On May 8, 2018, Google I/O was held at Shoreline Amphitheatre in Mountain View, California. If you are wondering what Google I/O is, don't worry, I've got your back. "Google I/O brings together developers from around the globe annually for talks, hands-on learning with Google experts, and the first look at Google's latest developer products." In the Keynote, Sundar Pichai, the CEO of Alphabet Inc. (Google's parent company), shared the then-latest developments that Google had been working on. One of the projects that he spoke about was something that maybe no one saw coming; an application of Artificial Intelligence (AI), soon to be on our own smartphones, that left the world in awe.

This Women in AI Podcast episode is with Juliet Waters, Chief Knowledge Officer at Kids Code Jeunesse, a Canadian charity with a mission to give every Canadian child access to digital skills education, with a focus on girls and underserved communities. KCJ teaches kids and their educators about topics including algorithm literacy and artificial intelligence, and how these integrate with the UN's Sustainable Development Goals to give kids the confidence and creative tools they need to build a better future. Listen to the podcast here. Thank you so much for joining us for the Woman in AI Podcast today. You're currently Chief Knowledge Officer at Kids Code Jeunesse so I wanted to, first of all, for any of our listeners that are not maybe familiar with KCJ, ask if you could share a brief overview. Sure, so we started a Canadian charity in around 2013, working alongside teachers in classrooms, trying to help develop some viable lesson plans that would help to bring computer programming into the classroom.

Oleg Shipitko, Chief Technical Director of Evocargo, an integrated logistics service company using autonomous vehicles speaks with Kate. Oleg talks about the need for automating operational logistics inside enclosed facilities centers and how their autonomous vehicles and other operational services can greatly improve the current way we transport goods within facilities such as ports, warehouses and factories. He has a bachelors and masters degree in autonomous information and control systems (bachelors: 4.96/5.0, Oleg has received numerous awards including: Best paper awarded at 32nd European Conference on Modeling and Simulation (ECMS-2018): Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image and Best paper awarded at IV International Conference on Information Technology and Nanotechnology (ITNT-2018): Gaussian filtering for FPGA based image processing with High-Level Synthesis tools.

Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases. In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM. We evaluate the target LM's replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280B parameter LM chatbot. We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty. Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot's own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation. Overall, LM-based red teaming is one promising tool (among many needed) for finding and fixing diverse, undesirable LM behaviors before impacting users.

The technology market share is just increasing like an oil spill in the ocean and becoming more and more complex and overwhelming to cope with. To help you learn and understand the ever-changing landscape of technology, we are extending a list of 30 Top LinkedIn Voice in Technology. Allie is the Global Head of Machine Learning Business Development, Startups, and Venture Capital at Amazon Web Service (AWS). Her area of expertise includes AI, Machine Learning, Crypto, Web3 & NFTs. Emmanuel is an undergraduate student of Chemical Engineering, named as the Young Influencer of the year by TIBA.

As the advent of machine learning continues to disrupt a swathe of industries, one of the things that is becoming increasingly clear is that machine learning needs lots of high-quality data to work well. According to the findings of a recently released survey, 99% of respondents reported having had an ML project completely canceled due to insufficient training data, and 100% of respondents reported experiencing project delays as a result of insufficient training data. Using synthetic data is one approach to get around the issues associated with obtaining and using high-quality data from the real world. We caught up with Rendered.ai Founder and CEO Nathan Kundtz to learn more about the use cases the platform can serve, and how it works under the hood.

Artificial intelligence can alleviate administrative burdens, improve diagnostic accuracy, identify patients most at risk for certain diseases and reduce unnecessary procedures, according to a recent paper. Yet, "most primary care providers do not know what it is, how it will impact them and their patients and what its key limitations and ethical pitfalls are," Steven Lin, MD, the author of the paper and family medicine service chief and head of technology innovation in the division of primary care and population health at Stanford Medicine, wrote in the Journal of the American Board of Family Medicine. He added that primary care is the ideal medical specialty to take charge in what he called the "health care artificial intelligence (AI) revolution." Lin shared more details on this emerging technology and how primary care can maximize its potential in an interview with Healio. Healio: Why should primary care lead the "health care AI revolution"?

There's an urban legend saying that if you wander around at night and go to a subway station and you ride a train with nobody on it, it might just take you to the other world,

Cleaning robot'Franzi' cleans in the entrance area of a hospital in Munich Neuperlach, southern Germany, on February 12, 2021. Every time someone mentions robots, I think of my grandmother. At 93, she was almost completely blind, in a wheelchair, and living in a nursing home. She was wheeled each morning into a room where a volunteer read the local newspaper. On my rare visits (I lived several states away), it was not uncommon for me to enter that room and find all the residents sleeping.

The "race" for dominance in AI is far from fair: Not only do a few developed economies possess certain material advantages right from the start, they also set the rules. They have an advantage in research and development, and possess a skilled workforce as well as wealth to invest in AI. We can also look at the state of inequity in AI in terms of governance: How "tech fluent" are policymakers in developing and underdeveloped countries? What barriers do they face in crafting regulations and industrial policy? Are they sufficiently represented and empowered at the international bodies that set rules and standards on AI? At the same time, there is an emerging challenge at the nexus of AI and climate change that could deepen this inequity.