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I think ML has potential that Blockchain doesn’t, but not in all the areas that are being hyped. I work in the public sector of Denmark, and we’re targeted by

by jaabe 7y ago

I think ML has potential that Blockchain doesn’t, but not in all the areas that are being hyped. I work in the public sector of Denmark, and we’re targeted by a lot of the hype. Which is worrisome, because it might actually lead to stupid projects if it becomes a political focus. So far it hasn’t though, and blockchain never did, so who knows. The thing about ML is that all the BI is worse than what we are already doing. Because it’s hyped we’ve naturally done proofs of concepts, with universities and with big tech, and no one has been capable of providing ML based analytics or BI that is even remotely close to what we already do. Because the simple truth is that we have been working with data for four decades. We have full time analysts who do nothing else, and they are simply lightyears ahead of anything ML we’ve seen, and, they can actually explain their results to our politicians and decision makers. Where ML does work, and the fact that it does separates it from blockchain, is for recognition. We have a lot of data, often in poor quality, and ML can troll through it faster and with higher quality than our human workers. We had to go through every casefile in a specific area, and identify which ones were missing a specific form. A casefile can be 500 A4 pages long, sometimes scanned in really terrible quality, and we had 500.000 of them. It took 12 people 6 months to do so, simultaneously we did a ML poc. It took 3 months to train the algorithm, but only one employee, and once it was done being trained, it took around five hours with a lot of Azure iron to troll through the data. ML had better results than our human effort and it was obviously way cheaper. So ML and AI may be a lot of hype, but it’s also more than that.

djsumdog 7y ago
I think what the post was going for is that things are often mislabeled as AI when really they're much simpler algorithms.
novalis78 7y ago
Just came back from the IOHKSummit in Miami with Stephen Wolfram and Caitlin Long as guest speakers. An incredible array of scientists (cryptography, theoretical computer science, mathematics, functional programming languages) presented their research and results of peer reviewed papers over the past few years. It was definitely the most substantial blockchain conference I have had a chance to attend in years. Fascinating talks on the ability to use standardized financial contracts (ARCTUS) via functional languages and gathered as library for developers in smart contracts blew my mind. The supply chain pro/con by Wyoming’s ranchers and their beef tracking USDA approved new standard showed the other end of the spectrum in terms of real world applications that already have an impact on industry. While there is a lot of hype and mania any time money is involved there is also an incredible universe of academic and tangible private progress being made using these distributed immutable / trustless (to higher degrees at least) ledgers.
- wolf550e 7y ago
  if by "blockchain" people mean "git for database records", that has obvious uses, but it existed before the craze. See the flowchart from NIST: https://twitter.com/arnabdotorg/status/1049116699927171077 https://twitter.com/arnabdotorg/status/1049116699927171077
jimbokun 7y ago
"We have full time analysts who do nothing else, and they are simply lightyears ahead of anything ML we’ve seen, and, they can actually explain their results to our politicians and decision makers." Time to rebrand your analysts as your in-house "big data machine learning AI system"!
ska 7y ago
This is a good point, often missed or buried in the noise. It goes beyond recognition though (e.g. more general classification). Another area is when you have fairly large sets with both breadth and depth, ML algorithms can sometimes pull out connections it is very difficult for analysts to even tackle. On a related note: I have no idea what Denmark is like for this, but I have seen other public sector analytics where the analysts themselves where good at what they did, but the whole system wasn’t very good at managing the data they consumed. This can introduce impressive delays and missed opportunities.
jgust 7y ago
This is the example I use when knowledge workers lose their minds over ML. Do you _enjoy_ menial tasks? If so, I'm sorry; they are probably going to be sucked up by machines. This is an awesome opportunity to offload busy work & improve your overall "product". What if, for example, these lawyers and paralegals could now spend more time with their clients for less money, or handle more clients, making top quality legal services more affordable for more people? That would be an awesome scenario to see play out, and possibly a major help to those who never had access to legal help.
ThomPete 7y ago
The first thing to realize is that AI is not coming for you or me or any other, it's coming for a subset of us. The subset of skills which can be rented out to an employer. AI and ML are just two things that are comming for that subset. Digitalization is also if not a bigger then at least a huge factor. The entire ecosystem of companies and people who were involved in supporting the music industry were more or less wiped out over the last 20 years. Leaving a thin layer of really successful people and then a huge group of people who makes no money. And the more things can be digitalized the more they are subject to pattern analysis which means the subsets of your that make you valuable are also easier to replace. Ironically a cleaning lady is probably the last person to loos her job because it's really hard to replace, but that just means the supply will increase too. So sure humans are better at BI but that's based on the assumption that how we do BI is the only way to do BI. I am not so sure it is. We will see.
- jaabe 7y ago
  We’ve seen a couple of examples with BI and analytics, I think you can put them into two categories. Prediction and automation. Prediction simply isn’t good enough. It may work well for google and Facebook, but that’s because failure is relatively harmless in advertising. No one dies just because you see a commercial for something you just bought. The failure rate is simply too high for us in the public sector, maybe that’ll change, but probably not. I say that because we’re severely limiting the access to data these years over privacy concerns. You could probably do some interesting things with medical data, but to get there, you’ll need to look at medical data and that’s just not happening in the current political climate. Then there is the automation. IBM wanted to sell us Watson analytics on the premise that it could recognise patterns and build the BI models our dedicated team does. So we let them try, and none of the models they came up with was even remotely useful. I can see this changing, but when? And what will our analytics department look like by then? It’s hard to say.
  - ThomPete 7y ago
    Yes but you also have to think about predictions a little more nuanced. You can make a prediction it might be true but it doesn't mean that you will be successful with it. I wouldn't be surprised is 90% of all BI predictions doesn't actually lead to more successful outcomes also in the public sector also in Denmark (I'm Danish too :) )
YeGoblynQueenne 7y ago
>> ML had better results than our human effort and it was obviously way cheaper. Would it be possible to give more details about how you evaluated the machine learning system and the human effort? e.g. did you have some idea about which, or how many, cases were missing the form of interest, etc?
- jaabe 7y ago
  We knew upfront that ML was going to be a proof of concept and that the job was going to be done manually either way. We are many things in the public sector, but we’re not big risk takers, and this was a job that had to be done as fast as possible because the missing documents are required by law. So what we did was that we cloned the data, and let the business do it’s thing while we did ours. This of course presented us with unique data on the results. The things we measured were quality, speed, economy and employee satisfaction. Quality was measured by keeping track of casefiles, that were flagged as missing the document by each process. That gave us two lists, one with the casefiles found by the manual team and one found by the ML team. We then made a few random checks of casefiles that appeared on both lists, and we checked every casefile that was only on one list. The ML team flagged more casefiles correctly, it both found more and made fewer errors. Of course this doesn’t tell us how many casefiles we didn’t find, but it does show us that ML was better. Speed was relatively simple, it was start to finish and ML was faster. We did rent a lot of iron in Azure to achieve this, we could have never done it without a major enterprise cloud agreement. We needed Microsoft to allocate the stuff we needed, it wasn’t even a simple task of using the automatic systems. Economically it’s a bit of a touchy subject. I won’t go into details on that, but basically we know what work costs. Renting iron in Azure wasn’t expensive compared to having that many full time workers dedicated to the task. Employee satisfaction is hard, but we don’t have people on staff who’s job it is to go through half a million casefiles and look at millions of documents. We had to pull people away from their regular jobs to do so, and even with 7000 people on staff, it’s really hard to find people who actually want to do this kind of work. HR did a bunch of HR magic, and basically people would prefer ML to do this sort of thing in the future.
  - YeGoblynQueenne 7y ago

by jaabe 7y ago

djsumdog 7y ago

I think what the post was going for is that things are often mislabeled as AI when really they're much simpler algorithms.

novalis78 7y ago

Just came back from the IOHKSummit in Miami with Stephen Wolfram and Caitlin Long as guest speakers. An incredible array of scientists (cryptography, theoretical computer science, mathematics, functional programming languages) presented their research and results of peer reviewed papers over the past few years. It was definitely the most substantial blockchain conference I have had a chance to attend in years. Fascinating talks on the ability to use standardized financial contracts (ARCTUS) via functional languages and gathered as library for developers in smart contracts blew my mind. The supply chain pro/con by Wyoming’s ranchers and their beef tracking USDA approved new standard showed the other end of the spectrum in terms of real world applications that already have an impact on industry. While there is a lot of hype and mania any time money is involved there is also an incredible universe of academic and tangible private progress being made using these distributed immutable / trustless (to higher degrees at least) ledgers.

wolf550e 7y ago
if by "blockchain" people mean "git for database records", that has obvious uses, but it existed before the craze. See the flowchart from NIST: https://twitter.com/arnabdotorg/status/1049116699927171077 https://twitter.com/arnabdotorg/status/1049116699927171077

jimbokun 7y ago

"We have full time analysts who do nothing else, and they are simply lightyears ahead of anything ML we’ve seen, and, they can actually explain their results to our politicians and decision makers." Time to rebrand your analysts as your in-house "big data machine learning AI system"!

ska 7y ago

This is a good point, often missed or buried in the noise. It goes beyond recognition though (e.g. more general classification). Another area is when you have fairly large sets with both breadth and depth, ML algorithms can sometimes pull out connections it is very difficult for analysts to even tackle. On a related note: I have no idea what Denmark is like for this, but I have seen other public sector analytics where the analysts themselves where good at what they did, but the whole system wasn’t very good at managing the data they consumed. This can introduce impressive delays and missed opportunities.

jgust 7y ago

This is the example I use when knowledge workers lose their minds over ML. Do you _enjoy_ menial tasks? If so, I'm sorry; they are probably going to be sucked up by machines. This is an awesome opportunity to offload busy work & improve your overall "product". What if, for example, these lawyers and paralegals could now spend more time with their clients for less money, or handle more clients, making top quality legal services more affordable for more people? That would be an awesome scenario to see play out, and possibly a major help to those who never had access to legal help.

ThomPete 7y ago

The first thing to realize is that AI is not coming for you or me or any other, it's coming for a subset of us. The subset of skills which can be rented out to an employer. AI and ML are just two things that are comming for that subset. Digitalization is also if not a bigger then at least a huge factor. The entire ecosystem of companies and people who were involved in supporting the music industry were more or less wiped out over the last 20 years. Leaving a thin layer of really successful people and then a huge group of people who makes no money. And the more things can be digitalized the more they are subject to pattern analysis which means the subsets of your that make you valuable are also easier to replace. Ironically a cleaning lady is probably the last person to loos her job because it's really hard to replace, but that just means the supply will increase too. So sure humans are better at BI but that's based on the assumption that how we do BI is the only way to do BI. I am not so sure it is. We will see.

jaabe 7y ago
We’ve seen a couple of examples with BI and analytics, I think you can put them into two categories. Prediction and automation. Prediction simply isn’t good enough. It may work well for google and Facebook, but that’s because failure is relatively harmless in advertising. No one dies just because you see a commercial for something you just bought. The failure rate is simply too high for us in the public sector, maybe that’ll change, but probably not. I say that because we’re severely limiting the access to data these years over privacy concerns. You could probably do some interesting things with medical data, but to get there, you’ll need to look at medical data and that’s just not happening in the current political climate. Then there is the automation. IBM wanted to sell us Watson analytics on the premise that it could recognise patterns and build the BI models our dedicated team does. So we let them try, and none of the models they came up with was even remotely useful. I can see this changing, but when? And what will our analytics department look like by then? It’s hard to say.
- ThomPete 7y ago
  Yes but you also have to think about predictions a little more nuanced. You can make a prediction it might be true but it doesn't mean that you will be successful with it. I wouldn't be surprised is 90% of all BI predictions doesn't actually lead to more successful outcomes also in the public sector also in Denmark (I'm Danish too :) )

YeGoblynQueenne 7y ago

>> ML had better results than our human effort and it was obviously way cheaper. Would it be possible to give more details about how you evaluated the machine learning system and the human effort? e.g. did you have some idea about which, or how many, cases were missing the form of interest, etc?

jaabe 7y ago
We knew upfront that ML was going to be a proof of concept and that the job was going to be done manually either way. We are many things in the public sector, but we’re not big risk takers, and this was a job that had to be done as fast as possible because the missing documents are required by law. So what we did was that we cloned the data, and let the business do it’s thing while we did ours. This of course presented us with unique data on the results. The things we measured were quality, speed, economy and employee satisfaction. Quality was measured by keeping track of casefiles, that were flagged as missing the document by each process. That gave us two lists, one with the casefiles found by the manual team and one found by the ML team. We then made a few random checks of casefiles that appeared on both lists, and we checked every casefile that was only on one list. The ML team flagged more casefiles correctly, it both found more and made fewer errors. Of course this doesn’t tell us how many casefiles we didn’t find, but it does show us that ML was better. Speed was relatively simple, it was start to finish and ML was faster. We did rent a lot of iron in Azure to achieve this, we could have never done it without a major enterprise cloud agreement. We needed Microsoft to allocate the stuff we needed, it wasn’t even a simple task of using the automatic systems. Economically it’s a bit of a touchy subject. I won’t go into details on that, but basically we know what work costs. Renting iron in Azure wasn’t expensive compared to having that many full time workers dedicated to the task. Employee satisfaction is hard, but we don’t have people on staff who’s job it is to go through half a million casefiles and look at millions of documents. We had to pull people away from their regular jobs to do so, and even with 7000 people on staff, it’s really hard to find people who actually want to do this kind of work. HR did a bunch of HR magic, and basically people would prefer ML to do this sort of thing in the future.
- YeGoblynQueenne 7y ago