Outline * The Promise versus Reality: Healthcare, Medical Imaging, and A.I. * The State of Deep Learning in Radiology and Medical Imaging Research * Understanding Radiology Workflow: What Problems Are Yet To Be Solved * The Top 5 Problems and Mitigations to Bringing Your AI Solution to Clinical Application * Call to Action: Datasets and Python Toolset Resources for Approaching Medical Imaging Description This talk will discuss the state of deep learning from a uniquely clinical perspective and offer the audience member an opportunity to understand the challenges facing the clinical medical imaging community and outline opportunities for the Python community to engage with clinicians. It will examine the disconnect between what has been promised versus what’s been delivered between radiology and artificial intelligence. A brief review of the most exciting and recent research and methodologies as applied to medical imaging will be performed. We will discuss how machine learning could be applied to the full spectrum of radiology workflow beyond simply computer assisted detection and characterization. There will be a broadly applicable discussion of the Top 5 problems and mitigations necessary for individuals and organizations bringing their A.I. solutions to a clinical setting. Finally, there will be a summary of datasets and Python resources that will assist interested developers in contributing to the growing medical imaging A.I. community. This material has not been presented previously elsewhere. Presentation page — https://2017.pycon.ca/schedule/13/
I think fears of artiicial super intelligence (in pop culture, specifically) are a bit overblown. I lay out my case in this vodeo. Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Hindsight Experience Replay course, with my subscription service. $24.99 a month gives you instant access to explanations and implementations of a dozen deep reinforcement learning algorithms. Not only will you learn everything from Deep Q Learning to Proximal Policy Optimization, but you will learn a repeatable system for learning new algorithms. Discounts available for Udemy students (enrolled longer than 30 days). Just send an email to sales@neuralnet.ai Courses Or, pickup my Udemy courses here: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-FEB-22 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-FEB-22 Curiosity Driven Deep Reinforcement Learning https://www.udemy.com/course/curiosity-driven-deep-reinforcement-learning/?couponCode=ICM-FEB-22 Natural Language Processing from First Principles: https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-FEB-22 Reinforcement Learning Fundamentals https://www.manning.com/livevideo/reinforcement-learning-in-motion Here are some books / courses I recommend (affiliate links): Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W Grokking Deep Learning: https://bit.ly/3yJ14gT Grokking Deep Reinforcement Learning: https://bit.ly/2VNAXql Come hang out on Discord here: https://discord.gg/Zr4VCdv Need personalized tutoring? Help on a programming project? Shoot me an email! phil@neuralnet.ai Website: https://www.neuralnet.ai Github: https://github.com/philtabor Twitter: https://twitter.com/MLWithPhil
Get the slides: https://www.datacouncil.ai/talks/building-a-lean-ai-startup-lessons-learned ABOUT THE TALK With recent developments in open source tools and cloud infrastructure, it has become easier to build Data Engineering applications. Lean Startup methodologies and MVPs have taken over product development. But can you apply them to building an AI product? How do you solve the Catch-22 of finding the data you need to get started? How do you iterate quickly (and cheaply) to a model that creates value for customers? What is an MVP in AI? How do you keep experimenting fast while ensuring the reliability of your data pipelines? With a team size that’s a rounding error at Uber or AirBnb? In this talk, we’ll share some of the issues we encountered trying to apply “lean startup” techniques to the development of a pure-play ML product and the techniques and tools we used to circumvent these difficulties. ABOUT THE SPEAKER Paul is the co-founder and CTO of MadKudu, a company using Machine Learning to optimize customer journeys at scale. Prior to MadKudu, he was a Product Manager in charge of the data platform at AgilOne. For reasons that made sense at the time, Paul holds MS Degrees in Nuclear Engineering from UC Berkeley and Ecole Polytechnique. ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with [More]
Michael’s talk discusses how AI is already in your everyday life, in ways you may not realize. He explains some myths and concerns while preparing you to look at AI in a different way than you likely do today. Michael L. Littman is a Computer Science Professor at Brown University, studying machine learning and decision making under uncertainty. He is co-director of Brown’s Humanity Centered Robotics Initiative, a Fellow of the Association for the Advancement of Artificial Intelligence, and has earned multiple awards for his teaching and research. An enthusiastic performer, Michael has had roles in numerous community theater productions and a TV commercial. 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
The renowned Berlin painter Roman Lipski has been working for two years with his Artificial Muse A.I.R., which inspires and augments him in his artistic work and pushes him to new frontiers. Now we present the latest generation of the muse, that is based on generative networks and allows for an intuitive and fluid interaction between artist and algorithm. Using Conditional Generative Adversarial Networks at its core, cGANs for short, A.I.R. translates sketches directly into new inspirations, facets and images. While the algorithm itself is mathematically complex and not easily accessible to human understanding, Lipski’s new approach to the muse exemplifies how curious discovery and experimentation can lead to intuitive understanding and ultimately trust, in a new generation of tools, in artificial intelligence per se. Explainability by interaction, trust by time. In our talk we will take a deep dive into the technical layer and share the learnings we made at “the inbetweens” of Roman and his Artificial Muse, of human and artificial intelligence. And this is just the beginning… Florian Dohmann, CEO, Birds on Mars Roman Lipski, Artist, Atelier Lipski
Thespian Theatre proudly presents “Ex_Machina, Or How I Learned to Stop Worrying and Love the A.I.” The play will be presented at Collège Jean-de-Brébeuf’s Salle Jacques-Maurice, on March 16 & 17, 2017 at 7:30 pm. You can find tickets for the show through members of the troupe ($10) or you can purchase them at the door ($15). This production is presented as part of Collège Jean-de-Brébeuf’s extracurricular activities. www.facebook.com/ThespianTheatreTroupe/ www.instagram.com/thespiantroupe/ *** Thespian Theatre présente fièrement “Ex_Machina, Or How I Learned to Stop Worrying and Love the A.I.” La pièce sera présentée au Collège Jean-de-Brébeuf, dans la Salle Jacques-Maurice le 16 et 17 mars 2017 à 19h30. Vous pouvez acheter des billets pour la pièce auprès des membres de la troupe ($10) ou à la porte ($15). La production est présentée dans le cadre des activités parascolaires du Collège Jean-de-Brébeuf. www.facebook.com/ThespianTheatreTroupe/ www.instagram.com/thespiantroupe/
Around the globe people ponder the future of employment in the digital age. This can be especially worrying after reading a few clickbait articles. What will we do for work? Will computers and robots take all the jobs? In this talk about the future of work, entrepreneur Steve Hilton addresses the question of why there are still so many jobs and comes up with a surprising and hopeful answer. How I learned to stop worrying and love AI 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
Day 3: April 6th 2018 Presentation by Frank Taylor (Head of AI, Swarm.Fund) Slides: https://app.box.com/s/wo0usj8dhjpuifcd2r8dl5tr754akw0p www.BLOCK2TheFuture.com @BLOCK2TheFutur #BLOCK2TheFuture
Max Howell draws back the curtain on the reality that artificial super intelligence will be here sooner than we think, and shares his approach to thinking about how it might impact our future. Max Howell has a master’s degree in chemistry, but after a year in the profession, abandoned it upon realizing chemistry is “super boring”. He began exploring open source coding. After working at Last.fm, then TweetDeck, Howell created Homebrew, an open source software manager that is today used by about 50 million people. He also authored a tweet about the interview process in the software industry that has been viewed more than 3 million times. Last year, he and his wife started a mobile app development company in Savannah. Max Howell has a master’s degree in chemistry, but after a year in the profession, abandoned it upon realizing chemistry is “super boring”. He began exploring open source coding. After working at Last.fm, then TweetDeck, Howell created Homebrew, an open source software manager that is today used by about 50 million people. He also authored a tweet about the interview process in the software industry that has been viewed more than 3 million times. Last year, he and his wife started a mobile app development company in Savannah. 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