July 18, 2024
1 Solar System Way, Planet Earth, USA

Silicon Valley's new big lie – Computerworld

“Can you go through all your old pitch decks and replace the word ‘crypto’ with ‘AI’?”

This title, part of a New Yorker cartoon by Benjamin Schwartzperfectly captures Silicon Valley's new AI-powered laundry ethos.

AI laundry sounds like just another spin cycle, but it’s actually a complex, multifaceted phenomenon. And it’s important for everyone reading this column — tech leaders, marketers, product creators, users, and IT professionals of all stripes — to understand the hype, distorted emphases, and outright lies we all encounter not just in marketing and sales, but also in the stories we read based on industry claims.

Understanding laundering with AI

AI whitewashing is a deceptive marketing practice that exaggerates the role of artificial intelligence in the product or service being promoted. The phrase is based on “greenwashing,” coined by environmentalist Jay Westerveld in 1986, which involves marketing consumer products as environmentally friendly without considering the environmental impact.

Products that use old-school algorithms are labeled “AI-powered,” taking advantage of the absence of a universally accepted definition of what AI is and isn’t. Startups build apps that plug into a publicly available generative AI API and market them as an AI app. Big, bold AI projects that are supposed to showcase the technology often rely on people working behind the scenes, because humans are the only way to make the ambitious AI solution work.

Let's talk more about this last one.

AI: It's made of people.

Retail giant Amazon launched 44 high-tech stores called Amazon Go and Amazon Fresh, which (as of 2016) used the company's “Just Walk Out” suite of technologies. I told you about this initiative in 2017.)

Amazon’s vision: stores where consumers could walk in, pick their items from the shelves and walk out without encountering a human behind a cash register. Sensors, including cameras, would feed artificial intelligence, which could figure out who bought what and charge accordingly — all without ever having to go through checkout. It looked like shoplifting, but legal.

The system was powered by advanced machine vision that monitored customers and what they picked up. Sensors on shelves transmitted the weight of items removed, confirming the type and quantity of items detected by the cameras. RFID-tagged items also added information to the mix. Advanced machine learning algorithms processed data from cameras and sensors to identify products and associate them with specific shoppers. Electronic entry and exit gates determined who came and went and when.

The algorithms were trained on millions of AI-generated images and videos to recognize products, human behaviors, and human actions.

For seven years, Amazon has been eager to talk about these components of its Just Walk Out technologies. But the tech giant has been hesitant to discuss the roughly 1,000 human beings hired to make it all more or less work, admitting the existence of these employees only after press reports exposed them. Even then, Amazon has obscured the specific role these employees played, saying only that they did not review videos.

Even with 1,000 employees monitoring and enabling 44 stores (verifying three-quarters of orders, according to reports), the technology has been plagued by problems, including delayed receipts, poorly managed orders and high operating costs.

This year, Amazon has been phasing out Just Walk Out technology from its main stores, but Still offers it as a service to other companies.

Another great example of humans behind the scenes of AI is the world of self-driving cars.

Alphabet's Waymo (the operation formerly known as Google's self-driving car initiative) has a NASA-style command center where employees monitor cars via cameras and intervene remotely when there's a problem. (Here's a time-lapse video I recently took of a Ride around San Francisco in a Waymo car.)

General Motors' Cruise subsidiary admits that its self-driving taxis need human assistance on average every 4 to 5 miles, and that each remote-control session lasts an average of 3 seconds.

Other autonomous driving companies are even more dependent on remote human operators. In fact, one German company called Vay Straight Up uses human operators to drive the cars, but remotely. The company recently launched a valet parking service in Las Vegas. The car is remotely driven to you and you drive it wherever you want. When you arrive at your destination, you simply get out and a remote operator parks it for you.

Amazon stores and self-driving cars are just two available examples of a widespread phenomenon.

Why does AI washing occur?

High-level, highly paid technologists building AI systems believe in AI and believe it can solve extremely complex problems. And, in theory, it can. They tell their superiors this. Those leaders tell their boards of directors this. Company executives tell investors this. And, as a company, they tell the public this.

There's just one small problem: it can't be done.

Most companies feel some responsibility for their lofty claims, and so they hide the degree to which the product or service depends on human beings behind the scenes making decisions, solving problems, and allowing the “magic” to happen.

The most shameless companies are undeterred by evidence that their AI is not as capable as they claimed or believed, so they just repeat their claims over and over again. Elon Musk, the CEO of Tesla, comes to mind.

In October 2016, Musk said Tesla would demonstrate fully autonomous driving from Los Angeles to New York by the end of 2017.

By April 2017, he predicted that in about two years, drivers would be able to sleep in their vehicle while it drives itself.

In 2018, Musk postponed his promise of Tesla's full self-driving car to the end of 2019.

In February 2019, Musk promised full self-driving “this year.”

In 2020, Musk claimed that Tesla would have more than 1 million self-driving robotaxis on the roads by the end of the year.

Even this year, Musk claimed that fully autonomous Teslas could arrive “by the end of this year.”

It's not going to happen. Musk is fooling himself and his customers. Musk is the clean lord of AI facelift.

The real problem with AI laundering

The cumulative effect of AI whitewashing is that it leads both the public and the tech industry astray. It fuels the illusion that AI can do things it can’t do. It makes people think that AI is either some kind of universal solution to all problems, or a slippery slope to dystopia, depending on one’s worldview.

AI-powered washing incentivizes inferior solutions, focusing on “magic” rather than quality. Claiming that your dog washing hose is “powered by AI” doesn’t mean that the dog you choose will end up cleaner. It just means you have an overpriced hose.

AI whitewashing distorts funding. Currently, investment in Silicon Valley is completely dominated by both real AI and AI whitewashing solutions. Even astute investors can overlook the hype and lies of AI whitewashing, knowing that the AI ​​story will sell in the market thanks to the naivety of the buyer.

The biggest problem, however, is not mis-selling by the industry, but self-deception. AI solution providers believe that human help is something dishonourable, when in fact I believe that human involvement would be welcomed with relief.

People really want humans involved in their shopping and driving experience.

What we need is more humans and fewer machines. As we speak, AI-generated garbage is flooding the zone with embarrassing prose and falsehoods, along with bizarre, sometimes horrifying images. Google is so eager to replace its search engine with an answers engine that We ended up with glue on our pizza..

What the public really wants is a search engine that points us to human-generated content, or at least a PageRank system that favors humans and labels AI-generated content.

The phenomenon of AI whitewashing is based on the illusion that people want machines to create and control everything, when they don't. It's based on the illusion that adding AI to something automatically makes it better, when it doesn't. And it's based on the illusion that employing people represents a failure of the technology, when it doesn't.

Enough with the AI ​​hoax! Sellers need to tell the truth about AI, and buyers need to demand proof that the AI ​​in the products and services we pay for actually does something useful.

I think I speak for all of us in the tech industry, the tech customer community, and the tech press when I say to Silicon Valley: stop misleading everyone about AI.

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