In the last years there has been quite some fuzz in the news about two seemingly unrelated topics: the decrease of bees and other pollinators and, simultaneously, the increase of artificial intelligence and data science. Reading up a bit on pollination and how research on pollination works I realized that although a lot is known, there is, as in all sciences, way more unknown. This triggered my curiosity, wouldn't it be possible to use AI for the study of pollinators? In this post I explain a simple way to do so. I wrote some code, which can be found om my github. But as a teaser: I wanted to quantify the number of visitations to the flowers in this movie.

Artificial intelligence is breaking out of science fiction and sprinting into reality. A robot can now identify a human from a photo of their face, trounce a Poker player and in theory, pilot a plane. Business applications for AI are growing, from Apple's Siri personal assistant to Amazon's delivery drones, and business schools want to ensure that their graduates have the skills to meet the future needs of industry. A wide range of master's degrees and electives within established courses that focus on AI are on offer. "Digital and AI are rapidly changing the way we live and work in significant ways," says Francisco Veloso, dean of London's Imperial College Business School. As businesses transform to keep up with the pace of technological change, he adds, "schools will need to do more to provide students with the tools they need to undertake careers or start businesses in areas such as blockchain, [and] fintech".

The first interactor--a muscular man in his fifties with a shaved head and a black V-neck sweater--walks into a conference room and sits in a low-slung blue armchair before a phalanx of video cameras and studio lights. The rest of the room is totally dark. He gazes at a black, hockey- puck-shaped object--an Amazon Echo--on a small table in front of him. "Alexa," he says, "let's chat." "Good morning, my friend," a female voice replies with synthetic agreeability, a purplish ring of light pulsing atop the Echo.

With the launch of NytDEMO, the Times looks to help ease one of marketers' top challenges in the digital era: targeting the right audience with the right message at the right time. Consumers increasingly crave personalization from brands, but marketers struggle with meeting these demands. A lack of personalization cost businesses as much $756 billion in 2016 and 41% of consumers reported switching companies due to lack of personalization, according to Accenture. An additional 31% of respondents said they find great value in services that automatically learn about their needs and tailor recommendations accordingly. AI-driven solutions provide more reliable insights that marketers can use to achieve higher levels of personalization, create higher-quality content and deploy more transparent campaigns.

I'm playing with CapsNet using Tensorflow to classify images taken from a traffic cam. I decided to start with just two classes (Fiat-Panda and Fiat-500) and I created a dataset with cropped images of car's rear. I reached a 0.56 accuracy on the testset which is pretty low for a 2-class classification problem. Here you can see the recap. The decoder on the final caps layer outputs mixed images of the two classes and the loss function on the test set doesn't converge.

Artificial intelligence (A.I.) is making the "rise of machines" -- once the stuff of science fiction -- a reality. As 60 Minutes reported on October 9, "It might not be long before machines begin thinking for themselves -- creatively, independently, and sometimes with better judgment than a human." On February 28, 2018, you're invited to "Meet the Future" at DePauw University as the Ubben Lecture Series presents the world's first artificial intelligence-fueled android, Sophia, and her creator, David Hanson. In a 7:30 p.m. program in Kresge Auditorium, Dr. Hanson -- founder, CEO and chief designer of Hong Kong-based Hanson Robotics -- will be joined by his one-of-a-kind robot character. At the free event, which is open to all, the two will deliver a speech, take questions from the audience, and offer insights into the world of tomorrow that we're already entering today.

The changes in user preferences can originate from substantial reasons, like personality shift, or transient and circumstantial ones, like seasonal changes in item popularities. Disregarding these temporal drifts in modelling user preferences can result in unhelpful recommendations. Moreover, different temporal patterns can be associated with various preference domains, and preference components and their combinations. These components comprise preferences over features, preferences over feature values, conditional dependencies between features, socially-influenced preferences, and bias. For example, in the movies domain, the user can change his rating behaviour (bias shift), her preference for genre over language (feature preference shift), or start favouring drama over comedy (feature value preference shift). In this paper, we first propose a novel latent factor model to capture the domain-dependent component-specific temporal patterns in preferences. The component-based approach followed in modelling the aspects of preferences and their temporal effects enables us to arbitrarily switch components on and off. We evaluate the proposed method on three popular recommendation datasets and show that it significantly outperforms the most accurate state-of-the-art static models. The experiments also demonstrate the greater robustness and stability of the proposed dynamic model in comparison with the most successful models to date. We also analyse the temporal behaviour of different preference components and their combinations and show that the dynamic behaviour of preference components is highly dependent on the preference dataset and domain. Therefore, the results also highlight the importance of modelling temporal effects but also underline the advantages of a component-based architecture that is better suited to capture domain-specific balances in the contributions of the aspects.

So is the Apple HomePod really worth the extra cost? MailOnline gave the smart speaker a test run to find out. Apple's HomePod stands at nearly 7 inches tall, which is slightly larger than the average smart speaker. It also feels heavier weighing in at 5.5lbs (2.5kg). But despite its size, it appears to blend into its surroundings.

I think it is a good program but I'm just not interested in web development anymore. I was originally positioning myself to become a UX Designer/Researcher, which I'm still interested in, but not particularly as it relates to web. Recently I've been getting much more interested in AI, since it touches on so many subjects I'm interested in, and obviously because it is such an important topic for the future. I definitely want to start positioning myself more towards this field as I continue my undegraduate field. But I don't really know where to go from here.

Back at IBC2017 there were three workflow demos of the DPP News Exchange project promoting the sharing of metadata, and this vital work has now arrived at prototype mode, with the prospect of publication in early April along with implementation guidance. The drivers of this project are the BBC, ITV, ITN, Sky, C5, Thomson Reuters, Sony, Avid, Telestream, Scisys, IPTC, and SAM (now Grass Valley). Confirming that a compliance program will also follow publication, Andy Wilson, DPP Head of Business Development and Delivery says: "News Exchange will enable the efficient movement of story metadata from the newsroom out to the reporter and cameraman in the field. "They record their video clips, and with that video file it will then send the metadata from the newsroom, now augmented with data from the camera and in an XML file, to be imported back into the newsroom. "This means the metadata with the story will pass with the video throughout the chain. There are immediate benefits to journalists like less tagging," he adds.