At the Tackling Climate Change workshop at this year's NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight. The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google's AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes. The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms. The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. One essential step in better addressing the world's pressing challenges, says Bengio, is changing the way AI research is valued.

If you ever get a chance, I HIGHLY recommend talking to people. Not just people around you *most* of the time, like your immediate or extended family, or the nosy neighbor, or the mail professional who happily delivers package upon package of online orders to your porch on what seems to be an hourly basis. No, I mean different people, ones you've never met before, especially around the world. I have had the unique opportunity to visit many countries in my still short life, and experienced several cultures, several languages, several routines and rites. I've broken bread around the world, and visited hundreds of heritage sites learning about the past and present of the indigenous.

On one hand, we know AI is the future of business. After all, manpower simply isn't fast enough to keep up with the pace of consumer demand. That said, there's a big difference between knowing AI is the future and actually implementing AI within your business successfully. That latter part--AI adoption--is where many companies are finding themselves stuck. No one said digital transformation would be easy--but you're not alone if you assumed AI adoption would be a cakewalk.

Algorithmic or automated decision systems use data and statistical analyses to classify people for the purpose of assessing their eligibility for a benefit or penalty. Such systems have been traditionally used for credit decisions, and currently are widely used for employment screening, insurance eligibility, and marketing. They are also used in the public sector, including for the delivery of government services, and in criminal justice sentencing and probation decisions. Most of these automated decision systems rely on traditional statistical techniques like regression analysis. Recently, though, these systems have incorporated machine learning to improve their accuracy and fairness. These advanced statistical techniques seek to find patterns in data without requiring the analyst to specify in advance which factors to use. They will often find new, unexpected connections that might not be obvious to the analyst or follow from a common sense or theoretic understanding of the subject matter. As a result, they can help to discover new factors that improve the accuracy of eligibility predictions and the decisions based on them. In many cases, they can also improve the fairness of these decisions, for instance, by expanding the pool of qualified job applicants to improve the diversity of a company's workforce.

The US government must spend $25 billion on artificial intelligence research by 2025, stem the loss of foreign AI talent, and find new ways to prevent critical AI technology from being stolen and exported, according to a policy report issued Tuesday. Otherwise it risks falling behind China and losing its standing on the world stage. The report, from the Center for New American Security (CNAS), is the latest to highlight the importance of AI to the future of the US. It argues that the technology will define economic, military, and geopolitical power in coming decades. Advanced technologies, including AI, 5G wireless services, and quantum computing, are already at the center of an emerging technological cold war between the US and China. The Trump administration has declared AI a national priority, and it has enacted policies, such as technology export controls, designed to limit China's progress in AI and related areas.

Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. The Association of University Radiologists (AUR9) in its role of organizing and representing the interests of academic radiologists and those of radiology at large, convened a roundtable to help radiologists and industry leaders share their points of view and their goals in order to foster a shared understanding about the impact and benefits of AI applications in the field of radiology. There is a clear mutual interdependence between the radiology community and industry partners, which, in the case of AI, should foster collaboration between the two groups. In order to advance radiological sciences and to bridge the gap between clinicians and engineers, members of both groups need to work together so as to ensure the development of common goals, shared understanding, and mutually productive efforts. This type of collaboration occurs most frequently at the local level between a single radiology academic department and a single manufacturer.

Calculating the average of a variable or a list of numbers is a common operation in machine learning. It is an operation you may use every day either directly, such as when summarizing data, or indirectly, such as a smaller step in a larger procedure when fitting a model. The average is a synonym for the mean, a number that represents the most likely value from a probability distribution. As such, there are multiple different ways to calculate the mean based on the type of data that you're working with. This can trip you up if you use the wrong mean for your data.

One of Amazon's most recent announcements was the release of their new tool called Amazon Rekognition Custom Labels. This advanced tool has the capability to improve machine learning on a whole new scale, allowing for better data analysis and object recognition. Amazon Rekognition will help users train their machine learning models more easily and allow them to understand a set of objects out of limited data. In other words, this capability will make machines more intelligent and capable of recognizing items with far less data sets than ever before. Machine learning includes a scientific study and adoption of algorithms that allow computers to learn new information and functionalities without needing direct instructions.

A prominent privacy expert has warned against allowing Amazon's voice assistant Alexa into the bedroom. Hannah Fry, a mathematician with expertise in the algorithms tech companies use, said she did not use the smart speaker in upstairs rooms of her house due to revelations that it was eavesdropping on private conversations. Amazon previously denied that its Echo devices were used to spy on people but earlier this year admitted that employees listen to customer voice recordings in order to improve speech recognition. It was also revealed that recordings of personal moments were inadvertently caught up after the smart speaker was triggered by words that sounded similar to "Alexa". After requesting audio data from Amazon that had been recorded by her Echo speaker, Dr Fry discovered it had picked up conversations that were never directed at the voice assistant.

GNN-Explainer is the first general tool for explaining predictions made by graph neural networks (GNNs). Given a trained GNN model and an instance as its input, the GNN-Explainer produces an explanation of the GNN model prediction via a compact subgraph structure, as well as a set of feature dimensions important for its prediction. Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models and explaining predictions made by GNNs remains unsolved.