By four years of age, girls tend to think that boys are academically inferior to them. Teachers often see their female students as academically superior, even when aptitude tests show the opposite pattern for their classes.
I’ve written about the people familiar with the matter pattern before—it means Reuters have anonymous insider sources that their reporters (and editors) find credible.
And yet, life expectancy about two years more than the national average is rather common in rich regions with selective migration, even in European countries with even access to health care, like France and Spain.
We have sort of how you treat academics and how you treat sports. And if we took the sports ethos and the athletic ethos in America and we applied it to academics, we would fix every academic problem in this country.
when you're working with a lot of data, the the difference can be massive if you structure it in one way versus structuring another way, right? Again, architectural decisions that have nothing to do with hotspots, they're how all the data is laid out and what the access pattern is, right?
AI checkers are essentially a cat-and-mouse game. AI checkers may learn to detect a certain pattern that is indicative of AI-generated content. Then, the next LLM may incidentally or deliberately not exhibit that pattern and avoid detection. The AI checker then has to be updated to detect said LLM, and so forth.
when the machine says I'm sorry you're so sick today it's very different from how your friend says it to you because the machine said that because it has learned through pattern when someone tells it I'm sick you should say I'm sorry you're sick instead of I'm so glad you're sick because that data exists.
Rather, the way that AI companies engage with communities-forcing NDAs, dangling billion-dollar promises, pushing environmental externalities far away from AI's wealthy user base-resembles a classic story about dark money in politics.
By the way, this is the pattern in a lot of regulation. It doesn't come from like some well-meaning person trying to keep the public safe. It comes from some entrenched, frankly, lazy interest that doesn't want to have to compete and runs to DC or runs to the local regulator and tries to use the power of government to shut their competition down. I think this is deeply unethical, but it happens all the time.
we feel this sense of ownership of products that we rely on every day. And we're angry when they change. Even if they change for the better, we're like angry because we go through life faster because of pattern recognition.
Um I think another similarity is I mean, the core notions uh behind Agile and extreme programming are solid and good, but a huge snake oil industry appeared around it, the Agile industrial complex as I like to refer to it. Um and that will happen. That is happening with AI right now, and it's often hard to see the difference between where is the snake oil and where is the real stuff.
Um and uh I think we would very rapidly abandon any cryptography based on the primes because if there was a one pattern that we didn't know about there's probably more.
Scrutiny applied to AI is way below other technologies that - even if you totally ignore the doomers - could put far fewer lives at risk than AI in a single incident.
I think especially if you have a product that has daily frequency like that's actually the retention that matters the most is that like of your existing user base that has developed a habitual pattern how sticky is your product and it's that retention rate that really compounds and build that builds that daily habit.
I actually in many ways see language learning as like their first vehicle. And what they have a superpower in is that again, the motivation, the habits, etc.
You know, interestingly, LM themselves are quite bad at playing chess. Like, they hallucinate moves. They look at patterns, right? They're they're very good at pattern recognition, but not so good at going super super super deep on a specific chess thing.
I've worked a lot of companies right but I'm wrong all the time and I think consumer behavior can be very fickle and especially when you work at a company you become a power user naturally. So sometimes you you may forget like what the actual user experience is for a brand new user.
If you do too much exploration, you can have your team feel a little bit too scattershot, just trying a 100 different random ideas. What's the through line? What's the strategy? How do you pattern match, you know, successes across them? And if you do too much in exploitation, which is often the MMO of growth teams, it can lead to this like saturation and stagnation where you're just locally maximizing a thing.
I'm now fairly pessimistic about ambitious interpretability (i.e. complete reverse-engineering), and I'm excited about model biology (studying qualitative high-level properties of models) and applied interpretability (rigorously doing useful things with interp).
I mean I've heard from multiple investors that foundation models are the fastest deteriorating asset of all time. And so if step one of your business is to burn through tens of millions or hundreds of millions of dollars of capital before you find product market fit and that asset has value for like a a week, I'm not sure it's like a great business model.
One performance anti-pattern is when a company, to debug one performance problem, installs a monitoring tool that periodically does work and causes application latency outliers.
AI is really great at spewing out code that does something that a million GitHub repositories already do because it's learned the underlying pattern. It's notoriously hard to get to do something new that hasn't been done before, especially when it's a complex task.
An unconventional guide to the craft of indie consulting and life in the gig economy, mixing philosophy, pattern language, and very practical guidance for building a client-based solo career.
Applied Corporate Finance (Wiley, 4th Ed): This is the book that is most closely tied to this class and represents my views of what should be in a corporate finance class most closely.
Until we develop better ways to bridge this gap - aligning what analysts understand with what is formally forecast - we'll continue to see the same pattern: reality consistently outperforming conservative predictions.
So Portland managed to build one very small line for fairly reasonable costs, but they were not cutting edge; this is a common pattern to Western US cities, in that the first line has reasonable costs and then things explode, even while staying self-funded and self-directed.
Shipbuilding is labor intensive, and we see a repeated pattern of it moving to countries with low-cost labor: from Britain to Japan, then to Korea, then to China.
One pattern is that for simple prompts, weak models can do (nearly) as well as strong models. For more challenging prompts, however, users are much more likely to prefer stronger models.
Obviousness comes from conforming to people’s existing mental models. Don’t waste time reinventing common UI patterns or paradigms unless they are at least 2x better, or you have some critical brand reason to do so.
Neurobiologists tell us that it takes two things to unlock and open up a neural pathway. The first is that the implicit must be made explicit. Sometimes you need help seeing what you don't see. But you must be open to the feedback. Second, there must be some sort of recoil, a sense of discrepancy, of "Oh no, I'm not sure I really want to keep doing that."
Brilliant insights into human nature. Moral reasoning, inventing victims, emotional reactions, the elephant (emotions) and its rider (explanations). Intuition reacts then logic confabulates a reason. Survival of the fittest applied to groups. Understanding religion and tradition. Great from start to finish.
What what I find generally is if I don't understand something, I I try to read backwards and and and kind of the further back back I read, the more universal things I find. And the more things that I think are kind of remarkable and unique to our era actually turn out to be quite universal.
this channel is full of high production quality and well-written videos. There are also several series on machine learning, the topics underlying self-driving cars, so this is certainly one of the more applied math channels out there.
The Sprint 225 uses one TriplePower LED bulb. Its light pattern is wonderfully smooth, with no distracting rings; it seems almost perfectly optimized for night-running and night-hiking — it focuses most of the light ahead, but still manages to illuminate the periphery.
Solve the fundamental, underlying issues, not the symptoms. We recommend starting with field studies and observations of actual practice (an applied ethnography). Ask “why?” at each issue.
It was probably the first book in English to bring serious academic scrutiny to Chinese food culture, and to use the scholarly techniques applied to other subjects to food, offering the views of different experts on different periods of Chinese history.
Put these two characteristics together and you have a key insight; Cities routinely trade near-term cash advantages associated with new growth for long-term financial obligations associated with maintenance of infrastructure.
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