Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them interrogators, as in the Turing test.

Accurate and reliable energy time series prediction is of great significance for power generation planning and allocation. At present, deep learning time series prediction has become the mainstream method. However, the multi-scale time dynamics and the irregularity of real data lead to the limitations of the existing methods. Therefore, we propose EnergyPatchTST, which is an extension of the Patch Time Series Transformer specially designed for energy forecasting. The main innovations of our method are as follows: (1) multi-scale feature extraction mechanism to capture patterns with different time resolutions; (2) probability prediction framework to estimate uncertainty through Monte Carlo elimination; (3) integration path of future known variables (such as temperature and wind conditions); And (4) Pre-training and Fine-tuning examples to enhance the performance of limited energy data sets. A series of experiments on common energy data sets show that EnergyPatchTST is superior to other commonly used methods, the prediction error is reduced by 7-12%, and reliable uncertainty estimation is provided, which provides an important reference for time series prediction in the energy field.

Wei Li, Intel's vice president and general manager of AI and analytics, says he's on a mission to make AI more accessible to developers. Intel, the world's largest manufacturer of CPUs and semiconductors, is well known for its chips, though it has struggled to stay at the top of the chip market. But hardware isn't Intel's only focus, according to Li. His team, the AI software group, is dedicated to bringing low-code/no-code AI software to the masses. One of the ways Li said Intel hopes to make AI more accessible is with AI reference kits, which include a chatbot and a document analyzer.

AI applications are starting to appear in almost all aspects of our everyday lives, from healthcare and finance to entertainment and environmental protection. But a large number of AI applications never make it from concept to implementation, and an even larger amount never even get started. How do we enable more data scientists and developers to quickly create the path from data to insights with the data and compute resources available to them? The key to traversing this path and enabling AI everywhere is software that both unlocks hardware performance and maximizes developer productivity. Dr. Wei Li is the Vice President and General Manager of AI & Analytics at Intel.

They explore the latest innovations in Artificial Intelligence that the team of "Engineering Magicians" at Intel have been working on, including: Delivering end-to-end productivity and performance, underpinned by openness, choice, and security, Intel's AI software portfolio makes AI easier to build and deploy, advancing the democratization of AI at scale. You can watch the full interview below. To find out more from Intel about Accelerating AI Everywhere, please click here. Dr. Wei Li is the Vice President and General Manager of AI & Analytics at Intel. After starting his career as a computer scientist for supercomputers, he received his Ph.D. in Computer Science from Cornell University and taught Advanced Compiling Techniques at Stanford University.