This is a talk from GOTO Chicago 2019 by Doug Lenat, Award-winning AI pioneer who created the landmark Machine Learning program, AM, in 1976 and CEO of Cycorp. I’ve dropped the full talk abstract below for a read before diving into the talk:

• Video
• Slides

Almost everyone who talks about Artificial Intelligence, nowadays, means training multi-level neural nets on big data. Developing and using those patterns is a lot like what our right brain hemispheres do; it enables AI’s to react quickly and – very often – adequately. But we human beings also make good use of our left brain hemisphere, which reasons more slowly, logically, and causally.

I will discuss this “other type of AI” – i.e., left brain AI, which comprises a formal representation language, a “seed” knowledge base with hand-engineered default rules of common sense and good domain-specific expert judgement written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base. I will describe the largest such platform, Cyc, and will demo a few commercial applications that were produced just by educating it as one might teach a new human employee.

But it is important to remember that human beings’ “super-power” is our ability to harness both types of reasoning, and I believe that the most powerful AI solutions in the coming decade will likewise be hybrids of right-brain-like “thinking fast” and left-brain-like “thinking slow”. That is the only path I see by which we will overcome the current dangerous inability of deep-learning AI’s to rationalize and explain their decisions, and will make AI’s far more trusted and – more importantly – far more trustworthy.

Anyone who understood this abstract and found it interesting should find my actual talk similarly accessible – and hopefully interesting!

Too bad I can’t create polls on Reddit…

I was talking with a data scientist friend about versioning data in ML projects (I know there are a lot of great solutions and this post is not meant to focus on any of them).

What he said really flipped the notion I had in my head that data is an integral part of data science source code.

He claimed that in most data science projects the data and artifacts (intermediate stages of data processing not including models) don’t change that much. This is to say, the source data might be changed, but it is just one file (So you can get away with not versioning it) and intermediate stages should always be determined by code so you just need to manage the code you used to create the stage and not the result (the only exception is if you have some painful or resource intensive processing where you wouldn’t want to repeat that process).

I was wondering, from people here with experience in real world projects, how versatile is your data? Do you feel it’s hard to manage the data and artifacts?

I’m confused and your input would be greatly appreciated.

I found that the word vectors loaded from en_core_web_md in spacy have some odd behavior. For instance, the words flamingo, penguin and igloo have precisely the same vectors (with nonzero values). I can’t find much on Google. Has anyone encountered this before?

• Project page: https://hyperconnect.github.io/MarioNETte/
• Paper: https://arxiv.org/abs/1911.08139
• Video: https://youtu.be/Y6HE1DtdJHg

Here’s a summary of the work. We can perform face reenactments under a few-shot or even a one-shot setting, where only a single target face image is provided. Previous approaches to face reenactments had a hard time preserving the identity of the target and tried to avoid the problem through fine-tuning or choosing a driver that does not diverge too much from the target. We tried to tackle this “identity preservation problem” through several novel components.

Instead of working with a spatial-agnostic representation of a driver or a target, we encode the style information to a spatial-information preserving representation. This allows us to maintain the details that easily get lost when utilizing spatial-agnostic representation such as those attained from AdaIN layers.

We proposed image attention blocks and feature alignment modules to attend to a specific location and warp feature-level information as well. Combining attention and flow allows us to naturally deal with multiple target images, making the proposed model to gracefully handle one-shot and few-shot settings without resorting to reductions such as sum/max/average pooling.

Another part of the contribution is the landmark transformer, where we alleviate the identity preservation problem even further. When the driver’s landmark differs a lot from that of the target, the reenacted face tends to resemble the driver’s facial characteristics. Landmark transformer disentangles the identity and expression and can be trained in an unsupervised fashion.

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Check out the video and tell us what you think. Thanks!

I imagine getting the cuda/cudnn issues right would be difficult. Not sure if there’s anything out there already.

https://www.google-colab.com

I started by cataloguing a few interesting notebooks in a github repo and want to make it more easy for others to share their work and receive comments.

If you are interested, the posts also push to twitter, linkedin, reddit and facebook

So why Google Colab? — Its is fairly easy to use with a click-and-run format and helps those with limited resources. And even though it has some issues, I am sure we are only at the start of a long-term project and that Colab would keep on improving. If you have any feedback, please let me know.

Hi r/machinelearning, I’m excited to announce the release of our new environment on robotic furniture assembly. We have over 80 furniture models from IKEA as well as support for Baxter and Sawyer robots (and a Cursor agent for those who don’t want to learn low-level control). We support a Gym interface and build on top of MuJoCo for ease of use with RL.

I’m excited to see what researchers will come up with to tackle furniture assembly, a complex and long-horizon task. Let me know if you have any questions or comments!

Website: www.clvrai.com/furniture Github: www.github.com/clvrai/furniture

Paper: https://arxiv.org/abs/1911.07246

I like machine learning. I’m interested in it. I don’t exactly understand how it works but I get the basic gist.

I was thinking… What if there was a website/social experiment thing where there was a machine learning bot and anyone could submit the training material? It would have a theme, maybe, and it would generate a result after a while.

Just an idea. I’m not smart enough to make it lol