Even as research and investment into artificial intelligence (AI) have gone through some 60 years of booms and busts, consumers' fascination with the technology persists. But it is with the recent rise of digitization, Big Data, improved algorithms, and processing speed that consumer applications of AI have begun to emerge into the mainstream. From movie recommendations to search engine queries, AI and, more specifically, deep learning are powering a range of applications that have recalibrated consumer expectations for the digital lifestyle. According to a new report from Tractica, consumer AI now accounts for the largest portion of AI software, hardware, and services revenue, reaching $1.9 billion in 2016 and rising to $2.7 billion by the end of 2017, which is greater than any other industry segment. Although the entire market, including consumer AI, remains in its infancy, Tractica forecasts strong growth ahead, with the market for consumer AI hardware, software, and services increasing to $42.1 billion by 2025.

Interpretability is an extremely important criterion when a machine learning model is applied in areas such as medicine, financial markets, and criminal justice (e.g., see the discussion paper by Lipton ([18]), as well as references therein). Many complex models, such as random forests, kernel methods, and deep neural networks, have been developed and employed to optimize prediction accuracy, which can compromise their ease of interpretation. In this paper, we focus on instancewise feature selection as a specific approach for model interpretation. Given a machine learning model, instancewise feature selection asks for the importance scores of each feature on the prediction of a given instance, and the relative importance of each feature are allowed to vary across instances. Thus, the importance scores can act as an explanation for the specific instance, indicating which features are the key for the model to make its prediction on that instance.

A particular event in 2008 was billed as a landmark in automotive engineering and received global media coverage. With 31 design and 37 technology patents filed, it was touted as a global land mark in affordable transportation. In terms of estimated market impact it was placed right beside the iconic Ford Model T and VW Beetle. The car was expected to revolutionize personnel transportation in the developing world, however much to disbelief initial sales were dismal and continued to remain a fraction of original expectations. A through analysis years later identified the main culprit.

Music streaming service Spotify may already be working on its own smart speaker. A trio of job listings posted by Spotify suggest that the company is set to start manufacturing its new upcoming hardware product. "Spotify is on its way to creating its first physical products and setting up an operational organization for manufacturing, supply chain, sales & marketing," Spotify said on its "operations manager" job listing. Other hardware related job listings include "senior project manager: hardware production" and "project manager: hardware." The job listings were first spotted by MusicAlly.

You only look once (YOLO) is a state-of-the-art, real-time object detection system. It comes with a few pre-trained classifiers but I decided to train with my own data to know how well it's made, the potential of Image Recognition in general and its application in real-life situations. If you are a fan of HBO's Silicon Valley TV series, you might be aware of the famous Not Hotdog app that Jìng-Yáng built. This is similar, very basic, and detects if an image has me or not. To get started, we need to install Darknet with two dependencies - OpenCV and CUDA for faster computation.

Maybe robot dinosaurs will encourage your kids to code. Mattel showed off a Jurassic World coding toy this weekend, the newest entry in its Kamigami line of educational robots, at the New York City Toy Fair. The robots are essentially the same as the original Kamigami, except the plastic covers you put over the robotic core builds out a dinosaur instead of a bug. You've likely seen similar concepts before. The dinosaurs pair over Bluetooth to a new Jurassic World companion iOS / Android app, and kids can drag and drop code to make them do things.

Spotify is working on a line of "category defining" hardware products and is ready to start setting up the manufacturing process. The streaming music company intends to create a hardware category "akin to Pebble Watch, Amazon Echo, and Snap Spectacles", according to job adverts posted over the past year. One ad for a senior product manager, posted last April, called for an expert to "define the product requirements for internet connected hardware [and] the software that powers it". Today, a trio of job adverts (spotted by industry site MusicAlly) have been posted, seeking an "operations manager", "senior project manager: hardware production", and "project manager: hardware production and engineering" for the hardware. The first of those adverts states that "Spotify is on its way [to] creating its first physical products and set-up an operational organisation for manufacturing, supply chain, sales and marketing." The new hire would manage the supply chain for the new product, suggesting the company is ready to begin the manufacturing process shortly.

"The Pensieve is an object used to review memories. It has the appearance of a shallow stone or metal basin, into which runes and strange symbols are carved and precious stones are fitted. It is filled with a silvery substance that appears to be a cloud-like liquid/gas; the collected memories of people who have siphoned their recollections into it." The images are a few photos from a roadtrip i took last summer. The model is trained on nearly 200 total. The caption is layered on via giphy.

Artificial Intelligence (AI) has the peculiar ability to simultaneously amaze, enthrall, leave us gasping and intimidate. The possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. What all we read in science fiction novels or saw in movies like'The Matrix' could …

Accenture Launches New Artificial Intelligence Testing Services Powered by a "Teach and Test" methodology, the new services help companies validate the safety, reliability and transparency of their artificial intelligence systems NEW YORK; Feb. 20, 2018 – Accenture (NYSE: ACN) has launched new services for testing artificial intelligence (AI) systems, powered by a unique "Teach and Test" methodology designed to help companies build, monitor and measure reliable AI systems within their own infrastructure or in the cloud. Accenture's "Teach and Test" methodology ensures that AI systems are producing the right decisions in two phases. The "Teach" phase focuses on the choice of data, models and algorithms that are used to train machine learning. This phase experiments and statistically evaluates different models to select the best performing model to be deployed into production, while avoiding gender, ethnic and other biases, as well as ethical and compliance risks. Accenture AI Testing Services from Accenture Technology During the "Test" phase, AI system outputs are compared to key performance indicators, and assessed for whether the system can explain how a decision or outcome was determined.