Technology has shaped human development since the dawn of time, from cave drawings that allowed stories to be recorded, to combustion engines bringing people closer. The World Economic Forum's Professor Klaus Schwab summed it up this way: "The first Industrial Revolution used steam power to mechanise production. The second used electric power to create mass production. The third used electronics and information technology to automate production. Now a fourth Industrial Revolution is building on the Third. It is characterised by a fusion of technologies that is blurring the lines between the physical, digital and biological spheres."

Machine learning will cost us jobs, a prominent technology executive acknowledged today, but she said job disruption isn't the insurmountable problem that many observers fear. Diane Greene, senior vice president in charge of Google Inc.'s cloud business, said at the Women in Data Science conference at Stanford University today that there's "no question" that machine learning, a branch of artificial intelligence that uses data to help computers learn rather than explicitly programming them, is replacing jobs. SiliconANGLE Media's mobile live video studio, theCUBE, is doing live interviews at the conference. Already, Greene said, "machines are better than humans" at some tasks. Recently they've started to do better at some kinds of image and speech recognition, and they're performing tasks such as finding signs of disease in photos better than humans.

In analytics, we retrieve information from various data sources; it can be structured or unstructured. The biggest challenge here is to retrieve information from unstructured data mainly texts. Here machine learning comes into the picture to overcome this challenge. Different algorithms have been designed in different platforms but here we will discuss one technique that can be applied in python. The process can be explained better by an example.

A silicon valley startup has developed an AI jacket with slits that automatically open or close to adjust your body temperature. The slits on the jacket help make it more breathable if, for example, you're skiing or just feeling hot on a stuffy train car during your commute. The slits are on the front and back of the jacket and can be operated manually - with the AI eventually learning your temperature preference and adjusting the slits automatically. The jacket has slits on the front and back, which open and close depending on your body temperature. The company, Omius, based in Menlo Park, California, modeled the jacket after the stomata of plants - the tiny pores that allow plants to let gas in and out. The jacket has slits on the front and back, which open and close depending on your body temperature.

This week, more than 2,000 Google employees walked out of work to protest President Trump's immigration ban. Far from disciplining them for leaving their desks, CEO Sundar Pichai and co-founder Sergey Brin treated workers to impassioned speeches of support. "Proud, moved, and touched to be at a company that boldly stands for its people," Googler Sam Tse tweeted. While Pichai and Brin were no doubt speaking from personal conviction--Brin's family fled the former Soviet Union when he was a boy--they also had little choice but to back their employees. Trump's directive cut to the heart of Silicon Valley's treasured values of globalism and openness, values widely embraced by the workers themselves.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Need help preparing a romantic dinner for two? ARMAR will give you a hand.

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The hope that comes with this discipline is that including the experience into its tasks will eventually improve the learning. But this improvement needs to happen in such a way that the learning itself becomes automatic so that humans like ourselves don't need to interfere anymore is the ultimate goal. There are close ties between this discipline and Knowledge Discovery, Data Mining, Artificial Intelligence (AI) and Statistics. Typical applications can be classified into scientific knowledge discovery and more commercial ones, ranging from the "Robot Scientist" to anti-spam filtering and recommender systems. But above all, you will know this discipline because it's one of the topics that you need to master if you want to excel in data science. Today's scikit-learn tutorial will introduce you to the basics of Python machine learning: step-by-step, it will show you how to use Python and its libraries to explore your data with the help of matplotlib, work with the well-known algorithms KMeans and Support Vector Machines (SVM) to construct models, to fit the data to these models, to predict values and to validate the models that you have build. The first step to about anything in data science is loading in your data. This is also the starting point of this scikit-learn tutorial.

For most of its 60-year history, the capabilities of artificial intelligence (AI) has been limited to science fiction movies. But with funding increasing by a compound annual growth rate of more than 40 percent over the last five years, AI technology has begun moving out of fiction and into reality. Computers like IBM's Watson have become Jeopardy! The applications for AI are expanding rapidly and customer support is one field that will see significant change with the growth of AI. Artificial intelligence and customer support have already become partially acquainted.

As a coding instructor and curriculum designer I spend a lot of time thinking about where the tech industry is headed, to prepare my students for that new world. Here are several trends I think will dominate software development in 2017. Increase in client-server hybrid systems. In 2017 we will see more software systems that blend local and cloud computing in a variety of different proportions. In traditional web programming, a browser connects to a backend server, which in turn does all the actual processing.

Minje Kim, an assistant professor of intelligent systems engineering at the School of Informatics and Computing at IU Bloomington, has received a gift from Intel to pursue a method of lowering the power and computing cost of deep learning processes in artificial intelligence. Intel sought a portfolio of research projects focused on compelling new human-computer interaction advancements that have HCI on the precipice of a breakthrough. As smart devices have become more ubiquitous, advances in deep learning have allowed AI to reach a near-human level. Deep learning allows complicated intelligence jobs -- such as computer vision, near real-time language translation and music recognition to be performed quickly, but such computing comes at a cost. Because neural networks present each of the millions of parameters of a computation in up to 64-bit forms, the computations required are both sizeable and hungry for power.