Japan's oldest "love doll" manufacturer wants to strip the sex toys of their seedy image and encourage people to see them as works of art instead. "Even now there is still a stigma," said Junpei Oguchi, a representative for Tokyo-based sex doll maker Orient Industry, which recently celebrated its 40th anniversary with a three-week exhibition showing the evolution of its dolls that drew over 10,000 visitors. "But at our exhibition there were lots of men and women visitors -- more women than men, in fact," he said. "There were young and old, men and women, a really wide range of people. I think people came because they had heard the reputation of how beautiful our dolls are. We want to get rid of the stigma."

Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quantization, low-rank decomposition, pruning, lossless compression and others. Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization. This results in a "learning-compression" algorithm, which alternates a learning step of the uncompressed model, independent of the compression type, with a compression step of the model parameters, independent of the learning task. This simple, efficient algorithm is guaranteed to find the best compressed model for the task in a local sense under standard assumptions. We present separately in several companion papers the development of this general framework into specific algorithms for model compression based on quantization, pruning and other variations, including experimental results on compressing neural nets and other models.

RAPHS are generic mathematical structures consisting of sets of vertices and edges, which are used for modeling pairwise relations (edges) between a number of objects (vertices). In practice, this representation is often extended to weighted graphs, for which a set of scalar values (weights) are assigned to edges and potentially to vertices. Thus, weighted graphs offer general and flexible representations for modeling affinity relations between the objects of interest. Many practical problems can be represented using weighted graphs. For example, a broad class of combinatorial problems such as weighted matching, shortest-path and network-flow [2] are defined using weighted graphs. In signal/data-oriented problems, weighted graphs provide concise (sparse) representations for robust modeling of signals/data [3]. Such graphbased models are also useful for analyzing and visualizing the relations between their samples/features. Moreover, weighted graphs naturally emerge in networked data applications, such as learning, signal processing and analysis on computer, social, sensor, energy, transportation and biological networks [4], where the signals/data are inherently related to a graph associated with the underlying network.

High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this dataset is used to train deep-learning-based models in order to accurately classify satellite images that contains or not zebra crossings. A novel dataset with more than 240,000 images from 3 continents, 9 countries and more than 20 cities was used in the experiments. Experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.

Early in 2015, artificial-intelligence researchers at Google created an obscure piece of software called TensorFlow. Two years later the tool, which is used in building machine- learning software, underpins many future ambitions of Google and its parent company, Alphabet. TensorFlow makes it much easier for the company's engineers to translate new approaches to artificial intelligence into practical code, improving services such as search and the accuracy of speech recognition. But just months after TensorFlow was released to Google's army of coders, the company also began offering it to the world for free. That decision could be seen as altruistic or possibly plain dumb, but nearly two years on, the benefits to Google of its great AI giveaway are increasingly evident.

Simon Sinek is by far one of the greatest thought leaders of our time. Almost religiously and on numerous occasions, I've implemented his "start with why" model and received positive results in doing so. When introduced, it challenged new and existing businesses to think differently, and pull on the emotions of consumers rather than their purse strings. It offered us the challenge of inventing a new way of thinking; outside of the box and with creative tact. Since the release of his now viral TED talk, we continue to see praise for the Sinek model, and it has been a for certain positive addition to education curriculums far and wide.

Radius is a fast-growing, venture-backed startup in the heart of San Francisco. Radius applies advanced data science to deliver the freshest, most accurate, and most comprehensive view on 20M US companies―from small businesses to the largest enterprises. We build cutting-edge machine learning solutions that help our customers discover markets, acquire customers, and measure performance through an app that's intuitive, secure, and enterprise-ready. As a Senior Software Engineer on the Predictive team, you will work on the Radius core machine learning engine that is responsible for modeling business behavior for a wide and diverse set of customers and goal definitions. Our Predictive engine is built to handle a diverse set of modeling problems in terms of training data size, data characteristics and complex labeling definitions which make our work both challenging and rewarding.

New technologies such as AI are just one part of digital transformation for retail and CPG companies, as 53% reported that say they are presently undergoing full-cycle digital transformation. Another 38% are transforming partially or in pockets and 9% are not currently transforming but will do so in the near future. One of the key initiatives that will move the needle on digital transformation is employee lifelong learning programs, cited by 58% of survey respondents.. Lifelong learning programs can accomplish a number of future-forward goals: The Infosys study, titled: Human Amplification in the Enterprise, surveyed more than 1,000 business leaders from U.S. organizations across various sectors, with annual revenue of $500 million or more. The Retail and CPG section of the report solely highlights the responses of 100 executives surveyed within those sectors.

At 26, tech guru Dave Asprey was a multi-millionaire, yet he struggled with mood swings and poor memory. Applying his computing skills to his brain, he created Bulletproof coffee (with butter and oil), which helped him focus. Asprey, now 45, runs a neuroscience institute and has devised a two-week plan called Head Strong to improve mental agility. Here, he reveals how small lifestyle, diet and exercise tweaks can help us all develop a sharper brain. Too much stress, lack of sleep and exercise, poor diet, and other forms of brain kryptonite affect our thinking power, leaving us tired, irritable, unfocused, and craving sugar.

Sony has followed the example of Google, Amazon and Facebook by open sourcing AI software in search for deep learning developers. Sony has followed in the path of Google, Facebook and Amazon, as it opens up access to its deep-learning software tools in an aim to attract artificial intelligence developers. The company announced that it has made its Neural Network Libraries available in open source, giving software engineers and designers access the core libraries for free to develop the necessary deep learning programs. Sony says the neural network design is a core development of any deep learning program and the shift to open source acts as a method to enable the development community to build on the core libraries' programs. The software in Sony's core libraries is written in C 11 and the programming language runs in different environments and operates on Linux, Windows and other platforms.