How you "feel" has never been more important in tech. This year, a number of companies are showing an interest in emotions, from Apple's purchase of Emotient for interpreting emotions from facial expressions in January, to IBM's release of new Watson APIs for analyzing emotion and tone in text. At Expert System, we've always believed in the value of a deeper understanding of text. While meaning and context can tell us so much, we're leaving a lot of valuable information on the table if we're not looking at one of the key things that makes us human. So, it's natural that emotion is the next frontier for conquering, albeit one whose subtleties and nuance make it even more difficult to determine with great accuracy.

At Retail Assist, we understand the importance of always having one eye on technology, in order to stay ahead of the curve. We've been researching the latest developments in Artificial Intelligence (AI) to bring you our opinion on consumer adoption of AI. In the news recently, we've noticed that the makers of Apple's iconic digital assistant, Siri, Dag Kittlaus and Adam Cheyer, have released a new AI platform, Viv. The key difference between their "next generation" tool and other AI platforms is that it isn't constrained by service provider, i.e. Being cross channel, it can "speak" in a device agnostic manner to vendors and third party suppliers in order to provide the most relevant information based on the request, i.e. to purchase relevant goods at the cheapest price.

Marie and I left mobile apps to new adventures with Opla's startup a few months ago. Apps changed the way we communicate and interact with our smartphones. I started programming apps 16 years ago, the market of apps is now mature. But another phenomenon has arise, which will change our daily life more deeply: Chatbots. They are not so tech-savvy and new. Now machines are so powerful that they could easily mimic human conversation using massive statistical algorithms.

Accenture has announced what looks like a major step forward in the use of artificial intelligence (AI) within IT and business process operations by forming a new practice with IPSoft. Accenture will set up an Accenture Amelia practice to'develop go-to-market strategies, solutions and consulting service offerings around deployments of virtual agent technology'. The aim is to focus initially on clients in the banking, insurance and travel sectors. Last November, Accenture also announced an investment in AI at its Centre for Innovation in Dublin to help clients to'accelerate the integration of intelligence and automation to transform their businesses'. IPSoft is one of a group of technology providers emerging in the AI/cognitive learning space, which we discuss in our reports into Intelligent Automation (see Business Process Automation – what is Intelligent Automation? and work back).

The secret to Viv is the system actually writes it's own code. In contrast to any other similar system, It is a profound and monumental giant leap forward. The structure of the Voice First world is held together by Intelligent Agents. Intelligent Agents use AI (Artificial Intelligence) and ML (Machine Learning) to decode volition and intent from an analyzed phrase or sentence. The AI in most current generation systems like Siri, Echo and Cortana focuses on speaker independent word recognition and to some extent the intent of predefined words or phrases that have a hard coded connection to a domain expertise.

Google is promoting natural language understanding with the open-sourcing of SyntaxNet, a neural network framework, and Parsey McParseface, an advanced parser for English text. Implemented in Google's open source TensorFlow machine intelligence library and released this month, SyntaxNet provides the code needed to train natural language understanding (NLU) models on data along with the Parsey McParseface parser for analysing English text. "Parsey McParseface is built on powerful machine learning algorithms that learn to analyse the linguistic structure of language and that can explain the functional role of each word in a given sentence," said Slav Petrov, Google senior staff research scientist. The project arose out of Google's pondering of how computers can read and understand human language in order to process it in intelligent ways. Accessible on GitHub, SyntaxNet serves as a framework for a syntactic parser, a key first component in many NLU systems, Petrov said.

Mitsubishi Electric has developed what may be a crucial next step in the development of artificial intelligence systems. Its "compact AI" technology eliminates the need for large servers and can be embedded in a far wider scope of devices and machines than existing AI systems can. The company says that, by filtering information necessary for analysis, the new technology can drastically reduce the processes involved in computation for AI systems. The development can trim the computation needed for certain tasks by as much as 90%, according to Mitsubishi Electric. It plans to start offering applications for compact AI technology, such as autonomous driving systems and smarter industrial robots and machine tools, as early as 2017, a company source said.

Goal In this week's summary we introduce the basic concepts behind reinforcement learning and some ways it is applied in very controlled environments. Motivation Reinforcement learning methods recently experienced a hype through AlphaGo ranking next to the best human Go players. Furthermore the complexity of Go might ease the transfer of reinforcement learning to very large NLP tasks like dialog handling. Steps Reinforcement Learning is usually applied to tasks, where an environment is partially observable and a certain action has to be taken. Any kind of game basically fits the former description.

The world's first artificial intelligence lawyer has been employed by a law firm in the US, which will use the robot to assist its various teams in legal research. The robot called'ROSS' is built upon Watson, IBM's cognitive computer. With the support of Watson's cognitive computing and natural language processing capabilities, lawyers can ask ROSS their research question and the robot reads through the law, gathers evidence, draws inferences and returns highly relevant, evidence-based answers. ROSS also monitors the law around the clock to notify users of new court decisions that can affect a case. The programme continually learns from the lawyers who use it to bring back better results each time.

If the founders of a new face recognition app get their way, anonymity in public could soon be a thing of the past. FindFace, launched two months ago and currently taking Russia by storm, allows users to photograph people in a crowd and work out their identities, with 70% reliability. It works by comparing photographs to profile pictures on Vkontakte, a social network popular in Russia and the former Soviet Union, with more than 200 million accounts. In future, the designers imagine a world where people walking past you on the street could find your social network profile by sneaking a photograph of you, and shops, advertisers and the police could pick your face out of crowds and track you down via social networks. In the short time since the launch, Findface has amassed 500,000 users and processed nearly 3m searches, according to its founders, 26-year-old Artem Kukharenko, and 29-year-old Alexander Kabakov.