In the past year, I've become convinced that machine learning is not hype. Strong AI/AGI is no longer a requirement for complex tasks. It doesn't matter that AGI is out of reach, since we don't need it in order for automation to take over vast swathes of the job market. I now think that domain specific ML is going to take 10% to 50% of all jobs in the next few decades. The obvious ones are transportation and logistics.

Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medical EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.

Dr. Stephen Parker, VP of professional graphics, took the stage to give an overview of the use of artificial intelligence in graphics applications. While the first working algorithm using deep feedforward perceptrons was published around 52 years ago in 1965 by Alexey Ivakhnenko and Valentin Lapa, deep learning in graphics applications has reached a combinatorial explosion thanks to some great work that has been recently accomplished by a group of researchers at the University of Toronto. In 2012, professor Geoffrey E Hinton and two students, Alex Krizhevsky and Ilya Sutskever, entered an image recognition content to build computer vision algorithms that learned to identify millions of objects in millions of pictures. Using the most efficient algorithms at the time, the team was able to take the error rate of an average human and cut it in half. They later created a company called DNNresearch, which Google bought the following year.

Top professional poker players have been been beaten by AI bots at no-limits hold'em. A complex variant of poker is the latest game to be mastered by artificial intelligence (AI). And it has been conquered not once, but twice, by two rival bots developed by separate research teams. Each algorithm -- which plays a'no limits' two-player version of Texas hold'em -- has in recent months hit a crucial AI milestone: they have beaten human professional players. The game first fell in December to DeepStack, developed by computer scientists at the University of Alberta in Edmonton, Canada, with collaborators from Charles University and the Czech Technical University in Prague. A month later, Libratus, developed by a team at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania, achieved the feat.

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?