>I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning
I don't see how this story gives a "misleading" view of deep learning. From my (admittedly limited) experience with self-driving RC cars, this type of mistake is quite easy for a neural net to make while being quite difficult to detect. In our case, after utilizing a visual back-prop method, we realized our car was using the lights above to direct itself rather than the lanes on the road.
Now, you can refute this and say "well clearly your data wasn't extensive enough" or "your behavioral model is too simple for a complicated task like driving" however as these tools become easier to use, more and more organizations will put them into practice without as much care as the researchers behind most of the current production efforts.
> Page: I think they had a really hard time getting along [Levandowski and another employee], and yet they worked together for a long time. And it was a constant -- yeah, constant management headache to help them get through that.
> Questioner: Do you recall that Anthony Levandowski was put on a personal improvement plan before he left?
> Page: I don't recall that.
> Questioner: Do you recall that Mr. Levandowski wanted to be head of the Project Chauffeur team?
> Page: I mean, that does not surprise me.
> Questioner: Do you recall having conversations with him, where he said to you that he wanted to be head of the team?
> Page: I don't recall, but it wouldn't be surprising, you know. I think he clearly felt things could be done better.
We’re building a savings app for people that struggle to save money. How? We’re using a new form of investment called prize-linked savings (new to the US as of 2014). The simple explanation is that you trade part of your interest for the chance to win from a prize pool of everyone's interest.
As a software engineer at Long Game you’ll be joining a small team of engineers and will have full exposure to all aspects of our product development processes.
We’re looking for developers that enjoy building fun mobile UX and/or engineers with considerable finance experience.
I think I disagree. The hard part is understanding why we have a sense of "self" one that can independently perceive it's place among the solar system and beyond.
The "colors, sounds, tastes, feels, etc" that you refer to can be seen as sensors that perceive narrow views of the various spectrums created by the energy bouncing around in the physical universe.
Examples:
- Hot vs. cold for a human is entirely different that hot vs. cold for a star.
- Color, from our perspective, is the spectrum of light that we need to utilize to effectively interact with our environment (different animals need and perceive different spectrums of light).
- Sound is a vibration that propagates as a [..] mechanical wave of pressure and displacement, through a transmission medium such as air or water. (taken from wikipedia.org/wiki/Sound)
We’re building a savings app for people that struggle to save money. How you ask? We’re using a new form of investment called prize-linked savings (new to the US as of 2014). The simple explanation is that you trade part of your interest for the chance to win from a prize pool of everyone's interest.
As a software engineer at Long Game you’ll be joining a small team of engineers and will have full exposure to all aspects of our product development processes.
We’re looking for developers that enjoy building fun mobile UX and/or engineers with considerable finance experience.
I rode this last night. It has a flaw that I haven't seen mentioned elsewhere.
In the midtown area, the streetcar tracks are in the outermost lanes of the 6-lane street. This causes a problem at intersections because the Qline can easily be blocked by cars that are trying to turn right off of Woodward (the main road).
The problem gets worse when you consider that this rail line was built to incentivize more people to come downtown. More foot traffic will inevitably block more cars at crosswalks, which in turn slows down the streetcar.
I'm assuming they can attempt to fix this by limiting the number of legal right hand turns or fix the lights to stay green until the streetcar passes through, but I'm not sure there will ever be enough pressure to do so.
What I'm trying to say is that Walmart isn't actually providing much value over the local bank, as the consumer costs are astronomical in comparison to the average account balance. The whole notion that they're helping the person "save" while charging these fees is ludicrous; and this is what the article is purporting.
You are correct though, local banks aren't necessarily helping people save either, especially those who are living paycheck to paycheck.
>Walmart sells the card for $1, and Green Dot charges the usual associated fees: $5 a month if your balance is less than $1,000; $2.50 for ATM withdrawals; etc.
This is absolutely not how one should encourage people to save. This is Walmart trying to steal business from local banks/and or provide banking services to people in rural locations. Personally, I think Walmart should offer this at zero-cost to help their customers live with less financial vulnerability (and thus shop more at Walmart).
Full disclosure: I work at a startup that's using prize-linked savings to help encourage people to save money, but we don't charge a monthly-fee and we actually give our users interest on top of what they win.
In reference to radioresistance: While an LD50 has been reported for wild type C. elegans individuals, an upper lethal limit has not been established, rather "nearly all animals were alive with no indication of excess lethality up to 800 Gy, the highest dose... measured."
Edit: Just to clarify, I don't agree with anything Levandwoski has done and both he and Uber should face repercussions for their actions. With that said, there's a lot of "conspiracy"-esque hype around this guy that I think may be undeserved. Everyone here seems to be focused on his monetary motivations for acting as he did; I'm simply trying to offer an alternative viewpoint that could explain the situation outside of pure financial gain.
A lot of people are blaming Levandwoski for deceiving Google as though he were some sort of evil mastermind. Given the lack of care he's taken to hide from these inevitable accusations, one must question the intelligence of his actions. Instead, I think there might be a more simple explanation.
Levandowski is clearly a brilliant character. Is it possible that he's not trying to maximize monetary gain but is instead optimizing for power over the technology? I sense that he's good with technical implementation but possibly terrible at technical leadership/the politics necessary for seeing one's vision through in a larger organization.
If viewed in this light, it seems that Levandowski has constantly been frustrated by the direction (or lack thereof) within the organizations he's been a part of. Rather than starting side companies and jumping ship for profit, he's really just trying to maintain control over whatever vision he has for lidar-enabled self-driving technology.
Now this strategy for power breaks down a bit, because he keeps selecting the local maxima in terms of opportunity. When Google comes calling, he accepts. When Uber comes calling, he accepts. Constantly convinced that the next place will give him the power/respect he thinks he deserves.
In the end, rather than building up IP from scratch and making a good name for himself, he's stolen a bunch of work from other engineers while potentially building a patent-infringing product. (thoughts x-posted from the other thread)
We’re building a savings app for people that struggle to save money. How you ask? We’re using a new form of investment called prize-linked savings (new to the US as of 2014). The simple explanation is that you trade part of your interest for the chance to win from a prize pool of everyone's interest.
As a software engineer at Long Game you’ll be joining a small team of engineers and will have full exposure to all aspects of our product development processes.
We’re looking for developers that enjoy building fun mobile UX and/or engineers with considerable finance experience.
I've witnessed this scenario before, almost verbatim.
Clearly the manager was wrong, but so was the employee. The issue here is that the employee is doing everything "right" from their perspective, without understanding the negative impact it has on the rest of the team. I'll spare my theories on how to "resolve" the situation, but suffice it to say, toxic employees can destroy companies; especially startups.
We’re building a savings app for people that struggle to save money. How you ask? We’re using a new form of investment called prize-linked savings (new to the US as of 2014). The simple explanation is that you trade part of your interest for the chance to win from a prize pool of everyone's interest.
As a software engineer at Long Game you’ll be joining a small team of engineers and will have full exposure to all aspects of our product development processes.
We’re looking for developers that enjoy building fun mobile UX and/or engineers with considerable finance experience.
Not to beat this analogy dead, but the reason Alan Kay et al. are so quick to discuss alternative computing methods should be quite obvious to anyone who doesn't limit their worldview to concepts that humans are already using.
Right now most processors are ridiculously general. They take a handful (ok, a couple thousand or so) instructions and they do their best to parallelize the instructions both on a single loop (core) and multiple cores. These instructions are of the "add, multiple, load, store" variety, with a few additional instructions for machine learning[1] and whatever HP wants[2].
This is it. This is the state of computing. How do bees work? Why can spiders hunt? When did crows start using tools? What makes us different than bonobos? How are all of these creatures so capable, yet so energy efficient?
We are taking a single solution, RISC/CISC architecture, and brute-forcing the hell out of it. Rather than build adaptive or purpose-built hardware, we're stuck on this concept of compile everything to x86/ARM and shrink the transistors (or try and offload parallel number crunching to the GPU).
What the author fails to realize is that computers are just fancy looping mechanisms. We use "HLLs" to compile abstract loops into instructions that run on general purpose machines. That's it.
The "apparently credible" people see the world in this light. They understand that the solution we've chosen is subpar, but the physics will make it work for some time.
A few other commenters have mentioned FPGAs. I'm not here to pitch a future on FPGAs; the die is still flat, the gates can only be reprogrammed so many times and they're generally "expensive."
I will say that we need better tools. FPGAs are a good start. Intel knows this[3]. Microsoft knows this[4].
With an FPGA you can dynamically program the exact logic a given operation will need. Whether it's real-time signal analysis, AI-built logic, or memcached, your logic will run exactly as specified.
Using purpose-built logic to run functions "natively" will drastically improve the efficiency of computation; both in time and energy.
It's really hard to build a horse that will fly to the moon. It's a lot easier to build a spaceship that can carry a horse to the moon.
My first idea was to build some sort of balloon/inflatable device that you drop into the hole. By affixing meat to the rope/hose, you can get the dog to climb on top of the balloon as it slowly inflates. Once you've got the dog out of the not-so-cylindrical bottom, you can deflate the balloon to the point of fitting the circumference of the hole. From here you carefully pull the dog up; making sure you stop if it's legs get caught/etc.
I don't see how this story gives a "misleading" view of deep learning. From my (admittedly limited) experience with self-driving RC cars, this type of mistake is quite easy for a neural net to make while being quite difficult to detect. In our case, after utilizing a visual back-prop method, we realized our car was using the lights above to direct itself rather than the lanes on the road.
Now, you can refute this and say "well clearly your data wasn't extensive enough" or "your behavioral model is too simple for a complicated task like driving" however as these tools become easier to use, more and more organizations will put them into practice without as much care as the researchers behind most of the current production efforts.