Fleur had the pleasure of speaking with Ian Kuali'i, a multi-disciplinary self-taught artist of Hawaiian/Apache ancestry working in the forms of murals, large-scale hand cut paper and site-specific installations. From a single sheet of paper using only an xacto blade as his tool, Ian's portraits, journal entries and scenes are masterfully rendered in hand cut paper with a blend of loose urban contemporary techniques and collaged found materials. Ian describes his creative process as "The meditative process of destroying to create." Fleur: Can you please introduce yourself? My name is Ian Joseph Kekoa Hardwick-Kuali'i or just simply Ian Kuali'i.

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.

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,