Will be attending.
CSIG (Cognitive Systems Institute Group) Talk - July 25, 2019 -10:30-11am US Eastern
Talk Title: Dialogue Mapping with IBIS for More Productive Meetings Speaker: Paul Fernhout, Software Developer
Abstract: Tens of billions of US dollars a year are wasted on unproductive unfun meetings. Worse, even "productive" meetings sometimes fail to consider a diversity of opinions and so produce suboptimal decisions (e.g. Fukushima Daiichi's seawall height). How can Cognitive Systems help people make better decisions in meetings more quickly? How can we help people with strong disagreements collaborate in mapping landscape of possibilities in a fun way? One option is to visualize the thinking going on in a meeting using Dialog Mapping(TM) developed by Jeff Conklin and associates, which visualizes discussions using the Issue-Based Information Systems (IBIS) grammar consisting of Issues/Questions, Options/Answers, and supporting Pros & Cons. This talk will explain more about Dialogue Mapping and (hopefully) provide a live demonstration.
Bio: Paul Fernhout is passionate about helping people collaborate to make better decisions more quickly using computers. He has worked as a software developer on decision-support projects for a wide variety of organizations ranging from non-profits to multi-nationals to governments, as well as on independent FOSS projects with his wife related to educational simulations, evolutionary design tools, information organizers, and Participative Narrative Inquiry. He has also written about technology and social change.
Zoom meeting Link: https://zoom.us/j/7371462221; Zoom Call in: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
http://cognitive-science.info/community/weekly-update/ for recordings & slides, and for any date & time changes
Join Group: https://www.linkedin.com/groups/6729452/ (CognitiveSystemesInstitute)to receive notifications Thu, July 25, 10:30am US Eastern https://zoom.us/j/7371462221
More Details Here : http://cognitive-science.info/community/weekly-update/
Via Karolyn Schalk, Susan Malaika
Showing posts with label @CSIGNews. Show all posts
Showing posts with label @CSIGNews. Show all posts
Tuesday, July 23, 2019
Tuesday, October 02, 2018
Talk: Improving Online Conversations
Interesting application idea. Improving conversation is a great idea. But I have often seen related approaches to essentially define 'good', as agreeing with me. Will this be able to temper interaction to make it meaningful, or will conversational AI go the way of 'correcting' my speech?
CSIG (Cognitive Systems Institute Group)
Talk Title: Improving On-Line Conversations through Machine Learning
Speaker: Marie Pellat, Google Oct 4, 2018 - 10:30-11am US Eastern
Abstract: Having a healthy discussion online is difficult. The sad reality of internet today is that people are harassed into suicide, journalists are threatened into silence, hate speech is normalized and sometimes organized. Approximately 1/4 of women face sexual harassment online and are physically threatened. Over the last few years, many organizations have simply turned off their comments sections as they became to hard to moderate. How can technology help? This talk will cover how the Conversation-Ai team at Google has used deep learning techniques to help improve conversations online. We will also discuss the limitations of machine learning algorithms with respect to unintended bias and go over some mitigation techniques.
Bio: Marie grew up in Paris and completed a BS in Mathematics and Physics at the Ecole Polytechnique. She later moved to California to complete a MS at Stanford University. After a couple years working on the ML team at Nest, Marie transferred to another Google bet called Jigsaw where she works on language models using artificial intelligence techniques and with the goal of improving conversations online.
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
Thu, Oct 4, 10:30am US Eastern https://zoom.us/j/7371462221
More Details Here : http://cognitive-science.info/community/weekly-update/ (Slides and recording here)
CSIG (Cognitive Systems Institute Group)
Talk Title: Improving On-Line Conversations through Machine Learning
Speaker: Marie Pellat, Google Oct 4, 2018 - 10:30-11am US Eastern
Abstract: Having a healthy discussion online is difficult. The sad reality of internet today is that people are harassed into suicide, journalists are threatened into silence, hate speech is normalized and sometimes organized. Approximately 1/4 of women face sexual harassment online and are physically threatened. Over the last few years, many organizations have simply turned off their comments sections as they became to hard to moderate. How can technology help? This talk will cover how the Conversation-Ai team at Google has used deep learning techniques to help improve conversations online. We will also discuss the limitations of machine learning algorithms with respect to unintended bias and go over some mitigation techniques.
Bio: Marie grew up in Paris and completed a BS in Mathematics and Physics at the Ecole Polytechnique. She later moved to California to complete a MS at Stanford University. After a couple years working on the ML team at Nest, Marie transferred to another Google bet called Jigsaw where she works on language models using artificial intelligence techniques and with the goal of improving conversations online.
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
Thu, Oct 4, 10:30am US Eastern https://zoom.us/j/7371462221
More Details Here : http://cognitive-science.info/community/weekly-update/ (Slides and recording here)
Tuesday, August 28, 2018
Cognitive Systems Talk: Machine Learning in a Snap
Invitation to the ISSIP Cognitive Systems Institute Group Webinar
Please join us for this call and invite your contacts - e.g., at universities, partners & clients. The call is in a series - and you can see the series here http://cognitive-science.info/community/weekly-update/
Talk Title: Machine Learning in a Snap August 30, 2018 - 10:30am US Eastern
Speaker: Thomas Parnell, IBM
Talk Description:
Generalized linear models, such as logistic regression and support vector machines, remain some of the most widely-used techniques in the machine learning field. Their enduring popularity can be attributed to their desirable theoretical properties, effective training algorithms, and relative ease of interpretability. In this talk we will introduce Snap Machine Learning: a new library for fast training of such models, that is designed to enable new real-time and large-scale applications. The library was designed from the ground up with performance in mind. It exploits parallelism at three different levels: across multiple machines in a network, across heterogeneous compute nodes within a machine (e.g. CPU and GPU), as well as the massive parallelism offered by modern GPUs. In this talk we will review this new architecture and give examples of how the library can be used via the various APIs that are provided (e.g. Python, Apache Spark, MPI). Finally, we will present benchmarking results using the publicly available Terabyte Click Logs dataset (from Criteo Labs) and show that Snap Machine Learning can train a logistic regression classifier in 1.53 minutes, 46x faster than any of the results that have been previously reported using the same dataset.
Bio:
Thomas received his B.Sc. and Ph.D. degrees in mathematics from the University of Warwick. U.K., in 2006 and 2011, respectively. He joined Arithmatica, Warwick, U.K., in 2005, where he was involved in FPGA design and electronic design automation. In 2007, he co-founded Siglead Europe, a U.K.-limited subsidiary of Yokohama-based Siglead Inc., where he was involved in developing signal processing and error-correction algorithms for HDD, flash, and emerging storage technologies. In 2013, he joined IBM Research in Zürich, Switzerland, where he is actively involved in the research and development of machine learning, compression and error-correction algorithms for IBM’s storage and AI products. His research interests include signal processing, information theory, machine learning and recommender systems.
Date and Time : August 30 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
(Check the website in case the date or time changes: http://cognitive-science.info/community/weekly-update/ )
Please retweet - https://twitter.com/sumalaika/status/1034299766266580992
Join LinkedIn Group https://www.linkedin.com/groups/6729452
Please join us for this call and invite your contacts - e.g., at universities, partners & clients. The call is in a series - and you can see the series here http://cognitive-science.info/community/weekly-update/
Talk Title: Machine Learning in a Snap August 30, 2018 - 10:30am US Eastern
Speaker: Thomas Parnell, IBM
Talk Description:
Generalized linear models, such as logistic regression and support vector machines, remain some of the most widely-used techniques in the machine learning field. Their enduring popularity can be attributed to their desirable theoretical properties, effective training algorithms, and relative ease of interpretability. In this talk we will introduce Snap Machine Learning: a new library for fast training of such models, that is designed to enable new real-time and large-scale applications. The library was designed from the ground up with performance in mind. It exploits parallelism at three different levels: across multiple machines in a network, across heterogeneous compute nodes within a machine (e.g. CPU and GPU), as well as the massive parallelism offered by modern GPUs. In this talk we will review this new architecture and give examples of how the library can be used via the various APIs that are provided (e.g. Python, Apache Spark, MPI). Finally, we will present benchmarking results using the publicly available Terabyte Click Logs dataset (from Criteo Labs) and show that Snap Machine Learning can train a logistic regression classifier in 1.53 minutes, 46x faster than any of the results that have been previously reported using the same dataset.
Bio:
Thomas received his B.Sc. and Ph.D. degrees in mathematics from the University of Warwick. U.K., in 2006 and 2011, respectively. He joined Arithmatica, Warwick, U.K., in 2005, where he was involved in FPGA design and electronic design automation. In 2007, he co-founded Siglead Europe, a U.K.-limited subsidiary of Yokohama-based Siglead Inc., where he was involved in developing signal processing and error-correction algorithms for HDD, flash, and emerging storage technologies. In 2013, he joined IBM Research in Zürich, Switzerland, where he is actively involved in the research and development of machine learning, compression and error-correction algorithms for IBM’s storage and AI products. His research interests include signal processing, information theory, machine learning and recommender systems.
Date and Time : August 30 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
(Check the website in case the date or time changes: http://cognitive-science.info/community/weekly-update/ )
Please retweet - https://twitter.com/sumalaika/status/1034299766266580992
Join LinkedIn Group https://www.linkedin.com/groups/6729452
Saturday, August 04, 2018
AI Needs Explaining
All analytics needs explaining. But primarily, why is what we have discovered as a pattern here trustable as a pattern in the future? And will that discovered pattern be useful in some business process today or in the future? Will some regulatory condition in the future prevent us from using this discovery?
AI, You’ve Got Some Explaining To Do By Alex Woodie in Datanami
Artificial intelligence has the potential to dramatically re-arrange our relationship with technology, hearkening a new era of human productivity, leisure, and wealth. But none of that good stuff is likely to happen unless AI practitioners can deliver on one simple request: Explain to us how the algorithms got their answers.
Businesses have never relied more heavily on machine learning algorithms to guide decision-making than they do right now. Buoyed by the rise of deep learning models that can act upon huge masses of data, the benefits of using machine learning algorithms to automate a host of decisions is simply too great to pass up. Indeed, some executives see it as a matter of business survival.
But the rush to capitalize on big data doesn’t come without risks, both to the machine learning practitioners and the people whom are being practiced upon. The risk posed to consumers by poorly implemented machine learning automation is fairly well-documented, and stories of algorithmic abuse are not hard to find.
And now, as a result of the European Union’s General Data Protection Regulation (GDPR), the risks are being pushed back to the companies practicing the machine learning arts, which can now be fined if they fail to adequately explain to a European citizen how a given machine learning model got its answer.
“In addition to all the rights around your personal data, the right to be removed and so forth, there’s a passage in GDPR talking about the right to an explanation,” says Jari Koister, FICO Vice President of Product and Technology. “The consumer actually has the right to ask, Why did you make that decision?”
AI, You’ve Got Some Explaining To Do By Alex Woodie in Datanami
Artificial intelligence has the potential to dramatically re-arrange our relationship with technology, hearkening a new era of human productivity, leisure, and wealth. But none of that good stuff is likely to happen unless AI practitioners can deliver on one simple request: Explain to us how the algorithms got their answers.
Businesses have never relied more heavily on machine learning algorithms to guide decision-making than they do right now. Buoyed by the rise of deep learning models that can act upon huge masses of data, the benefits of using machine learning algorithms to automate a host of decisions is simply too great to pass up. Indeed, some executives see it as a matter of business survival.
But the rush to capitalize on big data doesn’t come without risks, both to the machine learning practitioners and the people whom are being practiced upon. The risk posed to consumers by poorly implemented machine learning automation is fairly well-documented, and stories of algorithmic abuse are not hard to find.
And now, as a result of the European Union’s General Data Protection Regulation (GDPR), the risks are being pushed back to the companies practicing the machine learning arts, which can now be fined if they fail to adequately explain to a European citizen how a given machine learning model got its answer.
“In addition to all the rights around your personal data, the right to be removed and so forth, there’s a passage in GDPR talking about the right to an explanation,” says Jari Koister, FICO Vice President of Product and Technology. “The consumer actually has the right to ask, Why did you make that decision?”
Friday, August 03, 2018
Teaching Languages with AI Assistance
This week's CSIG talk featured Lewis Johnson of Alelo, a company that has an online AI assisted language teaching system. Natural language voice language processing is used. The assistance includes tracking progress, and analyzing how the teaching should be adapted. The things that good teaching should do. This essentially uses a chatbot to provide the teaching. To 'Empower Learning and Teaching'. When I taught at Columbia I had an excellent (human) teaching assistant, here we can see how the best of machine assistance might look. Would like to see how such a systems works in practice, and how it can add behavioral assistance to help learning. Could there be a gaming of competition included?
The slides are here. Recording of the talk here.
The slides are here. Recording of the talk here.
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