And Here are some Friday Big Ideas.
In “Literary Theory for Robots: How Computers Learned to Write” by Dennis Yi Tenen, the author explores the history of chatbots and their predecessors, tracing the blurring line between authors and the tools they use. From medieval Arabic divination circles to 17th-century German cabinetmakers, Tenen discusses the parallels between past literary robots and today’s AI. The Washington Post review noted that while the book may be confusing at times, it offers reassurance that the current AI moment is part of a larger historical pattern.
MIT Technology Review, too, noted, “We’ve been here before,” exploring the historical context of concerns over technological unemployment and drawing parallels to the current fears surrounding AI and job displacement. It highlights the distinction between the impact on industries as a whole and the effects on individuals, emphasizing the need for a grounded understanding of economics. The article argues against the notion of a jobless future. It emphasizes the importance of using AI to expand the economy and create new jobs while also calling for responsibility and consideration of workers in the development and implementation of AI technologies.
Donald Clark writes that the idea that AI will only augment jobs and not replace them is a lie. AI has already replaced jobs in various industries, such as print advertising, bookshops, and retail. It has also led to losing jobs in translation and online learning. AI technology will have multiple impacts, including job augmentation, job losses for non-adoption, gradual job losses, job automation, the disappearance of legacy companies, and the creation of new jobs. The timing of these impacts is uncertain, but they will happen. Middle management and graduate jobs involving text production are at risk. Human exceptionalism should not be overestimated, as AI is competent in various cognitive tasks.
Computer Weekly examines the potential for making money out of generative artificial intelligence (GenAI) in the channel. GenAI is seen as a game changer that can turbo-charge digital transformation and provide new opportunities for the channel. Use cases for GenAI span various business functions, including sales, marketing, HR, and operations. However, there are risks, such as data privacy and security. The channel can take advantage of GenAI by preparing clients for AI adoption, creating custom AI solutions, offering AI integration services, and providing user education. As the technology evolves, more use cases, certifications, and partner programs are expected to emerge.
An article in Forbes discusses the differences between public AI and private AI. Public AI is accessible to anyone with an internet connection and is built by researchers and developers who share their work openly. Private AI, on the other hand, is designed for data privacy and security and operates on confidential or proprietary data. The article highlights the customization and data sensitivity benefits of private AI and the collaborative and innovative nature of public AI. It also mentions the potential for an enterprise AI infrastructure combining both benefits.
Why do we care?
I prefer this section to be more about alternate opinions, so I will leave you to ponder.
I’m adding a podcast to my schedule, with a live show on YouTube and LinkedIn on Wednesdays at 3 pm Eastern. We’ll cover some of the show’s Big Ideas in discussion format, so your weekend thinking will be augmented by further discussion. Immediately available on YouTube, the show will drop in the podcast feed on Saturdays.