Deep learning is in the early stages of unlocking tremendous economic value outside its impact in large technology companies. While the algorithms have revolutionized consumer experiences in domains as varied as speech interfaces, image search, language translation and game AI, enterprises are still in the early stages with efforts to apply these algorithms to other areas - such as improving automotive speech interfaces, agricultural robotics, finding anomalies in IoT data, and more. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. The team at Nervana Systems (recently acquired by Intel) aims to change this, and have built a deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. At the 2016 Deep Learning Summit in London, Arjun Bansal, Co-founder and VP of Algorithms at Nervana, presented Catalyzing Deep Learning's Impact in the Enterprise.

Mumbai: Data scientists have begun betting on the use of machine learning, deep learning, Big Data and artificial intelligence (AI) technologies to help in the early detection of diseases and advance healthcare. Leading the way on the road to healthcare analytics are the world's five largest medical device companies--Johnson & Johnson, GE Healthcare, Siemens, Medtronic and Philips Healthcare. In the case of Dutch electronics, healthcare and lighting company, Philips NV, much of such innovative work is being done at the Philips Innovation Campus (PIC) in Bengaluru. PIC, according to the centre's chief executive Srinivas Prasad, initially started as a software centre in 1996, and has now developed into a product engineering site with a focus on delivering innovations for local and global markets. "Engineers and domain experts work on end-to-end products and solutions across the health continuum, from healthy living, to prevention, diagnosis and treatment. PIC is harnessing the power of technologies such as mobile, digital, cloud and Big Data analytics to improve patient outcomes through care coordination and patient empowerment. PIC takes pride in developing solutions to make healthcare affordable and accessible in India and other growth geographies like Africa and Indonesia," said Prasad.

Note: For training purposes, I highly recommend building a training/validation set using a steering wheel controller, and you'll want a labeled set of about 40K samples (though I have heard you can get by with much fewer, even unaugmented - my sample set actually used augmentation of about 8k real samples to boost it up to around 40k). You'll also want to use GPU and/or a generator or some other batch processing for training (otherwise, you'll run out of memory post-haste).

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. According to Mr. Sachin Garg - Associate Director at MarketsandMarkets who tracks the global semiconductor market, the global artificial intelligence chipset market is expected to be worth USD 16.06 Billion by 2022, growing at a CAGR of 62.9% between 2016 and 2022.

Law360, New York (February 15, 2017, 12:03 PM EST) -- Bruce J. Heiman Elana R. Reman It's hard to escape reading about the "AI revolution." Artificial intelligence or "AI" computer systems now can simulate surprising human-like processes including learning, decision-making, self-correction, speech and image recognition, reasoning and other tasks we typically think of as requiring "intelligence." The AI sprint has been propelled by recent growth in computing power, increasing availability and openness of large data sets, and "deep learning" algorithms that mimic neural networks.

Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.

Crowdsensing of air quality is a useful way to improve public awareness and supplement local air quality monitoring data. However, current air quality monitoring approaches are either too sophisticated, costly or bulky to be used effectively by the mass. In this paper, we describe AirTick, a mobile app that can turn any camera enabled smart mobile device into an air quality sensor, thereby enabling crowdsensing of air quality. AirTick leverages image analytics and deep learning techniques to produce accurate estimates of air quality following the Pollutant Standards Index (PSI). We report the results of an initial experimental and empirical evaluations of AirTick. The AirTick tool has been shown to achieve, on average, 87% accuracy in day time operation and 75% accuracy in night time operation. Feedbacks from 100 test users indicate that they perceive AirTick to be highly useful and easy to use. Our results provide a strong positive case for the benefits of applying artificial intelligence techniques for convenient and scalable crowdsensing of air quality.

Developing a Brain-Computer Interface~(BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patterns vary across different patients. Thus, finding a group of manually extracted features for the prediction task is not practical. Moreover, when using implanted electrodes for brain recording massive amounts of data are produced. This big data calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based BCI system for the analysis of this big EEG data is presented. First, a dimensionality-reduction technique is developed to increase classification accuracy as well as to decrease the communication bandwidth and computation time. Second, following a deep-learning approach, a stacked autoencoder is trained in two steps for unsupervised feature extraction and classification. Third, a cloud-computing solution is proposed for real-time analysis of big EEG data. The results on a benchmark clinical dataset illustrate the superiority of the proposed patient-specific BCI as an alternative method and its expected usefulness in real-life support of epilepsy patients.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. With all the quickly evolving nomenclature in the industry today, it's important to be able to differentiate between AI, machine learning and deep learning.

"Recently acquired by Hewlett Packard Enterprise, SGI is a trusted leader in technical computing with a focus on helping customers solve their most demanding business and technology challenges." Dr. Eng Lim Goh joined SGI in 1989, becoming a chief engineer in 1998 and then chief technology officer in 2000. He oversees technical computing programs with the goal to develop the next generation computer architecture for the new many-core era. His current research interest is in the progression from data intensive computing to analytics, machine learning, artificial specific to general intelligence and autonomous systems. Since joining SGI, he has continued his studies in human perception for user interfaces and virtual and augmented reality.