The EU is poised to effectively become the world’s AI police, creating binding rules on transparency, ethics, and more.
It’s done.
Category: robotics/AI – Page 572
It’s hard to believe, but generative AI — the seemingly ubiquitous technology behind ChatGPT — was launched just one year ago, in late November 2022.
Still, as technologists discover more and more use cases for saving time and money in the enterprise, schools, and businesses the world over are struggling to find the technology’s rightful balance in the “real world.”
As the year has progressed, the rapid onset and proliferation has led to not only rapid innovation and competitive leapfrogging, but a continued wave of moral and ethical debates and has even led to early regulation and executive orders on the implementation of AI around the world as well as global alliances — like the recent Meta + IBM AI Alliance — to try and develop open frameworks and greater standards in the implementation of safe and economically sustainable AI.
Nevertheless, a transformative year with almost daily shifts in this exciting technology story. The following is a brief history of the year in generative AI, and what it means for us moving forward.
Brain-inspired computing hardware could be used to address limitations in AI.
Scientists have created Brainoware, a computer software that makes computing more brain-like by integrating real, actual, human brain tissues.
Onboarding new employees has always been a pivotal part of HR’s responsibilities.
Onboarding has undergone a radical transformation. Explore how AI and data are reshaping how we welcome new hires and helping us deliver engaging onboarding experiences.
The promise and peril of AI have dominated conversations this year.
Innovator leverages large-language models for financial services tech tools that better analyst work and performance.
Microsoft research releases Phi-2 and promptbase.
Phi-2 outperforms other existing small language models, yet it’s small enough to run on a laptop or mobile device.
Over the past few months, our Machine Learning Foundations team at Microsoft Research has released a suite of small language models (SLMs) called “Phi” that achieve remarkable performance on a variety of benchmarks. Our first model, the 1.3 billion parameter Phi-1 (opens in new tab), achieved state-of-the-art performance on Python coding among existing SLMs (specifically on the HumanEval and MBPP benchmarks). We then extended our focus to common sense reasoning and language understanding and created a new 1.3 billion parameter model named Phi-1.5 (opens in new tab), with performance comparable to models 5x larger.
We are now releasing Phi-2 (opens in new tab), a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters. On complex benchmarks Phi-2 matches or outperforms models up to 25x larger, thanks to new innovations in model scaling and training data curation.
Scientists have grown a tiny brain-like organoid out of human stem cells, hooked it up to a computer, and demonstrated its potential as a kind of organic machine learning chip, showing it can quickly pick up speech recognition and math predictions.
As incredible as recent advances have been in machine learning, artificial intelligence still lags way behind the human brain in some important ways. For example, the brain happily learns and adapts all day long on an energy budget of about 20 watts, where a comparably powerful artificial neural network needs about 8 million watts to achieve anything remotely comparable.
What’s more, the human brain’s neural plasticity, its ability to grow new nervous tissue and expand existing connective channels, has granted it an ability to learn from noisy, low-quality data streams, with minimal training and energy expenditure. What AI systems accomplish with brute force and massive energy, the brain achieves with an effortless elegance. It’s a credit to the billions of years of high-stakes trial and error that delivered the human brain to the state it’s in today, in which it’s chiefly used to watch vast numbers of other people dancing while we’re on the toilet.
Tesla is pushing the boundaries of AI and supercomputing with the development of Dojo 2, aiming to build the world’s biggest supercomputer by the end of next year, and setting high goals for performance and cost efficiency.
Questions to inspire discussion.
Who is leading Tesla’s DOJO supercomputer project?
—Peter Bannon is the new leader of Tesla’s DOJO supercomputer project, replacing the previous head, Ganesh Thind.
Tesla’s Giga Texas factory is not only expanding production capacity for the Cybertruck, but also hinting at the development of a $25K compact car and showcasing innovative and advanced manufacturing processes.
Questions to inspire discussion.
What vehicles is Giga Texas producing?
—Giga Texas is producing the Cybertruck, Model Y, and a new $25K compact car.
One month after OpenAI unveiled a program that allows users to easily create their own customized ChatGPT programs, a research team at Northwestern University is warning of a “significant security vulnerability” that could lead to leaked data.
In November, OpenAI announced ChatGPT subscribers could create custom GPTs as easily “as starting a conversation, giving it instructions and extra knowledge, and picking what it can do, like searching the web, making images or analyzing data.” They boasted of its simplicity and emphasized that no coding skills are required.
“This democratization of AI technology has fostered a community of builders, ranging from educators to enthusiasts, who contribute to the growing repository of specialized GPTs,” said Jiahao Yu, a second-year doctoral student at Northwestern specializing in secure machine learning. But, he cautioned, “the high utility of these custom GPTs, the instruction-following nature of these models presents new challenges in security.”