The race to create more advanced artificial intelligence continues, realized most recently in the creation of personal digital assistants to help with daily tasks. Microsoft (MSFT) CEO Satya Nadella believes that those on the forefront of designing A.I. must remain principled in their approach. "I definitely fall in the camp of thinking of A.I. as augmenting human capability and capacity and then distilling a set of principles," Nadella said to CNBC's Kelly Evans on "Power Lunch" on Wednesday. "Algorithmically, for example, how can A.I. be programmed to care for humans, not have bias built in? How can it be trustworthy, how can it be transparent?"

A.I.: Artificial Intelligence--which was released 15 years ago today--was an unprecedented collaboration between two titans of cinema: Stanley Kubrick and Steven Spielberg. The film, based on the 1969 short story Supertoys Last All Summer Long, is set in the late 21st century and tells the story of a robot named David (Haley Joel Osment) who is programmed to feel human love for his parents, Henry and Monica. After Henry and Monica's human son Martin is brought back to life from suspended animation, his jealousy leads him to get David cast off into the wilderness with Teddy, his robotic teddy bear friend. David and Teddy soon befriend a robotic prostitute named Gigolo Joe (Jude Law), and David's quest to become a "real boy" begins in earnest. In 1983, 18 years before A.I. made it into theaters, Stanley Kubrick bought the movie rights to author Brian Aldiss' short story, Supertoys Last All Summer Long.

Time to get back to my Oceans of Data series! This week I am going to share an overview of the artificial intelligence market that includes a fantastic infographic courtesy of Max Wegner, who has graciously provided this content to me. Every year data grows exponentially. If you ever doubted the value of your data, the shocking 26 billion purchase of LinkedIN by Microsoft is proof that data is digital gold. LinkedIn was not bought for the app.

See the forest, see the trees. Here lies the challenge in both performing and presenting an analysis. As data scientists, analysts, and machine learning engineers faced with fulfilling business objectives, we find ourselves bridging the gap between The Two Cultures: sciences and humanities. After spending countless hours at the terminal devising a creative and elegant solution to a difficult problem, the insights and business applications are obvious in our minds. But how do you distill them into something you can communicate? Presenting my work is one of the surprising challenges I faced in my recent transition from academia to life as a data analyst at a market research and strategy firm.

H2O: A Platform for Big Math From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores. Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform.

The h2oEnsemble R package provides functionality to create ensembles from the base learning algorithms that are accessible via the h2o R package (H2O version 3.0 and above). This type of ensemble learning is called "super learning", "stacked regression" or "stacking." The Super Learner algorithm learns the optimal combination of the base learner fits. In a 2007 article titled, "Super Learner," it was shown that the super learner ensemble represents an asymptotically optimal system for learning. H2O Ensemble currently supports regression and binary classification.

Quick question....I've always wondered how people have time to compete on Kaggle AND work? Do you work full time as well and just dedicate your nights/weekend to competitions? I just finished an executive graduate program so will have more time outside of work but the winners seem to be doing this stuff full time.

Who has the advantage in artificial intelligence -- big companies, startups, or academia? Perhaps all three, especially as they work together when it comes to fields like this. One thing is clear though: A.I. and deep learning is where it's at. And that's why this year's newly anointed Andreessen Horowitz Distinguished Visiting Professor of Computer Science is Fei-Fei Li [who publishes under Li Fei-Fei], associate professor at Stanford University. Bridging entrepreneurs across academia and industry, we began the a16z Professor-in-Residence program just a couple years ago (most recently with Dan Boneh and beginning with Vijay Pande).

Much of the buzz around artificial intelligence has surrounded companies focused on general purpose artificial intelligence, as opposed to companies applying AI algorithms like machine learning and deep learning in specific industries. "From healthcare to finance to e-commerce, we're focused on changing people's lives." Taking a look at the core AI companies like Sentient -- companies focused on general-purpose AI applicable across a variety of industries -- a few have already received 50M or more in equity funding. These include Sentient, Ayasdi, Digital Reasoning, Vicarious (whose backers include angels Jeff Bezos, Elon Musk, and Marc Benioff) as well as DataRobot. We used CB Insights data to look at the most well-funded core AI companies, below.

This feature was produced in collaboration between Vox Creative and Pricewaterhouse Coopers. Recode editorial staff was not involved in the creation or production of this content. At a recent conference on artificial intelligence (AI) where I spoke, there was a lot of talk about the significance of AlphaGo. In the popular press, the historic March matches between Korean champion Lee Sedol and Google's AlphaGo AI program were characterized as man vs. machine. The world was captivated as the AI beat the Go world champion 4-1, with Sedel winning just one match.