# Can big data, everpresent sensors and AI replace the scientific method **URL:** \ **Category:** Great Debates\ **Created:** [August 26, 2017, 2:10am UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701 "2017-08-26T02:10:52Z")\ **Posts on this page:** 5\ **Page:** 2
**Author:** ![Voyager](https://sea3.discourse-cdn.com/straightdope/user_avatar/boards.straightdope.com/voyager/32/133_2.png) [@Voyager](https://boards.straightdope.com/u/Voyager)\ **Post date:** [November 7, 2017, 5:54pm UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701/21 "2017-11-07T17:54:53Z")
> [@Lemur866](#): > > I forgot that “expert system” is a term of art. I was just talking more generally. > > The point is, if you want a system that can tell the difference between cats and dogs, you have to show it 10,000,000 pictures labeled “cat” and 10,000,000 pictures labeled “dog”. You can’t just set up a system and have it casually inform you a couple days later, “Oh, by the way, I just figured out the difference between cats and dogs”. You have to define the problem for it, and define the data set, and define what success looks like. I’m not sure if the training set would have to be that large. But one of the reasons image recognition is taking off these days is that you can just direct the system to find pictures labeled cat and dog on Facebook or Google or wherever. You no longer have to show it anything. One way you can do this is through clustering. You place the images in an n-dimensional space where n is the number of useful features. 4 legs is not too useful here, whiskers would be. You adjust the weights on these until you can draw a line between images labeled cat and images labeled dog. (With some small degree of mislabeling.) Then you apply to a larger set to see if your training set worked. So success is defined by the labeling and some metrics of how much separation you get and how many mistakes you make. So in a sense the system does inform you that it figured out the difference between cats and dogs - but not in a verbal way. > [@](#): > > Yes, a current system that can tell the difference between cats and dogs doesn’t do so because the programmer gave it a list of rules about what a cat is and what a dog is, and the system follows the rules. The method the system uses is a black box, and the system might easily use methods that no human being would think to use, and might even use methods that human brains can’t understand. Expert systems were rule based systems, which was my point. It isn’t a black box really - you can look inside to see the weighting and for neural networks what they look like. We can understand it at a high level, but maybe not the details - but I don’t think there is any one person who understands the full details of Win 10 or how the Google algorithms work. I’m not sure I could say I understood the data mining system I wrote myself - it was over a period of years and way too complex to fit into my head. > [@](#): > > That still doesn’t mean a general purpose AI that can make human scientists obsolete. Such a system is no closer to reality than it was in 1968. Yeah, it’s easy to imagine, and it has been imagined since the first computers. But it’s not happening any time soon, and all our progress on artificial intelligence since 1968 hasn’t brought us any closer to Hal 9000. Yeah, we’ve got Alexa. But Alexa doesn’t work the way they imagined Hal 9000 would work. Hal 9000 works by understanding things. Alexa works in a completely different way. And in many ways, an AI that works nothing like a human being is actually better than a Hal 9000. Who needs a system that works like a human brain when we’ve got 7 billion spare human brains running around the planet, barely being used? This I agree with. One of my AI textbooks was from 1959, and it was just around the corner then. We are going to make a theme of our conference next year AI - it is really machine learning, but AI is a sexier term. That is why we hear so much about AI today, and why people confuse machine learning with real AI. ---
**Author:** ![Snarky\_Kong](https://avatars.discourse-cdn.com/v4/letter/s/a183cd/32.png) [@Snarky\_Kong](https://boards.straightdope.com/u/Snarky_Kong)\ **Post date:** [November 7, 2017, 5:56pm UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701/22 "2017-11-07T17:56:46Z")
> [@Mijin](#): > > Isn’t distilling science into human understanding already something of a problem? > ISTM that large chunks of physics and maths moved away from human intuition at least, a long time ago. Calculations are performed, and we try not to worry how to put it into visualizations or analogies, which are frequently misleading. > I think it’s entirely feasible to imagine a reality where we have enough information to verify the \*correctness \*of a program / machine, but are unable to distill what it’s doing into a neat set of equations. And being as such a machine would still be useful to us, we’d still want to use it. Will we just shrug and try not to worry about being one more level abstracted away? I would draw a distinction between things that aren’t intuitive and things that are incomprehensible. Quantum mechanics is unintuitive, but you can go do the math and get an answer. You can say why the particle tunnels through the potential well or whatever. If you have a 1,000 layer neural network, how the activation of neuron 4 in layer effects the output is so obscured that it’s basically a black box. There isn’t understanding of any underlying mechanism. Now, hopefully people develop tools that allow us to have some understanding, but it’s current very vague. ---
**Author:** ![Voyager](https://sea3.discourse-cdn.com/straightdope/user_avatar/boards.straightdope.com/voyager/32/133_2.png) [@Voyager](https://boards.straightdope.com/u/Voyager)\ **Post date:** [November 7, 2017, 6:02pm UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701/23 "2017-11-07T18:02:29Z")
> [@Mijin](#): > > I think there are a few steps leading up to the reality that the OP is describing. > > Firstly, doing big data analysis on scientific research will be very beneficial and I’m sure many people are working on this now. It might help us spot when research is confirming or refuting other research, which experiments may not have been rigorous and, most excitingly, combine observations: Right now, papers published in journals in different fields might never get associated, even if they’re describing facets of the same phenomenon. Big data might help us chain together human knowledge. > (that’s my understanding anyway, I’m not a scientist) This won’t really be Big Data - there won’t be enough of it. It is tempting to use big data techniques on little data, but you get misleading results. I never had enough volume in the stuff I studied to say anything statistically interesting. Searches are good for finding correlations between things in different fields, and there is a concept called [the Semantic Web](https://en.wikipedia.org/wiki/Semantic_Web) which might be of use. It is nothing new - lots of advances have been made when people in one field learn another and bring concepts from the first to the second. ---
**Author:** ![LSLGuy](https://sea3.discourse-cdn.com/straightdope/user_avatar/boards.straightdope.com/lslguy/32/5813_2.png) [@LSLGuy](https://boards.straightdope.com/u/LSLGuy)\ **Post date:** [November 7, 2017, 7:20pm UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701/24 "2017-11-07T19:20:36Z")
> [@Voyager](#): > > I’m not sure if the training set would have to be that large. But one of the reasons image recognition is taking off these days is that you can just direct the system to find pictures **labeled cat and dog on Facebook or Google or wherever**. You no longer have to show it anything. > One way you can do this is through clustering. You place the images in an n-dimensional space where n is the number of useful features. 4 legs is not too useful here, whiskers would be. You adjust the weights on these until you can draw a line between images labeled cat and images labeled dog. (With some small degree of mislabeling.) Then you apply to a larger set to see if your training set worked. So success is defined by the labeling and some metrics of how much separation you get and how many mistakes you make. > So in a sense the system does inform you that it figured out the difference between cats and dogs - but not in a verbal way. > … Bolding mine. Not disputing in any way your post or your expertise. But the bolded part brings up an interesting conundrum. Perhaps one worth discussing. How many of those cat and dog labels on all those pix were applied by humans and how many were applied by Google’s or Facebook’s AIs? If orgs are training their AIs using input that was generated by other AIs, there’s some risk of a feedback loop developing that goes off the rails. Sorta like the dueling pricing bots on eBay working themselves up to $10 million for a pair of socks. Yes, humans are in the loop some. But if Facebook starts using Google’s image labels as learning inputs while Google is using Facebook’s, we might get some very “interesting” interpretations of what’s what. It might be fun to watch. Then again, if in some future world the police departments’ automatic face recognition stuff gets confused and starts recognizing toddlers as crazed gunmen we \*might \*have problems. ---
**Author:** ![Mijin](https://sea3.discourse-cdn.com/straightdope/user_avatar/boards.straightdope.com/mijin/32/9369_2.png) [@Mijin](https://boards.straightdope.com/u/Mijin)\ **Post date:** [November 8, 2017, 3:49am UTC](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701/25 "2017-11-08T03:49:33Z")
> [@Voyager](#): > > This won’t really be Big Data - there won’t be enough of it. It is tempting to use big data techniques on little data, but you get misleading results. I never had enough volume in the stuff I studied to say anything statistically interesting. Ah, you are right. I made the common mistake of just talking about “big data” as if it just means “lots of data”. But in fact it doesn’t refer to a specific size of data; it’s more about to what degree we can process records, and the significance of individual records. [Previous page](https://boards.straightdope.com/t/can-big-data-everpresent-sensors-and-ai-replace-the-scientific-method/794701.md?page=1)