Microsoft at NeurIPS 2019

In many real-life scenarios, obtaining information is costly, and getting fully observed data is almost impossible. For example, in the recruiting world, obtaining relevant information (in other words, a feature value) for a company could mean performing time-consuming interviews. The same applies to many other scenarios, such as in education and the medical field, where each feature value is an often more complex answer to a question. Unfortunately, AI-aided decision making usually requires large amounts…

Imagine moving to a new city. You want to get from your new home to your new job. Unfamiliar with the area, you ask your co-workers for the best route, and as far as you can tell … they’re right! You get to work and back easily. But as you acclimate, you begin to wonder: Is there a more scenic route, perhaps, or a route that passes by a good coffee spot? The fundamental question…

There’s a lot of excitement around self-driving cars, delivery drones, and other intelligent, autonomous systems, but before they can be deployed at scale, they need to be both reliable and safe. That’s why Gurdeep Pall, CVP of Business AI at Microsoft, and Dr. Ashish Kapoor, who leads research in Aerial Informatics and Robotics, are using a simulated environment called AirSim to reduce the time, cost and risk of the testing necessary to get autonomous agents…

Memory is an important part of human intelligence and the human experience. It grounds us in the current moment, helping us understand where we are and, consequently, what we should do next. Consider the simple example of reading a book. The ultimate goal is to understand the story, and memory is the reason we’re able to do so. Memory allows us to efficiently store the information we encounter and later recall the details we’ve previously…

Artificial intelligence has evolved to become a revolutionary technology. It is rapidly changing the economy, both by creating new opportunities (it’s the backbone of the gig economy) and by bringing venerable institutions, like transportation, into the 21st century. Yet deep at its core something is amiss, and more and more experts are worried: the technology seems to be extremely brittle, a phenomenon epitomized by adversarial examples. Adversarial examples exploit weaknesses in modern AI. Today, most…

Drone racing has transformed from a niche activity sparked by enthusiastic hobbyists to an internationally televised sport. In parallel, computer vision and machine learning are making rapid progress, along with advances in agile trajectory planning, control, and state estimation for quadcopters. These advances enable increased autonomy and reliability for drones. More recently, the unmanned aerial vehicle (UAV) research community has begun to tackle the drone-racing problem. This has given rise to competitions, with the goal…

Having experience in deep learning doesn’t hurt when it comes to the often mysterious, time- and cost-consuming process of hunting down an appropriate neural architecture. But truth be told, no one really knows what works the best on a new dataset and task. Relying on well-known, top-performing networks provides few guarantees in a space where your dataset can look very different from anything those proven networks have encountered before. For example, a network that worked…

Text to speech (TTS) has attracted a lot of attention recently due to advancements in deep learning. Neural network-based TTS models (such as Tacotron 2, DeepVoice 3 and Transformer TTS) have outperformed conventional concatenative and statistical parametric approaches in terms of speech quality. Neural network-based TTS models usually first generate a mel-scale spectrogram (or mel-spectrogram) autoregressively from text input and then synthesize speech from the mel-spectrogram using a vocoder. (Note: the Mel scale is used…

In both language and mathematics, symbols and their mutual relationships play a central role. The equation x = 1/y asserts the symbols x and y—that is, what they stand for—are related reciprocally; Kim saw the movie asserts that Kim and the movie are perceiver and stimulus. People are extremely adept with the symbols of language and, with training, become adept with the symbols of mathematics. For many decades, cognitive science explained these human abilities by…

While reinforcement learning (RL) has seen significant successes over the past few years, modern deep RL methods are often criticized for how sensitive they are with respect to their hyper-parameters. One such hyper-parameter is the discount factor, which controls how future rewards are weighted compared to immediate rewards. The objective that one wants to optimize in RL is often best described as an undiscounted sum of rewards (for example, maximizing the total score in a…

Reinforcement learning has achieved great success in game scenarios, with RL agents beating human competitors in such games as Go and poker. Distributional reinforcement learning, in particular, has proven to be an effective approach for training an agent to maximize reward, producing state-of-the-art results on Atari games, which are widely used as benchmarks for testing RL algorithms. Because of the intrinsic randomness of game environments—with the roll of the dice in Monopoly, for example, you…

One of the core directions of Project Malmo is to develop AI capable of rich interactions. Whether that means learning new skills to apply to challenging problems, understanding complex environments, or knowing when to enlist the help of humans, reinforcement learning (RL) is a core enabling technology for building these types of AI. In order to perform RL well, agents need to do exploration efficiently, which means understanding when to try new things out and…