I have a job for the right AI

In the Media archives of this WordPress blog I have 3,467 images; 242 videos and 92 audio files. WordPress provides a hand DESCRIPTION file that I’m almost always too lazy to use. Doing so would make it much easier to find one of these files. Seems like a simple job for an AI to “look” at these (and the post in which they appear) and create a brief description. While we’re on the subject…

I’m pretty sure Google and Apple “know” more about me than I know/remember about myself. While I’m not losing any sleep about this, it would be interesting to see a summary of all that data. Something an AI could knock off without breaking a sweat.

Gmail, Drive, Calendar, Photos, iPhone, Messages, YouTube. Take a look/listen and write a report. Assumptions, judgements… I’m good with whatever form the AI chooses.

I’d be willing to pay for this.

The Scariest Beast Ever Created

“Many lesser troubles will appear in everyday private life. Simulated fake AI porn will likely be a big annoyance, since people like to pay attention to that. If you’re a gamer, AIs will be trained to cheat at your games. If you’re a schoolteacher, you’ll look askance at the kid at the back of the class who never raises his hand but turns in essays that read like Bertrand Russell. Fraudsters might fake the voices of your loved ones, and invent scams to demand money over the phone.”

AI is the Scariest Beast Ever Created, Says Sci-Fi Writer Bruce Sterling

How to use AI to do practical stuff

An overview (with lots of links) on how to use AI by Ethan Mollick.

Large Language Models like ChatGPT are extremely powerful, but are built in a way that encourages people to use them in the wrong way. When I talk to people who tried ChatGPT but didn’t find it useful, I tend to hear a similar story.

The first thing people try to do with AI is what it is worst at; using it like Google: tell me about my company, look up my name, and so on. These answers are terrible. Many of the models are not connected to the internet, and even the ones that are make up facts. AI is not Google. So people leave disappointed.

“A quick and sobering guide to cloning yourself”

I recently stumbled upon a Substack article by Ethan Mollick, a professor of management at the Wharton School of the University of Pennsylvania, titled “A quick and sobering guide to cloning yourself.”

“With just a photograph and 60 seconds of audio, you can now create a deepfake of yourself in just a matter of minutes by combining a few cheap AI tools. I’ve tried it myself, and the results are mind-blowing, even if they’re not completely convincing. Just a few months ago, this was impossible. Now, it’s a reality.”

As a former radio guy I was more interested in the audio portion of Professor Mollick’s experiment.

“Clone a voice from a clean sample recording. Samples should contain 1 speaker and be over a 1 minute long and not contain background noise. Currently works best on US-English accent.”

I created an account at 11ElevenLabs, picked a voice and uploaded some text from my blog bio.

For $5 a month (first month free) you can synthesize your own voice. I uploaded a recording of me reading that same bio.

Finally, I pasted in some text from one of my blog posts and my voice was “cloned.”

Just to be clear, the first audio is one of their “voices.” The the second audio is a recording of my voice. The real me, if you will. And the third audio is the synthesized Steve voice. I’m not sure someone could tell the difference. I sort of prefer the synthesized reading over my own. In two years (?), this technology will be so good it will be nearly impossible to tell real from cloned.

“It’s time to pay attention to A. I.”

I confess I haven’t been paying much attention to the ChatGPT phenomenon but the video below is a pretty good explanation.

In a follow-up video (Google Panics Over ChatGPT), we’re told Microsoft CEO Satay Nadella is reported to have said “…its impact will be at the magnitude of the personal computer, the internet, mobile devices and the cloud.” That’s a big impact. Or hyperbole. Or both.

Waiting on my AI buddy

I’d like to live long enough to have a chat with an AI that:

  • Has “read” my 5,700 blog posts
  • Has read my 4,000 Mastodon posts
  • Has watched/analyzed my 525 videos on YouTube
  • Has “read” the 858 books in my LibraryThing

Because there is no one who has or will get to know me that well. What would it be like to interact with an intelligence that “shares” those experiences? I cannot imagine.

AI Superpowers

“In his book “AI Superpowers: China, Silicon Valley, and the New World Order,” Kai-Fu Lee, a well-known artificial-intelligence expert, venture capitalist and former president of Google China, argues that China and Silicon Valley will lead the world in AI. But his highly readable book covers a lot of other ground as well, and among the most interesting insights are his descriptions of the differences between Chinese and Silicon Valley tech culture.” (Washington Post review) Not quite finished but here are some excerpts:

If artificial intelligence is the new electricity, Chinese entrepreneurs will be the tycoons and tinkerers who electrify everything from household appliances to homeowners’ insurance. […] Ambitious mayors across China are scrambling to turn their cities into showcases for new AI applications. They’re plotting driverless trucking routes, installing facial recognition systems on public transportation, and hooking traffic grids into “city brains” that optimize flows.

China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective. […] The core motivation for China’s market-driven entrepreneurs is not fame, glory, or changing the world. Those things are all nice side benefits, but the grand prize is getting rich, and it doesn’t matter how you get there.

Adoption of mobile payments happened at lightning speed. By the end of 2016, it was hard to find a shop in a major (Chinese) city that did not accept mobile payments. […] By the end of 2017, 65 percent of China’s over 753 million smartphone users had enabled mobile payments. […] It got to the point where beggars on the streets of Chinese cities began hanging pieces of paper around their necks with printouts of two QR codes, one for Alipay and one for WeChat. […]

For 2017, total transactions on China’s mobile payment platforms reportedly surpassed $ 17 trillion—greater than China’s GDP. […] Data from mobile payments is currently generating the richest maps of consumer activity the world has ever known, far exceeding the data from traditional credit-card purchases or online activity captured by e-commerce players like Amazon or platforms like Google and Yelp. […] Recent estimates have Chinese companies outstripping U.S. competitors ten to one in quantity of food deliveries and fifty to one in spending on mobile payments. China’s e-commerce purchases are roughly double the U.S. totals, and the gap is only growing.

U.S. federal funding for math and computer science research amounts to less than half of Google’s own R& D budget.

Between 2007 and 2017, China went from having zero high-speed rail lines to having more miles of high-speed rail operational than the rest of the world combined.

It no longer makes sense to think of oneself as “going online.” When you order a full meal just by speaking a sentence from your couch, are you online or offline? When your refrigerator at home tells your shopping cart at the store that you’re out of milk, are you moving through a physical world or a digital one?In the United States we build self-driving cars to adapt to our existing roads because we assume the roads can’t change. In China, there’s a sense that everything can change—including current roads. Indeed, local officials are already modifying existing highways, reorganizing freight patterns, and building cities that will be tailor-made for driverless cars.

Much of today’s white-collar workforce is paid to take in and process information, and then make a decision or recommendation based on that information—which is precisely what AI algorithms do best.

Going Postal

Once a month every company should survey (anonymously) all employees, asking which of their co-workers is most likely to come to work shooting. Maybe the top three most likely employees. Not sure what you’d do with that information but it wouldn’t surprise me if this woman’s name would have made the list.

Once ‘smarter’ AIs are running offices (monitoring all communication; video; etc) they’ll spot the shooters days in advance. Their metal detectors will know they’re packing when they walk through the door.

Now, take a few minutes to make your own list. The three people in your life most likely to go postal. You’re welcome.