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Being in the public sector, I have the joy of sitting in a lot of meetings with companies and consultants that want to sell us AI for analytics and prediction.

Being in the public sector, I have the joy of sitting in a lot of meetings with companies and consultants that want to sell us AI for analytics and prediction. I’m still unconvinced. The closest I’ve seen to anything useful was Watson, and all it really offered was stuff we’ve been doing for years. I mean, we have 5 people working with data and building models, and Watson could maybe automate some of that, but you’d still need people to qualify what it came up with and build the things it didn’t. We’ve run proof of concepts on stuff like traffic, and a small startup with a relationship with a university, build a route prediction thing that would make it easier to avoid jams. It worked, but it frankly didn’t work better than what our traffic department was already doing, using simple common sense. So we diverted the project to work on traffic light control for maximum flow, and it turned out that the settings our engineers had come up with were already optimal. Only our engineers had come up with the same results, using less resources. I’m beginning to think of AI as mostly hype. It does have some uses, like for recognition. We use ML and image recognition to troll through millions of old case files for various purposes. We use speech recognition to help people with dyslexia. But for analytics and prediction it’s been extremely useless compared to actual people. I’d love to be convinced otherwise by the way. If it actually does something, I’d like to be on the forefront with it so I don’t have to play catch up when Deloitte sells it to the mayor. But I have to admit that after 5 years of AI bullshit bingo I’m one sales meeting away from labeling AI in the same category as blockchain.

sgt101 8y ago
I think it depends on what you call AI and where you want to use it. Many people focus on "automation" - so do the analytics that an analyst would otherwise do. But as you note, mostly automation, workflow, analytic or chat, is only able to do very routine tasks, and it's still quite expensive to deploy. Another issue is that often when you tackle these domains it turns out that a commonsense non-technical approach is almost as good. On several occasions I've seen it's just a case of "stop doing that". On the other hand AI can be a powerful tool or assistant when humans are struggling with a task. AI that (for example) highlights all the suspicious places in an oncology image after an oncologist has had a look and labelled it as showing cancer could improve quality of outcome. I am deeply suspicious of people who want to chop the oncologist out though! More prosaically prediction systems to estimate future customer churn, identify sales opportunities (or in the public service case demand for services and needs), future traffic flows, complex settings for complex devices (especially in the face of change), diagnostics of problems, reasoning over millions of records, are all valuable. Often the theme is "can we make a very narrow very specialist component that could come to the same conclusions that a reasonably smart three year old would if we could make her concentrate and do it millions of times". So a bit smart, very autistic, very fast. What you will not do is create value from consultancy and procurement; you need to build a capability to get the value, and that is a slow, expensive and strategic game - think 4 high cost (but don't be bullshitted there are great people who will work for reasonable amounts out there so long as you don't expect them to be slaves and make bombs or crash the financial system - think doctor or lawyer cost and recruit with the support of your uni) fte for three years. If that is too expensive then I think it's going to be a waste of your time for now, and your current stance of sceptical openness is the right thing to do - eventually someone will come with a convincing shrink wrapped offer that is right for your domains, at the right price - then all is good.
antpls 8y ago
In my opinion, we cannot compare blockchain and AI : blockchains are a type of data structure based on hashes, while AI is a wide and fuzzy concept with different meanings over the years. Process automations and "AI" (let's say, complex autonomous systems) are parts of a bigger picture which is the digitalization of the society. Sometime, they are sold as a drop-in replacement for current human processes, but it actually works in only a few common cases. The issue with AI and automation is not necessarily a technical one, the issue is the shift to a different ownership model, trust model, maintenance model and faster improvement model than the ones being believed possible, or currently in place for the last decades. It's not that software cannot replace people, rather that big corporations are resistant to changes. We don't need to introduce AI to see the internal inefficiencies of big employers... Technologies fundamentally change the meaning and content of "work", but people cannot fundamentally change that fast. Budgets cannot change that fast, investments cannot change that fast, regulations cannot change that fast, etc. There will be a lot of preliminary work to do in changing the educational system, regulations and also changing the redistribution of wealthiness, before "AI" actually happens.
- hef19898 8y ago
  I understood the OP in that he put AI and blockchain in the same hype / bullshit trap category.
  - eksemplar 8y ago
    Yes.
hef19898 8y ago
The worst example I have seen in that regard was some kind of ride sharing thing. The core function apparently was route optimization based on multiple destinations for multiple passengers. They aimed to offer 30 minute time frames for arrival and departure. In an environment where complain about a 5 minute delay for busses. Quite unsurprisingly they failed in the end, still got funding so.
abandonliberty 8y ago
Watson is also people. Granted, those people are data scientists who can actually build an AI solution for you, but it's still a stretch from the marketing.
jorgemf 8y ago
> But for analytics and prediction it’s been extremely useless compared to actual people. Usually the problem is that companies are going to sell you an AI solution for your data, but these companies are not going to tell you that with your current data there is very little an AI system can do. I say this because I saw companies who wanted a prediction system with 2 years of data (40 datapoints per year). Any AI expert will see there is no way this will work but still the system is sold to the company. This is much worse in the public sector.
- eksemplar 8y ago
  We took this into account and worked with data from multiple municipalities. 25 years of data in a region with 1 million people is quite a lot. The problem for ML seem to be that we’ve been using the data for a while and that we’re already very good at it.
UncleEntity 8y ago
> We’ve run proof of concepts on stuff like traffic, and a small startup with a relationship with a university, build a route prediction thing that would make it easier to avoid jams. It worked, but it frankly didn’t work better than what our traffic department was already doing, using simple common sense. So we diverted the project to work on traffic light control for maximum flow, and it turned out that the settings our engineers had come up with were already optimal. Only our engineers had come up with the same results, using less resources. The AI came up with the optimal solution on an "already solved problem", twice, and this is a failure on the part of the AI?
- eksemplar 8y ago
  It’s a failure if it’s more expensive. It wasn’t very good at the first example though.

sgt101 8y ago

I think it depends on what you call AI and where you want to use it. Many people focus on "automation" - so do the analytics that an analyst would otherwise do. But as you note, mostly automation, workflow, analytic or chat, is only able to do very routine tasks, and it's still quite expensive to deploy. Another issue is that often when you tackle these domains it turns out that a commonsense non-technical approach is almost as good. On several occasions I've seen it's just a case of "stop doing that". On the other hand AI can be a powerful tool or assistant when humans are struggling with a task. AI that (for example) highlights all the suspicious places in an oncology image after an oncologist has had a look and labelled it as showing cancer could improve quality of outcome. I am deeply suspicious of people who want to chop the oncologist out though! More prosaically prediction systems to estimate future customer churn, identify sales opportunities (or in the public service case demand for services and needs), future traffic flows, complex settings for complex devices (especially in the face of change), diagnostics of problems, reasoning over millions of records, are all valuable. Often the theme is "can we make a very narrow very specialist component that could come to the same conclusions that a reasonably smart three year old would if we could make her concentrate and do it millions of times". So a bit smart, very autistic, very fast. What you will not do is create value from consultancy and procurement; you need to build a capability to get the value, and that is a slow, expensive and strategic game - think 4 high cost (but don't be bullshitted there are great people who will work for reasonable amounts out there so long as you don't expect them to be slaves and make bombs or crash the financial system - think doctor or lawyer cost and recruit with the support of your uni) fte for three years. If that is too expensive then I think it's going to be a waste of your time for now, and your current stance of sceptical openness is the right thing to do - eventually someone will come with a convincing shrink wrapped offer that is right for your domains, at the right price - then all is good.

antpls 8y ago

In my opinion, we cannot compare blockchain and AI : blockchains are a type of data structure based on hashes, while AI is a wide and fuzzy concept with different meanings over the years. Process automations and "AI" (let's say, complex autonomous systems) are parts of a bigger picture which is the digitalization of the society. Sometime, they are sold as a drop-in replacement for current human processes, but it actually works in only a few common cases. The issue with AI and automation is not necessarily a technical one, the issue is the shift to a different ownership model, trust model, maintenance model and faster improvement model than the ones being believed possible, or currently in place for the last decades. It's not that software cannot replace people, rather that big corporations are resistant to changes. We don't need to introduce AI to see the internal inefficiencies of big employers... Technologies fundamentally change the meaning and content of "work", but people cannot fundamentally change that fast. Budgets cannot change that fast, investments cannot change that fast, regulations cannot change that fast, etc. There will be a lot of preliminary work to do in changing the educational system, regulations and also changing the redistribution of wealthiness, before "AI" actually happens.

hef19898 8y ago
I understood the OP in that he put AI and blockchain in the same hype / bullshit trap category.
- eksemplar 8y ago
  Yes.

hef19898 8y ago

The worst example I have seen in that regard was some kind of ride sharing thing. The core function apparently was route optimization based on multiple destinations for multiple passengers. They aimed to offer 30 minute time frames for arrival and departure. In an environment where complain about a 5 minute delay for busses. Quite unsurprisingly they failed in the end, still got funding so.

abandonliberty 8y ago

Watson is also people. Granted, those people are data scientists who can actually build an AI solution for you, but it's still a stretch from the marketing.

jorgemf 8y ago

> But for analytics and prediction it’s been extremely useless compared to actual people. Usually the problem is that companies are going to sell you an AI solution for your data, but these companies are not going to tell you that with your current data there is very little an AI system can do. I say this because I saw companies who wanted a prediction system with 2 years of data (40 datapoints per year). Any AI expert will see there is no way this will work but still the system is sold to the company. This is much worse in the public sector.

eksemplar 8y ago
We took this into account and worked with data from multiple municipalities. 25 years of data in a region with 1 million people is quite a lot. The problem for ML seem to be that we’ve been using the data for a while and that we’re already very good at it.

UncleEntity 8y ago

> We’ve run proof of concepts on stuff like traffic, and a small startup with a relationship with a university, build a route prediction thing that would make it easier to avoid jams. It worked, but it frankly didn’t work better than what our traffic department was already doing, using simple common sense. So we diverted the project to work on traffic light control for maximum flow, and it turned out that the settings our engineers had come up with were already optimal. Only our engineers had come up with the same results, using less resources. The AI came up with the optimal solution on an "already solved problem", twice, and this is a failure on the part of the AI?

eksemplar 8y ago
It’s a failure if it’s more expensive. It wasn’t very good at the first example though.