AI Articles and Analysis about Process automation

Over the last 3-4 years, sentiment analysis has become a more and more common term - understood by marketers and businesspeople alike. The idea is simple: An artificial intelligence system that can detect the emotional “tone” or sentiment of a specific text document (as long as a book, or as short as a tweet).

Episode summary: In this episode of "AI in Industry" podcast, we speak with Manoj Saxena, the Executive Chairman of CognitiveScale, about how AI and automation are being applied to white-collar processes in the healthcare sector.

Robo-advisors are digital platforms that provide automated, algorithm-based financial planning services with little to no human supervision.

Garment and textile manufacturing have historically been labor-intensive industries, as seen in how many of world’s largest fashion, clothing and apparel brands seem to have a significant portion of their products manufactured in Asian countries such as China, India, Bangladesh, Vietnam, etc.

AI and machine learning software are beginning to integrate themselves as tools for efficiency and accuracy within pathology. According to a March 2018 digital pathology report produced by Allied Market Research, such software enables the “procurement, management and interpretation” of information and can be applied to multiple functions within the field of healthcare.

Episode summary: In the last two or three years we at Emerj have witnessed a definite uptick in AI applications like predictive maintenance and heavy industry. Many exciting business intelligence and sensor data applications are making their way into “stodgy” industries like transportation, oil and gas, and telecom - where machine vision has countless applications.

Deloitte, EY, PricewaterhouseCoopers (PwC) and KPMG are among the largest service providers in the accounting industry— collectively referred to as the Big Four. Many of the financial and consulting services offered by these firms - such as advising on investment decisions - involve finding patterns in very large sets of data. This data is often beyond the understanding of a single person (or team of persons) and is often “noisy” and without a set format.

Artificial intelligence -- especially machine learning -- is at it’s best when it is working with a large, analyzable data set, like text. But much of the data in the world isn’t in text form. Instead, it’s in the form of spoken words on video and audio recordings or even live events. This makes reliable voice transcription an important goal for artificial intelligence.