Techie-Talk: Are Robots Really Taking Over? Dean Wronowski talks AI and ML, and whether or not robots are really taking over.

Purple and White Artificial Intelligence with the Exterior of a Woman.

Eleanor Barlow 

Welcome back again Dean. Now, last time we spoke we covered your work at Pansensic, and lightly touched upon ML and AI, but we know that AI and ML is a great passion of yours! Outside of the office, and not relating to Pansensic in any way, how do you see AI and ML progressing? These days there are so many scaremongering stories about robots taking over, do you agree with that viewpoint?

Dean Wronowski: “Whether you hate it or love it, you have to go with it, because if you don’t go with it, you will be left behind without an understanding of it. Even though people are saying that they will be out of work because of all this new technology, you have to remember that all this new technology will also create jobs. That’s why you should just go with the flow, try and get into it, learn about it, get involved in it, so that it becomes less of a scary concept.

“I would probably say that, whether you like it or not, it will be involved in your life…if it isn’t already!  It’s like when you go on Facebook, an action most of us make every day, there are thousands and thousands of machine learning algorithms running all-day-long on there. You probably don’t realise it, but they are continuous.

“They have even started developing algorithms where they use machine learning to generate even more data, based on the data they already have. For example, people upload pictures in their homes, with their children or their dogs, sitting in the kitchen, conservatory, or sitting room- ordinary Facebook posts. Now say, over the last 5-10 years, you have taken a few photos in said rooms and posted them on your timeline, well Facebook then has a massive database of all these pictures. Facebook (or rather the machine learning behind it) can now actually work out that all these pictures are very close or very similar, so what it can do is regenerate your whole room based on those pictures. So, let’s say we took ten pictures of the room we are sitting in now, and these images were posted over however many years you have had a Facebook account, Facebook can use machine learning and basically regenerate this whole room in 3D.”

Fascinating…but what’s so interesting about Kevin’s kitchen or Susan’s conservatory?

“Memories. If you have moved to a new house, for example, or just want to return to a place in time, rather than just looking at static pictures, you can generate the whole room in 3D simply by putting on some of those ocular augmented 3D glasses. Once you put them on you are in a 3D space, and it will feel like you are back in the place you want to be. You can walk around the room, and even feel like you are walking through to other rooms. Every so often, if Facebook discovers that a certain static picture was taken it will update the 3D space to mirror the information in that image. So, you can look back on memories in the past as if they are right there.

“But it gets more interesting. Let’s say one person here in our little town of Bude has their room regenerated, and they have a cousin in America who also has their room regenerated, you can basically knock on the door of each other’s augmented reality space and go into each other’s rooms and actually see 3D objects.

“This is happening now, but it’s just the start of it because Facebook can now generate your whole living room in 3D models. You can basically just click on an object, grab it, hold on to it as if it’s in your hands, and take that 3D model back into your house and see what it looks like. Let’s say your friend has some curtains in their house that you really love, but not sure how it will look in yours, you can tap on them, pick them up and take them to your augmented reality home to see how they look before buying.

“But it doesn’t just stop there. Because you have a 3D blueprint of the whole model/object, and because we have 3D printers now, you can actually print what you like, because you have the spec of that model. You can basically print any object taken from any augmented space in the world. Obviously, the downside is that the more advanced this gets the more advanced hackers get, and things like gunpower and generating 3D weapons could become more common.”

[For a more in-depth description about this form of augmented reality, watch this video (about 29 mins in) presented by Mark Zuckerberg].

I think you will agree that that is incredible and terrifying at the same time. Whether you deem it as good or bad, it is a truly remarkable advancement in technology.

My next question for you is, ‘What new app/technologies out there are your favourite at the moment, and why?’

“For me, it just has to be machine learning. You can just do so much with it!”

Can you explain what routes you could go down with it?

“There are two routes you can go down. There are supervised machine learning and unsupervised machine learning.

“An example of supervised machine learning is when, say I want to find a picture of you amidst all the data online, I can teach the machine to only look for you. I can have a database with about fifty-thousand pictures of you that have been sorted and given labels, taken at different angles and in different lightings- and the more images and the more data I give the machine, the better it is at predicting. Because you have your cloud machines and cloud processing, it’s just really fast. And if you have a real-time camera, I could analyse the pictures in real-time and the machine will be able to work out which section of the photograph is you.

“But, of course, if I can do that then so can other people and businesses…it’s getting more and more available…so you need to know about it and be prepared for it.

“That is an example of supervised learning, because I’m basically telling the machine what to look for. Unsupervised machine learning is, essentially, when the machine works out different things itself. So, let’s say you have a picture of a flower, and you want to predict what type of flower that is, machine learning, if it is unsupervised, will actually try to work out the features in that picture and from those features try to name/categorise the flower. It will look at qualities such as the size, length and colour of the stem, the size, colour, and shape of the leaf, angle of the petal, shape of the flower head, and so on. The more features you give it to look for, the more it picks up.

“But because it can pick out all these features by itself it also has debates with itself; ‘Does this feature fit? Is this feature the same?’. And the thought of a debating robot is one that can put people on edge.

“A while back, I remember reading about how Facebook had a similar issue when they built two robots [Alice and Bob] on unsupervised machine learning and they ended up learning off of one another. Whether this was Facebook’s early days and they were still learning about it or not, I can’t remember, but because the two robots were able to learn from one another through deep learning, they (it) developed a language that only they could understand, humans could not understand it, so it all had to be turned off!”

[To read more about the ‘Facebook AI Robot Shutdown Incident’ click here.]

Unbelievable! Thank you, Dean, for another truly insightful interview.

 

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 (This interview has been lightly edited, for the purpose of clarity.)

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