At first glance, generative AI might seem like more of a curiosity than an enterprise-technology tool, said Peter van der Putten, director of the AI Lab at software firm Pegasystems Inc. "Creating cute pictures of a corgi in a house made of sushi isn't exactly a profitable business case, at least not for large enterprises," Mr. van der Putten said. And yet, he said, "generative AI startups are popping up left and right, in areas such as marketing, support, service and other content creation." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Jasper, an Austin, Texas-based startup launched last year, has developed a generative AI platform designed to auto-generate promotional blog posts and other marketing materials. Amid a sharp decline in venture-capital investing deals, Jasper last week announced a $125 million Series A fundraising round, which set its private-market valuation above $1 billion, the company said.

So, you know about the benefits of AI for business -- how it can reduce time spent on manual tasks, improve data-driven decision-making, and allow humans to focus on strategic business initiatives. But have you considered AI for social impact? Initiatives like IBM's Science for Social Good, in which the company partnered with 19 NGO and government agencies, are accelerating the pace of problem-solving to improve global challenges and positively impact human livelihood, while positively influencing business. The cost of computation and the volume of inputs required to solve vast problems using this powerful technology is more affordable and practical than ever before. "AI for good" is becoming an increasingly strategic priority for business and the public sector -- which need each other (and technology) to solve the world's most complex problems.

What is AI? Everything you need to know about Artificial Intelligence Like just about every other business sector, the real estate industry is quickly learning that data is one of its most valuable assets. After 36 years in the business, Keller Williams has plenty of data to work with -- as well as a vast network of real estate agents it can turn to for help keeping that data fresh and useful. When Neil Dholakia joined Keller Williams as chief product officer in 2017, the company was already intent on modernizing. "We didn't want to be the Blockbuster to Zillow's Netflix, or the Blockbuster to Redfin's Netflix," Dholakia told ZDNet. "You name the company that started up in the last five years, or 10 years for that matter -- they all look at us as an old guard company, and we're the Blockbuster that they're going to Netflix."

The next time you have trouble accessing a mission critical application and need to prove your identity, you may be making your case not to network administrators or IT support but to a machine learning algorithm. The oft-discussed machine learning model has already taken root in the information security industry, as several vendors have embraced the technology to improve malware and threat detection and displace traditional signature-based detection. But now machine learning is making its way into identity and access management (IAM) to make rulings for authentications and authorizations. Several experts at the 2017 Cloud Identity Summit this week discussed machine learning in cybersecurity applications for identity management systems, as well the risks and rewards of such applications. The appeal of machine learning in cybersecurity is straightforward: IAM increasingly relies on a growing number of factors – from physical and behavioral biometrics to geolocation data -- to determine the identity and authorizations of an individual, and companies are turning to algorithms to process and judge those factors for IAM systems.