Customer service interactions are time intensive and are frequently a source of customer irritation – particularly when interacting with a customer service agent that has little experience in your matter. Chatbots are increasingly effective in customer service interactions. Chatbots recognize and understand specific phrases in customer requests (typically written requests, but spoken natural language requests are rapidly advancing) and then respond to customers accordingly. Chatbots self-learn as they receive further information and customer requests, enabling them to improve accuracy and conduct conversations more naturally. I also include Apple's SIRI and Microsoft's Cortana in the category of chatbots – although Apple and Microsoft might disagree preferring to classify SIRI and Cortana as digital assistants.

"This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram. This has the potential to decrease unnecessary biopsies," says Stephen T. Wong, Ph.D., P.E., chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute. The team led by Wong and Jenny C. Chang, M.D., director of the Houston Methodist Cancer Center used the AI software to evaluate mammograms and pathology reports of 500 breast cancer patients. The software scanned patient charts, collected diagnostic features and correlated mammogram findings with breast cancer subtype. Clinicians used results, like the expression of tumor proteins, to accurately predict each patient's probability of breast cancer diagnosis.

The competition's goal was to automate the right whale recognition process using a dataset of aerial photographs of individual whales. The terms and conditions for the competition stated that to collect the prize, the winning team had to provide source code and a description of how to recreate the winning solution. A fair request, but as it turned out, the winning solution's authors spent about three weeks recreating all of the steps that led them to the winning machine learning model. When data scientists work on a problem, they need to test many different approaches -- various algorithms, neural network structures, numerous hyperparameter values that can be optimized etc. The process of validating one approach can be called an experiment.

The Senior Computer Vision Expert will also very closely work with Subject Matter Experts and Business Analysts to rapidly understand a specific business domain and iteratively refine the analyses and the learning models to create high-fidelity automated analytics solutions.

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. NOTE: You should NOT install TensorFlow with Anaconda as there are issues with the way Anaconda builds the python shared library that prevent dynamic linking from R. If you install TensorFlow within a Virtualenv environment you'll need to be sure to use that same environment when installing the tensorflow R package (see below for details).

Yes, it's just a TV show, but television plays a huge role in creating cultural phenomenons, and Mr. Robot has certainly found its place in pop culture. Anti-hero Elliot, a hacker whose ambition is to save the world from E Corp, struggles with mental illness. The voices inside his head are constantly in a conflict of good vs evil. As a result, Elliot's good intentions have paved his road to hell. Where does that leave the rest of us, though?

Most of us will be familiar with Siri, the personal assistant that lives inside your iPhone, answering questions, checking the weather, and setting your morning alarms. Now imagine Siri on a whole other level. Those of you that have recently updated your Apple devices to iOS 10 might have noticed the bot features that have been integrated into the messaging application. Bots are more accessible than ever, and can be reached without having to go at all out of your way. In fact, you could be using them regularly without even knowing it.

Microsoft is doubling down on its efforts to create better AI, with the establishment of a dedicated research group of over 5,000 staff to make Cortana and its other products much smarter. The decision taken by Microsoft to form a department solely focused on AI is no surprise, given the recent announcement of its involvement in the newly formed Partnership on AI to benefit People and Society. The partnership – that also includes Google, Facebook, Amazon and IBM, among others – is an attempt to democratise AI technology for the good of humankind. However, this doesn't prevent Microsoft from going it alone to compete with Google Now, Siri and Alexa in the intelligent personal assistant game. In a statement, Microsoft said that the division of 5,000 staff will be led by Microsoft veteran Harry Shum, who has worked in research and Bing engineering within the company.

Computers can already recognize you in an image, but can they see a video or real-world objects and tell exactly what's going on? Researchers are trying to make computer video recognition a reality, and they are using some image recognition techniques to make that happen. Researchers in and outside of Google are making progress in video recognition, but there are also challenges to overcome, Rajat Monga, engineering director of TensorFlow for Google's Brain team, said during a question-and-answer session on Quora this week. The benefits of video recognition are enormous. For example, a computer will be able to identify a person's activities, an event, or a location.

In the burgeoning field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as "black box" systems: Once a machine learning engine has been trained from a collection of example data to perform anything from facial recognition to malware detection, it can take in queries--Whose face is that? Is this app safe?--and spit out answers without anyone, not even its creators, fully understanding the mechanics of the decision-making inside that box. But researchers are increasingly proving that even when the inner workings of those machine learning engines are inscrutable, they aren't exactly secret. In fact, they've found that the guts of those black boxes can be reverse-engineered and even fully reproduced--stolen, as one group of researchers puts it--with the very same methods used to create them. In a paper they released earlier this month titled "Stealing Machine Learning Models via Prediction APIs," a team of computer scientists at Cornell Tech, the Swiss institute EPFL in Lausanne, and the University of North Carolina detail how they were able to reverse engineer machine learning-trained AIs based only on sending them queries and analyzing the responses.