IBM Hosted a Big Party for Watson With its CEO as the Star By Darryl K. Taft Posted 2016-10-28 Print NEWS ANALYSIS: IBM CEO Ginni Rometty keynoted the company's World of Watson conference in Las Vegas and drove home that Watson is the AI platform for business. LAS VEGAS–IBM held a big party for its Watson cognitive computing technology here this week in the form of the IBM World of Watson (IBM WoW) 2016 conference. Originally known as IBM Insight, the company augmented the name of the event to focus on Watson and the importance of cognitive technology to the company's future. Indeed, IBM CEO Ginni Rometty said hundreds of millions of users interact with Watson-powered systems today and, by the end of next year, she expects there to be more than 1 billion Watson users. If nothing else, the World of Watson event was a paean to the continuing adoption of Watson across a broad variety of industries and technology areas, including medicine and health care, financial services, retail, manufacturing and travel, as well as technology areas such as cloud computing, the internet of things (IoT), software development and more.

AI -- looms on the horizon as the next game-changer for business operations, including sales. Salespeople tend to adopt technologies as consumers first and then expand use into their professional lives, so sales leaders thinking about AI will play may be focusing on assistive intelligence like Amazon Echo, Cortana and Siri. There is strong consumer demand: Strategy Analytics predicts that the use of voice-activated AI assistants will rise to almost 350 million by 2020. It's also easy to imagine the ways a voice-enabled assistant like Siri could become a great sales sidekick. Picture AI providing a spoken-worded guided selling system in CPQ, interacting with sales management systems while the salesperson is behind the wheel on a road trip, producing custom reports based on the salesperson's spoken requests and then displaying the results on a screen.

Recent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenia brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered functional connectivity in the schizophrenia brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenia brain relative to healthy brain with various similarity measures using time-frequency domain analysis, while participants performing the working memory task in the MRI scanner. To assess the distinctiveness of the BOLD connectivity in the schizophrenia, recognition performances of pattern classifier machine trained by each similarity measure were compared. Interestingly, classifier machine trained by time-lagging patterns of low-frequency fluctuation (LFF) produced highest classifying accuracy than the machines trained by other measures. Also, classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that characteristics of altered functional network in the schizophrenia brain can hardly defined with single aspect of relationship across the multiple brain regions.

About 17,000 people attended this week's IBM World of Watson conference. Many of them were trying to figure out what exactly to do with the cognitive computing engine. After an initial Watson-specific conference drew just over 1,000 people to Brooklyn, N.Y., in May 2015, IBM this year combined that event into its much larger IBM Insight analytics conference and changed the name to World of Watson. But the growing interest shown in Watson by attendees is a testament to how hot all things related to artificial intelligence are right now. At the same time, many businesses are just starting to think about how they can use cognitive applications like Watson.

Companies worldwide will spend $3.5 trillion on IT in 2017, market research firm Gartner predicts. Above all else, companies are expected to increase their spending on software and services (as opposed to hardware), as they all rush to buy their tech via the cloud computing model, where tech is hosted in the vendor's data center and delivered as a service over the internet. Software spending is projected to be up 6 percent in 2016, and to grow another 7.2 percent in 2017 to a total of $357 billion. Meanwhile, companies will spend $943 billion on IT services, up nearly 5% over 2016 spending levels. All this means that there are billions of dollars at stake for the rising tech trends, things that emerged within that last few years are ready to take off and become mainstream next year.

Messaging platforms are coming of the age, and Chatbots are all the new trend now. They are touted as the next version of apps, like a huge change in the communication industry. Since Facebook has expanded access to its Messenger service, giving businesses the ability to reach customers better through different APIs, "Chatbots" has become the buzzword these days. Many questions are arising about Chatbots: What actually the Chatbots are? Are Chatbots a big opportunity for businesses?

The study was a success: the first two AIs learnt how to communicate securely from scratch. P input plaintext, K shared key, C encrypted text, and PEve and PBob are the computed plaintext outputs.The Google Brain team (which is based out in Mountain View and is separate from in London) started with three fairly vanilla neural networks called Alice, Bob, and Eve. Each neural network was given a very specific goal: Alice had to send a secure message to Bob; Bob had to try and decrypt the message; and Eve had to try and eavesdrop on the message and try to decrypt it. Alice and Bob have one advantage over Eve: they start with a shared secret key (i.e. this is symmetric encryption). Importantly, the AIs were not told how to encrypt stuff, or what crypto techniques to use: they were just given a loss function (a failure condition), and then they got on with it.

Researchers from the Google Brain deep learning project have already taught AI systems to make trippy works of art, but now they're moving on to something potentially darker: AI-generated, human-independent encryption. According to a new research paper, Googlers Martín Abadi and David G. Andersen have willingly allowed three test subjects -- neural networks named Alice, Bob and Eve -- to pass each other notes using an encryption method they created themselves. As the New Scientist reports, Abadi and Andersen assigned each AI a task: Alice had to send a secret message that only Bob could read, while Eve would try to figure out how to eavesdrop and decode the message herself. The experiment started with a plain-text message that Alice converted into unreadable gibberish, which Bob could decode using cipher key. At first, Alice and Bob were apparently bad at hiding their secrets, but over the course of 15,000 attempts Alice worked out her own encryption strategy and Bob simultaneously figured out how to decrypt it.

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Entering the hardware business is nothing unusual for an Internet giant like Google. But in launching its Pixel phone last week, Google GOOG is setting itself up to compete with its own direct customers. Unlike its previous Nexus smartphones, where most manufacturing and marketing is carried out by third parties--including Huawei, LG, and ASUS--the Pixel phone is completely Google's own creation. From design to development to branding to marketing, Google is behind it all. But what's peculiar is its determined pursuit in this less-profitable, asset-intensive side of business. Several studies, business academics, and consultants have shown the limits of asset builders, or companies that accumulate physical assets in order to market and distribute physical products.