There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to handle machine learning inference work, and they have to find new employees in a relatively shallow pool of available AI talent. None of this is easy, but the trend is irreversibly toward AI, machine learning and deep learning, so decisions need to be made, according to Karim Beguir.

Having an intuition for how machine learning algorithms work -- even in the most general sense -- is becoming an important business skill. As Andrew Ng has written: "Almost all of AI's recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B)." But how does this work? As you might imagine, many exciting machine learning problems can't be reduced to a simple equation like y mx b. But at their essence, supervised machine learning algorithms are solving for complex versions of m, based on labeled values for x and y, so that they can predict future y's from future x's.

Having an intuition for how machine learning algorithms work -- even in the most general sense -- is becoming an important business skill. As Andrew Ng has written: "Almost all of AI's recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B)." But how does this work? As you might imagine, many exciting machine learning problems can't be reduced to a simple equation like y mx b. But at their essence, supervised machine learning algorithms are solving for complex versions of m, based on labeled values for x and y, so that they can predict future y's from future x's.

Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson's adage applies well to AI adoption: The future is already here, it's just not evenly distributed. The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities.

In addition to targeting customers based on inferred wants and needs, a compelling facet of personalization is something Forrester Research identifies as "Operationalizing Emotion", which alludes to customers making purchasing decisions based as much (or more) on emotional experiences than on rational conclusions. Today's market is all the more risky for companies that don't provide a stellar customer experience from start to finish, and it's becoming more common for businesses to suffer longer-term revenue losses for a single negative experience, whether directly experienced by the customer or based on empathy for others' experiences. A quick and personalized response to any customer dissatisfaction seems almost an essential application for businesses that want to stay afloat for the long-haul.

Google is hiring and there are lots of opportunities to do Machine Learning-related work here. Kevin Murphy is applying Bayesian methods to video recommendation, Andrew Ng is working on a neural network that can run on millions of cores, and that's just the tip of the iceberg that I've discovered working here for last 3 months. There is machine learning work in both "researcher" and "engineer" positions, and the focus on applied research makes the distinction somewhat blurry.