New artificial intelligence algorithm better predicts corn yield


"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author of the study.

Using AI, people who are blind are able to find familiar faces


Cambridge, United Kingdom – Theo, a 12-year-old boy who is blind, is seated at a table in a crowded kitchen on a gray and drippy mid-December day. A headband that houses cameras, a depth sensor and speakers rings his sandy-brown hair. He swivels his head left and right until the camera in the front of the headband points at the nose of a person on the far side of a counter. Theo hears a bump sound followed by the name "Martin" through the headband's speakers, which are positioned above his ears. "It took me like five seconds to get you, Martin," Theo says, his head and body fixed in the direction of Martin Grayson, a senior research software development engineer with Microsoft's research lab in Cambridge.

How the Pentagon's JAIC Picks Its Artificial Intelligence-Driven Projects


The Pentagon launched its Joint Artificial Intelligence Center in 2018 to strategically unify and accelerate AI applications across the nation's defense and military enterprise. Insiders at the center have now spent about nine months executing that defense driven AI-support. At an ACT-IAC forum in Washington Wednesday, Rachael Martin, the JAIC's mission chief of Intelligent Business Automation Augmentation and Analytics, highlighted insiders' early approach to automation and innovation. "Our mission is to transform the [Defense] business process through AI technologies, to improve efficiency and accuracy--but really to do all those things so that we can improve our overall warfighter support," Martin said. Within her specific mission area, Martin and the team explore and develop automated applications that support a range of efforts across the Pentagon, such as business administration, human capital management, acquisitions, finance and budget training, and beyond.

Roberto G.E. Martín on LinkedIn: #AI #ReinforcementLearning


Using just a depth-sensing camera, GPS, and compass data, the AI Agent gets its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration.

Valid distribution-free inferential models for prediction Machine Learning

A fundamental problem in statistics and machine learning is that of using observed data to predict future observations. This is particularly challenging for model-based approaches because often the goal is to carry out this prediction with no or minimal model assumptions. For example, the inferential model (IM) approach is attractive because it has certain validity guarantees, but requires specification of a parametric model. Here we show that a new perspective on a recently developed generalized IM approach can be applied to construct an IM for prediction that satisfies the desirable validity guarantees without specification of a model. One important special case of this approach corresponds to the powerful conformal prediction framework and, consequently, the desirable properties of conformal prediction follow immediately from the general IM validity theory. Several numerical examples are presented to illustrate the theory and highlight the method's performance and flexibility.

Algorithmic Trading Strategies and Modelling Ideas


'Looks can be deceiving,' a wise person once said. The phrase holds true for Algorithmic Trading Strategies. The term'Algorithmic trading strategies' might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear. In this article, We will be telling you about algorithmic trading strategies with some interesting examples. If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading. Popular algorithmic trading strategies used in automated trading are covered in this article.

AsiaCollect is changing the way you think about debt collection: Startup Stories


During the 13 years he spent in banking, Tomasz Borowski's career spanned operations, risk management, and product management, where he also witnessed the brutality and unprofessional methods that conventional debt collection agencies adopted to retrieve outstanding balances. He observed the same thing when he moved to Ukraine in 2005 to continue his banking career. He did his own research on alternative methods and came to know about two digital debt collection companies--US-based TrueAccord and Polish debt collection agency Kruk SA--both worth over a billion US dollars today. "I decided to move to Southeast Asia and began exploring the market here. It was obvious to me that the situation here was similar to Ukraine. I decided to utilize my experience in finance and create a professional credit management services company to help change this market," Borowski told KrASIA.

Roberto G.E. Martín on LinkedIn: #AI #RL


On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long-time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems.

Three Ways Biased Data Can Ruin Your ML Models


Machine learning provides a powerful way to automate decision making, but the algorithms don't always get it right. When things go wrong, it's often the machine learning model that gets the blame. But more often than not, it's the data itself that's biased, not the algorithm or the model. That's been the experience of Cheryl Martin, Ph.D., who worked as an applied research scientist at the University of Texas, Austin and NASA for 14 years before joining the AI crowdsourcing outfit Alegion as its chief data scientist earlier this year. "You often hear that the algorithm is biased, or the machine learning is algorithmically biased," Martin tells Datanami.

Martin's Playtime with Tensorflow Lite / Dr Who image recognition


Sign in to report inappropriate content. Digital Maker's Martin Evans has been experimenting with TensorFlow Lite on the Raspberry Pi 4 to recognise Dr Who character shapes. This is a short video of the Pi camera recognising a Dalek & a Cyberman, with the output going to an Ada Fruit Display Screen.