Lifeboat Foundation

Over the past few months, I was asked multiple times by Staff of the House Committee on Oversight and Accountability whether I am available to testify before the U.S. Congress on Unidentified Anomalous Phenomena (UAPs). As a result, I cleared my calendar for November 13, 2024 and prepared the following written statement. At the end, I was not called to testify before Congress and so I am posting below my intended statement. The Galileo Project under my leadership is about to release this week unprecedented results from commissioning data of its unique Observatory at Harvard University. Half a million objects were monitored on the sky and their appearance was analyzed by state-of-the-art machine learning algorithms. Are any of them UAPs and if so — what are their flight characteristics? Unfortunately, the congressional hearing chairs chose not to hear about these scientific results, nor about the scientific findings from our ocean expedition to the site of the first reported meteor from interstellar space.

Stay tuned for the first extensive paper on the commissioning data from the first Galileo Project Observatory, to be posted publicly in the coming days. Here is my public statement.

Modern imaging systems, such as those used in smartphones, virtual reality (VR), and augmented reality (AR) devices, are constantly evolving to become more compact, efficient, and high-performing. Traditional optical systems rely on bulky glass lenses, which have limitations like chromatic aberrations, low efficiency at multiple wavelengths, and large physical sizes. These drawbacks present challenges when designing smaller, lighter systems that still produce high-quality images.

Readers are unable to reliably differentiate AI-generated from human-written poetry and are more likely to prefer AI poems, according to new research published in Scientific Reports. This tendency to rate AI poetry positively may be due to readers mistaking the complexity of human-written verse for incoherence created by AI and an underestimation of how human-like generative AI can appear.

Fortunately, linguists have developed sophisticated tools using information theory to interpret unknown languages.

Just as archaeologists piece together ancient languages from fragments, we use patterns in AI conversations to understand their linguistic structure. Sometimes we find surprising similarities to human languages, and other times we discover entirely novel ways of communication.

These tools help us peek into the “black box” of AI communication, revealing how AI agents develop their own unique ways of sharing information.

All lawyers, from in-house counsel and law firms, to barristers and judges, need to embrace the use of generative artificial intelligence (AI). Why? Because it will become an indispensable productivity tool across the legal profession.

A research team led by Lund University in Sweden has developed an AI tool that traces back the most recent places you have been to.

Microorganisms are organisms, such as bacteria, that are invisible to the naked eye. The word microbiome is used to describe all the microorganisms in a particular environment. Establishing the geographical source of a microbiome sample has been a considerable challenge up to now.

However, in a new study, published in the research journal Genome Biology and Evolution, a research team presents the tool Microbiome Geographic Population Structure (mGPS). It is a unique instrument that uses ground-breaking AI technology to localise samples to specific bodies or water, countries and cities. The researchers discovered that many places have unique bacteria populations, so when you touch a handrail at a train station or bus stop, you pick up bacteria that can then be used to link you back to the exact place.

Continue reading “New GPS system for microorganisms could revolutionise police work” »

“I can say with certainty that brains work on completely different principles than deep learning, and these differences matter.” writes Jeff Hawkins in a new essay.

Excerpt:

Why should we care about how the brain works?

Continue reading “For truly intelligent AI, we need to mimic the brain’s sensorimotor principles” »

Summary: Researchers have discovered that the NMDA receptor (NMDAR), known for its role in learning and memory, also stabilizes brain activity by setting baseline neural network activity. This stabilization supports the brain’s adaptability amid constant environmental and physiological changes.

The study revealed that blocking NMDARs disrupted this baseline, highlighting their critical role in maintaining neural homeostasis. Findings may revolutionize treatments for conditions like depression, Alzheimer’s, and epilepsy by leveraging NMDAR’s role in brain stability.

Summary: A deep learning AI model developed by researchers significantly accelerates the detection of pathology in animal and human tissue images, surpassing human accuracy in some cases. This AI, trained on high-resolution images from past studies, quickly identifies signs of diseases like cancer that typically take hours for pathologists to detect.

By analyzing gigapixel images with advanced neural networks, the model achieves results in weeks instead of months, revolutionizing research and diagnostic processes. The tool is already aiding disease research in animals and holds transformative potential for human medical diagnostics, particularly for cancer and gene-related illnesses.