The father of modern speech recognition, Frederick Jelinek, once famously said: 'Anytime a linguist leaves the group, the recognition rate goes up.' Based on this logic, if the domain experts – the phonologists, in his case – were to be exchanged with pure engineers, the performance of the system would improve. Would this theory apply to a system that heavily utilises machine learning? Do the domain experts increase the performance, or is it the lack of them that is best for the system? When working in a highly specialised domain, such as the legal arena, which has clear, well-defined tasks, technology is provided to support, augment and increase productivity. It is often the case that both supervised machine learning techniques (i.e.

Both Amazon and Google are advancing their efforts in machine learning, a type of artificial intelligence that lets computers learn without being explicitly programmed. It's often associated with cloud computing, because it requires the considerable computing power the cloud makes easily available. Amazon's efforts will get a boost when Alexander Smola, a professor in the machine-learning department at Carnegie Mellon University, leaves that position July 1 to head Amazon's cloud machine-learning platform. Smola revealed his plans in this blog post. "This is a terrific task, and it was an offer that I could not turn down," Smola wrote.

Boston: In a latest discovery by the scientists, including one of Indian-origin, have developed artificial software that can turn smartphone in to an eye tracking device. Eye-tracking technology - which can determine where in a visual scene people are directing their gaze - has been widely used in psychological experiments and marketing research, but the required pricey hardware has kept it from finding consumer applications. In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. "Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student. "Since there are no applications, there's no incentive for people to buy the devices. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.

ABSTRACT Deep neural networks (DNNs) have been successfully applied to a wide variety of acoustic modeling tasks in recent years. These include the applications of DNNs either in a discriminative feature extraction or in a hybrid acoustic modeling scenario. Despite the rapid progress in this area, a number of challenges remain in training DNNs. This paper presents an effective way of training DNNs using a manifold learning based regularization framework. In this framework, the parameters of the network are optimized to preserve underlying manifold based relationships between speech feature vectors while minimizing a measure of loss between network outputs and targets. This is achieved by incorporating manifold based locality constraints in the objective criterion of DNNs. Empirical evidence is provided to demonstrate that training a network with manifold constraints preserves structural compactness in the hidden layers of the network. Manifold regularization is applied to train bottleneck DNNs for feature extraction in hidden Markov model (HMM) based speech recognition. The experiments in this work are conducted on the Aurora-2 spoken digits and the Aurora-4 read news large vocabulary continuous speech recognition tasks. The performance is measured in terms of word error rate (WER) on these tasks. It is shown that the manifold regularized DNNs result in up to 37% reduction in WER relative to standard DNNs. Index Terms-- manifold learning, deep neural networks, manifold regularization, manifold regularized deep neural networks, speech recognition 1. INTRODUCTION Recently there has been a resurgence of research in the area of deep neural networks (DNNs) for acoustic modeling in automatic speech recognition (ASR) [1-6]. Much of this research has been concentrated on techniques for regularization of the algorithms used for DNN parameter estimation [7-9]. At the same time, there has also been a great deal of research on graph based techniques that facilitate the preservation of local neighborhood relationships among feature vectors for parameter estimation in a number of application areas [10-13]. Algorithms that preserve these local relationships are often referred to as having the effect of applying manifold based constraints.

A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when they see one. We present a new approach to interactive clustering for data exploration called TINDER, based on a particularly simple feedback mechanism, in which an analyst can reject a given clustering and request a new one, which is chosen to be different from the previous clustering while fitting the data well. We formalize this interaction in a Bayesian framework as a method for prior elicitation, in which each different clustering is produced by a prior distribution that is modified to discourage previously rejected clusterings. We show that TINDER successfully produces a diverse set of clusterings, each of equivalent quality, that are much more diverse than would be obtained by randomized restarts.

The 2016 Embedded Vision Summit recently took place in the heart of Silicon Valley. The summit started with a bang when Jeff Dean announced some impressive results using reduced precision deep learning models for inference. For embedded and edge applications of deep learning models, reduced precision inference is a big deal. A brief primer is that model size is reduced by four times since normally single precision uses 32 bits per value. The power draw is significantly reduced as 16 bit arithmetic is nearly two times as fast and memory transfers can account for the majority of the power budget.

Google announced Thursday that it is opening a dedicated machine learning (ML) center in Europe, which will be based in Zurich, Switzerland. It is extending its biggest non-U.S. The search giant revealed the new artificial intelligence research push on Thursday in a blog post. Through its latest effort, Google aims to build a centralized venue for machine language researchers and software engineers to join forces and develop ideas that further enhance the available technology today. The Zurich lab will focus on the development of research and products in the areas of Machine Intelligence.

Google also informed that why it took initiative to have a research center in Europe because world's top technical universities reside in Europe. Google is taking this opportunity to build up their own team for the betterment of future AI creations. Google Germany, the research center for artificial intelligence in a nonprofit and base home to 450 scientists, academics and other researchers who work mostly on language technology, embedded intelligence, augmented reality, knowledge management and multimedia analysis, and data mining. Google recently bought a UK based startup named DeepMind with 500 million USD. Google later invested Oxford Universities AI research team in the startup project.

Tech Insider recently published an article about the first artificially intelligent lawyer. ROSS – the name of the artificial intelligence (AI) – can search court rulings from 13 years ago, along with opinions about the case at hand and its significance to past court verdicts. ROSS also sifts through all of this information and provides the user with only highly relevant data. ROSS even operates in plain language, which is a huge step in artificial intelligence, as current systems search and process data in list form, which is not how court rulings and cases are documented. ROSS was recently'hired' at a bankruptcy firm, and several other firms have requested the AI lawyer as well.

The hype around artificial intelligence (AI) has reached a fever pitch in the past few months, as tech giants such as Google, Microsoft, Facebook and Apple have unveiled new AI technology that could bring it out of the realm of science fiction and into the mainstream. Google recently unveiled its Google Home platform as a competitor to Amazon's Echo AI, and blogged how its chip technology has pushed machine learning and intelligence "seven years into the future." Amazon's dedicated staff of 1,000 Alexa developers is fending off the big G by reportedly teaching its software to recognize your emotional state as it sells you stuff. And Facebook, IBM, and other tech giants are using AI to study your social media presence and search history to sell you goods and advertise products. Meanwhile, Apple announced at this week's WWDC that it's placing a smarter Siri on not just every iOS 10 device, but also macOS and allowing third-party developers access to the Siri SDK.