Rob at Pint of Science 2018

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So, tonight finds me in Furley and Co in the middle of Hull. I was there to give a talk about the Robots to be most afraid of. I was second on the bill, the first speaker was Dr Stephen Burwood, a lecturer on Philosophy, talking about Science and Human Nature. It was really interesting to hear a philosopher's take on science, and where it fits in. I learned a few new words including the word "aporia", which means an "impasse in reasoning".

The central tenet in Stephen's talk was that we seem to have used the scientific viewpoint to prove that we are really just a very clever kind of ape, but this leaves us with a really big thing about us that just don't seem to have a scientific explanation for; namely the things that we do that make us human.

Is there a scientific explanation of why we have things like good and bad, morals and stuff like that. If there is, then where is it? If there is no explanation, then what does that say about the scientific method? Deep stuff. Great exercise for the brain. 

Then it was time for me to do my stuff. I talked about my worries about machine learning, that we are building tools that will be making decisions for us based on potentially shaky reasoning and dodgy stats, and that we are using software in situations where an ethical framework is urgently needed. It was interesting how Stephen's discussion on reasoning collided nicely with my observations on Machine Learning. More great questions, more great discussion. 

Thanks to Phil for inviting me, and the audience for being awesome. I mentioned a few things in the talk that I'd link through to in the blog. Here they are.

One of the most accessible books on philosophy that I've ever read is Godel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter. You can track down a copy on Amazon here

The video of the Google software agent booking a haircut and using umms and ahhs in its speech is here

The Universal Paperclips game is here

You can find the Hull branch of the British Science Association here

Enter FameLab - I have

I've signed up for FameLab. I think you should too. Being able to handle yourself in front of an audience is a terribly useful skill. If you are going to start, it's best to start small. FameLab gives you three minutes to fill with something scientific that you feel passionate about. The Hull heat is on the third of February 2016 and you can find out more and sign up here

I'm going to speak about why Computer Science is the most important subject of all. Should be fun. 

Hull Knowledge Factory Student Talk

 At least now I know what happens if I try to use the Panorama feature of my camera to take a picture of the audience. Sorry if you were cut off.

At least now I know what happens if I try to use the Panorama feature of my camera to take a picture of the audience. Sorry if you were cut off.

I did a talk for a bunch of Knowledge Factory students today. These are folks who will be joining us at the end of the month as students, but have come along early to spend a few days getting a taste of university life.

The subject of the session was the joys of "Making Stuff" and it was great fun. Thanks for being a lovely audience folks. During the talk I mentioned some bits and bobs and I said I'd post references for anyone who fancies following up on the things I talked about.

Arduino

Arduino is the name of a family of embedded computers of different sizes. These are the kind of computers that you would put inside a device to control what it does. I use them in my wedding lights and other gadgets that I've made. You program them in C using a very easy to use framework that you can download for free from the Arduino web site.There are versions of the framework for Mac and PC. You put a program into the Arduino device and it runs that program each time the power is switched on.

You can buy Arduino branded devices but they are a bit pricey. It is much cheaper to go onto eBay and just search for Arduino. A company called Sintron makes some very nice kits of parts to play with, these start at around 30 pounds. Once you have the kit just search the web and you'll find loads of libraries, sample code and videos to get you started. 

If you want some books to read about the platform I'd look for books by Simon Monk. He has written some good Arduino primers, plus a few other fun books. 

Programming 

There is no such thing as the best programming language in the world, but I quite like C#. You can get a free C# book, plus a lot of teaching materials and sample programs, from here

If you want to learn some Python (and why not, it's great) we have a course of sorts here

3D Printing

My 3D printer is an Ultimaker. I call her Una and I made her from a kit a few years ago. You can find all my 3D printing posts here

Blogging 

Bloging is a great way to practice writing and maybe even make a name for yourself. I did a Rather Useful Seminar about blogging. You can find it here

Intelligence, Robots and Big Data

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What do Wal-Mart, Google and Lady Gaga all have in common? They are all fans of Big Data.

I didn’t know that until today, when Prof. Ping Jiang, who has just joined the department, gave his inaugural lecture. Prof. Jiang was talking about properly large amounts of content. Google create around 25 Petabytes of data every day (that’s a  1 with fifteen zeroes after it). Wal-Mart are registering over 1 million customer transactions an hour. And Lady Gaga (or more probably her manager Troy Carter) are getting input from 31 million Twitter followers and 51 million Facebook fans when they consider what to do next.

Big Data holds useful nuggets of information and lets you do do lots of powerful things. But the problem with big data is that it is, well, er, big. And we are not talking about sheer size here, we also need to consider the rate at which we are adding to the data, and the speed that we want to get useful things from the raw numbers.

It seems that the best way to decide when you are dealing with big data is when conventional techniques break down. If it would take your network of servers several hundred years to deliver the result of one query on your data set, then you are dealing with big data. And the only way to really deal with this is to divide and conquer by spreading the processing around as much as you can, and doing the maximum amount of work you can when you first get the data in.

Prof. Jaing took as an example the problem of machine vision, in the context of robots that can navigate around autonomously. This is a complex problem, with huge amounts of data coming in from the robot’s visual sensors alone. An intelligent robot would need to be very intelligent indeed just to be able to find its way from one office to another.

But if you spread the vision sensors around the building, getting them to perform all the motion and object tacking, you can reduce the intelligence that you need in the robot itself and lose a lot of complexity. Your robot can move a lot more confidently, as the systems controlling it can “see” much further ahead and react to changes in the environment. You are dealing with the big data coming into your system by processing the raw information as it arrives and converting it into a useful form that could be shared by all the devices navigating in an area.

It’s early days, but it did look to me like this did hold the prospect of actually having useful robots working with us.

Fascinating stuff.