In a recent Scientific American article entitled "Springtime for AI: The Rise of Deep Learning," computer scientist Yoshua Bengio explains why complex neural networks are the key to true artificial intelligence as people have long envisioned it. It seems logical that the way to make computers as smart as humans is to program them to behave like human brains. However, given how little we know of how the brain functions, this task seems more than a little daunting. So how does deep learning work? This visualization by Jen Christiansen explains the basic structure and function of neural networks.

Salesforce spent roughly 75 million on the three startups it acquired last quarter, according to a regulatory filing submitted Friday. Salesforce spent 32.8 million on MetaMind, the natural language processing and image recognition startup it bought in April. It spent an additional 41.6 million on two other companies it didn't name, but they are likely PredictionIO and Implisit Insights. This is the first time Salesforce publicly disclosed the financial details of these deals. One interesting nugget from the MetaMind acquisition is that Salesforce recorded 31.2 million in goodwill, which represents the amount paid for the company beyond what's valued on the balance sheet.

ICRA is almost over, and we hope you've been enjoying our coverage, which so far has featured robot moths, zipper actuators, machine learning, and duckies. We'll have lots more from the converence over the next few weeks, but for you impatient types, we're cramming Video Friday this week with a painstakingly curated selection of ICRA videos--emphasis on pain: there were nearly 500 videos! We tried to include videos from many different areas of robotics: control, sensing, humanoids, actuators, exoskeletons, manipulators, prosthetics, aerial vehicles, grasping, AI, VR, haptics, vision, and microrobots. We're posting the abstracts along with the videos, but if you have any questions about these projects, let us know and we'll get more details from the authors. Have a great weekend everyone! We present an adaptive filter model of cerebellar function applied to the calibration of a tactile sensory map to improve the accuracy of directed movements of a robotic manipulator.

This is such an exciting day for me, and has made me think about how my Mycroft journey started. About a year ago, Mycroft CEO Joshua Montgomery asked me if I would be interested in helping create an artificial intelligence for our makerspace. He wanted to create an environment like the one in Star Trek or Iron Man – where an on-site AI was able to control the lights, locks and communications of the facility. The more I thought about the idea of a voice-enabled artificial intelligence that inhabited a physical space, the more excited I became. I dreamed of an entity that could understand natural language and answer questions, perform tasks, and interact with the environment.

Alphabet (GOOGL)-owned Google has unveiled its Google Home device, a virtual assistant designed to answer questions and complete tasks, geared to take on the increasingly popular Amazon (AMZN) Echo. The Google assistant made its debut at Alphabet's developer conference Wednesday, after Consumer Intelligence Research Partners said last month that Amazon sold 3 million 180 Echo devices in less than two years on the market. Google is trying to position Home as a device with even more artificial intelligence capabilities, with the help of its own search platform built into the device. Google Home can change colors, and the company says it also has a learning algorithm to keep conversations going and get to know you better over time. The speaker won't be released until the fall, and the company has yet to share a price tag.

Google's gumdrop-shaped autonomous car looks like something you'd see in a Pixar movie, a cute and cuddly machine that makes the future look fun--until it ambushes you in a crosswalk, traps you like a fly in a web, and whisks you away. That horrific scenario comes to mind reading Google's recently approved patent for what amounts to slathering its cars in glue. To be fair, this has less to do with collecting humans than protecting them. Autonomous vehicles absolutely will reduce collisions and fatalities, but even the most ardent advocates concede one of them eventually will hit a pedestrian. Google engineers believe coating the front of a car with adhesive could prevent someone from bouncing onto the windshield, sliding under the wheels, or flying into the air and landing in the road.

At a dinner I attended some years ago, the distinguished differential geometer Eugenio Calabi volunteered to me his tongue-in-cheek distinction between pure and applied mathematicians. A pure mathematician, when stuck on the problem under study, often decides to narrow the problem further and so avoid the obstruction. An applied mathematician interprets being stuck as an indication that it is time to learn more mathematics and find better tools. A monthly column in which top researchers explore the process of discovery. This month's columnist, Ingrid Daubechies, is the James B. Duke Professor of Mathematics and Electrical and Computer Engineering at Duke University.

This repository provides a logistic regression implementation in python for our fair classification mechanism introduced in (Zafar et al., 2016). Please cite the paper when using the code. Fair classification corresponds to a scenario where we are learning classifiers from a dataset that is biased towards/against a specific demographic group, yet the classifier predictions are fair and do not show the biases contained in the data. For more details, have a look at Section 2 of our paper. Lets start off by generating a sample dataset where class labels are biased towards a certain group.

Google CEO Sundar Pichai said on Alphabet's Q1 earnings call: "In the long run, we will evolve in computing from a mobile-first to an AI-first world". This has prompted various speculation on what an AI-first world will look like. Pichai envisages that it will include "assistive" search, "especially on mobile," suggesting that artificial intelligence (AI) will be the platform for on-demand services accessed from any device – including smartphones. Dave Coplin, chief envisioning officer at Microsoft UK spoke at the AI Summit in London. He believes that AI first (AI as a platform) will "change how people relate to tech and to each other."

Algorithmia, a marketplace for building algorithms and monetizing them as APIs, has added support for four widely used languages: JavaScript, Python 3, Rust, and Ruby. When originally launched last year, Algorithmia supported only Java, Scala, and Python 2.x, but had plans for expanding the list. The revamped roster now covers most languages used for algorithm development in fields like machine learning and natural language processing -- areas where Algorithmia wants to provide a broad range of offerings. Algorithmia also now supports the standard repositories for third-party code used by each language: PyPI for Python, NPM for JavaScript, Crates.io for Rust, and Ruby Gems. The upshot is that any third-party package hosted on those services can be made part of an Algorithmia offering.