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Microsoft Word - Model versus Data AI.docx

On Apr 21, 2019
@ogrisel shared
RT @wellingmax: I wrote this response to Rich Sutton's "The Bitter Lesson" https://t.co/rpv22dqXr4
Open

The question seems to come in different flavors: symbolic AI or statistical AI, white box AI or black box AI, model driven or data driven AI, generative or discriminative AI? A recent blog by Rich Sutton adds to the list compute-driven AI versus human-knowledge based AI. A number of ...

staff.fnwi.uva.nl
On Apr 21, 2019
@ogrisel shared
RT @wellingmax: I wrote this response to Rich Sutton's "The Bitter Lesson" https://t.co/rpv22dqXr4
Open

Microsoft Word - Model versus Data AI.docx

The question seems to come in different flavors: symbolic AI or statistical AI, white box AI or black box AI, model driven or data driven AI, generative or discriminative AI? A recent blog ...



On Apr 16, 2019
@weballergy shared
RT @hardmaru: Probabilistic Model-Based Reinforcement Learning Using The Differentiable Neural Computer They replaced the MDN-LSTM in World Models with a Neural Turing Machine and tried to use MDN-NTMs (can learn algorithms from data, in theory) for model-based RL. https://t.co/FgBbfroH4S https://t.co/Nc4qdoJjKN
Open

Probabilistic Model-Based Reinforcement Learning Using The Differentiable Neural Computer

My experiments found that a model learned in a Differentiable Neural Computer outperformed a vanilla LSTM based model, on two gaming environments.

On Apr 15, 2019
@hugo_larochelle shared
RT @DBahdanau: Are you curious about systematic generalization? Do you like small, carefully controlled studies with intriguing conclusions? Check out our latest paper: https://t.co/xA4tYvdjF9. Code & data at https://t.co/ydCswLGkyW. Work done by @MILAMontreal with the help of @Element_ai https://t.co/BZNDgnq0ep
Open

Click here to read the article

The output of the last FiLM block hNq x undergoes an extra 1 × 1 convolution and max-pooling to produce hq x. MAC network of Hudson & Manning (2018) produces hq x by repeatedly applying a ...

On Apr 18, 2019
@Miles_Brundage shared
RT @tdietterich: Nice article on AI risks from an international strategic perspective by the always insightful @paul_scharre I strongly support his call for governments/militaries to initiate AI safety conversations. https://t.co/qZH9QVON7Z
Open

Killer Apps

The real danger of an AI arms race isn't that another country would win; it's that unsafe technologies would make everyone lose.

On Apr 21, 2019
@xamat shared
@f2harrell @danilobzdok @TheWagRocks @DataSciFact Your post is a quite good categorization of statistical properties of ML methods, but it is *not* a way to separate SL from ML. That distinction, as many other statisticians have come to accept, does not exist: https://t.co/sRbf4KImCs
Open

Machine Learning FAQ

This is the personal website of a data scientist and machine learning enthusiast with a big passion for Python and open source. Born and raised in Germany, now living in East Lansing, ...

On Apr 15, 2019
@gdb shared
@dan_s_becker Dota is a convenient testbed for pushing the limits of general-purpose deep RL technology. Here's a physical robotics problem we solved using the learning system we wrote for Dota: https://t.co/PQGhabYIR1
Open

Learning Dexterity

We've trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.

On Apr 16, 2019
@haldaume3 shared
RT @UMDscience: We're proud to announce the launch of the @UofMaryland Center for Machine Learning. @CapitalOne is an inaugural partner of the center, which will be located in the new Brendan Iribe Center. https://t.co/kInrRSrPW1 #UMDdiscovers #UMDinnovates #MachineLearning https://t.co/4bpoTScDi5
Open

University of Maryland Launches Center for Machine Learning

Initial funding for the center comes from the College of Computer, Mathematical, and Natural Sciences (CMNS) and UMIACS, which will provide technical and administrative support. An ...

On Apr 17, 2019
@petewarden shared
Excited to be presenting a keynote at @EmbVisionSummit, May 20-23, Santa Clara, CA: Why the Future of Machine Learning is Tiny. I’ve attended for many years and always enjoy the practical focus! https://t.co/98R3XMUTu0 #computervision #tinyml #tensorflow #evs19
Open

The Future of Computer Vision and Machine Learning is Tiny

The Future of Computer Vision and Machine Learning is Tiny" at the 2019 Embedded Vision Summit.

On Apr 15, 2019
@peteskomoroch shared
RT @dbeyer123: For the curious, here's an interview I did with Michael Osborne on AI, automation and its impact on labor. https://t.co/90f1AaqbqN
Open
On Apr 17, 2019
@Miles_Brundage shared
RT @ENERGY: ANNOUNCED TODAY: $20M in funding for artificial intelligence and machine learning R&D projects to: ✅Boost grid resilience, operation, and management. ✅Solve scientific problems and improve #AI predictions. ⬇⬇https://t.co/j6dtP4vKGd
Open

Department of Energy Announces $20 Million for Artificial Intelligence Research

Department of Energy Announces $20 Million for Artificial Intelligence Research

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On Apr 17, 2019
@RichardSocher shared
RT @SFResearch: And another PhD intern writes a publication accepted to @iclr2019! @AkhileshGotmare goes into detail on: 1. Cosine learning rate decay 2. Learning rate warmup 3. Knowledge distillation Learn more in this Q&A, and congrats on starting full-time this week! https://t.co/S2pAZIrbEH https://t.co/DhCLzsR1Cc
Open

Q&A with Salesforce Research Intern Akhilesh Gotmare on how "Optimization and Machine Learning" led him to ICLR.

In the research paper, “A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation” Futureforce PhD Intern Akhilesh Gotmare, worked with Research Scientist ...

On Apr 21, 2019
@shakir_za shared
RT @Khipu_AI: #khipu2019 listed as one of the top AI conferences to attend in 2019! https://t.co/4cC1ocw6OR Applications are open! More details in https://t.co/wK0ZLhCYnC
Open

Top AI Conferences in 2019

These are the top AI, machine learning, deep learning, conferences to attend this 2019, which will take place in the US and the world…

On Apr 21, 2019
@AlisonBLowndes shared
I finished the book, here's my take: https://t.co/8UoJV6VvVo via @danieldennett "intelligent designer". #ai #deplearning #EasterSunday https://t.co/Gwmd7VOi72
Open

Competence without comprehension

What follows is a review of Daniel C Dennett's book on the Evolution of Minds, accompanied by a few of my thoughts. My son gave me this book in December 2017 and I'm afraid, due to my ...

On Apr 17, 2019
@glouppe shared
RT @tommy_da_cat: Fantastic work on Differential learning with gaussian process flows from the group of @samikaski #aistats #MachineLearning https://t.co/WkR6E31eFl https://t.co/oZe7xbLpx7
Open

Deep learning with differential Gaussian process flows

We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression ...

On Apr 18, 2019
@graphific shared
RT @teropa: "Exploring the Creative Potential of Deep Learning through the Magenta Project" by @ada_rob A great introduction to generative AI models and using them for creative purposes, using examples of work by Magenta and others. Some of my stuff in there too 😊 https://t.co/y6CuC0NuYH
Open

Adam Roberts - Exploring the Creative Potential of Deep Learning through the Magenta Project

https://ai.google/research/people/104881 Exploring the Creative Potential of Deep Learning through the Magenta Project Over the past 3 years, the increase in...

On Apr 22, 2019
@jeremyphoward shared
In this clip, @elonmusk tells @lexfridman that adversarial examples are trivially easily fixed. @karpathy is that your experience at @tesla? @catherineols is that what the neurips adversarial challenge found? https://t.co/4OMIKcP67w
Open

Elon Musk: Tesla Autopilot | Artificial Intelligence (AI) Podcast

Elon Musk is the CEO of Tesla, SpaceX, Neuralink, and a co-founder of several other companies. This conversation is part of the Artificial Intelligence podca...

On Apr 17, 2019
@SebastianThrun shared
RT @udacity: Are you looking for a career in #AI? Here are 13 industries soon to be revolutionized by #AI via @Forbes https://t.co/73gl7w6SMR
Open

13 Industries Soon To Be Revolutionized By Artificial Intelligence

Experts from Forbes Technology Council offer their predictions on which industries are about to be revolutionized by artificial intelligence (AI).

On Apr 19, 2019
@SebastianThrun shared
Excited to announce expansion of our partnership with @Amazon Web Services (AWS) on our latest Machine Learning Nanodegree programs - Intro to ML and ML Engineer! Congratulations to the whole @udacity and AWS team! #Udacity #MachineLearning #DreamBig https://t.co/vQFDpzgOX2
Open

Become a Machine Learning Engineer

Build a solid foundation in Supervised, Unsupervised, Reinforcement, and Deep Learning. Then, use these skills to test and deploy machine learning models in a production environment.

On Apr 17, 2019
@Miles_Brundage shared
RT @scottniekum: Our new IRL algorithm, T-REX, can significantly outperform a suboptimal demonstrator and does not require a model or inference-time rollouts. We achieve SOTA imitation learning performance (vs. GAIL, BCO, etc.) on a variety of MuJoCo and Atari domains: https://t.co/rEcwTgDrNU
Open

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is a consequence of the general reliance of ...