AI Sector Overviews Articles and Reports

While there have been and continue to be innovative and significant machine learning applications in healthcare, the industry has been slower to come to and embrace the big data movement than other industries. But a snail's pace hasn't kept the data from mounting, and the underlying value in the data now available to health care providers and related service providers is a veritable goldmine. In this editorial, we provide an overview of where healthcare's big data actually comes from, and why providing robust data analytics services in this sector matters.

Predictive analytics for marketing would have been adopted years ago - if only the compute power were more ubiquitous, the data were more accessible, and the software were easier to use. Now "predictive analytics" itself is almost a buzzword, after nearly 30 years of backward-looking marketing tracking.
Today, well over 30 years after the founding of Lotus Software, even medium-sized businesses are often still operating their marketing "scoreboards" in Google Sheets or One Drive... "throw it in a spreadsheet" still works.
But businesses with an eye on the future want to know more than just what happened in the past. "Scoreboards" (most analytics tools and tracking) don't tell you what the score will be. Some of our recent "AI for marketing" articles have gained readership because more and more executives are searching for ways to look forward with their numbers, not just back. SAS defines the term well:

Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels - from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum.

As the term "machine learning" has heated up, interest in "robotics" (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).

In the hundreds of researcher and executive interviews we've been fortunate enough to conduct in the last three years, few artificial intelligence applications are brought up more than marketing and advertising. During talks with execs and researchers from companies ranging from Facebook to Baidu, and IBM to AT&T, marketing has been a perennial theme in conversations of AI's hottest applications.

Today's artificial intelligence market is not easy to quantify. Besides the lack of consensus on a coherent definition for "artificial intelligence" as a term, the field's nascent stage of development makes it difficult to carve out silos or hard barriers of where one industry or application ends, and another begins.