❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers.
📝 The paper “Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback” is available here:
https://www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds.
Continue reading “DeepMind’s New AI Surpasses Humans At Some Things!” »
DeepMind brought artificial intelligence mainstream. Now its CEO Demis Hassabis is issuing a warning.
HelloScribe’s powerful AI writing and brainstorming tools help Marketing, Advertising, and PR Professionals to do their best work 10x faster.
Victoria Krakovna is a Research Scientist at DeepMind working on AGI safety and a co-founder of the Future of Life Institute, a non-profit organization working to mitigate technological risks to humanity and increase the chances of a positive future. In this interview we discuss three of her recent LW posts, namely DeepMind Alignment Team Opinions On AGI Ruin Arguments, Refining The Sharp Left Turn Threat Model and Paradigms of AI Alignment.
Transcript & Audio: https://theinsideview.ai/victoria.
Continue reading “Victoria Krakovna–AGI Ruin, Sharp Left Turn, Paradigms of AI Alignment” »
Biorealistic organic electrochemical neurons enabled by ion-tunable antiambipolarity in mixed ion-electron conducting polymers.
An artificial organic neuron that closely mimics the characteristics of biological nerve cells has been created by researchers at Linköping University (LiU), Sweden. This artificial neuron can stimulate natural nerves, making it a promising technology for various medical treatments in the future.
Work to develop increasingly functional artificial nerve cells continues at the Laboratory for Organic Electronics, LOE. In 2022, a team of scientists led by associate professor Simone Fabiano demonstrated how an artificial organic neuron could be integrated into a living carnivorous plant to control the opening and closing of its maw. This synthetic nerve cell met 2 of the 20 characteristics that differentiate it from a biological nerve cell.
A common idea that our creativity is what makes us uniquely human has shaped society but strides of progress made in the domain of Generative Artificial Intelligence question this very notion. Generative AI is an emerging field that involves the creation of original content or data using machine learning algorithms.
As we think about a future where humans and AI partner in iterative creative cycles, we consider how generative AI could impact current businesses and possibly create new ones. Up until recently, machines were relegated to analysis and cognitive roles, but today algorithms are improving at generating original content. These technologies are iterative in principle, one is built on top of the last one, and each new iteration enhances the algorithm and increases the potential for discovery exponentially.
The technology presents itself as a more refined and mature breed of AI that has sent investors into a frenzy and among all this emerges a clear market leader — OpenAI. Its flagship products-ChatGPT and DALL-E proved to be industry disruptors and brought generative AI tools to the masses. DALL-E allows people to generate and edit photo-realistic images simply by describing what they want to see, while ChatGPT does the same through a text medium.
Startups implementing generative AI are already attracting big, early-stage rounds at hefty valuations — with little evidence they can become profitable.
As those who have read this column over time understand, I have a soapbox that involves authors, whether academics or consultants, pandering to management rather than teaching them. Sadly, Age of Invisible Machines.
The second, and larger issue was mentioned up top. Inventors have a habit, from long before Alfred Nobel, of ignoring the consequences of their inventions. The excuse is the same as scientists often give, that it’s not up to them to decide on the used and societal impact, they’re just discovering and inventing things. While that is true for theoretical science, it’s far past time for technologists focused on applications that directly impact society to give up that attempt to absolve themselves from societal impact.
The ethical AI movement is only an extension of regular movements in society, movements that try to understand how change impacts those societies and to do it from the beginning. Any good programmer looks at system issues from the design phase. Waiting until debugging is too late to create an effective system. Artificial intelligence will clearly impact society in major ways. It will redefine who can work and how society must address a change in the definition of work. It ties into the overvaluing of stocks because of the promise of solutions, in the lack of understanding of most people in what those solutions mean, and a real understanding, among a very few, of what that means.
Continue reading “Age Of Invisible Machines: An Impractical Guide To Hyperautomated Systems” »
The emergence in the last week of a particularly effective voice synthesis machine learning model called VALL-E has prompted a new wave of concern over the possibility of deepfake voices made quick and easy — quickfakes, if you will. But VALL-E is more iterative than breakthrough, and the capabilities aren’t so new as you might think. Whether that means you should be more or less worried is up to you.
Voice replication has been a subject of intense research for years, and the results have been good enough to power plenty of startups, like WellSaid, Papercup and Respeecher. The latter is even being used to create authorized voice reproductions of actors like James Earl Jones. Yes: from now on Darth Vader will be AI generated.
VALL-E, posted on GitHub by its creators at Microsoft last week, is a “neural codec language model” that uses a different approach to rendering voices than many before it. Its larger training corpus and some new methods allow it to create “high-quality personalized speech” using just three seconds of audio from a target speaker.
