Just skimmed the text and read the comments here TBH, but I can already see were this is going. Of course the brain is complex and we don't understand it and even if Elon might not succeed with Neuralink (in the decade to market plan) it is still worth exploring. So stop hating gizmodo.
If I recall correctly Google built its own network gear long time ago. Completely makes sense. Looking at Cisco product with regard to security & the recent NSA leaks you really don't want your network gear from them.
The big difference here is that states can simply buy access to, e.g., a Cisco Nexus and attack it from inside and out until they find a vulnerability in NX-OS, let's say, a malformed CLI-via-HTTP call.
Whereas, what software does a Google switch even run? What's the architecture, the APIs? You basically need someone inside Google, or for one of these things to fall off a truck. Way more involved and expensive than the 10k you might spend on a Nexus to throw it your lab and set your hackers on it.
Actually, Google has published papers and have presented talks (many of which are available on Youtube) on the type of gear they have developed. I don't know what their latest versions are, but recently they were using OpenFlow style infrastructure to provided fine-grained control (security, balancing, analysis) over flows through out their network. OpenFlow style constructs also provide a micro-segmentation style control (ie distributed firewall) over ingress/egress of traffic at the individual container/vm port level.
Ah shareholder value. Value is the keyword. It's not shareholder money per se. If a companies stock jumps because people think you are bold for making your own secure network equipment then you have created value. Security and privacy can make value. Look at Apple. Public perception of Apple makes up a nice part of their stock and it's based around (just to name some smaller ones) privacy and security.
Well mostly because they don't rely on any legacy code base but they can write it all by themselves. Also Google has somewhat of an reputation for security other than Cisco.
Think of it in the same terms as any other service you're contemplating: do you want to pay other people for proprietary stuff, or do you want to pay your own people to develop skills around open stuff?
cisco has an incentive to not look foolish, but they don't particularly care about any small or mid-size accounts. Your people have an incentive to care about your security and functionality. Weigh up the advantages and disadvantages and make your choice appropriately.
That's the sort of thing I'm talking about. Good implementations of known-good crypto.
I don't doubt that Google's trying to come up with new, novel cryptographic methods as well, but those take a long time to develop, test, and get reviewed. RSA didn't show up over-night and SHA3 has taken some time to get ready for production.
Sample size: 20 people. (Okay what? Are you even trying guys? Releasing something about the the human metabolism with a sample size of 20? Shame on you!)
Concluding a weight gain because Ghrelin levels are raised? What? Do you even science?
Ghrelin in the first place only tells your brain to look for more food because it signals an empty stomach.
Oh please. It's hard to attract funding for studies, it's perfectly reasonable tos tart with a small sample and tehn ask for funding to do a larger study once you have a working hypothesis.
Of course, I've noted that pharmacists often have a better idea about interactions than doctors do; i've been stopped taking medications concurrently because I find dangers when picking up my prescription. Perhaps doctors shouldn't have this privilege! I shouldn't have to double check my own doctor.
Doesn't matter. To general masses it just looks (and well you will have to change code) like they shut down another thing after short time people relied on. Bad move Google. Just stop offering it and keep in running in quiet for people who have it next time.
The general masses neither know nor care about this API. "Business" folks will see they have a year to transition to an improved version of the product.
Man, there are some enterprises still running mission-critical stuff on Windows XP. The idea that a year to transition something is a long time window for enterprises is absurd.
... and that they will need to allocate a budget to this migration project, and make sure it is delivered successfully. If they do not understand the benefits, "business" folks will just see it as an annoyance.
Will never happen. As a ml researcher, I'd absolutely love to apply ml to Healthcare but the data simply isn't there. Big companies will never release the data. Government intervention is needed,but that won't happen with the new administration...
Startups often have health data and an interest in machine learning. For example, we presented research at a NIPS workshop where we trained an LSTM to predict abnormal heart rhythms from about 793 million heart rate measurements. The heart rate measurements came from our Apple Watch app, Cardiogram, and the "gold standard" data came from a study we're running with UC San Francisco (wsj.com/articles/new-study-seeks-to-use-deep-learning-to-detect-heart-disease-1458240739).
It's not just us. The NIPS machine learning for healthcare workshop had hundreds of attendees this year from both industry and academia: https://www.nipsml4hc.ws
If you're an ML researcher or engineer and want to use machine learning to save lives, feel free to email me. I'm brandon@cardiogr.am. Happy to talk about our company or point you to relevant research.
I'd be more interested, as someone who has occasional atrial fibrillation, if my data results in any particular predictions for me as to severity, likely occurrence while I'm sleeping, etc. I'm 100% on board with my data benefitting all of humanity, but as a sufferer, how can it more immediately provide me with any actionable insights.
We do plan to expose predictions for you within Cardiogram. The biggest barrier is that any predictions relating to a health condition (like atrial fibrillation) are FDA-regulated, so there's a high level of scientific validation that must happen first.
Sounds like you have paroxysmal AF. I can't tell you much about any direct benefits ML could provide there.
I do work on data from persistent AF patients. Specifically, trying to predict AF recurrence after treatment with electrical cardioversion. Basically, electrical cardioversion is an effective treatment for some subset of persistent patients, but for another subset it is not. Doctors have a hard time deciding which patients can benefit from electrical cardioversion and which will not. If we can build a model that predicts this, we can avoid unnecessary procedures (which always carry some risk) and explore other treatment options instead. If this works well, it would directly benefit individuals.
The US is not the world. In some countries (e.g. UK's NHS), it might happen.
But the data isn't actually collected -- e.g., I was told Holter monitor recordings are not kept, only looked at and discarded if nothing is found; and data which is collected is often useless for automated analysis.
As someone who has worked in ML, I'm said that's the case. As someone who also worked in security, I dread the day this data is properly collected. It will not be properly anonymized, it will be available to shady people for the right price, and it's more likely your enemies and insurers will know when your heart is going to fail before you do.
NHS as I understand is state owned and a well-functioning organisation. So NHS if chooses can play a huge role in global health with all the ML and health-data. Again, as you have rightly pointed its about keeping bad guys out.
It seems remarkably well suited in concept for playing such a role. However, in this respect (among others), it's not a well-functioning organisation. It's not even just one organisation.
Silently downvote me all you like, but only in the last month have I seen code I wrote before we even used CVS (remember that?) for source control still being used daily.
18 months ago I submitted a proposal for some software I had ready to go, happy to discuss costs but suggested a small amount (£10/user/month IIRC) with a costed business plan showing the savings it could make. They declined it, fine, but then gave the proposal document to an in house developer who's been working full time on it since and still hasn't even shown a line of code to anyone.
I'd tried to get national level innovation funding for that so the local organisation wouldn't have to pay for it. NHS innovation money is only available for proven software. By which I mean having a full system tested by clinicians and ready to go isn't enough; it has to already be in production use to be innovation!
The last couple of months a marketing agency have been trying to sell some software I've written across the NHS to help people collaborate, but they just can't do it. They've concluded it has to be sold to one small area at a time, literally starting in a GP surgery for something that makes most sense rolled out across the organisation. Before I approached the marketing agency I'd contacted the front door email of about 5 NHS IT organisations claiming to help suppliers improve the NHS about how to start the ball rolling; none of them got back to me.
There's a clue to the fractured nature of the NHS in the article linked elsewhere in the comments about Google getting AI data; they've only been able to get the data for 1.6 million patients. The NHS deal with that many patients in 2-3 days.
A month or two ago their email hit national headlines for going into meltdown after someone spammed most of the staff with the mailing list in CC and they all ended up replying to all to ask to be removed from the list.
I've been interested in application of ML to healthcare for nearly 20 years. In the early days, researchers just refused to share data; they were worried that you (using some fancy math or ML) would upstage them. I actually heard this first-hand from a faculty member.
Fast forward to today, and there is more openness. I did skim the paper mentioned here, but did not see any links to the actual data, which is a shame.
I think this is still true today. I worked at large research institution, and the doctors were cut-throat when it came to keeping other people from using their data.
My understanding is that 70% of the difficulty medical research is collecting the data. So once you've done that you want to make sure you've protected your investment of time and energy.
From an individual stand point I understand why they do it. From a societal standpoint it's such a waste.
Also their trove of data is not a one shot. They might collect data a mountain of data on Leukemia but publish one study on Leukemia's correlation with high power lines. Publishing this paper wouldn't necessitate opening up all of their data from either a horizontal or vertical perspective.
Seems like government intervention is the problem -- specifically, occupational licensing. There is a huge barrier right now for ML-healthcare startups that already can provide better diagnoses than normal doctors in some cases (and at a fraction of the cost), but are at an impasse to license their technology to legally "practice medicine."
If I was a doctor I'd love to have a ML assistant who will also look over my patients' symptoms and find correlations that I missed. Is that any more "practicing medicine" than a reference book is?
So we need a free software effort to produce some kind of machine learning/statistical analysis tools that would not be sold but merely used by whoever wants them. The user should be a medical professional using the tool as an aid much like a reference book. A good doctor doesn't take the book as gospel (at least my GP doesn't) but weighs what it says together with the specifics of the case and so on together with his or her own experience. So perhaps a grassroots start could be possible.
Of course the major stumbling block of access to good data remains.
The UK's NHS recently opened up a large amount of its data to Google [0]. In parallel efforts, a company called Nuna is gathering and unifying data from state level Medicaid programs so it can be analyzed similarly [1].
A previous company I worked for was scared to death of holding any records that may be classified as health records because of the regulatory implications.
>As a ml researcher, I'd absolutely love to apply ml to Healthcare but the data simply isn't there. Big companies will never release the data.
The big-data regime isn't the only regime. Probabilistic models can encode doctors' prior knowledge and also be trained with only a few dozen data points.
Trampling privacy to extend life sounds like exactly the sort of thing Peter Thiel would be for and he seems to be running the show regarding the Trump administration and the FDA.
Is this true in all developed countries? What about healthcare systems owned and operated by the government. Seems plausible a government contract could provide access to such data.
> Will never happen. As a ml researcher, I'd absolutely love to apply ml to Healthcare but the data simply isn't there
A famous(in medical circles) cardiologist Eugene Braunwald once said after a stint practicing in mexico: "...they have the patients we have the technology..."
The US is not the world.
What sorts of data sets are you looking for? It is probably available from other countries or could be more easily collected. I have an interest in this area too
> It didn’t have to be this way. Ev could have RECONSIDERED. He surely had enough in the bank from previous exploits. Started slower. Not hired 150 people before there was a path to profitability, let alone sustainability. Maybe you will.
Well the Twitter folks is not known for clever business moves. Seriously though, how do you need 150 people for that website?!
