Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

Chances are, if you are searching for a tutorial on artificial neural networks (ANN) you already have some idea of what they are, and what they are capable of doing. But did you know that neural networks are the foundation of the new and exciting field of deep learning? Deep learning is the field of machine learning that is making many state-of-the-art advancements, from beating players at Go and Poker, to speeding up drug discovery and assisting self-driving cars. If these types of cutting edge applications excite you like they excite me, then you will be interesting in learning as much as you can about deep learning. However, that requires you to know quite a bit about how neural networks work. This tutorial article is designed to help you get up to speed in neural networks as quickly as possible. In this tutorial I'll be presenting some concepts, code and maths that will enable you to build and understand a simple neural network. Some tutorials focus only on the code and skip the maths – but this impedes understanding. I'll take things as slowly as possible, but it might help to brush up on your matrices and differentiation if you need to. The code will be in Python, so it will be beneficial if you have a basic understanding of how Python works. You'll pretty much get away with knowing about Python functions, loops and the basics of the numpy library. By the end of this neural networks tutorial you'll be able to build an ANN in Python that will correctly classify handwritten digits in images with a fair degree of accuracy. Once you're done with this tutorial, you can dive a little deeper with the following posts: All of the relevant code in this tutorial can be found here. Here's an outline of the tutorial, with links, so you can easily navigate to the parts you want: Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. We don't need to talk about the complex biology of our brain structures, but suffice to say, the brain contains neurons which are kind of like organic switches. These can change their output state depending on the strength of their electrical or chemical input. The neural network in a person's brain is a hugely interconnected network of neurons, where the output of any given neuron may be the input to thousands of other neurons.

The offer will be available in France for Orange employees from mid-May and for the general public from 6 July 2017. Customers can subscribe directly from the mobile application, online or in one of Orange's 140 certified stores. Innovative and specifically designed for mobile uses, the offer will provide customers from launch with a bank account, a debit card, overdraft protection and an interest-bearing savings account. Additional services, such as credit and insurance, will gradually be included in the offer. Right from the outset, the service will integrate a number of cutting-edge, digital and banking innovations including contactless mobile payments, sending money by SMS, instant bank balances, temporary freezing of the debit card and 24/7 access to a bank advisory service.

Stimulating the brain with electrical signals could help people suffering from memory loss to remember new events. Researchers found deep brain stimulation with electrodes helped people who usually struggle to remember things to create new memories. The discovery could one day be used to help people suffering from conditions such as dementia and epilepsy, the researchers claimed. The researchers studied 102 patients being treated for drug-resistant epilepsy. They recorded electrical activity from electrodes implanted in the patients' brains.

A majority of consumers are confused about what artificial intelligence really does, and have misplaced fears that inhibit them from embracing AI-based technology, according to a new study. Despite that, "these fears are often eased once they gain firsthand AI experience – which ironically many enjoy today without even realizing it," notes the study, conducted by Pegasystems Inc. In a survey of 6,000 customers in six countries, Pegasystems found that consumers appear hesitant to fully embrace AI devices and services. Only one in three (36 percent) are comfortable with businesses using AI to engage with them – even if this typically results in a better customer experience. "Almost three quarters (72 percent) express some sort of fear about AI, with one quarter (24 percent) of respondents even worried about robots taking over the world," the study finds.

Artificial intelligence (AI) will "significantly transform" the insurance industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to a new report from Accenture. The Technology Vision for Insurance 2017 report, called Technology for People, released on Wednesday by the global professional services company, found that while the technology will be empowering, insurers face challenges integrating AI into their existing technology. Insurers cite issues such as data quality, privacy and infrastructure compatibility. The report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries in North America, Europe, Asia-Pacific, Africa and South America, Accenture noted in a press release. The goal of the survey was to identify the key issues and priorities for technology adoption and investment.

"Will artificial intelligence replace marketers in the near future?" This is the compelling question posted by Loren McDonald of IBM Watson Marketing during his presentation at the recent Digital Summit conference in Los Angeles. While many marketers might consider this a provocative presentation opener, there are some blunt realities marketers need to consider if they want to remain in the field and be competitive. Artificial Intelligence is about the development of computers systems that are able to perform tasks that would normally require human intelligences such as visual identification speech recognition, decision-making and translating between languages. AI performs a role in many of the stems that you use everyday from using Siri on your phone, a chatbot on an ecommerce site like Staples or 1-800-Flowers or every time you use Google.

Amazon is the exception to nearly every rule in business. Rising from humble beginnings as a Seattle-based internet bookstore, Amazon has grown into a propulsive force in at least five different giant industries: retail, logistics, consumer technology, cloud computing, and most recently, media and entertainment. The company has had its share of missteps -- the expensive Fire phone flop comes to mind -- but is also rightly known for strokes of strategic genius that have put it ahead of competitors in promising new industries. This was the case with the launch of cloud business AWS in the mid-2000s, and more recently the surprising consumer hit in the Echo device and its Alexa AI assistant. Today's Amazon is far more than just an "everything store," it's a leader in consumer-facing AI and enterprise cloud services. And its insatiable appetite for new markets mean competitors must always be on guard against its next moves.

Accenture Labs and Akshaya Patra Use Disruptive Technologies to Enhance Efficiency in Mid-Day Meal Program for School Children "Million Meals" project applied artificial intelligence, the Internet of Things and blockchain to drive efficiency and timeliness of lunch program in government schools across India BENGALURU, India; Apr. 20, 2017 – Accenture (NYSE: ACN) and Akshaya Patra, the world's largest NGO-run Mid-Day Meal Program, collaborated on an innovative project that used disruptive technologies to exponentially increase the number of meals served to children in schools in India that are run and aided by the government. The "Million Meals" project revolutionized Akshaya Patra's supply chain and operations, resulting in improved food quality and expanded service reach. Rooted in a vision to eliminate child hunger, the "Million Meals" project demonstrated how disruptive technologies such as artificial intelligence (AI), the Internet of Things (IoT) and blockchain can help address significant challenges in mass meal production and delivery. Accenture Labs, the research and development arm of Accenture, executed the project over a period of six months in Akshaya Patra's Bengaluru kitchen. An analysis of the project indicated a potential to improve efficiency by 20 percent, which could boost the number of meals served by millions.