A team of researchers from the University of Alberta, Canada and tech giant IBM has developed artificial intelligence and machine learning algorithms, which can diagnose schizophrenia by studying the blood flow of the brain. Mina Gheiratmand, the lead author of the study published in the journal Nature, said the algorithm was able to provide diagnosis with 74 percent accuracy. Using the algorithm, doctors might not just be able to be diagnose the disease but even determine its severity using a simple MRI scan and the AI-based neural network, which studies the blood flow. "This unique, innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease," Serdar Dursun, a professor of psychiatry & neuroscience with the University of Alberta, said in a statement to Engadget. The researchers studied 95 subjects, including 46 schizophrenia patients.

Greece and Rome were, like many areas of the ancient world, multilingual, and so needed both translators and interpreters. My own thesis into English to Welsh translation – due to be published later this year – shows that a translator working to correct the output from machine translation makes for higher productivity and quicker translation. Well over 350,000 people speak Welsh every day, while local authorities across the UK are also translating into numerous other languages. Today, machine translation can create rough drafts of relatively simple language, and research shows that correcting this draft is usually more efficient than translation from scratch by a human.

The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese internet companies led by Alibaba Group Holding, Baidu and Tencent Holdings have been investing heavily in AI applications.

The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese internet companies led by Alibaba Group Holding, Baidu and Tencent Holdings have been investing heavily in AI applications.

The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese technology companies including Alibaba Group Holdings, Baidu Inc and Tencent Holdings have been investing heavily into AI applications.

Today it was made public that Google has acquired Halli Labs, a very young (its first public appearance was on May 22 of this year) startup based out of Bengaluru, India, that was focused on building deep learning and machine learning systems to address what it describes as "old problems." The company says it will be joining Google's Next Billion Users team "to help get more technology and information into more people's hands around the world." Halli announced the news itself earlier today in a short post on Medium, and Caesar Sengupta, a product management VP at Google, also confirmed the acquisition with a Tweet. Welcome @Pankaj and the team at @halli_labs to Google. Google has now also confirmed the acquisition with a short statement it provided to TechCrunch.

Mark Armstrong, vice president and managing director of international operations for APJ and EMEA, for Progress, discusses the company's mission to democratise data science and make technologies such as machine learning, predictive maintenance and Artificial Intelligence more accessible so businesses in Australia and New Zealand can succeed. Progress believes that democratisation of data science is the key to including all organisations, including SMBs, in the machine learning equation. Data RPM and its meta-learning approach to machine learning will enable us to democratise data science by making it scalable, which will allow businesses to build models without the need for an army of data scientists. Making machine learning and artificial intelligence (AI) accessible is exactly why we acquired DataRPM.

Global Deep Learning Market size is expected to reach USD 10.2 billion by 2025, according to a new report by Grand View Research, Inc. Considerable improvements in machine learning algorithms and advancements in deep learning chipsets are driving the industry growth. Rapid improvements in fast information storage capacity, high computing power, and parallelization have contributed to the swift uptake of the deep learning technology in end-use industries such as automotive and healthcare. Further, the need for understanding and analyzing visual contents among enterprises in order to gain meaningful insights, is expected to provide traction to the industry over the forecast period. The increasing prominence of Graphics Processing Unit (GPU)-accelerated applications is leading to increased adoption of the technology in scientific disciplines such as deep learning and data science. Organizations are utilizing deep learning neural networks to extract valuable insights from enormous amounts of data for providing innovative products and improving customer experience; thereby, increasing revenue opportunities.

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.