The mobile AI market was valued at USD 1.66 billion in 2017 and is expected to reach USD 17.83 billion by 2023, at a CAGR of 28.41% during the forecast period. The base year considered for this study is 2017, and the forecast period is between 2018 and 2023. The research methodology used to estimate and forecast the mobile AI market begins with capturing data on key vendor revenues through secondary research. The secondary sources include research papers and journals of Association for the Advancement of Artificial Intelligence (AAAI), European Association for Artificial Intelligence, Canadian Artificial Intelligence Association, Institute of Electrical and Electronics Engineers (IEEE), Data Science Association, International Association for AI and Law (IAAIL), and MarketsandMarkets Analysis. Moreover, product offerings of demand-side vendors were taken into consideration to determine the market segmentation.

Japanese IT service provider, NTT DATA has invested in medical imaging AI start-up company DeepTek. NTT has not disclosed the size of investment. Ajit Patil, co-founder, DeepTek, said this was a seed stage funding and NTT had picked up 10-15% stake in the company and they had formed a strategic equity partnership with NTT Data. This funding would take care of their need for around two and half years but they are looking at further global alliances and venture funding, he said. DeepTek is co-founded by Dr Amit Kharat a leading radiologist in the country, Aniruddha Pant an AI technology expert with a Berkeley University PhD, and Ajit Patil, a serial entrepreneur, whose earlier start-up exited to NTT DATA Corp.

CHINA'S "Sputnik moment" came on May 27th 2017. On that day an algorithm thrashed Ke Jie, the world's best player of Go, an ancient and demanding Chinese board game. Mr Ke's defeat by AlphaGo, an artificial intelligence (AI) system developed by DeepMind, a British firm that had been bought by Google, was as much a blow to China's psyche as the Soviet satellite was to America's self-esteem in 1957. Within months, China announced ambitious plans to dominate AI by 2030. Kai-Fu Lee thinks it will succeed. He is well placed to judge.

Image: AI algorithms match radiologists in detecting pathologies on chest X-rays and CT (Photo courtesy of Qure.ai). A clinical validation study confirms that an artificial intelligence (AI) driven algorithm can differentiate between normal and abnormal x-rays with unprecedented accuracy. Researchers at Columbia Asia Hospitals (Kuala Lumpur, Malaysia) and Qure.ai (San Mateo, CA, USA) trained a deep learning system to identify abnormal x-rays using 1.2 million x-rays and their corresponding radiology reports. Specific x-ray abnormalities included blunted costophrenic angle, calcification, cardiomegaly, cavity, consolidation, fibrosis, hilar enlargement, opacity and pleural effusion, among others. The system was tested against a three-radiologist majority analysis based on an independent, retrospectively collected, and de-identified set of 2,000 x-rays.

Companies have a never-ending list of cloud applications they rely on with crucial data becoming ever-more siloed. Yet, in order to make sense of all this information, they need IT to manage exporting, massaging and importing data from various platforms to effectively make data-based decisions. IT of course is spread thin and decisions often need to be made in real-time, meaning one-time imports and exports get old, quickly. This is where SnapLogic comes in. Their simple and fast IaaS-based enterprise integration platform empowers enterprise IT organizations and lines of business to connect apps, things and data.

Designing optimal controllers continues to be challenging as systems are becoming complex and are inherently nonlinear. The principal advantage of reinforcement learning (RL) is its ability to learn from the interaction with the environment and provide optimal control strategy. In this paper, RL is explored in the context of control of the benchmark cartpole dynamical system with no prior knowledge of the dynamics. RL algorithms such as temporal-difference, policy gradient actor-critic, and value function approximation are compared in this context with the standard LQR solution. Further, we propose a novel approach to integrate RL and swing-up controllers.

Interpreting the prediction mechanism of complex models is currently one of the most important tasks in the machine learning field, especially with layered neural networks, which have achieved high predictive performance with various practical data sets. To reveal the global structure of a trained neural network in an interpretable way, a series of clustering methods have been proposed, which decompose the units into clusters according to the similarity of their inference roles. The main problems in these studies were that (1) we have no prior knowledge about the optimal resolution for the decomposition, or the appropriate number of clusters, and (2) there was no method with which to acquire knowledge about whether the outputs of each cluster have a positive or negative correlation with the input and output dimension values. In this paper, to solve these problems, we propose a method for obtaining a hierarchical modular representation of a layered neural network. The application of a hierarchical clustering method to a trained network reveals a tree-structured relationship among hidden layer units, based on their feature vectors defined by their correlation with the input and output dimension values.

Many types of data can be represented as graphs. Edges may correspond to actual links in the data (e.g., users connected by some social network) or to levels of similarity induced from the data (e.g., users having liked a large common set of movies). The resulting graph is typically sparse in the sense that the number of edges is much lower than the total number of node pairs, which makes the data hard to exploit. A standard approach to the analysis of sparse graphs consists in embedding the graph in some vectorial space of low dimension, typically much smaller than the number of nodes [15, 19, 4]. Each node is represented by some vector in the embedding space so that close nodes in the graph (linked either directly or through many short paths in the graph) tend to be represented by close vectors in terms of the Euclidian distance.

T. Poggio observes, analyzes and predicts the evolution of deep learning from both mathematical and biological sides(which is the focus in our article) in [1]"Deep learning: mathematics and neuroscience". He mentions that, "it is telling that several of the algorithmic tricks that were touted as breakthroughs just a couple of years ago are now regarded as unnecessary ", while " some of the other ideas " such as residual learning " are more fundamental" "and likely to be more durable, though their exact form is bound to change somewhat " . In a word, he predicts that residual learning is a more durable component within the evolution of deep learning.

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success.