In March 2016 Insilico Medicine initiated a research collaboration with Life Extension to apply advanced bioinformatic methods and deep learning algorithms to screen for naturally occurring compounds that may slow down or even reverse the cellular and molecular mechanisms of aging. Today Life Extension (LE) launched a new line of nutraceuticals called GEROPROTECTTM, and the first product in the series called Ageless CellTM combines some of the natural compounds that were shortlisted by Insilico Medicine's algorithms and are generally recognized as safe (GRAS). "Life Extension's mission is to extend the healthy human lifespan; and as such, we are focused on identifying natural products with critical health and wellness properties," said Andrew G. Swick, PhD, senior vice president of scientific affairs, discovery research and product development for Life Extension. "Our collaboration with Insilico Medicine fostered a novel approach to formulating anti-aging supplements utilizing artificial intelligence and sophisticated biologically-inspired algorithms and resulted in the very first AI formulated supplement," Swick said. The global nutraceuticals market was valued at US$165.62 billion in 2014 by Transparency Market Research and is expected to reach US$278.96 billion by 2021.

WikiLeaks has published a huge trove of what appear to be CIA spying secrets. The files are the most comprehensive release of US spying files ever made public, according to Julian Assange. In all, there are 8,761 documents that account for "the entire hacking capacity of the CIA", Mr Assange claimed in a release, and the trove is just the first of a series of "Vault 7" leaks. Already, the files include far more pages than the Snowden files that exposed the vast hacking power of the NSA and other agencies. In publishing the documents, WikiLeaks had ensured that the CIA had "lost control of its arsenal", he claimed.

It appears Apple is serious with launching a new iPhone installment with facial recognition capabilities. The Cupertino giant has just been awarded with a patent for an invention that involves face detection using depth information. U.S. patent 9,589,177 was granted to Apple by the U.S. Patent and Trademark Office on Tuesday, and it is a patent that details how face detection or facial recognition will work on an upcoming device, which is very likely to be the iPhone 8. Based on this new document, Apple is working on a technology that would allow the recognition of faces in digital video feeds using depth map data and images of the scene or background. The depth map will help in plotting the coordinates of the human faces that will be detected at a particular location. A respective face detection window will then mark the human face.

Marketers are turning to machine learning to find patterns in data and use analytically derived insights to predict future actions at a more finite level. Organizations are putting machine learning to use in myriad ways, including powering personal voice assistants, enhancing recommendation engines, reducing fraud and guiding self-driving cars. If you're exploring how to put machine learning to work in your marketing, here's how you can use it to minimize guesswork and bolster four key marketing actions immediately. Use information from both external sources and customer interactions to provide more variant-rich customer views and enable personalization at micro levels not previously possible with traditional segmentation approaches. Machine learning allows organizations to more rapidly analyze and learn from high-volume, varied and detailed data -- whether structured, unstructured or semi-structured.

Yahoo has launched another bot, called Captain, with the specific mission of reminding you about things you need to do. This SMS-based tool will manage lists and reminders, and all you have to do is text "Hi" to 773-786 to begin the process. This Siri-meets-Evernote bot lets you input any reminders you want to receive, and when the deadline approaches texts you with a follow-up message. You can also easily create lists within the bot. While many developers produce bots for messaging apps, Yahoo has instead elected to center Captain around text messaging.

Given how much several tech giants throw around the terms "artificial intelligence," "machine learning" and "deep learning," it's easy to assume that they're all trying to do similar things. And in some cases, the companies are indeed using AI to tackle problems that have much in common, particularly when it comes to making sense out of real-life sounds, images and dialogue. But a closer look shows that tech giants are at times not only tackling very different challenges, but employing different AI techniques to do so. IBM (IBM), which has set a goal of obtaining $10 billion in annual revenue related to its Watson AI platform by 2023, is especially an outlier -- not seen as a leader when it comes to certain large-scale, consumer-focused AI solutions, but very much one when it comes to AI offerings that require a measure of industry-specific expertise. The companies plan to pair Watson's ability to make sense out of data unique to fields such as health care, retail and financial services with Salesforce's Einstein AI platform -- it relies on the data taken in by Salesforce apps -- to deliver industry-specific insights, predictions and recommendations that can be useful for customer interactions. One such example given by the companies: By having Salesforce's Marketing Cloud apps plug into Watson programming interfaces (APIs), weather, local shopping and retail industry data analyzed by Watson could be paired with customer-specific shopping data analyzed by Einstein to "send highly personalized and localized email campaigns to shoppers."

Today, IBM and Salesforce announced they will connect their artificial intelligence platforms to help companies target and serve customers. IBM's natural-language AI program Watson -- which conquered the television quiz show "Jeopardy" -- will be integrated with Salesforce's Einstein, the AI that helps mine its hugely popular customer relationship management software, to provide customer purchasing habits and shopping data for businesses that run Salesforce. That means that an insurance company that uses Salesforce, for example, can also use Watson's weather data to target customers before a snowstorm to help reduce potential damages. By combining Watson's data on local retail trends with specific customer data from Salesforce, companies will be able to send highly targeted campaigns to shoppers, according to the companies. Salesforce customers will be able to start using Watson's smarts to refine their customer targeting starting in the second half of this year.

They are able to infer what you want. As a result, they respond with appropriate actions. They operate autonomously i.e. without human supervision. They are able to infer what you want. As a result, they respond with appropriate actions.

Chinese electronics company Huawei is now the world's third-biggest mobile phone manufacturer – though it's still a brand many people don't know well. Its latest releases, the Huawei P10 and P10 Plus, build on last year's P9 by lavishing improvements all round and adding a notable extra focus on colour. While it's become common in recent years for premium phones to be presented in metallic colour variants (gold, silver, rose gold), it's been left to more affordable handsets, usually clad in plastic casing, to go for bright hues. Think the Apple iPhone 5C or models from HTC and, until recently, Microsoft. Huawei has gone further, producing its top-of-the-range P10 in eight shades, yes eight. One of them, rose gold, will only be available in China.

Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what's going on inside the box in order to know if you're truly optimizing this critical tool. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In this article we'll cover the five basic types of recommenders and their strengths and weaknesses. Given that Recommenders add 10% to 25% of incremental income to your ecommerce business you need to know exactly how these are working. Optimization will involve fine tuning as well as potentially combining different models.