Fairness ProjectLast week, I moderated a panel at TWIMLcon about how teams and organizations can operationalize responsible AI that combined perspectives from three people from different corners of the tech and AI community. Rachel Thomas is best known as cofounder of fast.ai, a popular free online deep learning course. In recent months, Thomas was named director of a new organization that mixes research, policy, and education called the Center for Applied Data Ethics at University of San Francisco. Guillaume Saint-Jacques is a senior software engineer at LinkedIn's Fairness Project, an applied research team that assesses the performance of the company's AI systems. Parinaz Sobhani is director of machine learning at Georgian Partners, an investor in SaaS startups with its own applied research lab to help portfolio companies apply machine learning.

Image registration is the process of transforming different images of one scene into the same coordinate system. These images can be taken at different times (multi-temporal registration), by different sensors (multi-modal registration), and/or from different viewpoints. The spatial relationships between these images can be rigid (translations and rotations), affine (shears for example), homographies, or complex large deformations models. Image registration has a wide variety of applications: it is essential as soon as the task at hand requires comparing multiple images of the same scene. It is very common in the field of medical imagery, as well as for satellite image analysis and optical flow. In this article, we will focus on a few different ways to perform image registration between a reference image and a sensed image.

In March, Yoshua Bengio received a share of the Turing Award, the highest accolade in computer science, for contributions to the development of deep learning--the technique that triggered a renaissance in artificial intelligence, leading to advances in self-driving cars, real-time speech translation, and facial recognition. Now, Bengio says deep learning needs to be fixed. He believes it won't realize its full potential, and won't deliver a true AI revolution, until it can go beyond pattern recognition and learn more about cause and effect. In other words, he says, deep learning needs to start asking why things happen. The 55-year-old professor at the University of Montreal, who sports bushy gray hair and eyebrows, says deep learning works well in idealized situations but won't come close to replicating human intelligence without being able to reason about causal relationships.

A NASA Frontier Development Lab (FDL) team has shown that by using deep learning, it is possible to virtually monitor the Sun's extreme ultraviolet (EUV) irradiance, which is a key driver of space weather. The Sun is vital for survival, but solar flares, which typically occur a few times a year, have the potential to cause severe disruptions in space and on Earth. These disruptions can impact spacecraft, satellites and even systems here on Earth, including GPS navigation, radio communications and the power grid. Deep learning can help get more value out of our current ability to monitor the Sun by providing virtual instruments to supplement physical devices. This research will be published in Science Advances on October 2, 2019 ("A deep learning virtual instrument for monitoring solar extreme ultraviolet spectral irradiance").

After releasing the beta version of TensorFlow 2.0 in June, Google announced its final release on Monday. This release comes with tighter integration with Keras, eager execution enabled by default, promises three times faster training performance, a cleaned-up API, and more. One of the important updates in TensorFlow 2.0 is its tighter integration with Keras, a popular high-level API used for easy and fast prototyping, building, and training deep learning models. This will enable developers to easily leverage its various model-building APIs including Sequential, Functional, and Subclassing. Explaining the motivation behind this change, the TensorFlow team wrote, "By establishing Keras as the high-level API for TensorFlow, we are making it easier for developers new to machine learning to get started with TensorFlow. A single high-level API reduces confusion and enables us to focus on providing advanced capabilities for researchers."

This is a clip from a conversation with Gary Marcus from Oct 2019. You can watch the full conversation here: https://www.youtube.com/watch?v vNOTD... (more links below) Podcast full episodes playlist: https://www.youtube.com/playlist?list... Podcasts clips playlist: https://www.youtube.com/playlist?list... Podcast website: https://lexfridman.com/ai Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel.

Each big step of progress in computing -- from mainframe to personal computer to internet to smartphone -- has opened opportunities for more people to invent on the digital frontier. But there is growing concern that trend is being reversed at tech's new leading edge, artificial intelligence. Computer scientists say AI research is becoming increasingly expensive, requiring complex calculations done by giant data centers, leaving fewer people with easy access to the computing firepower necessary to develop the technology behind futuristic products like self-driving cars or digital assistants that can see, talk and reason. The danger, they say, is that pioneering artificial intelligence research will be a field of haves and have-nots. And the haves will be mainly a few big tech companies like Google, Microsoft, Amazon and Facebook, which each spend billions a year building out their data centers.

Nine months ago, as part of a joint reference architecture launch with Nvidia, Pure Storage published TensorFlow deep learning performance results. The goal of creating a joint architecture with Nvidia was to identify and solve performance bottlenecks present in an end-to-end deep learning environment -- especially at scale. During creation of our reference architecture, my team identified and improved performance issues across storage, networking, and compute. Our system is a physical entity, and everything from cabling configuration and MTU size to Tensorflow prefetch buffer size can impact performance. The software and hardware stack in our test environment.

BackgroundLife expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning.

Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. These attacks target deep neural network models for end-to-end autonomous driving control.