Lifeboat Foundation

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Self-healing endpoint platform providers are under pressure to create new solutions to help CISOs consolidate tech stacks while improving cyber-resiliency. CISOs see the potential of self-healing platforms to reduce costs, increase visibility and capture real-time data that quantifies how cyber-resilient they are becoming. And reducing costs while increasing cyber-resilience is the risk profile their boards of directors want.

A self-healing endpoint is one that combines self-diagnostics with the adaptive intelligence to identify a suspected or actual breach attempt and take immediate action to stop it. Self-healing endpoints can shut themselves off, complete a re-check of all OS and application versioning, and then reset themselves to an optimized, secure configuration — all autonomously with no human intervention.

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Generative AI, which is based on Large Language Models (LLMs) and transformer neural networks, has certainly created a lot of buzz. Unlike hype cycles around new technologies such as the metaverse, crypto and Web3, generative AI tools such as Stable Diffusion and ChatGPT are poised to have tremendous, possibly revolutionary impacts. These tools are already disrupting multiple fields — including the film industry — and are a potential game-changer for enterprise software.

All of this has led Ben Thompson to declare in his Stratechery newsletter to declare generative AI advances as marking “a new epoch in technology.”

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Hyperbole aside, we have only scratched the surface of what the new technology may eventually become. ChatGPT has the markings of artificial narrow intelligence (ANI). That is, AI that is designed to perform specific tasks.

SUMMARY Researchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second. This innovation, which harnesses the massive parallelism of light, heralds a new era of optical signal processing for machine learning with numerous applications, including in self-driving cars, 5G networks, data-centers, biomedical diagnostics, data-security and more.

THE SITUATION Global demand for machine learning hardware is dramatically outpacing current computing power supplies. State-of-the-art electronic hardware, such as graphics processing units and tensor processing unit accelerators, help mitigate this, but are intrinsically challenged by serial data processing that requires iterative data processing and encounters delays from wiring and circuit constraints. Optical alternatives to electronic hardware could help speed up machine learning processes by simplifying the way information is processed in a non-iterative way. However, photonic-based machine learning is typically limited by the number of components that can be placed on photonic integrated circuits, limiting the interconnectivity, while free-space spatial-light-modulators are restricted to slow programming speeds.

THE SOLUTION To achieve a breakthrough in this optical machine learning system, the researchers replaced spatial light modulators with digital mirror-based technology, thus developing a system over 100 times faster. The non-iterative timing of this processor, in combination with rapid programmability and massive parallelization, enables this optical machine learning system to outperform even the top-of-the-line graphics processing units by over one order of magnitude, with room for further optimization beyond the initial prototype.

Is Director of the Division of Research, Innovation and Ventures (DRIVe — https://drive.hhs.gov/) at the Biomedical Advanced Research and Development Authority (https://aspr.hhs.gov/AboutASPR/ProgramOffices/BARDA/Pages/default.aspx), a U.S. Department of Health and Human Services (HHS) office responsible for the procurement and development of medical countermeasures, principally against bioterrorism, including chemical, biological, radiological and nuclear (CBRN) threats, as well as pandemic influenza and emerging diseases.

Dr. Patel is committed to advancing high-impact science, building new products, and launching collaborative programs and initiatives with public and private organizations to advance human health and wellness. As the DRIVe Director, Dr. Patel leads a dynamic team built to tackle complex national health security threats by rapidly developing and deploying innovative technologies and approaches that draw from a broad range of disciplines.

Continue reading “Dr. Sandeep Patel, Ph.D. — BARDA — Developing Effective Life-Saving Medical Countermeasures For All” »

Dr. Renee Wegrzyn, Ph.D. is the inaugural director of the Advanced Research Projects Agency for Health (ARPA-H — https://arpa-h.gov/), an agency that supports the development of high-impact research to drive biomedical and health breakthroughs to deliver transformative, sustainable, and equitable health solutions for everyone. ARPA-H’s mission focuses on leveraging research advances for real world impact.

Previously, Dr. Wegrzyn served as a vice president of business development at Ginkgo Bioworks and head of Innovation at Concentric by Ginkgo, where she focused on applying synthetic biology to outpace infectious diseases—including Covid-19—through biomanufacturing, vaccine innovation and biosurveillance of pathogens at scale.

Continue reading “Dr. Renee Wegrzyn, Ph.D. — ARPA-H — Transformative, Sustainable, Equitable Health Solutions For All” »

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Since its launch in 2020, Generative Pre-trained Transformer 3 (GPT-3) has been the talk of the town. The powerful large language model (LLM) trained on 45 TB of text data has been used to develop new tools across the spectrum — from getting code suggestions and building websites to performing meaning-driven searches. The best part? You just have to enter commands in plain language.

GPT-3’s emergence has also heralded a new era in scientific research. Since the LLM can process vast amounts of information quickly and accurately, it has opened up a wide range of possibilities for researchers: generating hypotheses, extracting information from large datasets, detecting patterns, simplifying literature searches, aiding the learning process and much more.

Monica P. Medina (https://www.state.gov/biographies/monica-p-medina/) is Assistant Secretary for the Bureau of Oceans and International Environmental and Scientific Affairs at the U.S. Department of State. She was also recently appointed as United States Special Envoy for Biodiversity and Water Resources.

Previously, Secretary Medina was an adjunct professor at Georgetown University’s School of Foreign Service. She was also a Senior Associate on the Stephenson Ocean Security Project at the Center for Strategic and International Studies, and Co-Founder and Publisher of Our Daily Planet, an e-newsletter on conservation and the environment.

Continue reading “Monica Medina, Assistant U.S. Secretary, Oceans & International Environmental & Scientific Affairs” »

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According to new reporting from the Financial Times, Google has invested $300 million in one of the most buzzy OpenAI rivals, Anthropic, whose recently-debuted generative AI model Claude is considered competitive with ChatGPT.

According to the reporting, Google will take a stake of around 10% and Anthropic will be required to use the money to buy computing resources from Google Cloud. The new funding will value the San Francisco-based company at around $5 billion.