At Pomato, we believe the future of recruiting certainly includes a place for AI. Our technology acts as a virtual screener to automatically assist recruiters to understand and properly validate technical resumes. Recently a number of articles and blog posts have been written as prognosticators weigh in on how much machine learning will affect the recruitment industry. So we decided to check them out and capture the best quotes and thoughts found in the recruiting blogosphere. However, if the'chatbot' technology lifts off, there should be a measured adoption to ensure recruitment companies protect the perception of their business in the recruitment sector.

SAP rolled out a series of partnerships and advancements at its Sapphire Now event. Google unveiled a host of initiatives at Google I/0. HP Enterprise introduced an Internet of Things (IoT) platform. AtScale, a BI for Hadoop platform provider, raised a new round of funding. We've got it covered in our Big Data Roundup for the week ended May 22, 2016.

Justin Gerrard speaks quickly, Brian Gerrard speaks slowly. If you met them separately, you would never guess they were brothers. But their oil-and-water partnership helped them create Bae, a dating app for black people. Bae works pretty much like Tinder, but tailor-made for black users. The Gerrards came up with the idea after they realized how difficult it is for black singles to find dates on existing platforms.

Alexy Khabrov's talk at LX Scala caused the Scala community to raise their arms for a revolution. After his talk, Alexy discussed his thoughts on how to ease the transition for Python data scientists into the Scala community, what Scala can learn from Python as a programming language, and how machine learning will influence the future of the Scala community within the next five years.

A place for visual representations of data: Graphs, charts, maps, etc. DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the aim of this subreddit. Check the best user-made visualizations of 2013 and 2014 (Jan-July Aug-Dec). Directly link to the original source article of the visualization (not an image file) or tag the post as [OC] if you made the visualization. Only [OC] posts may link to image files.

This five-fingered robot hand developed by University of Washington computer science and engineering researchers can learn how to perform dexterous manipulation -- like spinning a tube full of coffee beans -- on its own, rather than having humans program its actions. A University of Washington team of computer scientists and engineers has built what they say is one of the most highly capable five-fingered robot hands in the world. It can perform dexterous manipulation and learn from its own experience without needing humans to direct it. Their work is described in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation. "Hand manipulation is one of the hardest problems that roboticists have to solve," said lead author Vikash Kumar, a UW doctoral student in computer science and engineering. "A lot of robots today have pretty capable arms but the hand is as simple as a suction cup or maybe a claw or a gripper."

Robots and intelligent systems have gained a significant amount of attention recently. Various authors and reports detail advances in robotics and artificial intelligence and speculate on the role of humans in the robotic future. In such commentaries, robots are often presented as independent and tireless machines that are rational and efficient; they are removed from their creators and contexts of use. Unfortunately, as appealing as this image might be, it resembles more of a science fiction hero than the contextual nitty-gritty that revolves around the development and deployment of robotic applications. To obtain a more holistic picture, let us have a look at some contextual factors that surround robotics innovation today.

Artificial intelligence – what's the worst that can happen? Working with hacktivist and entrepreneur Federico Pistono, he has come up with a set of worst-case scenarios for a potential malevolent AI, from "enslaving mankind" to "destroying the universe". Yampolskiy argues that anticipating as many negative outcomes as possible – just as cybersecurity researchers do when looking for vulnerabilities – will help us guard against disaster. "The standard framework in AI thinking has always been to propose new safety mechanisms," he says. But looking at the issue with a cybersecurity mindset puts a different spin on things: starting with a list of all the things that could go wrong will make it easier to test any safeguards we may eventually want to put in place.

I was chatting with an old friend yesterday and he reminded me of a conversation we had nearly 50 years ago. I tried to explain to him what I did for living and he was trying to understand why getting computers to understand was more complicated than key word analysis. I explained about concepts underlying sentences and explained that sentences used words but that people really didn't use words in their minds except to get to the underlying ideas and that computers were having a hard time with that. Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.

Google is launching a new artificial intelligence group called "Magenta" to see if computers can create their own masterpieces, according to Quartz. The group was reportedly outlined on Sunday by Douglas Eck, a researcher at Google Brain (one of Google's well-established AI divisions), at the Moogfest tech and music festival in Durham, North Carolina. Magenta, due to launch more publicly at the start of June, will reportedly aim to establish whether AIs can be trained to create original pieces of music, art, or video. In order to determine whether this is possible, the Magenta team will use TensorFlow -- a software library for machine intelligence that Google built and opened up to the public at the end of last year. The first product to be released by Magenta will be a simple program that's designed to help researchers import music data from MIDI music files into TensorFlow, according to Quartz.