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Granted AI performs well at identifying, predicting how to respond through analyzing patterns and information, etc. However, AI is not completely hacker proof at this point. AI still requires close monitoring by humans. The bottom line is until the existing net infrastructure and digital platforms are Quantum based; it will be hard to make AI hacker proof and fully autonomous due to the risks with the existing digital technology.


In the new battle between man and machine, how does artificial intelligence impact the security professional?

Posted by Ben Rossi.

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I consider this as a nice interim step in maturing the digital platform environment for financial services. However, once Quantum Computing, Quantum Internet, etc. is available to the masses such as in China, etc. this solution will fail in protecting financial data and other PPI related information as recent research is showing us.

https://lnkd.in/bjcCJ-U


IBM is currently attempting to merge artificial intelligence and the blockchain into a single, powerful prototype.

With blockchain tech’s promise of near-frictionless value exchange and artificial intelligence’s ability to accelerate the analysis of massive amounts of data, the joining of the two could mark the beginning of an entirely new paradigm.

Could we see race car driver careers become all AI? Nvidia is testing the concept.


Formula E is going completely autonomous with the all-new Roborace series slated for the upcoming race season. At its GTC developer conference, Nvidia announced these autonomous, electric race cars will be powered by Nvidia Drive PX 2, a supercomputer built for self-driving cars.

Drive PX 2 is powered by 12 CPU cores and four Pascal GPUs that provides eight teraflops of computer power. The supercomputer-in-a-box is vital to deep learning and trains artificial intelligence to adapts to different driving conditions, including asphalt, rain and dirt.

Jen-Hsun

At a time when PCs have become rather boring and the market has stagnated, the Graphics Processing Unit (GPU) has become more interesting and not for what it has traditionally done (graphical user interface), but for what it can do going forward. GPUs are a key enabler for the PC and workstation market, both for enthusiast seeking to increase graphics performance for games and developers and designers looking to create realistic new videos and images. However, the traditional PC market has been in decline for several years as consumer shift to mobile computing solutions like smartphones. At the same time, the industry has been working to expand the use of GPUs as a computing accelerator because of the massive parallel compute capabilities, often providing the horsepower for top supercomputers. NVIDIA has been a pioneer in this GPU compute market with its CUDA platform, enabling leading researchers to perform leading edge research and continue to develop new uses for GPU acceleration.

Now, the industry is looking to leverage over 40 years of GPU history and innovation to create more advanced computer intelligence. Through the use of sensors, increased connectivity, and new learning technique, researchers can enable artificial intelligence (AI) applications for everything from autonomous vehicles to scientific research. This, however, requires unprecedented levels of computing power, something the NVIDIA is driven to provide. At the GPU Technology Conference (GTC) in San Jose, California, NVIDIA just announced a new GPU platform that takes computing to the extreme. NVIDIA introduced the Telsa P100 platform. NVIDIA CEO Jen-Hsun Huang described the Tesla P100 as the first GPU designed for hyperscale datacenter applications. It features NVIDIA’s new Pascal GPU architecture, the latest memory and semiconductor process, and packaging technology – all to create the densest compute platform to date.

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