AI Sector Overviews Articles and Reports

Banks and financial institutions are particularly opaque when it comes to how they implement and leverage AI for their business. Mastercard is a key example of this because they use most of their AI applications internally and have only recently begun to make their technology more transparent to the greater financial industry.

Northrop Grumman Corporation is a U.S. global technology company that provides autonomous, aerospace, cybersecurity, and defense solutions to military and commercial organizations.

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for.

The finance sector has proven itself an early adopter of AI in comparison to other industries. As such, the applications of artificial intelligence and machine learning in finance are myriad. Traders, wealth managers, insurers, and bankers are likely well aware of this in some form.

The focus of the National Aeronautics and Space Administration (NASA) is to provide information to civilian institutions to help them solve scientific problems at home and in space. This requires a continuous stream of raw data under a constantly shifting environment.  According to a 2017 interview with Kevin Murphy, Earth Science Data Systems Program Executive at NASA, the biggest challenge now is not going where no man has gone before, but managing the data.

Natural language processing (NLP) seems to see less use in pharma than AI approaches such as machine vision and predictive analytics, but nevertheless there are a few applications for NLP in pharma. The industry deals mostly with structured data, but in some business areas, unstructured data is the norm. In this article, we discuss how natural language processing can help pharmaceutical companies make sense of their unstructured data and use it to make decisions.

To some, facial recognition may feel like an AI technology with one chief use case and numerous niche ones that are not helpful outside of the client company that requested it. While security and anti-fraud solutions tend to dominate the conversation, there are many actionable possibilities for facial recognition software.

Business leaders in pharmaceuticals may be concerned with how machine vision will affect their operations in the clinical and scientific departments more than those of packaging and administration. However, prioritizing these types of operations may not provide the ROI as it’s hyped up to be. Instead, there are many possibilities for machine vision in pharmaceuticals related to packaging, shipping, and data entry.