It took our data science team led by Dr Xuxu Wang almost a year to develop the machine learning and deep learning models required," Mr Brown told …

It's no great secret that Apple's voice assistant has plenty of room for improvement. The Cupertino company is aware of this -- in June, it debuted an improved neural text-to-speech model capable of delivering a more natural-sounding voice without the use of samples. And in a newly published research paper on the preprint server Arxiv.org, a team of Apple scientists describe an approach for selecting training data for Siri's domain classifier -- the component that chooses whether a person's command relates to, say, their calendar rather than their alarms -- that leads to a substantial error reduction with only a small percentage of examples. As the researchers explain, Siri processes speech to suss out the intended domain with a classifier called the Domain Chooser, which helps identify a given user's intent. Once an utterance is matched to one of the over 60 defined domains, a component called the Statistical Parser assigns a parse label to each part of the utterance, after which the domain and parse labels predicted by the Domain Chooser and Statistical Parser are mapped into an intent representation that kicks off the appropriate action.

As a member of a research group involved in computer vision, I wanted to write this short article to briefly present what we call "Zero-shot learning" (ZSL), an interesting variant of transfer learning, and the current research related to it. Today, many machine learning methods focus on classifying instances whose classes have already been seen in training. Concretely, many applications require classifying instances whose classes have not been seen before. Zero-shot learning is a promising learning method, in which the classes covered by training instances and the classes we aim to classify are disjoint. In other words, Zero-shot learning is about leveraging supervised learning with no additional training data.

The future of Artificial Intelligence (AI) depends on many factors. Advancements in computing power and the implementation of regulations are just two external influences that could significantly impact what AI will be able to do in the years to come. Before we get to the exciting future uses, however, it's important to understand exactly where we are today. And that's not as clear as it should be. There are a lot of misconceptions about AI, machine learning, and deep learning.

Producing content for Massive Open Online Course (MOOC) platforms like Coursera and EdX might be academically rewarding (and potentially lucrative), but it's time-consuming -- particularly where videos are involved. Professional-level lecture clips require not only a veritable studio's worth of equipment, but significant resources to transfer, edit, and upload footage of each lesson. That's why research scientists formerly at Udacity, an online learning platform with over 150 courses, are investigating a machine learning framework that automatically generates lecture videos from audio narration alone. They claim in a preprint paper ("LumièreNet: Lecture Video Synthesis from Audio") on Arxiv.org that their AI system -- LumièreNet -- can synthesize footage of any length by directly mapping between audio and corresponding visuals. "In current video production pipeline, an AI machinery which semi (or fully) automates lecture video production at scale would be highly valuable to enable agile video content development (rather than reshooting each new video)," wrote the paper's coauthors.

This article will focus on the impact of AI, 5G, Edge Computing on the healthcare sector in the 2020s as well as a section on Quantum Computing's potential impact on AI, healthcare and financial services. The next in the series will deal with how we can use AI in the fight against climate change including the protection of the Amazon, smart cities and AGI. For those who are new to AI, Machine Learning and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed such as the need for improved sharing of data ...

Even the most powerful computers are still no match for the human brain when it comes to pattern recognition, risk management, and other similarly complex tasks. Recent advances in optical neural networks, however, are closing that gap by simulating the way neurons respond in the human brain. In a key step toward making large-scale optical neural networks practical, researchers have demonstrated a first-of-its-kind multilayer all-optical artificial neural network. Generally, this type of artificial intelligence can tackle complex problems that are impossible with traditional computational approaches, but current designs require extensive computational resources that are both time-consuming and energy intensive. For this reason, there is great interest developing practical optical artificial neural networks, which are faster and consume less power than those based on traditional computers.

The deep learning predictive AI algorithm developed by I Know First, a Fintech company that provides state of the art self-learning AI-based algorithmic stock market forecast solutions to uncover the best investment opportunities, has shown an accuracy of up to 95% in its predictions for Facebook (FB). That is according to aFacebook stock forecastevaluation report published by the company on August 25, 2019. The algorithm has demonstrated a higher accuracy rate for longer-term forecasts, as is often the case for predictive AI. "DeepFake" face-swapping technology created a buzz a couple of years back when a series of fake celebrity porn videos generated by the method spread rapidly across the Internet. Deepfake's unseemly side triggered widespread fears and concerns regarding misuse of the new tech, although its propagation was limited by the relatively advanced programming expertise required to perform convincing face-swapping. The recent explosion of interest in artificial intelligence, machine learning, and deep learning has been mirrored by an explosion in book titles on these same topics.

Operator-theoretic analysis of nonlinear dynamical systems has attracted much attention in a variety of engineering and scientific fields, endowed with practical estimation methods using data such as dynamic mode decomposition. In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating it using finite data, with consideration of the unboundedness of operators. For this purpose, we first consider Perron-Frobenius operators with kernel-mean embeddings for such systems. Then, we extend the Arnoldi method, which is the most classical type of Kryov subspace methods, so that it can be applied to the current case. Meanwhile, the Arnoldi method requires the assumption that the operator is bounded, which is not necessarily satisfied for transfer operators on nonlinear systems. We accordingly develop the shift-invert Arnoldi method for the Perron-Frobenius operators to avoid this problem. Also, we describe a way of evaluating the predictive accuracy by estimated operators on the basis of the maximum mean discrepancy, which is applicable, for example, to anomaly detection in complex systems. The empirical performance of our methods is investigated using synthetic and real-world healthcare data.

Cooperation-A ware Lane Change Control in Dense Traffic Sangjae Bae 1, Dhruv Saxena 2, Alireza Nakhaei 3, Chiho Choi 3, Kikuo Fujimura 3, and Scott Moura 1 Abstract -- This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane without cooperating with other drivers. In this case, classical robust controls may not apply since there is no "safe" area to merge to. That said, modeling complex and interactive human behaviors is nontrivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of human drivers in response to potential actions of the autonomous vehicle, which are then be systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented, showing a strong potential of the proposed framework. I NTRODUCTION An autonomous-driving vehicle is no longer a futuristic concept and extensive researches have been conducted in various aspects, spanning from localization, perceptions, and controls to implementations and validations. Particularly from the perspective of control engineers, designing a controller that secures safety, in various traffic conditions, such as driving on arterial-road/highway in free-flow/dense traffic with/without traffic lights, has been a principal research focus.