Lex Fridman interviews Chris Urmson, former CTO of the Google Self-Driving Car team, a key engineer and leader behind the Carnegie Mellon autonomous vehicle entries in the DARPA grand challenges and the winner of the DARPA urban challenge. Today he is the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber’s former autonomy and perception lead.
Two Minute Papers explores the paper “Direct speech-to-speech translation with a sequence-to-sequence model.” Check out the voice samples as well.
Over the last decade or so, open source has blossomed into a major movement and the backbone of the tech industry. For instance, check out this project that Uber, yes Uber, has open sourced.
Ludwig is a TensorFlow-based toolbox that allows you to train and test deep learning models without the need to write any of the code. Incubated at Uber for the last two years, Ludwig was finally open sourced this February to incorporate the contributions of the data science community. With Ludwig, a data scientist can train a deep learning model by simply providing a CSV file that contains the training data as well as the YAML file with the outputs and inputs of the model.
The battle for top talent in the AI space is heating up with Apple looking to boost their AI stable. This reminds me of the 90’s when major tech firms would poach talent from each other with impunity. It’s interesting to see how “hot” AI skills have become.
Artificial Intelligence Apple has poached another top engineer from Google as it continues to grow its artificial intelligence and machine learning divisions, with Google’s Dr. Ian Goodfellow having left his role as a “Senior Staff Research Scientist” with Google to join Apple as a “Director of Machine Learning” in […]
DeepMind is definitely at the top of its game with cutting edge projects like AlphaGo, AlphaStar, and, most recently, AlphaFold, but it has even bigger plans. Curiously, it will retain control of any AGI it creates. Granted, an AGI is still years, maybe even decades away. I do, however, find it interesting that DeepMind is already planning a corporate power struggle.
Very Blade Runner-esque, don’t you think?
DeepMind — quite prominently — claims to be the “world leader in artificial intelligence research.” AlphaGo and AlphaStar certainly lend credence to that title, but the Alphabet division’s end goal is artificial general intelligence (AGI). If it ever achieves that landmark accomplishment, DeepMind — and not its parent company — will reportedly retain control.
TensorFlow 2.0 has arrived, with a focus on ease of use, developer productivity, and scalability.
Now there’s a contest to show off your TF2 chops: The #PoweredByTF 2.0 Challenge.
Here’s a synopsis:
Developers of all ages, backgrounds, and skill levels are encouraged to submit projects. Teams may have between 1 and 6 participants. Participants are encouraged to expand the scope of an existing TensorFlow 1.x project, to migrate and continue work on a historic TensorFlow 1.x project; or to create an entirely new software solution using TensorFlow 2.0.
Keras and eager execution . Robust model deployment in production on any platform. […]
In this first episode of Cloud AI Adventures, Yufeng Guo explains machine learning from the ground up with concrete examples.
In this video, ColdFusion takes a closer look at the technology behind Google Duplex.
Here’s a video from Google introducing the techniques you can use to represent features – including Bucketing, Crossing, Hashing, and Embedding – and other utilities TensorFlow uses to help you create features.
The video is worth a watch just for the walkthrough of using TensorFlow Estimators to classify structured data.