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.

In the frantic race for artificial intelligence, driven by GAFA[1], with its increasingly efficient algorithms and ever-faster automated decisions, engineering is king, supporting this highly-prized form of innovation. So, does philosophy still have a role to play in this technological world that places progress at the heart of every objective? Armen Khatchatourov, a researcher in philosophy at Télécom École de Management, describes his own approach as acting as "an observer who needs to keep his distance from the general hype over anything new". Over the past several years he has worked on human-computer interactions and the issues of artificial intelligence (AI), examining the potentially negative effects of automation. In particular, he analyses the problematic issues arising from legal frameworks established to govern AI. His particular focus is on "ethics by design".

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.

When I left Big Firm, it was for personal reasons. I did not leave shaking my fist at a horrible culture and history of injustice. I did not experience that. I started thinking about a professional life staring at a different set of walls. I began to question my place in the Big Firm ecosystem and whether I could succeed without the safety net. For many attorneys, the choice will not be theirs to make.

The way Facebook is designed means people are destined to fall out and suffer severe emotional wounding, claims one professor. The site forces people to talk carelessly and not listen to each other, according to Glenn Sparks. And because people are "friends" with so many people, there are a lot of people to fall out with and users experiences traumatic scenarios as a result, the professor and communications expert claimed. All of that happens because the site is terrible at letting people talk with nuance, he said. That means that people are unable to express their feelings about politics or other sensitive topics properly – and end up falling out.

A new app promises to let people take selfies with anyone they like – including dead people or celebrities. ELROIS, a South Korean company, only needs a 3D scan of any person's face for its "With Me" app. Once it has one, it can'resurrect' that person – allowing them to react to what you say and move around, as well as letting you take pictures with them. The app has been explicitly created to allow people to recreate the image and personality of someone who has passed away, according to its creators. They said that it is a way of overcoming a broken heart by interacting with a virtual recreation of their deceased loved ones.

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI) awareness in the press. But most people probably can't name 3 applications for machine learning, other than self-driving cars and perhaps their voice activated assistant hiding in their phone. There's also a lot of confusion about where the Artificial Intelligence program actually exists. When you ask Siri to play a song or tell you what the weather will be like tomorrow, does "she" live in your phone or in the Apple cloud? And while you ponder those obscure question, many investors and technology recommenders are trying to determine whether,,, or will provide the best underlying hardware chips, for which application and why.