Editor's Note: A representative from Mashable's Branded Content team attended the Olay panel on a Global Skin Analysis Program at Mobile World Congress and gave a firsthand account of the event. This is where Olay premiered their Skin Advisor, which is a platform powered by artificial intelligence. Mashable's Chief Data Scientist, Haile Owusu, sat on this panel. I'd never really considered the myriad applications of artificial intelligence (AI) until I took my seat at the OLAY panel at Mobile World Congress in Barcelona. The subtle ways that AI is disrupting our lives are many and varied - and often in places you'd least expect.

The line for the SXSW panel'Eyes in the Sky: The Future of AI and Satellites' snaked around many corners in Austin's JW Marriot Hotel – understandably, AI coupled with space shit, bring it on. Spaceknow Inc's CEO Pavel Machalek did most of the talking during this session. Spaceknow is a San Francisco based company building an AI system that can process the petabytes of data from the hundreds of commercial satellites circling us up above. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. "We are digitizing the physical world, so we can build apps on top it," Machalek stated. According to the Czech CEO, we're currently going through a sea of change in how we use satellite data.

We are seeing today novel expressions of artificial intelligence (AI) which were just a while ago the stuff of sci-fi: autonomous vehicles, self-learning machines, fiction-writing programs which may win literary prizes. Yet, what if the AI goes awry? What if an autonomous vehicle malfunctions and damages your property? What if an AI robot hacks into a smart city's network and steals every citizen's personal data? Will our current legal liability rules give us satisfactory outcomes when applied to such scenarios?

The line for the SXSW panel'Eyes in the Sky: The Future of AI and Satellites' snaked around many corners in Austin's JW Marriot Hotel – understandably, AI coupled with space shit, bring it on. Spaceknow Inc's CEO Pavel Machalek did most of the talking during this session. Spaceknow is a San Francisco based company building an AI system that can process the petabytes of data from the hundreds of commercial satellites circling us up above. We're covering the weird and wonderful tech at SXSW, join us in the fun. "We are digitizing the physical world, so we can build apps on top it," Machalek stated. According to the Czech CEO, we're currently going through a sea of change in how we use satellite data.

In an economy where value is generated by digital efficiency, I believe that every organization should strive for digital flow. For an introduction to digital flow, see Digital Transformation Isn't A Goal. It's A Journey.There are three central forces of flow: Design Thinking, Platform Thinking, and Open Thinking. This article focuses on Design Thinking. Digital technology is complex, but users should never know it.

Microsoft claimed to reach a 5.9 percent word error rate last October using neural language models resembling associative word clouds. At the time, the company believed 5.9 percent was equivalent to human parity. But, IBM says it's not popping the champagne yet. "As part of our process in reaching today's milestone, we determined human parity is actually lower than what anyone has yet achieved -- at 5.1 percent," George Saon, IBM principal research scientist, wrote in a blog post this week. IBM reached the 5.5 percent milestone by combining so-called Long Short-Term Memory, an artificial neural network, and WaveNet language models with three strong acoustic models.

The word "trust" pops up a lot in conversations about human-robot interactions. In recent years, it's crossed an important threshold from the philosophical fodder of sci-fi novels into real-world concern. Robots have begun to play an increasing role in life and death scenarios, from rescue missions to complex surgical procedures. But the question of trust has largely been a one-way street. Should we trust robots with our lives?

I;m rewriting a project to use Node.js. I;d like to keep using MySQL as the DB (even though I don;t mind rewriting the schema).

I created a dataframe with 3 columns, feature_1, feature_2, and target, with the goal of having feature_1 and feature_2 predict the target. I standardized feature_1 and one-hot-encoded feature_2 (which has 100 categories and therefore creates 100 columns). I know for sure that feature_1 and target are correlated and so is feature_2 and target. The target is numbers from 1 to 10 and I create a correlated categorical (feature_2) variable by making a portion of the categorical values map perfectly with the target, for example, when target is a 3, set feature_2 to "A". Creating a model with only feature_1 vs target I get val_loss of 7.60.