It's the dream of any foodie who watches what they eat to be able to snap a photo of their meals and have their phone instantly tell them how many calories they're about to consume. That's the mission statement of a new startup called AVA, which promises to do away with the dreary manual logging process of rival healthy-eating apps in favor of an altogether more streamlined process. Using AVA's "intelligent eating" service, users will simply take a photo of their food, text it to AVA, and then receive health and caloric information in return. "We're using artificial intelligence to assist nutritionists in estimating calories as well as making recommendations, factoring in historical eating habits, diet patterns, location and behavioral analysis against a database of roughly 50,000 meals," Ian Brady, AVA's co-founder and CEO, tells Digital Trends. "We've seen in our early pilot that factoring in larger data sets makes for more accurate and personalized recommendations, and that these play a considerable role in driving engagement and overall effectiveness of our programs." Since AVA is still in private beta mode, Brady's not spilling the beans on exactly how the technology works but he says that it's a "a combination of image recognition, human recognition and AI algorithms."

An interesting sampling of thoughts about the future of AI, the obstacles to "human-level" artificial intelligence, and how we might overcome those obstacles is found in the business week article with a link below). I find several interesting issues in the article. In this post, we explore the first; viz., the idea of "human-level" intelligence implicitly assumes that intelligence has levels. Within a very specific framework, it might make sense to talk about levels. For instance, if you are building a machine vision program to recognize hand-printed characters, and you have a very large sample of such hand printed characters to test on, then, it makes sense to measure your improvement in terms of accuracy.

The current situation with malware naming conventions is in disarray. Different antivirus vendors use different naming conventions and sometimes they don't follow their own standards. Let's look at a few results for a random virus. These are the results from VirusTotal, a meta-antivirus scanning service. We can see that it is a Trojan malware with some vendors (Dr.Web and TrendMicro) setting the family as StartPage, some saying it's in the Agent family, some saying it is in the FakeAV family and some saying it is Generic "KR" malware.

In my opinion, the marriage of the leading professional social network and the world's largest software company demonstrates that we are decidedly at the start of a new era in software, where proprietary data is king, and will start to come bundled together with software. We've seen this rise in the consumer realm, where technology companies are fundamentally aggregating and analyzing user behavior, and providing value back to users (and, of course, advertisers.) There are countless other examples that also demonstrate that consumer technology puts behavioral and user data front and center, in a way that I expect we will start to see from the enterprise as the divide between these two segments starts to collapse. Taken together, this demonstrates that proven machine learning algorithms have both the horsepower and access to granular datasets that are unprecedented.

NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.

SugarCRM has put A.I. at the core of its product plans and is working on a new intelligence service along with a Siri-like agent named Candace. Tapping the company's recent acquisitions of Stitch and Contastic, the new technology will be designed to help businesses spend less time entering data into their customer relationship management (CRM) software, and more time learning from and acting upon it. SugarCRM is scheduled to demonstrate the new capabilities Wednesday at its SugarCon conference in San Francisco. "In the CRM space, we want people to focus on what they're good at: Relating to others, such as customers and partners," Rich Green, SugarCRM's chief product officer, said in an interview last week. "As data becomes more and more available, it typically has required quite a bit of labor to ensure that your CRM system stays up to date," Green explained.

The Earth isn't getting any bigger, so we need to start finding more efficient ways to feed the projected 10 billion people by 2050 using the same amount of land. Researchers from EPFL in Switzerland and Penn State University used the Caffe deep learning framework and Tesla K40 GPUs to train a model that identifies crop diseases. For now, the researchers created a website, Plant Village, an open access database of 50,000 images of healthy and diseased crops. The goal is to launch a mobile app to help farmers around the world by providing them with the ability to snap a photo of their diseased plant and the app would automatically diagnose it. Silicon Valley-based Blue River Technology has developed a deep learning solution called LettuceBot that rolls through a field photographing 5,000 young plants a minute, using algorithms and machine vision to identify each sprout as lettuce or a weed.

Once the stuff of sci-fi movies and books, artificial intelligence (AI) is quickly becoming a reality. One thing is quite clear: AI has transformative potential for most if not all industries, including banking. A recent survey conducted by Synechron and TABB Group found that over 71 percent of companies believe that AI will be a hugely important technology in financial services over the next 10 years. The same survey found that 30 percent of companies already have deployments in AI. According to Gartner, by 2018, 40 percent of outsourced services will leverage smart machine technology.

As what was once mere data evolves into actionable intelligence, the context that binds that data becomes ever more essential. With no context around those four letters, you might not understand the reference or make any sort of connection. But if you add just one word to "java," such as "development," "island," or "coffee," the reference changes completely--and that's with just a single word of context. This is the type of active context and connection that the Brainspace engine provides. "Context is a very important part of what we do. When we analyze documents, we take the context into consideration," says Ravi Sathyanna, vice president of technology and product management at Brainspace.

At its annual Worldwide Developers Conference (WWDC) in San Francisco this week, Apple suggested that it was more than willing to compete toe-to-toe against Google, Amazon, and Microsoft in the artificial intelligence (A.I.) arena. As those rivals build increasingly sophisticated bots that can respond to users' natural-language commands, Apple has opened its own digital assistant, Siri, to third-party developers. If things pan out as the company expects, Siri will rapidly add more functionality, essentially becoming a butler for a growing collection of tasks. And that's just the first of what will surely be many A.I.-related initiatives on Apple's part. But how can Apple reconcile its need for personal data--the fuel of an effective A.I. platform--with its rigorous user privacy stance?