Summary: With concepts of single-feature linear-regression, cost function, gradient descent (from Part 1), epoch, learn-rate, gradient descent variation (from Part 2) under our belt, we are ready to progress to multi-feature linear regression with TensorFlow (TF). If you are already familiar with matrices and multi-feature linear regression, skip to the end for the multi-feature Tensorflow code cheatsheet, or even skip this entire article. The premise of the previous articles was: given any house size (square meters/sqm), which is the feature, we want to predict the house price (), the outcome. In reality, any prediction relies on multiple features, so we advance from single-feature to 2-feature linear regression; we chose 2 features to keep visualization and comprehension simple, but the concept generalizes to any number of features. We introduce a new feature, 'Rooms' (number of units in the house).

What will happen as we enter the era of human augmentation, artificial intelligence and government-by-algorithm? James Barrat, author of Our Final Invention, said: "Coexisting safely and ethically with intelligent machines is the central challenge of the twenty-first century." A lot of folks are earnestly exploring the topic. "Will scientists soon be able to create supercomputers that can read a newspaper with understanding, or write a news story, or create novels, or even formulate laws?" asks J. Storrs Hall in Beyond AI: Creating the Conscience of the Machine (2007). "And if machine intelligence advances beyond human intelligence, will we need to start talking about a computer's intentions?" Sharing this concern, SpaceX/Tesla entrepreneur Elon Musk has joined with YCombinator founder Sam Altman to establish OpenAI, an endeavor that aims to keep artificial intelligence research – and its products – accountable by maximizing transparency and openness. Among the most-worried is Swiss author Gerd Leonhard, whose new book Technology Vs. Humanity: The Coming Clash Between Man and Machine, coins an interesting term, "androrithm," to contrast with the algorithms that are implemented in every digital calculating engine or computer.

Artificial intelligence, or "AI," is the current direction of technological innovation, but if the U.S. does not step up its game, another world power may lead the future. The number of journal articles mentioning "deep learning" or "deep neural networks" produced by China exceeds the number published about similar technological advances by the U.S. The same is true for cited publications, reveals a National Science and Technology Council (NSTC) report. The report's findings indicate that not only is China doing more AI research, but they are having a greater influence on this growing field. Chinese venture capitalists and tech giants like Baidu, a company commonly referred to as China's Google, is investing heavily in AI. For Baidu, major projects include driverless vehicles, search engine optimization, and improved speech recognition software.

Human beings have an uneasy relationship with robots. We're fascinated by the prospect of intelligent machines. At the same time, we're wary of the existential threat they pose, one emboldened by decades of Hollywood tropes. In the near-term, robots are supposed to pose a threat to our livelihood, with automation promising to replace human workers while the steady march of artificial intelligence puts a machine behind every fast food counter, toll booth, and steering wheel. The palm-sized robot, from San Francisco-based company Anki, is both a harmless toy and a bold refutation of that uneasy relationship so loved by film and television.

With the appearance of robotic financial advisers, self-driving cars and personal digital assistants come many unresolved problems. We have already experienced market crashes caused by intelligent trading software, accidents caused by self-driving cars and hate speech from chat-bots that turned racist. Today's narrowly focused artificial intelligence (AI) systems are good only at specific assigned tasks. Their failures are just a warning: Once humans develop general AI capable of accomplishing a much wider range of tasks, expressions of prejudice will be the least of our concerns. It is not easy to make a machine that can perceive, learn and synthesize information to accomplish a set of tasks.

IoT fire is catching up in every industry. It is changing the businesses drastically than we imagined. This blog covers some of the aspects that will fuel the realization of IoT and brings businesses into a new territory. Thing – refers to devices, sensor, software and everything else. Billions of Things are already connected to the Internet.

With changes in ecommerce taking place at a faster rate than ever before, two major shifts in consumer behavior have impacted retailers in a way that they cannot ignore. The first is the increasing importance placed on consumer reviews. This trend has grown in significance, with one survey stating that 88 percent of consumers trust online reviews as much as a personal recommendation. Google itself is paying more attention to reviews, with industry analysts noting that consumer opinions are impacting page ranking more than ever before. The second shift is around retailers who are using semantic search technology to power their search bar.

As far back as 3,500 years ago ancient Egyptian doctors were performing invasive surgeries. Even though our tools and knowledge have improved drastically over time, until very recently surgery was still a manual task for human hands. When it came out about 15 years ago, Intuitive Surgical's da Vinci surgical robot was a major innovation. The da Vinci robot helps surgeons be more precise and dexterous and to remove natural hand tremors during surgery. In the years since da Vinci first came out, many other surgical robots have arrived.

AMD, Dell EMC, Google, Hewlett Packard Enterprise, IBM, Mellanox Technologies, Micron, NVIDIA and Xilinx announced a new, open specification that can increase datacenter server performance by up to 10 times. The new standard is called Open Coherent Accelerator Processor Interface (OpenCAPI). It is an open consortium to provide a high bandwidth, low latency open interface design specification. It will enable corporate and cloud data centers to speed up big data, machine learning, analytics, and other emerging workloads. Servers and related products based on the new standard are expected in the second half of 2017. Capable of 25Gbits per second data rate, OpenCAPI outperforms the current PCIe specification which offers a maximum data transfer rate of 16Gbits per second.

Today, artificial intelligence (AI) is rapidly emerging out of R&D labs and into the mainstream. Smart technologies are changing every aspect of our lives, from the way we work, to health care, education, travel, and transportation. One example: the self-driving cars produced by Google and Tesla. There are also many successful applications in the computer vision space. But what about the non-vision applications of AI: that is, areas including non-spatial data--most importantly, text and numbers?