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Massachusetts-headquartered Dynatrace, which provides an intelligence layer to monitor and optimize application development, performance and security, today announced key updates for its core platform, including a new AutomationEngine that enables teams to streamline monitoring and other activity across a variety of workflows.
Developers, security specialists, operations personnel and even business users can tap into the platform. The company made the announcement at its annual cloud observability conference in Las Vegas.
Security researchers are seeing threat actors switching to a new and open-source command and control (C2) framework known as Havoc as an alternative to paid options such as Cobalt Strike and Brute Ratel.
Among its most interesting capabilities, Havoc is cross-platform and it bypasses Microsoft Defender on up-to-date Windows 11 devices using sleep obfuscation, return address stack spoofing, and indirect syscalls.
Like other exploitation kits, Havoc includes a wide variety of modules allowing pen testers (and hackers) to perform various tasks on exploited devices, including executing commands, managing processes, downloading additional payloads, manipulating Windows tokens, and executing shellcode.
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Generative AI has been all the rage in the recent months, but it is typically generic and not specifically focused on the specific needs of any one company.
San Francisco based startup Jasper is aiming to help make generative AI less generic. The company made a series of announcements today at its Gen AI conference.
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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.
Dr. Patel brings extensive experience in public-private partnerships to DRIVe. Prior to joining the DRIVe team, he served as the HHS Open Innovation Manager. In that role, he focused on advancing innovative policy and funding solutions to complex, long-standing problems in healthcare. During his tenure, he successfully built KidneyX, a public-private partnership to spur development of an artificial kidney, helped design and execute the Advancing American Kidney Health Initiative, designed to catalyze innovation, double the number of organs available for transplant, and shift the paradigm of kidney care to be patient-centric and preventative, and included a Presidential Executive Order signed in July 2019. He also created the largest public-facing open innovation program in the U.S. government with more than 190 competitions and $45 million in awards since 2011.
Prior to his tenure at HHS, Dr. Patel co-founded Omusono Labs, a 3D printing and prototyping services company based in Kampala, Uganda; served as a scientific analyst with Discovery Logic, (a Thomson Reuters company) a provider of systems, data, and analytics for real-time portfolio management; and was a Mirzayan Science and Technology Policy Fellow at The National Academies of Science, Engineering, and Medicine. He also served as a scientist at a nanotechnology startup, Kava Technology.
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.
Prior to Ginkgo, Dr. Wegrzyn was program manager in the Biological Technologies Office at DARPA, where she leveraged the tools of synthetic biology and gene editing to enhance biosecurity, promote public health and support the domestic bioeconomy. Her DARPA portfolio included the Living Foundries: 1,000 Molecules, Safe Genes, Preemptive Expression of Protective Alleles and Response Elements and the Detect it with Gene Editing Technologies programs.
Dr. Wegrzyn received the Superior Public Service Medal for her work and contributions at DARPA. Prior to joining DARPA, she led technical teams in private industry in the areas of biosecurity, gene therapies, emerging infectious disease, neuromodulation, synthetic biology, as well as research and development teams commercializing multiplex immunoassays and peptide-based disease diagnostics.
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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.
