Lifeboat Foundation

The efforts of Jeff Hawkins and Numenta to understand how the brain works started over 30 years ago and culminated in the last two years with the publication of the Thousand Brains Theory of Intelligence. Since then, we’ve been thinking about how to apply our insights about the neocortex to artificial intelligence. As described in this theory, it is clear that the brain works on principles fundamentally different from current AI systems. To build the kind of efficient and robust intelligence that we know humans are capable of, we need to design a new type of artificial intelligence. This is what the Thousand Brains Project is about.

In the past Numenta has been very open with their research, posting meeting recordings, making code open-source and building a large community around our algorithms. We are happy to announce that we are returning to this practice with the Thousand Brains Project. With funding from the Gates Foundation, among others, we are significantly expanding our internal research efforts and also calling for researchers around the world to follow, or even join this exciting project.

Today we are releasing a short technical document describing the core principles of the platform we are building. To be notified when the code and other resources are released, please sign up for the newsletter below. If you have a specific inquiry please send us an email to [email protected].

The reliable generation of random numbers has become a central component of information and communications technology. In fact, random number generators, algorithms or devices that can produce random sequences of numbers, are now helping to secure communications between different devices, produce statistical samples, and for various other applications.

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Science publisher Springer Nature has developed two new AI tools to detect fake research and duplicate images in scientific papers, helping to protect the integrity of published studies.

The growing number of cases of fake research is already putting a strain on the scientific publishing industry, according to Springer Nature. Following a pilot phase, the publisher is now rolling out two AI tools to identify papers with AI-generated fake content and problematic images — both red flags for research integrity issues.

Continue reading “Springer Nature cracks down on AI research fraud using AI tools” »

This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA.

This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Colin Jacobs, PhD, assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen, The Netherlands, and Kiran Vaidhya Venkadesh, a second-year PhD candidate with the Diagnostic Image Analysis Group at Radboud University Medical Center discuss their 2021 Radiology study, which used CT images from the National Lung Cancer Screening Trial (NLST) to train a deep learning algorithm to estimate the malignancy risk of lung nodules.

Viewers like you help make PBS (Thank you 😃). Support your local PBS Member Station here: https://to.pbs.org/DonateSPACE

Be sure to check out the Infinite Series episode Singularities Explained • Singularities Explained | Infinite Se… or How I Learned to Stop Worrying and Divide by Zero.

Continue reading “PBS Space Time” »

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., has issued a solicitation (DARPA-PA-23–03-11) for the Defense Applications of Innovative Remote Sensing (DAIRS) project.

Primary emphasis will be in the high frequency (HF) band nominally at 4 to 15 MHz. Key applications in this frequency band are SWOTHR for aircraft, ship, and boat tracking, oceanographic SWOTHR, and sounding for ionospheric characterization.

When it comes to quantum computing, that chilling effect on research and development would enormously jeopardize U.S. national security. Our projects received ample funding from defense and intelligence agencies for good reason. Quantum computing may soon become the https://www.cyberdefensemagazine.com/quantum-security-is-nat...at%20allow, codebreaking%20attacks%20against%20traditional%20encryption" rel="noopener" class="">gold standard technology for codebreaking and defending large computer networks against cyberattacks.

Adopting the proposed march-in framework would also have major implications for our future economic stability. While still a nascent technology today, quantum computing’s ability to rapidly process huge volumes of data is set to revolutionize business in the coming decades. It may be the only way to capture the complexity needed for future AI and machine learning in, say, self-driving vehicles. It may enable companies to hone their supply chains and other logistical operations, such as manufacturing, with unprecedented precision. It may also transform finance by allowing portfolio managers to create new, superior investment algorithms and strategies.

Given the technology’s immense potential, it’s no mystery why China committed what is believed to be more than https://www.mckinsey.com/featured-insights/sustainable-inclu…n-quantum” rel=“noopener” class=””>$15 billion in 2022 to develop its quantum computing capacity–more than double the budget for quantum computing of EU countries and eight times what the U.S. government plans to spend.

Mark Hamilton, an MIT Ph.D. student in electrical engineering and computer science and affiliate of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), wants to use machines to understand how animals communicate. To do that, he set out first to create a system that can learn human language “from scratch.”