Lifeboat Foundation

The artificial intelligence (AI) language model ChatGPT has captured the world’s attention in recent months. This trained computer chatbot can generate text, answer questions, provide translations, and learn based on the user’s feedback. Large language models like ChatGPT may have many applications in science and business, but how much do these tools understand what we say to them, and how do they decide what to say back?

In new paper published in Neural Computation on February 17, 2023, Salk Professor Terrence Sejnowski, author of “The Deep Learning Revolution,” explores the relationship between the human interviewer and language models to uncover why chatbots respond in particular ways, why those responses vary, and how to improve them in the future.

According to Sejnowski, language models reflect the intelligence and diversity of their interviewer.

Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector of image Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straight-forward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at this https URL.

The authors have declared no competing interest.

OpenAI announced its latest language model, GPT-4, but many in the AI community were disappointed by the lack of public information. Their complaints track increasing tensions in the AI world over safety.

Yesterday, OpenAI announced GPT-4, its long-awaited next-generation AI language model.

Should AI research be open or closed? Experts disagree.

Continue reading “OpenAI co-founder on company’s past approach to openly sharing research: ‘We were wrong’” »

Earlier this month, when LinkedIn started seeding “AI-powered conversation starters” in people’s news feeds to boost engagement on its platform, the move saw more than little engagement of its own, none of it too positive.

But the truth of the matter with LinkedIn is that it’s been using a lot of AI and other kinds of automation across different aspects of its platform for years, primarily behind the scenes with how it builds and operates its network. Now, with its owner Microsoft going all-in on OpenAI, it looks like it’s becoming a more prominent part of the strategy for LinkedIn on the front end, too — with the latest coming today in the areas of LinkedIn profiles, recruitment and LinkedIn Learning.

The company is today introducing AI-powered writing suggestions, which will initially be offered to people to spruce up their LinkedIn profiles, and to recruiters writing job descriptions. Both are built on advanced GPT models, said Tomer Cohen, LinkedIn’s chief product officer. LinkedIn is using GPT-4 for personalized profiles, with GPT-3.5 for job descriptions. Alongside this, the company is also creating a bigger focus on AI in LinkedIn Learning, corralling 100 courses around the subject and adding 20 more focused just on generative AI.

As we hurtle towards a future filled with artificial intelligence, many commentators are wondering aloud whether we’re moving too fast. The tech giants, the researchers, and the investors all seem to be in a mad dash to develop the most advanced AI. But are they considering the risks, the worriers ask?

The question is not entirely moot, and rest assured that there are hundreds of incisive minds considering the dystopian possibilities — and ways to avoid them. But the fact is that the future is unknown, the implications of this powerful new technology are as unimagined as was social media at the advent of the Internet. There will be good and there will be bad, but there will be powerful artificial intelligence systems in our future and even more powerful AIs in the futures of our grandchildren. It can’t be stopped, but it can be understood.

I spoke about this new technology with Ilya Stutskeve r, a co-founder of OpenAI, the not-for-profit AI research institute whose spinoffs are likely to be among the most profitable entities on earth. My conversation with Ilya was shortly before the release of GPT-4, the latest iteration of OpenAI’s giant AI system, which has consumed billions of words of text — more than any one human could possibly read in a lifetime.

16,000 financial advisors of the bank must be nervous.

Multinational investment management and financial services company Morgan Stanley is deploying a sophisticated chatbot to support the bank’s army of financial advisors powered by the most recent OpenAI technology, according to CNBC

The tool’s goal is to help the bank’s advisors access its data.

Continue reading “ChatGPT for financial advice? Morgan Stanley tries AI” »

Inspired by nature, these soft robots received their amphibious upgrade with the help of bistable actuators.

Researchers at Carnegie Mellon University have created a soft robot that can effortlessly transition from walking to swimming or from crawling to rolling.

“We were inspired by nature to develop a robot that can perform different tasks and adapt to its environment without adding actuators or complexity,” said Dinesh K. Patel, a postdoctoral fellow in the Morphing Matter Lab in the School of Computer Science’s Human-Computer Interaction Institute. “Our bistable actuator is simple, stable and durable, and lays the foundation for future work on dynamic, reconfigurable soft robotics.”

Long-term microgravity exposure causes various biological changes, ranging from bone loss to changes in cardiovascular function.

Towards this, SpaceX’s Dragon cargo ship is set to deliver cardiac tissue chips to the International Space Station (ISS). According to NASA, the cargo spacecraft is expected to autonomously dock with the ISS at 7:52 am EDT Thursday, March 16.

Scientists have long known that mitochondria play a crucial role in the metabolism and energy production of cancer cells. However, until now, little was known about the relationship between the structural organization of mitochondrial networks and their functional bioenergetic activity at the level of whole tumors.

In a new study, published in Nature, researchers from the UCLA Jonsson Comprehensive Cancer Center used positron emission tomography (PET) in combination with electron microscopy to generate 3-dimensional ultra-resolution maps of mitochondrial networks in lung tumors of genetically engineered mice.

They categorized the tumors based on mitochondrial activity and other factors using an artificial intelligence technique called deep learning, quantifying the mitochondrial architecture across hundreds of cells and thousands of mitochondria throughout the tumor.