After plans to break physical barriers with his metaverse initiative, Meta CEO Mark Zuckerberg revealed plans for another globe-spanning artificial intelligence (AI) project earlier this year, this time a universal translation tool unlike any other. At the same time, the company that made itself famous (and notorious) for its social media networks also introduced another AI-powered tool, a virtual assistant. Both of these intelligent applications were intended to have practical use cases in Zuckerberg's metaverse, those were their intended uses but they will also have wider business applications that Meta is all too aware of. AI virtual assistants, of course, are already in wider use by organizations as chatbots to handle basic customer requests and interactions across a variety of digital services– including Meta's own popular platforms like Facebook Messenger, Instagram, and WhatsApp Business. The other, less well-known AI use case(s) is the language and translation exercises that provide alternatives to relying on human translators to provide accurate, expert-quality translations in real-time.

Chloe Sweden is the Founder and CEO of Plants and Perks, a service for rewarding employees with sustainable perks. Chad talks to Chloe about supporting employees on plant-based sustainability journies by gifting free samples and high-value prizes, choosing a co-founder, and being strategic with the types of businesses they've approached. Become a Sponsor of Giant Robots! CHAD: This is the Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. And with me today is Chloe Sweden, the Founder, and CEO of Plants and Perks, a service for rewarding employees with sustainable perks. Chloe, thank you so much for joining me. CHLOE: Thank you for having me. CHAD: So you officially started Plants and Perks, at least according to your LinkedIn, in July of 2020. The idea was in your head for longer than that. So, where does the idea from Plants and Perks come from? And when did you start to noodle it? I also think that the LinkedIn algorithm isn't 100% correct. CHLOE: And it always seems to add time. I always get this sort of like, "Oh my God, you've been doing this for like two years?" I'm like, "No, I'm sure it can't be. It must be shorter than that." So Plants and Perks, Plants and Perks originally started out life as the Green Shoot Institute, which, I think, if you Google us, still there's remnants of the Green Shoot Institute that exists. That is still our company holding name. And that was kind of, I guess, the first thought of the idea. I was at the time heading up commercial relationships at a large parenting platform in the UK. And we had started to go on our own plant-based journey, so thinking about cutting back on meat and dairy consumption. I guess that was sort of my own personal journey that started to make me, as a parent, and as a consumer, and as a senior leader within business to, start to think about things outside of myself, and my family, and my business. And really, that was kind of the spark of thinking about how we, as employers, don't really do much to support employees on the plant-based sustainability journey. That was the sort of the embryo of the idea.

We have a huge audience of developers, engineers, and programmers who want to transition to becoming successful entrepreneurs. This conversation explores the journey of such a developer. Fantastic story! Sramana Mitra: Let's go to the very beginning of your journey. Where were you born, raised, and in what kind of background? David Moss: I was born in Arizona. I grew up in a small town. There wasn't a lot happening out in this town. I was interested in building things and taught myself how to program in the C language when I was 12. I continued by starting a business when I was in high school fixing computers, building websites, and so on. I always had an idea that I was going to do a startup as I got older. Those dreams did eventually come true. Getting there was an interesting path. I ended up going to college and studying electrical engineering partially because I was a little bored with software at that age. I had been doing software for so long, I wanted to learn more

Put a microphone in front of Phil Spencer and the guy will always deliver. Spencer, who you may or may not know as the head of Microsoft Gaming (he's in charge of all things Xbox), sat down with the Wall Street Journal at its WSJ Tech Live event for a wide-ranging interview about everything from Microsoft's plans for mobile gaming to Spencer's personal feelings on the metaverse. Spencer is one of the few industry leaders who actually gives real answers to questions on occasion (and comes across as "one of the good guys" for it), so let's break down the highlights. Undoubtedly the funniest thing Spencer said at WSJ Tech Live came at the expense of Meta's Mark Zuckerberg-fueled metaverse efforts. As you can see at about the 1:15 mark in this clip from the WSJ YouTube channel, Spencer seemed to take a bit of a jab at Meta's work-focused metaverse, without naming names of course.

Check out this awesome Embedding visualization toolbox by TensorFlow: https://lnkd.in/dXw2FV25! I feel that one of the silent heroes in the emergence of Data Science in the past decade has been the expansion of data visualization tools. It is less glamorous than many Deep Learning discoveries, but I am not sure we would be we are without those. One heavy influence on the world of data science has been the"The Grammar of Graphics" book by Leland Wilkinson in 2005 (https://lnkd.in/dWQRGwUD) This led to the creation of Ggplot2 in 2007 (https://lnkd.in/dEmQ4juJ).

Welcome to our October 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we learn about a Nigerian sign language dataset, hear from researchers working on different robotic control projects, and dig into the latest governmental AI policies. Steven Kolawole created a pioneering dataset for Nigerian sign language, in collaboration with a TV sign language broadcaster and two schools in Nigeria. He used this dataset of over 8000 images to create a model to convert sign language to text or speech. In this interview, Steven told us about the goals of this research, his methodology, and how the work has inspired research in other languages.

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HolisticBias, which includes nearly 600 descriptor terms across 13 different demographic axes. HolisticBias was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HolisticBias is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.

I have never lived in Los Angeles, but I have probably spent more time thinking about L.A. than any other city that I haven't resided in. This is partly the fault of Hollywood, of Ice Cube and The White Album, of Curb Your Enthusiasm and Party Down, of the despised Lakers, but it's mostly the fault of Mike Davis. Davis, the historian and urban theorist who died on Tuesday, was probably my favorite writer about cities that I have ever read. He didn't only write about L.A., not by a long shot, but L.A. was his Beatrice, his Dark Lady. Every time I visit Los Angeles Davis' work floods through my brain, often down to specific words, phrases, and sentences.

Welcome to your official daily AI research tl;dr (often with code and news) for AI professionals where I share the most exciting papers I find daily, along with a one-liner summary to help you quickly determine if the article (and code) is worth investigating. "We propose a novel method for pre-training text-toimage generation model on image-only datasets." "We present our techniques to train a) a policy that can perform robust dexterous manipulation on an anthropomorphic robot hand and b) a robust pose estimator suitable for providing reliable real-time information on the state of the object being manipulated." The best paper award goes to Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh for their paper called "On the Versatile Uses of Partial Distance Correlation in Deep Learning". "In this paper, we revisit a (less widely known) from statistics, called distance correlation (and its partial variant), designed to evaluate correlation between feature spaces of different dimensions."