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Traditionally, software is built by programmers who consider all the possible situations and write rules to deal with them. But lately, many applications are created by machine learning: the programmer is replaced by a trainer, who shows the computer examples until it learns to complete the task. This shift in the way software is built is opening up exciting new possibilities and posing new challenges. Peter Norvig is a director of research at Google Inc. Previously he was head of Google’s core search algorithms group and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAI (Association for the Advancement of Artificial Intelligence), ACM (Association for Computing Machinery), California Academy of Science and American Academy of Arts & Sciences.
What if your favorite artist was a computer? Could you detect the difference between a real musician and a synthetic one? Artificial Intelligence has recently gained a crucial role in our lives, influencing even music. Valerio Velardo, founder of “The Sound of AI”, a world wide community of researchers and experts on this field, is going to explore the topic of generative music starting from the origins and going to the future developments and risks. Originally streamed live on Polifonia’s Twitch channel. Guest: Valerio Velardo Moderator: Luigi Attorresi Join The Sound Of AI Slack community: https://valeriovelardo.com/the-sound-of-ai-community Make sure to follow us to be updated on the last news and events! Instagram: https://www.instagram.com/polifoniapolimi Facebook: https://www.facebook.com/polifoniapolimi Website: https://en.polifoniapolimi.it/ Twitch: https://www.twitch.tv/polifoniapolimi Linkedin: https://www.linkedin.com/company/polifonia-polimi If you’d like to support us, you can offer us a coffee: https://ko-fi.com/polifonia
Artificial intelligence has the ability to revolutionise and personalise targeted healthcare for individual patients. The regulatory frameworks for AI in healthcare are a critical component in managing and maximising accurate healthcare predictions. Navid holds a PhD in Biomedical Engineering and Medical Device Development. He has previously worked as a postdoctoral researcher focusing on translation and commercialization of biodegradable implants. Navid is currently focused on enabling artificial intelligence-based healthcare solutions to be commercially developed and applied in clinical settings. Dr Navid Toosi Saidy is the Digital Health Lead at Max Kelsen, driving the implementation of digital innovation in healthcare. Navid holds a PhD in Biomedical Engineering and Medical Device Development. He has previously worked as a postdoctoral researcher focusing on translation and commercialization of biodegradable implants. Drawing on years of experience collaborating at the University research level, and with medical device companies and regulators, Navid is currently focused on enabling artificial intelligence-based healthcare solutions to be commercially developed and applied in real world clinical settings. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
April 13-14, 2021 – The NHGRI Genomic Data Science Working Group hosted Machine Learning in Genomics: Tools, Resources, Clinical Applications and Ethics. This virtual workshop highlights the opportunities and obstacles that occur when applying machine learning methods to basic genome sciences and genomic medicine. Workshop Agenda: https://www.genome.gov/event-calendar/Machine-Learning-in-Genomics-Tools-Resources-Clinical-Applications-and-Ethics ————— Welcome ————— Co-chairs: Trey Ideker, Ph.D., University of California San Diego Mark Craven, Ph.D., University of Wisconsin Speaker: Eric Green, M.D., Ph.D., Director, National Human Genome Research Institute ————– Keynote: ————– Moderator: Shannon McWeeney, Ph.D., Oregon Health and Sciences University Presenters: Eric Topol, M.D., Scripps Research Brad Malin, Ph.D., Vanderbilt University Medical Center Chapters: 0:00 – Start 0:09 – Welcome (Mark Craven) 0:52 – Welcome (Trey Ideker) 1:28 – Opening Remarks (Eric Green) 9:41 – Keynote Presentation (Eric Topol) 34:54 – Keynote Presentation (Brad Malin) 59:00 – Q&A Session with Shannon McWeeney, Eric Topol and Brad Malin
Conversational interfaces have emerged as a new way to interact with AI assistants on smartphones, smart speakers, and other devices. However, questions have been raised about the usability of these systems and whether the technologies are sufficiently mature. There are two main approaches to the development of conversational systems: the traditional approach in which systems are designed according to best practice guidelines and the behaviours of the systems and based on handcrafted rules; and the new paradigm of conversational AI in which systems are trained from data using various machine learning techniques. The pros and cons of each approach are highlighted. The presentation concludes with a recommendation that in addition to focusing on technical issues it is also important to consider issues relating to user experience by eliciting user requirements in a process of co-creation and conducting detailed studies of how users actually engage with conversational systems in everyday situations. Speaker: Michael McTear Home
Artificial Intelligence (AI) is no longer sci-fi. From driverless cars to the use of machine learning algorithms to improve healthcare services and the financial industry, AI and algorithms are shaping our daily practices, and a fast-growing number of fundamental aspects of our societies. This can lead to dangerous situations in which vital decision making is automated – for instance in credit scoring, or sentencing – but limited policies exist for citizens subject to such AI technologies embedded in our social institutions to seek redress. Similarly, well-intended technologists might release AI into society that is ethically unsound. A growing body of literature on improving the auditability and transparency of algorithms is being developed. Yet, more is needed to develop a shared understanding about the fundamental issues at the heart of the debate on AI, algorithms, the law, and ethics. These issues taken together are leading to a renewed focus on, and increasing concern about, the ethical and legal impact of AI on our societies. In this panel we bring together five thought leaders on AI from the corporate sector, academia, politics, and civil society to discuss. We will hear from Paul Nemitz, Monica Beltrametti, Alan Winfield, Vidushi Marda and Sandra Wachter, the conversation will be moderated by Corinne Cath. Panel: – Paul Nemitz – Director responsible for Fundamental rights and Union citizenship in the Directorate-General Justice of the European Commission – Monica Beltrametti – Director at NAVER Labs Europe – Alan Winfield – Professor of Robot Ethics University of the West [More]
There have been many conversations around AI and automation in the past few years. Some people are excited to embrace the coming growth of AI, while others are frightened and worried about how our jobs and our lives will change because of it. This video explores what AI really is, what challenges and benefits it creates and what we can do to ensure we are ready for the oncoming growth of AI. Humans have long been obsessed with creating AI ever since the question, “Can machines think?”, was posed by Alan Turing in 1950. With the prediction that AI will grow 300% this year over last year, it is clear that this trend is not going anywhere anytime soon. What are the two biggest developments to bring AI to the forefront? What are the biggest benefits and challenges with AI? What can individuals do to prepare for the oncoming AI growth? Some people are excited to embrace the coming growth of AI, while others are frightened and worried about how our jobs and our lives will change because of it. One thing that is clear is that AI is a big part of the future of work, so get ready. ———————————————- ABOUT THE FUTURE IN FIVE Will AI take over the world? Will cars drive themselves? What will the employees, managers, and organizations of the future look like and will we all have jobs we love? Join best-selling author and futurist Jacob Morgan each week as he explores these topics [More]
Organised By National Science Centre, Delhi (National Council of Science Museums) Ministry of Culture, Govt. of India Moderator: Shri N. Ramdas Iyer Head (Education) & Curator ‘G’, NSC, Delhi Panel: 1. Mathew James Bailey Recognised Authority- Innovation Ecosystems Artificial Intelligence, Smart Cities, IoT (Global Impact) 2. D Kishore Balaji Director- Global Trade Policy, Intel, India 3. Dr. Rahul Katarya Associate Professor Department of Computer Science & Engineering Delhi Technological University, Delhi 4. Ms. Nuriya Ansari Managing Partner Learning Link Foundation, India
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that’s useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I’ve yet encountered. Here are links to the individual talks and the full live streams for the two days: 1. Foundations of Deep Learning (Hugo Larochelle, Twitter) – https://youtu.be/zij_FTbJHsk 2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) – https://youtu.be/u6aEYuemt0M 3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) – https://youtu.be/oGk1v1jQITw 4. TensorFlow Tutorial (Sherry Moore, Google Brain) – https://youtu.be/Ejec3ID_h0w 5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) – https://youtu.be/rK6bchqeaN8 6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) – https://youtu.be/F1ka6a13S9I 7. Deep Reinforcement Learning (John Schulman, OpenAI) – https://youtu.be/PtAIh9KSnjo 8. Theano Tutorial (Pascal Lamblin, MILA) – https://youtu.be/OU8I1oJ9HhI 9. Deep Learning for Speech Recognition (Adam Coates, Baidu) – https://youtu.be/g-sndkf7mCs 10. Torch Tutorial (Alex Wiltschko, Twitter) – https://youtu.be/L1sHcj3qDNc 11. Sequence to Sequence Deep Learning (Quoc Le, Google) – https://youtu.be/G5RY_SUJih4 12. Foundations and Challenges of Deep Learning (Yoshua Bengio) – https://youtu.be/11rsu_WwZTc Full Day Live Streams: Day 1: https://youtu.be/eyovmAtoUx0 Day 2: https://youtu.be/9dXiAecyJrY Go to http://www.bayareadlschool.org for more information on the event, speaker [More]
World-known pioneer Yoshua Bengio (MILA) discusses the challenges ahead for deep learning toward artificial intelligence. #FranceisAI Web – https://franceisai.com/ Twitter – https://twitter.com/franceisai LinkedIn – https://linkedin.com/company/franceisai/ Video credit: VLAM
AI researchers are striving to create intelligent machines that complement human reasoning and enrich human experiences and capabilities. At the core is the ability to harness the explosion of digital data and computational power with advanced algorithms that extend the ability for machines to learn, reason, sense, and understand—enabling collaborative and natural interactions between machines and humans. The Academic Research Summit 2019 is focused on the theme of Data Science and AI. See more at https://www.microsoft.com/en-us/research/video/panel-discussion…ortunities-in-ai/
More information: http://www.iiep.unesco.org/en/iiep-strategic-debate-can-education-systems-anticipate-challenges-artificial-intelligence-4491 Speakers: – Stuart Elliott, Henry David Visiting Scholar, U.S. National Academies of Sciences, Engineering and Medicine. See his presentation: https://box.iiep.unesco.org/index.php/s/5idmfptBQJFGRq8 – Steven Vosloo, Senior Project Officer, Section of Youth, Literacy and Skills Development, UNESCO (discussant). See his presentation: https://box.iiep.unesco.org/index.php/s/8xfCc2D6qdLy56W – Suzanne Grant Lewis, Director, IIEP-UNESCO (moderator) Tuesday, 15 May 2018, 4 p.m. to 6 p.m. Join us on Twitter for live updates. Use the hashtag #StrategicDebate and follow @IIEP_UNESCO. Don’t forget to retweet!
Presented by Peter Skomoroch, Co-Founder and CEO, SkipFlag (acquired by Workday) Enterprise and consumer applications increasingly apply machine learning to create conversational interfaces. Adding a conversational UX presents a number of challenges for machine learning practicioners attempting to build intelligent applications. This session will describe some lessons learned from the recent wave of bots and from building SkipFlag, an intelligent knowledge base that integrated with Slack. Should you develop your own algorithms or make use of NLP as a service? How should you plan to include humans in the loop? To what degree do you need to specialize your models for the industry you’re working in? We’ll cover these questions and more.
This presentation took place at the Deep Learning Summit in London on 24-25 Sept 2015: https://www.re-work.co/events/deep-learning-london-2015 #reworkDL The Challenges of Human Labour Automatization with Deep Learning in the Transport Industry The key idea behind deep learning is to automate human labour, reduce the costs and reduce the time and increase precision in which a task can be done. In the transport industry things like cargo number recognition and counting of objects were first to be automated, and are now improved to a very high precision. However, there are many other tasks in the industry that can be automated, but computers are currently lacking the precision to guarantee the appliance with the industry security standards. This presentation will discuss how we have overcome some of the challenges and give an insight of the upcoming applications and their effects on industry. Juris Pūce an adventurous entrepreneur, always looking for new challenges and business to build. Interested in all things technologically innovative and somewhat unknown, hence most of his companies are IT related. With over 15 years of experience in technology related business management, Juris Pūce currently divides his work between being a visionary for various start-ups as well as being the CTO of KleinTech, a company that specialises in complex machine vision and deep learning technology solutions for transport and security industries.
The Future of Management in an Artificial Intelligence-Based World For more info about the conference: https://bit.ly/2J30TD3 Live Session: Artificial Intelligence: New Challenges for Leadership and Management -Dario Gil, Vice President of Science and Solutions, IBM Research -Tomo Noda, Founder and Chair, Shizenkan University Graduate School of Leadership and Innovation, Japan Moderator: Sandra Sieber, Professor, IESE