New York, Oct 19 (IANS) In a major breakthrough in the field of speech recognition, Microsoft researchers have created a technology that accurately recognises the words in a conversation like humans do. The team from Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard "Switchboard" speech recognition task. This is an historic achievement," said Xuedong Huang, the company's chief speech scientist in a Microsoft blog post. The milestone means that, for the first time, a computer can recognise the words in a conversation as well as a person would. In doing so, the team has beat a goal they set less than a year ago - and greatly exceeded everyone else's expectations as well. "Even five years ago, I wouldn't have thought we could have achieved this.

Microsoft has made a major breakthrough in speech recognition, creating a technology that understands a conversation as well as a person does. In a paper published Monday, a team of researchers and engineers in Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard Switchboard speech recognition task.

Microsoft's Artificial Intelligence and Research branch have created a speech recognition system that can pick up the words in a conversation as well as most humans can. The new system was announced by Microsoft in a blog post and the team's findings were published on Monday. The record-breaking tech has hit what the team is calling "human parity" -- that is, it's not perfect, but it makes the same or fewer mistakes in transcription than human professionals. The word error rate of the system was down to 5.9 percent, from the 6.3 percent error rate reported just last month. That's more impressive than it sounds, as humans will commonly mishear words like "have" for "is", or "a" for "the" in transcribing.

British AI talent is about to be given a boost after Founders Factory, a company specialising in growing startups, announced today that it will receive a "multimillion pound" investment from a top Chinese private equity firm. CSC Group is one of China's largest private equity firms, specialises in tech investment, and has previously pumped 400m into AngelList, an US startup accelerator. Now, the firm is turning its attention to the UK. CSC has agreed to a five-year deal with Founders Factory to invest in and scale five AI startups and co-create two new companies every year. "The partnership will build a bridge between AI talent in Europe and China; giving Founders Factory startups access to the Chinese market and Chinese talent the opportunity to enter the European tech ecosystem," Founders Factory said.

DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. This Smart News Release features multimedia. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.

Every year there is a new hot topic in tech. The difference between now and the past is that everything is becoming interconnected at a faster rate. We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here.

Analysis Research in AI is expanding quickly, and the UK and US governments have begun to notice. Official reports about the new technology and future strategies were dropped by both governments this month. Blighty's Science and Technology Committee released Robotics and Artificial Intelligence, while the White House delivered Preparing for the Future of Artificial Intelligence and National Artificial Intelligence Research and Development Strategic Plan. The titles of the British and American reports provide a clue as to how both governments are responding. There is no "preparing" or "strategic plan" in the UK's reports.

Topic identification is a top-of-the-list need for organizations working with large volumes of online, social, and enterprise text. Along with entity resolution, relation extraction, summarization, and sentiment analysis, topic modeling is a key natural language processing (NLP) function. Premise number 2: Applied NLP -- text analytics -- remains as much art as science, requiring a combination of domain and technical expertise. How better to explore topic modeling and NLP advances than via an interview with a leading practitioner? This article features an interview with Lev Konstantinovskiy, a data scientist who is community manager for gensim, which offers open-source topic modeling for Python programmers.

The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first consider the similarity of elements in the data as the connectivity of nodes in an undirected graph, then present the concept of a connection center and regard it as the cluster center of the data. Based on this definition, the determination of cluster centers and the assignment of class are very simple, natural and effective. One more crucial finding is that the cluster centers of different scales can be obtained easily by the different powers of a similarity matrix and the change of power from small to large leads to the dynamic evolution of cluster centers from local (microscopic) to global (microscopic). Further, in this process of evolution, the number of categories changes discontinuously, which means that the presented method can automatically skip the unreasonable number of clusters, suggest appropriate observation scales and provide corresponding cluster results.

Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent. Though ICA has proven useful and has been employed in many applications, complete statistical independence can be too restrictive an assumption in practice. Additionally, important prior information about the data, such as sparsity, is usually available. Sparsity is a natural property of the data, a form of diversity, which, if incorporated into the ICA model, can relax the independence assumption, resulting in an improvement in the overall separation performance. In this work, we propose a new variant of ICA by entropy bound minimization (ICA-EBM)-a flexible, yet parameter-free algorithm-through the direct exploitation of sparsity. Using this new SparseICA-EBM algorithm, we study the synergy of independence and sparsity through simulations on synthetic as well as functional magnetic resonance imaging (fMRI)-like data.