So… AI is worse than a third grader at understanding language dialects

By looking at these hype cycles, we can clearly see them, how they fail, and how they morph into the next set of promises, hyping that sucker with the next round of astronomical valuations in this high-tech Ponzi scheme.

Failure begat failure – the founders of failed startups somehow had experience and received funding in the next startup due to some skewed level of experience. However, it is rather promises, illusions and hype that create valuation; failure or mediocrity/simplicity will be damned. Rinse and repeat as the financial world puzzles over how the Silicon Valley economy works.

Not so smart Holmes

Let’s follow the example that started as Smart Homes and Smart Buildings in the 2010s and included sensors to provide inputs for home automation. When the smart thermostat craze died down, it only made sense to use a wider range of sensors, and why not, the entire Internet. This is how the Internet of Things (IoT) was born.

The Internet of Things has never lived up to its promise, although Canadians somehow remain optimistic about AIoT (AI+IoT) and “digitalization” (a word that irritates me for some reason), since there was no way to process all the information from potentially billions sensors This gave way to Big Data, which promised to sort, search and make sense of large volumes of data, sets of which were too large for human comprehension. Elementary, my dear Watson.

But then why bother about people at all? Why not train machines to work with these large data sets? This premise has created a cycle of artificial intelligence hype that I believe is now nearing its peak? The rats are jumping off a sinking ship, but stocks are approaching a peak as the news cycle begins to turn negative.

But rather than making the initial promise of improving energy efficiency in the operation of a home, commercial building, vehicle or city, AI has made a new promise to CFOs that it can replace knowledge workers. After all, the AI ​​had the same information as humans after doing a Google search at work.

AI appealed to executives who saw an evolution toward a five-person workforce at a Fortune 50 company who would collect stock options and let AI do the rest of the work. This frenzy fed on itself, and companies began implementing AI into everyday activities.

Everyone is in training

The terms of service for providers were secretly changed, giving Zoom potential access to call content to train its AI. Yes, those sales report slides from quarterly calls can now become training for a machine that can advise your company as well as your competitors if someone decides to use Zoom tools to summarize the meeting or if the meeting organizer allows AI training .

Reddit, with its communities of experts, began training its AI on answers that experts volunteered to help those who want to learn more about how to do something or just get answers to a college homework assignment.

LinkedIn did the smart thing and started training its AI by opting out, forcing many of us to train its AI without even knowing we were doing it (I opted out as soon as I found out about it). But that’s not the trick: they prey on the vanity of “experts”, those who want to demonstrate their skills and knowledge to headhunters and recruitment companies by asking experts to answer questions.

Many brilliant people in my LinkedIn network are still answering these LinkedIn questions, surprising their peers and inadvertently putting a nail in the coffin of their careers and the careers of their students by training the AI ​​that will replace them.

The original idea of ​​“making the world a better place” about energy efficiency in homes, networks, grids, etc. using IoT and big data has evaporated. The race is now on to fuel the AI ​​hype with tons of electricity and cooling water (the latest madness in this hype madness), assuring Bill Gates that he is not left holding the bag on his small modular (nuclear) reactor, as useless as solar wind , and the battery hits the economy. They all know that the AI ​​hype will fail unless they can provide the power needed to run AI data centers and maintain NVIDIA’s PE ratio quickly enough.

AI: what’s good about this?

I dabbled in AI when it came out, just to see what all the fuss was about. Don’t confuse this with generative design, which I think is a fantastic tool in the hands of people who know what they’re doing – as opposed to those who don’t know how to properly constrain a problem or apply load paths. The result is at the operator level.

Being an engineer, the artistic side of my brain had atrophied a lot, so I decided to try my hand at “art” using Mid-journey AI when it first came out. Using text prompts, I had about a week of fun absorbing various forms of AI-generated “art” before I got bored. (Fig. 3).