Citizens often do not make the connection between AI and the amazing new products and services broadly available on the market. But the impact of AI goes far beyond innovative consumer products. Historically, humans exclusively created and delivered knowledge. Now, in the era of cognitive computing, machines have the potential to help people unlock knowledge and insights from massive volumes of data. This combination of human and machine makes AI both powerful and transformative.

Sorry for the explicit language in the title, but examples, like MD Anderson having to bench Watson in its "eradicate cancer" moon shot, after a 3-year, $62 million dollar effort, make my head explode. Rocket science is relatively simple, compared to a space program. AI is the (relatively) easy part of the business problem they are trying to solve with AI. From almost 30 years dancing in and around AI, including implementing multi-million dollar expert systems at some of the world's largest companies, these are my three steps to help ensure your $62 million or even $6200 does not meet the same fate as the Oncology Expert Advisor. As the audit report on the project says, "results herein should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state."

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C API. For this course, we will be using Python.

Computer vision engineers have used machine learning techniques for decades to detect objects of interest in images and to classify or identify categories of objects. They extract features representing points, regions, or objects of interest and then use those features to train a model to classify or learn patterns in the image data. In traditional machine learning, feature selection is a time-consuming manual process. Feature extraction usually involves processing each image with one or more image processing operations, such as calculating gradient to extract the discriminative information from each image. Deep learning algorithms can learn features, representations, and tasks directly from images, text, and sound, eliminating the need for manual feature selection.

"Smart money" VCs have an enviable investment history, with a pattern of successful exited companies and high returns. And these firms' portfolios can provide important indicators of where tech and innovation is going. We define smart money venture capital firms (VCs) as those firms with the best combination of portfolio valuations and investment outcomes. Our methodology led us to identify 24 venture capital firms that stand above the rest in terms of financial success. So where is smart money going?

Advancement in computers and technology have been incredibly positive for music creators around the globe. You can learn how to produce music on YouTube, purchase the same sample libraries as your favorite composers and create epic scores from your laptop, anywhere in the world. As a result, there are more music producers than ever. Computers helping humans create original music has become today's standard, but there are several companies looking to turn the tables on this process. Computer-generated songs are a fast-growing sector of the industry, looking to disrupt creation, licensing and access for musicians and non-musicians alike.

And after all that, you reach a customer service agent who can't give you the help you need. No wonder we hate customer service. A San Francisco startup founded by particle physics researchers is working to ease that agony, with help from AI and GPUs. The company, Deepgram, created technology that businesses can use to quickly assess customer calls to improve service. "You really don't want to be calling customer service, and you don't want your time wasted," said Scott Stephenson, Deepgram co-founder and CEO.

Several weeks after winning U.S. patent protection for its data preparation platform, Tamr Inc. announced a deal with Hewlett Packard Enterprise to resell its data unification software. The reseller deal certifies that Tamr's technology has been validated for interoperability with HPE systems as part of its third-party software and hardware offerings. It also raises the visibility of the startup's platform given the breadth of HPE's sales channel. Hewlett Packard Pathfinder was an early investor in Tamr, which was spun out of the Massachusetts Institute of Technologies' Computer Science and Artificial Intelligence Laboratory in 2014. The startup, based in Cambridge, Mass., raised more than $25 million in a Series B funding round in June 2015.

The Washington fight over the future of Obamacare will have enormous repercussions for our health care system, which now accounts for nearly 18 percent of the U.S. economy. First there was "open washing," the marketing strategy for dressing up proprietary software as open source. Next came "cloud washing," whereby … Advances in artificial intelligence, machine learning, and deep learning are impacting businesses. But, the terms are often used interchangeably. As consumer banking becomes increasingly virtual, banks are setting the bar high for their non-human ambassadors.

The Internet of Things is a powerful technological force. So many devices and activities in our personal and professional lives are connected to it--from smartphones and activity trackers to huge manufacturing centers and medical devices, more and more things are being connected to the IoT every day and transforming how we live and work. At the same time, artificial intelligence technology is also developing rapidly, with 80% of the world's largest companies having incorporated some kind of cognitive technology into their products. AI trains computers to take on some human forms and tasks and expands the world of technological possibilities to combine machine learning and human creativity. With two large technological movements making waves around the world, it makes sense to combine them and add an AI layer to the IoT.