This article follows my previous one on Bayesian probability & probabilistic programming that I published few months ago on LinkedIn. And for the purpose of this article, I am going to assume that most this article readers have some idea what a Neural Network or Artificial Neural Network is. Neural Network is a non-linear function approximator. We can think of it as a parameterized function where the parameters are the weights & biases of Neural Network through which we will be typically passing our data (inputs), that will be converted to a probability between 0 and 1, to some kind of non-linearity such as a sigmoid function and help make our predictions or estimations. These non-linear functions can be composed together hence Deep Learning Neural Network with multiple layers of this function compositions.
End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data. Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty. In this paper, we propose a Bayesian NN for end-to-end control that estimates uncertainty by exploiting feature map correlation during training. This approach achieves improved model fits, as well as tighter uncertainty estimates, than traditional element-wise dropout. We evaluate our algorithms on a challenging dataset collected over many different road types, times of day, and weather conditions, and demonstrate how uncertainties can be used in conjunction with a human controller in a parallel autonomous setting.
Artificial intelligence has begun seeping its way into every tech product and service. Now, companies are changing the underlying hardware to accommodate this shift. Apple is the latest company creating a dedicated AI processing chip to speed up the AI algorithms and save battery life on its devices, according to Bloomberg. The Bloomberg report said the chip is internally known as the Apple Neural Engine and will be used to assist devices for facial and speech recognition tasks. The latest iPhone 7 runs some of its AI tasks (mostly related to photographer) using the image signal processor and the graphics processing unit integrated on its A10 Fusion chip.
Microsoft Corp. is setting up a new research lab focused on artificial intelligence with the goal of creating more general-purpose learning systems. The new lab, called Microsoft Research AI, will be based at the company's headquarters in Redmond, Washington, and involve more than 100 scientists from across various sub-fields of artificial intelligence research, including perception, learning, reasoning and natural language processing. The goal, said Eric Horvitz, the director of Microsoft Research Labs, is to combine these disciplines to work toward more general artificial intelligence, meaning a single system that can tackle a wide-range of tasks and problems. Such a system, for instance, might be able to both plan the best route to drive through a city and also figure out how to minimize your income tax bill, while also understanding difficult human concepts like sarcasm or gestures. This differs from so-called narrow AIs, which are just designed to perform a single task well -- for instance, recognize faces in digital photographs.