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.

Artificial Intelligence News: Google Brain AI Can Create, Decode And Hide Its Own Encryption Only ... MIT's'Nightmare Machine' uses AI to give your photos a horrifying Halloween makeover Apple's touch-enabled MacBook Pro, tough Q4 results, 'Apple Car' China's Best Robots, Artificially Intelligent (AI) Assistants Can Understand How People Think Is Google AI's Encryption Better Than Human Encryption? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

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.

MIT's'Nightmare Machine' uses AI to give your photos a horrifying Halloween makeover Apple's touch-enabled MacBook Pro, tough Q4 results, 'Apple Car' Is Google AI's Encryption Better Than Human Encryption? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

On 26 October 2016, UBS held its second UBS Future of Finance Forum, this time in Zurich. The conference saw leaders and experts from the financial industry, fintechs, academia and regulators come together to discuss the future of Intelligent Automation and start to build a common understanding of what a successful approach would look like. We discussed evolving client preferences, what constitutes a'good' intelligent agent and how AI will impact our business. We sounded the drivers for automation and attempted to set its ethical boundaries, investigate the optimal human / co-bot collaboration, control risks and manage the new workforce. We debated sensible requirements from regulator and bank perspective and how to identify and manage liability.

I'm Evan, co-founder and CTO of Fuzzy.ai. Our team is very excited to be opening our doors for new registration today. These rules are optimized over time due to feedback from production use. We've applied this technique to a number of interesting business cases -- fraud detection, recommendations, content optimization, and dynamic pricing. Our Web-based developer environment makes it easy to design an agent quickly, and our SDKs for different programming languages make integrating with our REST API pretty painless.

Predictive analytics / machine learning / artificial intelligence is a hot topic – what's it about? Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like data mining, predictive analytics, and advanced analytics are considered too geeky or old for industry marketers and headline writers.

Microsoft this week released an updated version of its Microsoft Cognitive Toolkit as an open source Beta. The deep learning system is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and Nvidia GPUs. It also works with Microsoft's Azure GPU offering. The Microsoft computer scientists who developed the toolkit initially were looking for a tool to speed up and improve their own research. Initially called "Microsoft/CNTK," it morphed into an offering that Microsoft customers and flagship product groups depend on for a wide variety of deep learning tasks, the company said.