Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, scientists conducted a fascinating experiment to predict how "market-driven" platforms like food delivery and ride-hailing businesses affect the overall economy when they're optimized for different objectives, like maximizing revenue. Elsewhere, demonstrating the versatility of AI, a team hailing from ETH Zurich developed a system that can read tree heights from satellite images, while a separate group of researchers tested a system to predict a startup's success from public web data. The market-driven platform work builds on Salesforce's AI Economist, an open source research environment for understanding how AI could improve economic policy.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week AI applications have been found in several unexpected niches due to its ability to sort through large amounts of data, or alternatively make sensible predictions based on limited evidence. We've seen machine learning models taking on big data sets in biotech and finance, but researchers at ETH Zurich and LMU Munich are applying similar techniques to the data generated by international development aid projects such as disaster relief and housing. The team trained its model on millions of projects (amounting to $2.8 trillion in funding) from the last 20 years, an enormous dataset that is too complex to be manually analyzed in detail.

Depending on the theory of intelligence to which you subscribe, achieving "human-level" AI will require a system that can leverage multiple modalities -- e.g., sound, vision and text -- to reason about the world. For example, when shown an image of a toppled truck and a police cruiser on a snowy freeway, a human-level AI might infer that dangerous road conditions caused an accident. Or, running on a robot, when asked to grab a can of soda from the refrigerator, they'd navigate around people, furniture and pets to retrieve the can and place it within reach of the requester. But new research shows signs of encouraging progress, from robots that can figure out steps to satisfy basic commands (e.g., "get a water bottle") to text-producing systems that learn from explanations. In this revived edition of Deep Science, our weekly series about the latest developments in AI and the broader scientific field, we're covering work out of DeepMind, Google and OpenAI that makes strides toward systems that can -- if not perfectly understand the world -- solve narrow tasks like generating images with impressive robustness.

Research papers come out far too frequently for anyone to read them all. That's especially true in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the more interesting recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week we have a number of entries aimed at identifying or confirming bias or cheating behaviors in machine learning systems, or failures in the data that support them. But first a purely visually appealing project from the University of Washington being presented at the Conference on Computer Vision and Pattern Recognition.

Research papers come out far too frequently for anyone to read them all. That's especially true in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This edition, we have a lot of items concerned with the interface between AI or robotics and the real world. Of course most applications of this type of technology have real-world applications, but specifically this research is about the inevitable difficulties that occur due to limitations on either side of the real-virtual divide.

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in but not limited to artificial intelligence -- and explain why they matter. This week brings a few unusual applications of or developments in machine learning, as well as a particularly unusual rejection of the method for pandemic-related analysis. One hardly expects to find machine learning in the domain of government regulation, if only because one assumes federal regulators are hopelessly behind the times when it comes to this sort of thing. So it may surprise you that the U.S. Environmental Protection Agency has partnered with researchers at Stanford to algorithmically root out violators of environmental rules.

There's more AI news out there than anyone can possibly keep up with. But you can stay tolerably up to date on the most interesting developments with this column, which collects AI and machine learning advancements from around the world and explains why they might be important to tech, startups or civilization. To begin on a lighthearted note: The ways researchers find to apply machine learning to the arts are always interesting -- though not always practical. A team from the University of Washington wanted to see if a computer vision system could learn to tell what is being played on a piano just from an overhead view of the keys and the player's hands. Audeo, the system trained by Eli Shlizerman, Kun Su and Xiulong Liu, watches video of piano playing and first extracts a piano-roll-like simple sequence of key presses. Then it adds expression in the form of length and strength of the presses, and lastly polishes it up for input into a MIDI synthesizer for output.

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers -- particularly in but not limited to artificial intelligence -- and explain why they matter. This week has a bit more "basic research" than consumer applications. Machine learning can be applied to advantage in many ways users benefit from, but it's also transformative in areas like seismology and biology, where enormous backlogs of data can be leveraged to train AI models or as raw material to be mined for insights. We're surrounded by natural phenomena that we don't really understand -- obviously we know where earthquakes and storms come from, but how exactly do they propagate?

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers -- particularly in but not limited to artificial intelligence -- and explain why they matter. This week, a startup that's using UAV drones for mapping forests, a look at how machine learning can map social media networks and predict Alzheimer's, improving computer vision for space-based sensors and other news regarding recent technological advances. Machine learning tools are being used to aid diagnosis in many ways, since they're sensitive to patterns that humans find difficult to detect. IBM researchers have potentially found such patterns in speech that are predictive of the speaker developing Alzheimer's disease.

This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why …