In this week’s roundup, learn how artificial intelligence (AI) is being used to improve contract management and what five developments will shape AI in 2021 and beyond. Explore how machine learning (ML) is being used to further materials science and predict key factors for suicide attempts. Finally, learn why marketers need to use both rule-based and ML-based personalization.
Business Contracts Remain Complex, And Artificial Intelligence Can Improve Contract Management
By David A. Twitch, contributing writer for Forbes.com
Business contracts can be extremely complex, covering a massive amount of details. Many companies struggle to understand, manage, and evaluate the large amount of text involved in these interactions. While AI has been used for basic legal discovery, it is now beginning to be applied to full contract management. Learn how one company, ThoughtTrace, is working to bring the power of AI to the challenge.
These Five AI Developments Will Shape 2021 and Beyond
by Baidu for TechnologyReview.com
2020 was a difficult year for citizens, companies, and governments globally. The spread of COVID-19 required far-reaching health and safety restrictions. AI applications played a crucial role in saving lives and allowing economic resilience. Baidu was at the forefront of many AI breakthroughs. Here are five advances with implications for combating COVID-19 as well as transforming the future of the economy and society.
Why Marketers Need Both Rule-Based and Machine Learning-Based Personalization
By Yaniv Navot, contributing writer for ClickZ.com
Personalization is now a basic standard of service due to rising consumer expectations and competitive pressure. In the past, companies have stuck to rule-based personalization. But in order to scale personalization efforts, relying on this manual approach isn’t efficient. So many are gravitating to machine learning algorithms to assist in the decision-making process. There are advantages and disadvantages to both approaches, which is why many marketers use both.
Accurate Machine Learning in Materials Science Facilitated by Using Diverse Data Sources
By Rohit Bahtra, contributing writer for Nature.com
Scientists are on the constant hunt for materials that have superior properties. Using a range of techniques, they are constantly synthesizing, characterizing, and measuring the properties of new materials. A ML strategy has been developed that exploits the fact that data are being collected in different ways with varying levels of accuracy. This approach was developed to build a model that predicts a key property of materials. Learn more about this approach and the impact it has had on materials science.
Machine Learning Flags Key Risk Factors for Suicide Attempts
By Batya Swift Yasgur, MA, LSW, contributing writer for Medscape.com
A ML model was applied to data on over 34,500 adults and 2,500 survey questions with the goal of identifying predictors of who might be at risk for later suicide attempts. The findings suggest that a history of suicidal behaviors, functional impairment related to mental health disorders, and socioeconomic disadvantage are the three biggest risk factors that can predict a suicide attempt. Learn more about the study, findings, and implications.
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