Blows my mind that 1:1 tutoring dwarfs the impact of other factors such as socioeconomic status, reinforcement, assigned homework, classroom morale, etc (at least according to the researchers).
Does anyone know if this thesis has been replicated? Or if these results hold in modern times (original study was 40 years ago)?
The article states that Anaina and Burke separately conducted their tests, but social robots [1] have been shown to be effective in individual tutoring. Human tutoring is not always better than a well-designed computer program [2]. There have been issues with how studies interpret their effect on group size / scalability [3].
In my experience with 1:1 before and during my Masters in Education, nothing could compare to the learning and growth my students had from that highly personalized (and personal) instruction.
It's super common for students to not understand material or express that they don't, but it's just not possible to drill into each specific student's particular knowledge or skill level in the classroom environment.
On the social and behavioral side, many students who struggle in a classroom environment transform into model students when taught with both the care and privacy of 1:1.
For me, I feel it's a combination of hyper-personalized instruction plus compassion in a relationship where it feels safer for the student to accept the value of improving at something without social pressure or embarrassment.
Seeing your launch hn made me happy. I tested one of your products in 2021. It's cool to see you and Parsagon working through iterations and finding customer traction. Mad props!
The author hits on a powerful point that is getting missed in this HN discussion. That is: talented and driven students are limited by the US education system.
Some of those young people cultivate skill by getting practice during youth. Doing that while young builds a compounding machine of personal interest + confidence + progress.
I have never seen broad data to support this, so discussions revolve around anecdotes[1]. That's fine by me though because we have countless examples of the legends of their craft who fit that mold: bill gates, zuck, warren buffett, taylor swift, mozart, da vinci... the list is long.
No single system will work for every single student. But that isn't the point. The point is that the best of the best deserve to feed their interests at a young age, which the current US upbringing limits. How many more bill gates and zuck-level creators could the world have if more talented youths could cultivate their talents very early in life?
Well, just look at the design. State education is designed to get ~97% of pupils to some minimum education level.
That means the coursework and schedules are designed specifically for the lowest common denominator of a student.
This means that if you're anything but, say, the bottom 20% of students, public school isn't an efficient use of time for you. You should be learning more in the same amount of time.
There are a lot of other problems with it too, but that's the most egregious. If education was more efficient, a lot of the other problems with it could be solved as well.
While I really want to agree with you because I spent 10 years of my education with people who were exactly the bottom 20% which was beyond frustrating, unfortunately the resources are limited, so if you try to create a society where the top 10% has all the opportunities to develop to their full potential, you'll end up leaving behind the other 90%, which will make average voter even less informed about the world around them.
> create a society where the top 10% has all the opportunities
Fixing the inefficiency doesn't necessarily mean paying more attention to the top 10% at the cost of denying resources to the bottom 90%.
One path is to develop individualized plans that allow students to work at their own pace. Instead of advancement at the end of the year, advance at attainment of a proven proficiency level.
Still require kids to physically go to school, but transform the classroom for the modern age.
Have teachers balance working with local students with working with ones in a nation-wide online network. Leverage that network of instructors and bring it to bear on a child's education, instead of leaving it entirely to those in geographic proximity.
Since most of them are teaching the same topics, start recording the lectures and promote the best of them. Balance live and recorded lecture with live hands-on local assistance as well as online Q&A.
This wouldn't increase inequality. If anything, it can't be worse than sending the richest 10% to private school while the other 90% are left to.. what it is now.
Holding back capable people from reaching their potential is a unique kind of evil to me.
It's not even about giving them more resources, but the taking away that infuriates me. The single most valuable thing a child can have is curiosity, the second most is their time. Anyone who takes away one or the other are fundamentally an enemy to me.
Forcing kids who have great potential to go at the pace of the worst is taking away both of those things at the same time. These kids don't need much babysitting, they are also completely able to learn from anything. They do not need a live instructor, that's for sure.
this assumes that the point of school is to maximize student learning. i think it's better to look at it as free daycare so that adults can work. the whole system makes more sense in that context
With a competent tutor, material and emotional support you don't need to cultivate talents, you simply create complex skills. You typically don't search so much for a hidden talent in a child as leverage their neuroplasticity and accelerated learning to lay a life-long foundation.
But this doesn't come cheap, and tutoring is also going a bit out of style, regrettably.
>On February 7, 2024, Senator Scott Wiener introduced Senate Bill 1047 (SB-1047) – known as the Known as the Safe and Secure Innovation for Frontier Artificial Intelligence Systems Act (the Act) – into the California State Legislature. Aiming to regulate the development and use of advanced artificial intelligence (AI) models, the Act mandates developers to make certain safety determinations before training AI models, comply with various safety requirements, and report AI safety incidents. It further establishes the Frontier Model Division within the Department of Technology for oversight of these AI models and introduces civil penalties for violations of the Act.
You've got a company valued at $80 billion and you can get legislation put forward to kneecap your primary competition for a the price of a 2009 Honda Accord with 150,000 miles on the odometer? What great value for money!
This typically works the other way. You find politicians who support you, due to personal views or electoral idiosyncrasies, and then give them money to boost them.
The lack of data surprises me. It reads like the author has a vendetta against this brand.
They say that Air Jordans are “deflating” because auctions end at the low end of their estimated ranges, though still setting records for sneakers. All asset prices were inflated during zero-interest rate policy, and have fallen since then. What makes Air Jordans unique?
Showing sales data for Air Jordans would go further to making this point, and comparing that to Nike or sneaker sales overall, but they don’t offer that data or analysis.
Edit:
This site says that Jordan show sales were $6.6B in 2023, up 30% annually. This Atlantic write up seems misguided, if that data is accurate https://runrepeat.com/jordan-shoes-statistics
i have to disagree, there was a point between like 2013-2021 where every jordan 1 ,4 and certain 11 & 3's would resale for an arm & a leg. Attempting to get a pair for retail was a nightmare from long queues to sneaker bots making it humanly impossible to pick up a pair
now its possible to get most jordans close to retail price in the resale market or pick them up at retail as demands for most jordans is gone, unless its some crazy collab
Exactly this. This market is so hot there are bot rentals that cost $1,000s/mo to run and a whole black market of AWS accounts with 25-100K credits to run those bots to “cop a drop”.
This article doesn’t address when cameras are blocked, which is the obvious issue with camera-only self driving. Teslas have crashed when cameras were blinded by the sun. Now throw in snow, rain, dust… Is that solvable with lots of cameras and different types? …Does it need to be solved?
Maybe the bigger question - anyone know the status of low cost lidar? Dozens of startups and larger companies were working on it 10 years ago, yet Lidar still costs “thousands” according to the article
There's something bizarre going on with lidar manufacturing/pricing. When I was looking into it for a specific application it was much cheaper to buy entire made-in-china products containing the exact lidar module than it is to buy the part separately, even at bulk pricing.
This is very common with all kind of components. There are economies of scale your vendor can achieve when they sell someone a million of the same thing. Also the company buying a milion of the same thing is going to pay the vendor a significant sum, even if they get all kind of discounts, and that puts them at a much better negotiating position than you buying a single one.
Hobbyist buying a few units of a component, even if they are buying it with a significant margin, will net the producer peanuts. So not surprising they don’t worry much about serving them that market.
Yes, I'm aware of that. That's why I added the bit about bulk pricing.
In my case I was looking at buying quite a number of units, outside of a hobbyist application. In fact, I would say it was a higher number than the cheaper China-made products could possibly sell (different market sizes).
It seemed to me that they didn't want to sell for any price really but would make an exception if they could really, really rip me off.
As others have pointed out, lidar doesn't denote capability.
1d lidars that have a range of 8 meters indoors are quite cheap <$15 volume.
"2d" lidar, that is one measuring one plane's depth, are generally a lost more costly. Not only that they are bigger and eat more power. again indoor only.
3d lidars are more expensive still, and if you want it to work outdoors, even more.
What does a human driver do when their vision is obstructed?
1. Attempt to use the vehicle's built-in windscreen wiper to remove the obstruction.
2. Failing that, stop the car. Preferably before the vision gets so badly obstructed that the car cannot safely be brought to a stop. But stop the car even so.
3. Get out and clear the obstruction. Admittedly the AI will have trouble with this, but it is vanishingly rare anyway, and if the car is carrying passengers, this task can be given to the passengers.
When the human continues driving in incliment weather, and runs into the back of a van full of kids and kills all of them, we put them in jail for making a bad judgement call.
How do we handle the AI mowing over a pedestrian when it makes a bad judgement call? Right now, the status quo is that we do jack and shit, and I can't help but feel like that's not a good plan.
It’s an interesting conundrum, but in a full AI world the hope is that it’s so rare we don’t feel the need to be punitive at all and can chock it up to bad luck and try to learn from it. Perhaps more similar to when a airplane crashes and people die.
People and ADAS have their own, different, and critical weaknesses. Neither is a panacea. (Which is why mass transit investment should be prioritized over scifi fantasy ADAS.)
> Teslas have crashed when cameras were blinded by the sun. Now throw in snow, rain, dust…
I used to think the more sensors the better, but after listening to George Hotz talk about it I can see the logic of focusing on ambient spectrum in visual and near range. Of course, he will talk up his approach as best, but here it is as best as I recall:
1. more sensors ~= more signal
2. more sensors means
a. longer processing pipeline for fusing data streams (timing, registration)
b. more software, thus more surface area for defects
c. decisions about response when 1 sensor modality fails
3. visual range spectrum is
a. well adapted for environment
b. has inexpensive and high quality sensors
c. sufficient for humans so is sufficient to get to human-like driving by a computer
The answer to blocked cameras is:
1. to have protocols to slow down and stop gracefully
2. maintain enough of a spatial model of the vehicle surroundings to perform the above (Simultaneous Localization and Mapping, SLAM)
Our eyeballs are not cameras and have way more depth info from their function than just two arrays of pixels that you can derive parallax from, and all the claims that "humans only use their eyes" fundamentally ignore all the other parts we use, up to and including an intrinsic simulation of physics in our brain.
Yes, sure. Cameras and biological light sensing have different tradeoffs. My lay person's understanding is that the eye-brain neuron pathway bandwidth is not theoretically sufficient for what we perceive and so our brain is effectively running an ongoing simulation of the future a few miliseconds ahead of now and correcting based on sensory input.
The book "An Immense World: How Animal Senses Reveal the Hidden Realms Around Us" by Ed Yong [0] is really great for understanding how sensory input informs but isn't the same as a mental model of the world built into the operations of a living thing.
Likewise ADAS and similar systems do not operate simply on what is sensed at any particular moment. Even ahead of things like being blinded by a sunset, there are occlusions when one object moves behind another and cannot be directly detected but can be inferred by an object model that predicts future positions given the the earlier known velocity and acceleration. [1]
More than that, I mean eyes have more data than just what light is hitting their retinas. The work that the brain and neurons do to aim and focus your eyes at a distant object essentially solves several math problems that give you very direct distance info. Your brain knows that, if the angular deviation of your eyes away from parallel is X to aim at an object, then it is ~Y distance away. It also knows that, these muscles have to flex this much to focus on that object, which ALSO provides depth info to your brain. Solid state image sensors cannot provide either of those datasets.
These two processes are actually why VR can be difficult on the eyes, because while the main way your brain senses depth is the parallax (the classic "binocular vision" way people think of), the sense of focus is telling your brain that everything is right in front of your eyes.
The first rangefinder, micking this process mechanically, was invented in 1769. You’re essentially arguing for Lidar / sensor fusion.
Do you have any sources for this being a significant factor in human depth estimation? “Infinity” focus starts at 6 meters, yet we’re able to estimate much larger distances with great accuracy.
I looked up the history of the rangefinder and the work of Watt in the 1770s is kind of obscure. For one, he called it a “micrometer” [0] even though he also created something like what is called a micrometer today, only he called it an “end measuring machine.” Additional confusion comes from “telemeter” as an early term for a rangefinder. Only Watt was also there at the beginning of what we now call telemetry: “additions to his steam engines for monitoring from a (near) distance such as the mercury pressure gauge and the fly-ball governor.” [2]
Watt's micrometer, designed between 1770 and 1771, was what we would now call a 'rangefinder'. It was used for measuring distances, and was essential for his canal surveying work.
Adapted from a telescope, with adjustable cross-hairs in the eye-piece, it was particularly useful for measuring distances between hills or across water.
Blows my mind that 1:1 tutoring dwarfs the impact of other factors such as socioeconomic status, reinforcement, assigned homework, classroom morale, etc (at least according to the researchers).
Does anyone know if this thesis has been replicated? Or if these results hold in modern times (original study was 40 years ago)?