A major problem with LLM AIs is their core nature is not understood by the vast majority of everyone - developers included. They are an embodiment of literature, and if that confuses you you're probably operating on an incorrect definition of them.
I like to think of them as idiot savants with exponential more savant than your typical fictional idiot savant. They pivot on every word you use, each word in your series activating areas of training knowledge, until your prompt completes and then the LLM is logically located at some biased perspective of the topic you seek (if your wording was not vague and using implied references). Few seem to realize there is no "one topic" for each topic an LLM knows, there are numerous perspectives on every topic. Those perspectives reflect the reason one person/group is using that topic, and their technical seriousness within that topic. How you word your prompts dictates which of these perspectives your ultimate answer is generated.
When people say their use of AI reflects a mid level understanding of whatever they prompted, that is because the prompt is worded with the language used by "mid level understanding persons". If you want the LLM to respond with expert guidance, you have to prompt it using the same language and terms that the expert you want would use. That is how you activate their area of training to generate a response from them.
This goes further when using coding AI. If your code has the coding structure of a mid level developer, that causes a strong preference for mid level developer guidance - because that is relevant to your code structure. It requires a well written prompt using PhD/Professorial terminology in computer science to operate with a mid level code base and then get advice that would improve that code above it's mid level architecture.
In more words, "of course it's stupid, it's as complex as a mid-sized rodent where we taught it purely by selective breeding on getting answers right while carefully preventing any mutations which made their brains any bigger".
It's basically plotting the dots of all easily accessible written word to find your words, finding the words that are answers to your words and then charting a line through them no matter how scattered those points may be, and spitting that back out. It doesn't "know" anything nor is it reasoning even if the results are similar.
You have to come into it with the same "these people are only stupid and lack the experience to answer my questions despite thinking they do, they lack the world view to even process how I arrived at the parameters of my question(s)" apprehension like you would if asking reddit about some hazardous thing that would make them all screech. AI is the margarine to that butter.
It's a technology with potential to deliver great value, but there are limitations...
Not to put too fine a point on your metaphor, but the different training methods deployed by ChatGPT vs Claude, for example, changes that a bit regarding who did the “selective breeding”, arguably nurture vs nature, respectively
Isn't this where the research plan implement loop comes in though? Assuming comprehension used effectively?
You should be learning alongside the llm through the research phase of anything. Updating your understanding of what is possible and best practices with rigorous checks and limiting scope to a high fidelity to leave little room for doubt. In-line commenting and questioning and asking for more passes on the living document of the area you are working on and then judiciously breaking it down further when you think there is too broad a scope for an llm to understand and synthesise properly.
If you do end up with too much vagueness, you need to limit scope more or break up the feature, implementation etc to be specific and applies enough to again, properly research and decide the plan.
I guess this is not so easy because lot of it depends on your own ability of reading comprehension, but I've had great success learning niche topics because I research (as a sub agent usually) essentially any topic that is mysterious until every level of the puzzle is properly mapped out to the specificity required.
Do I think most people are doing this? No. So I guess the statistics make sense. It's not intuitive to many people I think - because as you said, it's an embodiment of literature that is a tangled web of thought patterns and perspectives, so you need to pare it's answers down to the specific level, direction and area of ideas you want to get out of it. Way easier to do than it sounds, but it requires finesse in comprehension rather than getting lazy with it - normalcy of deviance comes to mind.
Former author of one of the top 5 facial recognition servers in the world for multiple years running, here's what's going on: the industry has solved this issue, but the potential clients are seeking the lowest bidder, and picking the newer companies, the nepostically created not really players but well connected, and those companies have terrible implementations. This is not a case of the technology not there yet, we solved all these racial bias issues 10 years ago. But new companies with new training sets and new ML engineers that do not know any of the industry's history are now landing contracts with terrible quality models, but well connected sales channels.
This study finds a higher rate of correct identification for black people than for other ethnic groups, whereas a few years ago the problem seemed to be that the software was less effective at identifying black people.
Do you have some insight about why this reversal might have occurred?
To have a high quality facial recognition system it needs to include every possible combination of ethnicity, in addition to all of those they each need to include variations of daylight, of dappled light, of partial obscuring, night time illumination, across every variation of season, variations of expression and face angle, across variations of weather, variations of distance, across variations of things placed on a person's face, and then across all kinds of variations of video compression. All these face image variations in the training set enable the trained model to find and track the features that persist through all these variations. In truth it requires hundreds of millions of facial images to create an accurate facial recognition system. Most new companies and many that have been around for respectable periods are not realizing how much data collection, annotation and additional variation creation it requires for a high quality FR training set. The company I worked at spent 20 years collecting laser scans of real people to then create the augmented real person data set with several hundred million faces.
Your problem is evident in your question. What does “black” mean? It’s entirely subjective. Dwayne Johnson? Liv Tyler? Nelson Mandela? Barack Obama? Mariah Carey?
This is a semantic issue. Ethnic groups are constructs. A system which misidentifies people identifies all people poorly.
It doesn’t track across regions either. People labelled “white” by law in some countries (Brazil, South Africa, etc) would be classified as “black” elsewhere.
In England, the example here, we do not classify people the way the Us does, with its history of “one drop” politics. Many British people considered “white” are “black” in the US.
There is no scientifically valid way of defining who counts as “black” so any discussion of tuning a system based on this definition is a disaster.
Even the people commenting are talking about different groups based on their own culture and prejudices.
I believe the term "Black" in reference to a person when discussing the topic of facial recognition is only used in journalism. There is no "Black" in the facial recognition industry. There really is no identification of ethnicity in facial recognition. It is all just variations of human appearance, in a unbroken spectrum. The natural and ever present population of mixed race people basically destroy any sense of "race" or "ethnicity" within the software. The ONLY time race and ethnicity are included in facial recognition discussions is when some group trains an algorithm with biased data, creating a biased trained algorithm. That is a human failure to understand the problem they trained their data, not grasping the lack of critical data and its impact on the trained model's use. The technology itself operated exactly as designed, it was literally humans not understanding the subtle nature of what they were doing that is the issue.
> Many British people considered “white” are “black” in the US.
I'm also British, can you give an example of that? A minor celebrity/TV personality say?
To the extent you have a point though I think it's irrelevant anyway—they paused the programme because they found, according to whatever definition they measured with, it had that skew.
How recently? We had a home security camera and every time our (Black) son walked up to the door, the camera would classify him as an “animal”. This was as recently as 2022
In the other direction, my camera regularly identifies cats, crows, and shadows as people. I think recognition in security cameras has a very long way to go.
I think for people starting out - rule 5 isn't perhaps that obvious.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
If want to solve a problem - it's natural to think about logic flow and the code that implements that first and the data structures are an after thought, whereas Rule 5 is spot on.
Conputers are machines that transform an input to an output.
> If want to solve a problem - it's natural to think about logic flow and the code that implements that first and the data structures are an after thought, whereas Rule 5 is spot on.
It is?
How can you conceive of a precise idea of how to solve a problem without a similarly precise idea of how you intend to represent the information fundamental to it? They are inseparable.
Obviously they are linked - the question is where do you start your thinking.
Do you start with the logical task first and structure the data second, or do you actually think about the data structures first?
Let's say I have a optimisation problem - I have a simple scoring function - and I just want to find the solution with the best score. Starting with the logic.
for all solutions, score, keep if max.
Simple eh? Problem is it's a combinatorial solution space. The key to solving this before the entropic death of the universe is to think about the structure of the solution space.
I mean - no. If you're coming to a completely new domain you have to decide what the important entities are, and what transformations you want to apply.
Neither data structures nor algorithms, but entities and tasks, from the user POV, one level up from any kind of implementation detail.
There's no point trying to do something if you have no idea what you're doing, or why.
When you know the what and why you can start worrying about the how.
Iff this is your 50th CRUD app you can probably skip this stage. But if it's green field development - no.
Sure context is important - and the important context you appear to have missed is the 5 rules aren't about building websites. It's about solving the kind of problems which are easy to state but hard to do (well) .
Also, "why these 5 in particular" is definitely not obvious -- there are a great many possible "obvious in some sense but also true in an important way" epigrams to choose from (the Perlis link from another comment has over a hundred). That Pike picked these 5 to emphasise tells you something about his view of programming, and doubly so given that they are rather overlapping in what they're talking about.
Well, for one, by eliminating external tool calling, the model gains an amount of security. This occurs because the tools being called by an LLM can be corrupted, and in this scenario corrupted tools would not be called.
There will be no "unlocking of AGI" until we develop a new science capable of artificial comprehension. Comprehension is the cornucopia that produces everything we are, given raw stimulus an entire communicating Universe is generated with a plethora of highly advanceds predator/prey characters in an infinitely complex dynamic, and human science and technology have no lead how to artificially make sense of that in a simultaneous unifying whole. That's comprehension.
The fear of missing out is driving all of this. The article even states that the majority of users have no use case. Except handing around $20 a day to the service providers.
I just have no words. There will be so many scams and other issues if (or when) OpenClaw is hacked... Identity thefts, bank transfers, deleted accounts, stolen photos, etc.
I gave this advice to a non-developer friend yesterday: There is a huge interest in creating all types of automation. Don’t waste your time doing this also. Someone else is going to create a way to automate things that is much more secure than the extremely insecure manners the crowd is experimenting with right now. Let them experiment, make messes, and probably figure out this method, that method, and 10 others people dream up and try are not good for long term use. Let them drive wreaked infrastructure for a while, and then pick up the working, clean and safe methods 2–3 years from now. Long after this early experimenters crowd have cognitively damaged themselves and intellectually exhausted themselves multiple times over. Let their pain be your future easy solution.
But who am I kidding, this is fashion and has nothing to do with tech.
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