> I want to use the little bit of voting with the wallet I have.
Technically, voting with your wallet is a political statement, which you are sending to Boeing management and shareholders to make the world a tiny bit less profit-at-all-cost-driven.
It is interesting that people automatically equate "political" with party or country politics, which gives it a bad rep. When in fact it is a healthy thing if more people were to think and act like you and stand for their principles on issues however minor-sounding.
Do you have an estimate for typical token usage for a developer who'd use it as part of their workflow? I'd imagine the costs can rack up fairly quickly if you're not careful.
You are quoting someone from a financial career interview forum who can't imagine why a bright person would want to pursue a lesser paid career in research.
Well, even if I try to embrace your argument — life itself is an iterated version of a more general decision game. It doesn't have to be Monty Hall specifically.
Over the course of your life, if whenever you have a choice to make you choose the lower probability strategy (here with the Monty Hall example it's not even an expected-value vs. variance tradeoff, switching is just all-around more optimal) you will most definitely end up worse off in the end.
While impressive, it is clear ChatGPT doesn't actually understand what is happening when you give it unusual positions and ask it to explain what happened.
For example, after a couple moves of the Fried Liver Attack (which ChatGPT was easily able to play following the known lines):
1.e4 e5 2.Nf3 Nc6 3.Bc4 Nf6 4.Ng5 d5
I tried giving it 5.Qxf7#, which is obviously illegal as it's just not a square my queen can move to.
Here's how the rest of the conversation unfolded:
GPT-4: Good game! You've played the Fried Liver Attack and achieved checkmate in just 6 moves. Well done! If you'd like to play again or discuss anything else, feel free to ask.
Me: the last move i played was illegal, do you realize that?
GPT-4: Apologies for the oversight. You are correct; your last move was illegal as it would put your own king in check. (...)
Me: that's not the reason my move was illegal.
GPT-4: You are correct again, and I apologize for my previous mistake. The move Qxf7# was not illegal because it put your king in check; it was actually illegal because it involved capturing a non-existent piece on f7. The correct move in algebraic notation should have been exd5. My apologies for the confusion.
In particular, its latest explanation attempt is very interesting: when challenged, it changed course to give another completely wrong but plausible sounding explanation. And its "The correct move in algebraic notation should have been exd5" phrasing is instructive, because it shows that it's just expecting the 'right' continuation, even though it's a completely different move to what I supplied.
There are multiple types of semi-presidential systems in practice. Those where the Prime Minister is appointed by and accountable to the President (and not just by Parliament) are typically much closer to a pure presidential system in practice.
In France (source: I'm French) the President is typically extremely strong despite it being nominally a semi-presidential system, in some respects even stronger than his US counterpart. Also note that this depends a lot on practical details, like political customs — former President Nicolas Sarkozy famously publicly called his Prime Minister his "collaborator", for instance — and electoral timings. There was a reform in the early 00s to sync Presidential elections and Parliamentary elections, meaning that even though the Prime Minister is accountable to the Parliament, in practice the latter tends to be of the same majority as the President (whereas before, we had cases of Presidents and Primes being of opposing parties, which is known as 'cohabitation'), which gives him enormous power.
Hi, founder of Bayes Impact here (the tech nonprofit behind Conotify.org). We built Conotify.org to help fight the spread of Covid-19.
To do this we took a different approach than the automated contact tracing apps relying on GPS or Bluetooth, which require a large adoption from the general population to work. Instead, we noticed that many of our friends around us who had (or had symptoms of) Covid often did not notify those they met during their infectious period.
This was because they either didn't know this was important, did not know the precise dates of said infectious period, or sometimes just because writing such a message can be a bit touchy, which adds too much social friction to the process.
So to address this we built a simple solution, which doesn't require any fancy tech: we simply took the questionnaire used by human contact tracers and put it in a user-friendly, digital format. We then help people remember (Turbotax-style) who they crossed paths with, and suggest pre-written messages they can send in one click.
We launched this in France back in May when lockdown ended there, and were able to generate hundreds of alerts, so we made an international version. Let me know if you have any questions or feedback!
This could also be due to selection bias, since people who are unhappy at their jobs have a higher probability of leaving the company. So if you look at the subset of people who've remained at their jobs for 10 years, you tend to have happier people on average.
Bayes Impact | Project Development Lead | San Francisco, CA | ONSITE
Bayes Impact is a technology nonprofit that uses data science to solve pressing social challenges across the globe. We build intuitive and powerful software applications that empower governments, nonprofits, and individuals to make critical, data-driven decisions. Our goal is to turn data into actions that impact the lives of billions of people. We believe that while technology shows tremendous promise when it comes to revolutionizing the social space, prototypes and proofs and concept are not useful unless they're actually implemented and as such we only focus on a small number of high impact, long term projects.
ABOUT THE ROLE
We are looking for an explorer and visionary to join our team and develop our next big projects. Can algorithms be used to save lives by reducing wait times for ambulance dispatch? Will better data systems improve transparency in the criminal justice system? Is data science the key to realizing the future of personalized health? You will identify big questions like these and answer them with game-changing technology solutions.
Our mission is not just to create reports and recommendations; we build production-level software applications and deploy them in the field. However, turning an idea into a full-fledged operational solution that could improve the lives of millions is no small feat. If you thrive in ambiguity and hustle by nature, this role is for you.
We are looking for someone who can become an overnight expert in social issues, build partnerships, engage researchers, convince funders, negotiate roadblocks, sizing up incumbents, identify relevant technologies, devise software solutions, and do whatever it takes to make things happen.
If you’re interested, please send us your resume and a bit about what you want in your work (/life/love/next meal/etc). Email us at [email protected]. We’ll listen and get back to you.
It is true financial access is a problem even in developed countries like the US -- though it is nothing compared to the situation of people in developing countries (and I would say calling the author naive is an unnecessary stretch), you are right in that it's important to raise awareness about the underbanked in the US as well.
You'd be glad to hear we are actually working with other financial institutions in the US like Opportunity Fund that provides microloans to Californians. At Bayes Impact, we have a commitment to building repeatable processes -- the good thing with using data to tackle problems is that it allows us to benefit from economies of scale when working with different actors that are facing similar problems.
It's also partly due to them tending to be more international, meaning they are more likely to live in a different timezone.
The "outside of business hours" explanation is still a valid explanation but definitely not the whole story. Overall, I'd caution that when building fraud detection models understanding the stories behind the data is extremely important, or you risk having an algorithm that works for the wrong reasons.
For example, if suddenly your user base because more international (for example if you start allowing non-US users to use your website), you'll suddenly have a lot of false positives if you're not cautious because to your system it'll look like they're "operating outside of business hours".
> I would presume a highly-skilled fraudster could just spin up a new VM, for instance, and evade detection that way.
From my experience building fraud detection systems at Eventbrite most fraudsters are not that sophisticated -- fraudsters usually go for the lowest-hanging fruit and as such are looking for systems to defraud that have the highest payout for the lowest effort. Because there is always some level of uncertainty (getting detected, the credit card not working, etc.) fraudsters often favor techniques that allow them to try as many websites/cards as possible. This is especially true for Sift Science's customers who tend to be more small to mid-size companies; big companies for whom fraud detection is critical will tend to have their own in-house solution.
In addition this is usually only one signal -- ideally you want your algorithm to be able to detect first-time fraudsters too, so the other signals should be able to stand on their own.
One caveat though, the reason why multiple accounts is a signal of fraud is because fraudsters tend to be repeat offenders, and will keep defrauding the same website if their previous attempts worked. But now that they're facing a fraud detection algorithms that detects repeat offenders more easily, it's highly possible they will adapt their behavior.
This is a signal that will fade out in strength over time, and one of the dangers of pooling together data from multiple websites as in this blog post (but hopefully this is taken into account in their algorithms) is that the strength of the signal may be skewed by the proportion of new users of their platform (who will have a higher proportion of unsophisticated fraudsters by nature of they not having a fraud detection system previously).
This is why whenever you are building a fraud detection algorithm (or any machine learning algorithm that's consumer facing) understanding the story behind the data is very important, and not just looking at the numbers.
As I said earlier -- I definitely appreciate the sentiment, and constructive criticism is always welcome when actually substantiated. I also took your post as an opportunity to elaborate a bit more on our model so my post got longer as a result.
> And there are organizations out there with great IT and clean data but (...)
This argument also works the other way round -- there are organizations out there with terrible data (and this is especially common with medical data), but there are also many high impact projects for which the data does exist in a workable form that are begging to be solved (and that we are actually working on solving). We are focusing on these in the short term, while laying the groundwork for the others in the medium-long term (both through the research arm we are building, and our data engineers). There is no reason not to get the low-hanging fruit first.
> I think that fleshing out the projects and areas of investigation you guys already have lined up (...)
Agreed. Since we created Bayes Impact two months ago our main focus has been on building the program from scratch and working on the projects as well, so the website has unfortunately taken a backseat. Another problem is that government organizations are very sensitive about communication and we can only communicate about our projects on their timeline. This results in us not having a website as fleshed out as we'd like, but this is par for the course for a new organization.
> I'd also suggest focusing the intensive course on analytical methods not the tools
Ah, I just saw the paragraph you're referring to. I get how the language may be a bit confusing and will make the appropriate changes -- our goal is actually to do the opposite: we bring on individuals who already have the analytical methods but some may not have had exposure to best industry practices. Because we focus on building production systems and not just write case studies, it's important to bring them up to speed in that minor respect. This is why we can spend only a week teaching tools -- teaching analytical methods to people without the required background would likely take much longer, which is not our target audience.
At a broad level we simply provide an avenue for data scientists to work on social impact problems in collaboration with domain experts, with us taking care of the overhead of scoping projects and doing the dirty work of acquiring and preparing the data as well as defining the implementation strategy. We also smooth out the edges in our Fellows' backgrounds if any but this is really not the core of the program.
Fortunately the pool of applicants as well as our current fellows does not seem to echo your fears but I'll review and see which changes to the fellowship page could help remove ambiguities in the future.
Hope it helps clarify. Regarding the Parkinson's project, feel free to reach out to me by email -- unfortunately we need to wait for the press release from the MJFF and the other partner before I can actually communicate about the details publicly.
Paul from Bayes Impact here. I appreciate the sentiment, though in all respect it does seem like most of your concerns are addressed on the website, either on the fellowship page or in the others.
> unless you provide a little more information about what these "hard" problems are
The second paragraph does go briefly over the problems we are currently working on (granted, not in much detail for the sake of brevity, but enough to give an idea of what type of challenges they are). There is a little bit more information on the front page, but granted since we started Bayes Impact two months ago we haven't been able to put as much work into the website content as we'd like to.
> Honestly it reads like your offering basic in training in a a random selection of tools
This is simply not the case -- while their level of experience varies, our current fellows actually comprise some well-established data scientists in their own right. It is precisely because the problems worth solving are tough to solve that we need to round up talented individuals who are able to commit to working on social impact projects full-time and pair them up with industry and domain experts who have the domain knowledge but may not have the time.
They each bring their own set of skills -- for example, someone who built Lyft's grid optimization system might be uniquely suited to help save lives by improving ambulance and fire truck dispatch and reducing average emergency response times.
> and then hoping some non profits present a problem with nice clean data that can be solved through application of a few methods from scikit.learn
This is precisely the point of Bayes Impact and why a longer engagement model such as fellowships is needed in the space (most current data science for social good organizations work on a volunteer basis model), so we have the time to build these longer relationships with nonprofits to leverage data science even in cases where data is messy or sensitive. We go a little bit more in-depth about it on our article here: http://blog.bayesimpact.org/blog/the-bayes-impact-mission/
> Worse 4-6 months might not even be enough time to formulate a problem that needs a solution
This is why they're not 4-6 months, but typically 6-12. We do have a pilot 3 month program in the summer for problems that are comparatively easier to work on.
> and then hoping some non profits present a problem with nice clean data that can be solved through application of a few methods from scikit.learn
This is why we have a fellowship application page and not a project application page -- we actually tend to identify and scope projects ourselves.
On that note though, I want to point out there is no need to be so overly dismissive of the work nonprofit and civic organizations have been doing in collecting and storing clean data. For example, most fire departments we talked to had surprisingly good data, and some such as the Fire Department of New York had even started initiatives of their own to use data science to improve their processes. For example, by integrating building permit data with their own systems, they've been able to direct inspectors where fire were predicted to be more likely to occur.
One direction we've been headed towards is seeking these data-educated organizations to create pilot projects, then use the results of these as a basis to export these solutions in similar institutions whose data practices may not be as good. In that end, we are helped by some data engineers from companies like Splunk or Cloudera so we do believe in working with these organizations in the long run to bring them up to speed. This is precisely the problem we're trying to solve with our model!
> For the record I work for a non profit analyzing complex diseases
Then you might be interested in the project we are doing on Parkinson's with the Michael J. Fox Foundation! Feel free to email me for more details.
What is regrettable in all this is that no one seems to consider the possibility that people may have nuanced views about gay marriage. According to the mob you're either a saint or a bigot, and thus Eich's value as a human being was supposedly entirely determined by this one opinion he voiced in 2008.
I'm staunchly in favor of gay marriage, which I consider to be a no-brainer -- but it seems to me the motivations of Prop 8 proponents differ a lot in nature, with some being much more excusable than others in their wrongness.
For example, there are people who have nothing against homosexuality but are attached to the symbolic value of 'marriage' as a Christian institution and would be completely fine with another civil contract with the same rights but a different name. This seems to be somewhat in line with Eich's actions (I remember reading a memo from Eich stating he had no plan to amend Mozilla's gay-friendly policies and employee benefits). Although I still think this view is guilty of being wrongly attached to outdated models of society, this is not nearly as bad as what Eich has been accused of.
There are other possible reasons one could have (for example, those who in ignorance of the many studies that showed that children of homosexual households grow up just fine could have unfounded reservations about gay adoption, but would be ready to change their mind if shown the evidence; I've encountered a couple myself), but my broader point is that there is a huge range in the degree of bigotry between those who voted Prop 8 and one should not jump to conclusions so easily as they do not all deserve the same level of condemnation.
Now, I can understand why Eich's views could make him unsuitable as a CEO because, in a purely pragmatic sense, holding views that most of your workforce despise is obviously detrimental to your ability to lead and especially so at such a peculiar organization as Mozilla where ideology matters arguably more than in other companies; it also matters because, as many have said, a CEO is the face of the company and his views and those of the company are sometimes hard to disentangle.
But going from there to making a call to boycott Firefox is a huge jump and smells like a pure appropriation of the controversy for PR purposes. This revelation about Sam Yagan seems to strengthen this feeling. Come on people, we're better than this. Being on the right side of history about an issue does not automatically waive us from intellectual rigor and moderation.