I've lost track of the number of times I've heard somebody say recently that Timnit Gebru is saving the world. Her co-lead of AI ethics at Google, Margaret Mitchell, said that about her a few days ago when Gebru led some events at Google around race. Her work with Joy Buolamwini that found race and gender bias in facial recognition is in part why lawmakers in Congress want to prohibit federal government use of the technology. That work also played a major role in Amazon, IBM, and Microsoft agreeing to halt or end facial recognition sales to police. Earlier in the week, organizers of the Computer Vision and Pattern Recognition (CVPR) conference, one of the biggest AI research conferences in the world, took the unusual step of calling her CVPR tutorial about how bias in AI goes far beyond data "required viewing for us all."

I had the opportunity to work with Spell on a challenge for Omdena to preprocessing our datasets. This was a great experience because on my laptop this process took many hours while on Spell it was much faster and my laptop didn't freeze. Additionally, Spell had logs where I can monitor my process, kill the run if it is necessary, and I can see how long the run is so I can monitor performance in my code.

Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.

Fractal #ArtificalIntelligence has captured the imagination of humanity in recent times. This video features a session led by Dr. Li Deng, Former Chief Scientist of AI and Partner Research Manager at Microsoft in Redmond. Dr. Deng founded and led Microsoft's Deep Learning Technology Center (2013-2017). He explores deep learning approaches like supervised and unsupervised learning and reinforcement learning, which are driving #SpeechRecognition, #ImageRecognition, games, and #ChatBot functionalities. Watch this video to see Dr. Deng derive principles and applications from real-world examples, which demonstrate how deep learning can be applied to business environments.

A widely used technique for model reuse, complementary to feature extraction, is fine-tuning. Fine-tuning involves unfreezing some layers of the top layer of the frozen model library for feature extraction, and jointly training the newly added part of the model (in this case, a fully connected classifier) and these top layers. This is called fine-tuning because it slightly adjusts the more abstract representations of the model being reused, in order to make them more relevant for the problem at hand. Fine-tuning is a process to take a network model that has already been trained for a given task and makes it perform a second similar task. Assuming that the original task is similar to the new task, using a designed and trained network allows us to take full advantage of the feature extraction that occurs in the top layer of the network without having to develop a feature extraction network from scratch. It depends on the deep learning framework.

With two releases every year, you may find it challenging to keep up with the latest features.* In fact, some people who work here feel the same way! This release, I asked the Product Managers about the new features related to deep learning that they think you should know about in release 20a. Here are their responses: Deep Learning Starting with Deep Learning Toolbox, there are three new features to get excited about in 20a. Experiment Manager (new) - A new app that keeps track all conditions when training neural networks.

"Machine learning does not have a built-in mechanism for investigating or discussing the social and political merits of its outputs." Two thousand two hundred twelve expert researchers and practitioners across a variety of technical, scientific, and humanistic fields, including statistics, machine learning and artificial intelligence, law, sociology, history, communication studies and anthropology have joined hands to sign a petition to stop Springer Nature from publishing a potentially malicious research paper. Springer Nature plans to publish an article "A Deep Neural Network Model to Predict Criminality Using Image Processing" that revives long discredited physiognomist pseudoscience. Sign this petition to urge @SpringerNature to refrain from publishing. The argument here is that the uncritical acceptance of default assumptions inevitably leads to discriminatory design in algorithmic systems, reproducing ideas which normalize social hierarchies and legitimise violence against marginalised groups.

One of the many benefits of using artificial intelligence (AI) is to help us view societal problems from a different perspective. While there's been much hubbub about how AI might be misused, we must not overlook the many ways AI can be used for good. Our global issues are complex, and AI provides us with a valuable tool to augment human efforts to come up with solutions to vexing problems. Here are 10 of the best ways artificial intelligence is used for good. Artificial intelligence, powered by deep-learning algorithms, is already in use in healthcare.

Facebook researchers believe the game NetHack is well-tailored to training, testing, and evaluating AI models. To this end, they today released the NetHack Learning Environment, a research tool for benchmarking the robustness and generalization of reinforcement learning agents. For decades, games have served as benchmarks for AI. But things really kicked into gear in 2013 -- the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. Rather, they're informing the development of systems that might one day diagnose illnesses, predict complicated protein structures, and segment CT scans.

Typically this requires casting the original deterministic physics-based model into a probabilistic framework where inputs or control variables (CVs) are treated as random variables with probability distributions derived from available experimental data, manufacturing constraints, design criteria, expert judgment, and/or other domain knowledge (e.g., see [1]). Running the physics-based model with CVs sampled according to these distributions yields corresponding realizations of the system response as characterized by quantities of interest (QoIs). Analysis of the uncertainty propagation from the CVs to the QoIs informs decision-making, e.g., it informs engineering decisions aimed at improving the quality and reliability of designed products and helps identify potential risks at early stages in the design and manufacturing process. Quantitatively assessing uncertainty propagation presents a fundamental challenge due to the computational cost of the underlying physics-based model. Even for a low number of CVs and QoIs, uncertainty quantification (UQ) for, e.g., accelerating the simulation-aided design of multiscale systems and data-centric engineering tasks more generally ([2]), requires a large number of repeated observations of QoIs to achieve a high degree of confidence in such an analysis. The sampling cost is further exacerbated in real-world applications where distributions on QoIs are typically non-Gaussian, skewed, and/or mutually correlated, and therefore need to be characterized by their full probability density function (PDF) rather than through summary statistics such as mean and variance. The computational cost of nonparametric methods to estimate these densities can become prohibitively high when using a fully-featured physics-based model to compute each sample. One approach to alleviate the computational burden is to derive a cheaper-to-compute surrogate for the physicsbased model's response enabling much faster generation of output data and thus overcoming computational bottlenecks.