All fair points, and I don’t deny predictive text generation is at the core of what’s happening. I think it’s a fair statement that most people hear “predictive text” and think it’s like the suggested words in a text message, which it’s more than that.

I also don’t think Turing Tests are particularly useful long term because humans are so fallible. We too hallucinate all the time with our convictions based on false memories. Getting an AI to have what seems like an emotional response or show uncertainty or confusion in a Turing test is a great way to trick people.

The algorithm is already a black box as is the mechanics of our own intelligence. We have no idea where the ceiling is for this technology yet. This debate quickly goes into the ontological and epistemological discussion about what it means to be intelligent…if the AI predictive text generation is complex enough where you simply cannot tell a difference, then is there a meaningful difference? What if we are just insanely complex algorithms?

I also don’t trust that what the market sees in AI products is indicative of the current limits. AGI isn’t here yet, but LLMs are a scary big step in that direction.

Pragmatically, I will maintain that AI is a different form of intelligence because I think it shortcuts to better discussions around policy and how we want this tech in our lives. I would gladly welcome the news that tells me I’m wrong.

Talk to anyone who consumes Fox News daily and you’ll get incorrect predictive text generated quite confidently. You may also deny them their intelligence and lack of humanity with the fallacies they uphold.

I also think intelligence is a gradient—is an ant intelligent? What about a dog? Chimp? Who gets to draw the line?

It very may be a very complex predictive text generator that hallucinates but I’m concerned that it minimizes its capabilities for better or worse—Its ability to maintain context and has enough plasticity to reason and change its response points to something more, even if we’re at an early stage.

First, we don’t understand our own neurons enough to model them.

AI’s “neuron” or node is a math equation that takes a numeric input with a variable “weight” that affects the output. An actual neuron a cell with something like 6000 synaptic connections each and 600 trillion synapses total. How do you simulate that? I’d argue the magic of AI is how much more efficient it is comparatively with only 176 billion parameters in GPT4.

They’re two fundamentally different systems and so is the resulting knowledge. AI doesn’t need to learn like a baby, because the model is the brain. The magic of our neurons is their plasticity and our ability to freely move around in this world and be creative. AI is just a model of what it’s been fed, so how do you get new ideas? But it seems that with LLMs, the more data and parameters, the more emergent abilities. So we just need to scale it up and eventually we can raise the.

AI does pretty amazing and bizarre things today we don’t understand, and they are already using giant expensive server farms to do it. AI is super compute heavy and require a ton of energy to run. So, the cost is a rate limiting the scale of AI.

There are also issues related to how to get more data. Generative AI is already everywhere and what good s is it to train on its own shit? Also, how do you ethically or legally get that data? Does that data violate our right to privacy?

Finally, I think AI actually possess an intelligence with an ability to reason, like us. But it’s fundamentally a different form of intelligence.

Speaking as a designer, it’s important to separate the style/trend of a UI from its function. I think what you’re looking for is actually UX design.

As a discipline, User Experience uses evidence-based research to understand how and why users behave they do. This leads to specific design patterns and principles that underlie all the good UI design seen from the giants like Apple, Google, Microsoft, etc. It gives you the language to evaluate designs. This is the foundation of your UI and the rest is just style — fonts, colors, imagery and icons which is subjective and less important. I lost ambition to be a trendy UI designer, so every design looks the same, but usability will shines through. Clean, simple and accessible is timeless.

Study the articles from nngroup.com. They pretty much established the field of UX Design, with content talking about user behavior in the 1990s. https://lawsofux.com is a more attractive and consumable option, also heavily influenced by NN Group. Finally, accessible design is good design for all, not just those with disabilities. Understand the guidelines set by the W3C for accessibility, like minimum font sizes or contrast ratios for colors.

The point is not to say all is lost so fuck it, but to highlight that maybe there are systemic issues with an unregulated free markets. Networks have consolidated into a handful of streaming services to a point where there are really no other options for consumers.

What are you going to do? Read a book? Go back to DVDs? They can afford the relatively few people willing to take an all or nothing proposition to squeeze consumers for all they got. They are also really good at lobbying to keep the law on their side to keep it that way.

Good concept in theory but consolidation of streaming services to a handful of providers in an $88 billion dollar industry means the reality for most is that you can culturally isolate yourself by not consuming or seek illegal means of getting your entertainment.

Voting with your dollars works for mom and pop shops, but a loss in viewership due to changes in fees was calculated and note in the ledger.

Yep, I spent a month refactoring a few thousand lines of code using GPT4 and I felt like I was working with the best senior developer with infinite patience and availability.

I could vaguely describe what I was after and it would identify the established programming patterns and provide examples based on all the code snippets I fed it. It was amazing and a little terrifying what an LLM is capable of. It didn’t write the code for me but it increased my productivity 2 fold… I’m a developer now a getting rusty being 5 years into management rather than delivering functional code, so just having that copilot was invaluable.

Then one day it just stopped. It lost all context for my project. I asked what it thought what we were working on and it replied with something to do with TCP relays instead of my little Lua pet project dealing with music sequencing and MIDI processing… not even close to the fucking ballpark’s overflow lot.

It’s like my trusty senior developer got smashed in the head with a brick. And as described, would just give me nonsense hand wavy answers.

This is what I thought as well. Creaming butter and sugar properly gives the cookie better structure and spread less.

The butter also needs to be the right temperature before baking—chilling dough is sometimes needed. Also regularly scrapping the sides of the bowl while mixing is important to have a nice homogeneous cookie without gobs of dry flour or butter.

I’ve definitely seen GPT-4 become faster and the output has been sanitized a bit. I still find it incredibly effective in helping with code reviews where GPT-3 was never helpful in producing useable code snippets. At some point it stopped trying to write large swaths of code and started being a little more prescriptive and you still need to actually implement snippets it provides. But as a tool, it’s still fantastic. It’s like a sage senior developer you can rubber duck anytime you want.

I probably fall in the minority of people who thinks releasing a castrated version of GPT is the ethical approach. People outside the technology bubble don’t have a comprehension of how these models work and the capacity for harm. Disinformation, fake news and engagement algorithms are already social ills that manipulate us emotionally and most people are too technologically illiterate to see how pervasive these problems are already.