Transcription of legal proceedings is very important to enable access to justice. However, speech transcription is an expensive and slow process. In this paper we describe part of a combined research and industrial project for building an automated transcription tool designed specifically for the Justice sector in the UK. We explain the challenges involved in transcribing court room hearings and the Natural Language Processing (NLP) techniques we employ to tackle these challenges. We will show that fine-tuning a generic off-the-shelf pre-trained Automatic Speech Recognition (ASR) system with an in-domain language model as well as infusing common phrases extracted with a collocation detection model can improve not only the Word Error Rate (WER) of the transcribed hearings but avoid critical errors that are specific of the legal jargon and terminology commonly used in British courts.

The CyberPeace Institute is an independent and neutral non governmental organization who works to enhance the stability of cyberspace by decreasing the frequency, impact, and scale of destructive cyberattacks. The Institute works in close collaboration with relevant partners to reduce the harms from cyberattacks on people's lives worldwide, and provide them assistance. By analyzing cyberattacks, the Institute exposes their societal impact, how international laws and norms are being violated, and advances responsible behavior to enforce cyberpeace. We are looking for a highly motivated intern to join the CyberPeace Institute. We work on a wide ranging area of machine learning and data science.

Arthur C. Clarke once remarked, "Any sufficiently advanced technology is indistinguishable from magic." That ambient sense of magic has been missing from the past decade of internet history. Each new tablet and smartphone is only a modest improvement over its predecessor. The expected revolutions--the metaverse, blockchain, self-driving cars--have plodded along, always with promises that the real transformation is just a few years away. The one exception this year has been in the field of generative AI.

Honestly, I didn't expect to be hallucinating with Artificial Intelligence and Deep Learning so soon. Dall-E 2, Midjourney and Stable Diffussion Whisper CHATGPT (as an extra we could include them in the GPT-3 family). Well, I could leave it there, and it would make a good tweet, or at most a small thread, but since I don't like to talk without trying to demonstrate what I'm saying, I'm going to do a small experiment. Here it goes: I'm going to choose a speech, a motivating and public speech, and I will work on it with the help of these AIs. At first I wanted to select a speech by my General Manager in Spain, a person whom I admire, and I would have loved to do this on one of his last public speeches; but then I got scared, because I haven't asked for permission from the Legal Department or the Communications Department of the company.

Orly Lobel believes technology can make the world a better place--and she knows in 2022, that makes her a bit of a contrarian. Lobel, a law professor specializing in labor and employment at the University of San Diego in California, has studied how technology and the gig economy affects workers. That has made her familiar with the potential disruptions caused by tools like automated résumé screening and apps that use algorithms to assign work to people. Yet Lobel feels discussion about automation and AI is too stuck on the harms these systems create. In her book The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, Lobel encourages a sunnier view.

This year, it feels like artificial intelligence-generated art has been everywhere. In the summer, many of us entered goofy prompts into DALL-E Mini (now called Craiyon), yielding a series of nine comedically janky AI-generated images. But more recently, there's been a boom of AI-powered apps that can create cool avatars. MyHeritage AI Time Machine generates images of users in historical styles and settings, and AI TikTok filters have become popular for creating anime versions of people. This past week, "magic avatars" from Lensa AI flooded social media platforms like Twitter with illustrative and painterly renderings of people's headshots, as if truly made by magic. These avatars, created using Stable Diffusion -- which allows the AI to "learn" someone's features based off of submitted images -- also opened an ethical can of worms about AI's application.

This week, Dana, Julia, and Stephen get started by discussing Avatar: The Way of Water. Then they discuss the new Netflix documentary series Harry & Meghan. Finally, they finish by talking about the new Lensa AI art app and all the photos it's generating online. In Slate Plus, the panel answers a listener question about which works of art they like to revisit again and again? Outro music is "Lonely Calling" by Arc De Soleil

The board dinner event, "The Board's role in strategy, governing ecosystems and artificial intelligence", held in Stockholm, Sweden in mid November 2019, was co-organized by two board research projects: SISU Boards led by Professor Mats Magnusson of the Royal Institute of Technology (KTH) and 4Boards.ai The event, as well as both research projects, are co-sponsored by Vinnova – the Swedish Innovation Agency. The event was divided into two sets of activities: 1) speaker presentations and 2) roundtable discussions. Speakers included two globally leading experts, Anastassia Lauterbach and Sebastian Herzog, as well as Researchers Chairman Liselotte Engstam and Professor Mats Magnusson. Below is a summary of the speakers' presentations as well as some insights gained through roundtable discussions with event participants.

Non-target corporations Kairos and Amazon have overall error rates of 6.60% and 8.66%, respectively. These are the worst current performances of the companies analyzed in the follow-up audit. Nonetheless, when comparing to the previous May 2017 performance of target corporations, the Kairos and Amazon error rates are lower than the former error rates of IBM (12.1%) and Face (9.9%) and only slightly higher than Microsoft's performance (6.2%) from the initial study.

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.