These capabilities will in many cases be integrated into our living systems. We thus face the question: What might remain uniquely human? And does this question even matter? AIs will become better and faster than unenhanced homo sapiens at nearly everything. AIs will become better and faster than unenhanced homo sapiens at nearly everything.

Insights gleaned from business intelligence reports and dashboards are worthwhile for decision support, early warning signals or identifying trends that warrant further investigation before taking action. However, advances in analytical technology (cognitive and machine learning) for example--combined with the proliferation of customer engagement channels has created the potential to fuel omni-channel customer journey design. It should come as no surprise that enterprise-level omni-channel customer analytics, similar to Big Data Analytics (BDA), encompass numerous information sources, multiple technologies (software and hardware), nimble business processes and specialized human expertise in advanced analytics. Not surprisingly, this Hypatia Research Group study found that global organizations are successfully utilizing various analytical techniques in the form of these types of engagement. Further analysis is available in our latest Galaxy study.

Blackbirds, the company's resource group for black employee, created the bot to provide users with facts, events in major cities, and viral content from black users.

When it comes to customer experience, most machines just aren't very smart. Every day, customers around the world are suggested products to buy or use that have little or no application to their lives and for which they aren't a customer. It is frustrating to get recommendations for items you would never actually purchase. Luckily, machine learning is evolving and changing how we use technology in the customer experience. Machine learning is solely focused on writing software that learns from past experiences and creates a more personalized and relevant experience for customers.

Think your dog talks like people? Scientists say you might just be right.

Machine learning, artificial intelligence, deep learning… Unless you've been living under a rock, chances are you've heard these terms before. Indeed, they seem to have become a must for market researchers. Unfortunately, so many precise terms have never meant so little! For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless. My engineers would severely chastise me if I used these words incorrectly--an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.

Many of us know Jack and Jill went up a hill to fetch a pail of water. But did you know that Jill then went to the Georgia Institute of Technology? That's right -- Jill went on to college and is now a teaching assistant in a course on artificial intelligence (AI) in Georgia Tech's computer science program. Jill assists Ashok Goel, professor in the School of Interactive Computing. Jill, implemented on IBM's Watson platform, was first used during the spring 2016 semester to successfully answer frequently asked student questions without the help of humans.

I attended a talk by Andrew Ng at Stanford last week where he discussed the future of artificial intelligence. He reinforced my thinking around the importance of data to the success of any company working on a specific application of A.I. He also confirmed my hypothesis that the large players like Google and Baidu are open sourcing their algorithms and applications in order to gather even more tagged data for their training sets. While there are emerging techniques that may allow for training off of smaller data sets or ones that are completely unstructured in the future, today these techniques are still in their infancy. As a result, to build a defensible business, a company must create a large, proprietary data set that will lead to the best trained algorithms in its field.

The roots of machine and deep learning come out the study of the human brain, but while that may have been a starting point, modern AI doesn't function like a human brain today. As modern machines are structured differently to human brains, it stands to reason that they can learn and process information in different ways as well. In a fascinating blog post, the Google Translate team set out how they can use machine learning to translate between languages, even when the machine has never seen a direct example of this language pairing before. The AI here effectively translates all languages into a brand new language (clustering) it has developed from its learning - an approach to linguistics no human would ever take. Transfer learning of this type, combined with flexible design, has the potential to offer insights you'd never see from a human perspective.

How human vigor and algorithmic rigor are joining forces in the sales function. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. We live in a data-saturated world where a great many of our interactions with other humans happen online. It makes sense then that one of the most human of business activities -- sales -- is currently undergoing a digital renaissance. While the sales function has historically relied on metrics, today there is far more sales-centric data, and far richer data, than ever.