https://en.m.wikipedia.org/wiki/Google_self-driving_car
Tesla has more than 100,000 model S driving around and in just 7 months their auto pilots have already surpassed the km driven by Google for the past 7 years by almost a factor 100 (roughly 200M km). That is real use in many places, situations and weathers, not just test drives in California.
I realise it's not apples to apples and that Google's cars may be more autonomous for now. But with the numbers stacked against it like that I doubt it will be long before Tesla's auto pilot is vastly superior.
As I see it, given the lack of a Google car, they will have to team up with a major car company to get enough cars out there. And that requires a more elegant hardware solution than what they currently put on the rooftops.
http://www.extremetech.com/extreme/231097-tesla-records-its-...
I think that is an understatement. This video shows why staying in your lane on a highway (what Tesla autopilot does) and dealing with city streets are two just completely different things: https://youtu.be/tiwVMrTLUWg?t=8m49s
I think Tesla has a ton of catching up to do to compete with where Google is right now.
That's not to say that tesla isn't developing full automation it's just that what they sell now is just clever branding on something you can get elsewhere.
Musk is literally too good friends with Sergey Brin and Larry Page for this not too happen.
http://motherboard.vice.com/read/elon-musk-and-larry-page-ha...
http://www.businessinsider.com/googles-secret-apartment-elon...
Tesla, with a large, update-able fleet in the field, has a much larger data-collection platform than Google. Even if they're not using it for full automation now, the collection itself is very valuable.
This is almost certainly why Uber is putting self-driving cars into the field now as well, even though they are clearly not ready for prime-time. They need those sensor packages to be driving around on real roads and in real traffic.
Tesla (currently) has one radar, one camera and 4 short distance ultrasonic sensors. Google has LIDAR plus a lot more.
Tesla's suite may or may not be sufficient for operation. But for training, good data is critical.
Additionally, I've read some really interesting articles about research other car manufacturers have done. For instance, Google has never tested in bad weather, but Ford has been working on self-driving cars that work in snow. And while Google just assumes the humans are meant to be 'along for the ride', Volkswagon did some really good UI work, in terms of figuring out how to make the car's actions predictable, and hence, less scary. (Essentially, the car indicated to the driver what it was about to do before it executed a maneuver.)
Google is really good at capitalizing on their self-driving car project for marketing purposes, but it's extremely unlikely it'll ever be a market leader.
>> Google's sensor platform is the most expensive out there.
>> Google has never tested in bad weather
What about a self-driving cars as a service ? they can be the first to start a very profitable service that is limited in area and in weather even thought the sensors are more expensive(and they can claim "we aren't cutting corners like everybody else!")
And that could be a great place to be in, strategically.
Since this is a core part of your argument, I'm gonna go ahead and [citation needed]
I've not heard anything of the sort and I've seen Google's cars on all sorts of roads. Plenty of their talks have been about the cars detecting anomalous situations and reacting appropriately, as well.
* Even if the projects get 10, 100 millions $ in revenue / profit, it is meaningless compare to billions in profit from search. The folks in those projects are not likely to benefit significantly from it.
* The smart folks probably know it. If they join the project (self driving car), some of motives are to learn as much as possible using Google's resources, name, connections and set it up for their own next venture.
Reading this article makes me wonder if it was a good idea to put all of them into one division though. Even when you go in knowing a project is a long-shot it can be demoralizing when it fails. I can't imagine how hard it would be to work in a whole division of mostly failed projects.
Instead of getting lean startups that have to move quickly because of resource scarcity you have bloated startups that feel no pressure to move quickly because of the unlimited resources they are receiving.
It just operates on hype curve. GoogleX takes projects from "technological trigger" to "peak of inflated expectations": https://en.wikipedia.org/wiki/Hype_cycle
- Verify/Life Sciences is failing pretty hard and has failed to develop any actual products. https://www.statnews.com/2016/03/28/google-life-sciences-exo...
- Glass is an absolute failure. I own one, I loved mine. It's still a failure.
- Gcam went to... Google Research? It went from moonshot R&D to normal R&D. That's not success.
- Google Brain... went to Research as well. And really, it is likely just a rebranded version of whatever DeepMind was already doing when Google bought them.
- Project Tango went from Google's moonshot R&D to Motorola's R&D (ATAP) which Google kept ownership of. That's also not success.
Android Wear, Flux, and Project Insight (Indoor Maps) probably all count as actual successes for X, but that's about it.
Unsurprising. The valley (and tech in general) is forgetting it's power doesn't come from management and politics.
In a academic context, there is no subtext of creating products. Yeah, churning out papers is as contrived...but I still feel like the timelines in academia are more generous.
Google talks a good press event, highlighting their embrace of failure, their desire to take on crazy moonshots etc...but its all in the context of quarterly reports. Something has to give.
Isn't the whole idea of a "moonshot", in this context, a project that has both a long window before any payoff, and a high risk of failure. So isn't this a very much "water is wet" story?
If you're familiar with the internals of Google (or have ever worked there), then you wouldn't even be slightly surprised by the content of this article or the ever present stream of product failures from Google. In fact, if you're aware, then you know things are likely to remain this way until something is done about all the BS present internally.
You see the "difficult to work-- FOR" and almost (or all out) sociopathic leaders being outed to some extent in the last year or so: commonplace. That is, Google likes to position itself as being above such stupidity, but if you listen to what they say more carefully, you see this is their default/go-to strategy. That is, they explicitly seek out such personalities (similar to many VCs), as they believe (based on "data") that it's what's more likely to lead to success.
Also, while it would be easy to believe Google is all about the "moon shots" they like touting/hyping (especially given how much money they are dumping in that direction), if you look at who they are putting in key positions, and how everything is setup, you immediately realize that (regardless of what they are doing) it will all likely go nowhere.
Something is very strange about that place. It's like they have no brain. It's like they are just outright stupid. Which I suppose is hard to say (and have believed), especially after years of hype (and supporting anecdotes) about its exceptionally talented pool of employees.
Essentially, it often seems like a sea of INTJs who like to parade around as though they know what to do with data (and are above bias and feeding into their own BS, because they are "data-driven"), but that at the end of the day are just going based on whim/gut, one which is more self-centered and out of touch than "in tune with" and reflective of the world at large (or where it's going).
I suppose if you spin around in your own shht enough, and surround yourself with more of-- yourself-- then eventually, you'll fall into line believing your own BS and that you must be right.
I remember when I was at Google for a short time in 2010. The place was a source of endless annoyance and irritation. The field was wide open, and it was all there for the taking, but then they just consistently and continually kept making the dumbest decisions. And they'd defend those decisions as though they were God almighty and immune to being wrong. It was the greatest consistent stream of stupidity I had ever seen. And by the looks of it, nothing has really changed. It's just been shht, then more shht, then more BS trying to explain away the shht, as though shht isn't what it is.
It's not that they "fail often due to releasing more and sooner" or "see something beyond the field of view of many;" it's that they just plain failed and it was most likely due to stupidity/credentials (you heard what I said) being heard over repeated statements of what made more sense (or of what would be more likely to go somewhere). Also, that failure that looks like a half-assed piece of crap likely was likely 2 years (or more) in the making, rather than the 6-8 weeks it seems went into it.
It's just shht every time, and as soon as you step into realizing it, you'll see that it's always just more shht from them. Their only successes (even in their "main" business) have come from the competition "falling off" (F'ing themselves over), rather than from them releasing things that are worthwhile or better.
The place was extremely infuriating to me, and I couldn't wait to leave. I was in silent shock the majority of the time I was there, and it seems that even though 6+ years have gone by, not much has changed!
I'll say it outright and in plain English: the "almighty" Google-- the almighty enterprise of innovation-- the almighty force for pushing the web/world forward-- is completely full of shht! They couldn't put out an innovative (or even just quality) product to save their lives! If anything surfaces from them that's not more garbage, then it was likely from an acquisition. And even then, it seems they are F'ing even those avenues up more frequently as time goes by.
Google Photos: acquisition!
Google DeepMind: acquisition!
Google ATAP: acquisition!
Etc.
They've got nothing.
Who's to blame?!
Attention, Decision, Interest, Action. AIDA.
We're adding a little something to this month's sales contest. As you all know, first prize is a Cadillac Eldorado. Anybody want to see second prize? (second prize is a set of steak knives.)
Third prize is you're fired.
These are The New Leads. These are the Glengarry leads. And to you they're gold. And you don't get them. Why? Because to give them to you would be throwing them away. They're for closers.
If Google X is really just a nerd-PR exercise, let's call it that.
X, on the other hand, has been around for over 6 years now and as far as I know, its only marginal success to date has been Glass. I don't think it's unreasonable to say that X's success rate so far has been lower than many people expected.
That was probably true originally. Lately, (especially post-Alphabet) it seems like the pressure is on them to produce commercial products, albeit ones that might be many years out - my impression is that researchers at PARC were not, realistically, under the same pressure (see: the many innovations that were never commercialized).
That makes sense, though - Google is actively in search of new business models, while Xerox enjoyed such enormous market share while PARC was well-funded that I doubt they felt that kind of pressure until later.
One thing did jump out at me in this article.
Mike Cassidy, who stepped down from Loon, ran the team “like a fire drill,” a former employee said.
I read this as, "Leadership likes to change its mind about direction and focus ... constantly". That's a recipe for disaster. Focus is key. You need to find the right direction as quick as possible, then execute.
It may sound hackneyed, but I believe necessity is the mother of invention and it doesn't seem there is that kind of motivation here...it feels forced.
The self-driving car is the project that seems to have the biggest potential impact, but the string of high-profile departures over the last year or two is worrisome.
Also, the leader wearing Rollerblades to meetings seems far too much like a parody of "quirky tech visionary" for my comfort. But maybe I'm more traditional/close-minded than most here.
Imagine the difference between, say, trying to determine a user's video preferences based upon their hit statistics on IMDB vs direct viewing data from Netflix that includes everything they've watched, how long (1 minute vs the entire show), when, the order they've watched (eg, mood predictors), frequency, etc.
I'm not saying that this is the difference between Tesla and Google, just that quantity and quality don't necessarily equate.
Tesla is far more limited in both what it's collecting and how it's collecting it as it needs to go over a cell signal. For example they are using a camera for a hefty part of the autopilot system. They obviously can't stream every frame of that camera up to their servers for deeper analysis. Besides being infeasible bandwidth requirements it would be an insane privacy violation of the owner.
Google's Koala cars are functional under only the most idyllic, constrained and carefully monitored conditions. There's a huge laundry list of unsolved, and unknown problems between what Google has demonstrated so far and where they need to be technology-wise to run a robust, reliable, profit generating service at the scale needed to cover their R&D.
The casual thought experimenter generally fails to recognize the frequency with which they utilize higher level reasoning when driving that's well beyond the limits of the current state of the art in AI. Nobody has the slightest idea of how to solve this, let alone dig into all the as-of-yet not understood logistical problems inherent in commercializing the technology, an unexplored realm rife with any number of unknown unknowns.
The real world is a very messy place. Unlike Google, Uber is eyeballs deep in the messiness of the real world, so they're probably better poised, though a lot can change in 5 or 10 years. The competitive playing field has been so dramatically altered in the past 2 or 3 years that the days when Google was the only company anyone took seriously feels like ancient history.
With regards to the sensors, my bet is that by the time AI's capacity to reason is where it needs to be, the sensors and software needed to see and interpret the dynamic driving environment will be dirt cheap. Probably all you'll need is cameras, their cost keeps going down and the state of the art in image processing is progressing and will continue to progress.
But you also have to realize that you'll also highlight the weaknesses of the technology. People may not be able to easily specify to the car where they'd like to disembark. What if people want to be taken just outside the service area? Uber is including a human with their self-driving project for now, which doesn't save them (or you) any money.
Google learned from Glass that a small number of users and a lot of public attention and hype about a product can quickly eviscerate it. The technology was good, but people without hands-on experience misunderstood it, and a couple small incidents became national news. A small rollout can just as easily kill your project as kick it off.
>> What if people want to be taken just outside the service area?
Google is currently trying to be the comparison search engine for people who want to order rides - via their Google Maps. If they sucsseed, they'll just fit you with the right service according the limitations of the self-driving car ,etc.
And regarding Google Glass - IDK. Even Uber is marketed on a city-by-city basis,
Tesla can't do the inverse.
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Oh how sweet it would be
for it to be me
that's the one to reveal
in days before
the IPO of many of these
(and the main other 3)
that what the world
has been waiting for
is sitting with me--
with open arms--
desperately ready to be--
with you--
always.
.
.
.
Some say with wonder
other with haste
all that's to be said
but even in this case
where is it again
what makes it all
seem sudden
a change
leaving the world a part
tense at the seams
knowing even then
aside from the waves
crashing toward the bitter end
it was dealt
squarely
only pausing to be
as there it is again.
But if you have the most cars collecting the freshest data, you still have to bear in mind, the car collecting that data, is currently relying on the old data. Which means your cars can't trust the map data. And the reality is, with how much things in the real world change, your map data will never be trustworthy. You can have it, but you can't rely on it.
Which is a better self-driving car? The car that can look at a cloud-based 3D map of every object in the entire town, assuming it's current, and decide on a route? Or the car that can ping Google Maps, get told "turn left on Main, right on Washington" then entirely from it's live surroundings, decide how to drive?
I think even just the cameras alone would be valuable. It seems a Tesla has a better idea of what's going on around it than a human would, so these sensors should be at least close to sufficient for training an autonomous driving agent.
I mean, everything is obvious in retrospect. I'm sure there were some PARC guys bemoaning that all the fundamental discoveries were already made in the 1950s... Google today probably has bigger budgets than Xerox had then (tho' that's just a guess on my part)
For example: http://blogs.wsj.com/ideas-market/2011/02/07/the-difficulty-...