I don't get it when people all claim that AGI is a winner takes all game. It is not (unless it is used as a weapon). When one company reaches AGI, there will be a dozen very close to AGI, given time. Also a winner is not going to drive everyone else out of business, it is the opposite, one winner will have people betting on the second and the third winners, the technology will also help other develops. Once you have a good enough model, everything will be incremental. I think the hardware capability will be the real burden, not the model itself. If AGI is as powerful as it sounds, maybe hardware won't be a problem any more.
how could I naively think this is going to be the technology to free human from being the slave of work.... of course, it is going to be a weapon, and it is only powerful when no one else has it.
I have been on Douyin a lot, there is a lot chat rooms for people to debate politics, so it is a 50/50 divide for opinions (for and against). Many of them will say 'China sucks, and US rules'. People learn a way to speak avoiding keywords, if you are not careful, you get kicked out of the chat. Many of them you can tell are against the government, but these are not ordinary people, they probably don't work, and spending most of their time online talking about politics.
That happened to me, lost 16 years old gmail account, which is my main account for my digital life. It happened after I disabled some tracking, and Google was no longer able to recognize me, even though I had my phone number registered, it was not enough.
I suspect this will happen to me soon, though all I do with it is occasionally sign in just to keep it registered. It now refuses to log me in unless I am on a specific IP address, no matter how many MFA steps it requests and I pass.
Same. Lost my 2004 Gmail because they silently enabled 2FA and the phone number on the account is a long lost one. I have the username/password and the recovery email is set to me. The account also forwards all emails to me, so I still get the mail, but I can't log into the account.
Not yet found someone to do a SIM swap for me and get the 2FA code...
Sorry to branch out:
How does this one meal per day work for you? There is recommended calories for a person, do you have to follow it somehow to make sure you have enough energy and exercise?
I just eat double portion at dinner, and then nibble on snacks before bed - I haven’t had breakfast in decades, then since I moved to India, with the carby nature of the food it was hard to stay in shape with 2 meals, so I decided to try and skip the lunch too. With fun work it is actually quite easy, and babysitting 4 claudes and helping out colleagues is very entertaining.
Now I either do gym before dinner (heavy exercise) or social dance after.
I’ve been given a lot of advice how I “should” be structuring it - like “don’t eat too much before bed” or “never eat before exercise” … but I haven’t had any issues with what I’m doing so far (~2 years)
In my case, I think slower model makes it hard to manage context and tasks in parallel. I would much prefer to work in one task only, and finish it, take a break, and work on another task. Currently I have three tabs for three tasks in parallel, it is much worse than because constantly context switching is painful. I think a faster model would mean that you don't have to start a new task while waiting.
the extra 5% time you will need to help AI with multiple turns and information it needed. These 5% time reasoning rarely is enough to finish the task. i.e. 5% time AI is just not enough to complete the task without a lot help.
The page says Starbucks, not a random area, random coffee shop. I think it is a valid test. Starbucks is properly staffs, in reasonably busy area, the shop is enclosed.
Starbucks locations in the US are not limited to areas that are low crime. They are as ubiquitous as McDonald’s if not more so.
Mostly I think this website is just a stereotype collector and in that sense is very counterproductive. It’s going to be a bunch of people who want to report their biases.
You'll also need to just chop New York into two and split the city off. NYC data skews the rest of the state so hard...
(north country anectedote: we leave our doors unlocked and laptops, keys, wallets, and iphones straight up in plain view in parking lots up here in rural nowhere. people are dumb.)
I don't think he is hired for coding, he is hired for the product. It is not that he is going to join a product team and code, he probably will lead and influence the product, where other software engineers can help to fulfill.
"In the camera+LIDAR case, you conceptually require AND(x.ok for all x) before you accelerate."
This can be learnt by the model. Let's assume vision is 100% correct, the model would learn to ignore LIDAR, so the worst case scenario is that LIDAR is extra cost for zero benefit.
This is not going to be true for a very long time, at least so long as one's definition of "vision" is something like "low-cost passive planar high-resolution imaging sensors sensitive to the visual and IR spectrum" (I include "low-cost" on the basis that while SWIR, MWIR, and LWIR sensors do provide useful capabilities for self-driving applications, they are often equally expensive, if not much more so, than LIDARs). Camera sensors have gotten quite good, but they are still fundamentally much less capable than the human eyes plus visual cortex in terms of useful dynamic range, motion sensitivity, and depth cues - and human eyes regularly encounter driving conditions which interfere or prohibit safe driving (e.g. mist/ fog, heavy rain/snow, blowing sand/dust, low-angle sunlight at sunrise/sunset/winter). One of the best features of LIDAR is that it is either immune or much less sensitive to these phenomena at the ranges we care about for driving.
Of course, LIDAR is not without its own failings, and the ideal system really is one that combines cameras, LIDARs, and RADARs. The problem there is that building automotive RADAR with sufficient spatial resolution to reliably discriminate between stationary obstacles (e.g. a car stalled ahead) and nearby clutter (e.g. a bridge above the road) is something of an unsolved problem.
The worst case scenario is that LIDAR is a rapidly falling extra cost for zero benefit? Sounds like it's a good idea to invest into cheap LIDAR just in case the worst case doesn't happen. Even better, you can get a head start by investing in the solution early and abandon it when it has obsolete.
By the way, Tesla engineers secretly trained their vision systems using LIDAR data because that's how you get training data. When Elon Musk found out, he fired them.
Finally, your premise is nonsensical. Using end to end learning for self driving sounds batshit crazy to me. Traffic rules are very rigid and differ depending on the location. Tesla's self driving solution gets you ticketed for traffic violations in China. Machine learning is generally used to "parse" the sensor output into a machine representation and then classical algorithms do most of the work.
The rationale for being against LIDAR seems to be "Elon Musk said LIDAR is bad" and is not based on any deficiency in LIDAR technology.
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