ANALYSIS: Ray Dalio, the founder of Bridgewater Associates, the largest hedge fund in the world, hired IBM's Watson programming team to incorporate artificial intelligence into the investment process. Later this year, I will be doing something similar, so stay tuned to learn more about this exciting endeavour. Today we have Jamie Wise from BUZZ Indexes based in Toronto who recently launched an Artificial Intelligence driven ETF based index fund BUZ (US). The investment process uses natural language processing software and artificial intelligence to scour the internet for tweets, blogs, and searches for company names. One of the most interesting things is that the software is able distinguish between someone typing Amazon when talking about a bad product they bought on line and AMZN and a bad outlook for the stock.

Adrian Zumbrunnen was terrified of what conversational interfaces meant for him as a UX designer. "The conversational interface is scary," he says. "Will I still have a place in this industry when pushing pixels around is no longer the thing that designers do?" So Zumbrunnen decided to confront his fear head on. He redesigned his personal website so that it ran off a Quartz-style chatbot.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

The benefits of machine learning continue to trickle down to smartphones and gadgets, and chipmaker Qualcomm wants to help speed up the process. The company is launching a new software development kit for its "machine intelligence platform" Zeroth. This SDK will make it easier for companies to run deep learning programs directly on devices like smartphones and drones -- if they're powered by one of Qualcomm's chips, of course. Right now, you're probably using all sorts of deep learning programs you don't know about. Companies like Google and Facebook use this sort of software for things like image and voice recognition, but usually, this process happens in the cloud, with the results beamed to your phone.

If you want to build an information system today, well, you need to build it and design it, code it, test it, maintain it, and so on. And although millions and billions of dollars are spent in information systems we often forget the reason why we need those information systems in the first place: to be able to do something else. Information is always the means not the end, an insight that often goes amiss in the huge investments that companies make in information systems. Going back to the comparison with electricity, it is as if every time a company needs something to do it needs to create it own electricity plant that is unique and fit for purpose. Indeed, it needs to build many electricity plants each powering a different operation, from marketing and sales, to manufacturing and logistics.

The vendor's Neural Processing Engine SDK will run on smartphones and other devices powered by Qualcomm's Snapdragon 820 SoCs.

As unstructured data continues to pile up, different approaches and platforms are emerging to help businesses make greater analytical use of what otherwise might amount to clutter. Among the latest approaches is an "intelligent crowdsourcing platform" from Spare5, a Seattle-based startup specializing in, among other things, data "cleanup." Spare5's proprietary platform unveiled Wednesday (April 13) incorporates a crowdsourcing approach that leverages the experience of domain specialists to perform "custom micro-tasks." Once "filtered for quality," the resulting tasks can be used for applications ranging from training artificial intelligence models and improving searches to augmenting directories. The crowdsourcing platform combines human insights with machine learning techniques to untangle and promote wider analytics use of unstructured data.

The most recent development in hearables comes courtesy of giant tech company Microsoft. Still in its early development stages, the Cortana device, known as Clip, has been targeted for release in 2016. This will be a wearable aimed at women on the go and could potentially be designed as a piece of jewelry. The prototype is whispered to be ready by end of 2015. Microsoft s objective is to design a voice activated hearable device that will allow the wearer to hear messages and also interact with a virtual assistant.

Qualcomm announced the deep learning software development kit (SDK) for the company's Snapdragon 820 SoC. Qualcomm's deep learning SDK is called the Snapdragon Neural Processing Engine, and it's powered by the company's Zeroth Machine Intelligence Platform, with optimizations designed to take advantage of the heterogeneous compute capabilities of the Snapdragon SoC. Among other things, it may be able to more effectively block malware on mobile devices. Qualcomm said that the Snapdragon Neural Processing Engine SDK will give developers a "powerful, energy efficient platform" that will help them create mobile neural network tools that can be used for scene detection, text recognition, natural language processing and more. The idea is that OEMs can run their own neural network applications locally, on-device, and don't have to communicate with the cloud.