Among the various companies, non-profits and researchers using tech company Google's TensorFlow platform, one application that has caught the attention of developers at the internet giant is PlantMD. Created by high school students Shaza Mehdi and Nile Ravenell, the app can detect diseases in plants. The duo, who showcased the app at Google's I/O annual developer conference this year, built it based on the Internet company's open-source machine learning library for data programming--TensorFlow. "PlantMD's machine learning model was inspired by a dataset from PlantVillage, a research and development unit at Penn State University. PlantVillage created an app called Nuru, Swahili for'light', to assist farmers to grow better cassava, a crop in Africa that provides food for over half a billion people daily," Fred Alcober, a member of Google's TensorFlow team, wrote in a blog post. Cassava plants, wrote Alcober, though very tolerant of harsh weather conditions, is susceptible to pests and diseases.

On her first day at NASA in 1999, Dr. Ayanna Howard walked into the Telerobotics Research and Applications Group at the Jet Propulsion Laboratory, excited to begin programming Mars rovers with her newly assigned staff. But a male staff member barely registered her presence, saying "The secretaries work down the hall." So began the rise of one of the few female African American roboticists in America. To be fair, Dr. Howard's staffer probably didn't realize that the young woman entering the lab was his boss because he had never met a female robotics Ph.D. Even today, although 74 percent of girls are interested in technology-related fields such as computer science, as adults they represent only 25 percent of all computing occupations.

If you think drones aka unmanned aerial vehicles (UAVs) are at the peak of their evolution, it's time to think again because China is using the technology as birds to spy on its residents. We all know that the basic job of a drone involves monitoring ground activity and conducting critical reconnaissance missions. Most countries in the world are employing the technology for this purpose, but in order to ensure the success of such missions, it is crucial that the UAV remains unseen. This is why engineers across the globe are working to improve the element of stealth. However, just recently, a report from South China Morning Post (SCMP) revealed that China's government and military agencies have taken a unique approach to the case.

Data management and data analytics are two critical fundamental resources that should be used by Thai enterprises and tech startups to develop innovative services backed by artificial intelligence (AI) technology, instead of working to develop intelligent products or services to compete with global players. Chai Wutiwiwatchai, research unit director of the National Electronics and Computer Technology Center (Nectec), said local enterprises can benefit from the many data sets held by state agencies through 20 ministries and the private sector. "Intelligent products and services driven by AI may not be easy to enter for local enterprises and startups, as there are too many global tech players and AI tech-embedded tools available for free in the market," he said. But the government must urgently digitise the existing data sets of all agencies, 70% of which are stored on paper and in portable document format (PDF) files. Speaking on the sidelines of the "AI Shapes the Future" forum last week, Mr Chai said innovative products and services embedded with AI tech have been increasingly accessible in the global market for four years, especially through popular use cases of image recognition, biometrics, cybersecurity and smart speakers.

For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing massive amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach. Companies like Google, Facebook and Microsoft have poured money into deep learning. And the technology's perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars. But now some scientists are asking whether deep learning is really so deep after all.

Learning of dynamical systems, or state-space models, is central to many machine learning problems, such as reinforcement learning, sequence modeling, and autonomous systems. Furthermore, state-space models are at the core of recent model developments within the machine learning area, such as Gaussian process state-space models (Frigola et al. 2014a; Mattos et al. 2016; etc.), infinite factorial dynamical models (Gael et al., 2009; Valera et al., 2015), and stochastic recurrent neural networks (Fraccaro et al., 2016, for example). A strategy to learn state-space models, independently suggested by Digalakis et al. (1993) and Ghahramani and Hinton (1996), is the use of the Expectation Maximization (EM, Dempster et al. 1977) method. Even though originally proposed only for maximum likelihood estimation of linear models with Gaussian noise, the strategy can be generalized to the more challenging nonlinear and non-Gaussian cases, as well as the empirical Bayes setting. Many contributions have been made during the last decade, and this paper takes another step along the path towards a more computationally efficient method with a solid theoretical ground for learning of nonlinear dynamical systems.

Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for sampling from target distributions which are not easy to evaluate point-wise. However, pmMH requires good proposal distributions to sample efficiently from the target, which can be problematic to construct in practice. This is especially a problem for high-dimensional targets when the standard random-walk proposal is inefficient. We extend pmMH to allow for constructing the proposal based on information from multiple past iterations. As a consequence, quasi-Newton (qN) methods can be employed to form proposals which utilize gradient information to guide the Markov chain to areas of high probability and to construct approximations of the local curvature to scale step sizes. The proposed method is demonstrated on several problems which indicate that qN proposals can perform better than other common Hessian-based proposals.

In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show that an agent trained with various accuracy requirements sampled randomly learns more efficiently than when asked to be very accurate at all times. Then we show that adaptive selection of accuracy requirements, based on a local measure of competence progress, automatically generates a curriculum where difficulty progressively increases, resulting in a better learning efficiency than sampling randomly.

While the worst-case complexity of exp-concave stochastic optimization is fairly understood ([22, 28, 18, 15]), a promising avenue is to investigate these complexities under distributional assumptions. One common possibility is the common fast eigendecay assumption ([12, 5, 25, 1]). Namely, in many machine learning problems, the eigenvalues associated with the empirical covariance matrix exhibit a fast decay, where the tail of the eigenvalues are significantly smaller than the desired precision. Naturally, this phenomenon suggests a sketch-and-solve approach, where a sufficiently accurate solution is obtained by projecting the data onto a low-dimensional space and solving the smaller problem. Indeed, many algorithmic ideas in this spirit have been suggested in the recent years ([3, 25]). A more sophisticated approach, which we name sketch-to-precondition, ([2, 8]) is to enhance the performance of first-order optimization methods via preconditioning, where the preconditioner is based on a coarse low-rank approximation to the data matrix. The main message of our paper is as follows: 1 The sample complexity of exp-concave stochastic optimization scales optimally with the effective dimension, rendering the sketch-and-solve approach useless in this context. On the other hand, the sketch-to-precondition approach is effective and can be accelerated via model selection.

The Space Invaders arcade video game, celebrating its 40th anniversary, is a classic piece of software credited as one of the earliest digital shooting games. As a game designer and teacher of games, I know how meaning is carried from designer to the mechanics of play. As a game studies researcher, I also know how games reveal myth, meaning and culture. An analysis of Pac-Man, for instance, shows how that game embodies many values of its day – including consumerism, drug use and gender politics. The message in Space Invaders is as basic as the graphics: when faced with conflict, players have no option except to blast it away.