By now, most people who run small and midsize businesses know that they ought to take advantage of artificial intelligence to make their companies competitive in the digital age. But many don't know how to go about it. To meet this demand, Northeastern, which invested $50 million in a new artificial intelligence research institute last year, will lead a new Massachusetts program, AI Jump Start, to connect small business owners in the state with academic faculty experts to learn how machine learning can grow their companies. The initiative is aimed at a broad range of small and midsize enterprises in defense, manufacturing, health, and other industries whose leaders would like to incorporate artificial intelligence but aren't quite sure where to turn. It's open also to companies that want to upgrade data-driven computing to glean new insights into customers, suppliers, and competitors.

Now that this attention-grabbing headline has drawn you in, let me clarify. Data scientists should not partake in illegal drugs. Data scientists should participate in pharmacological research, as artificial intelligence and machine learning can add value, even when the data scientist does not have a background or training in physics, biology, chemistry, or medicine. The CAIA Association and FDP Institute had a recent conversation with Woody Sherman, the CSO of Silicon Therapeutics. While many of us can be left behind in a discussion of computational drug discovery, it seems that almost everyone today is a budding epidemiologist trying to better understand the prevention and spread of COVID-19, so let's continue.

The U.S. military's approach to AI is equal parts offense and defense, acknowledging that primary adversary China could also weaponize the technology as a form of asymmetrical warfare in which U.S. military superiority is blunted by dual-used AI technologies. Hence, the service said it would seek to organize and leverage its vast trove of operational data to develop new algorithms for future sensor as well as command and control systems. "Depending on the strategic choices we make now, our ability to operate around the globe may be blunted or bolstered by the adoption of--or hardening against--artificial intelligence," notes the U.S. Air Force's AI strategy released in September. The Air Force plan is part of a broader Defense Department effort to ramp up military AI capabilities under a Joint Artificial Intelligence Center created last year to coordinate AI R&D. The DoD strategy relies heavily on partnering with technology companies, a stance that has generated push-back at leading AI developers like Google.

To power AI applications and research across engineering, science, and medicine, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory Supercomputing Center has just installed a new GPU-accelerated supercomputer, powered by 896 NVIDIA Tensor Core V100 GPUs. According to MIT, the new system named TX-GAIA for Green AI Accelerator was ranked by TOP500 as the most powerful AI supercomputer at any university in the world. "We are thrilled by the opportunity to enable researchers across Lincoln and MIT to achieve incredible scientific and engineering breakthroughs," said Jeremy Kepner, a Lincoln Laboratory Fellow who heads the Lincoln Laboratory Supercomputing Center. "TX-GAIA will play a large role in supporting AI, physical simulation, and data analysis across all Laboratory missions," he added. The new supercomputer has a peak performance of 100 AI petaFLOPs, as measured by the computing speed required to perform mixed-precision floating-point operations commonly used in building deep neural networks.

Today HPE announced announced the deployment of a new supercomputer at the MIT Lincoln Laboratory Supercomputing Center for compute-intensive AI applications and bolstering research across engineering, science, and medicine. Called TX-GAIA (Green AI Accelerator), the new supercomputer converges HPC and AI to support workloads such as modeling and simulation and perform complex deep neural networks (DNN) and other machine learning training. It is based on the HPE Apollo 2000 system, which is purpose-built for HPC and optimized for AI, by integrating the latest Intel Xeon Scalable processors and NVIDIA GPU accelerators. At the MIT Lincoln Laboratory Supercomputing Center, our mission is to solve the nation's hardest technical challenges by advancing computationally intensive science, engineering, and medicine," said Jeremy Kepner, head and founder, at MIT Lincoln Laboratory Supercomputing Center (LLSC). "By collaborating with HPC leaders like HPE, we are expanding technical capabilities to run emerging AI workloads in our supercomputer and accelerate innovation." The new supercomputer has a measured performance of 4.725 Petaflops and will be used to support research projects that will fuel innovation in weather forecasting, medical data analysis, autonomous systems, synthetic DNA design, and new materials and devices. Additionally, the MIT Lincoln Laboratory Supercomputing Center's new system gets an AI performance boost, as measured by the computing speed required to perform DNNs, of a peak performance of 100 AI Petaflops. This will greatly accelerate the processing of deep neural networks and other compute-intensive AI workloads in order to improve training in areas such as image recognition, speech and natural language processing and computer vision. The TX-GAIA system comprises nearly 900 Intel processors and 900 Nvidia GPU accelerators. The new system is housed in a modular data center facility, co-developed with HPE and designed to speed deployment and reduce overall IT resources. It is located in Holyoke, Massachusetts, where it is powered by abundant green energy, and will go into production in the fall of 2019. We've seen strong industry demand for scalable performance to train higher volumes of AI that will advance science and engineering, and make breakthroughs across industries," said Bill Mannel, vice president and general manager, HPC and AI at HPE. "Our continued partnership with MIT Lincoln Laboratory Supercomputing Center extends the power of our HPC technologies to boost AI R&D and create new experiences."

The new TX-GAIA (Green AI Accelerator) computing system at the Lincoln Laboratory Supercomputing Center (LLSC) has been ranked as the most powerful artificial intelligence supercomputer at any university in the world. The ranking comes from TOP500, which publishes a list of the top supercomputers in various categories biannually. The system, which was built by Hewlett Packard Enterprise, combines traditional high-performance computing hardware -- nearly 900 Intel processors -- with hardware optimized for AI applications -- 900 Nvidia graphics processing unit (GPU) accelerators. "We are thrilled by the opportunity to enable researchers across Lincoln and MIT to achieve incredible scientific and engineering breakthroughs," says Jeremy Kepner, a Lincoln Laboratory fellow who heads the LLSC. "TX-GAIA will play a large role in supporting AI, physical simulation, and data analysis across all laboratory missions." TOP500 rankings are based on a LINPACK Benchmark, which is a measure of a system's floating-point computing power, or how fast a computer solves a dense system of linear equations.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

UMass today rolled out its new GPU cluster – Gypsum – aimed at deep learning. Like many institutions, UMass is seeking to attract Ph.D. students drawn to deep learning and artificial intelligence. At 400 GPUs, Gypsum is on the large side for academic GPU clusters according the university. The new systems will be housed at the Massachusetts Green High Performance Computing Center in Holyoke, Mass., and is the result of a five-year, $5 million grant to the campus from the Massachusetts Technology Collaborative made last year. It represents a one-third match to a $15 million gift supporting data science and cybersecurity research from the MassMutual Foundation of Springfield.

Activity on the tech labs front is happening faster than we can get to it these days, so here are a few "in case you missed it" items... The state of Ohio, JobsOhio and the Ohio State University are putting $45 million into an expansion of the Transportation Research Center's (TRC) 540-acre Smart Mobility Advanced Research and Test (SMART) Center in the Columbus area. Research will focus both on connected and driverless vehicles within this section of the 4,500-acre TRC expanse. This first phase of SMART expansion will include the industry's largest high-speed intersection, an urban network of intersections (i.e., roundabouts, or what we in the Northeast call rotaries), a rural network that includes wooded roads and a neighborhood network for slower speeds. TRC provides the largest independent vehicle testing facility in North America, according to TRC CEO Mark-Tami Hotta.