|
luxury handset posted:the industry is quietly admitting it, to a degree. at least the large vehicle manufacturers are. there's a ton of people who have interest in hyping the technology though, both uber and tesla depend on it to varying degrees for survival, and then you've got this guy I commute to Los Angeles once a week from the San Diego area and sleep on the way. Self-driving car? No, Amtrak.
|
#
?
Nov 7, 2019 18:17
|
|
|
|
| # ? Jun 9, 2024 06:19 |
|
|
I, for one, am shocked that these people have managed to make some poor investments
|
|
#
?
Nov 7, 2019 19:14
|
|
|
Morbus posted:I, for one, am shocked that these people have managed to make some poor investments They have a huge amount of assets whose value is very real and not at all self-reported, inflated, speculative, or otherwise bullshit, though. Nothing will happen to them.
|
|
#
?
Nov 7, 2019 19:39
|
|
|
i mean, they do? you could look this up and prove it for yourself. or not, that's an option also, many people choose to continue believing things without merit because to verify otherwise is inconvenient
|
|
#
?
Nov 7, 2019 19:44
|
|
|
Does Uber have a crypto yet?
|
|
#
?
Nov 7, 2019 19:49
|
|
|
Uber/WeWork merger when?????
|
|
#
?
Nov 7, 2019 22:07
|
|
|
Mister Facetious posted:Uber/WeWork merger when????? Finally I can get into a strangers box to travel to another strangers box inside a strangers office. Watxh the uber-wework try to rent conference rooms inside SUVs
|
|
#
?
Nov 7, 2019 22:10
|
|
|
luxury handset posted:i mean, they do? you could look this up and prove it for yourself. or not, that's an option also, many people choose to continue believing things without merit because to verify otherwise is inconvenient Hey here's a thought, state with proof and evidence of the third party certifying their financials or stop replying like this every time somebody makes a half-assed joke about a trillion dollar multinational.
|
|
#
?
Nov 7, 2019 22:46
|
|
|
ryonguy posted:Hey here's a thought, state with proof and evidence of the third party certifying their financials or stop replying like this every time somebody makes a half-assed joke about a trillion dollar multinational. yeah it's deloitte https://group.softbank/en/corp/d/annual-reports/2017/common/file/pdf/p180_report_softbank_annual_report_2017.pdf dont get me wrong, i would like to see softbank stumble and all capital to the thrown into the sea, i just demand goons have better ideas than "are you aware that 2009 was a year that happened?" but that's a fault of my expectations being to high perhaps
|
|
#
?
Nov 7, 2019 22:59
|
|
|
lol deloitte, yes, very credible
|
|
#
?
Nov 7, 2019 23:13
|
|
|
hey, i'm only answering the question asked aka being the resident google receptionist 
|
|
#
?
Nov 7, 2019 23:18
|
|
|
Megillah Gorilla posted:What's also worrying is that is seems the car's programmed to ignore anyone crossing the road except at places where it has been told there are crossings. Nah man, self-driving cars are 18 months out. Totally. They're absolutely safe and aren't going to straight up murder pedestrians on the daily.
|
|
#
?
Nov 8, 2019 00:49
|
|
|
I know I've seen it posted here but i cannot find it. What is the problem with all the Tesla, Google, OpenAI stuff? Everyone i know is fawning over all the AI talk that comes out of these tech companies and its all just marketing and slick buzzwords but I can't find anything concrete to explain it to them. Laypeople with next to 0 understanding of technical systems are now crowing about automation and AI replacing everything in a matter of years and I just can't figure out how to explain that it is not even remotely that simple. I remember someone posting how Silicon valley AI is a lot of hype with no substance and wildly different from the academia focused research.
|
|
#
?
Nov 8, 2019 01:16
|
|
|
https://thecorrespondent.com/100/the-new-dot-com-bubble-is-here-its-called-online-advertising/13228924500-22d5fd24 I have always been skeptical about the value of online advertising, but assumed that big companies involved must have done some studies to determine its value. Turns out they did, but their studies are garbage.
|
|
#
?
Nov 8, 2019 01:19
|
|
|
Automation is steadily improving and long term that does threaten jobs, but it'll happen gradually, not overnight. For example, Boston Dynamics has been working on robots for a long rear end time now and they only recently did a big commercial launch with Spot. Spot looks...well it certainly has some uses, but we're still decades away from I, Robot-style general purpose robots taking our jerbs.
|
|
#
?
Nov 8, 2019 01:20
|
|
|
pentyne posted:I know I've seen it posted here but i cannot find it. What is the problem with all the Tesla, Google, OpenAI stuff? Everyone i know is fawning over all the AI talk that comes out of these tech companies and its all just marketing and slick buzzwords but I can't find anything concrete to explain it to them. Most of the Silicon Valley "AI" stuff is someone grabbing some open source neural network library, finding a large enough dataset to train it, and producing a barely functioning model out of it that they use to milk VC money.
|
|
#
?
Nov 8, 2019 01:24
|
|
|
pentyne posted:I know I've seen it posted here but i cannot find it. What is the problem with all the Tesla, Google, OpenAI stuff? Everyone i know is fawning over all the AI talk that comes out of these tech companies and its all just marketing and slick buzzwords but I can't find anything concrete to explain it to them. I mean you have to go by application to actually get into why it's generally somewhere between 'oversold' and 'total horseshit'. But a good example is photo recognition and its bad habit of deciding anything tall in a photo is a giraffe, because it turns out the training data is enriched in giraffes compared to the real world because people like taking pictures of giraffes when they see them.
|
|
#
?
Nov 8, 2019 01:24
|
|
|
pentyne posted:I know I've seen it posted here but i cannot find it. What is the problem with all the Tesla, Google, OpenAI stuff? Everyone i know is fawning over all the AI talk that comes out of these tech companies and its all just marketing and slick buzzwords but I can't find anything concrete to explain it to them. General Purpose AI is still a pipe dream, and most of what is being sold as AI now is just highly complex if/then trees. It has some value in automating policy application, but it isn't the placebo for decision-making and analysis that SV wants to sell it as, much less the religion chuds like Eliezer Yudkowsky want to portray it as. There's a whole death cult in SV right now that believes that the Singularity is going to happen Any Day Now and we'll suddenly be post-scarcity so they never have to work again and are hailed as gods.
|
|
#
?
Nov 8, 2019 01:39
|
|
|
Megillah Gorilla posted:What about when construction has blocked off a sidewalk and pedestrians have to walk on the road "protected" by barriers? Geez couldn't they at least use some jersey barriers here?
|
|
#
?
Nov 8, 2019 01:43
|
|
|
Liquid Communism posted:General Purpose AI is still a pipe dream, and most of what is being sold as AI now is just highly complex if/then trees. It has some value in automating policy application, but it isn't the placebo for decision-making and analysis that SV wants to sell it as, much less the religion chuds like Eliezer Yudkowsky want to portray it as. If someone describes it as AI instead of "machine learning" you know they are either profiting from it or drank the kool aid. AI is the buzzword bingo free square right in the middle.
|
|
#
?
Nov 8, 2019 01:57
|
|
|
pentyne posted:I know I've seen it posted here but i cannot find it. What is the problem with all the Tesla, Google, OpenAI stuff? Everyone i know is fawning over all the AI talk that comes out of these tech companies and its all just marketing and slick buzzwords but I can't find anything concrete to explain it to them. The reason for all the AI hype is that, around 10 years ago, some very long-standing problems in object classification / machine learning saw some really impressive progress. A lot of this had to do with some breakthroughs involving neural networks. Like, for decades, it was a really hard problem to get a computer to accurately classify an object based on an image, and progress had been very tepid. Then all of a sudden you had algorithms [op up that could do a really good job. I mean classifiers still have problems today, but in comparison the state of the art in like 2007 was just complete and utter dogshit, as it had been for decades. The notion that it is at all technologically feasible to train a computer to recognize semi-reliably a thing based on an image of it is a recent development. This was and is exciting 1.) because there are some obvious piratical applications and 2.) because neural networks, as a concept, had been kind of dismissed for a long time as not being very useful, and now with some new tricks there was an exciting new tool in the toolbox. So in a short period of time, some previously very hard problems were solved or saw lots of progress. Google's Go playing AI, for example, is something that really wasn't possible using the methods that existed pre-2012. So because you had a sudden and exciting burst of progress in a field nobody understands but which has clear applications, you've got huge opportunities to hype the poo poo out of vaporware and make $$$$. If you're really interested in some of the underlying concepts, I think this series from 3Blue1Brown (which is an outstanding channel for it's other content too) is a good intro: https://www.youtube.com/watch?v=aircAruvnKk
|
|
#
?
Nov 8, 2019 02:17
|
|
|
Morbus posted:The reason for all the AI hype is that, around 10 years ago, some very long-standing problems in object classification / machine learning saw some really impressive progress. A lot of this had to do with some breakthroughs involving neural networks. Like, for decades, it was a really hard problem to get a computer to accurately classify an object based on an image, and progress had been very tepid. Then all of a sudden you had algorithms [op up that could do a really good job. I mean classifiers still have problems today, but in comparison the state of the art in like 2007 was just complete and utter dogshit, as it had been for decades. The notion that it is at all technologically feasible to train a computer to recognize semi-reliably a thing based on an image of it is a recent development. Just to add on to this. A number of the most impressive results in reinforcement learning are the result of throwing tons and tons of computing power at the problem. There are definitely improvements in the methods (details that are boring to outsiders like reward efficiency) but AlphaStar is a much more impressive engineering feat than it is a demonstration of general AI. Lots of problems totally unrelated to AI become tractable if you have millions of dollars to spend years developing a highly skilled workforce and an incredibly optimized system to work with. Its neat, but my opinion is that it is a multimillion dollar parlor trick.
|
|
#
?
Nov 8, 2019 02:44
|
|
|
Cicero posted:Automation is steadily improving and long term that does threaten jobs, but it'll happen gradually, not overnight. It's also people latching onto the more drastic examples rather than the picky reality. People as a whole aren't being replaced. Technology is just forcing less people to do more work, with some getting shuffled out of the workforce. It's much more striking to write articles about "Wendy's/McDonald's Is Getting Rid of Cashiers" when the reality of "Wendy's is offer a self-serve kiosk that a lot of people won't bother to use for a lot of minor bullshit reasons, but it will somewhat reduce the load on checkout allowing them to probably cut a cashier" isn't quite as interesting or attention grabbing. An AI circling the item to pick out of a box for an Amazon warehouse worker, thereby forcing them to work a second faster on each pick all day, isn't a good headline.
|
|
#
?
Nov 8, 2019 03:25
|
|
|
Might be time to restart the thread with a new poll: how long until SoftBank goes under:
|
|
#
?
Nov 8, 2019 03:35
|
|
|
Always remember that the same people trying to sell you on AI, or autonomous cars, or really any app-based solution to problem they've invented by writing an app are impossibly gullible and broken human beings. https://www.nytimes.com/2019/11/07/style/dopamine-fasting.html quote:SAN FRANCISCO — Everything was going really well for the men of Tennessee Street. Women wanted to talk to them, investors wanted to invest, their new site got traffic, phones were buzzing, their Magic: The Gathering cards were appreciating. This all was exactly the problem. Someone just told techbros about the hedonic treadmill. quote:A dopamine fast is simple because it is basically a fast of everything.
|
|
#
?
Nov 8, 2019 03:50
|
|
|
I am shocked, shocked, shocked that WeWork would lay off rank-and-file employees and require a noncompete in order to get a severance package. https://www.vox.com/recode/2019/11/7/20953930/wework-adam-neumann-severance-laid-off-employees-layoffs-buyout-unicorn-ipo quote:“It’s kind of insulting they’re describing this as generous,” said the anonymous former Meetup employee about the terms of the layoff packages. The employee said that they were told not to discuss the terms of the deal and that WeWork is “monitoring” staff and would take “aggressive action” if anyone disclosed details. kitten smoothie fucked around with this message at 04:24 on Nov 8, 2019 |
|
#
?
Nov 8, 2019 04:22
|
|
|
kitten smoothie posted:I am shocked, shocked, shocked that WeWork would lay off rank-and-file employees and require a noncompete in order to get a severance package. Speaking of layoffs: https://twitter.com/itsa_talia/status/1192281064137510912
|
|
#
?
Nov 8, 2019 04:26
|
|
|
Silicon Valley appears to be what would happen if there were a bunch of clones of Kramer from Seinfeld and they were all real. Except some of his ideas were better.
|
|
#
?
Nov 8, 2019 04:32
|
|
|
quote:Hi, I'm the guy with 1.2M Ford. Update and AMA I guess (self.wallstreetbets) https://www.reddit.com/r/wallstreetbets/comments/dt4p70/hi_im_the_guy_with_12m_ford_update_and_ama_i_guess/ Here is Matt Levine’s article. He also talks about SoftBank.
|
|
#
?
Nov 8, 2019 04:38
|
|
|
Oakland Martini posted:https://thecorrespondent.com/100/the-new-dot-com-bubble-is-here-its-called-online-advertising/13228924500-22d5fd24 This was a really fun read, thanks for posting it. As someone who works in a field where I can usually flip a switch and turn on/off the effect I'm trying to measure, looking for sufficient statistical power in a field full of people who would rather not know sounds maddening.
|
|
#
?
Nov 8, 2019 04:56
|
|
|
In general, Matt Levine is the only markets writer worth reading. What he’s written about SoftBank alone chould really be bound as Volume II of Das Kapital.
|
|
#
?
Nov 8, 2019 05:39
|
|
|
Yeah, that's a good article and captures my general question of why the gently caress they did it. Like, throwing several thousand dollars on the line for the lulz is a weird thing. I also noticed that r/WallStreetBets has a banner that turns the Wall Street Kid into The Joker when you mouse over it, which captures the atmosphere of that place better than any words ever could. A slightly funnier point from the subreddit thread on it noted that Robinhood's margin policy isn't as forgiving as they assumed. You can pay $5 a month to trade on margin, but it's only good for up to $1,000. Any margin above that is charged 5% yearly interested assessed daily and billed monthly. Some idiot who's in for a million in long term puts on Apple and Tesla would owe $60,000 in interest if they actually let him use the glitch as he hoped. Still crazy, if the AMAs are true, that Robinhood is still relying on generic emails and not assigning some type of crisis manager to the cases. I guess it is possible that they could gently caress up handling this so bad that the buyer isn't liable.
|
|
#
?
Nov 8, 2019 06:21
|
|
|
MickeyFinn posted:Just to add on to this. A number of the most impressive results in reinforcement learning are the result of throwing tons and tons of computing power at the problem. There are definitely improvements in the methods (details that are boring to outsiders like reward efficiency) but AlphaStar is a much more impressive engineering feat than it is a demonstration of general AI. Lots of problems totally unrelated to AI become tractable if you have millions of dollars to spend years developing a highly skilled workforce and an incredibly optimized system to work with. Its neat, but my opinion is that it is a multimillion dollar parlor trick. I think this is a wrong take if you look at the history. The improvement in computing power from 1990 to 2005 absolutely dwarfs the improvement that occurred from 2006 to 2019. Like it's not even close. And yet, prior to ~2012, the performance of (mostly autocorrelation based) image classifiers was just laughably bad, with only marginal improvements despite multiple order-of-magnitude gains in available computing power. Those older techniques are still dogshit even if you run them on modern supercomputers. The sudden increase in interest you see in the last 5+ years was very much the result of some (arguably long past due) breakthroughs in algorithms and methods. That being said, absolutely nothing that has been achieved is even remotely close to being a "demonstration of general AI". Some new and very useful tools were added to the toolbox, and it's now possible to solve problems that we were pretty bad at. But this has happened many times before in the history of AI, and there are always people who are eager to jump on each new cool trick as the harbinger of general AI when the fact is we are really no closer to that than we were 50 years ago.
|
|
#
?
Nov 8, 2019 06:40
|
|
|
Morbus posted:I think this is a wrong take if you look at the history. The improvement in computing power from 1990 to 2005 absolutely dwarfs the improvement that occurred from 2006 to 2019. Like it's not even close. And yet, prior to ~2012, the performance of (mostly autocorrelation based) image classifiers was just laughably bad, with only marginal improvements despite multiple order-of-magnitude gains in available computing power. Those older techniques are still dogshit even if you run them on modern supercomputers. computing power is roughly the same over those spans, Moore's law is still mostly holding after all these years probably the bigger change was the advent of 64 bit addressing becoming mainstream allowed for a much larger space to be parsed X86-64 was '99 and that's.as good as any.milestone to point to as for when 64 but processors started moving out of purposeful niche prodict to the standard commercial offering OJ MIST 2 THE DICK fucked around with this message at 06:59 on Nov 8, 2019 |
|
#
?
Nov 8, 2019 06:56
|
|
|
Morbus posted:
Except that just isn't true. The breakthrough image classification models weren't new tech. They were built on decades old research. They just comically upped the number of free parameters and most importantly trained it on datasets that are orders of magnitude larger than previous. Before 2012ish often researchers had to train on datasets of several dozen examples per class. As a results the methods had to be drastically constrained to get what was then good performance. The rise of companies harvesting as much data as possible and devoting clusters upon clusters to just tune hyperparameters constantly is what is the difference. Also the reason why Google et al often tend to self publish and not compare against baselines in their work. Often the algorithms add complexity for the sake of complexity and new headlines without actually outperforming previous work. Billy Gnosis fucked around with this message at 08:00 on Nov 8, 2019 |
|
#
?
Nov 8, 2019 07:52
|
|
|
Billy Gnosis posted:Except that just isn't true. The breakthrough image classification models weren't new tech. They were built on decades old research. They just comically upped the number of free parameters and most importantly trained it on datasets that are orders of magnitude larger than previous. Before 2012ish often researchers had to train on datasets of several dozen examples per class. As a results the methods had to be drastically constrained to get what was then good performance. hum yes just highly complex if/then trees, yes
|
|
#
?
Nov 8, 2019 07:59
|
|
|
Absurd Alhazred posted:Might be time to restart the thread with a new poll: how long until SoftBank goes under: Never, because he's got an ungodly huge ball of capital fed by a very profitable core telecom business that is implicitly backed by the Japanese government? icantfindaname fucked around with this message at 09:23 on Nov 8, 2019 |
|
#
?
Nov 8, 2019 09:18
|
|
|
Delta-Wye posted:hum yes just highly complex if/then trees, yes That is, in fact, what an image classification model is. You can build a basic one in Python in under an hour. The difference between that and the big ones Goggle Images uses is magnitudes of scale more processing power and millions of user-tagged images to train it on.
|
|
#
?
Nov 8, 2019 09:23
|
|
|
In what sense is a convolutional neural network just an “if–then tree”?
|
|
#
?
Nov 8, 2019 09:35
|
|
|
|
| # ? Jun 9, 2024 06:19 |
|
|
animist posted:okay, so, first thing you gotta understand is what people mean by "deep neural networks". a neural network is made up of "neurons", which are functions of weighted sums. thats it. here's a "neuron":
|
|
#
?
Nov 8, 2019 10:25
|
|
