Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty $22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

The O'Reilly Artificial Intelligence Conference call for speakers is open Underneath all the AI hype, real breakthroughs are happening--and obstacles to applied AI are being overcome--allowing AI developers to create software that doesn't just do what it's told, but has the ability to anticipate the needs of its users through a combination of pattern recognition, knowledge, planning, and reasoning. Enterprise bots are emerging to participate in conversations and carry out repetitive tasks. Deep learning toolkits are becoming essential tools for software engineers and data scientists. Frameworks are being developed that promise point-and-click development of intelligent conversational interfaces to relatively unsophisticated developers. There is a growing--and urgent--need for information on applied AI, as opposed to the kind of research presented at academic conferences.

MogIA, an artificial intelligence system and election predictor, has successfully predicted its fourth election in a row. The system, which learns in real-time by examining data on the internet, placed its bets on Donald Trump to win the 2016 US Presidential Election in October after also successfully predicting both the Democratic and Republican party primaries. "If Trump loses, it will defy the data trend for the first time in the last 12 years since Internet engagement began in full earnest," said MogIA's developer Sanjiv Rai in October, who then was still unaware of the certainty of a Trump win. "If you look at the primaries, in the primaries, there were immense amount of negative conversations that happen with regards to Trump," he continued. "However, when these conversations started picking up pace, in the final days, it meant a huge game opening for Trump and he won the Primaries with a good margin."

With visual data, it's sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque. At the Association for Computational Linguistics' Conference on Empirical Methods in Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions. "In real-world applications, sometimes people really want to know why the model makes the predictions it does," says Tao Lei, an MIT graduate student in electrical engineering and computer science and first author on the new paper. "One major reason that doctors don't trust machine-learning methods is that there's no evidence."

It's one thing to track endangered animals on land, but it's another to follow them when they're in the water. How do you spot individual critters when all you have are large-scale aerial photos? Queensland University researchers have used Google's TensorFlow machine learning to create a detector that automatically spots sea cows in ocean images. Instead of making people spend ages coming through tens of thousands of photos, the team just has to feed photos through an image recognition system that knows to look for the cows' telltale body shapes. An initial version could spot 80 percent of the sea cows that had been confirmed in existing photos.

GENEVA – A new device has allowed two monkeys to regain use of their paralyzed legs by transmitting brain signals wirelessly, bypassing their spinal cord lesions, a study released Wednesday by the journal Nature said. The implantable device, called a neuroprosthetic interface, was developed by an international team led by researchers at the Federal Polytechnic School of Lausanne (EPFL) and may soon be tested as a remedy for paralysis in humans. "For the first time, I can imagine a completely paralyzed patient able to move their legs through this brain-spine interface," Jocelyne Bloch, a neurosurgeon at the Lausanne University Hospital, said in a press release from EPFL. The interface conceived at EPFL is a multicomponent brain-spine connector, which decodes signals from the part of the motor cortex responsible for leg movements. It then relays those signals in real time to the lumbar region of the spinal cord that activates leg muscles to walk.

It's fun to buy a drone from a store, but perhaps more satisfying to build one from scratch. Intel in December will start shipping a fully loaded drone kit to let you do just that, with all the parts including the rotors, software, 3D camera and flight controller. Intel's Aero Ready to Fly Drone kit will go on sale on the company's website. An Intel spokeperson couldn't immediately provide a price. But it won't be cheap -- likely more than $600.

As technology progresses and society moves further into the digital age, the volume of data that needs to be processed and distributed is increasing at a rapid rate. As if that weren't enough, the data is becoming increasingly complex as well, with some modern software systems consisting of millions of lines of code. Their maintenance requires huge data centers with large teams of software developers, and they consume a ton of energy and financial resources. To mitigate this industry-wide problem, data science experts from Lancaster University have developed an artificially intelligent computer software system that can rapidly self-assemble itself into the most efficient form without human input. The system is called REx, and it uses machine learning to perform.

Constellation Research estimates that by 2020, at least 60 percent of the data considered to be mission-critical will live outside of the corporate firewall instead of in on-premises data centers. This trend has accelerated through the mobile, social and big data movements, with data and applications increasingly living and running outside of the four walls of the enterprise. As organizations shift from ownership to access, artificial intelligence (AI), Internet of things (IOT), and block chain will all require contextual insights to be delivered at the speed of thought. Against this backdrop, Integration Platform as a Service (IPaaS) is emerging as a one-stop, cloud-based choice for supporting both data integration and application integration to help address the new requirements and challenges that organizations face. IPaaS provides the foundational and mission-critical capabilities required to support disruptive business models.

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