Lifeboat Foundation

With the development of computing and data, autonomous agents are gaining power. The need for humans to have some say over the policies learned by agents and to check that they align with their goals becomes all the more apparent in light of this.

Currently, users either 1) create reward functions for desired actions or 2) provide extensive labeled data. Both strategies present difficulties and are unlikely to be implemented in practice. Agents are vulnerable to reward hacking, making it challenging to design reward functions that strike a balance between competing goals. Yet, a reward function can be learned from annotated examples. However, enormous amounts of labeled data are needed to capture the subtleties of individual users’ tastes and objectives, which has proven expensive. Furthermore, reward functions must be redesigned, or the dataset should be re-collected for a new user population with different goals.

New research by Stanford University and DeepMind aims to design a system that makes it simpler for users to share their preferences, with an interface that is more natural than writing a reward function and a cost-effective approach to define those preferences using only a few instances. Their work uses large language models (LLMs) that have been trained on massive amounts of text data from the internet and have proven adept at learning in context with no or very few training examples. According to the researchers, LLMs are excellent contextual learners because they have been trained on a large enough dataset to incorporate important commonsense priors about human behavior.

To divine that wonderful arts lie hid behind trivial and childish things is a conception for superhuman talents.

Microsoft’s latest layoffs throw ethics out the window and we should all be worried.

“That’s got molecules in it that will prevent cancer, among other things” like anti-inflammatory properties, he said. Some older research has shown, for example, that green tea consumption might be linked to a lower risk of stomach cancer.

Sinclair also said he takes supplements (like those sold on the Tally Health website) that contain resveratrol, which his team’s research has shown can extend the lifespan of organisms like yeast and worms.

While the compound, famously found in red wine, is known to have anti-inflammatory, anti-cancer, heart health, and brain health benefits, the research is mixed on if or how well such benefits can be achieved in humans through a pill.

These days, we don’t have to wait long until the next breakthrough in artificial intelligence impresses everyone with capabilities that previously belonged only in science fiction.

In 2022, AI art generation tools such as Open AI’s DALL-E 2, Google’s Imagen, and Stable Diffusion took the internet by storm, with users generating high-quality images from text descriptions.

Continue reading “AI Might Be Seemingly Everywhere, but There Are Still Plenty of Things It Can’t Do—For Now” »

ChatGPT’s successor GPT-4 has not just passed various major exams, but has actually aced them. Elon Musk has now reacted to the news and says, ‘What will humans do?’

It will be released with a new tool called MakerSuite, which lets you prototype ideas, do prompt engineering, synthetic data generation and custom-model tuning. Waitlist available soon.

Microsoft’s Kosmos-1 can take image and audio prompts, paving the way for the next stage beyond ChatGPT’s text prompts.

Microsoft has unveiled Kosmos-1, which it describes as a multimodal large language model (MLLM) that can not only respond to language prompts but also visual cues, which can be used for an array of tasks, including image captioning, visual question answering, and more.

OpenAI’s ChatGPT has helped popularize the concept of LLMs, such as the GPT (Generative Pre-trained Transformer) model, and the possibility of transforming a text prompt or input into an output.

The bank has been testing the artificial intelligence tool with 300 advisors and plans to roll it out widely in the coming months, according to Jeff McMillan, head of analytics, data and innovation at the firm’s wealth management division.

Morgan Stanley’s move is one of the first announcements by a financial incumbent after the success of OpenAI’s ChatGPT, which went viral late last year by generating human-sounding responses to questions. The bank is a juggernaut in wealth management with more than $4.2 trillion in client assets. The promise and perils of artificial intelligence have been written about for years, but seemingly only after ChatGPT did mainstream users understand the ramifications of the technology.

The idea behind the tool, which has been in development for the past year, is to help the bank’s 16,000 or so advisors tap the bank’s enormous repository of research and data, said McMillan.