etendue

I'd be very wary of using complex SOUP like TensorFlow, even if brought under my quality system. I think a good answer here is that once one goes under design control the subset of functionality needed should be implemented in-house under the organization's SDLC.

I think the simple answer is that it is not easy. To start, rigorous design processes with risk analysis upfront are certainly necessary, as are well-defined operational contexts for the autonomous functionality, and a very disciplined approach to clearly defining safety-critical subsystems and minimizing their surface area.

There's a surprising amount of work in the literature that serves as a guide for using neural networks in safety-critical contexts, e.g., http://dl.acm.org/citation.cfm?id=2156661 and http://dl.acm.org/citation.cfm?id=582141.

The report makes reference to "Assessment of Safety Standards for Automotive Electronic Control Systems" by NHTSA, which itself reviews ISO 26262, MIL-STD-882E, DO-178C, the FMVSS, AUTOSAR, and MISRA C.

In this context, they mean verification and validation in the systems engineering sense. Software would be included in that it is a part of the whole system.

N.B., this policy is mainly concerned with Highly Automated Vehicles (HAVs), which are defined as SAE Level 3 ("capable of monitoring the driving environment") and above.

edit: as to SAE Level 2, it has this (and more) to say:

> Furthermore, manufacturers and other entities should place significant emphasis on assessing the risk of driver complacency and misuse of Level 2 systems, and develop effective countermeasures to assist drivers in properly using the system as the manufacturer expects. Complacency has been defined as, “... [when an operator] over-relies on and excessively trusts the automation, and subsequently fails to exercise his or her vigilance and/or supervisory duties” (Parasuraman, 1997).

also,

> Manufacturers and other entities should assume that the technical distinction between the levels of automation (e.g., between Level 2 and Level 3) may not be clear to all users or to the general public.

I think this point from the post deserves emphasis, for consideration in future discussions:

> If Tesla makes a "major" change to software, pretty much all bets are off as to whether the new software will be better or worse in practice unless a safety critical assurance methodology (e.g., ISO 26262) has been used. (In fact, one can argue that any change invalidates previous test data, but that is a fine point beyond what I want to cover here.) Tesla says they're making a dramatic switch to radar as a primary sensor with this version. That sounds like it could be a major change. It would be no surprise if this software version resets the clock to zero miles of experience in terms of software field reliability both for better and for worse.

I am admittedly a broken record on this point, but Tesla moves very fast and very nimble on a system that is under design controls. It would be very educational to learn about their development process and how it maps to ISO 26262.

Thinking of training data sets as an example: they contain a lot of personal information (faces, license plates, times, locations, speeds, etc.). Depends on jurisdiction, but there likely aren't legal issues in amassing and using this data when it is collected in plain view from public areas; I envision possible ethical issues though. Could you release training data for public use, without obscuring identifying information? There may not be legal issues in releasing raw data, but keeping in mind that the information is sensitive and recorded without consent, and that an engineer's first duty is to the public health, safety, and well-being, might there be a professional obligation to sanitize the data?

My example might be a bit contrived, but I think there are going to be many valid (and far better!) questions in this discipline that should be asked and considered, and I think your graduates need to be equipped to do so.

Will ethics (e.g., the collection and storage of personal information, or professional responsibilities in regulated industries) be given any treatment?

I believe that the comment you are responding to was speaking to the asymmetry that people with experience in computational biology have an easier time moving to general data science problems than do people with experience in general data science working on computational biology problems.

I agree that the asymmetry exists: there is a tremendous baseline of scientific knowledge and experience that is needed to make significant contributions to the field. I personally have worked with people with backgrounds in programming or CS on medical problems, and it has been frustrating because they lack what I would term "scientific common sense". I would personally prefer, and would be able to make more progress with, working with (for example) anyone who has completed a sequence of education sufficient for pre-med requirements and has some programming experience over a "full stack data engineer". Even if someone with a programming or CS background were inclined to pick up the textbooks and amass the baseline scientific knowledge (I'm sure they exist, although I haven't met them yet), they'd still lack the years of laboratory work and experience of applying this knowledge.

My original comment was apparently poorly worded because it was interpreted by the responders differently than I intended, but delightfully, it resulted in very thoughtful comments. I am very skeptical that one can make even small contributions to genetics without the experience of years of specialized work. There are ancillary problems that could be done by someone with a programming or CS background, e.g., a better LIMS system, or perhaps protocol management, but I don't see those tasks as leading to later making meaningful contributions to the field of genetics. The MD or PhD isn't required, but all the work done leading up to it is, and so as I see it those prepared to make the contributions are most likely going to have gotten the degree on the way.

How would one go about meaningfully contributing to solving problems in genetics without having done the work leading to a MD or PhD (or both)?

> Or is "that's disturbing" somehow considered to be an indication of dismissal or disagreement?

Actually, you said "It disturbs me that all the examples so far are science fiction." which (a bit out of context and ignoring the principle of charity) could be interpreted as a cursory dismissal along the lines of, "these examples are too ridiculous to consider further".

Even with the principle of charity, I find "they're getting it from sci-fi movies" to be an unfair summary of my point, but perhaps I'm doing a poor job making my case clearly.

Thinking on it, would it be fair to expect any example of an autopilot function on a car to be from a type other than science fiction or fantasy?

I am not trying to imply that people are having difficulty distinguishing fiction from reality.

I am noting that real auto companies have deliberately placed product concepts in media to prime people's expectations of what future products will look like and what they will be able to do. Independently, there are also proper level 4 systems under active development getting plenty of popular press coverage.

I also note that the very public face of Tesla frequently makes very public and (in my opinion, overly optimistic) declarations of their product's capabilities both present and future, for example (Jan 2016), "The Model S is 'probably better than humans at this point in highway driving' according to Musk." [0]

It doesn't strike me to be all that far of a leap for an average person to conclude that the future has arrived.

[0] https://www.theguardian.com/technology/2016/jan/11/elon-musk...

I don't see why my examples should in any way "disturb you". Science fiction frequently is an inspiration for products and ideas that are made real, examples are legion.

I judiciously selected I Robot and Minority Report because Audi and Lexus, respectively, had product placement for future design concepts.

Demolition Man, I Robot, or Minority Report

The attrition rate for a PhD program isn't 90%, but 50% is not unknown; my own was around 30% (measured from matriculation to defense, my class year). Some students were forced out of the program, others left on their own accord.

Also, it is unusual for a dissertation to be outright rejected because of how it reflects on the advisor and committee: the committee is (supposed to be) kept up to date on the student's progress and will recommend against defending if the student is unlikely to pass. Slightly less unusual would be a student being allowed to defend, but then needing to do major revisions to their dissertation for it to be accepted. Keep in mind that at the point one is defending, quite a bit of time and money has been invested in the candidate so there is a good incentive to see the candidate succeed for no other reason. Unsuited students are (ideally) dismissed much earlier, i.e., at admission to candidacy.

One absolutely worries about being scooped on papers, since those are the currency of academia and being scooped usually results in needing to publish your own (now less novel) work in a lesser journal. And as another commenter points out: a professor taking on 10 students with only 1 succeeding, if one defines success as being tenured, isn't that far off from reality.

As an aside, I personally think forming a research group at a university isn't all that different from creating a startup.

This is an absolutely incorrect assertion: drug discovery is but one part of the drug development process. You're ignoring (among other things) the work in medicinal chemistry, retrosynthesis, and scale up/chemical engineering that go into creating a drug. The compound at the end of the development process rarely looks like the one identified in initial screening, and the work done to manufacture it and bring it to market unquestionably count as inventive.

> The $999 price point is designed to be affordable, and is possible because of the components Comma uses in its product, which tend to be inexpensive off-the-shelf electronics.

"Inexpensive off-the-shelf electronics" aren't rated for automotive environmental conditions, nor do they have the immunity to interference (e.g., single event upsets) required for safety-critical systems.

I doubt I would, even though I use Python for other purposes.

Lua isn't the only one in this space: I personally use Tcl (or Jim Tcl) for this sort of work, and it (IMHO) excels in this area. What advantages would you see your Python implementation having over Tcl or Lua which were designed with this area in mind?

The Live Image with my sister is an MP4 file, 852KB, 480x640 resolution, H.264 encoding.

We are on the same page I think, the skepticism is shared: I'm in medical and not automotive, but much of our PLM process is shaped by what was learned in the automotive and aerospace worlds. It boggles my mind to imagine how Tesla can get all of the responsible parties to even sign off on the documentation alone at the speed they work. I am quite serious (and I think correct) that if Tesla's process is as efficient as it appears, they are sitting on a gold mine for consulting considering all the organizations in regulated industries that are incapable as moving as fast. Musk wants to change the world? Sharing this secret would count.

Another question that popped in my head: what did the requirements look like that allowed such a huge (I assume) CPU and memory budget available that they could improve the system with "six times as many radar objects with the same hardware with a lot more information per object."

I was on vacation recently and used Pix all over---I got many great photos that were clearly better than what I would have gotten with the stock app (the built-in comparison feature is a great idea), even on landscapes where there weren't faces to focus on.

Pix also managed to take a fantastic Live Image of my sister and me, which is really special because I don't get to see her very often. Thanks to you all for that.

Chief area that I think needs improvement is on general speed optimizations, startup is slow and feedback when taking a shot could be clearer (i.e., when is it done after I hit the shutter?).

Oops, my question was ambiguous—I was actually wondering how you measured "mental load", as you put it.

Completely agree---it could also make for a very lucrative consulting business.

> In my small sample set(~30k AP miles) I've found the mental load for driving long distances and rush hour traffic to be significantly less.

Interesting---how did you measure this?

The speed with which these changes are rolled out, especially considering the scale of the changes, is also … impressive while under design controls.