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.

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 …

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. The topics in this week's Deep Science column are a real grab bag that range from planetary science to whale tracking. There are also some interesting insights from tracking how social media is used and some work that attempts to shift computer vision systems closer to human perception (good luck with that). One of machine learning's most reliable use cases is training a model on a target pattern, say a particular shape or radio signal, and setting it loose on a huge body of noisy data to find possible hits that humans might struggle to perceive.

Research papers come out at far too rapid a rate 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 in Deep Science spans the stars all the way down to human anatomy, with research concerning exoplanets and Mars exploration, as well as understanding the subtlest habits and most hidden parts of the body. Let's proceed in order of distance from Earth. First is the confirmation of 50 new exoplanets by researchers at the University of Warwick.

Police in the US state of Delaware are poised to deploy "smart" cameras in cruisers to help authorities detect a vehicle carrying a fugitive, missing child or straying senior. The video feeds will be analyzed using artificial intelligence to identify vehicles by license plate or other features and "give an extra set of eyes" to officers on patrol, says David Hinojosa of Coban Technologies, the company providing the equipment. "We are helping officers keep their focus on their jobs," said Hinojosa, who touts the new technology as a "dashcam on steroids." The program is part of a growing trend to use vision-based AI to thwart crime and improve public safety, a trend which has stirred concerns among privacy and civil liberties activists who fear the technology could lead to secret "profiling" and misuse of data. US-based startup Deep Science is using the same technology to help retail stores detect in real time if an armed robbery is in progress, by identifying guns or masked assailants.