{"id":25482,"date":"2022-01-14T00:00:00","date_gmt":"2024-09-13T08:48:52","guid":{"rendered":"https:\/\/wordpress-583806-4798031.cloudwaysapps.com\/deep-ml-on-gpus-ai-industry-and-rd-soar\/"},"modified":"2024-11-15T16:21:23","modified_gmt":"2024-11-15T15:21:23","slug":"deep-ml-on-gpus-ai-industry-and-rd-soar","status":"publish","type":"post","link":"https:\/\/informator.se\/en\/deep-ml-on-gpus-ai-industry-and-rd-soar\/","title":{"rendered":"Deep ML on GPUs: AI, industry, and R&D soar."},"content":{"rendered":"
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Time for another \u201cthank you gamers, for fueling GPU-vendors\u201d; that is, indirectly boosting deep-learning HW. A year ago, BioNTech <\/strong>and InstaDeep <\/strong>(a partner of London-based <\/em>DeepMind<\/a><\/em><\/strong>) launched their <\/em>AI innovation lab<\/a><\/em><\/strong> in London and Mainz for next-generation vaccines<\/a> and biopharmaceuticals, combining InstaDeep\u2019s DeepChain platform with BioNTech\u2019s Covid know-how and technology. It\u2019s already transforming research and IT-architecture alike: This week, the lab announced their high-risk Covid-variants Early Warning System (EWS<\/a>),<\/strong> ranking the Omicron a high-risk one \u2013 on the same day<\/strong> its sequence became available.<\/em><\/p>\n\n\n\n

This marks a \u201c2.0\u201d of the long-lasting win-win relationship between gene-based models and AI (see also diagram example<\/a> from Informator\u2019s course, 2020). The EWS can evaluate new variants in a matter of minutes, and it can (risk-)monitor variant lineages in real time. In other words, save months <\/em><\/strong>of precious time. 10,000 variant sequences being discovered every week, humans simply can\u2019t cope. <\/p>\n\n\n\n

AlphaFold2<\/a> by another InstaDeep partner in London, DeepMind, has predicted the shapes of nearly every protein in the human body, and their AI-team leader John Jumper<\/a> is one of \u201cNature\u2019s 10\u201d<\/strong> who shaped science in 2021. Karolinska Institutet\u2019s KI News<\/a><\/strong> wrote: AI may allow (\u2026) to accurately simulate highly complex biological scenarios in silico<\/strong><\/em>.<\/p>\n\n\n\n

(Figure by DeepMind blog<\/a>: Complex 3D shapes emerge from a string of amino acids). <\/em><\/p>\n\n\n\n

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The IT-architect part of my brain is reasonably aware of HW-vendors who underpin this second wave of SciFi-come-true in AI apps. For 2022, InstaDeep\u2019s HW partner NVIDIA <\/strong>lists Top 5 Trends<\/a><\/strong> to watch this year:<\/p>\n\n\n\n

1.<\/strong> To move to production, edge AI management<\/strong> will become the responsibility of IT<\/strong> departments.<\/p>\n\n\n\n

2. Multimodal AI<\/strong>: more intelligent applications responding to what they see, hear,<\/strong> sense<\/strong>. These employ skills like NL understanding, conversational AI, pose estimation, inspection and visualization. Expect an expansion into robotics, healthcare, and more.<\/p>\n\n\n\n

3. AI and Industrial IoT: <\/strong>\u201caccording to Gartner, by 2027 ML & deep learning will be included in 65+ percent of edge use cases, up from less than 10 percent in 2021.\u201d Factories can add AI onto cameras and other sensors for inspection and predictive maintenance. For instant action, RT responses are made possible by connecting the AI inference application with the IoT platforms that manage the assembly lines, robotic arms etc.<\/p>\n\n\n\n

4. AI-on-5G in Industry 4.0:<\/strong> ultra-low latency, guaranteed quality-of-service. Expansion into plant automation, robots, monitoring\/inspection, vehicle telemetry, and more.<\/p>\n\n\n\n

5. ML Ops<\/a><\/strong> shifting to edge computing, to drive the flow to and from the edge – ingesting new data or insights from the edge, retraining, testing, and redeploying ML apps.<\/p>\n\n\n\n

Finally, are we rushing into a blind alley of ever-bigger Big Data with ever-bigger hunger for resources and carbon footprint? Not necessarily. For architects, there\u2019s most often a workaround. Not least, in innovative algorithms. Remember DeepStack<\/strong>.AI<\/a> running on a laptop<\/a><\/strong> with a \u201cgamer\u201d NVIDIA GPU as an add-on, instead of a supercomputer?<\/p>\n\n\n\n

A month ago, DeepMind\u2019s NL processing AI RETRO<\/a><\/strong> (Retrieval-Enhanced Transformer) matched the performance of deep NNs that are 25 times<\/strong> its size. It uses an external database of text passages, like a crib, to generate new human-like sentences. Along with a drop in cost and in training time, it\u2019s database also takes it closer to eXplainable<\/a> AI. By the way, several AI researchers from Michael Bowling\u2019s DeepStack<\/a> team<\/a> from Edmonton and Prague are now co-developing DeepMind\u2019s new universal Player of Games<\/a>.<\/p>\n\n\n\n

Happy New 2022 to all architects, linguists, CTOs\/CIOs, bioscientists, Industry 4.0 innovators, gamers, the ECDC, AI & data scientists, and dozens of other specialists!<\/p>\n\n\n\n

by <\/em>Milan Kratochvil<\/a><\/u><\/em><\/em><\/p>\n\n\n\n

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<\/a> Trainer at Informator, senior modeling and architecture consultant at Kiseldalen\u2019s, main author: UML Extra Light (Cambridge University Press) and Growing Modular (Springer). Advanced UML2 Professional (OCUP<\/a> cert level 3\/3).<\/em><\/p>\n\n\n\n

Milan and Informator collaborate since 1996 on architecture, AI, rules, modeling, UML, requirements, and design. You can meet him this year at these courses <\/em>in English or Swedish (remote<\/strong> participation is offered, encouraged, and strongly recommended by epidemiology experts) <\/em>:<\/em><\/p>\n\n\n\n

AI, Architecture, and Machine Learning<\/a><\/em><\/em><\/p>\n\n\n\n

Agile Architechture Fundamentals<\/a><\/em><\/p>\n\n\n\n

Agile Modeling with UML<\/a><\/em><\/p>\n\n\n\n

Avancerad objektmodellering med UML<\/a><\/em><\/em><\/p>\n\n\n\n

(on demand: Modular Product Line Architecture<\/em> )<\/p>\n<\/span><\/span>\r\n

\u00a0<\/span><\/span><\/h4>\r\n<\/span><\/span><\/div><\/div>","protected":false},"excerpt":{"rendered":"

Time for another \u201cthank you gamers, for fueling GPU-vendors\u201d; that is, indirectly boosting deep-learning HW. A year ago, BioNTech and InstaDeep (a partner of London-based DeepMind) launched their AI innovation lab in London and Mainz for next-generation vaccines and biopharmaceuticals, combining InstaDeep\u2019s DeepChain platform with BioNTech\u2019s Covid know-how and technology. It\u2019s already transforming research and IT-architecture alike: This week, the lab […]<\/p>\n","protected":false},"author":6,"featured_media":26647,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[570],"tags":[],"class_list":["post-25482","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogg"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t