And if you wanted to place an ad online to say, reach everyone in the US of driving age (car ad), or in Toronto during an NBA game (Pizza ad) what are all your options?
885 karma · joined March 1, 2013
Being able to work with existing infrastructure and vehicles and other components designed to work with gas is a huge advantage. And of course the simpler storage of gas vs. hydrogen is nice too.
In terms of efficiency we can do a little back of envelope calcualtions. Prometheus converts electricity to gas at 60 kWh a gallon (33.7 kWh) or about 56% efficient. Hydrogen production via electrolysis comes in at between 44.5 - 55 kWh/kg-H2 (I've seen a wide range of estimates) for a kg of Hydrogen (33 kWh) so about 60-70% efficient.
Of course then there's the reverse process of burning gas or using a fuel cell for electricity production. The Toyota Mirai gets about 68 miles per kg of hydrogen and the roughly equivalent Toyota Camry hybrid gets about 52 mpg.
So boiling this down (if I got this right and my data is good):
Gas method: 60kWh -> 1 gallon of gas -> 52 miles = .86 miles / kWh Hydrogen method: 55 kWh -> 1 kg of h2 -> 68 miles = 1.23 miles / kWh
Of course this ignores all the challenges to building the hydrogen infrastructure, public fear (warranted or not) about hydrogen, capital costs of both systems, etc. It's also likely the hydrogen conversion gets more efficient than the 55kWh to product 1kg but wanted to keep this conservative.
On the minus side the publishers and developers are still compensated by the amount of time users spend in their articles/games so the incentive is still to develop addictive and time hogging services or click bait. To be fair this is a difficult problem as there's not an objective measure for quality that compensation could be based on. Perhaps more thought could have gone into this though.
Interesting. Does this signal that AWS believes some enterprise workloads will remain in private DCs? This is certainly a big value to those running on-prem databases.
Anyone have an idea on pricing?
"...the bill would allow California residents to find out what information businesses and data brokers collect about them, where that information comes from, and how it's shared. It would give people the power to ask for their data to be deleted and to order businesses to stop selling their personal information. It places limits on selling data on users younger than 16 years of age, and prohibits businesses from denying service to users for exercising their rights under the bill."
If you don't collect personal information it's easy. If you do collect it, just don't sell it and let the customer tell you to delete it.
Doesn't seem too different from dealing with credit card payments and lots of companies have figured out how to do that.
To be clear I'm not arguing for or against parking or public transportation. But I do think we should be forming policies with more data and less opinion.
As an example San Francisco spends about $1 billion of its $9 billion city budget funding Muni - more than 10% of the entire city budget.
More broadly, however, we'll likely see many more relationships develop between:
1. the multitude of smaller and relatively less capitalized self-driving companies (Drive.ai <i>only</i> raised ~$50 million in its last round) who may be more challenged to collect / simulate driving data
2. the better capitalized competitors (GM, Waymo/Google, Bosch, Tesal etc.) who have more means to collect / create data
3. those who have access to consumers (Uber, Lyft, Google/Waze/Maps, Ford/Chariot, GM cars & Cruise building it's own ride sharing app)
4. fleet service companies (commercial and rental)
5. freight services (UPS, Fedex, etc.)
6. OEMs / Tier 1s / manufacturers with different strategies (Ford, GM, BMW, FCA, Continental, Bosch, ...)
It's a wild, mixed up, confused world out there. Very interesting to see from a business strategy perspective.
The argument is essentially the same for Google/Facebook et al. Try telling someone who has used gmail for the last 10 years before Google altered their privacy policy for Google's benefit that if they want to keep their email private they're free to switch to Yahoo or Outlook. And that's a case where consumers have a choice. Consumers have no voice in web sites use of Google Analytics or Doubleclick. Or Facebook's like button. Or Adbobe's audience tracking....
One could apply the same logic to ISPs, after all. If you don't like Verizon's privacy policy you're free to switch to Comcast, or ATT. Oh - they all have the same privacy sapping policy? Too bad.
Certainly it should not be too much to ask that a "normal" non-technical internet user should feel free to roam the open internet without every click and view being tracked. I cannot expect to train my grandmother avoid google/facebook, use privacy badger and/or ublock and know what to do when her video doesn't load or play because privacy badger has blocked some adobe tracker....
This is hacker news. Let's not accept cringeworthy arguments that could have been proudly published by lobbyists for Facebook here. If this audience cannot scale higher what hope do we have for society at large? So yes, we should have regulations to limit what ISPs can track. And yes, those policies should apply to extremely large internet ecosystems as well.
What's fascinating is that other online privacy areas that I would argue are much more invasive and threatening to consumer privacy are completely ignored. I'm referring, of course, to the ecosystems of Google, Facebook, the new Verizon (AOL, Yahoo), and the many other companies looking to amass as much consumer data as possible for profit.
These FCC rules feel a bit like the FCC was patting themselves on the back for fixing a drip in the bathroom faucet while ignoring the broken pipe to the water main.
From a business perspective I can also see why ISPs would be so upset about their businesses having to abide by these rules while their competitors (again, Facebook, Google, et al) are left to collect data unbridled. The all sell ads after all.
Theano support will continue for as long as Keras exists. This integration changes nothing for existing Keras users, only for TF users. - https://twitter.com/fchollet/status/821090410659344384
Rather than a "new" Keras it sounds (I could be wrong) as if the Keras API will be included now with TF as an alternative way of interacting with TF (as it has been for some time) and simple bundled with TF.
Senator Dianne Feinstein Urges Senate Support for USA-Patriot Act Reauthorization http://www.feinstein.senate.gov/public/index.cfm/press-relea...
1. Business model / strategy is as important as the technology. Way too many people believe that all they need to do is build a great product (by all accounts it seems RethinkDB did) and the rest will take care of itself. Enterprise software is a very difficult business and at the moment going through tremendous change and while there is good opportunity it takes much more than good tech.
2. I would put RethinkDB in the "hard tech" category - the category that YC recently said they'd like to fund more of. Given the success of the technology but failure of the company it raises the question of what value YC brings to the table for a hard tech company? Many hard tech companies are very different than the companies that appear to enjoy the most success from YC (e.g. very different than AirBNB, Dropbox,...).
Note that there are exceptions and this can be overcome.
> If so... do the challenges listed in this post resonate? Do you believe the shifts identified are the right ones to focus on?
I'm applying deep learning in a healthcare application and certainly the points you raise resonate and are generally the right areas to address for utilizing AI (for points 2 and 3 really any innovative technology) but let me expand on them a bit.
#2: Deployment and the Outside-In Principle - Very often new technologies are coupled with business model innovation to bring about change. The complex and often perverse mechanics of the healthcare system in this country make this exceedingly difficult. I agree that models that more rationally couple risk with reward (e.g. Accountable Care Organizations, Bundled Payments, and payer/provider organizations like Kaiser) provide the right incentives to reduce cost while increasing the quality of care. This is an environment where technology can make a difference; not so in the fee for service model. I think the software "deployment" model is much less of an issue.
#3: Regulation and Fear - This is actually a significant challenge. The FDA has significant incentive to be very conservative with their approvals (lives may hang in the balance) and for them the risk of failure (i.e. a death caused by a device/test they approved and likely makes the news headlines) is MUCH more traumatic and negative than the rewards of success (some costs are reduced for a specific treatment or diagnostic that almost no one will ever hear about). Additionally the FDA, Doctors, and medical administrators suffer the same fear of formulaic decisions that most people do. We simply don't trust an algorithm to make decisions and so will resist them or hold them to significantly higher standards than we hold human driven decisions. This means even if you get through the FDA you'll potentially have resistance of doctors and patients.
If we export some of the other posts about radiology I can just imagine a patient's experience in a hospital. I'm sure a doctor or radiologist will share an anecdote like the one in this thread (radiologist saw an occlusion due to a poor image and detected a tumor) and scare a patient into thinking a radiologist is better than an algorithm at reading an MRI/x-ray despite statistics that will (eventually) show that the deep learning algorithm will be more consistent and accurate. To be clear I'm not arguing that a radiologist not continue to be involved and review the diagnosis but that it will be difficult for this technology to be established in high-stakes applications due to fear.
Well it would be nice to first validate the results somewhere else (peer review should actually be completed) and no doubt some real world trials proceed and their results evaluated. This is what any FDA approval, at a minimum, would entail. Keep in mind what the author's themselves note in how this study may differ from the "real world":
"One limitation of this study is that cases submitted for TCGA and TMA databases might be biased in terms of having mostly images in which the morphological patterns of disease are definitive, which could be different from what pathologists encounter at their day-to-day practice."
Putting this into practice also requires looking at the bigger picture; not just the accuracy of the diagnosis vs. humans.
For example I have found that in studies doctors are much more conservative in their diagnosis than an academic algorithm with no implication on patient outcome. For instance if the question is a) "cancer" or b) "not cancer" the doctor, fearing patient harm, malpractice suits, career derailment, will be biased to identifying "a) cancer" because the perceived costs of a false positive (treating for a nonexistent cancer which may have significant ill effects) are lower than a false negative (not treating a real cancer leading potentially to death). This will reduce the human doctor's accuracy on diagnosis.
What the "right" bias is in an individual case can bring in many more factors and moral questions out of scope for this study.
This is not to argue that tools like these should not be developed and utilized but practical application is often more difficult than just solving the technology challenge. I'm certain that we'll see many advances in healthcare due to ML.
Given how many applications we're seeing for AI it seems that there may be a battle for attention of lawmakers and others for which AI application we put into production first given the limited bandwidth our regulators have; it's not like congress has been especially productive over the last many years.
http://www.economist.com/news/briefing/21695385-profits-are-...
Also lets Nervana scale and potentially get their Neon deep learning framework out there in the face of bigger players (a la TensorFlow).
All in all it's good to see the competition in this space.
1. Why not use OpenID Connect (http://openid.net/connect/) on top of OAuth?
2. How does this compare to Okta, Ping, etc.? Seems like this may be a solved problem in the enterprise space and I'm curious what Slack brings to the table that couldn't be done with existing solutions.
Personally I think some of the most enjoyable parts of starting are debating these points, creating inexpensive tests to validate (or invalidate) them, and evolve the idea.
Let's not forget that most of the email system is still not encrypted (last time I checked anyway) even though the last mile between your mail server and your client may be.
You're right. Sorry for my sloppy use of AuthN and AuthZ. My point is that for day to day authentication into 3rd party sites which is what I think most people use "Sign in with Google" and the like for might be better served by a 3rd party with less or no data. Less chance of accidents like the subject of this HN thread.
Of course as others have suggested Google could implement a more serious authorization system for elevated or unusual privileges in order to get users, such as this one, to pay attention.
It does make me think that perhaps authentication (OAuth) would be better provided by an independent organization that didn't house so much personal data (that is, not an email provider nor a social network). An independent provider that didn't have much, if any, personal info would prevent this accidental release of information and control. That way if someone _really_ wanted to give a third party access to and control of their email at Google they would have to actually take the extra step of logging into Google and deliberately providing the access. In this case introducing friction into the process may save the user from shooting himself in the foot.
Email providers don't have significant incentive to become Identity Providers; Web site owners do not have significant incentive to accept persona at least until many users adopt/request it; Users are mostly unaware of Persona's existence and the argument in favor for many is rather weak as the people who seem to care about privacy seem to be in an exclusive club.
There are a few keys to success that I can think of off the top of my head:
1. Solid awareness campaign to explain benefits to non-HN type users. Would depend on solid benefits of security, convenience/simplicity, and privacy.
2. Fantastic "default" identity provider as most email providers will not, in the short term, become Identity Providers. This has to work without sacrificing UX, security, convenience/simplicity, and privacy.
3. Adoption by at least 1 major email provider (e.g. FastMail).
While I don't bring much in the way of development skills (not my strength at all), I would be very willing to help on the strategy, partnerships / business development, marketing etc. front.