Following examples set by the likes of Google and others, Amazon has made its Deep Scalable Sparse Tensor Network Engine (DSSTNE) generally available to researchers, developers and everyone else. The engine, which is used to provide product recommendations to Amazon shoppers -- usually under the "You may also be interested in" -- is now available on Github. The package includes examples, instructions for setup, FAQs, User guide and holds a business-friendly Apache 2.0 license. We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations. We hope that researchers around the world can collaborate to improve it.

This post provides an in-depth introduction to using Keras for deep language modeling. Includes sections on word embedding, characterizing recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for sentence vectors. Each section includes examples on how to implement it using Keras. This post explains the code in this Github repository. Question answering has received more focus as large search engines have basically mastered general information retrieval and are starting to cover more edge cases. Question answering happens to be one of those edge cases, because it could involve a lot of syntatic nuance that doesn't get captured by standard information retrieval models, like LDA or LSI. Hypothetically, deep learning models would be better suited to this type of task because of their ability to capture higher-order syntax. Two papers, "Applying deep learning to answer selection: a study and an open task" (Feng et.

Management consulting company Accenture has teamed up with IT automater IPsoft to launch an artificially intelligent agent called Amelia, who has been designed to encourage companies to embrace machine learning. Similar to Apple's Siri, Amelia will tackle client queries in "conversational" language, answering questions and managing processes. For example, she is able to help customers open bank accounts, or check out insurance policies. She will also be on hand to help employees within businesses, providing internal services like HR and guiding staff on company policies. As well as boasting natural language capabilities, Amelia has an element of machine learning - meaning she can be taught how to carry out various functions, like answering calls, via a simple uploading process.

If you were learning to code, you screwed up. Less than a year ago, Bloomberg was answering the question of "What is code?" and now we don't even need it anymore. Tanz is correct that machine learning is a new (for most people) and exciting way for humans to interact with computers. Instead of requiring coding knowledge, programs, functions, and algorithms can be improved more or less by training them. Large companies are already doing this, and the thing that has gone unmentioned in Tanz's piece is that, well, only large companies can do this.

The idea of artificial intelligence (AI) has been around for thousands of years, dating back to some of the earliest Greek myths. Those early stories show our infatuation with the concept that we could imbue machines with that most human of qualities: independent thought. Like many of humanity's greatest technical achievements, the quest to create AI is fundamentally about making our lives easier. We are now entering an era where the reality of AI is beginning to catch up with the myths and science fiction stories of our youth. Advances in the area of machine learning (ML) and natural language processing (NLP) have resulted in devices and applications that we interact with daily.

Smarter and more adaptive machines are rapidly becoming as much a part of our lives as the internet, and more of our decisions are being handed over to intelligent algorithms that learn from ever-increasing volumes and varieties of data. As these "robots" become a bigger part of our lives, we don't have any framework for evaluating which decisions we should be comfortable delegating to algorithms and which ones humans should retain. That's surprising, given the high stakes involved. I propose a risk-oriented framework for deciding when and how to allocate decision problems between humans and machine-based decision makers. I've developed this framework based on the experiences that my collaborators and I have had implementing prediction systems over the last 25 years in domains like finance, healthcare, education, and sports.

As a taxi driver, you need to pick up and drop off passengers as fast as possible.

While assisting customers through their digital transformation is the current priority for SAP, CEO Bill McDermott has predicted over the next five to 10 years the hype will be around machine learning, artificial intelligence, and augmented reality. "I think very strongly that intelligent applications will fundamentally change the way you do work in the enterprise and the way you collaborate with your trading partners outside of the enterprise," he said during his keynote at 2016 SAP Sapphire Now. He went on to say it's no longer viable for businesses to just automate internal processes, rather the future needs to focus on using automated systems to make intelligent predictions. "This idea of CRM and SFA -- that's dead; everybody has got that. We're not just automating internal sales process anymore so the sales director has a sales forecast that makes sense. Even with all that technology investment you still only have about 22 percent confidence on the sales forecast, while other companies have 44 percent, so there's something wrong with the sales forecast," he said.

Japan's Sony said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a U.S. startup. The electronics maker has invested an undisclosed sum in California-based Cogitai. The year-old firm, founded by three researchers, focuses on technology that allows machines to learn continually and autonomously from interaction in the real world. The move comes a time when major technology companies such as Facebook, Apple, and Alphabet's Google are spending aggressively on AI ventures. "From an objective perspective, we are lagging behind," Hiroaki Kitano, chief executive of Sony Computer Science Laboratories, said in an interview. "But there are still unexplored areas -- some in cyberspace but vastly more in the physical world," Kitano said.

Snips is a new take on the virtual personal assistant. Science fiction has enrapt the world with conversational artificial intelligence. Iron Man's Jarvis is the most notable culprit, but Her's Samantha and even the droids in Star Wars imagine a world where supercomputers integrate seamlessly into our everyday life. But that's not the only way for computers to help. Snips, a French company just launching in the United States, has a different idea about how to serve as a personal assistant. Snips connects to every relevant data stream in your phone--contacts, email, calendar, accelerometer, location data--and logs everything.