Before getting into this report, we have to inform readers that none of the companies discussed below claim to offer software that provides diagnostics, except Cognoa, which has FDA approval to call itself a diagnostic tool. We suspect this is because these companies are not legally allowed to do so. We usually don't refer to a dictionary to determine what constitutes a concept, preferring to create our own informed definitions, such as in our What is Machine Learning? The companies listed in this report seem to provide diagnostics based on that definition, but again, readers should be informed that these companies do not technically provide diagnoses for illnesses and conditions, except Cognoa. Rather, they provide information to users on their symptoms (for legal reasons).

It seems that the majority of AI solutions for payment processing are focused on fraud detection and prevention. Some companies claim to offer straight-through processing software as well. We'll get started with background information about AI in payment processing, and then we'll explore the vendor use cases in depth individually. The companies discussed in this report vary in their densities of AI talent, which is one of the three rules of thumb we use when determining whether or not a company is actually leveraging AI or using it more for marketing purposes. We look for companies with AI talent in their C-suites first and foremost, but perhaps equally important is the number of data scientists employed at the company.

Founded in London in 2010 and acquired by Google in 2014, AI company DeepMind Technologies Ltd. reportedly reduced the amount of energy required to cool Google's data centers by 40 percent. DeepMind reported these results in July 2016, however, the company claims that it first began applying machine learning two years prior to improve energy usage. Specifically, a set of data center operating scenarios and parameters were used to train a system of neural networks. The neural network "learned" how the data center functioned and began identifying opportunities for optimization. Google claims that data was pulled from thousands of sensors located in the data centers.

Chatbots are gradually being adopted into the healthcare industry and are generally in the early phases of implementation. Market research firm Grand View Research estimates that the global chatbot market will reach $1.23 billion by 2025. This projected growth reflects a compounded annual growth rate (CAGR) of 24.3 percent. Healthcare has become an attractive market for companies developing chatbot applications for patients and clinicians. In this article we'll explore 5 representative examples of chatbots in the healthcare industry.

Natural language processing, or NLP, is one AI-based technology that's finding its way into a variety of verticals. We covered the business applications of NLP and where it comes into play in finance broadly in our previous reports. We intend to cover the technology's applications in banking specifically in this report. NLP might allow a company to garner insights that can be used to assess a creditor's risk or gauge brand-related sentiment across the web.

A 2017 Nielsen report titled "Young and Ready to Travel (and Shop)" revealed that the millennial generation travels more than any other generation, including Baby Boomers. The report suggests that, unlike previous generations, the travel industry will need to shift in order to cater to the millennial's unique preferences and "lack of predictability."

Stockbrokerage might be viewed by investors as a traditionally human-based service allowing them to buy and sell equities. When looking at the shift in how stock brokerage is different today compared to the early 2000s, the largest change seems to be in software-based automation. Put simply, a lot of what was being done by humans (such as executing trades, giving advice to investors, discretionary trading) can now be done through software.

According to the US Government Accountability Office, the Federal Bureau of Investigation's database contains over 30 million mugshots of criminals and ID card images from 16 states. This is just one of many law enforcement databases which also contain further identity information, including fingerprints and text data. With needs to improve investigation times and streamline the task of matching suspect images within a pool of numerous identities, government officials, law enforcement offices, and commercial vendors are researching how AI, specifically computer vision, can be used to improve facial recognition. Through our research, we aim to show insights on how various law enforcement agencies and companies are implementing facial recognition technologies. Readers interested in AI for law enforcement might be interested in our founder's presentation at a joint INTERPOL-UN conference on AI in law enforcement given in the summer of 2018.