6 ms·

Just like any truly transformative tech, AI takes long-term planning, tons of competence and picking the parts that make most sense for your current business go

Just like any truly transformative tech, AI takes long-term planning, tons of competence and picking the parts that make most sense for your current business goals. So obviously AI fails for most organizations because they lack all of these things and the fact that most never operate in scale or tackle problems difficult enough to need AI. Maybe one solution is finding the AI equivalent of Microsoft Office, ie a group of cheap tools that are so powerful, flexible and integrated that they can be used by individual employees and teams across all industries and business needs.

st1x7 6y ago
Doesn't the first part of your comment contradict the second? If it does require a lot of long-term planning and competence, maybe the last thing you want to do is to distribute it to indivudal employees.
- mindcrime 6y ago
  I suspect that it's hierarchical in nature, in the end. That is, you have the aspects of "using AI" that are big, complex, enterprise-wide and do require lots of long-term planning and a strategic focus. But there are also the "low hanging fruit" where you can utilize the "MS Office of AI" to achieve smaller, more tactical goals.
  - Valgrim 6y ago
    Isn't this kind of tool doomed to suffer the "AI Effect"? https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect I feel like I use a ton of tools everyday that would have been considered "AI" 10 years ago, like content-aware fill in Photoshop, translation and correcting software, etc.
    - mindcrime 6y ago
      Yes, no doubt. That's one thing that makes it so hard to have conversations about AI. The goalposts are constantly moving. :-(
asdfman123 6y ago
Azure ML Studio is a good tool for your non-data-science developer to play around with ML tools. I picked it up at my company and it was fairly easy to make models and predictions with. Of course, once you actually start to get good at it you want to switch back to using code, but it's a good way to start.
nradov 6y ago
How do we know that AI is truly transformative tech? The null hypothesis is that most businesses could achieve equivalent ROI by using older, simpler data analysis techniques like linear regression.
- ativzzz 6y ago
  You could replace "AI" there with any other newly developed tech paradigms, like "Big Data" several years ago. Your null hypothesis is probably correct for most companies, but there will always be some companies for whom the new tech is beneficial. Most companies do not need big data, and are fine running a single postgres instance for the entirety of their lifetime, just like most companies probably don't need AI.
  - jonathankoren 6y ago
    This is true, but I think the real benefit of the “Big Data” paradigm (scare quotes included) was the spreading the idea that you should actually measure things, and then make a decision. You’d think this was obvious, but apparently it wasn’t. (Think back to the book Lean Startup, whose premise is, “A/B test hypotheses.”) Similarly, the “AI Revolution”, could be boiled down to, “run a regression.”
- thinkharderdev 6y ago
  I would argue that linear regression is under the umbrella of ML. And probably the most important ML technique for existing businesses. A lot of the "low hanging fruit" for existing business cases is not necessarily discovering how to use regression analysis on existing data (which is probably already happening at a lot of places) but how to operationalize and continually update linear regression models so you can make them a mission-critical part of the infrastructure.
  - nradov 6y ago
    Linear regression has been around since before computers. It's a real stretch to put that under the umbrella of AI. Nor is AI required to continually update linear regression models; a "for" loop will suffice.
peterthehacker 6y ago
I agree that AI is very difficult and requires hard work by competent operators to be successful but I think there’s a deeper problem - most business data is irrelevant noise. It won’t matter how talented your DS team is if there is no signal in your data relevant to business operations for the ML model to identify.
- ergest 6y ago
  Completely agree. Most business data is noise and most of the signals are already discovered as simple rules and heuristics. On the other hand, if you have a strong signal in your data, even a simple algorithm like linear/logistic regression will be able to help. What I’ll call “signal hunting” is probably the best use of DS resources and also the hardest thing to do. I’ve done my share of experiments with ML/AI and where I’ve seen the most interesting value has been NLP applications (such as categorizing customer comments or assigning categories to products based in description) and finding “factors that influence behavior x” which then can be turned into either a model or a few simple rules.
- yes-the-day 6y ago
  I'm convinced this is one of the main sources of inefficiency in modern management, whether using AI or not. Managers are incentivized to be action prone so as to demonstrate their value continuously, and they are also expected to put in processes. Often these processes are on way too tight of a cadence and what ends up happening is that managers spend their entire time evaluating and chasing noise.
maxlamb 6y ago
Although that’s exactly the argument made in the early 1990’s about why expert systems were not successful “yet”.

st1x7 6y ago

Doesn't the first part of your comment contradict the second? If it does require a lot of long-term planning and competence, maybe the last thing you want to do is to distribute it to indivudal employees.

mindcrime 6y ago
I suspect that it's hierarchical in nature, in the end. That is, you have the aspects of "using AI" that are big, complex, enterprise-wide and do require lots of long-term planning and a strategic focus. But there are also the "low hanging fruit" where you can utilize the "MS Office of AI" to achieve smaller, more tactical goals.
- Valgrim 6y ago
  Isn't this kind of tool doomed to suffer the "AI Effect"? https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect I feel like I use a ton of tools everyday that would have been considered "AI" 10 years ago, like content-aware fill in Photoshop, translation and correcting software, etc.
  - mindcrime 6y ago
    Yes, no doubt. That's one thing that makes it so hard to have conversations about AI. The goalposts are constantly moving. :-(

asdfman123 6y ago

Azure ML Studio is a good tool for your non-data-science developer to play around with ML tools. I picked it up at my company and it was fairly easy to make models and predictions with. Of course, once you actually start to get good at it you want to switch back to using code, but it's a good way to start.

nradov 6y ago

How do we know that AI is truly transformative tech? The null hypothesis is that most businesses could achieve equivalent ROI by using older, simpler data analysis techniques like linear regression.

ativzzz 6y ago
You could replace "AI" there with any other newly developed tech paradigms, like "Big Data" several years ago. Your null hypothesis is probably correct for most companies, but there will always be some companies for whom the new tech is beneficial. Most companies do not need big data, and are fine running a single postgres instance for the entirety of their lifetime, just like most companies probably don't need AI.
- jonathankoren 6y ago
  This is true, but I think the real benefit of the “Big Data” paradigm (scare quotes included) was the spreading the idea that you should actually measure things, and then make a decision. You’d think this was obvious, but apparently it wasn’t. (Think back to the book Lean Startup, whose premise is, “A/B test hypotheses.”) Similarly, the “AI Revolution”, could be boiled down to, “run a regression.”
thinkharderdev 6y ago
I would argue that linear regression is under the umbrella of ML. And probably the most important ML technique for existing businesses. A lot of the "low hanging fruit" for existing business cases is not necessarily discovering how to use regression analysis on existing data (which is probably already happening at a lot of places) but how to operationalize and continually update linear regression models so you can make them a mission-critical part of the infrastructure.
- nradov 6y ago
  Linear regression has been around since before computers. It's a real stretch to put that under the umbrella of AI. Nor is AI required to continually update linear regression models; a "for" loop will suffice.

peterthehacker 6y ago

I agree that AI is very difficult and requires hard work by competent operators to be successful but I think there’s a deeper problem - most business data is irrelevant noise. It won’t matter how talented your DS team is if there is no signal in your data relevant to business operations for the ML model to identify.

ergest 6y ago
Completely agree. Most business data is noise and most of the signals are already discovered as simple rules and heuristics. On the other hand, if you have a strong signal in your data, even a simple algorithm like linear/logistic regression will be able to help. What I’ll call “signal hunting” is probably the best use of DS resources and also the hardest thing to do. I’ve done my share of experiments with ML/AI and where I’ve seen the most interesting value has been NLP applications (such as categorizing customer comments or assigning categories to products based in description) and finding “factors that influence behavior x” which then can be turned into either a model or a few simple rules.
yes-the-day 6y ago
I'm convinced this is one of the main sources of inefficiency in modern management, whether using AI or not. Managers are incentivized to be action prone so as to demonstrate their value continuously, and they are also expected to put in processes. Often these processes are on way too tight of a cadence and what ends up happening is that managers spend their entire time evaluating and chasing noise.

maxlamb 6y ago

Although that’s exactly the argument made in the early 1990’s about why expert systems were not successful “yet”.