Wharton doctoral candidate Alejandro Lopez-Lira used machine learning to analyze companies’ annual reports and assess the top four systematic risks that affect most firms. …Read More
Using a Company’s Own Words to Assess Its Risks
machine learning
The Evolution of Machine Intelligence: How Algorithms are Shaping Our Lives
Wharton professor Kartik Hosanagar, author of “A Human’s Guide to Machine Intelligence,” walks through the evolution of artificial intelligence and points to the developments that lie ahead.…Read More
The Algorithm-Driven Matchmaking Platform for Service Providers and Customers
Marco Zappacosta, co-founder and CEO of Thumbtack, explains his solution to the fragmentation of the gig economy, effectively linking customers with suitable service providers.…Read More
Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns
Using unsupervised machine learning, I introduce interpretable and economically relevant risk factors that characterize the cross-section of returns better than the leading factor models, furthermore, I do not use any information from the past returns to select the risk factors. …Read More
The Acceleration of Artificial Intelligence Through Gaming
Danny Lange, VP of Artificial Intelligence and Machine Learning at Unity Technologies, discusses how new technologies combined with gaming have kicked the pace of machine learning into high gear.…Read More
A Dynamic Duo: The Case for Combining Artificial Intelligence and Human Creativity
Artificial intelligence expert Angela Zutavern explores machine learning’s potential to enhance innovation efforts by complementing our uniquely human powers of creativity.…Read More
Bias-aware AI for Human Capital Management: An Innovative Approach for Algorithmic Job Screening
A well-known maxim in management is that “your people are your greatest asset”. Recruitment strategies in particular have been linked to firms’ innovative capacity, emphasizing the importance of maintaining competitive advantages in HR as a key goal of effective innovation management. …Read More
Adoption of Predictive Analytics: Impact of Model Interpretability
One of the most important trends in business in recent years has been the growth of Big Data and predictive analytics. The trend started with traditional analytics and the emergence of decision support systems. With advances in machine learning (ML), systems can now take in large amounts of data, learn how human decision-makers have made decisions in the past, and make decisions autonomously (achieving human-level or superhuman performance in many activities). …Read More
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and Natural Language Processing algorithms.…Read More
Using Machine Learning to Predict High-Impact General Technologies
Can machine learning techniques be used to predict high-impact, general technologies? We find that an ensemble of deep learning models that analyze both the text of patents as well as their bibliometric information can ex-ante identify such patents, accurately identifying 80 of the top 100 high generality patents in the hold-out sample. …Read More
As Brands Embrace Artificial Intelligence, It’s The Human Touch That Will Drive Value
Justin Reilly (W’10), head of customer experience innovation for Verizon Fios, projects that 80% of value creation in artificial intelligence (AI) will be in business-to-business [B2B] applications, and the rest in consumer services.…Read More