Ai open powermeetupmarch25th
- 1. AI and OpenPOWER Meetup Spend a half day learning about Artificial Intelligence's latest real-world impact and gather the latest cutting-edge insights from the pioneers in your industry. Discover advances in deep learning tools and techniques from the world’s leading innovators across industry, research and financial sector We’re excited to announce that the prominent speakers will be joining us at the AI and OpenPOWER ADG Meet up, on 25th March,2018 At 4.00 pm to 7.30 pm 2603 Camino Ramon #200, San Ramon, CA 94583, USA
- 2. Title : Learning at Leadership Scale: Performance, Deployment Experiences, and Best Practices Abstract: HPC centers have been traditionally configured for simulation workloads, but data analytics (including deep learning) methods and frameworks have been increasingly applied alongside simulation on scientific datasets. These frameworks do not always fit well with job schedulers, large parallel file systems, and MPI backends, and also vary in performance based on whether the underlying architecture is a CPU or an accelerator like a GPU or a combination. We discuss examples of how machine learning and deep learning workflows are being deployed on next- generation systems at the Oak Ridge Leadership Computing Facility including the lead up to the Power and Volta-based Summit system from experiences on OLCF’s Titan and SummitDev. We will share benchmarks between native compiled versus containers-based systems as well as best practices for deploying learning and models on leadership resources supporting scientific workflows. Jack Wells is the Director of Science for the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science national user facility, and home to the Titan and upcoming Summit supercomputers, located at Oak Ridge National Laboratory (ORNL). Wells is responsible for the scientific outcomes of the OLCF’s user programs. Wells has a Ph.D. in physics from Vanderbilt University, and has authored or co-authored over 100 scientific papers and edited 1 book, spanning nanoscience, materials science and engineering, nuclear and atomic physics computational science, applied mathematics, and novel analytics measuring the impact of scientific publications.
- 3. Key Note speaker : Graham Mackintosh Title : AI Research and OpenPOWER at the NASA Frontier Development Lab Abstract: The NASA Frontier Development Lab (FDL) is an AI research accelerator established to apply emerging AI technologies to space science challenges which are central to NASA's mission priorities. The program is managed by the SETI Institute, and brings together the AI capabilities of NASA, academic institutions, and commercial partners to tackle complex space science problems, such as predicting extreme solar events that can damage satellites and endanger the lives of astronauts. In this session, we will present a summary of the NASA FDL research results to date, and outline how the SETI Institute, as a member of the OpenPower Foundation, plans to leverage the Power platform to expand its FDL 2018 research program. Graham Mackintosh is a pioneer in the field of advanced analytics, and has applied his thought leadership into multiple new domains for big data analysis, high performance cloud computing, AI and Deep Learning. He currently works as an AI consultant for NASA and the SETI Institute to provide technical and program management support across a range of space science domains, including the application of deep learning technologies to space weather, planetary defense and astrobiology. Prior to his current role as an AI consultant, Graham worked in the Emerging Technology division of IBM Corporation, and led successful AI initiatives at CERN, the SETI Institute, NASA Frontier Development Lab, and with the US Federal Government.
- 4. Dr Sudha Jamthe is the CEO of IoTDisruptions.com and a globally recognized thought leader at the junction of IoT and Autonomous Vehicles. She brings twenty years of digital transformation experience from building organizations, shaping new technology ecosystems and mentoring leaders at eBay, PayPal, Harcourt, and GTE. She teaches the IoT Business course and "The Business of Self-Driving Cars" Course at Stanford Continuing Studies Program and enjoys mentor industry professionals to shape emerging technology ecosystems. She advises corporate and city leaders on regional economic development using technology with a focus on innovation gaps and social equality. Title: AI Trends towards a Driverless World Speaker is author of '2030 The Driverless World' about the junction of Autonomous cars and Cognitive IoT. She is the author of three IoT books, 'IoT Disruptions,' 'IoT Disruptions 2020' and 'The Internet of Things Business Primer'. She is the producer of 'The IoT Show' on YouTube. Sudha is a champion for STEM programs and 'Girls Who Code,' and hosts mentor programs for kids. Key Note speaker : Sudha Jamthe
- 5. Jim Spohrer is IBM Director, Cognitive Opentech Group (COG) leading open source AI work at IBM. Previously, he was Director IBM Global University Programs, co-founded IBM Almaden Service Research Group, ISSIP Service Science community, and was founding CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist Technologist (DEST), he developed next generation learning platforms. Earlier (19740-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company for speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and winner of the Gummesson Service Research award as well as Vargo & Lusch Service-Dominant Logic award. Title: The Future of AI: Measuring Progress and Preparing Abstract: An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated (hint: befriend someone studying GitHub open AI code + data + models + containers). Key Note speaker : Jim Spohrer (IBM)
- 6. Vinod Iyengar Vinod Iyengar heads alliances and product marketing at H2O. He is also a trained data scientist and works extensively with our customers and partners to spread the word of how artificial intelligence can help enterprise transform their businesses. Title: Driverless AI Abstract: Driverless AI accelerates data science workflows by automating feature engineering, model tuning, ensembling and model deployment. Driverless AI turns Kaggle-winning recipes into production-ready code and is specifically designed to avoid common mistakes such as under or overfitting, data leakage or improper model validation. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy With Driverless AI, everyone can now train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client/server API through a variety of language such as Python, Java, C++, go, C# and many more. To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest computer hardware. Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU on IBM Power platform, both in the cloud or on-premise. There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and models. Key Note speaker : Vinod Iyengar
- 7. Title: AI In Bank( Citi Bank ) Abstract: Improved Customer Focus: There is a growing expectation to personalize the services provided to the customer and retain the customer. AI has the Capability to segment the Data into various groups and can be used to segment the customers based on the data from the conversation through telephonic & chat, visits to Branch & ATM, Usage across the various Channels (Net banking, App) and personalize the Service. Based on the Customer Actions, Transactions, Behaviors and Sentimental Analysis performed using AI Bank can provide offers, products or the Next Best Action for a customer. This can lead to better Customer Loyalty and Retention. Mundane Manual tasks by the Customers can be automated using the AI technology. Provide support in the validation of the various KYC Documents, searching Documents for providing guidance, etc. Suggest Investments opportunities based on data gathered. Models trained based on the Historical Data, Macro Economic Data can support the Bank in adopting Investment Strategies. Uppili Rajagopalan Senior Executive in Financial Industry where he has 20 years of experience . Uppili leads Global O&T Alternate Customer Contact team. He provides leadership and expertise for ASIA & EMEA regional delivery and application development for Chat, Digital Virtual Agent, eDelivery and Robotic process automation initiatives. Uppili holds a Bachelors in Computer Science & Masters in Business Administration Key Note speaker : Uppili Rajagopalan
- 8. Dr. Berthold Reinwald is a Principal RSM at IBM Research - Almaden. He is the technical lead for Apache SystemML. His research interests include scalable analytics platforms and database technology which he contributes to IBM Watson. Title: Scalable Machine/Deep Learning with Apache SystemML on Power Abstract: We will present perspectives and challenges of machine/deep learning in the enterprise. We will cover a variety of use cases from different vertical industries, discuss the state of the art, and take a critical look at challenges in systems development. We will draw from the experience in the development of Apache SystemML, an open source project for declarative, large scale machine/deep learning, and show deep learning examples running on Power. speaker : Berthold Reinwald (IBM Research)
- 9. Leo Reiter : Chief Technology office at Nimbix and is a virtualization and cloud computing pioneer with over 20 years of experience in software development and technology strategy. Prior to Nimbix, Mr. Reiter was co-founder and CTO of Virtual Bridges, an early innovator in server-based computing and private cloud platforms for Enterprise. Mr. Reiter is an entrepreneur with a strong background in Lean Startup and Agile methodologies. Title: PowerAI on Nimbix Cloud Abstract: Leo will be sharing the PowerAI and HPC features running Nimbix Cloud . He will also share the various industry based use cases and customer experiences on the Nimbix Cloud platform . Key Note speaker : Leo Reiter from Nimbix ( AI Cloud )
- 10. Jussi kukkonun , self-directed and driven vice president with a comprehensive background in system integration and IT solution provider business delivering data center solutions for enterprise, high performance computing and cloud and hosting provider customers and leading cross-functional teams to ensure success and achieve goals. Known as an innovative thinker with strong product marketing, business development, go-to-market strategy, partnership and data storage acumen. Recognized for maximizing performance by implementing appropriate strategies through analysis of details to gain understanding of the competitive position, emerging issues, trends and relationships.
- 11. Vasanth Ram is a Hardware Professional and has built GPUs, Tablets, Laptops at Imagination Technologies, AMD & Intel with an emphasis on Low Power. He also does researchinLowPowerDatacenterschedulersuitableforIaaSCloudwithMachineLearning based Workload Prediction and Host Provisioning. He is passionate about solving real- world problems and has built Computer Vision-based Autonomous and semi-autonomous Robots. He also consults in Hyper-scale & Hyper-converged Datacenters Architectures (Compute, Storage & Network) and end-to-end ASIC paper to production for CPUs and GPUs for startups, consulting firms and enterprises. He Has Master of Science in Computer Engineering from University of California Santa Barbara and Bachelor of EngineeringinElectronicsEngineeringfromCoimbatoreInstituteofTechnology,India AGKarunakaranwasthefoundingPresidentandCEOofGDATechnologiesInc., aleadingIntellectualPropertylicensingandelectronicsdesignservicesCompany.GDAwas purchasedbyL&TInfotech,IndiainMarch2007.AtGDA,hewasresponsibleforleadership development,growthstrategy,prudentcashmanagementandworkedwithleading Semiconductorcompaniestocommercializethesiliconintellectualpropertyblocks. UnderhisleadershipGDAmadeittotheINC500list,Si100lists,grewto400employees, deliveredonsignificantproductdevelopmentengagementswithleadingsystemsand semiconductorcompaniesandbecamealeadingsupplierofHighspeedSerialI/O Semiconductor Intellectual blocks. He has deep experience in bootstrapping companies andinstrategicM&Atransactions,havingconsummateddealsforGDAwithL&TInfotech andRambus.
Editor's Notes
- #5: The Future of AI: Measuring Progress and Preparing An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated. Speaker Bio: Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award. More information here: Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14 Bio and CV: http://service-science.info/archives/2233 Optional Business, Marketing, and Technical Pre-reads: IBM Bluemine: Industry Predictions 2018: "2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent." Another predication to consider: ...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings. See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html Also, see Tencent paper and Github code: ArXiv: https://arxiv.org/abs/1606.01549 Github: https://github.com/bdhingra/ga-reader IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/ And to understand why solving AI is still very, very, very hard, in spite of all the hype: Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf Optional Pre-read for Societal Implications: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.
- #6: The Future of AI: Measuring Progress and Preparing An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated. Speaker Bio: Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award. More information here: Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14 Bio and CV: http://service-science.info/archives/2233 Optional Business, Marketing, and Technical Pre-reads: IBM Bluemine: Industry Predictions 2018: "2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent." Another predication to consider: ...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings. See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html Also, see Tencent paper and Github code: ArXiv: https://arxiv.org/abs/1606.01549 Github: https://github.com/bdhingra/ga-reader IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/ And to understand why solving AI is still very, very, very hard, in spite of all the hype: Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf Optional Pre-read for Societal Implications: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.
- #9: The Future of AI: Measuring Progress and Preparing An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated. Speaker Bio: Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award. More information here: Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14 Bio and CV: http://service-science.info/archives/2233 Optional Business, Marketing, and Technical Pre-reads: IBM Bluemine: Industry Predictions 2018: "2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent." Another predication to consider: ...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings. See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html Also, see Tencent paper and Github code: ArXiv: https://arxiv.org/abs/1606.01549 Github: https://github.com/bdhingra/ga-reader IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/ And to understand why solving AI is still very, very, very hard, in spite of all the hype: Ernie Davis (NYU) pointers: Real “reading”with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim”which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf Optional Pre-read for Societal Implications: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. We are not quite at 2030, but I believe we have reached the “Keynes point,”where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.