curioushacking's comments

Similar to another poster. Our 6 year old was just diagnosed with it.

The formal test was a short survey about our childs behavior in various situations. It was highly subjective with questions similar to "My child has difficulty sitting still" (Never, Sometimes, Often).

There was not example of what is considered sometimes or often and what is age appropriate for a 6 year old.

The survey was required to be answered by the parents and kindergarten teacher. That was all that was necessary to have the option of medication.

Even more surprising is that with only slight more effort we now have the option to put our child on an IEP (indivdualized education plan) where there will now be a full additional teacher in the classroom assisting her.

Overall it has been an eye opening experience. Compared to our first child, our 6 year old is more spirited, but is not violent or defiant. She is mostly concerned with her own interests and will admittedly have difficulty focusing for long periods of time. I would be very curious if her behavior is above the threshold for ADHD in other countries.

This was just our experience and it is very possible that our child was an obvious case so further analysis was not necessary. For now we have opted not to use medication. Interestingly our diagnosing pediatrician has two children with ADHD and said that they put both children on medication at 6 years old.

I was in the exact same situation. I loved my team at Meta and learned a ton, but after the increased intensity QA from Zuck I went through two rounds of re-structuring and was shuffled to new teams. I found that I didn't have the motivation to learn the specific implementation of the huge layers of abstraction the new team was creating.

Couple this with a general questioning my life goals and if I want to spend more of it on making ad serving slightly better. I was left searching. I've landed at a new gig and I've been so much happier despite a sizable pay cut at my new role.

In general, while it clearly created huge turmoil, I wonder if one large round where you potentially cut too deep would be better on company health than many smaller rounds. I'd be curious if the sentiment of engineers at Twitter is now more positive then that at Meta.

As a general financial instrument there seems to be too large an opportunity for fraud and scams that it seems to generally not be worth it outside of hedges against hyper inflation.

But the general idea of web3, personal ownership of data that you can seamlessly move to different interfaces, and as a way to trust documents still all seem like great reasons to have public ledgers. This may not be in the currency space, but I feel like general crypto is something I would like to see in the future.

I also love the idea of moving fully to stable coins for better tracking related to tax implications. I'm not sure of how this would work fully, but there seems far too much opportunity for tax evasion and fraud under the current systems and moving to a stable coin seems like it could aid in that regard.

I'm not trying to say that there hasn't been a history of racism nor even that it isn't prevalent today. I just want to understand how we accurately measure the actual effects of it so that we can understand how much effort to put into solving it or measuring if it is getting better over time. And some of the most used measures I find as evidence seem to be about the distribution of races in various jobs which on its own doesn't necessarily seem like a reliable metric to me.

Others pointed out some studies which showed potential biases in hiring and that seems like a great potential proxy to understand the current level of racism in hiring.

This seems to be the largest potential issue then with my understanding of requiring research a-priori to match an assumed outcome or ideal. I wonder if an academic wanted to rigorously attempt to isolate between these, would they be allowed to publish the results were found that systemic issues were not significant. It seems potentially dangerous if we stifle publications of studies that find minimal impact of racism because it could have the impact of only highlighting the cases of racism, but not the net impact.

My understand for how we measure systemic racism issues seems to typically be predicated on assumed outcomes. For example that if the distribution of employees race does not match the general population then there must be a systemic cause for this.

What I don’t understand is why that is assumed true. If we want to encourage many different cultures to live together wouldn’t it naturally make sense that different cultures would have different outcomes in job preferences? How do you separate potential racism from cultural differences?

My fear is if there are strong cultural differences that lead to disparate racial outcomes so organizations will always be able to point out that systemic issues exist even when they may be eradicated. I don’t know how we measure this.

I need to check again, but from my understanding the damage is exponential based on the vehicle weight, but also that real road damage is functionally nothing until you get to very large heavy vehicles

Exactly. While ML interpretability may be tough, I don't really read it like that. More here are X number of algorithms and each one is aiming to do Y. Chronological, Engagement, Happiness, Current Trends, etc.

I think it may be hard to see technology changes in the moment, but over the last decade alone I think we've had a number of large changes that are starting to be taken for granted.

We have the proliferation of electric vehicles and (at least in the US) a large charging network. There is also a massive difference in the wearable markets. The watches are getting so good that health monitoring and early warning signs are either already here or on the horizon. Cell phones are significantly better. A decade ago a video call would be a novelty and now it is done without a second thought. We finally have the futuristic edge to edge screens and foldables are now on the market.

There are others but they may not fall into the technology that we interact with daily. Things such as the drug technology used to make the covid vaccine. There are also changes in ML with the significant improvements in deep learning and model training.

I'm not sure if any of those count as an incredible leap, I think each one was developed incrementally, but when looking over the course of a decade it feels like a large leap to me.

Depending on how correlated the verifications between the human and AI system are, this could be used as a verification system to determine if consensus needs to happen. I.E. Always run the ML system and only ask for a consensus if the ML system disagrees with the diagnosis. This could still provide a lot of value I would assume.

We could tune the material to whatever wavelength we wanted, but for ours we targeted above 850nm as that was a common filter. Regarding the actual camera to IR, you are right. Even the front facing camera on a iPhone is sufficient to read them.

Many cars use front facing cameras that with minimal adjustment could read at the proper wave lengths, but one issue for a lot of vehicles right now is that the windshield has an IR filter to minimize heat and interior damage. For cameras behind the rear view mirror, the standard windshield creates an issue. Windshields with a small cut of the film would be sufficient, but they are not manufactured to my knowledge.

For general fiducials not related to these, I had hoped to put them everywhere. Think even hidden everywhere and read by phones. But at least for a while I think the read facing cameras on phones will continue to have the filter and using the phone backwards with the front facing camera is awkward.

For the fiducials we previously worked on, the goals was to maximize error correction for the types of occlusions commonly on signs. Because most signs are vertical, there is a lower likely hood of a ton of snow on them. The more common occlusion is an edge occlusion from either a natural road feature, or other vehicles.

For snow that generally disrupts the sign fiducial, we had a few solutions. The first is that if fiducials are dense enough, then dead reckoning may be sufficient until the next fiducial is observerd. The second is to try and build different layers of data with different error correction capabilities. One system developed could relay a low number of bits from a far distance and reasonable error correction capability. The remaining bits were then much smaller and readable from up close. The thought being that if you are able to fully resolve a fiducial in one area then assuming an apiori map of fiducials, the first 16 to 24 bits of a 64 bit code is likely enough to accurately resolve location.

I worked for a number of years on making invisible fiducials that can appear only in infrared, and am one of the chief inventors on 3M's smart code. The idea was that on any retroreflective surface you can place this fiducial sticker and relay some information. We were originally looking at approximately 64 bits of information. This isn't a ton, but we thought it a good balance of error correction, and module (the pixels in the code) size.

At least from an IR perspective, it was generally loved by nearly everyone we spoke to, but there was no takers in the end due to the IR requirement and such cameras not being everywhere yet.

Here is a link to some of our first work. That picture of a stop sign is our first prototype with hand cut special film.

https://www.businessinsider.com/3m-hides-tech-in-sides-to-he...

Agreed. It was a great post. I tend to think of my self as a "liberal-tarian." With the tenet being to set a goal for a standard of living and social / environment targets that benefits as many as possible, but anything beyond that get out of the way.

In this way I am for socialized health care, significantly stricter regulations or punitive measures regarding the environment. However for most other issues I would rather have a competitive market.

In the end I think some form of UBI may help us get there most efficiently. Ensure the environmental and health sustainability and give people a minimum amount of money to ensure they can reasonably sustain themselves, then let the market consider the rest.

I happen to be a white male from the US and during my PhD I was my Advisor's only native English speaker. Also, at most of my jobs, I've been either the only or one of two native English speakers.

While I don't have the numbers, it would not surprise me that for at least CS/Software and likely other many other professions white male is by no means the run-of-the-mill applicant.

I whole-heartedly agree. I not sure of another community that has this level of well reasoned discourse anywhere else. Hacker News is a special place.

I agree with you that the biological inspiration for a neural network is tenuous at best. Especially the basic two-layer network outlined here.

However at least for visual perception, there is some scientific basis for convolutional neural networks having some properties to biological visual perception. The work of Hubel and Wiesel demonstrated visual cortex activations that look very similar to the first layers of CNN kernels.

ref: https://knowingneurons.com/2014/10/29/hubel-and-wiesel-the-n...

One of the reasons I enjoy hacker news is that the discussions on many posts are often richer than the post content. This site has managed to maintain consistent, high quality discussions unlike reddit and twitter. Without strict moderation however, is there a proven way to incentivize or gamify rich discussion?