OpenPOWER and AI workshop at Barcelona Supercomputing Center

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.
• #7: 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.
• #8: 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.