What do we call the collection of technologies that make up what we used to call "artificial intelligence?" This conundrum reminds me of a Raymond Carver short story (and book) called What We Talk About When We Talk About Love. I was prompted to discuss this issue by a conversation with Jeremy Achin, the young CEO of DataRobot. We were preparing for a collaborative presentation at the Open Data Science Conference in Boston a few weeks ago, and I told him I could present on "The Cognitive Company." Achin, who doesn't mince words, wrinkled up his nose and said he really didn't like the use of the word "cognitive".

Together with The Heart Fund, 42.cx wants to solve public health challenges by modelizing diseases patterns through applied artificial intelligence to support THF doctors during their medical tasks. The generated prediction system will run on portable devices to disrupt healthcare in developing countries and to fight cardiovascular disease. "The Heart Fund combats cardiovascular diseases by offering access to care to millions of children in developing countries. For the first time in history with this partnership, artificial intelligence scientists have access to thousands of electrocardiograms (ECG), blood panels, evaluations and resulting diagnosis. This allows us to build more accurate models than ever before. With our technology a highly qualified diagnosis can be performed at thousands of different locations at the same time, around the clock anywhere in the world. It's practically like having the know-how and knowledge of the best cardiologic surgeons combined and being present at the same time anywhere.,"

What does the creation of new artificial intelligence products look like today, and what do experts in this field foresee realistically happening in the near future? One thing's for sure, the way we work and function in life will change as a result of growth in this field. Listen and find out more. Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast.

Among the list of technologies that have radically changed our economy in the last year is a handful that did not receive the same level of attention as artificial intelligence or self-driving cars. One, in particular, is called Neural Machine Translation (NMT), a major breakthrough in language technology that some believe is a turning point in how business gets done. The Internet and the connectivity it facilitates is primarily responsible for what we now call the global economy. Emails, web pages, and mobile applications have created a marketplace for ideas and products, as well as empowered organizations to collaborate instantly from thousands of miles away. But for as small as the world is today, it can get smaller, and language is a major part of that.

One of the big trends we've seen over the last five years is automation. At the same time, we're also seeing more intelligence built into tools we already have, like phones and computers. Where do you see this process in five years? In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things. You will have more intelligent systems in the physical world, too -- not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world.

During an interview with Walt Mossberg tonight at the Code Conference, Android creator and now Essential Products CEO Andy Rubin showed off his latest creation, the Essential Phone. When it was announced this morning and reservations opened the company didn't say much about when to expect shipments, but in response to a question from Mossberg, Rubin said that he "wouldn't take reservations if it wasn't going to ship in, you know, 30 days or so." He also mentioned that the company's Home assistant would be able to support AI helpers from other companies like Google, Amazon or Apple (if they choose to make them available on its Ambient OS). Essential has its own AI assistant that runs on both the PH-1 phone and Home, but as Rubin put it, "All these people that have ecosystem envy...they have a thing. Like SmartThings as an example which is Samsung's thing...but consumers don't want just Samsung's stuff in their house."

Data scientists generally ascribe to the "machine learning process" which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project. Let's drill down into each of the "7 steps from raw data to insight" to get a sense for how to approach this important process. Step 1: Multiple data streams – where information comes in from numerous source and formats. The data for analysis may come from a data warehouse, data mart, data lake, or even Internet-of-Things (IoT) sensors.

Google CEO Sundar Pichai was obviously excited when he spoke to developers about a blockbuster result from his machine-learning lab earlier this month. Researchers had figured out how to automate some of the work of crafting machine-learning software, something that could make it much easier to deploy the technology in new situations and industries. But the project had already gained a reputation among AI researchers for another reason: the way it illustrated the vast computing resources needed to compete at the cutting edge of machine learning. A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). They told MIT Technology Review that the project had tied up hundreds of the chips for two weeks solid--making the technique too resource-intensive to be more than a research project even at Google.

This is an invitation to collaborate. In particular, it is an invitation to collaborate in framing how we look at and develop machine intelligence. Even more specifically, it is an invitation to collaborate in the construction of a Periodic Table of AI. Thinking about Artificial Intelligence has proven to be difficult for us. We argue constantly about what is and is not AI.

This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameter-free, and usable and easy-to-interpret by non-statisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. Readers are invited to further formalize the technology outlined here, and challenge my proposed methodology.