The interplay between AI and Sentiment analysis is also a new area. There are already many synergies between AI and Sentiment analysis because many functions of AI apps need sentiment analysis features. "The common interest areas where Artificial Intelligence (AI) meets sentiment analysis can be viewed from four aspects of the problem and the aspects can be grouped as Object identification, Feature extraction, Orientation classification and Integration. The existing reported solutions or available systems are still far from being perfect or fail to meet the satisfaction level of the end users. The main issue may be that there are many conceptual rules that govern sentiment and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being."

Nvidia's new partner in bringing AI-powered self-driving tech to the masses definitely has the experience needed to go truly mass-market – it's Bosch, leading tier one auto industry supplier. Bosch will build an AI supercomputer designed for use in vehicles using Nvidia tech, which means Nvidia now has a partner that works as a tier one supplier to all major car maker in the world. It's only the latest partner for Nvidia's AI-powered self-driving car tech, which also include automakers like Audi and Mercedes-Benz, but it's the one that could potentially have the most impact in terms of giving Nvidia reach and influence across the industry. This is the kind of strategic tie-up that lets both partners do what they do best – Nvidia can focus on developing the core AI supercomputing tech, and Bosch can provide relationships and sales operations that offer true scale and reach. Nvidia's deep learning model does not depend on specific rules being coded for each individual situation; instead, it provides the systems with a number of examples from human behavior, and then the AI can determine on its own what to do in specific scenarios. The mid-step implementation of this tech is Nvidia's AI co-pilot, which will allow the vehicle to work with a human driver to understand where their attention is directed and provide warnings about undetected hazards, as well as read a driver's lips and use audio cues to understand commands regardless of the in-vehicle noise environment.

Cater to the "market of the one" -- this has always been the holy grail of marketing. Brands and marketers have always strived to understand individual consumer necessities and tried to cater to them directly through an open dialog, at scale. While this was long a pipe-dream, with the advent of deep neural networks, the current crop of machine learning algorithms, and advancements in artificial intelligence (AI) research, the age-old spray and pray marketing is coming to an end. Now, with machine learning, brands have a good shot of being truly coherent in their narrative and engaging consumers with a consistent voice, tailored to individuals across omnichannel end-points. To break it down, let's take a concrete example of advertising a kid's video game, such as "Plants vs. Zombies -- Garden Warfare 2" and compare the two marketing options. In the marketing world, the best course of action for such a game would involve defining the genre of the game, the intended audience behavior and the market segment to advertise.

Expanding on their Jetson TX1 and TK1 products for embedded computing, NVIDIA announced last week their Jetson TX2 platform--a hardware and software platform the size of a credit card designed to deliver AI computing at the edge. NVIDIA touts Jetson TX2 as delivering "unprecedented deep learning capabilities," and based on the form factor, it may be right as it paves the way for a number of cutting-edge uses--from highly intelligent factory robots and commercial drones, to cameras with AI for smart cities. NVIDIA has been running on all cylinders lately with datacenter machine learning, and I think this release, if it performs as promised, will solidify their place at the top of the machine learning class in certain classes of devices. NVIDIA announced the TX2 at an event I attended last week in San Francisco with many tier 1 vendors and startups with some interesting use cases. Jetson, by design, isn't targeted at every embedded device, it's for those non-mobile devices who need strong deep neural network performance at a given power draw. The TX2 is a significant step up from its predecessor.

A data-sharing deal between Google DeepMind and the Royal Free London NHS Foundation Trust was riddled with "inexcusable" mistakes, according to an academic paper published on Thursday. The "Google DeepMind and healthcare in an age of algorithms" paper -- coauthored by Cambridge University's Julia Powles and The Economist's Hal Hodson -- questions why DeepMind was given permission to process millions of NHS patient records so easily and without patient approval. "There remain many ongoing issues and it was important to document how the deal was set up, how it played out in public, and to try to caution against another deal from happening in this way in the future," Powles told Business Insider in Berlin the day before the paper was published. DeepMind and Royal Free say that the study "completely misrepresents the reality of how the NHS uses technology to process data" and that it contains "significant mistakes." Powles and Hodson said the accusations of misrepresentation and factual inaccuracy were unsubstantiated, and invited the parties to respond on the record in an open forum.

Researchers studying a deal in which Google's artificial intelligence subsidiary, DeepMind, acquired access to millions of sensitive NHS patient records have warned that more must be done to regulate data transfers from public bodies to private firms. The academic study says that "inexcusable" mistakes were made when, in 2015, the Royal Free NHS Foundation Trust in London signed an agreement with Google DeepMind. This allowed the British AI firm to access sensitive information about 1.6 million patients who use the Trust's hospitals each year. The access was used to create monitoring software for mobile devices, called Streams, which promises to improve clinicians' ability to support patients with Acute Kidney Injury (AKI). But according to the study's authors, the purposes stated in the agreement were far less specific, and made more open-ended references to using data to improve services.

As part of its projects with Britain's National Health Service, Google's artificial intelligence unit DeepMind announced last week it's developing a new way to protect confidential health data--from itself. Its problem: How to assure hospitals, and the public at large, that patient confidentiality isn't compromised as it processes the sensitive medical health records entrusted to it. DeepMind's proposed solution is to create an indelible data log that can't be tampered with. It would show when a piece of data was used, and for what purpose. Importantly, DeepMind itself wouldn't be able to modify logs to use the data nefariously.

The road to human-level artificial intelligence is long and wildly uncertain. Most AI programs today are one-trick ponies. They can recognise faces, the sound of your voice, translate foreign languages, trade stocks and play chess. They may well have got the trick down pat, but one-trick ponies they remain. Google's DeepMind program, AlphaGo, can beat the best human players at Go, but it hasn't a clue how to play tiddlywinks, shove ha'penny, or tell one end of a horse from the other.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

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