The new plugin gives businesses a simple way to create and deploy customer support messenger bots; providing their customers with a real-time chat experience that combines a hybrid of AI self-service support and on-demand live agent chat. Integrated with Inbenta's powerful semantic search technology, the Chatbot Creation Platform enables a company to leverage its existing knowledge base to create the chatbot experience. The knowledge base -- or customer FAQs -- are no more than a sequence of answers to common questions; because the content is founded on natural language, the customer exchange becomes a two-way conversation. Inbenta specializes in Natural Language Processing and semantic search to improve the customer experience online through Artificial Intelligence-powered technology that helps businesses increase the efficiency of its customer service, call centers, e-Commerce, FAQs and social media platforms. Support services such as dynamic FAQs, knowledge management and and virtual assistants improve business website searches, customer self-service, and e-Commerce conversions.

With the rising anxiety about robots taking over human jobs, now even lawyers may have a reason to be on the edge of their seats. A 19-year-old Stanford University student, Joshua Browder, impressively taught himself how to code and ended up creating what he claims is the "world's first robot lawyer," according to The Guardian. Over the past 21 months, the artificial intelligence (AI) lawyer chatbot has successfully helped void 160,000 parking tickets in London and New York. The bot has done it all for free. Remarkably, DoNotPay chatbot has taken on a quarter of a million parking ticket cases, and the AI lawyer has won 64 percent of them. To quantify the impact, the chatbot has saved about 4 million in fines that no longer have to be paid.

Last week New York University hosted the final workshop of a series sponsored by the White House on the transformative potential of Artificial Intelligence. Rather than focusing on the technical bits and bytes, the NYU-hosted schedule centered around the near-term social and economic impact of automation, mass data collection and new analytics. This leads directly into the White House's July 22nd deadline for its Request for Information on "Preparing for the Future of Artificial Intelligence." While we often fantasize about the fallout from the coming robot apocalypse, that is simply not today's challenge. Today, we need to focus on the near-term impact of smart-er automation systems on labor and social structures.

Allison Parrish is a computer programer, educator, and poet whose work deals with the materiality of language. With a background in both the formal study of language (she received her BA in Linguistics from UC Berkeley) and years of experience as a software developer, Parrish's work has lead her to explore words not only as symbols but also as objects in their own right, teasing the boundaries between medium and message to ask questions about how communication happens in a digital context. Electronic media open up entirely new possibilities for the manipulation of language, but they also lay bare some of the thorniest problems of textual interpretation. If we are writing for other humans we can assume a baseline understanding of how language works, an understanding which computers still do not share. If this seems abstract, consider the way that language comes to be represented as a text file in a computer: words are compressed into ASCII characters and stored as bits on a hard drive; the fundaments of human communication, having emerged out of obscure prehistory and evolved for centuries, accumulating layers of connotation and nuance along the way, are now encoded as tiny electrical charges in a matrix of transistors.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in the preparation of the article. Competing interests: The authors have declared that no competing interests exist.

In my job as a data scientist, I spend my time driving machine learning research and making it work in the real world. NEW for 2016: Featuring a comprehensive market overview, the top questions to ask providers, and full one-page profiles of the top-28 vendors for your enterprise. The challenge with cleaning data for use in all types of machine learning is that each data set is different, and understanding a certain data set typically requires considerable "tribal" knowledge. He provides guidance for SAS and its customers on deriving substantive value from machine learning technologies and designs new data mining & machine learning approaches, focusing on neural networks and clustering.

SEBASTOPOL, CA–(Marketwired – July 07, 2016) – The inaugural O'Reilly Artificial Intelligence Conference explores the real-world opportunities of applied AI on September 26 and 27, 2016, in New York City. O'Reilly Media founder and CEO Tim O'Reilly says that the explosion of intelligent software has just begun. Companies and developers working on applied AI require a different kind of knowledge than the research presented by existing academic conferences. The O'Reilly AI Conference fills that need with deeply practical sessions on AI today -- how to implement and interact with AI, use cases, and best practices -- as well as inquiries into the future of intelligence engineering. Peter Norvig and Tim O'Reilly serve as honorary program chairs for the first O'Reilly AI conference, with Ben Lorica and Roger Chen as program chairs.

Session 1: Introduction to Tensorflow We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images. Session 2: Training A Network W/ Tensorflow We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image. Session 3: Unsupervised And Supervised Learning This session goes deep.

Ants, it turns out, are extremely good at estimating the concentration of other ants in their vicinity. This ability appears to play a role in several communal activities, particularly in the voting procedure whereby an ant colony selects a new nest. Biologists have long suspected that ants base their population-density estimates on the frequency with which they--literally--bump into other ants while randomly exploring their environments. That theory gets new support from a theoretical paper that researchers from MIT's Computer Science and Artificial Intelligence Laboratory will present at the Association for Computing Machinery's Symposium on Principles of Distributed Computing conference later this month. The paper shows that observations from random exploration of the environment converge very quickly on an accurate estimate of population density.

CHENNAI: Societe Generale's 10-week accelerator programme Catalyst has eight Indian startups working on various themes and three of them -Uniphore Software Systems, FixNix and Gavs Technologies are from Chennai, working on various risk and analytics tools that may be used by SocGen in future. FixNix, founded by Shanmugavel Sankaran, is trying to develop an early prediction based warning system for employee retention and proactive management of employee expectations. "We have a three-member team working on risk analytics in the accelerator. FixNix is getting aggressively into security and risk analytics for Indian and international banks, including Societe Generale, UBS," said Sankaran. The SaaS based governance, risk and compliance (GRC) startup has funding from ex-CIO of Tesla Jay Vijayan and other angel investors.