Under the umbrella of his artificial-intelligence nonprofit, OpenAI, Musk is working with Sam Altman, the sneaker-loving president of Y Combinator, to create an "off-the-shelf" robot that will execute basic housework, according to a company blog post. Signed by two OpenAI executives in addition to Musk and Altman, the announcement outlines the nonprofit's mission to "build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible." Once the team manages to build this robotic maid for the general population, it plans to up the intelligence of this robotic servant so it can hold conversations and problem-solve better than most humans. Let's hope the Three Laws of Robotics come pre-programmed.

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. Despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. AI is coming--it's going to drive our cars, be our personal assistant, and take the role of our doctor.

Google can see a future where robots help us unload the dishwasher and sweep the floor. The challenge is making sure they don't inadvertently knock over a vase --- or worse -- while doing so. Researchers at Alphabet Inc. unit Google, along with collaborators at Stanford University, the University of California at Berkeley, and OpenAI -- an artificial intelligence development company backed by Elon Musk -- have some ideas about how to design robot minds that won't lead to undesirable consequences for the people they serve. They published a technical paper Tuesday outlining their thinking. The motivation for the research is the immense popularity of artificial intelligence, software that can learn about the world and act within it.

In NLP we use sentiment to refer to the positive or negative emotions a person expresses in their language. It may be measured categorically (negative) or numerically (-1.2). It may be measured at the utterance level or as applied to a particular entity or topic. It may be calculated via any number of algorithms, but from the standpoint of a user of text analytics software, you provide text to a program and get back information about the sentiment. When you design the conversational paths of your chatbot you are concerned primarily with the topics of conversation.

Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes during training. In this post you will discover how you can use different learning rate schedules for your neural network models in Python using the Keras deep learning library. Using Learning Rate Schedules for Deep Learning Models in Python with Keras Photo by Columbia GSAPP, some rights reserved.

Did you know that OpenCV can detect cat faces in images…right out-of-the-box with no extras? But after Kendrick Tan broke the story, I had to check it out for myself…and do a little investigative work to see how this cat detector seemed to sneak its way into the OpenCV repository without me noticing (much like a cat sliding into an empty cereal box, just waiting to be discovered). In the remainder of this blog post, I'll demonstrate how to use OpenCV's cat detector to detect cat faces in images. This same technique can be applied to video streams as well. If you take a look at the OpenCV repository, specifically within the haarcascades directory (where OpenCV stores all its pre-trained Haar classifiers to detect various objects, body parts, etc.), you'll notice two files: Both of these Haar cascades can be used detecting "cat faces" in images.

Waterloo, Ontario, is a boom town. An hour west of Toronto, the city rumbles with construction work. Even the Older Mennonites of St. Jacobs, one town over, are digging up their main street, forcing their horse-and-buggies to detour. The region's growth stems largely from the University of Waterloo, whose intensive internship programs have made it a magnet for tech recruiting. In the '90s the city birthed Research in Motion and its Blackberry platform, which briefly dominated the mobile industry. Today, Waterloo is also a bot town.

Musk and some of the other leaders of the nonprofit artificial intelligence group Open AI jointly announced that they are "working to enable a physical robot ... to perform basic housework." "We believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot," said in the blog post. It was signed by Musk as well as Sam Altman, the president of venture firm Y Combinator, and two staff members at Open AI. Musk is the CEO of both Tesla Motors (TSLA) and SpaceX. They hope to build a robot that can asked to perform a task by simply speaking to it.

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives. The machines have all the answer. The question is how long they will still need humans. I want you to spend 30 minutes of your day composing an employee motivation video that includes the maximum number of nightmare scenarios and buzzwordy cliches. If any of them bite, we here at Technically Incorrect want to see if it beats the quite body-shivering, bloviatory effort of Tronc.

Back in 1997, Andy Grove, then chief executive officer of Intel, became one of the first corporate titans to embrace the teachings of Harvard Business School professor Clayton Christensen. Sensing that Intel might be undercut by PC chip rivals with cheaper wares, Grove invited Christensen to speak to his team about industrial leaders of the past who had waited too long to address emerging threats. Within a few quarters, Intel had brought out a line of lower-end Celeron chips for PCs, which pretty much smashed the dreams of Intel wannabes such as Advanced Micro Devices. Intel is no longer a case study in adaptability. On the contrary, it has whiffed in the market for mobile chips used in smartphones and tablets, by far the largest new opportunity for chip makers in the past 10 years.