AI is transforming the business world, creating enormous benefits for firms and customers. John Deere is one example of a company that is using AI to improve its products and services. The coming wave of foundation models is likely to turn a lot more AI boring, with applications that range from computer programming to story-telling. However, businesses must tread carefully as they deploy more AI, as foundation models can sometimes reflect humanity's darker side. The age of "boring AI" will be anything but Business

Maintenance can be a complex undertaking, requiring thorough planning and an astute understanding of a facility's risk profile. This is particularly true of high-risk facilities. Maintenance does not occur in a'vacuum' and can result in costly downtimes if it is unexpected or unplanned. Even planned maintenance can lead to lengthy downtimes resulting in huge losses. For example, oil refineries in the US alone lose an estimated $47 billion from 213,000 hours of downtime each year.

A few weeks ago, Wharton professor Ethan Mollick told his MBA students to play around with GPT, an artificial intelligence model, and see if the technology could write an essay based on one of the topics discussed in his course. The assignment was, admittedly, mostly a gimmick meant to illustrate the power of the technology. Still, the algorithmically generated essays -- although not perfect and a tad over-reliant on the passive voice -- were at least reasonable, Mollick recalled. They also passed another critical test: a screening by Turnitin, a popular anti-plagiarism software. AI, it seems, had suddenly gotten pretty good.

Today I bring you a fresh perspective on a topic I've written about a lot on TAB: AI imperfection. But, instead of enumerating the ways in which AI systems fail, as I typically do, I'm going to change my point of view to give you a new--and rather convincing--argument that I haven't seen written anywhere else. Let's start from the beginning. A few days ago, before ChatGPT was a thing, I was scrolling Twitter and saw this picture (try to recognize what you're looking at): It took me a whole minute to realize it's just a little doggy. Then it struck me: humans are nowhere near perfect.

The graph represents a network of 1,611 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 07 December 2022 at 13:45 UTC. The requested start date was Wednesday, 07 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 19-day, 4-hour, 28-minute period from Thursday, 17 November 2022 at 16:32 UTC to Tuesday, 06 December 2022 at 21:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The manufacturing industry (largely) welcomed artificial intelligence with open arms. However, planning for mechanical assemblies still requires more than scratching out some sketches, of course--it's a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies. Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential to be riddled with errors. In a quest to ease some of said burdens, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that's accurate, efficient and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multi-part assembly, and then searches for a physically realistic motion path for each step.

The episode goes back to the 90s when IBM achieved a significant milestone when they were able to build a program that can beat "Gary Kasparov" who used to be a world chess champion. Clearly, it's the intelligence displayed by machines rather than living creatures. This artificial intelligence is the discipline of science and engineering for building intelligent machines that can act automatically to accomplish the required task in a complex environment. More and more companies are turning to artificial intelligence and exploring ways to use AI for achieving excellence in multiple areas. A lot of companies like Microsoft, Google etc. are investing a lot in AI.

As a Harry Potter fan and a lover of writing, I was curious to see what would happen if I asked ChatGPT (Generative Pretrained Transformer) to write a Harry Potter fan fiction. So, I fed ChatGPT a few prompts and let it do its magic. The result was a piece of fan fiction titled "The Lost Diadem of Ravenclaw", which follows the story of Harry, Ron, and Hermione as they embark on a quest to find the lost diadem of Ravenclaw. The diadem, which is said to enhance the intelligence of its wearer, has been missing for centuries and is believed to be hidden in the Forbidden Forest. As they journey through the forest, the trio encounters a number of obstacles and challenges, including an encounter with a pack of werewolves and a showdown with the infamous Death Eater Bellatrix Lestrange. Despite the challenges they face, Harry, Ron, and Hermione persevere and eventually find the lost diadem.

While simple calculations and graphs have long been part of the spreadsheet experience, machine learning (ML) has not.

One promising technology is machine learning, or ML. ML and artificial intelligence (AI) have long been used in radiology and other diagnostic fields.