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Future of Healthcare with AI Panel | CogX17 Highlights | CogX

Join the CogX Global Leadership Summit and Festival of AI and Breakthroughs Technology – June 8th to 10th 2020 – https://cogx.co/ Subscribe to our epic newsletters for free https://cognitionx.com/newsletter-subscribe/ Healthcare has been the hottest area for investment in AI, with 270 deals made since 2012. An aging population, increasing demand and rising drug costs has […]
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#74 Dr. ANDREW LAMPINEN – Symbolic behaviour in AI [UNPLUGGED]

Please note that in this interview Dr. Lampinen was expressing his personal opinions and they do not necessarily represent those of DeepMind. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Pod version: https://anchor.fm/machinelearningstreettalk/episodes/74-Dr–ANDREW-LAMPINEN—Symbolic-behaviour-in-AI-UNPLUGGED-e1h6far Dr. Andrew Lampinen is a Senior Research Scientist at DeepMind, and he thinks that symbols are subjective in the relativistic sense. Dr. Lampinen completed his PhD […]
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#70 – LETITIA PARCALABESCU – Symbolics, Linguistics [UNPLUGGED]

Today we are having a discussion with Letitia Parcalabescu from the AI Coffee Break youtube channel! We discuss linguistics, symbolic AI and our respective Youtube channels. Make sure you subscribe to her channel! In the first 15 minutes Tim dissects the recent article from Gary Marcus “Deep learning has hit a wall”. Patreon: https://www.patreon.com/mlst Discord: […]
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01:14:31

SingularityNET: General Theory of General Intelligence: Specializing Maximally General AGI (6/10)

This is Episode 6 in a series of videos discussing the General Theory of General Intelligence as overviewed in the paper Goertzel, Ben. “The General Theory of General Intelligence: A Pragmatic Patternist Perspective.” https://arxiv.org/pdf/2103.15100 This episode overviews some work (some quite recent and some older) formulating a general mathematical and conceptual framework for AGI algorithms […]
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SingularityNET: Ben Goertzel Reviews Weak Links by Peter Csermely – Book Reviews With Ben Goertzel

Ben Goertzel discusses Peter Csermely’s book, Weak Links: The Universal Key of Stability of Networks and Complex Systems in his latest book review. To order the book: https://www.amazon.com/Weak-Links-Universal-Stability-Collection/dp/3540311513 ‘The extracellular matrix as a key regulator of intracellular signalling networks’ Article: https://bpspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/bph.14195 The Graph Of A Social Network: https://griffsgraphs.wordpress.com/2012/07/02/a-facebook-network/ ——– SingularityNET is a decentralized marketplace for […]
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SingularityNET: General Theory of General Intelligence: Critical Priors for Human-Like General Intelligence

This is Episode 7 in a series of videos discussing the General Theory of General Intelligence as overviewed in the paper Goertzel, Ben. “The General Theory of General Intelligence: A Pragmatic Patternist Perspective.” https://arxiv.org/pdf/2103.15100 This episode overviews ideas regarding how the particular nature and requirements of *human-like-ness* can be used guide the design and education […]
Powered by TensorFlow: Creating a custom, machine learning-powered drumming arm
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Powered by TensorFlow: Creating a custom, machine learning-powered drumming arm

Jason Barnes (@cybrnetx), a drummer and composer from Atlanta, lost his lower right arm after an electrical accident in 2012. Determined to continue pursuing music, Jason teamed up with a group of Georgia Tech researchers to create a custom, TensorFlow-powered drumming arm that enables him to drum almost as naturally as if he was playing […]
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AISTATS 2021: Calibrated Adaptive Probabilistic ODE Solvers

This is the video presentation for AISTATS 2021 for Calibrated Adaptive Probabilistic ODE Solvers Nathanael Bosch, Philipp Hennig, Filip Tronarp Paper: https://arxiv.org/abs/2012.08202 Experiments: https://github.com/nathanaelbosch/capos Julia: https://github.com/nathanaelbosch/ProbNumDiffEq.jl Python: https://github.com/probabilistic-numerics/probnum more about our research can be found at https://uni-tuebingen.de/en/134428 Source of this “Tübingen Machine Learning” AI Video
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Probabilistic Numerics for ODEs 5: Try it out yourself!

This video is part of a ten-part spotlight series on Probabilistic Numerical Methods for (ordinary) differential equations. In this fifth video, Nico Krämer returns to introduce probnum, a library of research code under active development at http://probnum.org. Among other things, it provides easy access to probabilistic ODE solvers with an interface that allows drop-in replacement […]
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DeepLearning.AI Learner Community Event ft. Jan Jitse Venselaar

deeplearning.ai presents virtual mini-event series for our Learner Community of those taking the AI for Medicine specialization! #MeetYourMentor: Mentors sharing their personal experiences of breaking into AI #DeepDive: A deep dive into a specific area that the guest speaker is working on and more! This event will be from 10am to 10:30 am on May […]
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3D Deep Learning with PyTorch 3D w/ Georgia Gkioxari – #408

In this episode, we’re joined by Georgia Gkioxari, a research scientist at Facebook AI Research. Georgia was hand-picked by the TWIML community to discuss her work on the recently released open-source library PyTorch3D. In our conversation, Georgia describes her experiences as a computer vision researcher prior to the 2012 deep learning explosion, and how the […]
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01:28:05

Probabilistic ML – Lecture 3 – Continuous Variables (updated 2021)

This is the third lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig in the Summer Term 2020 at the University of Tübingen, updated in 2021 Time-stamped slides available at https://uni-tuebingen.de/en/180804. Contents: * Random Variables and Borel Measures * Beta Distributions and a Conjugate Prior Example Some of the slides for this lecture […]
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36:47

3D Deep Learning with PyTorch 3D w/ Georgia Gkioxari – #408

Today we’re joined by Georgia Gkioxari, a research scientist at Facebook AI Research. Georgia was hand-picked by the TWIML community to discuss her work on the recently released open-source library PyTorch3D. In our conversation, Georgia describes her experiences as a computer vision researcher prior to the 2012 deep learning explosion, and how the entire landscape […]
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01:09:28

Lecture 18 – Epilogue

Epilogue – The map of machine learning. Brief views of Bayesian learning and aggregation methods. Lecture 18 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media […]
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Lecture 17 – Three Learning Principles

Three Learning Principles – Major pitfalls for machine learning practitioners; Occam’s razor, sampling bias, and data snooping. Lecture 17 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech […]
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01:22:08

Lecture 16 – Radial Basis Functions

Radial Basis Functions – An important learning model that connects several machine learning models and techniques. Lecture 16 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic […]
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01:18:19

Lecture 15 – Kernel Methods

Kernel Methods – Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins. Lecture 15 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association […]
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01:14:16

Lecture 14 – Support Vector Machines

Support Vector Machines – One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html […]
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01:26:12

Lecture 13 – Validation

Validation – Taking a peek out of sample. Model selection and data contamination. Cross validation. Lecture 13 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media […]
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01:15:14

Lecture 12 – Regularization

Regularization – Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 12 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with […]
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Lecture 11 – Overfitting

Overfitting – Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise. Lecture 11 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media […]
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Lecture 10 – Neural Networks

Neural Networks – A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies […]
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Lecture 09 – The Linear Model II

The Linear Model II – More about linear models. Logistic regression, maximum likelihood, and gradient descent. Lecture 9 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website – http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic […]