Well File:

designer


How do we keep bias out of AI?

#artificialintelligence

From the coining of the term back in the 1950's to now, AI has taken remarkable leaps forward and only continues to grow in relevance and sophistication But despite these advancements, there's one problem that continues to plague AI technology – the internal bias and prejudice of its human creators. The issue of AI bias cannot be brushed under the carpet, given the potential detrimental effects it can have. A recent survey showed that 36% of respondents reported that their businesses suffered from AI bias in at least one algorithm, resulting in unequal treatment of users based on race, gender, sexual orientation, religion or age. These instances incurred a direct commercial impact: of those respondents, two-thirds reported that as a result they lost revenue (62%), customers (61%), or employees (43%). And 35% incurred legal fees because of lawsuits or legal action.


The unexpected magic of an Arya Stark-Bugs Bunny team-up is 'MultiVersus' sweet spot

Mashable

Based on early appearances, you should expect the unexpected when characters from Game of Thrones, Looney Tunes, and other popular Warner Bros. franchises team up and scrap together in WB Interactive Entertainment's upcoming MultiVersus. The "platform fighter" from the developers at Player First Games is built like a gaming sandbox where magical moments of play emerge from happy accidents and inventive players. The wascally wabbit can toss a projectile-blocking safe on the ground, but it's also a physics-based object that can be moved -- which means a punch can knock it into other players. Arya Stark, meanwhile, steps into the battlefield armed with a throwing knife that she can teleport herself over to, even if a teammate -- or, say, a cartoon safe -- is touching it. "Bugs Bunny will knock the safe up in the air and [Arya] will throw the dagger and teleport to the safe...and then re-direct it."


Designing societally beneficial Reinforcement Learning (RL) systems

Robohub

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind's work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution – for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research. At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. The focus of these research efforts to date has been to account for shortcomings of datasets or supervised learning practices that can harm individuals.


How To Become A Master In Machine Learning With Python?

#artificialintelligence

Machine learning training is something long-term holds. We need more choices for personalization, great proposals, and brilliantly look highlights. The address presently emerges: which is the finest programming language for machine learning? Python training is the arrangement for this. Machine learning online course is best learned in Python online training.


HUMAN + MACHINE

#artificialintelligence

The limitation of using human manual manipulation is time consuming in the process of form finding. In this project, the human design rules were set by six major curves which were spread on the roof surface. These rules were used in generating new designs. Each designer has their own strengths. The strength of each designer (human, generative design algorithm, and GA) was used in creating a creative design generator.


AI Audits with Ryan Carrier - The Machine Ethics Podcast

#artificialintelligence

Ryan Carrier founded ForHumanity after a 25 year career in finance. His global business experience, risk management expertise and unique perspective on how to manage the risk led him to launch the non-profit entity, ForHumanity, personally. Ryan focused on Independent Audit of AI Systems as one means to mitigate the risk associated with artificial intelligence and began to build the business model associated a first-of-its-kind process for auditing corporate AIs, using a globally, open-source, crowd-sourced process to determine "best-practices". Ryan serves as ForHumanity's Executive Director and Chairman of the Board of Directors, in these roles he is responsible for the day-to-day function of ForHumanity and the overall process of Independent Audit. Prior to founding ForHumanity, Ryan owned and operated Nautical Capital, a quantitative hedge fund which employed artificial intelligence algorithms.


The Machine Ethics Podcast: Ethics of digital worlds with Richard Bartle

AIHub

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. Richard Bartle joins us again after his appearance on episode 65 to chat about the metaverse, different ways to design AI controlled NPC, the lack of progress of AI in games, ethical considerations of games designers, ethics of AI life, virtualism, 'smart' AI, robot rights and more… Dr Richard A. Bartle is Honorary Professor of Computer Game Design at the University of Essex, UK. He is best known for having co-written in 1978 the first virtual world, MUD, the progenitor of the £30bn Massively-Multiplayer Online Role-Playing Game industry. His 1996 Player Types model has seen widespread adoption by MMO developers and the games industry in general. His 2003 book, Designing Virtual Worlds, is the standard text on the subject, and he is an influential writer on all aspects of MMO design and development.


Why we need human-centered AI

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.


OpenAI's new image generator sparks both excitement and fear

#artificialintelligence

OpenAI has unveiled a new AI tool that turns text into images -- and the results are stunning. Named DALL-E 2, the system is the successor to a model unveiled last year. While its predecessor generated some impressive outputs, the new version is a major upgrade. DALL-E-2 adds enhanced textual comprehension, faster image generation, and four times greater resolution. "When approaching DALL-E 2 we focused on improving the image resolution quality and improving latency, rather than building a bigger system," OpenAI researcher Aditya Ramesh told TNW.


What Happens When Twelve Thousand Game Developers Converge?

The New Yorker

Nick Kaman, the co-founder and art director of Aggro Crab, an indie-game studio in Seattle, is twenty-six years old, with messy, brass-bleached hair, large round eyeglasses, and a small silver hoop in each earlobe; self-deprecating and sincere, with a sarcastic streak, he speaks with slacker chill. At the University of Washington, he studied human-centered design and engineering--"Pretty cringe," he said--while teaching himself how to make video games. Eventually, he started running the on-campus game-development club, which taught students how to build games along the lines of Flappy Bird using Unity, a game engine. "You can make that game in half an hour, but by doing that you've learned all these fundamentals of game-making," Kaman said. "Like, how do I do player input? How do I do jump physics? How do I spawn in pipes that move from the right to the left?"