Do Telecom Networks Dreams of Siamese Memories?

In this post I will try to make understandable a Deep Neural Network I developed lately. We are still in early stages and a lot of improvements will need to get in, but the preliminary results are positive. I have been told I am not so great at explaining things high level, so a word of warning, some part may go deep technical! So, let start with the buzz words: what I will describe is a One-Shot Learning approach using a Siamese Deep Neural Network which characterize ongoing data traffic patterns in a telecom network to identify faults in real-time.

Telecom network nodes (think piece of equipment) often suffer from recurring faults. There are things which are done by human operator, or traffic pattern exhibited by the users, or situations in adjacent nodes which can impact the performance of a specific node. Once degradation is identified, an analyst goes through the alarms raised by the equipment or the logs, figure out the issue and fix it. Some of those faults are recurring for a reason or another. Analysts probably gets better and better at identifying and fixing those, but still it takes some of their precious time. Would not it be nice if we could identify those automatically and then act to fix the problem? This is pretty much in line with the ONAP vision of a complete life-cycle management of a service. Let say it is a small part of the mechanism required to make that vision real.

The objective is to develop a Machine Learning trained analytic module for a specific set of Network Function Virtualization (NFV) components which can feed into the ONAP policy engine architecture. The analytic module monitors in real-time the NFV service levels and informs the policy engine about the NFV service status i.e. normal working status or degraded/failure mode and in such a case why it is failing.

Ideally, we want a trained analytic module which knows about a lot of different errors characteristics and can adapt as easily as possible to different network conditions i.e. nodes in different networks may be subject to different traffic patterns, but still be subjected to the same errors. In other terms, it would be nice to be able to deploy this module to supervise nodes deployed at different operators without having to retrain it completely…

For the purpose of this experiment we use as data traffic information collected by probes on the control plane traffic coming into/out of a specific node (P-CSCF (a Proxy Server) of an IP Multimedia Subsystem (IMS)). The probes are part of an Ericsson product, the Ericsson Expert Analytics and takes care of the collection and storage of the data from the NFV component. The P-CSCF is part of a test network we created for the experiment and is subject to a realistic traffic model simulated by network traffic generation servers.

The first step is to characterize statistically the traffic going through the P-CSCF and collected by the probes. In this step we create a set of about 130 statistical features based on 1 minute intervals describing the traffic. For example: Number of Registrations in a minute; Number of Session Initiations; Number of  operations presenting error codes and count of those error codes e.g. number of Registrations with return code 2xx, 3xx, … ; Average time required to complete operations; Standard Deviation of those times; etc.

A first choice is how long of a stream should be base our decision on? We decided to go with 1-hour intervals thus we use 60 consecutive examples of those 130 or so features vector for training and for predictions. We label our examples such that if for the whole period there is no error present it is “normal traffic”, or if we introduced an error during that 60 minutes period, thus the example exhibits in part a specific error then it is labelled as per this error.

To fulfil our need for easy adaptation of the trained analytic module we decided to go with One-Shot learning approach. Our hope is that we can train a Deep Neural Network which characterize the traffic it is presented with on a “small” vector (here we initially selected a vector of 10 values), akin to words embedding in Natural Language Processing (NLP). We also hope then that vector arithmetic properties observed in that field for translation purpose will hold e.g. king – man + woman = queen; paris – france + Poland = warsaw. If such property hold, deployment of the trained analytic module in a different environment will consist simply in observing a few examples of regular traffic and adjusting to the specific traffic pattern through arithmetic operations. But I am getting ahead of myself here!

To perform training according to One-Shot learning strategy we developed a base LSTM-based Deep Neural Network (DNN) which is trained in a Siamese Network framework akin what is done for Image Recognition. To do so we create triplets of Anchor-Positive-Negative of 60 minutes/130 features data. In other words, we select an anchor label e.g. normal traffic, or error X, we then select a second example of the same category and a third example from another label category. This triplet of examples becomes what we provide as examples to our Siamese framework to train our LSTM-based DNN. Our initial results were obtained with as little as 100k triplets thus we expect better results when we will train with more examples.

Our Siamese framework can be described as following: The three data points from a triplet are evaluated through the base LSTM-based DNN and our loss function see to minimize the distance between Anchor-Positive examples and maximize the distance between Anchor-Negative examples. The base LSTM-based DNN is highly inspired from my precious trial with time-series and consist in the following:

_________________________________________________________________
Layer (type)                 Output Shape              Param #  
=================================================================
batch_normalization_1 (Batch (None, 60, 132)           528      
_________________________________________________________________
lstm_1 (LSTM)                (None, 60, 512)           1320960  
_________________________________________________________________
lstm_2 (LSTM)                (None, 512)               2099200  
_________________________________________________________________
batch_normalization_2 (Batch (None, 512)               2048     
_________________________________________________________________
dense_1 (Dense)              (None, 512)               262656   
_________________________________________________________________
batch_normalization_3 (Batch (None, 512)               2048     
_________________________________________________________________
dense_2 (Dense)              (None, 10)                5130     
_________________________________________________________________
batch_normalization_4 (Batch (None, 10)                40        
=================================================================
Total params: 3,692,610
Trainable params: 3,690,278
Non-trainable params: 2,332
_________________________________________________________________

Once the base LSTM-based DNN is trained, we can compute the vector representation of each of the traffic case we are interested in e.g. Normal Traffic, Error X traffic, … and store them.

When we want to evaluate real-time the status of the node, we pick the last hour of traffic data and compute its vector representation through the trained base LSTM-based DNN. The closest match from the stored vector representation of the traffic cases and the current traffic is our predicted current traffic state.

At this point in time we only data collected for one specific error, where a link between the P-CSCF and the Home Subscriber Server (HSS) is down. Below diagram shows our predictions on a previously unseen validation set i.e. not used for training.

siameseresults.png
Siamese LSTM-based Deep Neural Network traffic condition prediction on real-time traffic.

As we can see there is quite a few small false predictions along the way, but when the real error is presented to the trained model it can identify it correctly.

Our next steps will be to collect data for other errors and train our model accordingly. As I said in the beginning this is quite early results but promising nonetheless. So keep tuned-in for more in the new year!

 

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The Fallacious Simplicity of Deep Learning: zat is ze question?

This post is the fifth and last in a series of posts about the “Fallacious Simplicity of Deep Learning”. I have seen too many comments from non-practitioner who thinks Machine Learning (ML) and Deep Learning (DL) are easy. That any computer programmer following a few hours of training should be able to tackle any problem because after all there are plenty of libraries nowadays… (or other such excuses). This series of posts is adapted from a presentation I will give at the Ericsson Business Area Digital Services technology day on December 5th. So, for my Ericsson fellows, if you happen to be in Kista that day, don’t hesitate to come see it!

In the last posts, we’ve seen that the first complexity lay around the size of the machine learning and deep learning community. There are not enough skilled and knowledgeable peoples in the field. The second complexity lay in the fact that the technology is relatively new, thus the frameworks are quickly evolving and requires software stacks that range from all the way down to the specialized hardware we use. The third complexity was all about hyper-parameter setting, a skill specific to machine learning and that you will need to acquire. The fourth complexity dealt with data, how to obtain it, how to clean it, how to tame it.

The next challenge we will look at is ze question. When we start a new machine learning project, we might have a question we want to answer. Through data exploration we might find that this question cannot be answered directly with the data we have. Maybe we figure the question is not that interesting after all. It all boils down to fast feedback. You need to explore your data, try to answer a question and see where it leads. Then it is time for discussion with the stakeholders, is it what they are looking for? Does it bring value?

There is different type of questions machine learning can answer, but it is not unlimited. Do we want to sort things in similar buckets, do we want to predict such and such value, do we want to find examples that are abnormal? In order for a machine learning exercise to be successful, you need a really specific question to answer: Is this subscriber an IoT device or a Human? Then you need proper data. Using the Canadian census data to try to answer to figure out if a mobile phone subscriber is a Human or a Machine might not work! But with the proper data we could start to explore if there is a model between some data, for example IP addresses visited, time of those visits, etc. and the fact the subscriber is a Machine or a Human.

Often the question will evolve with time, through discussion. You need to be ready for that evolution, for that change. You might have to bring in new data, new methods, new algorithms. It is all about searching and researching, trials and errors, finding new paths. Getting access to the data might be the biggest difficulty, but finding the right question is certainly the second.

The fifth complexity: finding the right question.

Through this series I have detailed five complexities to deep learning (and machine learning in general). There are many more. The machine learning “process” is not like software development. In general, it requires a lot more exploration and researching than regular software development. It requires an higher level of “artistic flair” than you would need to write a regular software application. There are other things that differentiate machine learning from software development, but I think those are the five first and biggest complexity one can face when developing a machine learning model:

  • First you need access to data, and that might not be trivial. You will also need to clean that data and ensure you have a consistent flow.
  • Second you will need to find the right question. This may require many iterations and might require new data sources as well.
  • Third, the results may look simple, but the code itself does not show everything that is hidden behind the curtain. A lot has to do with hyper-parameter setting and tweaking and there is no cookbook for this. The APIs do not tell you what values will give good results.
  • Fourth, machine learning requires specific competence. Some of those competences have to do with software development, but some other are quite different. It is a relatively new domain and the community size is still smaller than others in software development. Moreover, this is a highly in demand skill set and hard to find in the wild.
  • Finally, this is a quickly evolving domain which range from specialized hardware to specialized software stacks. In software development peoples are accustomed to quickly evolving environment, but the pace and breath at which it goes in machine learning might well be unprecedented.

I hope this series could give you a better appreciation of the complexities of deep learning and machine learning. It may look easy when you look at the results and the code that supports it, but there is a lot of things you are not given to see! Artificial Intelligence field will continue to grow in the coming years and will enter in way more aspect of your daily lives. This will require peoples trained in deep learning, machine learning and data science as this is not simply the usage of yet another software library.


Originally published at medium.com/@TheLoneNut on November 28, 2017.

IMA MAG AGE

A small pause in my series on the complexities of Deep Learning (or in general Machine Learning). Today I will try to give an honest review of the latest course I followed: “Convolutional Neural Networks” on Coursera by Andrew Ng.

I first followed the “Machine Learning” course from Andrew Ng in 2014. I really liked his pace of delivery and the way he builds up the knowledge to bring you up to speed. So, when his new nano degree on Deep Learning has been released in August of this year, I was one of the first to jump on it and I swiftly completed the three available courses. A couple of weeks back, the fourth out of five courses was released. I immediately went on the “Convolutional Neural Networks” course to continue my journey.

The course is well structured, as expected and given at a proper pace. The content is split on four weeks. The first week builds the foundations of Convolutional Neural Networks (CNN) and explains how those convolutions are computed, the mechanics. It explains its foundations in computer vision, then will detail the convolution with the padding, the stride and the pooling layers (max pooling, average pooling).

The second week looks at a few “classic” CNNs and explains how the architecture was built by adding new concepts on top of the previously existing ones. It then goes on explaining ResNets (a concept which can apply to other networks than only CNNs) and then build toward Inception Networks (yes, Inception as in the movie)

Third week introduces two new practical concepts, object localization and object detection. Figuring out where in a picture an object is, and secondly how many objects we can detect in a picture and where they each are. It nicely shows how bounding box predictions are made and evaluated followed by the use of Anchor boxes.

Finally, in the fourth week you will learn some of the coolest and funniest things about CNNs: face recognition and neural style transfer. Some important concepts introduced there are the one shot learning (another of those things which can apply to other networks than CNNs) and Siamese networks.

All in all, this is a good course. There are some glitches along the way. Some of the videos are not as polished as they could be i.e. some “bloopers” needs removal and a few errors which needs correction on the slides, but very minor. My biggest gripes is about the programming assignments. First, they are not self-contained. They right from the start refer to the introduction to Tensorflow made in the 3rd week of the second course of the nano degree: “Improving deep neural networks”. This makes things a little bit awkward when you do not use Tensorflow day to day (usually I’m fine with Keras) and there is 2–3 months in between the courses…

Second, the assignments are a bit less instructed than the other courses in the nano degree i.e. you will need to spend more time figuring out small bits of programming where not are not as much taken by the hand as in previous courses. This is not so much a problem as a statement, however it becomes a problem when combined with the problematic Coursera assignment submission engine (well, honestly, I do not know if it is Coursera fault or the course fault, but the result is the same). Sometimes it will refuse to correctly grade your assignment for things which are not even errors, or will introduce artificial boundaries without telling you those are there… I hope those get resolved soon, as it will not deter early adopters as myself, but will most probably discourage more than one in the future.

Lastly the jupyter kernels used on the site are troublesome. Server availability seems sketchy at times. You will often lose your work even if you saved it (you should save/export to your local machine regularly to alleviate those issues). In short, it is still a far way from the Kaggle kernels handling. The same issues have been reported by a colleague of mine following another Coursera course, so this is not unique to the CNN course. Also as the access to Coursera is now subscription based, you will lose access to your kernels if you do not renew your subscription after the course. Hence if you do not want to lose your work (as it is as much a reference as the course video themselves) you will have to store them locally on your machine or preferred cloud storage.

All that being said, it is an excellent course and taught me many things which I did not knew before, so an overall 4/5! But be ready for the glitches especially in the programming assignments. I especially enjoyed the one shot learning which I intend to apply to one of my deep neural network problem at work (not CNN related) and the neural style transfer. In that last programming exercise you complete the code of a neural style transfer algorithm. With a little push from my good friend Marc-Olivier, I went a little bit further and implemented a multi-style transfer algorithm. Here are my youngest kids with different level of style transfer from Edvard MunchPablo PicassoVincent Van Gogh and Georges Braque.

ZackAbby4Styles
My youngest kids with style transferred left to right, top to bottom: Edvard Munch, Pablo Picasso, Vincent Van Gogh and Georges Braques.

Originally published at medium.com/@TheLoneNut on November 22, 2017.

The Fallacious Simplicity of Deep Learning: wild data

This post is the fourth in a series of posts about the “Fallacious Simplicity of Deep Learning”. I have seen too many comments from non-practitioner who thinks Machine Learning (ML) and Deep Learning (DL) are easy. That any computer programmer following a few hours of training should be able to tackle any problem because after all there are plenty of libraries nowadays… (or other such excuses). This series of posts is adapted from a presentation I will give at the Ericsson Business Area Digital Services technology day on December 5th. So, for my Ericsson fellows, if you happen to be in Kista that day, don’t hesitate to come see it!

In the last posts, we’ve seen that the first complexity lay around the size of the machine learning and deep learning community. There are not enough skilled and knowledgeable peoples in the field. The second complexity lay in the fact that the technology is relatively new, thus the frameworks are quickly evolving and requires software stacks that range from all the way down to the specialized hardware we use. The third complexity was all about hyper-parameter setting, a skill specific to machine learning and that you will need to acquire.

Next challenge with machine learning, or should I say first challenge is the data! You need data to perform a machine learning task. For deep learning, you arguably need even more said data.

This is an angle that you will not see if you take courses (online or in schools) about data science, machine learning or deep learning. At least I’ve never seen it being properly presented as a difficulty. When you take a course, the data is most of the time provided. Or you get a clear indication as where to obtain it from. In most case this is well curated data. Well explained and documented fields and formats. If the data is not already available and the exercise wants you to concentrate on the data scraping aspect, then the other parameters of the exercise will be well defined. They will tell you where to scrape the data from and what is of importance for the exercise. It will make sure the scraping can be done in a structure fashion. If the exercise wants you to concentrate on data cleaning, or data augmentation, the dataset will have been prepared to properly show you how this can be done. Not to say that those courses or exercises are easy, but they do not show the real difficulty of wild data.

I think that for most companies, it all started with wild data. As data science grows in a company, peoples put structure and pipelines around data. But this comes with time and size. And it certainly does not prevent the entry of some wild data beast in the zoo from time to time. So, assuming you have access to data, it might really be wild data and you will have to tame it. Some says deep learning is less about data engineering and more about model architecture creation. It is certainly true in computer vision where the format of the data has been agreed on some time ago. But what about another domain, is the format so widely accepted? You might still have to do some feature engineering to input your data in your model. Adding the feature engineering problem to the hyper-parameter tuning problem…

On the other hand, you might well be in a situation where you do not have access to data. What if you are used to sell a specific type of equipment. That equipment might not be connected to a communication network. If it is, the customer might never have been asked if you could use its data. How do you put in place a contract or license that allows for you to collect data? Does the legislation in the regions where you sell that equipment allows for that collection or is there any restrictions you need to dance with? Is there personally identifiable information you will need to deal with? Will you need to anonymize the data? If you start a new business, based on a new business model, you might be able to build it in your product. But if you have a continuing business, how to you incorporate it in your product? Does it need to be gradual?

I guess you now have a good understanding of our fourth complexity. Where do you get your data from?

Data might be hard to come by. It might be wild and messy. It might not even relate to the question you want to answer… and that will be the subject of our fifth complication: do you have the right question?

The Fallacious Simplicity of Deep Learning: hyper-parameters tuning

This post is the third in a series of posts about the “Fallacious Simplicity of Deep Learning”. I have seen too many comments from non-practitioner who thinks Machine Learning (ML) and Deep Learning (DL) are easy. That any computer programmer following a few hours of training should be able to tackle any problem because after all there are plenty of libraries nowadays… (or other such excuses). This series of posts is adapted from a presentation I will give at the Ericsson Business Area Digital Services technology day on December 5th. So, for my Ericsson fellows, if you happen to be in Kista that day, don’t hesitate to come see it!

In the last posts, we’ve seen that the first complexity lay around the size of the machine learning and deep learning community. There are not enough skilled and knowledgeable peoples in the field. The second complexity lay in the fact that the technology is relatively new, thus the frameworks are quickly evolving and requires software stacks that range from all the way down to the specialized hardware we use. We also said that to illustrate the complexities we would show an example of deep learning using keras. I have described the model I use in a previous post This is not me blogging!. The model can generate new blog post looking like mine from being trained on all my previous posts. So without any further ado, here is the short code example we will use.

 

KerasExample
A Keras code example.

In these few lines of code you can see the gist of how one would program a text generating neural network such as the one pictured besides the code. There is more code required to prepare the data and generate text from model predictions, than simply the call to model.predict. But the part of the code which related to create, train and make predictions with a deep neural network is all in those few lines.

You can easily see the definition of each layers: the embedding in green, the two Long Short Term Memory (LSTM) layers, a form of Recurrent Neural Network, here in blue. And a fully connected dense layer in orange. You can see that the inputs are passed when we train the model or fit it, in yellow as well as the expected output, our labels in orange. Here that label is given the beginning of a sentence, what would be the next character in the sequence. The subject of our prediction. Once you trained that network, you can ask for the next character, and the next, and the next… until you have a new blog post… more or less, as you have seen in a previous post.

For people with programming background, there is nothing complicated here. You have a library, Keras, you look at its API and you code accordingly, right? Well, how do you choose which layers to use and their order? The API will not tell you that… there is no cookbook. So, the selection of layers is part our next complexity. But before stating it as such let me introduce a piece of terminology: Hyper-parameters. Hyper-parameters are to deep learning and machine learning any parameter for which value you can vary, but ultimately have to finetune to your data if you want you model to behave properly.

So according to that definition of hyper-parameter, the deep neural network topology or architecture is an hyper-parameter. You must decide which layer to use and in what order. Hyper-parameter selection does not stops at the neural network topology though. Each layer has its own set of hyper parameters.

The first layer is an embedding layer. It converts in this case character input into a vector of real numbers, after all, computers can only work with numbers. How big this encoding vector will be? How long the sentences we train with will be? Those are all hyper-parameters.

On the LSTM layers, how wide or how many neurons will we use? Will we use all the outputs all the time or drop some of them (a technique called dropout which help regularizing neural network and reduce cases of overfitting)? Overfitting is when a neural network learns so well your training examples that it cannot generalize to new examples. Meaning that when you try to predict on a new value, the results are erratic. Not a situation you desire.

You have hyper-parameter to select and tweak up until the model compilation time and the model training (fit) time. How big the tweaking to your neural network weights will be at each computation pass? How big each pass will be in terms of examples you give to the neural network? How many passes will you perform?

If you take all of this into consideration, you end up with most of the code written being subject to hyper-parameters selection. And again, there is no cookbook or recipe yet to tell you how to set them. The API tells you how to enter those values in the framework, but cannot tell you what the effect will be. And the effect will be different for each problem.

It’s a little bit like if what you would give as argument to a print statement, you know like print(“hello world!”) would not be “hello word”, but some values which would print something based on that value (the hyper-parameter) and whatever has been printed in the past and you would have to tweak it so that at training time you get the expected results!!! This would make any good programmer become insane. But currently there is no other way with deep neural networks.

HelloWorldHyperParam2
Hello World with Hyper-Parameters.

So our fourth complexity is not only the selection of the neural network topology but as well as all the hyper-parameters that comes with it.

It sometimes requires insight on the mathematics behind the neural net, as well as imagination, lots of trials and error while being rigorous about what you try. This definitely does not follow the “normal” software development experience. As said in my previous post, you need a special crowd to perform machine learning or deep learning.

My next post will look at what is sometime the biggest difficulty for machine learning: obtaining the right data. Before anything can really be done, you need data. This is not always a trivial task…

 

The Fallacious Simplicity of Deep Learning: the proliferation of frameworks.

This post is the second in a series of posts about the “Fallacious Simplicity of Deep Learning”. I have seen too many comments from non-practitioner who thinks Machine Learning (ML) and Deep Learning (DL) are easy. That any computer programmer following a few hours of training should be able to tackle any problem because after all there are plenty of libraries nowadays… (or other such excuses). This series of posts is adapted from a presentation I will give at the Ericsson Business Area Digital Services technology day on December 5th. So, for my Ericsson fellows, if you happen to be in Kista that day, don’t hesitate to come see it!

In the last post, we’ve seen that the first complexity lay around the size of the machine learning and deep learning community. There are not enough skilled and knowledgeable peoples in the field. To illustrate the other complexities, I’ll show an example of deep learning using keras. Don’t worry if you are not used to it and even if you have not programmed in a while, or at all, I’ll keep it simple. Below is one of the software stack you can use in order to perform deep learning. This one can be deployed on both CPU, so your usual computer or computer server; but it can also be deployed on Graphic Processing Units or GPU. Basically, the video card in your computer.

kerasstack
The stack we will use for demonstration.

To use the video card to do the type of computation required for deep learning, one of the GPU manufacturer, Nvidia, has created a software layer to program those GPU. CUDA is the Compute Unified Device Architecture, and allow someone to program the GPU to do any highly parallelizable task. On top of that layer, Nvidia still has created another layer targeting the task of running deep neural network. This is the cuDNN layer, or CUDA Deep Neural Network library. For my example, I’ll use on top of cuDNN the google framework for graph computation, Tensorflow. Lastly, to simplify my task, since I won’t build new kind of neurons or new kind of layers, I’ll use google Keras librairy which makes simpler the process of defining a deep neural network, deploying it, training it and testing it. For something simple, we already have 5 layers of librairies, and I don’t even mention the language I’ll use and the libraries required for it as well (note that in the latest release of TensorFlow, keras has been integrated). But no biggies, in software development we are used to have many layers of software piling up.

The software stack I’m using for this example is only one of the possible one to make use of. Just for the nVidia GPU there are already more than a dozen of frameworks that builds on top of cuDNN. Moreover, Intel, AMD and google are coming up with their deep neural network hardware accelerator. Many other companies are doing the same, creating accelerated hardware for deep neural networks. All this new hardware will come with their equivalent of CUDA and cuDNN and frameworks will proliferate for a while.

cuDNNAccelereted
Some of the cuDNN accelerated frameworks.

I’m not even going to talk about the next layer of frameworks (e.g. Tensorflow and keras). Hopefully, they will adapt to the new hardware… otherwise, we’ll have even more frameworks. Same for the next layer e.g. keras builds on top of tensorflow (or theano or CNTK but let’s not open that door now). Hence, we can see our next complexity.

Second complexity, the piling of frameworks (including specialized hardware) and the proliferation of frameworks. Which one to learn? Which one will become irrelevant?

The machine learning but especially the deep learning landscape is evolving rapidly. To be efficient it requires new kind of hardware that we did not see as common in industrial servers even a few years ago. This means that the whole development stack, from hardware to released data product is evolving quickly. Changing requirement is a known issue in software development, it is not different in data product development.

My next post will tackle through an example the next complexity: Hyper-parameter tuning, something you do not see in software development but which is necessary for the development of a data product.

The Fallacious Simplicity of Deep Learning: the lack of skills.

This post is the first in a series of posts about the “Fallacious Simplicity of Deep Learning”. I have seen too many comments from non-practitioner who thinks Machine Learning (ML) and Deep Learning (DL) are easy. That any computer programmer following a few hours of training should be able to tackle any problem because after all there are plenty of libraries nowadays… (or other such excuses). This series of posts is adapted from a presentation I will give at the Ericsson Business Area Digital Services technology day on December 5th. So, for my Ericsson fellows, if you happen to be in Kista that day, don’t hesitate to come see it!

Artificial Intelligence (AI) is currently touching our lives, it is present on your phone, your personal assistants, your thermostats, pretty much all web sites you visit. It helps you choose what you will watch, what you will listen to, what you will read, what you will purchase and so on. AI is becoming a cornerstone of user experience.

Today, if you look at AI cutting edge technology, you must conclude that you can no longer trust what you see and hear. Some neural network techniques allow researchers to impersonate anybody in video, saying whatever they want with the right intonations, the right visual cues, etc. Neural networks are creating art piece, for example applying the style of great paint masters to any of your photography.

Soon, it will become even more relevant to your everyday life. We can already see looming the days of the autonomous cars. Eventually it will be all transportation, then regulation of all technology aspect of our life and even further…

The Artificial Intelligence technology reach growth is so fast and so transformational that we sometime have the impression that it must be all easy to apply. But is it so?

AI may look easy if you look at all the available resources, all the books available. The plethora of online courses, you cannot visit a web page nowadays without getting a machine learning course being proposed to you! There are tons of video available online. From those courses, but also from enthusiasts, or university teachers. And if you start digging in the available software frameworks, you’ll find plenty of them.

So why would someone like Andrew Ng, one of the world’s best-known AI expert would come forth with the mission of training a million AI experts? Well, Machine Learning, Deep Learning and Artificial Intelligence knowledge is still sparse. Big companies have grabbed a lot of the talented peoples leaving universities empty. A lot of Universities still don’t have programs dedicated to that topic. For those who have courses, most only propose a couple of introductory courses. Then from the online front, there will be countless number of people who will start to learn but will abandon it along the way for many reasons. Online course completion rate is much lower than University program completion rate.

Moreover, this is quite a difficult subject matter. There are many considerations which are quite different from what you would have from a software development background.

A first complexity, not enough skilled and knowledgeable peoples, a smaller community than say: web programming.

Stay tuned for my next post which will tackle the next complexity: the piling and proliferation of frameworks.