Each Friday, I embrace the adage that "a picture is worth a thousand words" and hand-select compelling graphics to put important investment news into perspective. All it takes is a quick glance and you'll be up to speed. The company behind popular messaging app Snapchat priced its IPO on Wednesday and began trading yesterday. As Reuters reports, "Investors set aside concerns about its lack of profits and voting rights for a piece of the hottest tech IPO in years." As I previously warned, Snap is hardly a "fist-pounding buy." And let's get real, Reuters.

A smartphone app that uses deep learning lets people with Parkinson's disease test their symptoms at home in just 4 minutes. The app could help people monitor the disease's progression more closely, and uncover how lifestyle factors may affect their symptoms. "There's very little understanding as to how Parkinson's arises, and patients say that every day the condition is different," says George Roussos at Birkbeck, University of London. People report symptom changes related to everything from exercise to socialising to diet, but it's not yet possible to build a solid picture of how these factors interact. "To understand these differences, we need to monitor the condition regularly, in a quick and easy way, over a long period of time," says Roussos.

The thing about technology evolution that the movies don't quite tell you is that it desperately relies on adoption. The most amazing invention on the planet could be created and if there's not widespread consumer demand, or if it's prohibitively expensive for businesses, that breakthrough isn't going to actually break through. The old Henry Ford quote is more relevant than ever: "If I had asked people what they wanted, they would have said faster horses." However, by demonstrating the value and efficiency of new tools, we can push modern business forward exponentially – unignorably. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations.

Ten years on from its first 1.0 release, OpenCV (Short for Open Computer Vision) is a C library that lets you create software with real-time computer vision. Originally developed in Russia for Intel, it's fully open source and includes machine learning. It's BSD licensed so can be used freely. This is a very large library, with over 200 MB in various DLLs though you can also statically link to it if you prefer. It has several hundred algorithms in C with C, Python, Java and MATLAB interfaces and runs on Windows, Linux, Android, iOS and Mac OS.

One of the groundbreaking features of DataSonar is that it doesn't require users to have deep Machine Learning knowledge to perform their analysis. Nevertheless, we've created a quizz to test your Machine Learning skills. Each day we'll post 3 questions and the answers will be provided the following day. What is the minimum number of features/variables required to perform clustering? 1) Zero 2) One 3) Two 4) Three Remember your choices, as tomorrow we'll provide the answers. Learn more about DataSonar's Machine Learning component here or request a demo to see it in action.

Connie Brenton, senior director of legal operations at NetApp and chairman of the board of the Corporate Legal Operations Consortium (CLOC), said AI technology takes "significant resources to get up and running." Speaking at Legalweek: The Experience 2017 Conference, she joined other experts in explaining the financial reality of AI. "We're talking years before AI fundamentally changes the way we practice law," Brenton said. Jennifer McCarron, technology program manager at Cisco, said lawyers have to account for the costs behind licensing and purchasing the solutions. With Riverview Law's KIM virtual assistant, for example, she estimated the "starting point" for 10 users is about $30,000 to automate and create in-house workflows and processes. She said tailor-made programs with "templates for auto-generated documents" also increase the cost.

Now that the world has connected "things," we need to build these devices as truly intelligent in order to create instantaneous and precise results. This means you have to do as much of the processing at the point of entry as you can: at the edge. The killer use cases for IoT are becoming manifest through AI engines on edge devices. An autonomous car has this dual edge/cloud analytics model, producing precise, real-time results. In his session at @ThingsExpo, John Crupi, Vice President and Engineering System Architect at Greenwave Systems, will discuss how as more smart, AI-enabled "things" operate at the edges of our networks, we'll see things moving beyond being connected and into actively embodying intelligence.

The Internet of Things (IoT) represents new opportunities for manufacturers to capitalize on the value of data for their business. One of those opportunities is through leveraging an approach called machine learning, which is a branch of artificial intelligence that enables machines (or virtual representations of machines in the cloud) to learn new behaviors based on their external environments, internal health, and changing inputs. Analytics enable humans to give machines a voice – the ability for the machines to provide or derive insights based on data. Making machines more human with a voice in this way doesn't happen overnight, but the road that leads there starts with basic connectivity and data collection. Once data is collected, further levels of analytics maturity may be achieved, including the ability to remotely describe a problem, diagnose the root cause of a problem, and predict a problem that may happen in the future.

Today, there is incredible interest in anything that is even remotely related to #Artificial Intelligence (AI). AI is dominating the conversation on a variety of levels. Philosophers and thinkers are debating the moral implications and risks for human kind of a world where intelligent machines are ubiquitous. In the media, it seems that a new movie or TV series on AI is launched every month. Academic papers on the topic are receiving attention from far beyond the scope of the usual research audience.