Right good morning, everybody.
My name's Alex whistles, very warm welcome, too well to SQL bits and also to this session really great to see you all here. Hope you having a fun day. All my contact details on the screen there. Please do feel free to get in touch with any questions. You may have on ML or anything by related I'ma be, I consult.
Data warehouses cubes, etc ET L&ML is becoming more and more a part of our world, so any questions. You have please? Do feel free to get in touch over the Twitterverse on the emails etc.
I will reiterate I've been speaking totally for 4 days. My voice is going so please. Do bear with me. If you can't hear will turn the volume up a little bit and I'll start with spring, but I'll try and make it through right. So who am II'm on the organizing committee sequel relay. So I'll be off of SQL related. Thank you all for being here. I also help run the Data Relay Conference.
Happening in October if you want to register for that are on the Birmingham data meet up platform data platform meet up and I got a Masters degree in business intelligence. A third of which was data warehousing data modeling. A third, which was MBA and a third of which was machine learning and starts to 3 do tie in very, very stronger together.
Change tracking that you're tall coming out with almost and afterwards right enough for that business. Let's get onto the meat of this? What are you talking about machine learning?
Machine learning is everywhere isn't it everywhere. You look there's new stuff coming out is the buzzword. That's everywhere alot of their focuses on the technology on how to do it well necessarily what it is, and where it should be used and how it impacts our day-to-day life. There beyond consultant. All data warehouse systems that we create lead into the next.
OK, now I can report on what I've got, I've got some solid status. Table data of my business. What else can I do with it? How can I start predicting? Which of my customers are going to leave? What my pricing model should be these kind of things and so ML is a natural evolution. For anyone doing any data work or be. I work the basic principle is to have.
Quality data once you've got that then ML comes in. But what does that look like so I'll be looking at you real world examples of where it's used in day-to-day life will be looking at a few confusing terminology? What's the difference between AI machine learning and deep learning that all bounded round is different words. However, same thing? Are they not will have a look at that will do a couple of demos.
I will get those later on, and then we'll look at more of the moral side of it. What are the potential problems we can encounter with doing machine learning? What should we have to watch out for from a human perspective on where this is going all y'all doing there is going to be nice rosy future.
Is that OK with everyone?
Great well, we've got quite a lot to cover in the session. If you have got questions going through please. Do raise your hands in those questions as we go through. Obviously, the more questions. We get we may not be able to cover all the content, but we can balance that whatever you want to talk about it, we can talk about.
Right so as we all know machine learning isn't evil plot to make a slave, so I'm robot master overlords.
Sci-fi films have paint a pretty bleak picture of what machine learning and AI is what robotics is in reality. It's a little more mundane than that. We see it every day of our lives and we go onto Amazon people who bought this.
People who bought this also bought this Amazon $136 Billion sales company. They have 2,000,000,000 products and was there like a third of a billion customers. Something like that with a big organization. There's too much data there to try and manually associate products together and they change to freak.
Then I have some algorithm that is doing it automatically Association rules trying to bind relationships of products together is not just products sold products and demographics so it's not just people who bought this also bought that people who bought this and are similar to you in age color race, Creed location geographically.
The kind of property you live in which they know from your delivery address. Those kind of people? What did they buy 'cause. They're more likely to be relevant to you so it's much deeper than just product Association other examples. You may see uh things like credit scoring when you try and get a loan from a bank or a credit card.
Gone are the days where you know the bank manager. They know your parents. They know who you are, they trust you. Now your details go into a computer computer says yes computer says no. How did that happen? Well, they look at the history of other people have taken out loans look at with a similar demographic to you similar location age income level.
Etc and look to see their credit rating in their profile and try and apply a risk to you with your specific details and then create a prediction model of how likely you are to default on that loan if you're bad risk. They'll give you a higher APR to try and mitigate that risk etc.
That's why when you ask them why have I been refused alone. They don't know 'cause. They haven't made a decision are computers made that decision for them. There's no visibility of Why?
Customer segmentation let's say you run a holiday company, a travel travel agents and you're trying to push a 5 star all inclusive executive hold off the mall. Deves you've got 100 thousand people on your mailing list if you send out a flight to all those 100 thousand people. That's going to cost you a lot of money if you can filter that list down.
Most likely to respond well to that advert you're not going to be sound selling their holiday to a single parent or a young student or someone who's unemployed that's a premium holiday. You want someone who lives in a big house big income kids have left home. That kind of demographic are you going to manually try and filter that out, and try.
Self, who those people are going to be or are you going to look at the history of who's bought those kind of whole days before feed that into an ML model and let it decide for you. What makes the appropriate demographic for that kind of holiday and use that to predict of your main mailing list. Which of those 5000 people are more likely to respond?
Lock it to them that saving from sending 100 thousand Flyers to 5000. Flyers will pay for the ML model in itself in one male shot and you'll get pretty much the same response.
When he said Alexa.
The post code you put on the letter is the primary purpose. The primary point of how that messages letters routed through the post office system. You haven't got a team of trained people sitting there looking at post codes. It takes a photo or scan of that address the first algorithm works out, which is the most likely lying to be the postcode normally is the last for the last maybe.
Another lost want it finds the postcode. It'll end pass it to another algorithm. It will split it up into individual letters and then it will try and workout using image recognition and deep deep learning what the most likely letter is for that to be and it will give it a confidence level that'ST with 99% confidence.
Say, an eye with 60% 'cause it could be a now we don't know for one once it's got all those confidences of all the letters. It'll piece them together use the most relevant letter that I think it is, is it a valid postcode if it is great if it's not well. Let's try changing all those letters out to the next most likely and see if that's a better post code and all.
These image recognition systems take along time to train with millions of samples being fed into them. But you can use them in query them. Just like that. They have almost instant to query, which is why you can pass through very quickly that make sense so far excellence right so artificial intelligence.
Sign for quite a long time. AI is the umbrella term for all of this stuff.
If we look back in the 1950s things started to use computers to simulate intelligence who has played pac-man.
Quite a few of us when you're trying to escape the little little little ghosts. They are quite intelligent aren't they hunt you down like a pack of wolves, and they will get you they exhibit of intelligence on how they chase you around the board to try and hunt. If you actually look at the details of how that's been programmed. There's no intelligence there at all.
Algorithms so blinky the red one he's programmed up to go straight for you, simple as that then pinky pink. One is programs to try and target for squares ahead of where you're going, then the next one in key the blue one prompted Airporter Line from.
Through to you propagate that forward a names for that point and the final one. Clyde the Orange. One doesn't think he just chills. Now the 4 of them together have different very simple algorithms. But they are different, and together they appear to be intelligence. But they are very simple algorithms that a human is sit there and worked out and program.
So it's almost the A It looks intelligent, but it's not as simple programming. So the next evolution came with machine learning where we don't want a programmer to sit there and write all the rules. We want the system to learn itself or those rules should be.
So look at things like example of this so we take existing history and use historical events to feed into the algorithm to workout? What that model should be so let's say we want to workout whether a call.
Emma is going to leave us or not, we could look at historic customers and look at their activity in the month or so prior to leaving to see what they were doing and use that behavior to build a model of what represents that behavior and then apply that to new customers. Now we're not telling the algorithm what?
Rules are which is giving examples and saying, You learn from this and try and find common patterns that you can find in this data that will then be useful to create these algorithms internally that we can use them predict stuff. So we're letting the machine learning decide its own rules based on examples that we're giving it.
These are still relatively simple rules in that example of customer churn is likely to be a decision tree so you simply got a list of tree of decisions are they over 35. Yes, or no is there income over 40,000. Yes, or no are they male or female, male lots of decisions that go down that you can easily understand is a human and interpret the model of the.
But the next level is deep learning deep learning is far far more advanced and can be used for a much deeper set of inherent understanding of a server system rather than simple rules. It tries to mimic and replicate the human brain and it's basically a digital version, a simplified digital ver.
Human brain and so we again, giving examples, but it's not creating simple rules is creating fenomenale complex got feel understanding of and environments that we can't really understand what it's doing it. A black box and so it takes it to a whole. Another level and we look at example out later on that makes any questions.
Right stuff right so let's look at training with all these models basically from machine learning onwards. We have to train. Our models using data using historical data in order for them to build their own model, so the analogy here is a classroom.
When you send your kids to school. The teachers are not there to make them learn powered fashion memorizing answers to the question. There there to teach your kids generalized concepts. You don't want to know that for this particular triangle with these particular dimensions and angles? What the third angle is going to be.
Teach them the concept of Pythagoras, how to apply a generalized understanding of that problem, so when they learn that general understanding the kick and then apply that to any triangle and understand how to work out the missing angle. That's the concept. So you understand after a generalized understanding of it. So to do that the teacher provides worked examples this.
This is the missing thing. This is the Pythagoreans. Theorem. This is how you calculated to get that answer you give enough of those examples to a kid. They start to understand the generalized concept of how to apply it at the end of the year, you get to an exam.
The kids never seen that particular triangle before, but they understand the general concepts and employed at this new thing they haven't seen before.
The critical point here is the teacher Nord teacher already knows the answer to the exam question. But the kid doesn't so the kid gives their best answer and the teacher then marks it against what they know the answer is and looks at how good that kid is learned that generalized concepts. So you have 3 pages. You train the kid with examples. You then give them an exam where you know the answer, but they'd.
Score them on that to workout how good their answers are that's how school works, yeah, so that's how machine learning works as well. We do exactly the same process we give the em algorithm trained data sets of historical behavior. We give it lots. And lots and lots of examples an it learns these generalized concepts.
When we test it, we score it and then the best scoring algorithm that performs best we can input into production as are good prediction model, the best kids in the exam goes University get the best jobs there the best models to use.
Time for a demo.
Right.
So we're going to do 2 demos. The first one, is going to be using a tool in these are sweet called the Microsoft Azure Machine Learning Studio Best of all this is free. It's a great Sampat area to play around with building your own machine learning models.
So it's entirely online and it will create productionized models that you can use and consume so we're going to create a model is going to predict how good the fuel consumption is of a vehicle so I'm going to create a new model here. There are loads of sample experiments that you can load up. But I'm going to create a blank one here and try and get through.
Much in the way of time, so there's loads of save data sets here datasets. The samples that we can look at the centers. Information Bill Gates is RGB image and all sorts of fun stuff and that's play with I'm going to look for a data set called MPG you can load your own data into this, if you want as well. But I'm going to use a sample one now if I visualize this.
The visualization on there there, we go 5 visualize. This data set we can see we got the MPG there. The number of cylinders displacement. It's American so it's cubic inches, etc. But you get the point. The weight acceleration near the only field were not going to be using his actual name of the vehicle. 'cause the name doesn't have any correlation rarely to the MPG we're going to get rid of that.
So the first thing to do is to turn this MPG into a simple boolean good or bad is it a higher PG or low MPG vehicle and will use that to build our model on so I'm going to write a sequel transformation and almost use integration services will see this data flow.
Well, I'm going to write an extra case statements here to say case when MPG breakdown equal to 25, then that's good.
Else, bad and
As let's go high low MPG so now I've got that I can then run this just to make sure that that.
The query is actually writing valid give a second to run through and it'll just cast it and make sure it's OK.
So the first thing we want to do with this now is we need to split. This sample data up so this is our training data that we're going to use to train this model up. We need to split it into 2 different sets of data one which we're going to use to train. The model and then we're going to keep some back to give it a test at the end.
At the end of your exam to see how good about it is so we're going to split the states are up and all that data. I'm going to choose 80%. So I'm going to change that to not .8. So is 80% to keep and train and leave 20% free, as our tests at the end of the process.
Now I want to remove a couple of columns, so because we've now turned the numerical MPG.
Um into the into the high low. We no longer wants that'll numerical value. There 'cause, it confuses the model so we're going to get rid of.
Why is not working?
That should give Maine.
Don't live around I wanna do select first.
So I'm going to get rid of everything going to get rid of the model name and the actual MPG value and leave. Everything else in there now. I want to go through and to do the splits.
If I type that right.
Split it into my 80% for training and 20% for testing. Uh certainly around North .8.
If I can type right now, I want to train my model, so I can bring on my.
Train model if I can find it now we go and I can pass in the data that I'm going to use for my training but, I want to tell it what algorithm to use now. This is where I get a little bit more tricky do you want a neural network decision tree random forest you need to know which kind of model to choose on this? Which is where it gets a little bit more tricky.
There are various blogs and books on how to do this. There's a CHEAT SHEET, which I can put in the right direction. But I know for this. I want a 2 class boosted decision tree 2 class because we are predicting 2. Outputs Yes, or no high or low good or bad and we need to tell it, which model what column we want to predict so I will.
High low MPG.
And that's basically it but now we need to give it a test we do our interview exam. So I'm going to school my model.
And I'm going to pass through the trained model and then the 20% of data that I kept back as my test. I'm going to plug into my exam into my tests. So I'm going to run. This model through some predictions here and create a prediction on what these new date is going to be.
I then need to give it an evaluation so I need to mark the exam results and see how closely those predictions actually matched the reality of the results that I know is the case so I'm going to run that and that's now going to train my model is going to build this prediction model. There's 2 class boosted decision tree and then give it the 25th.
Side feed that Aiden user to create some predictions and then see how good those predictions are.
When we do this we end up with what's called a rock charter receiver operating characteristics chart, which basically tells us is it good or is it bad?
And when they finish running through.
You may want to create a model with multiple different algorithms to see which one performs best and you can do that in here, but we haven't got time to go through the full demo of that. But if I look at the output of my evaluate model. We got our rock shot now. If we had a purely random algorithm we would get a line something like that.
100% perfect prediction model, we would get a line like that. You never get that. If you do you've overtrained your model is something wrong? You've given it too much information but the goal is to get the line up in that corner as much as you can.
The higher you get up to the line the better a prediction model is the higher their exam results.
If you ever manage to get a line going down in this direction. You need to give us a partner back as you've created an artificially intelligent machine learning algorithm that performs worse than random chance gas work that that would be truly impressive.
So I recognize a pretty good model that's giving us a far better than random chance prediction. So let's go with that and that's production noise. This so I'm going to set up a web service here, so this is going to build my model and say, I'm going to production. Wise this and turn it into an actual production web service on the web. I'm going to do a little bit of manipulation of this.
Obviously all of this stuff up here is all data preparation for training. We don't need to do that. In reality when we actually production eyes. It so this is our web service inputs or whatever. We type in a website will come in there, it will be the results of our trained model. It'll give us a prediction at like with the results so now we've got that let's run that through to make sure that's going to work OK and give it a test.
Shimun get loads and I sticks.
We cannot deploy web service. This is compiling it, compiling the actual prediction model and put it up in the clouds web service and we can now test. It so this is not fully productions web service. We could write some C code or some python code to now send details about.
Up to this web service will do a prediction give us result back by luckily. They provided a nice test harness to do this with so I'm going to put in some random data. So we can see here. We're going to test. Let's do that. We're going to test this data, with 8 cylinders 3 or 7 displacement 130 horsepower that waited.
And we're going to request a prediction and it predicts it's going to be a bad MPG vehicle and that is pretty damn near close to zero now zero would be a perfect prediction of a bad MPG. One will be a perfect prediction of a good MPG any number in between gives us that confidence of how.
Prediction so let's start playing around with some of these. I'm going to change to a 1990 acceleration's going down to 25 and maybe the weight goes down to 2 and half thousand. Let's give that ago. It's still bad, but is now North .47, there's pretty much in the middle. It could go either way. Let's play around with a bit more set a 4 cylinder it's uh.
200 displacements let's go 100 horsepower and now we get that's not a good prediction with a .7 confidence and we can keep on going and if we can give it anything. We want and it'll give us this prediction.
Now, if you want the code for this you can click on this little button here and it will actually give you the documentation for that. API sample request. Jason it'll give you all of the C Sharp code so if you embed that in your application that will wrap up the code you need to send the information off to the web service, so get its prediction to get the results back.
Redline your applications and it really is easy as that now. You don't get any control over the nitty gritty what inside that model. But at this stage, you really want that you want to play with it and see how it can work then once you understand how it all works how the models reacts and things like that, then you can progress to using ML studio and more.
Python code, etc to get into it in a bit more detail. This is a really, really good starting point to get a feel for it.
Any questions on that is that all awesome stuff.
Question.
Good question So what if the data changes and what what determines a good or bad MPG changes part of one of the inputs. There was year would obviously were in 2019. We haven't got 20 cars. Yet so we need that would change the model so the model is fixed at that point if you if your data set changes and your.
Changes you need to get a new data set and retrain that's a model. So yes, you do need to keep retraining it every time Amazon making selling you thing they have to keep retraining their model, then do every day that it wants a culture or something like that, once a month. 'cause it takes a week to retrain but yes. You do need to keep retraining the model's data changes absolutely good question. But other question.
Can you go into the parameters another good question. No, you can't not in this but there are some very basic premises. You can chewing but you can't really get to the gritty of it. This is about a drag and drop environment. If you wanted junior parameters use are you spy thermostat are and go into more detail in the question.
Can you extract the model I have it? No, you can't you can't even upgrade this into a mail server so you could do that. This is a black box is designed to be a starting point in today's free, as well is not designed to be an enterprise grade solution is to help you get started with it. But you can use it in production. So now you would need to use this to learn the concepts not to write the code.
What's that, yeah, so what's the best use case of doing this? I wouldn't advise using this? A highly scalable corporate environments for your prediction models of customer churn. This is about really you playing around with it in a sample terrier to get a feel for what works better at 2 class precision tree or random forest algorithm a neural network.
How do they work if you buy machine learning for dummies is going to talk about all these algorithms you need somewhere to play with it and this is a really nice environment to play with it to understand how these things work. You can production wise it. I would say if you're doing, it in a corporate environment. You wanted more control of your code. You want to natural machines data scientist to write the code for you, but this is.
Right there's crackdowns let's do something a little bit more fun. So this is machine learning. This is a data set that we've fed in and this is creating some simple decision algorithms that do it. If we could see the code. We will be able to read and understand what it was doing, but nothing would move now to deep learning with the Neural Network. Let's try and recreate the human brain.
Is anyone ever the kids got a big stick put on your hand and try to balance it?
Did anyone ever tell you how to do that or did you just pick it up one day and try it and just play with it until you until you got to build a balance. You try an experiment so let's build a deep learning neural network to learn how to do that by itself, and will do that in a couple of minutes.
So for this we moving outside the Microsoft World into python. So this is local python install on my laptop. You can run python in Microsoft Tech in Databricks. For example, natively in SQL server. These days, but this is pure python installed in my machine. We've got a python library called Jim.
A suite of open source games that people have written and wrapped up in a library that you can import into python and interact with to save you writing all the graphics yourself. We've also got a library called TF learn now that this is based on Tensorflow. Tensorflow is one of the most common open source libraries for accessing machine.
TF learn is a wrapper on top of that that makes it even easier so this code is about 180 lines of python code and we're going to be using this cart pole games were importing this from our gym library and this is the thing that balances the pole. So the first thing we're going to do is jump down and play some Rand.
So this literally loads up the game and says do I move my hand left or right? I want to choose a 5050 choice and do something randomly. Let's play a few of those, and see what happens.
I'll see it run about 20 games and every time it starts to fall over it will reset and try again and you can see that it's randomly trying stuff and you can see the score here of how many moves is doing before it gets to a point where it's going to fall over the goal is to get to 200 moves and still be up right and we've averaged what 19.85 which is.
Bar for a random guess every time.
So we want to build a machine learning deep learning neural network that can narrow play this and try and learn what's happening. So this is the important bit of the code? What we're doing here is creating a multilayer neural network with lots of different numbers of neurons 128, No 2565, 12, but one half thousand.
Inside the human brain is made up of billions of neurons all connected with synopsis. This is a similar simplification of it and we're creating 5 layers of these human brain equivalence and we're going to feed data into this and this brain is going to learn what makes a good move or a bad move now we could sit here.
And just create program up some scenarios, and feed it in as to what's going to bad we got time for that. So why don't we just play some purely random games and it will be about 5000 random games and take those that purely randomly work quite well filter those off and feed those into.
Neural network and say will randomly these actually work quite well figure out what made these work OK learn the patterns and use that to train yourself and how to play it so the first thing we're going to do is we're going to commentate are random games. We're going to run 5000 purely random test games.
Causation will take too long, so it'll be behind the scenes, then of the filtered successful ones will train our model on that data and then we'll play a new set of games using that model and we'll see how that works.
So the first thing we're going to do is see it train up itself with all of these random games that is running.
So this is going through a number of cycles of these 5000 random scenarios training itself on what happens and this is the result.
So that got to 200 successfully.
Another 200.
Oh, it's going and it made it perfect score every time so in that 5 seconds. That was just learning. It's taught itself how to play this game imbalance that pole without me, giving it any information about gravity or angles or weight of the pole, etc and it just works.
Now, if I run this again, I may not get a perfect result to made over 290 or 180 every time you run it. It's really learning from scratch. If you give a kid a different teacher or teacher different kid. You gotta get different results. You'll get different results. Every time you train. A neural net model in this case, we've hit a winner, we got a perfect score.
Is this is 180 lines of python code machine learning is not easy, but it's iaccessible now gotta days? We have to write 10000 lines and have to be a PhD student to do it. We can all get into this now. Now I need to say a big shout out to where I got this code from in this demo is.
YouTube video if I can get back to that.
Of Harrison Kinsley at cent tax and he's got a fantastic. I think the 4 part. YouTube video on how to write that exact same demo and how it's rather code and what all that code does so big. Thank you to him for that demo and go to his YouTube to see how it all works behind the scenes.
He's a huge channel of all this kind of stuff all in python really useful resource. You've also got the studioML.net which is the first example. I gave you and then the gym library that has all these games on the PAC man space invaders. All sorts in there that you can play with in the gym library. That's all at gym open AOL.com.
Is it won't go back these slides will be on the single best website for download afterwards if you want?
Well, OK any questions on that.
This exact cool Art Naschel excellent right so we talk about the human brain the human brain. If you magnify it to whatever level looks something like that. You have about 100 billion neurons. These little blobs in the middle in your in an average human brain pop and robbers got 200,000,000,000 in the back.
The connections between them are synapses and every experience you have builds the connections between these neurons and help strengthen the connection between a good whiskey and enjoyment or sequel bits and fun and learning everything you experience is all about feeding information in.
Connect these neurons together with synapses to build a picture of these complex connections that associate one thing with another and that's how we learn and that's exactly how the neural networks in machine learning. Each neuron each 100,000,000,000 neurons have got about 7.
Actions to other neurons so you can imagine the complexity in the brain is quite significant with the neural network, we work on a similar basis. We have all of our inputs on the left. We have layers of hidden neurons in the middle. All connected using these synapses and then all of the connections between.
We can as you give to give training data to its and together they end up populating some outputs out the other end now that you never get one that simple. In reality, even that is ridiculously. Simple one the game. We played you saw the 5 levels of hidden neurons with about one and a half thousand neurons in there and that's just to play a little game so you can see.
We need to make these things the average frog has about 16:16 million neurons and we're now getting AI systems and computers that can handle about that level of intelligence right so to look at how prominent these things becoming all of these connections are very.
Operations when your training data very similar to Bitcoin mining when you're doing lots. And lots of little calculations, but millions of in parallel the best technology to do this kind of stuff is with gpus graphical processing units. If you look at the prevalence of Bitcoin mining and Neural Networks ML over recent years, you can see why the sherp.
Is skyrocketing because they make this stuff every other data center is now kitted out with banks and banks of gpus to make doing this stuff faster.
Right so now let's there's enough of the demos. Let's talk about are we doomed if it's a good thing or a bad thing. Now we all know from a business perspective self driving cars prediction models playing games. There's some awesome things. This stuff can do. But there's also some potential downsides and we have to be aware of what those are.
Ethically, there's a decisions. We made on whether something should be done not necessarily can it be done take Netflix. There's a common example with Netflix where different people will see different thumbnails of the same film when they look at Netflix, depending on what Netflix think you want to see.
So if you say goodwill hunting it would either show you a thumbnail of Robin Williams or Matt Damon and Minnie driver depending on whether Netflix think you prefer comedies or romances now that's not particularly problematic. I'm fine with that. But then there's another level where it's been shown that if Netflix think your ethnic minority.
It's more likely to show you a thumbnail of an ethnic minority even if they don't really play a big part in the film, if I just an extra now that's using their knowledge about your habits to sell you something that isn't really relevant because they know something about you now is that good or bad on my way to judge. I'm just kind of saying somewhere there is a line.
Just thinking about these things.
The common example is a self driving car you're going down the street and you have no option but to carry on, and knock over a child or do you swerve and knock over an hour to AP? What's the right answer. No one can program up a car on how to handle that situation, but also it had changes around the world are recent survey was done.
A lot of countries around the world as to how people would react in that situation and in the West. It was the majority would avoid the kid and Hideo AP in Japan. It was the opposite in Japan. It was avoid the kid and hit the oop. Sorry avoid yeah. That's right savior, I painted the kid because the van, the perceived value in SOC between childre.
Adults is different in different cultures is not an easy problem to solve you can't program. This algorithmically and there's no real answer to it, and nothing we're going to have to face over the next few years.
How do we have control of these things as anyone read Isaac Asimov's complete robots phenomenal book author Kasimov created 3 laws of robotics in science fiction that have pretty much been using every sci-fi robotic AI film. Since then first one, is a robot can't harm a human second one is uh.
Self unless that contradicts the first rule, the third one is a robot has to obey orders unless it contradicts the first 2 now. Something as simple as that Congo wrong. They are basic simple rules. But this book. The complete robot is a whole shares a sort stories that show how badly that can go wrong and how we perceive.
Simple but in reality, it's not going to manage this complexity as we go.
Question.
Zero floor might be what I was it was a while since about it.
Right OK. Thank you so the zero floor. The robots came up with was SOC is more important than an individual person. Therefore, for the good of the for the greater good, which had his own problems. As we know from other films. So yeah, thank you so it's a complex problem.
Going to solve Prasanta.
Killing machines how any technology is going to be used by the military at some point in his life, including AI Algorithms E lawn. Musk is quoted as saying. AI poses an existential threat to human civilization and existence, Stephen Hawking and we all hopefully respects told BBC.
For AI could spell the end of the human race now. These are very clever. People who don't speak lightly about this kind of stuff are they right are they wrong. No one knows at the moment but there are issues there, but there are problems, as well about things that we take for granted as humans that when we start creating machines.
And cause destruction without realizing the potential consequences in World War 2 the Russians trained dogs to be suicide dogs. They wrapped up in suicide vests and train them to go and lie underneath tanks and then they would be definative to blob tanks from the underside and so this work very well in training and practice.
And I sent him to the fields setting these dogs off towards towards the Germans and they turn right back around again run back and lay on the Russian tanks and blew them up because we understand the tank is a tank is a tank. A dog doesn't, he's been trained on Russian tanks, not German tanks.
So things that we take for granted that make sense you train. A dog or an AI algorithm. We don't think about giving you that information, but there are consequences that we may not expect.
Another one about picking up training data. Another World War II example was the Navy. We're trying to look at where airplanes get shot. Most aware they should put extra armor plating and so all the planes were coming back. They were saying well. They've been shot in the wings and they've been shot at the back of the fuselage and the.
So that we need to put more shielding and so they started to do this and it made no difference in my planes coming back and then it statistician old version of machines of data scientists pointed out that actually the ones that get shot in the wings make it back. You're only using the planes that make it back for your test set all the ones that gets shot in the main engine.
Those don't come back there for your data set in your test set or your sample set is exactly the opposite of what you really want to be very, very careful when using your input datasets that isn't being affected by human bias or by other situations.
Who can tell the difference between those 2? I'll give you a clue ones real ones not who thinks that all the left Israel hands up.
Who thinks there on the right is real?
Most of you are wrong.
Oh, I haven't got it to be having audio feed into the sound on this.
The other reasons, OK, so I'm just going to you won't be able to hear this. But this is a Chinese news station who have created an AI algorithm to read the news for them.
So this is the artificial version of the first guy and he's not reading the news 24/7.
Now I speak Chinese.
It's also in English version.
And you can still say it a little bit stifled and not entirely perfect, but as this improves.
It's going to get better and better.
So these are not running live in Chinese, new stations and the beauty of that, if they have no morals. They have no thought process. They're not going to start deciding whether they want to read news on lot based on their own personal opinion. They're going to read what that holds whether it's propaganda whether it's genuine fact whether it's good or bad they don't interpret they'd.
They'll read whatever they're given.
And of course, he's cheap works 20247 doesn't go on strike doesn't need paying doesn't need feeding, etc, etc. So the question then is what happens to employment does this affect our jobs and our lives. Yes, it is going to there is no question about that price. Waterhouse Cooper predicted 30% of jobs in the UK.
If it by a IOM algorithms in the next 15 years.
Now 82% of UK companies are expecting to implement AI solutions and ML systems in their in their business. Only 32% have re. Skilling programs signals people to do other things, so this is going to be a problem in our lifetime of a lot of jobs. Now we've seen this.
Industrial Revolution of one point back in the 15 hundreds 58% of the UK population was employed in farming.
Now it's less than 1% that's gone down from 2 and a half percent in the last 40 years. That's over half the population are now unemployed yet. We live in a world where we have the highest employments than we've ever ever had. So it hasn't made it unemployed is changed the nature of the work. We do is made us become more skills.
Building different technologies, writing this stuff managing this stuff rather than just become unemployed for it. But there was a very, very painful transition to get there and that is going to affect our generation and specifically our children's generation.
That is everything I could talk about so I hope that's been useful do we have any time we got.
-3 seconds look at that do we have any questions before we wrap up?
If you do all have around here for the next few minutes by all means come on down. Ask any questions, but thank you very much coming to this session have a great rest of the day. Please do filling your feedback forms. I really appreciate that and enjoy the rest of the day. Thank you.