Doing some quick googling, usbnet appears to be a non standard feature that requires some additional setup. Practically, that means I'm back to buying all the peripherals to get started again. If I'm wrong, that's cool since it would address my primary complaint.
Serial line over gpio? What is this, the 80s?;) I had a lot of fun with serial lines back then and will happily never go back.
Ok, that's kinda not true since I do a lot with MIDI hardware and software, which is serial. But, midi also supports my main point: I have midi cables that are 25 years old that still work fine. Across 30+ years of midi hacking, I've never had to change the cables or invest in superfluous adapters just to make them work (since 1987, I've bought exactly 4 midi interfaces - 2 serial and 2 usb. They all still work.)
I have a stack of model b pis that I use for various projects. What I love about them is that all I need is an Ethernet cable to use them. No keyboard, mouse, display - just a standard Ethernet cable and an ssh terminal. You know, that cable that hasn't changed in decades and that I have spares of in spades?
For the zero, I would need to invest in a collection of peripherals just to start it up. It's been years since I've had a USB mouse or keyboard (Bluetooth for all).
Peter's got the money to pull an Uber on the energy industry: break the law with a new business model, show that people like it and it benefits society, hire lobbyists to change the laws, profit!
Seriously, isn't this what the Uber experiment is all about? It's the VC experiment to show that if you have enough money, you can ignore laws that the rest of us have to follow to build new business models.
Peter's one of the few people out there with enough money to pull this off. Be bold, Peter! Make a lasting legacy! Anyone can blog about it, few have the resources to do something about it.
Who knew we would already segment into different fanboy camps for commercial space flight.
In one camp we have the SpaceXers quickly pointing out that New Shepard "only" made it to the Karman line, which really, any mall drone can do. Pshh.
In the other camp, we have the Blue Origin supporters pointing out that getting a rocket to the edge of space and _landing_ it is a pretty cool feat in and of itself.
Then there are the Rutans, (rightly) pointing out that SpaceShipOne did this a few years back. So, what's new?
The Armadillos, unfortunately, are still trying to avoid becoming roadkill on the way to the party.
Here's the camp I'm in: This is commercial spaceflight! Non-states are succeeding in getting rockets and such into space! Let me repeat: we have companies sending craft to FREAKING SPACE! THIS IS AWESOME! THEY ARE ALL AWESOME!
I'm late to this thread, but this seems like a good place to add one more key point that's important and likely lost on the causal crowd here.
The DNA referenced here is just that: DNA in a blood sample. Except for the basic genotyping done looking for specific diseases (which looks at miniscule portion of the DNA), there's almost no data associated with these samples.
To actually sequence the DNA from these samples would be a tremendous undertaking and be very expensive. For example, pre-natal sequencing firms (Sequenom, Natera, et al) sequence around 100k samples a year each using techniques that look at maybe 10% of the genome, still a long way from fully sequencing individuals. And, those methods are all sequencing a blend of mother/fetals blood, so they're not really identifying.
There's little to fear today from nefarious use of these samples. But, the long term issues are real and something the legal and scientific communities need to address.
Where are mod points when I need them? You nailed the real issue. All the other comments on this thread seem focused on the outliers and the semantics of "unlimited". The reality is that data caps are used to mis-price resources in order to extract more money from the customers.
Luckily, I'm still on AT&T unlimited for my iPhone and have Grande as an option for un-monitored home service (1Gb, mostly symmetric;) ). I'm dreading the day when I lose cellular unlimited and need to start worrying about overages.
I'm not defending Elsevier's business practice, but I'm pointing out that your trust in academic's experience is misplaced. While academics serve on the editorial boards, they aren't involved in the day-to-day business of the journals and privy to all it takes to manage one. Their job as editors is selecting research for review and publication, not managing its actual publication.
I have friends on the editorial boards of these journals. They're great researchers, but they are in no way qualified to be managing the operational aspects of disseminating research. And even if they could, is that really the best use of their time and resources?
I'll disagree with this. As someone who's spent a fair amount of time in both academia and industry, it's always shocking how little the academic side understands the true cost of things. So much in that world is paid for indirectly via the institution or someone else's grants (for instance, most university supercomputing resources are paid for by grants that the end users are never involved with). On top of that, academic labor is very cheap. Grad students and post-docs typically cost a quarter of their counterparts in industry.
The "best" path forward they have is to use grant-funded university computing resources to host the journals and grad student labor to maintain the infrastructure. This takes away the computing resources from their stated goal as research resources. You could claim that hosting research papers is a legitimate use, but given that there's no research value in developing the software for an open access journal (every publisher has something like this and PLOS does it too - there's nothing novel about it), this isn't research.
More importantly, using academic labor for this undermines the careers of those academics. Grad students and post-docs should be advancing their research careers, not developing and maintaining software infrastructure. This is abuse, plain and simple.
So, in protesting fees that basically cover the cost of managing journals and infrastructures, the academics will shift the infrastructure and labor costs to themselves. This will compromise their ability to do research (there are only so many hours in the day) and lead to an inferior product (grad students are not qualified to manage complex, production software projects).
I'll agree that we need a new model in publishing, but this isn't it.
This year I added some interactive sound effects to the porch using some synths and a Theremin.
For the base soundscape, I used two synths running loops that were out of sync to create a basic gloom-and-doom texture. The first synth (Korg Kaos Pad, using SYN-9 with Pad Motion for the loop) had a low-frequency sound that moved around a bit to create the sonic floor. The other synth (Korg Monotribe) was looping at at the lowest temp setting (maybe 1 Hz?) with a simple noise-based sound with the LFO set to sweep both pitch and filter to create a knocking sound. It was creepy.
For the interactive element, I placed a plastic skeleton on the vertical antenna of a Theremin (Moog Theremini) and set it so it would start "screaming" when kids were about 2 feet away (I initially set a larger radius, but that led to it constantly sounding when kids were on the porch and diluted the effect). A note on the skeleton invited kids to shake its hand
I placed my studio monitors under the table with the Theremin. They had enough bass to let the synth effects sound spooky (rather than hollow). Combined with some lights and the fog machine (fog machines work fine - I just have the cheap one from Walmart), the effect was pretty good. Some kids refused to get near the skeleton after they heard it the first time, but others would play around with it and try to figure it out (the Theremin was covered in a blanket, so it wasn't obvious how it worked).
Next year I plan to expand the set up a bit and add some additional speakers and proximity effects around the walkway.
"We're probably already there, in general human drivers suck."
Data, please? People make this claim all the time, but given that there are over a billion trips a day in the US and only around 120 fatalities, I'd say humans drivers pretty much have this thing down. The fact that people can make it around in their cars in myriad weather conditions, successfully navigate unfamiliar terrain, and quickly respond to sudden changes in circumstances (kid darting out in front of them) speaks volumes to how good human drivers are.
I watched a Google self-driving car cross an intersection this weekend (in Austin). It was moving very cautiously and then slowed down to a walking pace on the other side of the intersection, leaving a trail of human-driven cars stuck in the intersection while it decided to turn down a side street.
The "human drivers suck" crowd sounds very much like the "there's a thug with a gun around every corner" crowd. Some people seem to enjoy thinking the world is more dangerous than it really is.
I'm not sure this will attract the best candidates. For a Master's program, candidates from from three pools:
(1) Students who just finished undergrad and want additional specialization before entering the workforce (2) Working professionals who want to return to school to gain additional skills or enter a new field (3) Those who never found a job and are trying to wait out the market in school
Of these, only (1) and (3) likely have the time to commit to a MOOC. (2) could (and many people do this), but will always have their normal responsibilities taking priority.
The problem is that a MOOC is a huge time commitment. If it's the only way to get into a Master's program, you're taking a huge risk if you're already working and have responsibilities. The GRE/GMAT + an application + interview is reasonable to ask for something that's not guaranteed and likely has an acceptance rate of 10-20%. A three month time commitment isn't. This will simply exclude the most desireable and qualified group of students and limit the pool of applicants to those who had the free time to commit to it.
It's kinda like companies that require programming assignments prior to interviews. That tactic, while trendy and popular, tends to exclude the top 10% coders simply because they have better ways to spend their weekends and evenings and know it.
Uber is great in the same way Pets.com was great: they're burning their investor's money to run an unsustainable business. I loved getting 40lb bags of dog food delivered for free and I love paying less than the driver is making for my Uber rides. As a consumer, I win!
What's new about Uber compared to Pets.com is that Uber is the VC world's experiment in seeing if they can create illegal businesses and then use their huge piles of money to change the law in their favor. This is what should really scare everyone.
Huh? We're a small shop and use Jira just fine. But, we also don't blindly apply Agile(tm) either. We use agile (as in the manifesto version, not the Certified versions).
Jira is a productivity tool for managing tasks and workloads. If it's not effective for you, find another way to manage things. But, do find a way to manage your tasks and issues in a traceable manner. If you don't see the value in that, your process is likely the problem and no tool will fix it.
The basic idea behind the Mythical Man Month is essentially Amdahl's Law for human (instead of compute) resources. At some point, there's just no getting around it.
But, just like with parallel and distributed computing, there are always people who don't understand the basic tenets of it and think they've found a way to transcend it (I'm looking at you Hadoop users).
*sigh* The core concept of progressivism is what most of us want - policy based on our current best understanding of how the natural and social worlds work. The fact that it's been used to promote questionable policies in the past shows its flexibility: as we learn more, those policies are abandoned. The alternative, blindly holding on to policies that have been proven not to work (supply side economics on the right, Marxism on the left) just shows... what? That adherents are too proud to admit mistakes and evolve?
It's true that in modern American/Western politics the term has a slightly different connotation, but to pretend that the idea of using data and knowledge to find good policies is new (which the OP claimed - millennials are the first generation to use data!) is silly. Smart people for centuries have been trying this approach and it's never caught on with the general public.
The fact that my original post got modded "funny" shows just how hard it is to get people to think seriously about this approach.
That's not exactly correct... the Google cars have incredibly precise maps of the roads they're on, not just the route, but maps of the actual surface of the road (e.g., where the potholes are). That level of detail available to the onboard computers is pretty much the same as having sensors on the road. It requires an incredible amount of prep work. Of course, map updates could be handled by sensors on other cars constantly providing real time information. It's a cool approach, but only practical when you have that level of detail available.
Google, et al, are showing very controlled research projects. Even though they're testing in the real world, they're still highly controlled experiments.
Sure, many of the problems are resolvable using this approach, but what we don't know is what new problems will evolve once there are more than a handful of self driving cars on the road. More research will help identify these, but anyone who's done real science or engineering knows that what works at small scale rarely scales as you would hope/expect.
Let's do the math: drop tens of millions of dollars of your investor's money to be the beat out the other institutional investors to a company with a cool prototype built from Kickstarter funds. Realize a few months into it that the tech, while kick ass, isn't quite ready for prime time and won't have an available market of 10M users for at least a few years. Do some more math and realize they'll run out of money before then and have to take on additional VC money, possibly in a down round that will affect your position. Call one of your successful investments with money to burn, ask them to buy you out of your investment and give everyone a good return. Repeat with the next cool tech. Claim you have the unique ability to spot unicorns. Raise more money for your fund.
_That's_ how you think like a modern VC.
Of course, this is just a variation on the IPO scams of the first boom. In this case, the few successful companies (Facebook, Google, etc) replace the role of the public in providing quick returns on questionable investments. The public foots the bill indirectly: some IPO money and shares are used for the buyouts and ad revenue provides the rest of it.
That's pretty much my point: most people can't even get it right with simple statistical models. I advocate sticking with the simple mathematical models and dumping the "turn-key", simple statistical models that keep getting people in trouble. p value.05, publish!
Those that truly grok the statistics behind the more complex models can still use them, but the bar for acceptance by the community needs to be much higher. (which goes into fixing peer review and down that whole rabbit hole...)
To illustrate, the summary could easily be restated this way:
"...field of [data science] frequently uses [machine learning] in an unhealthy way. Many [data scientists] don't use [machine learning] as a tool to describe reality, but rather as an abstract foundation for whatever theory they've come up with."
Replacing "math" with "machine learning" isn't going to make a difference if the practitioners don't understand how to use it properly. Machine learning models are much more subtle and complex than simple mathematical models and very easy to misuse. To use them properly, you really need a much stronger understanding of the math behind them than most people have.
See the entire field of psychology and most GWAS studies for an example of where over reliance on (simple) models can get you into a lot of trouble.
That reminds me of a Java "class" I took in the late 90s. The "instructor" kept talking about how great "jay vac" was and how it made Java run faster. Yeah, that was javac he was referring too ("java see"). Took all of us programmers half the day to figure that one out. I still call it jayvac when I want to mess with people.
Indeed. In mountaineering circles it's always been Denali as well. Pretty much every group that has a physical connection to the mountain has always called it Denali.
Replication and reproducibility are not the same. Simply getting the source code and re-running the results is just replicating the study. It doesn't tell you anything about how reproducible the results are.
To be reproducible, someone should be able to use similar methods and get the same results. If a result is completely dependent on a specific build of the software, it's not robust enough to be considered reproducible.
Publications should require a concise written description of the method and solution that is complete enough that a competent practitioner could reproduce the results using whatever appropriate tools they want.
I'm dismayed that in CS that the academic community is putting so much emphasis on replication and not enough on robust reproducibility.
Slashdot Mirror
Doing some quick googling, usbnet appears to be a non standard feature that requires some additional setup. Practically, that means I'm back to buying all the peripherals to get started again. If I'm wrong, that's cool since it would address my primary complaint.
Serial line over gpio? What is this, the 80s? ;) I had a lot of fun with serial lines back then and will happily never go back.
Ok, that's kinda not true since I do a lot with MIDI hardware and software, which is serial. But, midi also supports my main point: I have midi cables that are 25 years old that still work fine. Across 30+ years of midi hacking, I've never had to change the cables or invest in superfluous adapters just to make them work (since 1987, I've bought exactly 4 midi interfaces - 2 serial and 2 usb. They all still work.)
I have a stack of model b pis that I use for various projects. What I love about them is that all I need is an Ethernet cable to use them. No keyboard, mouse, display - just a standard Ethernet cable and an ssh terminal. You know, that cable that hasn't changed in decades and that I have spares of in spades?
For the zero, I would need to invest in a collection of peripherals just to start it up. It's been years since I've had a USB mouse or keyboard (Bluetooth for all).
This was a big miss.
-Chris
Peter's got the money to pull an Uber on the energy industry: break the law with a new business model, show that people like it and it benefits society, hire lobbyists to change the laws, profit!
Seriously, isn't this what the Uber experiment is all about? It's the VC experiment to show that if you have enough money, you can ignore laws that the rest of us have to follow to build new business models.
Peter's one of the few people out there with enough money to pull this off. Be bold, Peter! Make a lasting legacy! Anyone can blog about it, few have the resources to do something about it.
Who knew we would already segment into different fanboy camps for commercial space flight.
In one camp we have the SpaceXers quickly pointing out that New Shepard "only" made it to the Karman line, which really, any mall drone can do. Pshh.
In the other camp, we have the Blue Origin supporters pointing out that getting a rocket to the edge of space and _landing_ it is a pretty cool feat in and of itself.
Then there are the Rutans, (rightly) pointing out that SpaceShipOne did this a few years back. So, what's new?
The Armadillos, unfortunately, are still trying to avoid becoming roadkill on the way to the party.
Here's the camp I'm in: This is commercial spaceflight! Non-states are succeeding in getting rockets and such into space! Let me repeat: we have companies sending craft to FREAKING SPACE! THIS IS AWESOME! THEY ARE ALL AWESOME!
-Chris
I'm late to this thread, but this seems like a good place to add one more key point that's important and likely lost on the causal crowd here.
The DNA referenced here is just that: DNA in a blood sample. Except for the basic genotyping done looking for specific diseases (which looks at miniscule portion of the DNA), there's almost no data associated with these samples.
To actually sequence the DNA from these samples would be a tremendous undertaking and be very expensive. For example, pre-natal sequencing firms (Sequenom, Natera, et al) sequence around 100k samples a year each using techniques that look at maybe 10% of the genome, still a long way from fully sequencing individuals. And, those methods are all sequencing a blend of mother/fetals blood, so they're not really identifying.
There's little to fear today from nefarious use of these samples. But, the long term issues are real and something the legal and scientific communities need to address.
-Chris
Where are mod points when I need them? You nailed the real issue. All the other comments on this thread seem focused on the outliers and the semantics of "unlimited". The reality is that data caps are used to mis-price resources in order to extract more money from the customers.
Luckily, I'm still on AT&T unlimited for my iPhone and have Grande as an option for un-monitored home service (1Gb, mostly symmetric ;) ). I'm dreading the day when I lose cellular unlimited and need to start worrying about overages.
-Chris
I'm not defending Elsevier's business practice, but I'm pointing out that your trust in academic's experience is misplaced. While academics serve on the editorial boards, they aren't involved in the day-to-day business of the journals and privy to all it takes to manage one. Their job as editors is selecting research for review and publication, not managing its actual publication.
I have friends on the editorial boards of these journals. They're great researchers, but they are in no way qualified to be managing the operational aspects of disseminating research. And even if they could, is that really the best use of their time and resources?
-Chris
I'll disagree with this. As someone who's spent a fair amount of time in both academia and industry, it's always shocking how little the academic side understands the true cost of things. So much in that world is paid for indirectly via the institution or someone else's grants (for instance, most university supercomputing resources are paid for by grants that the end users are never involved with). On top of that, academic labor is very cheap. Grad students and post-docs typically cost a quarter of their counterparts in industry.
The "best" path forward they have is to use grant-funded university computing resources to host the journals and grad student labor to maintain the infrastructure. This takes away the computing resources from their stated goal as research resources. You could claim that hosting research papers is a legitimate use, but given that there's no research value in developing the software for an open access journal (every publisher has something like this and PLOS does it too - there's nothing novel about it), this isn't research.
More importantly, using academic labor for this undermines the careers of those academics. Grad students and post-docs should be advancing their research careers, not developing and maintaining software infrastructure. This is abuse, plain and simple.
So, in protesting fees that basically cover the cost of managing journals and infrastructures, the academics will shift the infrastructure and labor costs to themselves. This will compromise their ability to do research (there are only so many hours in the day) and lead to an inferior product (grad students are not qualified to manage complex, production software projects).
I'll agree that we need a new model in publishing, but this isn't it.
-Chris
This year I added some interactive sound effects to the porch using some synths and a Theremin.
For the base soundscape, I used two synths running loops that were out of sync to create a basic gloom-and-doom texture. The first synth (Korg Kaos Pad, using SYN-9 with Pad Motion for the loop) had a low-frequency sound that moved around a bit to create the sonic floor. The other synth (Korg Monotribe) was looping at at the lowest temp setting (maybe 1 Hz?) with a simple noise-based sound with the LFO set to sweep both pitch and filter to create a knocking sound. It was creepy.
For the interactive element, I placed a plastic skeleton on the vertical antenna of a Theremin (Moog Theremini) and set it so it would start "screaming" when kids were about 2 feet away (I initially set a larger radius, but that led to it constantly sounding when kids were on the porch and diluted the effect). A note on the skeleton invited kids to shake its hand
I placed my studio monitors under the table with the Theremin. They had enough bass to let the synth effects sound spooky (rather than hollow). Combined with some lights and the fog machine (fog machines work fine - I just have the cheap one from Walmart), the effect was pretty good. Some kids refused to get near the skeleton after they heard it the first time, but others would play around with it and try to figure it out (the Theremin was covered in a blanket, so it wasn't obvious how it worked).
Next year I plan to expand the set up a bit and add some additional speakers and proximity effects around the walkway.
Fun stuff.
-Chris
"We're probably already there, in general human drivers suck."
Data, please? People make this claim all the time, but given that there are over a billion trips a day in the US and only around 120 fatalities, I'd say humans drivers pretty much have this thing down. The fact that people can make it around in their cars in myriad weather conditions, successfully navigate unfamiliar terrain, and quickly respond to sudden changes in circumstances (kid darting out in front of them) speaks volumes to how good human drivers are.
I watched a Google self-driving car cross an intersection this weekend (in Austin). It was moving very cautiously and then slowed down to a walking pace on the other side of the intersection, leaving a trail of human-driven cars stuck in the intersection while it decided to turn down a side street.
The "human drivers suck" crowd sounds very much like the "there's a thug with a gun around every corner" crowd. Some people seem to enjoy thinking the world is more dangerous than it really is.
-Chris
Some sources:
https://www.rita.dot.gov/bts/s...
http://www.nytimes.com/2004/03...
(Old academic joke)
-Chris
I'm not sure this will attract the best candidates. For a Master's program, candidates from from three pools:
(1) Students who just finished undergrad and want additional specialization before entering the workforce
(2) Working professionals who want to return to school to gain additional skills or enter a new field
(3) Those who never found a job and are trying to wait out the market in school
Of these, only (1) and (3) likely have the time to commit to a MOOC. (2) could (and many people do this), but will always have their normal responsibilities taking priority.
The problem is that a MOOC is a huge time commitment. If it's the only way to get into a Master's program, you're taking a huge risk if you're already working and have responsibilities. The GRE/GMAT + an application + interview is reasonable to ask for something that's not guaranteed and likely has an acceptance rate of 10-20%. A three month time commitment isn't. This will simply exclude the most desireable and qualified group of students and limit the pool of applicants to those who had the free time to commit to it.
It's kinda like companies that require programming assignments prior to interviews. That tactic, while trendy and popular, tends to exclude the top 10% coders simply because they have better ways to spend their weekends and evenings and know it.
-Chris
Uber is great in the same way Pets.com was great: they're burning their investor's money to run an unsustainable business. I loved getting 40lb bags of dog food delivered for free and I love paying less than the driver is making for my Uber rides. As a consumer, I win!
What's new about Uber compared to Pets.com is that Uber is the VC world's experiment in seeing if they can create illegal businesses and then use their huge piles of money to change the law in their favor. This is what should really scare everyone.
-Chris
Huh? We're a small shop and use Jira just fine. But, we also don't blindly apply Agile(tm) either. We use agile (as in the manifesto version, not the Certified versions).
Jira is a productivity tool for managing tasks and workloads. If it's not effective for you, find another way to manage things. But, do find a way to manage your tasks and issues in a traceable manner. If you don't see the value in that, your process is likely the problem and no tool will fix it.
-Chris
The basic idea behind the Mythical Man Month is essentially Amdahl's Law for human (instead of compute) resources. At some point, there's just no getting around it.
But, just like with parallel and distributed computing, there are always people who don't understand the basic tenets of it and think they've found a way to transcend it (I'm looking at you Hadoop users).
Learn it and never forget it:
https://en.m.wikipedia.org/wik...
-Chris
*sigh* The core concept of progressivism is what most of us want - policy based on our current best understanding of how the natural and social worlds work. The fact that it's been used to promote questionable policies in the past shows its flexibility: as we learn more, those policies are abandoned. The alternative, blindly holding on to policies that have been proven not to work (supply side economics on the right, Marxism on the left) just shows... what? That adherents are too proud to admit mistakes and evolve?
It's true that in modern American/Western politics the term has a slightly different connotation, but to pretend that the idea of using data and knowledge to find good policies is new (which the OP claimed - millennials are the first generation to use data!) is silly. Smart people for centuries have been trying this approach and it's never caught on with the general public.
The fact that my original post got modded "funny" shows just how hard it is to get people to think seriously about this approach.
-Chris
Data driven politics has a name. No need to reinvent it. Unfortunately, it's always struggled to get a strong following.
-Chris
That's not exactly correct... the Google cars have incredibly precise maps of the roads they're on, not just the route, but maps of the actual surface of the road (e.g., where the potholes are). That level of detail available to the onboard computers is pretty much the same as having sensors on the road. It requires an incredible amount of prep work. Of course, map updates could be handled by sensors on other cars constantly providing real time information. It's a cool approach, but only practical when you have that level of detail available.
Google, et al, are showing very controlled research projects. Even though they're testing in the real world, they're still highly controlled experiments.
Sure, many of the problems are resolvable using this approach, but what we don't know is what new problems will evolve once there are more than a handful of self driving cars on the road. More research will help identify these, but anyone who's done real science or engineering knows that what works at small scale rarely scales as you would hope/expect.
-Chris
Well, if we find a way to measure either of those using high-energy experiments, we'll get a few more decades out of the field.
Just when we think we're done, we're usually just at the beginning...
-Chris
Um, you shouldn't be one, either.
Let's do the math: drop tens of millions of dollars of your investor's money to be the beat out the other institutional investors to a company with a cool prototype built from Kickstarter funds. Realize a few months into it that the tech, while kick ass, isn't quite ready for prime time and won't have an available market of 10M users for at least a few years. Do some more math and realize they'll run out of money before then and have to take on additional VC money, possibly in a down round that will affect your position. Call one of your successful investments with money to burn, ask them to buy you out of your investment and give everyone a good return. Repeat with the next cool tech. Claim you have the unique ability to spot unicorns. Raise more money for your fund.
_That's_ how you think like a modern VC.
Of course, this is just a variation on the IPO scams of the first boom. In this case, the few successful companies (Facebook, Google, etc) replace the role of the public in providing quick returns on questionable investments. The public foots the bill indirectly: some IPO money and shares are used for the buyouts and ad revenue provides the rest of it.
-Chris
That's pretty much my point: most people can't even get it right with simple statistical models. I advocate sticking with the simple mathematical models and dumping the "turn-key", simple statistical models that keep getting people in trouble. p value .05, publish!
Those that truly grok the statistics behind the more complex models can still use them, but the bar for acceptance by the community needs to be much higher. (which goes into fixing peer review and down that whole rabbit hole...)
-Chris
To illustrate, the summary could easily be restated this way:
"...field of [data science] frequently uses [machine learning] in an unhealthy way. Many [data scientists] don't use [machine learning] as a tool to describe reality, but rather as an abstract foundation for whatever theory they've come up with."
Replacing "math" with "machine learning" isn't going to make a difference if the practitioners don't understand how to use it properly. Machine learning models are much more subtle and complex than simple mathematical models and very easy to misuse. To use them properly, you really need a much stronger understanding of the math behind them than most people have.
See the entire field of psychology and most GWAS studies for an example of where over reliance on (simple) models can get you into a lot of trouble.
-Chris
That reminds me of a Java "class" I took in the late 90s. The "instructor" kept talking about how great "jay vac" was and how it made Java run faster. Yeah, that was javac he was referring too ("java see"). Took all of us programmers half the day to figure that one out. I still call it jayvac when I want to mess with people.
-Chris
Indeed. In mountaineering circles it's always been Denali as well. Pretty much every group that has a physical connection to the mountain has always called it Denali.
-Chris
Replication and reproducibility are not the same. Simply getting the source code and re-running the results is just replicating the study. It doesn't tell you anything about how reproducible the results are.
To be reproducible, someone should be able to use similar methods and get the same results. If a result is completely dependent on a specific build of the software, it's not robust enough to be considered reproducible.
Publications should require a concise written description of the method and solution that is complete enough that a competent practitioner could reproduce the results using whatever appropriate tools they want.
I'm dismayed that in CS that the academic community is putting so much emphasis on replication and not enough on robust reproducibility.
-Chris