CHENNAI: With a PhD in mathematics, Bharat Ramakrishna was preparing content for school children when suddenly he found a well-paying job in the machine learning & data sciences space. No longer is a mathematics background purely academic. Maths majors are now in demand for a job in artificial intelligence and data sciences. "After graduating from the University of Utah, I was into preparing question banks for students. Now, concepts such as matrices, linear algebra and calculus are being used in artificial intelligence and it is easier for a mathematics graduate to learn coding than vice versa," said Ramakrishna, data scientist at Skillenza.

You already know what all of your friends are eating, so you might as well know how to make it, too. You already know what all of your friends are eating, so you might as well know how to make it, too. When someone posts a photo of food on social media, do you get cranky? Is it because you just don't care what other people are eating? Or is it because they're enjoying an herb-and-garlic crusted halibut at a seaside restaurant while you sit at your computer with a slice of two-day-old pizza?

The program is efficient enough to run on phones and is so fast that it can display retouched images in real-time, making it possible for users to see the final version of the image while still framing the shot. Researchers from Google and MIT's Computer Science and Artificial Intelligence Laboratory unveiled it this week at Siggraph, the premier digital graphics conference. The work builds on an earlier project from the MIT researchers that involved a similar process, but it occurred in the cloud. A phone would send a low-resolution version of an image to a web server, which would then send back a'transform recipe' that could be used to retouch the high-resolution version of the image on the phone, reducing bandwidth consumption. 'Google heard about the work I'd done on the transform recipe,' says Michaël Gharbi, an MIT graduate student in electrical engineering and computer science and first author on both the original and new papers.

Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. Besides, Dropout can be interpreted as an approximate model aggregation technique, where an exponential number of smaller networks are averaged in order to get a more powerful ensemble. In this paper, we show that using a fixed dropout probability during training is a suboptimal choice. We thus propose a time scheduling for the probability of retaining neurons in the network. This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem. This idea of "starting easy" and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models. Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations within the network architecture. Experiments on seven image classification datasets and different network architectures show that our method, named Curriculum Dropout, frequently yields to better generalization and, at worst, performs just as well as the standard Dropout method.

Evidence from AI Experts, conducted by researcher Katja Grace from the Future of Humanity Institute at Oxford University, 350 artificial intelligence experts predict that AI will outperform human beings within the next ten years in activities such as translating languages (in the year 2024), essay writing at secondary school level (in 2026) and truck driving (in 2027). The central theme, of course, in both instances is the risk that jobs will be replaced by technology," senior economist at the ILO, Steven Tobin, tells Equal Times. The problem is that the advantages or profits derived from the increased productivity resulting from automation do not necessarily benefit all social groups in equal measure. "The aim is to study what can be done to ensure that all the people residing in the city of Barcelona have access to the minimum level of income needed to live a decent life.

For the past three summers, around two dozen would-be computer scientists have come to Stanford University to learn about artificial intelligence from some of the field's brightest. The attendees, culled from hundreds of applicants, take day trips to nearby tech companies, interact with social robots and hexacopters, and learn about computational linguistics (what machines do when words have multiple meanings, say) and the importance of time management (very). But if your mental picture of AI is a bunch of guys creating wilier enemies for their favorite videogames, well, this isn't that. All the students here at the Stanford Artificial Intelligence Laboratory's Outreach Summer (SAILORS) program are girls who have just completed ninth grade, and their studies focus on finding ways to improve lives, not enhance their game play: How do we use AI to keep jumbo jets from careening into one another? To ensure that doctors wash their hands before hitting the OR? "Our goal was to rethink AI education in a way that encourages diversity and students from all walks of life," says Fei-Fei Li, director of Stanford's AI lab and a founder of the SAILORS program.

If you're deep into the world of artificial intelligence, you certainly know Kaggle, the Google Cloud-owned platform where AI coders compete on projects, often with financial rewards for the winning solutions. The platform recently passed 1 million members, a testament to what a hotbed the field of AI is. He's entered 39 competitions over the past year, recently placing second in a contest to develop an algorithm that can detect duplicate ads on the same platform. With his skill, enthusiasm, and cooperative attitude within the community, Mikel is very much the template of a rising star in the Kaggle and greater AI communities. Except for one thing: Mikel is just 16 years old.

Using the previously developed concepts of semantic spacetime, I explore the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory. By assigning interpretations to phenomena, from observers to observed, we may approach a simple description of knowledge-based functional systems, with direct practical utility. The focus is especially on the interpretation of concepts, associative knowledge, and context awareness. The inference seems to be that most if not all of these concepts emerge from purely semantic spacetime properties, which opens the possibility for a more generalized understanding of what constitutes a learning, or even `intelligent' system. Some key principles emerge for effective knowledge representation: 1) separation of spacetime scales, 2) the recurrence of four irreducible types of association, by which intent propagates: aggregation, causation, cooperation, and similarity, 3) the need for discrimination of identities (discrete), which is assisted by distinguishing timeline simultaneity from sequential events, and 4) the ability to learn (memory). It is at least plausible that emergent knowledge abstraction capabilities have their origin in basic spacetime structures. These notes present a unified view of mostly well-known results; they allow us to see information models, knowledge representations, machine learning, and semantic networking (transport and information base) in a common framework. The notion of `smart spaces' thus encompasses artificial systems as well as living systems, across many different scales, e.g. smart cities and organizations.

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10 question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network -- and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first 'visual chatbot'! Our dataset, code, trained models and visual chatbot are available on https://visualdialog.org

Machine learning is a technology that has grown to prominence over the past ten years (as at this time of writing) and is fast paving the way for the "Age of Automation". This post provides a holistic view of the vital constituents that characterizes machine learning. At the end of this piece, the reader can be able to grasp the major landmarks and foundation stones of the field. Also, this overview provides a structured framework to wade deeper into murkier waters without getting overly overwhelmed. Machine learning is a set of computational tools and mathematical techniques for predicting the future state or classifying the outcomes of a particular variable (or unit of measurement) based on its interactions with other variables in a data set.