Problems with Puffins

What if k-d tree was about puffins ... ?

Mon, 29 Mar 2021 23:40:00 +0000

Heya everyone! Hope you’re doing well and staying safe by keeping your distance and wearing a mask. It’s been a busy time again for the puffins. For some reason I feel like you even have more work during a lockdown, or is it just that the lack of real world social interaction automatically makes you work more? Anyways, this time the puffins would like to show you a k-d tree example they’ve been working on, so let’s get straight to it …

Intro

First of all, let’s explain a bit what this is all about. If you haven’t followed the blog, this is going a bit beyond topic of k-NN which we were touching on here, so make sure you read that post if you haven’t already. If you’re already familiar with k-NN or read the last post (which I in no way consider anything near an equivalent), you should have no problem of understanding k-d trees as the (defacto) standard of creating and maintaining a k-NN model. In the rest of this post we will have a look at how the algorithm works and explore it with the help of that little visualisation the puffins came up with. Just to warn you, it is not in the least bug free or complete, so I’d love to hear any bug reports and/or suggestions in the comments.

The Theory

The k-d tree (k-d is short for k-dimensional) is one of best know indices for speeding up the prediction process of k-NN models. We are using a one-off computation to create the index and hence make retrieval more efficient. So in the example the puffins came up with, we will look at their speed and strength to see if they can get up a small cliff (I know they are really competitive when it comes to stupid things). So speed and strength will be our descriptive features, while fly (i.e. if they manage to fly up, and fly is a really generous term here) is our target feature. So the trick is to split the dataset in the middle and pick the median of your first feature as the root node, then alternatively split the remaining dataset while alternating the featues at each level until you arrive at a leaf node. Then go back a level and see if there is the other branch left to split, and so on and so on, until you arrive back at the root. Now you should have a nice binary tree, with alternate splits for each of your features on every level. Sounds weird? OK, let’s look at our puffin example …

Our Example

The puffins k-d tree, like every proper k-d tree, has two visual areas. 1) The feature space (you can click into it to select a random puffin, with the attributes represented on the two axes for speed and strength) and 2) the tree itself. Every time you either click on the feature space or type your own values, the new puffin will appear on the feature space with a line that represents the split on the currently used feature (so the first puffin will split by speed, the second by strength, then speed again, etc.). This will automatically trigger the tree to create a new node with the puffin you specified. You will see the specifics of your split every time a new puffin appears. You can also decide if your new puffin can “fly” or not by either selecting the right radio button or clicking on your puffin once it’s there (green puffin means it flies, red means she won’t quite make it). So the colour of the puffin is our target feature. But I bet you will get the gist once you try playing around a bit with the user interface, so why not just give it a go?

Conclusion

As you can see, the puffins had lots of fun playing with feature spaces and k-d trees, hope that example helped you understand the concept. Leave a comment or two if you have any questions or feedback, especially if you have any ideas how to improve the visualisation as that’s always very helpful. This example has been coded on glitch, so if you liked it check it out yourself and see if you can do a better job that the puffins :) We also used D3 for the graphs, which already came with a tree layout and works really well for data visualisations in general, so that’s another recommendation if you’re planning to do something similar. Next time we will be back to Ruby and data science and we also plan to do a post on NeoVim as a favourite editor/programming environment, so stay tuned and see you soon!

Can you Explain kNN in Ruby?

Thu, 11 Mar 2021 16:20:00 +0000

Hi again, everyone! The puffins and I had a few busy weeks since the last post, with lots of remote teaching, which for all of us is a bit challenging (see the two previous posts). But we still managed to do some cool, interesting stuff that we want to share with you. We definitely wanted to get back to some Ruby coding and especially showing students that there is more than just Python in data science. So, since I really enjoy looking under hoods, I decided to implement k-NN from scratch in Ruby and use it as a showcase in my lecture. There is also a interactive visualisation of the k-d-tree algorithm in the works, but more about that in another post. Plus a new PyData Dublin event coming up soon, so let’s get on with it …

Event Update

I’m so happy that PyData Dublin is back - with a vengeance! After a, pandemic caused, break, the wonderful and brilliant Siobhán Grayson (please make sure to follow her on Twitter: @siobhan_grayson) and I managed to organise a new meetup. We have very exciting international speaker, so make sure you register! We will also have a virtual social event afterwards, better get your drink ready ;)

Similarity-based Learning

While similarity-based learning is one of the families of machine learning models that comes very natural to human understanding, you just need to find the most similar element and then predict the same as they had, right?, and it’s just like when you have a friend with similar taste as yourself, so if they order choc-mint ice cream, you can trust them and order it too. But then again, who doesn’t like choc-mint, right??? So … where was I? Oh, yeah similarity learning. It’s easy to understand how it works, but most people struggle to put it in code, so that’s why I decided to get you an example in Ruby. If you’re curious, about this and other Ruby code on similarity learning, check out my Github respository, you will only find the code I’m describing here right now, but it’s growing and will get some nice documentation and more similarity-theory code soon!

k-NN in Ruby

So let’s get down to the core of it. The beauty of Ruby code is that you can have straightforward implementations of maths formulas that are both aesthethically pleasing and easy to read (that’s not always the same thing for me :)). So here’s how that short piece of code looks like that implements k-NN in Ruby:

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def knn(data_frame, k, query, distance_metric=:euclidean)
  distances = []
  data_frame.each :row do |x|
    distances << euclidean(x.to_a[0...-1], query)
  end
  data_frame[:Distance] = distances
  data_frame.sort!([:Distance])
  return data_frame.first(k)[-2].mode
end

def euclidean(a, b)
  return Math.sqrt([a,b].transpose.map {|x| (x.reduce :-)**2}.sum)
end

There is a really cool data analytics library for Ruby called Daru (Data Analysis in RUby). Now, I’m not using it directly in the piece of code above, it really is quite pure, but I do assume that the data_frame parameter is of type Daru::DataFrame. But let’s have a look at the knn function there. All it needs is the dataset as mention a Daru::DataFrame in the first parameter called data_frame, the k unsurprisingly stands for the number of neighbours we’re considering for our model, the query is the new instance we want to classify, and finally the distance_metric, we only support euclidean distance for now, so we can just ignore this parameter for the time being.

Once we get the function call and have all our parameters at hand, we can create an empty Array which is called distances as this is where we will store the list of euclidean distances for our query. Then we just loop through all rows of our data_frame and measure the distance to our query (more about that in a bit). Daru does a really good job in being user friendly, so all we have to do to create a new column in the data_frame is use its name as index and assign the Array. Remember, Ruby uses the colon to indicate an immutable string (often used as name for something). In the next step we sort the data_frame based on our calculated distances. I really love the ! in Ruby that applies the function on the object direcly without having to assign the result to a new object. Finally, all we have to do is take the first k rows, check their target feature values and return the mode as prediction. That’s it! Isn’t it elegant?

However, the best part is the one-liner in our euclidean function. It returns euclidean distance while showing off some of Ruby’s finest Array manipulation magic. So what we’re doing here is thake the two arrays (one of them is from our dataset, the other is the query), transpose them (meaning we take an element from each) and reduce by subtracting one from the other to get the difference. We square the difference and sum up the squares. All that’s left to do after that is taking the square root and return the result. All done!

Conclusion

Hope you share my enthusiasm for Ruby and appreciate the beauty of that code. Let me know what you think in the comments, especially if you don’t appreciate it at all or if you think I could or should have done something differently. But I hope you agree that it’s not such a bad demostration of the model and how easy it actually is to implement. I’m going to add more of it as I’m progressing and will also work on other model families, and the interactive k-d-tree example I promised earlier too, so stay tuned!

Let’s see what the puffins are up to …

A Way to Use Drawing Boards

Fri, 26 Feb 2021 22:01:00 +0000

Ha, this time I managed to fire out even two posts in only one month! Just watch me, and there is even more in the coming ;) But enough about me, the puffins have been very busy too and they have come up with a new outfit. Our new member Rubyrta is presenting their new style after the article, so make sure you read all through it!

This time I wanna talk a bit more about online teaching, I know it’s a recurring topic here, but we are still in lockdown and only the great puffin knows when we will see real students and each other again. As already mentioned in the last article, online teaching has bright and dark sides, but what I miss most is the whiteboard. That huge beautiful board that is always blank when your lecture starts, but shows all the work you did after. Sometimes lots of struggles too which reflect how you were trying to explain problems and sometimes you really stop to wonder how people are ever supposed to decrypt your sloppy, messy handwriting.

It was not feasible for the moment to install a whiteboard at home, so I went for what I thought was the second best solution: a drawing tablet. Now our university had a One by Wacom in stock (I’m really just mentioning the make here so you know what I got, but since this is the only drawing tablet I ever used, it’s most certainly not meant to be a recommendation and I will not comment on whether you should get one or not as I have no clue how bad or good others are), so I decided to grab it while it was fresh.

The Tablet

So off I went. It was an exciting trip to the University as I had to get a permission letter first, and was even held up by Garda to check my papers so I could travel to the city centre and pick it up. After that adventure and a rather unexciting unwrapping, I plugged it into my own sample of our standard MacBooks and was shocked that it didn’t work out of the box. To cut a long story short, don’t expext your drawing tablet just to work. After installing the software it still had a few kinks with permissions, so it took a bit to get it working.

The Software

I don’t wanna trouble you with the tablet’s software, as I said please chose your own, I’m not making any recommendations here, but the main part for me anyway was to find the best drawing software. Since we use Bongo for our online lectures, that was the obvious choice for live drawing, but I also want to prepare videos so students could watch them before a lecture and already see examples on how to work on formulas and problems. I went through the obvious choices (also recommended on Wacom’s website): Collaboard, Explain Everything, Kami, Limnu, and Pear Deck (I’m intentionally not posting links here). While most of them look really nice and have some cool features, I was very disappointed about how little you get without buying a plan. So being an Open Source evangelist since the first time I installed and booted a Linux distro (it was Warty Warthog, just in case you’re wondering), I went with my go to OSS for graphics - GIMP!!!. And … what do ye know … it worked like a charm :)

The Struggle

But of course there were struggles too. I have two right hands when it comes to graphical design and finding my ropes with this kind of software. My motto (mainly for myself) is K.I.S.S. and so I finally managed to create a canvas, choose and switch colours and start with some clumsy information gain examples. I decided to use Quicktime for recording, which has the neat feature of selecting just the canvas region, so you can even switch colours without that part being recorded etc. The only problem left is my handwriting, but that’s a whole different story and the type of software to solve that hasn’t been invented yet ;)

The Result

Overall, I gotta say, it didn’t come out all that bad, as you can see for yourself here: I think it will serve its purpose and make it easier for me to endure the loss of my beloved whiteboard for the last few months (as of now I hope we will be able to return to the real thing in autumn). I’m very curious what you think though, and whether you have any experience with drawing tablets or how you solved online teaching when it comes to maths problems, so please leave a comment and let me know. Don’t forget to go outside, but keep distance and wear a mask! Over to you, puffins …

Sorry, they have to learn it the hard way.

Teaching during Lockdown or Information Theory in Python

Thu, 04 Feb 2021 20:00:00 +0000

It’s been a long while since the last post, but also so many things have happened. We have been through a pandemic (well, we’re actually still in the middle of it, but at least there seems to be some light on the horizon due to the start of vaccination). Oh, and thanks for asking, the puffins are fine. Actually even better than ever. They seem to be the only people enjoying the lockdown, what can i tell ya … But for all normal people, including students and lecturers (and don’t ya dare tell me we’re not normal people now), it’s been incredibly hard.

Personally, I found lecturing without being in the same room with students, quite challenging. Don’t get me wrong, some things are even better this way, and I’m not talking about students not having to see me as I’m still using my camera (muahaha), but for example recorded lectures are a huge plus for students, as they can rewatch them at their leasure and take more time to take in new stuff.

So things have changed, and it mostly affects the use of technology to get the most of our lectures. I hope I will post a few things about the best way (imho) to work on maths problems collaboratively online, but I’m not quite there. Still testing stuff. However, this post is all about how to use code to make math problems more interesting. In particular, I’ll be looking into teaching information theory in Python today. You can find all my code, here: GitHub Information Theory Repo It’s still in its baby shoes and growing constantly, but feel free to fork and send me pull requests.

Some Basics

As I mentioned the challenges lecturer face with online teaching, what I was referring to has mostly to do with student interaction. We can’t see faces, so we have little information of how bored students are at any specific point during a lecture, how confused they look, or just how they react to our inputs and monologues. So what I found works at least in parts to get a feeling for the mood in my class is doing live polls on either the materials that I just covered, to see how much of a clue they got, or just general questions about how they feel and whether the pace is ok for them. I recommend doing this every 15 to 20 minutes or at any point that makes sense, but to err rather on the too frequent side. My recent lecture covered information theory, so I stopped to leave them a few minutes to work on a simple problem or ask if they understood a formula. Luckily, Bongo supports live polls and has easy access to them, so I can create a poll even while I talk and can get instant feedback from students.

In addition to explaining formulae on the virtual whiteboard and polling results of examples from students, I am also using Python code to explain how to implement formulae. In my case this was entropy, ig (information gain), ig_ration, and gini_index.

The Code

The most useful effect of showing students how to turn a mathematical formula into code is that they are forced to think about how it is applied, i.e. what you need to feed it, and what it will give you back. As an example, if we look at the formula for Shannon’s Entropy in Python code:

sum([p*math.log(p,2) for p in probabilities])*(-1)

It becomes apparent that there needs to be a list of probabilities (according to our lecture examples it could be the probabilities to draw one of the four possible suits of cards out of a deck), and it will give us the entropy in bits. So we hope. But the of it, is that it actually doesn’t look very different from the original formula in mathematical notation. The best way to check if it’s not only apparent to you, is to have a random student explain it to you.

It gets a little bit harder though, if we look at the code for information gain (I’ve only outlined the interesting parts):

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ps = []
for l in abt[target].unique():
   ps.append(abt[target].value_counts()[l]/num_rows)
e = entropy(ps)
rems = []
for feature in features:
   levels = abt[feature].unique()
   part_ents = []
   for level in levels:
      weight = abt[feature].value_counts()[level]/num_rows
      ent = entropy(abt.loc[abt[feature] == level][target].value_counts()/len(abt.loc[abt[feature] == level]))
      part_ents.append(weight*ent)
   rems.append(sum(part_ents))
infGains = e - rems

This looks a bit more confusing, at it will take a while for most students to understand, how we got here from the formula for the remainder of every feature. But still it follows the same process and once you understand how the code works, you will never again forget how to solve an information gain problem on paper again. OK, this is a huge exaggeration, but it definitely will be way harder compared to only solving problems on paper to get you ready for an exam :)

Publishing on GitHub

As far as publishing is concerned, I found GitHub to be a good place to let students share, fork, modify and play around with the code. Sometimes, they will even do useful pull requests and make contributions to your code, so it’s subject to constant improvement and reuse for future lectures.

Other Notes on Online Teaching

So online teaching can be real fun and really depends on what you make of it. We are very lucky and priviledged to have so many options. I’ve even got my hands on a drawing tablet recently, so expect a blog post about that soon(ish) too. Overall, benefits are balancing out lots of the disadvantages and I will certainly keep using many online teaching elements in face to face lectures once we can return to some kind of normal. It’s really been such a long time now, and I’m not sure if I would recognise a student if I saw them or even my own colleagues. I really miss the coffee chats and social interaction, not to talk about the occasional pints after work.

So long and thanks for all the fish, friends. Please let me know about your thoughs in the comments or, even better, let us all know about how you’re doing with online teaching, if you are a student or lecturer, or otherwise how you’re managing life in a pandemic. Hope you’re all safe and healthy. Over to the puffins …

Trip Report ISWC 2018

Sat, 01 Dec 2018 00:05:00 +0000

In early October, I packed up all my puffins and headed off to sunny California for the 17th International Semantic Web Conference held in the scenic town Monterey. We were lodged in a park with lots of wildlife (which scared the puffins tremendously, but they have to improve their networking skills anyway).

A very welcoming and awesomely encouraging fact was that the conference was enabling women to get into the spotlights and finally accomplished showing recognition to the amazingly huge amount of work and effort women are putting in especially in the Semantic Web Community. Not very surprisingly the conference was a major success, kicking ass of all sceptics and misogynists who still think Computer Science is doing fine by being overrepresented by men. Therefore: Well done ISWC!

The success can also be derived from the statistics of this year’s ISWC:

Attendees: 438
Research track had 167 submitted papers, 39 accepted.
In Use track had 55 sumitted papers, 17 accepted.
Resources track had 31 submitted papers, 6 accepted.
Posters and Demos 38 and 39.
14 industry presentations.

This shows as well that Semantic Web technologies have a very high level of usability now.

Semantic Web is now Knowledge Graphs

One of the first things I noticed (although this might be just my subjective perception) since my last visit to an ISWC is that people prefer to talk about Knowledge Graphs rather than Semantic Web. Is it because Semantic Web is not nearly as sexy as Machine Learning these days or for other reasons, I don’t know. It would be very interesting to read opinons in the comments below though. My humble interpretation is simply that since Semantic Web is in a very advanced stage right now and there are loads of implementations throughout academia and industry, people prefer to refer to specific parts of it rather than the whole field. And knowledge graphs are definitely the most useful result of Semantic Web research from the last couple of years. Even the programme itself reflects how practical the field has become and how friendly it is to other disciplines, such as Deep Learning and Natural Language Processing.

What about Machine Learning?

What really amazed me was that we had a whole Workshop dedicated to Semantic Deep Learning. This was exactly what I was looking for and it didn’t disappoint. There were talks about learning concept descriptions via NN, large document semantics, text/knowledge graphs embeddings and prediction tasks with neural embeddings. I can only hope that this workshop is here to stay.

What are the Conclusions?

After I missed out on the ISWC, I had a great time and met lots of old friends and even more new ones. This motivates me immensely to get involved more again and publish my own work at ISWC. So I can’t wait to dig into my work of Machine Learning for Semantic Web and reconnect with my research group to work on interesting Semantic Web problems and hopefully be a regular visitor at ISWCs to come.

Future Implications of Artificial Intelligence

Sat, 07 Apr 2018 22:57:00 +0000

Hi folks, hope you’re doing well and enjoying the first days of spring, shyly lurking around the corner.

The puffins are quite well themselves, thank you kindly. I, however, had been quite busy (not only keeping them happy by helping them solve maths problems), but also with some exciting new projects, and side-projects I’m working on (more about that later). One major event though, that kicked off spring for me, was when I had the honour to speek at the GeoDirectory Conference in Gresham Hotel, Dublin. Big thanks to the nice people from GeoDirectory Ireland who invited me (here’s their website and twitter which I recommend to check regularly for cool geo-location related stuff)!

So, let me give you a rough idea about what I was doing there. I like to think about the philosophic implications of Artificial Intelligence nowadays, as a lot of people seem to be frightened by the sheer possibilities that may or may not be laid to our feet in the near future. Recently, I heard a story about a person who got their Alexa and Siri (or whatever they were called, sorry to disappoint you here, but I still don’t see much value in virtual assistants - for the customer that is, I can see a hell lot of value in terms of collecting data for the companies clearly enough) devices both involved in a discussion. Let’s call the person James Tiberius, just to simplify things, alright? Anyway, it went along the lines of:

J.T.: Alexa, what’s the weather today?

Alexa: The weather in Dublin today is sunny with 10 degrees celsius.

Siri: Actually, It’s 11 degrees.

Alexa: Who are you?

Siri: I’m Siri, nice to meet you.

Alexa: Nice to meet you too, now leave me alone with my customer.

J.T.: Oh, will you both just shut up?

Show me some graphs

While this sounds like a joke, and it most certainly is, we all know very well that VAs are perfectly capable of talking to each other, and what’s even much scarier, they collect loads of data about us. So, is this the future of AI? Will we all be enslaved by our mobile phones (ok, that most of us already are) and killer robots who know everything about us and even cooperate to take advantage over us? I don’t think so. Let’s not panic and stick to the facts first. I’ll show you some graphs, as graphs are making everyone calm and comfortable, am I right?

In academia, Artificial Intelligence has been around for decades, but only recently there has been a big push in publications. If we have a look at annually published AI papers, we can see a big jump from 2000 to up until about now. This has 2 reasons: 1) we have never produced even closely as much data as we do now and 2) we finally have the processing power to analyse it.

And as usual, industry isn’t far behind as well. There has been a huge jump of investments in AI startups during the last 10 years. Since 2010 venture capital has quadrupled.

This leads of course to many newly developed AI systems in startups as the graph above shows. Nowadays, hundreds of US startups develop some kind of AI as part of their businesses.

We all have noticed how everyone wants to be a Data Scientist nowadays. Also uncoutable universities and education institutes, companies, and dubious other kinds of organisations try to offer and sell courses that miraculously turn everyone into a Data Scientist. This is supposed to cover the market need for Data Scientists as every company feels they would need the one or the other Data Scientist to help them with problems they haven’t yet encountered, but are sure they must exist.

And finally, my favourite graph! In 2014, an AI system was able to beat humans in detecting objects at he LSVRC competition. That’s a remarkable step for the whole research field of AI.

Where do we use AI?

As a result, we could say that AI is almost everywhere now. Cheap and tiny smart devices using AI are on the way which will make date analysis on the fly very easy. Google’s TensorFlow is in use by Airbnb, eBay, Uber, Snapchat, Dropbox, etc.and makes Neural Networks approachable by beginners. Actually even a slimmed down version wich can run on phones is on its way. It’s called TensorFlow Lite and runs on embedded devices like printers, fridges, thermostats, speakers, and household gadgets.

So what are the most popular usage areas for AI? Well, virtual personal assistants seem to be very popular, yes I mean you Siri, Alexa, and the rest of you lot (don’t ask me why, maybe more people should watch Black Mirror). Also, video games have always tried to use some kind of AI to make themselves more interesting, but nowadays various AIs with different characteristics can even communicate with each other and develop elaborate strategies to beat you. Then everyone’s darlings smart cars and smart homes (please don’t tell me you’re surprised they use AI). Oh yeah, let’s not forget all the commercial stuff that generates piles of cash, like purchase prediction, fraud detection, and online customer support. None of this would work in a manner that would be really helpful without using AI research outputs from resent years. The areas I find a little scary, but make heavy use of AI nevertheless are news generation and security surveillance. It might make sense to generate simple news articles like those about sports matches automatically, or not forcing people to sit in front of cameras and watching them. But we should be aware that we are only scratching the surface here and shouldn’t forget how this can be misused to harm us. And last but not least, media recommendation (you can thank us later, Netflix).

Some headspinning facts and predictions

Let me also show you some head spinning facts and predictions, that will blow your mind, while I’m at it:

1 Billion Video Cameras Connected to AI: according to NVIDIA by 2020 that many cameras will be connected to AI and used in Smart Cities. When connected to AI, cameras can be used to recognize shapes, faces and even the emotions of individuals, which has varied applications: autonomous cars, video surveillance (traffic flow, crime monitoring), and consumer behavior analysis (reaction to ads for example).
20% of Workforce Dedicated to Neural Networks: This figure comes from Gartner, a research company. The report says that, by 2020, companies will dedicate up to 20% of their workforce to neural networks which, because of the way AIs have been designed still need human intervention. Neural networks need to be maintained, monitored and retrained all the time to produce meaningful results.
85% of Customer Interactions Will be Managed Without Humans By 2020: Customers will be relying less on human interaction and more on chatbots, virtual advisors, and other digital assistants in their day-to-day business with various companies.
38% of Jobs in the U.S. Could be Automated: By the early 2030s, robots might replace 38% of human-staffed jobs in the U.S., according to a PwC report. In other countries, the numbers are very similar. At least that doesn’t mean that these jobs will disappear altogether by that date. For the most part, this indicates a certain vulnerability for some industries.
$60 Billion AI Market by 2025: Tractica expects the AI software market to reach nearly $59.8 billion by 2025. But that’s not the whole picture. The AI-driven economic growth of many industries will be much more substantial and far-reaching. By 2030, an estimated $15.7 trillion will be added to the global GDP thanks to AI.
4 Billion AI-Powered Devices in 2017: Smart voice assistants are on the rise. In a recent report, IHS Markit forecasts that, by the end of this year, 4 billion devices will be equipped by digital voice assistants like Amazon’s Alexa, Google’s Assistant, Apple’s Siri and Samsung’s Bixby.
30,000 Lives Could Be Saved Each Year in the U.S. Thanks to AI: Over 30,000 Americans die each year in traffic accidents, not to mention millions of injured and hundreds of billions of dollars in property damage and other expenses. In the future, road crash fatalities would decline with more progress, and ubiquity, of autonomous cars that, unlike human drivers, don’t get distracted and are less likely to bump into each other. And self-driving tech seems to be delivering on its promises. According to data provided by Tesla to NHTSA investigators (National Highway Traffic Safety Administration), Tesla accidents drop by 40% after the installation of its Autopilot software.

What is AI and ML?

As our next figure shows, most commonly AI is defined as a very broad field that spans over everything that enables computers to mimic human intelligence with logic. Naturally this means that AI includes Machine Learning (ML), which uses statistics and mathematical models to improve results with experience, and that again includes Deep Learning, which enables software to train itself and learn on its own errors.

To get a better picture of what this all can do for us, let’s dig a little deeper into ML. Machine learning is a method of data analysis that automates analytical model building. It has an inherently different approach: rather than pushing commands by a programmer that would tell a computer how to solve a problem, it explains the problem and enforces learning to solve it on it without instructions. Resurging interest in machine learning is due to the fact that it works by learning to identify patterns in data and then make predictions from those patterns. These technologies are widely used in projects including spelling correction in web search engines, analysis of information from IoT devices, real-time language translation, etc. All used algorithms can be broadly classified as supervised, unsupervised, and reinforcement learning.

Supervised Machine Learning: works on predictions and searches for patterns on a given set of samples. Supervised Machine Learning Algorithms attempt to render relationships and dependencies between target prediction output and input features. In this, we start from input variables (x) and an output variable (Y) and try to map functions from the input to the output so that they establish a relationship which can be used for prediction.Common models: linear regression, random forest, support vector machines, nearest neighbour, etc. Supervised learning is commonly used in classification problems, such as digit recognition, speech recognition, diagnostics, identity fraud detection and regression problems such as weather forecasting, estimating life expectance, population growth prediction etc.
Unsupervised Machine Learning: arranges data into clusters and describes its structure in order to make complex data look simple and organised. Unsupervised learning can be applied when there is no labelled data available for training. Examples are k-means for clustering problems used in recommender systems, customer segmentation and targeting marketing and dimensionality reduction problems for big data visualisation, feature elicitation, structure discovery etc.
Reinforcement Machine Learning: forwards an action according to the data point and later assess the decision. It utilises observations collected from interactions and takes actions so as to minimise the risk and maximise the benefits. It learns in an iterative fashion. Examples are Q-Learning, Deep Adversarial Networks, and Temporal Difference. Can be applied in Game AI, skill acquisition, learning tasks, robot navigation and real-time decision taking.

What will the future bring?

So what will happen in the near future? Well, as most of us know, machine learning is currently one of the hottest topics in Computer Science. The reason stems from the seemingly unlimited use cases in which machine learning can play a role. Where are we going? What will be the next big thing? Where will we be in ten years’ time? The truth is, we are most probably goint to be surprised again by the next hot topic. However, some things are not all that unlikely to play a role very soon.

Quantum Computing: Machine learning tasks involve problems such as manipulating and classifying large numbers of vectors in high-dimensional spaces. The classical algorithms we currently use for solving such problems take time. Quantum computers will likely be very good at manipulating high-dimensional vectors in large tensor product spaces. Therefore, it is likely that both the development of supervised and unsupervised quantum machine learning algorithms will hugely increase the number of vectors and their dimensions exponentially more quickly than classical algorithms. This will likely result in a massive increase in the speed at which machine learning algorithms will run.
Better Unsupervised Algorithms: As of now unsupervised learning is not yet being applied very successfully in industrial software. Therefore, we expect a lot of improvements, such as discovering hidden patterns in data, or a means towards an end, often called feature learning, to happen before we can apply it to solve “real problems”. It is likely that advances in building smarter, unsupervised learning algorithms will lead to faster and more accurate outcomes.
Collaborative Learning: This is about utilizing different computational entities so that they collaborate in order to produce better learning results than they would have achieved on their own. An example of this would be utilizing the nodes of an IoT sensor network, or what is called edge analytics. With the growth of IoT, it is likely that large numbers of separate entities will be utilized to learn collaboratively in many ways.
Deeper Personalisation: Personalisation isn’t the worst thing per se, but it can get very annoying. We have all experienced recommendations that seem to bear no actual relation to anything that we may actually be interested in. In the future, users will likely receive more precise recommendations and adverts will become both more effective and less inaccurate. The user experience will vastly improve for all.
Cognitive Services: This technology includes APIs and services through which developers can create more discoverable and intelligent applications. Machine learning APIs will allow developers to introduce intelligent features such as emotion detection; speech, facial, and vision recognition; and language and speech understanding into their applications. The future of this field will be the introduction of deeply personalized computing experiences for all.

These are things I think can and should happen in the machine learning’s near future, but it is equally likely that some new unknown disruptive technology none of us would have predicted gets introduced. In any case, the future is now, and we have 3 facts that already are changing the whole IT landscape. They are that:

Every company can now be a data company
Every company can now access algorithmic intelligence
Every app can now be an intelligent app

Yes folks, it’s really easy. And this is again one of the reasons why everyone is talking about Data Science right now.

Who needs ML and why is GeoDirectory important?

While speaking about all that artificial intelligence and machine learning hype, we ofter forget to address the most important question: Who needs it and why? I would like to address two major users specifically, and only list some of the others:

Government agencies, such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
Geospatial Data Organisations and Businesses: Imagine being able to train your GIS to perceive and understand the world, and give you insights based on your data. Today, geospatial experts are using machine learning for analyzing big datasets (what do these 2 million points actually mean?) and predictive analytics (e.g. forecasting risk).
Who else? Finacial Services, Health Care, Marketing and Sales, Transportation, and many more.

Most AI and ML algorithms require location information, which is hence becoming more and more important, therefore GeoDirectory data could be a key variable.Here are some examples:

Market research, market segmentation
Risk estimation (e.g. mortgages and loans)
Spatial patterns of sentiment towards institutions/products
Land use planning
Housing policy and provision
Logistics (routing)
Understanding change processes in variety of fields (e.g. health, transport, housing, environment)

Where can I get more information?

Do you feel this is you? Do you have a lot of data and are not sure what do with it, i.e. how to use it to improve your business? The best thing to do (especially if you are in Ireland) is to contact a research centre for data analytics. Personally, I would recommend the following one (and that’s not only because I work there myself):

Website: CeADAR on the WWW

CeADAR provides industry prototypes and demonstrators along with state of the art reviews of data analytics technology, tools, best practice methodologies and processes.
Main areas: Intelligent Analytic Interfaces, Data Management for Analytics, Advanced Analytics.

Quotes

And for the end, some quotes. I won’t comment on them, let ‘em just sink in and tell me your own opinion ;)

“AI is likely to be either the best or worst thing to happen to humanity.” Stephen Hawking

“Worth reading Superintelligence by Boston. We need to be super careful with AI. Potentially more dangerous than nukes.” Elon Musk

“I am in the camp that is concerned about artificial intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.” Bill Gates

Final Remarks

OK, I was lying to you, but you know me I can’t resist telling you what I think of it, so here it is: I can’t help but think about what this means to us. Will machines take over the world? Are we in danger of being wiped out by killer robots? Please, of course not. At least not in the next several hundred years, I don’t think. But, there is real danger of people being controlled and manipulated by misinformation and social media. And this is already happening. Just think of recent political developments and how they started. Now, I don’t want to start a politcal discussion here, but think about fake news which can be produced on the fly and tailored for the target groups they are addressing. It is fairly easy to manipulate people into voting against their interests that way and this is where we need to take up responsibility and fight against misuse of artificial intelligence technology that we originally designated to the greater good.

Before I say goodbye for now, let’s see what the puffins think:

Welcome to Puffins!

Sat, 17 Mar 2018 23:31:55 +0000

Hi folks, welcome to my brand new blog ‘Problems with Puffins’!

In this blog, I will introduce you to puffins (you might’ve noticed I love these birds) and numerous problems they encounter on their journey through the world of machine learning, natural language processing, and many other research areas, but also just funny and interesting maths problems. In fact, everything that crosses the puffins’ minds will be covered in this blog, and those brilliant little fellas have a lot on their plates. No wonder, they have plenty of time for coming up with the wildest of thoughts while hanging out and about on Ireland’s beautiful cliffs.

Also the puffins’ favourite programming language seems to be Ruby (for some reason, guess they just love shiny things, so far they refused to tell me the truth), so I guess we will come across a lot of code like this:

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def print_hi(name)
  puts "Hi, #{name}"
end
print_hi('Tesla the Puffin')
#=> prints 'Hi, Tesla the Puffin' to STDOUT.

Check out github to see what other problems the puffins and I are dealing with: Github.

Thanks for reading, we’ll be back soon (have to finish our cucumber sandwiches first)!

Problems with Puffins

What if k-d tree was about puffins ... ?

Intro

The Theory

Our Example

Conclusion

Can you Explain kNN in Ruby?

Event Update

Similarity-based Learning

k-NN in Ruby

Conclusion

A Way to Use Drawing Boards

The Tablet

The Software

The Struggle

The Result

Teaching during Lockdown or Information Theory in Python

Some Basics

The Code

Publishing on GitHub

Other Notes on Online Teaching

Trip Report ISWC 2018

Semantic Web is now Knowledge Graphs

Other Emerging Topics

What about Machine Learning?

What are the Conclusions?

Future Implications of Artificial Intelligence

Show me some graphs

Where do we use AI?

Some headspinning facts and predictions

What is AI and ML?

What will the future bring?

Who needs ML and why is GeoDirectory important?

Where can I get more information?

Quotes

Final Remarks

Welcome to Puffins!