You may remember that back in February, the folks at Microsoft Research shared a series of tools meant to help create safer and more aware Unmanned Aerial Vehicles (UAVs). However, the search for improvement never stops, and the Research division has now shared something else they’re working on, inspired by the way nature operates.

Taking cues from birds and the way they stay aloft, the team has created an AI system which can keep a sailplane in the air “without using a motor, by autonomously finding and catching rides on naturally occurring thermals”.

In contrast to a bird, which does this naturally, for an AI to even be able to keep a vehicle in the air autonomously, it needs to not only gather a multitude of data points – be it wind speed, temperature, and others -, but it also needs to make predictions regarding how these data points will affect its trajectory. Moreover, the system needs to be sophisticated enough to also act in accordance to these predictions.

Similar to Facebook’s take, Ashish Kapoor, principal researcher at Microsoft, says that one day, this autonomous sailplane could replace cellular towers, therefore negating the need for any ground infrastructure. Not only that, but in conjunction with solar panels, the vehicle could stay aloft indefinitely.

The sailplane itself is still in testing, so it does include a battery and a motor, the latter of which is there in case a ground operator needs to take manual control. That said, when the vehicle gets in the air, it’s designed to adjust itself sans the need for an operator or a motor.

To achieve this high level of self-sufficiency on the AI’s part, the team has combined a few so-called frameworks of thinking, chief among which, the “partially observable Markov decision process”. This is used in cases where you need to plan for decisions “in an environment in which you can’t know everything”. Building upon this initial idea, for the AI to absorb and learn as much of the environment cues as quickly as possible, the Markov framework was combined with Bayesian reinforcement learning. Finally, in order for the AI to elect the most promising course of action, the team has also combined the previous two approaches with something called the Monte Carlo tree search.

For the AI system to be ever perfected, it’s split into two parts, the low-level and high-level planners. The high-level planner takes into account all sensory data from the environment, thus creating policies for trajectories the vehicle should take in order to go and look for thermals. In essence, the more times it does this, the better it gets at making these predictions. In the words of researcher Andrey Kolobov, “The system will perform better on Friday than on Thursday because it incorporates information based on past flights.” The low-level planner however, incorporates the Bayesian reinforcement learning approach to detect thermals in real time, or what’s thought of as “learning by doing”.

After months of work on Microsoft’s Redmond campus, the testing was finally conducted in Hawthorne, Nevada for a number of reasons. First, the open area allowed for higher flexibility, and the fact that it was in the real world as opposed to a simulation, revealed problems that need to be fixed in order to improve the system’s operation – such as the fact that it needs to know when to avoid obstacles like mountains, steer clear of restricted air space, a lake, or “scores of munitions that the U.S. Army stores in the area near the test flight site”.

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Microsoft Corp. is pushing further into the artificial intelligence market, announcing a new lab dubbed Microsoft Research AI, which is aimed at developing more general-purpose learning systems.

The lab, which will be located at its Redmond, Wash., headquarters will house more than 100 scientists from all areas of AI: learning, natural language, perception and reasoning. The idea behind the lab is multifaceted, with one goal being the creation of AI-enabled systems that can handle a variety of problems or tasks rather than a single product or device to tackle a particular problem, like getting around in traffic.

Microsoft said the integrated approach lets it develop tools that can do complex, multifaceted tasks. “Every day, computers are getting better at doing individual tasks like recognizing faces in photos or words in a conversation, using functionality such as pattern recognition and classification,” said Harry Shum, executive vice president of Microsoft AI and Research Group, in a blog post. “We believe AI will be even more helpful when we can create tools that combine those functions and add some of the abilities that come naturally to people.”

Ethics and Intelligence

Shum said the army of scientists and engineers will work closely with Microsoft’s research labs and product groups. They will be tasked with solving some of the toughest problems in AI and to accelerate the integration of advances into products and services. Another goal of the research lab is to make sure AI development remains transparent and ethical.

“As technology that uses AI gets smarter, we want to ensure that we take a responsible approach to our progress—and one that will ultimately provide the most benefit to our customers and to society as a whole,” said Shum. “This is uncharted territory, and we recognize that the decisions we make will have profound implications.”

Microsoft’s new lab puts it it in direct competition with other research firms focused on AI. With most of them racing to develop general learning systems, the company will be going up against DeepMind in the U.K. and Google Brain in San Francisco, both of which are units of Alphabet Inc. OpenAI is another AI research lab that’s backed by Elon Musk and Peter Thiel, reported Bloomberg. For the software giant, AI is becoming an important aspect of its business, showing up in Cortana, its voice-activated virtual assistant and in its cloud computing offering. It also overhauled the structure of the company last week to better align with the cloud business and advanced technologies.

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