bias

Our Own Wall.

~ Taran ~ 2 Comments

One of the more profound biases we have when it comes to our technology is just how stupid we can be. Ignorant, too, because we often forget just how little we know in the grand scheme of things which is well beyond our sight at any time, no matter how well informed we are.

It’s the Dunning-Kruger effect at levels depending on which mob we talk about and what that particular mob is made up of. Are they more open minded than close minded? Are they open to surprises?

We always end up surprised by something, and that’s a good thing. We don’t get new knowledge without being surprised in some way.

To be surprised means that something has to come leaping out of the dense grass of our biases and attacks us or helps us in some way. Surprise is important.

Personally, I like being surprised because it means something is new to me.

I’m not writing about a chocolate cake lurking in the dark room, I’m writing about expecting a result and getting something different, though exploring a new chocolate cake is also something I don’t mind. No, what I’m writing about is that unexpected outcome that has you wondering why it was unexpected.

That leads us to find out why, and that’s where we get new knowledge from. Asking the right questions.

It occurs to me that in creating this marketing of ‘artificial intelligence’ that we’ve created idiots. I thought we had enough, but apparently we need more. They don’t ask questions. They are better informed than our idiots, mind you, but someone gets to pick what distilled learning model they’re informed about.

I call them idiots not because they give us answers, sometimes wrong, but because they don’t ask questions. They don’t learn. We have a fair amount of systems on the planet we created that are in stasis instead of learning, and we’ve added new ones to the list.

I expect the marketers will send out a catalog soon enough of dumb systems marketed as smart technology.

Meanwhile, new generations may forget questioning, and that seems like it’s something we shouldn’t forget.

Bubbles Distilled By Time.

~ Taran ~ 3 Comments

We all perceive the world through our own little bubbles. As far as our senses go, we only have touch, taste, feeling, hearing, smell and sight to go by. The rest comes from what we glean through those things, be it other people, technology, language, culture, etc.

If the bubble is too small, we feel it a prison and do our best to expand it. Once it’s comfortable, we don’t push it outward as much.

These little bubbles contain ideas that have passed down through the generations, how others have helped us translate our world and all that is in it, etc. We’re part of a greater distillation process, where because of our own limitations we can’t possibly carry everything from previous generations.

If we consider all the stuff that creates our bubble as little bubbles themselves that we pass on to the next generation, it’s a distillation of our knowledge and ideas over time. Some fall away, like the idea of the Earth being the center of the Universe. Some stay with us despite not being used as much as we might like – such as the whole concept of, ‘be nice to each other’.

If we view traffic as something going through time, bubbles are racing toward the future all at the same time, sometimes aggregating, sometimes not. The traffic of ideas and knowledge is distilled as we move forward in time, one generation at a time. Generally speaking, until broadcast media this was a very local process. Thus, red dots trying to get us to do things, wielded by those who wish us to do things from purchasing products to voting for politicians with their financial interests at heart.

Broadcast media made it global by at first giving people information and then by broadcasting opinions to become sustainable through advertising. Social media has become the same thing. How will artificial intelligences differ? Will ChatGPT suddenly spew out, “Eat at Joes!”? I doubt that.

However, those with fiscal interests can decide what the deep learning of artificial intelligences are exposed to. Machine learning is largely about clever algorithms and pruning the data that the algorithms are trained on, and those doing that are certainly not the most unbiased of humanity. I wouldn’t say that they are the most biased either – we’re all biased by our bubbles.

It’s Pandora’s Box. How do we decide what should go in and what should stay out? Well, we can’t, really. Nobody is actually telling us what’s in them now. Our education systems, too, show us that this is not necessarily something we’re good at.

Bias in AI, Social Media, and Beyond.

~ Taran ~ 6 Comments

One of the things that is hard to convey to many people is how bias actually affects things. So I’ll offer a unique perspective, one that involves hamburgers.

All good stories should have a good burger of some sort, whatever your meat or lack of meat allows for. Some people will see ‘burger’ and go for the default of beef in their head, some people will think chicken or turkey or lamb or mushroom or… that right there is a bias.

I’ll go a bit further.

My father, well into his 50s, felt like having a hamburger and I asked him why we didn’t just make them instead of going out and buying some crappy burgers. He admitted something that floored me.

He didn’t know how to make them. Here he was, having lived decades eating burgers, but he never learned how to make burger patties. My father. The guy who always seemed within 10 feet of a burger joint when it came to feeding times.

Now, why was that?

First, he grew up in a Hindu home, and beef was not on the menu at home. He never would have been exposed in that household on how to make a beef patty – or a beef anything, for that matter. So he had an implicit bias from the start on not knowing how to make a hamburger.

He did, according to his oral history, like eating hamburgers, and would go to a place near his school to eat some. His eyes would glow when he discussed that memory, as simple as it might be.

Now, he also got married in the 1970s in the U.S., and Mom handled all the cooking. We cooked burgers there, but he managed to not learn about making the patties. He worked night shift, and so he wasn’t around most of the day anyway. More bias on him not learning how to make a hamburger, which an American of his generation generally considers an art form – but he was not American. More bias.

After decades, he assumed that learning how to make them was beyond him – which seemed peculiar considering how much time and care he would put into an omelette.

If my father were an AI of some sort and you asked him about how to make a beef patty, he would have likely said, “they come in stores.” While not knowing how to make burger patties is a pretty low threshold when compared to human extinction– it’s not hard to see how omitting information can be a handicap and create a bias.

It’s also not hard to see that by creating information or perspectives can also create bias. If we don’t teach AI about weight loss, an AI might suggest amputation for someone wondering how to lose weight – and even recommend low weight prosthetics. Ridiculous, but we never thought kids would be eating tide pods. We don’t exactly have as high a threshold as we might like to think.

There are good and bad biases, and they’re largely subjective. We see systemic biases now over all sorts of things – can you imagine them happening faster and more efficiently?

Aside from the large sweeping biases of culture, the artificial construct of race, and the availability of information, what other biases do you think can impact an artificial intelligence? Social media? Beyond?

Through A Blurry Looking Glass.

~ Taran ~ Leave a comment

_web_Reviewing Code Frosted GlassI’ve been spending, like so many, an inordinate amount of time considering the future of what we accuse of being artificial intelligence, particularly since I’ve been focusing on my writing and suddenly we have people getting things written for them by ChatGPT. I’ll add that the present quality doesn’t disturb me as much as the reliance on it.

Much of what these artificial intelligences pull from is on the Internet, and if you’ve spent much time on the Internet, you should be worried. It goes a bit beyond that if you think a bit ahead.

Imagine, if you would, artificial intelligences quoting artificial intelligences trained by artificial intelligences. It’s really not that far away and may have already begun as bloggers looking to capitalize on generating content quickly thrash their keyboards to provide prompts to ChatGPT and it’s ilk to create blog posts such that when they market their content it pops up in search engine results. Large language models (of which ChatGPT is one) suddenly think this is great content because what is repeated most makes predictive models say, “Aha! This must be what they mean or want!”.

long email ai

This cartoon at Marketoonist.com pokes at the problem with “AI Written, AI Read”, but not the recursive issue. Fortunately, on a personal blog I can use it otherwise it would take us down the path of copyright and the stable diffusion lawsuit.

This has the potential to be a scary future. People who look forward a few steps have started an open letter, which of course I signed, asking to Pause Giant AI Experiments. It’s a symbolic thing, I fully expect, because the people who are behind these artificial intelligences aren’t really too interested in much more than their bottom line, with the notable exception of DeepAI.org as far as I know.

Meanwhile, so many seem to think that these Large Language Models are a nice replacement for the nerds doing their homework because they don’t have to hang out with them. There’s a reason I bring this up. One of the reasons Artificial Intelligences, Large Language Model or otherwise, are considered creative is because they are basically well read people with mega-gobs of information at their digital synapses. They make it easier to find the intersectionality, a realm that has become more and more important and less and less thought about in our society where specializations require diplomas to do things that… one really doesn’t need a diploma to do.

And it doesn’t know everything. For example, I know a bit about Trinidad and Tobago, so I posed a question to ChatGPT related to the economy of Trinidad and Tobago (response in italics):

What are good exports that Trinidad and Tobago could focus on to help build the economy and better the socioeconomic status of it’s citizens?

Trinidad and Tobago has a diversified economy with a strong focus on the energy sector, particularly oil and gas production. However, there are several other sectors that the country could focus on to diversify its exports and boost its economy. Some potential export industries for Trinidad and Tobago include:

Tourism: Trinidad and Tobago has a rich cultural heritage, beautiful beaches, and unique natural attractions such as the Caroni Swamp and the Asa Wright Nature Centre. The country has the potential to further develop its tourism industry and attract visitors from around the world.

Agriculture: Trinidad and Tobago has a favorable climate for agriculture and could focus on exporting products such as cocoa, coffee, citrus fruits, and vegetables. The country could also explore the potential for organic farming and niche products such as spices and herbs.

Creative industries: Trinidad and Tobago has a vibrant cultural scene and could focus on exporting its music, film, and other creative products. The country has a rich tradition of Carnival, which could be further developed as a major tourist attraction and cultural export.

Information and communication technology (ICT): Trinidad and Tobago has a well-educated and tech-savvy workforce, and the country could focus on developing its ICT sector and exporting software, IT services, and other digital products.

Manufacturing: Trinidad and Tobago could focus on developing its manufacturing industry and exporting products such as chemicals, plastics, and processed foods.

Of course, developing any of these industries would require significant investment and effort, and there may be other factors to consider such as infrastructure, labor costs, and market demand. However, focusing on these industries could help diversify the economy and provide new opportunities for the citizens of Trinidad and Tobago.

Sounds very good, right? Devil is in the details.

Tourism is a definite possibility, but the decades long crime situation (which myself and others believe is because of socioeconomics related to the lack of diversity in the economy), as well as flash flooding and a focus on marketing rather than quality… no. I do like that it mentioned the Asa Wright center, and if anyone actually does come down this way, I can happily point you to other places that you won’t find in the tourist brochures.

Agricultural land has been used by the the government to build housing, so arable land use is decreasing with every the Housing Development Corporation creates, as well as with every agricultural plot of land converted to residential, commercial or industrial depending on who greases the wheels.

Manufacturing would be brilliant. Very little is made in Trinidad and Tobago, but if you’re going to be competing with robots and artificial intelligences in the developed world, we can throw that out.

ICT is my personal favorite, coming from a chatbot that has already got people generating code with it. Seriously, ChatGPT?

Everything ChatGPT has presented has been said more than once in the context of diversifying the economy of Trinidad and Tobago, and it’s a deep topic that most people only understand in a very cursory way. The best way to judge an economy is to observe it over time. In the grand scale of global discourse, the estimated population of 1.5 million people in a dual island nation is not as interesting to the rest of the world as Trinbagonians would like to think it is – like any other nation, most people think it’s the center of the universe – but it’s not a big market, for opportunities young intelligent people leave as soon as they can (brain drain), and what we are left with aspires to mediocrity while hiring friends over competency. A bit harsh, but a fair estimation in my opinion.

How did ChatGPT come up with this? With data it could access, and in that regard since it’s a infinitesimal slice of the global interest, not much content is generated about it other than government press releases by politicians who want to be re-elected so that they can keep their positions, a situation endemic to any democracy that elects politicians, but in Trinidad and Tobago, there are no maximum terms for some reason. A friend sailing through the Caribbean mentioned how hard it was to depart an island in the Caribbean, and I responded with, “Welcome to the Caribbean, where every European colonial bureaucracy has been perpetuated into stagnancy.“

The limitations using Trinidad and Tobago as a test case, an outlier of data in the global information database that we call the internet, can be pretty revealing in that there is a bias it doesn’t know about because the data it feeds on is in itself biased, and unlikely to change.

But It’s Not All Bad.
I love the idea that these large language models can help us find the intersectionality between specialties. Much of the decades of my life have been spent doing just that. I read all sorts of things, and much of what I have done in my lifetime has been cross referencing ideas from different specialties that I have read up on. I solved a memory issue in a program on the Microsoft Windows operating system by pondering Costa Rican addresses over lunch one day. Intersectionality is where many things wander off to die these days.

Sir Isaac Newton pulled from intersection. One biography describes him as a multilingual alchemist, whose notes were done in multiple languages which, one must consider, is probably a reflection of his internal dialogue. He didn’t really discover gravity – people knew things fell down well before him, I’m certain – but he was able to pull from various sources and come up with a theory that he could publish, something he became famous for, and something in academia that he was infamous for with respect to the politics of academia.

J.R.R Tolkien, who has recently had a great movie done on his life, was a linguist who was able to pull from many different cultures to put together fiction that has transcended beyond his death. His book, “The Hobbit”, and the later trilogy of “The Lord of the Rings” have inspired various genres of fantasy fiction, board games and much more. 

These two examples show how pulling from multiple cultures and languages, and specialties, are historically significant. Large Language Models are much the same.

Yet there are practical things to consider. Copyrights. Patents. Whether they are legal entities or not. The implicit biases on what they are fed, with the old software engineering ‘GIGO’ (Garbage in, garbage out) coming to mind with the potential for irrevocable recursion of supercharging that garbage and spewing it out to the silly humans who, as we have seen over the last decades, will believe anything. Our technology and marketing of it are well beyond what most people can comprehend.

We are sleeping, and our dreams of electric sheep come with an invisible electric fence with the capacity to thin the herd significantly.