sensitive or personal data which, if processed with care with the use of AI, can help unlock valuable bespoke experiences for consumers.
If you have been on Facebook or Instagram recently, you will have noticed the “10 Year Challenge”. Users post a profile picture of themselves from 10 years ago and another from now. It is meant to be a harmless meme that laughs at ourselves and late 2000s fashion. But could there be something more sinister to it? Katie O’Neil wondered in Wired if the “10 Year Challenge” is actually helping Facebook develop a facial recognition algorithm.
Imagine that you wanted to train a facial recognition algorithm on age-related characteristics and, more specifically, on age progression (e.g., how people are likely to look as they get older). Ideally, you’d want a broad and rigorous dataset with lots of people’s pictures. It would help if you knew they were taken a fixed number of years apart—say, 10 years. Sure, you could mine Facebook for profile pictures and look at posting dates or EXIF data. But that whole set of profile pictures could end up generating a lot of useless noise…In other words, it would help if you had a clean, simple, helpfully labeled set of then-and-now photos.