SYDNEY (BLOOMBERG) Does the idea to use artificial intelligence (AI), drones and electric force fields to prevent sharks from eating human bathers have teeth? Several tech start-ups in Australia say yes. Officials in the United States are watching the advancements keenly, aware that climate change is altering migration patterns and threatening to push great whites ever closer to American shores. This summer, sharks have attacked teenagers on beaches from California to New York. Sharks typically frequent lower latitudes, but warming oceans are pushing their prey north, said Florida Atlantic University Professor Stephen Kajiura.

In the spring of 2017, Urmila Mahadev found herself in what most graduate students would consider a pretty sweet position. She had just solved a major problem in quantum computation, the study of computers that derive their power from the strange laws of quantum physics. Combined with her earlier papers, Mahadev's new result, on what is called blind computation, made it "clear she was a rising star," said Scott Aaronson, a computer scientist at the University of Texas, Austin. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Mahadev, who was 28 at the time, was already in her seventh year of graduate school at the University of California, Berkeley -- long past the stage when most students become impatient to graduate.

Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. It provides you with that "ah ha!" moment where it finally clicks, and you understand what's really going on under the hood. Some algorithms are just more complicated than others, so start with something simple, such as the single layer Perceptron. I'll walk you through the following 6-step process to write algorithms from scratch, using the Perceptron as a case-study: This goes back to what I originally stated. If you don't understand the basics, don't tackle an algorithm from scratch. For the Perceptron, let's go ahead and answer these questions: After you have a basic understanding of the model, it's time to start doing your research. Some people learn better with textbooks, some people learn better with video.

It's becoming ubiquitous and invisible,

"But there are also unknown unknowns – the ones we don't know we don't know." An artificial intelligence strategy is the corporate equivalent of your spleen: everyone has one, but not everyone understands quite what it will accomplish. There are bold plans afoot everywhere in the world of AI to be sure, but its reality is still distant from the vision of artificial general intelligence (AGI) – i.e., machines displaying intelligence equivalent to the natural intelligence of humans – of popular imagination. Investors in particular need a sober and realistic view of what's achievable in the field of machine learning-driven AI today, versus what promises nothing more than a waste of time and money. There are many business problems that map to the attributes above. The key to success in AI is to focus on these classes of practical problems and solutions.

Transfer learning has been proven effective when within-target labeled data is scarce. A lot of works have developed successful algorithms and empirically observed positive transfer effect that improves target generalization error using source knowledge. However, theoretical analysis of transfer learning is more challenging due to the nature of the problem and thus is less studied. In this report, we do a survey of theoretical works in transfer learning and summarize key theoretical guarantees that prove the effectiveness of transfer learning. The theoretical bounds are derived using model complexity and learning algorithm stability. As we should see, these works exhibit a trade-off between tight bounds and restrictive assumptions. Moreover, we also prove a new generalization bound for the multi-source transfer learning problem using the VC-theory, which is more informative than the one proved in previous work.

These sources contain images that viewers would have to interpret themselves. Most images do not have a description, but the human can largely understand them without their detailed captions. However, machine needs to interpret some form of image captions if humans need automatic image captions from it. Image captioning is important for many reasons. For example, they can be used for automatic image indexing. Image indexing is important for Content-Based Image Retrieval (CBIR) and therefore, it can be applied to many areas, including biomedicine, commerce, the military, education, digital libraries, and web searching. Social media platforms such as Facebook and Twitter can directly generate descriptions from images. The descriptions can include where we are (e.g., beach, cafe), what we wear and importantly what we are doing there.

Regularized online learning is widely used in machine learning applications. In this paper we analyze a class of regularized online algorithms without linearizing the loss function or the regularizer, which we call \emph{fully implicit online learning} (FIOL). We show that the FIOL algorithm admits a better regret than the linearization approximate algorithm if each iteration in FIOL can be solved exactly. Then we show that by exploring the structure of a large class of loss functions and regularizers, the computational complexity of FIOL in each iteration is comparable to its linearized part, even if no closed-form solution exists. Experiments validate the proposed approaches.

If you're at all interested in Artificial Intelligence (AI), it's unlikely to be news to you that there is an AI skills shortage. Businesses are increasingly looking to invest in AI and are on the hunt for suitably skilled workers since traditional software teams without the experience of AI often encounter a number of challenges, as I described in a recent article over on DZone. Anyone thinking about joining the AI workforce will want to learn the subject, initially by doing some reading and research, but without committing to paying too much. As the need to recruit skilled AI staff has grown, so a number of businesses and individuals have set out to provide training courses, books, and e-learning, and the price and quality of these vary, as you would expect. As with all education, if you commit a chunk of your time, you don't want to find it wasted on out-of-date or incorrect information or to find that you are missing out on key skills after spending time and money on a course that promises to equip you appropriately.