Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Traditional contract lifecycle management (CLM) tools focus on improving document workflows. However, Icertis seeks to take the field to the next level with contract intelligence that uses artificial intelligence (AI) and machine learning (ML) to automatically extract contract data at scale. These tools are designed to structure contracts' commercial, legal and operational data and connect that data to procurement, ERP and human capital management apps to help companies accelerate revenue, reduce costs, improve risk management and ensure compliance. When VentureBeat previously covered Icertis in 2019, the CLM market was expected to be worth $3.16 billion by 2023.

As both the written and spoken-language corpora available to us explode in ubiquity, natural language processing (NLP) has become an invaluable tool to researchers, organizations, and even hobbyists. It lets us summarize documents, analyze sentiment, categorize content, translate languages – and one day potentially even converse at a human level. Like any AI and ML discipline, NLP is a fast-moving discipline undergoing rapid change as both practitioners and researchers dive into the prospects it presents. While the NLP landscape is rapidly changing, here are some of the trends and opportunities I see on the horizon. "Prompting" is a technique that involves adding a piece of text to your input examples to encourage a language model to perform a task you're interested in.

Thirteen percent of calls in the healthcare industry are disconnected before the caller is routed to an agent, and 67% of callers hang up the phone because they are frustrated at not being able to speak to a representative, according to a 2019 survey finding from 8x8, a unified communications vendor. In 2021, call center frustration persists for most healthcare customers. "The most common issues in healthcare call centers revolve around inefficient and expensive operations," said Joe Hagan, chief product officer at LumenVox, a speech recognition vendor. "As a result of the rapid shift to remote work in early 2020, it became clear that more often than not, contact centers have disparate systems and incompatible software making it difficult to meet the increased call volumes and demands on live agents." Being in the midst of the COVID-19 pandemic hasn't helped, either.

Although healthcare is squarely in the era of big data and data analytics, it remains difficult in clinical research to accurately identify patients with complex conditions like valvular heart disease through medical records. And if researchers cannot identify these patients, they cannot study them, track practice patterns or conduct population management. Part of the problem is that the current methods used to identify highly specific conditions like valvular heart disease use diagnosis or procedure codes. These were created primarily for billing purposes and often are not very useful for clinical care because they can be quite nonspecific and not include detailed data about the condition. "For example, a patient with moderate or severe aortic stenosis, which is a narrowing of one of the primary heart valves, is entirely different than a patient with mild valve disease," said Dr. Matthew Solomon, a cardiologist at the Permanente Medical Group and a physician researcher at the Kaiser Permanente Division of Research in Oakland, California.

This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Robots, AI and NLP in Drug Discovery. Abstract: Natural Language Processing (NLP) has been used in drug discovery for decades.

Every one is talking about the mighty, great, futuristic language model by OpenAI, founded by Tesla CEO Elon Musk, Y Combinator partner Sam Altman and other Silicon Valley big shots like Google researchers and ex CTO of Stripe. It is truly eye opening. We also want to tell you how exciting it is. Why is GPT-3 so hyped right now? Probably because GPT-3 has the coolest video demos ever: based on just a few English sentences it can generate a TODO app (write code by itself), generate Excel spreadsheets, automatically translate, generate quizzes based on content. Every one is writing about GPT-3, but because we are technical, our article will give you the important technical details and background you need to understand OpenAI's GPT-3.

All said, I'm extremely excited to see which new technologies are built on GPT-3 and how OpenAI continues to improve on its model. Increased attention and funding in NLP and GPT-3 might be enough to ward off fears from many critics that an AI winter might be coming (myself included). Despite the shortfalls of the model, I am hoping that everyone can be optimistic about a future where humans and machines will communicate with each other in a unified language and the ability to create tools using technology will be accessible to billions of more people.

You may have heard how artificial intelligence (AI) is being deployed within the information industry to combat fake news, detect plagiarism, and even recommend content to users. Until now however, AI has had minimal impact on the content creation and editorial functions of the publishing ecosystem. For scholarly publishers in particular, AI capabilities have advanced to a degree that they can actually automate significant portions of their workflows, with massive implications for their businesses, their authors and the research community. AI is a method by which humans train machines to identify patterns and learn new patterns. It involves developing algorithms that enable machines to quickly process large swaths of data, recognize the patterns within that data, and make decisions or recommendations based on that analysis.

Huff: The last few years is when the use of artificial intelligence (AI) and machine learning (ML) technology has come into its own; it's not just an idea …

There's been a huge amount of focus on the improvements artificial intelligence can make to individual consumers, Dave Damer, CEO and Founder of Testfire Labs, explores the strategic productivity gains that could be recognized by SMEs, corporates, and other organizations. Spend any time reading about artificial intelligence (AI), and you might think all the innovation is in the consumer space. While there's plenty of exciting development -- the growing skills of Alexa, the natural language processing (NLP) of Google Assistant, or the ubiquity of Siri, that's only a fraction of what AI and NLP can do. There's another, seemingly intractable problem where AI and NLP could be revolutionary -- business productivity, specifically the area that gets a big collective eye roll from employees -- meetings. The idea of most meetings is to draw people together, discuss ideas and options, create a consensus, and develop actions to achieve business tasks. That's the ideal, the reality is a little different: This is compounded as workforces become more culturally and geographically diverse.