As a former lighting designer, we would call this “task lighting” and you are completely correct. Generally speaking, task lighting is MUCH more pleasant that simply relying on overhead lights, recessed or otherwise.
189 karma · joined December 5, 2024
Grats to the team.
How is this different from nearest-neighbor within traditional vector search, used by RAG systems? What am I missing?
1. Compute input embeddings 2. Concurrently compute distance-to/logits-of runtime-specified (sic cached) label embeddings 3. Return greatest/closest label
I do appreciate the Kahneman reference.
MacOS certainly feels like a toy to me in many regards.
60% of the time, the framework works, every time.
The initial debacle that was the Recall recall and announcing the level of AI stuffing of win 11 that made me actively try out Linux mint (which has been quite nice and smooth). LLMs help to diagnose and fix items if you aren’t familiar with Linux. You no longer have to scour message boards for up-front answers (only use them as a “confirmation backstop”).
For work I now use macOS which feels like a toy but is ok overall (probably still some personal habit-based things).
If it weren’t for the fact I still develop plugins for a single, very specific windows-only app in my spare time, I would completely ditch windows, with contempt. Outside of Windows I am continually stubbing my toe on every single use of every single MSFT product with which I interact; primarily outlook, teams, and some azure stuff. Their products feel highly “feature optimized” with absolutely zero (perhaps negative) cross-feature cohesion. e.g. it’s as if the various outlook panes and the boxes of content were designed so modularly and independently that they don’t re-size or scale themselves cooperatively.
I perpetually experience stark jamais vu using their products, i.e. I am forced to use them every day and each time feels as unfamiliar, clunky, and unintuitive as the first time I ever touched them.
THEY’RE USING YOUR DATA
As they involve themselves directly, explicitly in the process of scientific research and discovery, it becomes a question of scientific and professional ethics.
Strange times.
- LoZelda Ocarina of time& majora’s mask
- starfox 64
- Mario party
- extreme G racing
- jet force Gemini
- golden eye
- conkers bad fur day
- Mario 64
- Tony hawk
Man… so much nostalgia
I mostly used KA between 2010-2015, and received immense benefit. These were the years it was MOSTLY “just videos”. Having revisited more recently, I found the gamification, quizzes, etc. simply distracting.
My understanding is that he started KA as a private YouTube channel to tutor his own family members, made it public, and it grew in popularity organically.
I haven’t used khanmigo, but given the climate and evolution of LLMs over the last 6 years, it is a completely reasonable goal to attempt to employ them in learning (though it sounds like it was not as effective as planned for.. reasons).
KA never pretended to be “all you need” for learning. It was always at least an excellent supplement to a given curriculum, and Sal’s genuine interest in the videos he narrates always sounded (to me) as palpably genuine which also goes a long way. There is a reason most textbook chapters have an initial section called “motivation”. The primary job of a teacher is to incite interest, and potentially direction at strategic points…
As the adage goes: You can lead a horse to water, but you can’t make it drink.
The names just hit different.
The article was a nice read, but I was having a hard time understanding why attempt to coin a new word when the exact phenomenon is already (very very) well known.
Between the years of 2018-2025, I led very many professional discussions and educational presentations regarding the theory and application of AI/ML in the org I worked (a ~120 person org lacking in any substantive expertise or awareness of the field).
One of the most important topics within those conversations was demystification. Several times I was asked (in earnest, by adults, in public fora) about “terminator/skynet scenarios”, sometimes intended metaphorically (related to the profession) and sometimes literally.
My response every single time:
I don’t fear “skynet”. My biggest fears surrounding the continued proliferation of AI are the mundane, boring, and nascent uses that completely bastardize the world as we know it. Uses like mortgage/loan approval, healthcare coverage approval, hiring, legal adjudications etc., etc.
Uses where the decision outcomes have a direct, major, intimate impact on people’s lives.
I am generally pro-AI/ML (when applied thoughtfully), but my greatest fears are and have ALWAYS been in knowing just how much easier it enables these kinds of decisions to inevitably be outsourced or made in an otherwise (more) thoughtless, dehumanizing way.
While it’s easy (and at times cathartic) to criticize big corp leaders, the CEO and company described in the article gives me a visceral reaction deeply within the uncanny valley that is actually difficult to describe.
sometimes starting from scratch just faster and/or easier.
- any project that involves hardware or physical objects (especially ones you are manufacturing!) is impressive
- the craftsmanship
- references to established standards
- documentation
- actually publishing
As others have said here you can drop the “wannabe” tag (or don’t, we’re all wannabes at heart :))
Nice job and keep up the good work!
I would have liked a comparison to something like Kanban which tries to aggregate to a (seemingly?) similar metric but is a more prominently known system.
With interest in this space you may be interested to check out:
- That Open Company in the BIM industry (not personally affiliated, but admirer). - Fovea ArchVision (also not directly affiliated, but can attest that the team are great people.)
It is _intelligent_ in the sense that it optimizes a thing that is hard for humans to not anthropomorphize.
Things like percolation theory, swarm theory, and others are similar topics in “complex systems”. Neural networks are interesting because they combine aspects of both complex systems and dynamical/adaptive systems (ie systems with a feedback loop).
A neural network at its very core is a function fitting algorithm. It stores matrices of parameters (ie weights and biases) such that every parameter (and combinations thereof) captures a relationship of your data in exactly the same way the slope and intercept are obtained through Linear Interpolation. All of the Regularization tricks applied just are attempts to incentivize a given parameter to not encode any trends too specifically (think an instance vs a type).
In this way you can ask yourself “how many aspects of your data are required to capture it adequately?” This is what scaling offers.
Contributors responded by going to delete their own respective contributions en masse. Upon doing so, were banned by the platform mid-process which then led to people going back to revise their contributions to be false rather than deleted.
I guess that’s “LLM related”
> “insultingly naive”, “overly simplistic”, “immature and self absorbed”
..exactly what I would expect from someone partly or majority through a PhD about tools. I like tools, their design, creation, and deployment more than average (though probably less than the author), but you need to come up for air, man.
Your interpretation of that phrase is oddly specific, highly esoteric, and completely different from EVERYONE not doing a PhD on tools.
> AI is just a tool
That word “just” seems to be doing a lot of heavy lifting for you. This sentiment is not intended to downplay the importance of tools on society. The generally understood intent of this phrase is to quell the current hysterics and to reassure anyone unfamiliar with the term _back propagation_ that AI, in fact, is neither alive nor sentient (in the sci-fi sense), and that it is “just” statistical modeling. Do not mystify the technology.
> a car/hammer is just a tool
While these are unanimously considered tools, I could entertain a long discussion and the “particular specialness” of tools and things that are inherently dynamical systems.
> what prototyping _should_ be about
The huge majority of the rest of your rant (or what I read of it) is awfully presumptuous and weirdly confrontational. Who can say what prototyping _should_ be? You of many people should understand that the creation and use of tools is contextual, sentimental, highly personal, intimate even.
> “But artificial intelligence… intends us not just to sit forward
From the first part of “The phrase”… AI is inanimate. Humans DO tend to anthropomorphize, and the whole point of saying “it’s just tool” is to remind people that… inanimate things don’t have an intent!
> our tools are using us
I find this notion somewhat trite. “The tail wagging the dog”. I’m not arguing that we aren’t impacted by the tools we use nor that the use and proliferation of AI is not impacting society, but it takes a PhD level of mental gymnastics to push that concept as far as you have in your rant.
The important takeaway though, is that you are a human, with agency and autonomy.
> it only matters how you use it
The implication of “How you use it” is that YOU GET TO CHOOSE. what you think, how you think, “how you use it”™, and even whether you use it! The choice is yours.
I don’t myself have a PhD, but seriously, do yourself a favor and come up for air. Read the parable of “The Empty Boat”.
LLMs are good at traversing traditional file systems because file/tree-like structures very likely encompass an overwhelming supermajority of their training set. By contrast, other approaches like graph-based vector/data discovery via sql queries seem equally or more promising (to me), not to mention the ability to run curl/http queries. Either way, it’s still all prompt engineering approaches to discover/manage context. In this way, files seem like a “local minimum” ie a medium that dually optimizes interpretability and accessibility for both LLMs AND humans.
Just riffing here, but this could even broadly be considered a discussion of memory-vs-storage (somewhat analogous to fluid vs crystallized memory in humans). In this way, one could imagine the models performing context compaction/backup by periodically dumping their context to files (or some other non-volatile storage) WITHOUT coming back to feature space.. just dump/load the tokens directly.
Personally, I am much more interested in even other approaches like VLMs (using visual tokens), architectures like auto encoders and its variations, jepa architectures and other approaches that emphasize operating primarily within the latent space.
To be fair, my particular interests have always been more in the computer vision area, and have been amused to watch the attention mechanism rise to prominence even over CNNs (given the contrast |similarity in their mechanics). Then again, I began my journey in the ML field when GANs were still the hotness, but I digress……
for LLMs, it’s tokens all the way down, and the name of the game is how discoverable and accessible can you make them?
Effective people managers (of whom I would not specifically consider myself) have known these tenets for as long as history. “Be concise”, “state your intent clearly”, funny how these are touted as novel “strategies” with which to expertly direct AI.
I don’t agree that “everything is a file”. Files are arrays of bytes. For an LLM, everything is a vector of tokens/embeddings.
An aphorism I recently heard: "All sufficiently advanced technology eventually becomes a web browser".
… seems apt especially in the context of the progression from chat-windows to harnesses and onwards to “harnesses that can do anything”.
Restated, the tool understands the process and what a “good” result/outcome looks like. It correctly presumes relevant and important information (especially given the current stage of your full task), and can “fill the gaps” between start and finish.
A good tool can distinguish between what you wanted, what you thought you wanted, and what you “should have” wanted.
A good tool makes it easy to do the “right” thing and hard to do the “wrong” thing.
A good tool doesn’t function to be understood, but to not be misunderstood.
I have spent a lot of time sitting in this question, having spent years creating design and drafting automation tools for architects, designers, and engineers in the building-design industry.