Siri, Cortana, and Google Now have all ceded some of the spotlight to new technologies (such as the Ambient User Experience we discussed yesterday), but at the end of the day, Virtual Personal Assistants (VPAs) such as the three listed above, will lead the way to Artificial Intelligence. AI may seem like a pipe dream but here's the shocker: we're already living in a world of AI. A Virtual Personal Assistant like Siri is an AI – just a very limited one. We tend to think of AI as C-3PO, or Sonny from I, Robot, but those are just advanced (fictionalized) versions of AI. This is an AI like Siri or Chess Wiz cum Cognitive Chef IBM's Watson.

The Naive Bayes classifier is a simple classifier that is often used as a baseline for comparison with more complex classifiers. We will use the famous MNIST data set (pre-processed via PCA and normalized [TODO]) for this tutorial, so our class labels are {0, 1, …, 9}. If you're like me, you may have found this notation a little confusing at first. We can read the left side P(C X) as "the probability that the class is C given the data X". We can read the right side P(X C) as "the probability that the data X belongs to the class C". (this is called the "likelihood") And we can compute the probability that the class 0 given the data, probability that the class 1 given the data, etc. just by computing the probability of the data for each class (how well the data fits a model of each class).

Posted by Kaz Sato, Staff Developer Advocate, Google and Ray Sakai, Product Manager, Reactive Inc. At GCP NEXT 2016, the biggest Google Cloud Platform event held this year in San Francisco, Jeff Dean, Google Senior Fellow, presented the Cloud Vision API with Cloud Vision Explorer. This amazing demo is now available for anyone and we warmly invite you to give it a try. To recap, Cloud Vision API is an image analysis service that's part of Cloud Platform. It enables you to understand the content of images by encapsulating powerful machine learning models in an easy-to-use REST API.

She continues, "Multivariate analysis is by far the greatest strength of AI, because it allows the kind of contextual decision-making intelligence used in systems like the human mind, while also drawing from the eidetic memory of a hard disk. No parsing through the emotions is required, and there are no attentional omissions. AI doesn't need sleep, and doesn't get fatigued after focusing on one topic for too long. At the same time, AI has the benefit of massively parallel processing. The ability to handle huge volumes of data is of increasing value, and AI can drink from the firehose. With enough memory and processing power, a medical AI could hold a whole family tree's worth of medical records in context, scour databases for pertinent diagnostic information, and call up banks of medical and social resources – all at the same time."

Technological advances and artificial intelligence (AI) are going to totally transform the way healthcare is delivered over the next five to 10 years. This is the view of Tony Young, National Clinical Director for Innovation at NHS England. But he warns that with the advent of life-changing technologies, we must not lose sight of what it means to be human. As with the arrival of the printing press 500 years ago which gave everyone access to the written word, medicine today is having its own "Gutenberg moment". Technology, such as smartphones and wearables, is giving patients access to medical knowledge and empowering them to take charge of their health and well-being.

Turns out a "useless" humanities degree can get you a job in Silicon Valley, thanks to the rise of artificial intelligence. The brains behind Apple's Siri, Amazon's Alexa, and Microsoft's Cortana realized that to make their A.I.s sound more like people, they needed to hire workers whose creativity was less digital and more personal. According to the Washington Post, the teams behind many everyday A.I.s are made up of poets, writers, and comedians, who help give the robots more personality. Now, we're not saying that Silicon Valley's tech community is full of sociopaths or psychos, but software companies seem to have realized that hiring engineers from fields outside their own can help make their A.I.s more personable, and it's a much easier way to teach computers to think than turning them loose on the internet, which inevitably turns them into racists. Some companies are taking artificial assistants one step further.

Stock photo giant Shutterstock is boosting its artificial intelligence (AI) credentials today with the launch of a new reverse image search feature within its iOS app. The New York-based company offers more than 80 million images for bloggers and media outlets, but keyword searches aren't always the most effective way to find images relevant to a story. If you want to search for photos that are similar to ones you already have in your possession, or if you want to find alternative photos based on the shapes, mood, color scheme, and general mise en scène around you, reverse image search comes into play. You can search Shutterstock by using the camera on your iPhone or the photos on your camera roll to find similar images. The launch comes three months after Shutterstock first introduced the feature through its desktop version, though extending it to smartphones does feel like a natural move, given that smartphones are cameras in their own right.

The collaboration will accelerate the creation of Zebra's imaging analytics engine and create neural networks that will use Zebra's vast imaging dataset to assist radiologists with automated diagnostic algorithms. Kibbutz Shefayim Israel, May 24, 2016 - Zebra Medical Vision is announcing a new collaboration with Intermountain Healthcare, one of the top performing integrated care providers in the U.S. Intermountain plans to work with Zebra to accelerate the creation of meaningful imaging algorithms to improve patient care. Zebra is also announcing today an additional financing round of 12 million led by Intermountain Healthcare, with the participation of existing investors. Zebra Medical Vision was founded in 2014 with the vision of teaching computers to automatically read and diagnose medical imaging data. The company's analytics engine helps physicians and healthcare providers analyze millions of imaging records, in an effort to close the diagnostic gap created by a billion people worldwide joining the middle class in the coming decade, who will require diagnostic services.

International Business Machines Corp. (IBM) is perhaps one of the most obvious stocks to buy for investors interested in machine learning. The company's super-computer, Watson, has been touted as one of the most promising machine-learning ventures. The computer is able to sift through volumes of data in order to identify patterns and learn from past inputs, making it a valuable asset for several industries. Healthcare is one place Watson has excelled, by helping doctors to diagnose patients and make connections between symptoms and diseases. While the healthcare space represents a lucrative market for IBM's machine-learning technology, cybersecurity could be the biggest reason to invest in IBM.

With the new Google Analytics 360 Suite, machine learning comes to center stage as marketers contend with how to use this technology in their digital strategies. The recently launched enterprise marketing cloud suite is working to bring together the once siloed data of disparate Google products. Just before the launch, eMarketer's Jillian Ryan sat down with Google analytics evangelist Justin Cutroni to discuss how Google approaches machine learning and what these advancements mean for marketers using Google products. We're really interested in leveraging machine learning, so that businesses can take more action on the data. There are marketing plans and economic forces, so teaching a machine to really understand all of those nuances is challenging, but what we have been trying to do is look at how we implement machine learning at a very basic level.