How to speed up claims processing with automated car damage detection

AI drives automation, not only in industrial production or for autonomous driving, but above all in dealing with bureaucracy. It is an realy enabler for lean management!

One example is the use of Deep Learning (as part of Artificial Intelligence) for image object detection. A car insurance company checks the amount of the damage by a damage report after car accidents. This process is actually performed by human professionals. With AI, we can partially automate this process using image data (photos of car damages). After an AI training with millions of photos in relation to real costs for repair or replacement, the cost estimation gets suprising accurate and supports the process in speed and quality.

AI drives automation and DATANOMIQ drives this automation with you! You can download the Infographic as PDF.

How to speed up claims processing with automated car damage detection

How to speed up claims processing
with automated car damage detection

Download this Infographic as PDF now by clicking here!

We wrote this article in cooperation with pixolution, a company for computer vision and AI-bases visual search. Interested in introducing AI / Deep Learning to your organization? Do not hesitate to get in touch with us!

DATANOMIQ is the independent consulting and service partner for business intelligence, process mining and data science. We are opening up the diverse possibilities offered by big data and artificial intelligence in all areas of the value chain. We rely on the best minds and the most comprehensive method and technology portfolio for the use of data for business optimization.

Data Dimensionality Reduction Series: Random Forest

Hello lovely individuals, I hope everyone is doing well, is fantastic, and is smiling more than usual. In this blog we shall discuss a very interesting term used to build many models in the Data science industry as well as the cyber security industry.


Random forest is a form of Supervised Machine Learning Algorithm that operates on the majority rule. For example, if we have a number of different algorithms working on the same issue but producing different answers, the majority of the findings are taken into account. Random forests, also known as random selection forests, are an ensemble learning approach for classification, regression, and other problems that works by generating a jumble of decision trees during training.

When it comes to regression and classification, random forest can handle both categorical and continuous variable data sets. It typically helps us outperform other algorithms and overcome challenges like overfitting and the curse of dimensionality.


Uncle John wants to see a doctor for his acute abdominal discomfort, so he goes to his pals for recommendations on the top doctors in town. After consulting with a number of friends and family members, Atlas chooses to visit the doctor who received the highest recommendations.

So, what does this mean? The same is true for random forests. It builds decision trees from several samples and utilises their majority vote for classification and average for regression.


  1. BIAS
  • The algorithm’s accuracy or quality is measured.
  • High bias means a poor match
  • The accuracy or specificity of the match is measured.
  • A high variance means a weak match

We would like to minimise each of these. But, unfortunately we can’t do this independently, since there is a trade-off


Bias vs Variance Tradeoff


Every other data dimensionality reduction method, such as missing value ratio and principal component analysis, must be built from the scratch, but the best thing about random forest is that it comes with built-in features and is a tree-based model that uses a combination of decision trees for non-linear data classification and regression.

Without wasting much time, let’s move to the main part where we’ll discuss the working of RANDOM FOREST:


As we saw in the analogy, RANDOM FOREST operates on the basis of ensemble technique; however, what precisely does ensemble technique mean? It’s actually rather straightforward. Ensemble simply refers to the combination of numerous models. As a result, rather than a single model, a group of models is utilised to create predictions.


Ensemble Learning: Bagging and Boosting



Let’s dive deep to understand things better:



Bagging simply helps us to reduce the variance in a loud datasets. It works on an ensemble technique.

  1. Algorithm independent : general purpose technique
  2. Well suited for high variance algorithms
  3. Variance reduction is achieved by averaging a group of data.
  4. Choose # of classifiers to build (B)


  1. Sample Training Data with Replacement
  2. Same algorithm on different subsets of training data


  1. Use with high variance algorithms (DT, NN)
  2. Easy to parallelize
  3. Limitation: Loss of Interpretability
  4. Limitation: What if one of the features dominates?


  1. Ensemble approach = Bootstrap Aggregation.
  2. In bagging a random dataset is selected as shown in the above figure and then a model is built using those random data samples which is termed as bootstrapping.
  3. Now, when we train this random sample data it is not mendidate to select data points only once, while training the sample data we can select the individual data point more then once.
  4. Now each of these models is built and trained and results are obtained.
  5. Lastly the majority results are being considered.

We can even calculate  the error from this thing know as random forest OOB error:


▪ From each bootstrapped sample, 1/3rd of it is kept aside as “Test”

▪ Tree built on remaining 2/3rd

▪ Average error from each of the “Test” samples is called “Out-of-Bag Error”

▪ OOB error provides a good estimate of model error

▪ No need for separate cross validation


Boosting in short helps us to improve our prediction by reducing error in predictive data analysis.

Weak Learner: only needs to generate a hypothesis with a training accuracy greater than 0.5, i.e., < 50% error over any distribution.


  1. Strong learners are very difficult to construct
  2. Constructing weaker Learners is relatively easy influence with the empirical squared improvement when assigned to the model


  1. Start with a ML algorithm for finding the rough rules of thumb (a.k.a. “weak” or “base” algorithm)
  2. Call the base algorithm repeatedly, each time feeding it a different subset of the training examples
  3. The basic learning algorithm creates a new weak prediction rule each time it is invoked.
  4. After several rounds, the boosting algorithm must merge these weak rules into a single prediction rule that, hopefully, is considerably more accurate than any of the weak rules alone.


  1. In each round, how is the distribution selected ?
  2. What is the best way to merge the weak rules into a single rule?

BOOSTING is classified into two types:



As far as the Random forest is concerned it is said that it follows the bagging method, not a boosting method. As the name implies, boosting involves learning from others, which in turn increases learning. Random forests have trees that run in parallel. While creating the trees, there is no interaction between them.

Boosting helps us reduce the error by decreasing the bias whereas, on other hand, Bagging is a manner to decrease the variance within the prediction with the aid of generating additional information for schooling from the dataset using mixtures with repetitions to provide multi-sets of the original information.

How Bagging helps with variance – A Simple Example


  1. Decision Trees have high variance
  2. The resultant tree (model) is determined by the training data.
  3. (Unpruned) Decision Trees tend to overfit
  4. One option: Cost Complexity Pruning


  1. Sample with replacement (1 Training set → Multiple training sets)
  2. Train model on each bootstrapped training set
  3. Multiple trees; each different : A garden ☺
  4. Each DT predicts; Mean / Majority vote prediction
  5. Choose # of trees to build (B)


Reduce model variance / instability.



▪ Each time a tree is split due to a variable m, Gini impurity index of the parent node is higher than that of the child nodes

▪ Adding up all Gini index decreases due to variable m over all trees in the forest, gives a measure of variable importance


  1. Diversity :
  2. Immunity to the curse of dimensionality :
  3. Parallelization :
  4. Train-Test split :
  5. Stability :
  6. Gini significance (or mean reduction impurity) :
  7. Mean Decrease Accuracy :


  1. maximum_features :

Increasing max features often increases model performance since each node now has a greater number of alternatives to examine.

  1. n_estimators :

The number of trees you wish to create before calculating the maximum voting or prediction averages. A greater number of trees improves speed but slows down your code.

  1. min_sample_leaf :

If you’ve ever designed a decision tree, you’ll understand the significance of the minimal sample leaf size. A leaf is the decision tree’s last node. A smaller leaf increases the likelihood of the model collecting noise in train data.

  1. n_jobs :

This option instructs the engine on how many processors it is permitted to utilise.

  1. random_state :

This argument makes it simple to duplicate a solution. If given the same parameters and training data, a definite value of random state will always provide the same results.

  1. oob_score:

A random forest cross validation approach is used here. It is similar to the leave one out validation procedure, except it is significantly faster.


Step1: Choose T- number of trees to grow

Step2: Choose m<p (p is the number of total features) —number of features used to calculate the best split at each node (typically 30% for regression, sqrt(p) for classification)

Step3: For each tree, choose a training set by choosing N times (N is the number of training examples) with replacement from the training set

Step4: For each node, calculate the best split, Fully grown and not pruned.

Step5: Use majority voting among all the trees

Following is a full case study and implementation of all the principles we just covered, in the form of a jupyter notebook including every concept and all you ever wanted to know about RANDOM FOREST.

GITHUB Repository for this blog article:

AI for games, games for AI

1, Who is playing or being played?

Since playing Japanese video games named “Demon’s Souls” and “Dark Souls” when they were released by From Software, I had played almost no video games for many years. During the period, From Software established one genre named soul-like games. Soul-like games are called  死にゲー in Japanese, which means “dying games,” and they are also called マゾゲー, which means “masochistic games.”  As the words imply, you have to be almost masochistic to play such video games because you have to die numerous times in them. And I think recently it has been one of the most remarkable times for From Software because in November of 2021 “Dark Souls” was selected the best video game ever by Golden Joystick Awards. And in the end of last February a new video game by From Software called “Elden Ring” was finally released. After it proved that Miyazaki Hidetaka, the director of Soul series, collaborated with George RR Martin, the author of the original of “Game of Thrones,” “Elden Ring” had been one of the most anticipated video games. In spite of its notorious difficulty as well as other soul-like games so far, “Elden Ring” became a big hit, and I think Miyazak Hidetaka is now the second most famous Miyazaki in the world.  A lot of people have been playing it, raging, and screaming. I was no exception, and it took me around 90 hours to finish the video game, breaking a game controller by the end of it. It was a long time since I had been so childishly emotional last time, and I was almost addicted to trial and errors the video game provides. At the same time, one question crossed my mind: is it the video game or us that is being played?

The childhood nightmare strikes back. Left: the iconic and notorious boss duo Ornstein and Smough in Dark Souls (2011), right: Godskin Duo in Elden Ring (2022).

Miyazaki Hidetaka entered From Software in 2004 and in the beginning worked as a programmer of game AI, which controls video games in various ways. In the same year an AI researcher Miyake Youichiro also joined From Software, and I studied a little about game AI by his book after playing “Elden Ring.” I found that he also joined “Demon’s Souls,” in which enemies with merciless game AI were arranged, and I had to conquer them to reach the demon in the end at every dungeon. Every time I died, even in the terminal place with the boss fight, I had to restart from the start, with all enemies reviving. That requires a lot of trial and errors, and that was the beginning of soul-like video games today.  In the book by the game AI researcher who was creating my tense and almost traumatizing childhood experiences, I found that very sophisticated techniques have been developed to force players to do trial and errors. They were sophisticated even at a level of controlling players at a more emotional level. Even though I am familiar with both of video games and AI at least more than average, it was not until this year that I took care about this field. After technical breakthroughs mainly made Western countries, video game industry showed rapid progress, and industry is now a huge entertainment industry, whose scale is now bigger that those of movies and music combined. Also the news that Facebook changed its named to Meta and that Microsoft announced to buy Activision Blizzard were sensational recently. However media coverage about those events would just give you impressions that those giant tech companies are making uses of the new virtual media as metaverse or new subscription services. At least I suspect these are juts limited sides of investments on the video game industry.

The book on game AI also made me rethink AI technologies also because I am currently writing an article series on reinforcement learning (RL) as a kind of my study note. RL is a type of training of an AI agent through trial-and-error-like processes. Rather than a labeled dataset, RL needs an environment. Such environment receives an action from an agent and gives the consequent state and next reward. From a view point of the agent, it give an action and gets the consequent next state and a corresponding reward, which looks like playing a video game. RL mainly considers a more simplified version of video-game-like environments called a Markov decision processes (MDPs), and in an MDP at a time step t an RL agents takes an action A_t, and gets the next state S_t and a corresponding reward R_t. An MDP is often displayed as a graph at the left side below or the graphical model at the right side.

Compared to a normal labeled dataset used for other machine learning, such environment is something hard to prepare. The video game industry has been a successful manufacturer of such environments, and as a matter of fact video games of Atari or Nintendo Entertainment System (NES) are used as benchmarks of theoretical papers on RL. Such video games might be too primitive for considering practical uses, but researches on RL are little by little tackling more and more complicated video games or simulations. But also I am sure creating AI that plays video games better than us would not be their goals. The situation seems like they are cultivating a form of more general intelligence inside computer simulations which is also effective to the real world. Someday, experiences or intelligence grown in such virtual reality might be dragged to our real world.

Testing systems in simulations has been a fascinating idea, and that is also true of AI research. As I mentioned, video games are frequently used to evaluate RL performances, and there are some tools for making RL environments with modern video game engines. Providing a variety of such sophisticated computer simulations will be indispensable for researches on AI. RL models need to be trained in simulations before being applied on physical devices because most real machines would not endure numerous trial and errors RL often requires. And I believe the video game industry has a potential of developing such experimental fields of AI fueled by commercial success in entertainment. I think the ideas of testing systems or training AI in simulations is getting a bit more realistic due to recent development of transfer learning.

Transfer learning is a subfield of machine learning which apply intelligence or experiences accumulated in datasets or tasks to other datasets or tasks. This is not only applicable to RL but also to more general machine learning tasks like regression or classification. Or rather it is said that transfer learning in general machine learning would show greater progress at a commercial level than RL for the time being. And transfer learning techniques like using pre-trained CNN or BERT is already attracting a lot of attentions. But I would say this is only about a limited type of transfer learning. According to Matsui Kota in RIKEN AIP Data Driven Biomedical Science Team, transfer learning has progressed rapidly after the advent of deep learning, but many types of tasks and approaches are scattered in the field of transfer learning. As he says, the term transfer learning should be more carefully used. I would like to say the type of transfer learning discussed these days are a family of approaches for tackling lack of labels. At the same time some of current researches on transfer learning is also showing possibilities that experiences or intelligence in computer simulations are transferable to the real world. But I think we need to wait for more progress in RL before such things are enabled.


In this article I would like to explain how video games or computer simulations can provide experiences to the real world in two ways. I am first going to briefly explain how video game industry in the first place has been making game AI to provide game users with tense experiences. And next I will explain how RL has become a promising technique to beat such games which were originally invented to moderately harass human players. And in the end, I am going to briefly introduce ideas of transfer learning applicable to video games or computer simulations. What I can talk in this article is very limited for these huge study areas or industries. But I hope you would see the video game industry and transfer learning in different ways after reading this article, and that might give you some hints about how those industries interact to each other in the future. And also please keep it in mind that I am not going to talk so much about growing video game markets, computer graphics, or metaverse. Here I focus on aspects of interweaving knowledge and experiences generated in simulation or real physical worlds.

2, Game AI

The fact that “Dark Souls” was selected the best game ever at least implies that current video game industry makes much of experiences of discoveries and accomplishments while playing video games, rather than cinematic and realistic computer graphics or iconic and world widely popular characters. That is a kind of returning to the origin of video games. Video games used to be just hard because the more easily players fail, the more money they would drop in arcade games. But I guess this aspect of video games tend to be missed when you talk about video games as a video game fan. What you see in advertisements of video games are more of beautiful graphics, a new world, characters there, and new gadgets. And it has been actually said that qualities of computer graphics have a big correlation with video game sales. In the third article of my series on recurrent neural networks (RNN), I explained how video game industry laid a foundation of the third AI boom during the second AI winter in 1990s. To be more concrete, graphic cards developed rapidly to realize more photo realistic graphics in PC games, and the graphic card used in Xbox was one of the first programmable GPU for commercial uses. But actually video games developed side by side with computer science also outside graphics. Thus in this section I am going to how video games have developed by focusing on game AI, which creates intelligence in video games in several ways. And my explanations on game AI is going to be a rough introduction to a huge and great educational works by Miyake Youichiro.

Playing video games is made up by decision makings, and such decision makings are made in react to game AI. In other words, a display is input into your eyes or sight nerves, and sequential decision makings, that is how you have been moving fingers are outputs. Complication of the experiences, namely hardness of video games, highly depend on game AI.  Game AI is mainly used to design enemies in video games to hunt down players. Ideally game AI has to be both rational and human. Rational game AI implemented in enemies frustrate or sometimes despair users by ruining users’ efforts to attack them, to dodge their attacks, or to use items. At the same time enemies have to retain some room for irrationality, that is they have to be imperfect. If enemies can perfectly conquer players’ efforts by instantly recognizing their commands, such video games would be theoretically impossible to beat. Trying to defeat such enemies is nothing but frustrating. Ideal enemies let down their guard and give some timings for attacking and trying to conquer them. Sophisticated game AI is inevitable to make grownups all over the world childishly emotional while playing video games.

These behaviors of game AI are mainly functions of character AI, which is a part of game AI. In order to explain game AI, I also have to explain a more general idea of AI, which is not the one often called “AI” these days. Artificial intelligence (AI) is in short a family of technologies to create intelligence, with computers. And AI can be divided into two types, symbolism AI and connectionism AI. Roughly speaking, the former is manual and the latter is automatic. Symbolism AI is described with a lot of rules, mainly “if” or “else” statements in code. For example very simply “If the score is greater than 5, the speed of the enemy is 10.” Or rather many people just call it “programming.”

*Note that in contexts of RL, “game AI” often means AI which plays video games or board games. But “game AI” in video games is a more comprehensive idea orchestrating video games.

This meme describes symbolism AI well.

What people usually call “AI” in this 3rd AI boom is the latter, connictionism AI. Connectionism AI basically means neural networks, which is said to be inspired by connections of neurons. But the more you study neural networks, the more you would see such AI just as “functions capable of universal approximation based on data.” That means, a function f, which you would have learned in school such as y = f(x) = ax + b is replaced with a complicated black box, and such black box f is automatically learned with many combinations of (x, y). And such black boxes are called neural networks, and the combinations of (x, y) datasets. Connectionism AI might sound more ideal, but in practice it would be hard to design characters in AI based on such training with datasets.

*Connectionism, or deep learning is of course also programming. But in deep learning we largely depend on libraries, and a lot of parameters of AI models are updated automatically as long as we properly set datasets. In that sense, I would connectionism is more automatic. As I am going to explain, game AI largely depends on symbolism AI, namely manual adjustment of lesser parameters, but such symbolism AI would behave much more like humans than so called “AI” these days when you play video games.

Digital game AI today is application of the both types of AI in video games. It initially started mainly with symbolism AI till around 2010, and as video games get more and more complicated connectionism AI are also introduced in game AI. Video game AI can be classified to mainly navigation AI, character AI, meta AI, procedural AI, and AI outside video games. The figure below shows relations of general AI and types of game AI.

Very simply putting, video game AI traced a history like this: the initial video games were mainly composed of navigation AI showing levels, maps, and objects which move deterministically based on programming.  Players used to just move around such navigation AI. Sooner or later, enemies got certain “intelligence” and learned to chase or hunt down players, and that is the advent of character AI. But of course such “intelligence” is nothing but just manual programs. After rapid progress of video games and their industry, meta AI was invented to control difficulties of video games, thereby controlling players’ emotions. Procedural AI automatically generates contents of video games, so video games are these days becoming more and more massive. And as modern video games are too huge and complicated to debug or maintain manually, AI technologies including deep learning are used. The figure below is a chronicle of development of video games and AI technologies covered in this article. Let’s see a brief history of video games and game AI by taking a closer look at each type of game AI a little more precisely.

Navigation AI

Navigation AI is the most basic type of game AI, and that allows character AI to recognize the world in video games. Even though I think character AI, which enables characters in video games to behave like humans, would be the most famous type of game AI, it is said navigation AI has an older history. One important function of navigation AI is to control objects in video games, such as lifts, item blocks, including attacks by such objects. The next aspect of navigation AI is that it provides character AI with recognition of worlds. Unlike humans, who can almost instantly roughly recognize circumstances, character AI cannot do that as we do. Even if you feel as if the character you are controlling are moving around mountains, cities, or battle fields, sometimes escaping from attacks by other AI, for character AI that is just moving on certain graphs. The figure below are some examples of world representations adopted in some popular video games. There are a variety of such representations, and please let me skip explaining the details of them. An important point is, relatively wide and global recognition of worlds by characters in video games depend on how navigation AI is designed.

Source: Youichiro Miyake, “AI Technologies in Game Industry”, (2020)

The next important feature of navigation AI is path finding. If you have learned engineering or programming, you should be already familiar with pathfiniding algorithms. They had been known since a long time ago, but it was not until “Counter-Strike” in 2000 the techniques were implemented at an satisfying level for navigating characters in a 3d world. Improvements of pathfinding in video games released game AI from fixed places and enabled them to be more dynamic.

*According to Miyake Youichiro, the advent of pathfinding in video games released character AI from staying in a narrow space and enable much more dynamic and human-like movements of them. And that changed game AI from just static objects to more intelligent entity.

Navigation meshes in “Counter-Strike (2000).” Thanks to these meshes, continuous 3d world can be processed as discrete nodes of graphs. Source:

Character AI

Character AI is something you would first imagine from the term AI. It controls characters’ actions in video games. And differences between navigation AI and character AI can be ambiguous. It is said Pac-Man is one of the very first character AI. Compared to aliens in Space Invader deterministically moved horizontally, enemies in Pac-Man chase a player, and this is the most straightforward difference between navigation AI and character AI.


Character AI is a bunch of sophisticated planning algorithms, so I can introduce only a limited part of it just like navigation AI. In this article I would like to take an example of “F.E.A.R.” released in 2005. It is said goal-oriented action planning (GOAP) adopted in this video game was a breakthrough in character AI. GOAP is classified to backward planning, and if there exists backward ones, there is also forward ones. Using a game tree is an examples of forward planning. The figure below is an example of a tree game of tic-tac-toe. There are only 9 possible actions at maximum at each phase, so the number of possible states is relatively limited.

But with more options of actions like most of video games, forward plannings have to deal much larger sizes of future action combinations. GOAP enables realistic behaviors of character AI with a heuristic idea of planning backward. To borrow Miyake Youichiro’s expression, GOAP processes actions like sticky notes. On each sticky note, there is a combination of symbols like “whether a target is dead,” “whether a weapon is armed,” or “whether the weapon is loaded.” A sticky note composed of such symbols form an action, and each action comprises a prerequisite, an action, and an effect. And behaviors of character AI is conducted with planning like pasting the sticky notes.

Based on: Youichiro Miyake, “AI Technologies in Game Industry”, (2020)

More practically sticky notes, namely actions are stored in actions pools. For a decision making, as displayed in the left side of the figure below, actions are connected as a chain. First an action of a goal is first set, and an action can be connected to the prerequisite of the goal via its effect. Just as well corresponding former actions are selected until the initial state.  In the example of chaining below, the goal is “kSymbol_TargetIsDead,” and actions are chained via “kSymbol_TargetIsDead,” “kSymbol_WeaponLoaded,” “kSymbol_WeaponArmed,” and “None.” And there are several combinations of actions to reach a certain goal, so more practically each action has a cost, and the most ideal behavior of character AI is chosen by pathfinding on a graph like the right side of the figure below. And the best planning is chosen by a pathfinding algorithm.

Based on: Youichiro Miyake, “AI Technologies in Game Industry”, (2020)

Even though many of highly intelligent behaviors of character AI are implemented as backward plannings as I explained, planning forward can be very effective in some situations. Board game AI is a good example. A searching algorithm named Monte Carlo tree search is said to be one breakthroughs in board game AI. The searching algorithm randomly plays a game until the end, which is called playout. Numerous times of playouts enables evaluations of possibilities of winning. Monte Carlo Tree search also enables more efficient searches of games trees.

Meta AI

Meta AI is a type of AI such that controls a whole video game to enhance player’s experiences. To be more concrete, it adjusts difficulties of video games by for example dynamically arranging enemies. I think differences between meta AI and navigation AI or character AI can be also ambiguous. As I explained, the earliest video games were composed mainly with navigation AI, or rather just objects. Even if there are aliens or monsters, they can be just part of interactive objects as long as they move deterministically. I said character AI gave some diversities to their behaviors, but how challenging a video game is depends on dynamic arrangements of such objects or enemies. And some of classical video games like “Xevious,” as a matter of fact implemented such adjustments of difficulties of game plays. That is an advent of meta AI, but I think they were not so much distinguished from other types of AI, and I guess meta AI has been unconsciously just a part of programming.

It is said a turning point of modern meta AI is a shooting game “Left 4 Dead” released in 2008, where zombies are dynamically arranged. As well as many masterpiece thriller films, realistic and tense terrors are made by combinations of intensities and relaxations. Tons of monsters or zombies coming up one after another and just shooting them look stupid or almost like comedies. .And analyzing the success of “Counter-Strike,” they realized that users liked rhythms of intensity and relaxation, so they implemented that explicitly in “Left 4 Dead.” The graphs below concisely shows how meta AI works in the video game. When the survivor intensity, namely players’ intensity is low, the meta AI arrange some enemies. Survivor intensity increases as players fight with zombies or something, and then meta AI places fewer enemies so that players can relax. While players re relatively relaxing, desired population of enemies increases when they actually show up in video games, again the phase of intensity comes.

Source: Michael Booth, “Replayable Cooperative Game Design: Left 4 Dead”, (2009), Valve

*Soul series video games do not seem to use meta AI so much. Characters in the games are rearranged in more or less the same ways every time players fail. Soul-like games make much of experiences that players find solutions by themselves, which means that manual but very careful arrangements of enemies and interactive objects are also very effective.

Meta AI can be used to make video games more addictive using data analysis. Recent social network games can record logs of game plays. Therefore if you can observe a trend that more users unsubscribe when they get less rewards in certain online events, operating companies of the game can adjust chances of getting “rare” items.

Procedural AI and AI outside video games

How clearly you can have an image of what I am going to explain in this subsection would depend how recently you have played video games. If your memories of playing video games stops with good old days of playing side-scrolling ones like Super Mario Brothers, you should at first look up some videos of playing open world games. Open world means a use of a virtual reality in which players can move an behave with a high degree of freedom. The term open world is often used as opposed to the linear games, where players have process games in the order they are given. Once you are immersed in photorealistic computer graphic worlds in such open world games, you would soon understand why metaverse is attracting attentions these days. Open world games for example like “Fallout 4” are astonishing in that you can even talk to almost everyone in them. Just as “Elden Ring” changed former soul series video games into an open world one, it seems providing open world games is one way to keep competitive in the video game industry. And such massive world can be made also with a help of procedural AI. Procedural AI can be seen as a part of meta AI, and it generates components of games such as buildings, roads, plants, and even stories. Thanks to procedural AI, video game companies with relatively small domestic markets like Poland can make a top-level open world game such as “The Witcher 3: Wild Hunt.”

An example of technique of procedural AI adopted in “The Witcher 3: Wild Hunt” for automatically creating the massive open world. Source: Marcin Gollent, “Landscape creation and rendering in REDengine 3”, (2014), Game Developers Conference

Creating a massive world also means needs of tons of debugging and quality assurance (QA). Combining works by programmers, designers, and procedural AI will cause a lot of unexpected troubles when it is actually played. AI outside game can be used to find these problems for quality assurance. Debugging and and QA have been basically done manually, and especially when it comes to QA, video game manufacturer have to employ a lot of gamer to let them just play prototype of their products. However as video games get bigger and bigger, their products are not something that can be maintained manually anymore. If you have played even one open world game, that would be easy to imagine, so automatic QA would remain indispensable in the video game industry. For example an open world game “Horizon Zero Dawn” is a video game where a player can very freely move around a massive world like a jungle. The QA team of this video game prepared bug maps so that they can visualize errors in video games. And they also adopted a system named “Apollo-Autonomous Automated Autobots” to let game AI automatically play the video game and record bugs.

As most video games both in consoles or PCs are connected to the internet these days, these bugs can be fixed soon with updates. In addition, logs of data of how players played video games or how they failed can be stored to adjust difficulties of video games or train game AI. As you can see, video games are not something manufacturers just release. They are now something develop interactively between users and developers, and players’ data is all exploited just as your browsing history on the Internet.

I have briefly explained AI used for video games over four topics. In the next two sections, I am going to explain how board games and video games can be used for AI research.

3, Reinforcement learning: we might be a sort of well-made game AI models

Machine learning, especially RL is replacing humans with computers, however with incredible computation resources. Invention of game AI, in this context including computers playing board games, has been milestones of development of AI for decades. As Western countries had been leading researches on AI, defeating humans in chess, a symbol of intelligence, had been one of goals. Even Alan Turing, one of the fathers of computers, programmed game AI to play chess with one of the earliest calculators. Searching algorithms with game trees were mainly studied in the beginning. Game trees are a type of tree graphs to show how games proceed, by expressing future possibilities with diverging tree structures. And searching algorithms are often used on tree graphs to ignore future steps which are not likely to be effective, which often looks like cutting off branches of trees. As a matter of fact, chess was so “simple” that searching algorithms alone were enough to defeat Garry Kasparov, the world chess champion at that time in 1997. That is, growing trees and trimming them was enough for the “simplicity” of chess as long as a super computer of IBM was available. After that computer defeated one of the top players of shogi, a Japanese version of chess, in 2013. And remarkably, in 2016 AlphaGo of DeepMind under Google defeated the world go champion. Game AI has been gradually mastering board games in order of increasing search space size.


We can say combinations of techniques which developed in different streams converged into game AI today, like I display in the figure below. In AlphaGo or maybe also general game AI, neural networks enable “intuition” on phases of board games, searching algorithms enables “foreseeing,” and RL “experiences.” And as almost no one can defeat computers in board games anymore, the next step of game AI is how to conquer other video games.  Since progress of convolutional neural network (CNN) in this 3rd AI boom, computers got “eyes” like we do, and the invention of ResNet in 2015 is remarkable. Thus we can now use displays of video games as inputs to neural networks. And combinations of reinforcement learning and neural networks like (CNN) is called deep reinforcement learning. Since the advent of deep reinforcement learning, many people are trying to apply it on various video games, and they show impressive results. But in general that is successful in bird’s-eye view games. Even if some of researches can be competitive or outperform human players, even in first person shooting video games, they require too much computational resources and heuristic techniques. And usually they take too much time and computer resource to achieve the level.

*Even though CNN is mainly used as “eyes” of computers, it is also used to process a phase of a board game. That means each phase of is processed like an arrangement of pixels of an image. This is what I mean by “intuition” of deep learning. Just as neural networks can recognize objects, depending on training methods they can recognize boards at a high level.

Now I would like you to think about what “smartness” means. Competency in board games tend to have correlations with mathematical skills. And actually in many cases people proficient in mathematics are also competent in board games. Even though AI can defeat incredibly smart top board game players to the best of my knowledge game AI has yet to play complicated video games with more realistic computer graphics. As I explained, behaviors of character AI is in practice implemented as simpler graphs, and tactics taken in such graphs will not be as complicated as game trees of competitive board games. And the idea of game AI playing video games itself not new, and it is also used in debugging of video games. Thus the difficulties of computers playing video games would come more from how to associate what they see on displays with more long-term and more abstract plannings. And currently, kids would more flexibly switch to other video games and play them more professionally in no time. I would say the difference is due to frames of tasks. A frame roughly means a domain or a range which is related to a task. When you play a board game, its frame is relatively small because everything you can do is limited in the rule of the game which can be expressed as simple data structure. But playing video games has a wider frame in that you have to recognize only the necessary parts important for playing video games from its constantly changing displays, namely sequences of RGB images. And in the real world, even a trivial action like putting a class on a table is selected from countless frames like what your room looks like, how soft the floor is, or what the temperature is. Human brains are great in that they can pick up only necessary frames instantly.

As many researchers would already realize, making smaller models with lower resources which can learn more variety of tasks is going to be needed, and it is a main topic these days not only in RL but also in other machine learning. And to be honest, I am skeptical about industrial or academic benefits of inventing specialized AI models for beating human players with gigantic computation resources. That would be sensational and might be effective for gathering attentions and funds. But as many AI researchers would already realize, inventing a more general intelligence which would more flexibly adjust to various tasks is more important. Among various topics of researches on the problem, I am going to pick up transfer learning in the next section, but in a more futuristic and dreamy sense.

4, Transfer learning and game for AI

In an event with some young shogi players, to a question “What would you like to request to a god?” Fujii Sota, the youngest top shogi player ever, answered “If he exists, I would like to ask him to play a game with me.” People there were stunned by the answer. The young genius, contrary to his sleepy face, has an ambition which only the most intrepid figures in mythology would have had. But instead of playing with gods, he is training himself with game AI of shogi. His hobby is assembling computers with high end CPUs, whose performance is monstrous for personal home uses. But in my opinion such situation comes from a fact that humans are already a kind of well-made machine learning models and that highly intelligent games for humans have very limited frames for computers.

*It seems it is not only computers that need huge energy consumption to play board games. Japanese media often show how gorgeous and high caloric shogi players’ meals are during breaks. And more often than not, how fancy their feasts are is the only thing most normal spectators like me in front of TVs can understand, albeit highly intellectual tactics made beneath the wooden boards.

As I have explained, the video game industry has been providing complicated simulational worlds with sophisticated ensemble of game AI in both symbolism and connectionism ways. And such simulations, initially invented to hunt down players, are these days being conquered especially by RL models, and the trend showed conspicuous progress after the advent of deep learning, that is after computers getting “eyes.” The next problem is how to transfer the intelligence or experiences cultivated in such simulations to the real world. Only humans can successfully train themselves with computer simulations today as far as I know, but more practically it is desired to transfer experiences with wider frames to more inflexible entities like robots. Such technologies would be ideal especially for RL because physical devices cannot make numerous trial and errors in the real world. They should be trained in advance in computer simulations. And transfer learning could be one way to take advantages of experiences in computer simulations to the real world. But before talking about such transfer learning, we need to be careful about the term “transfer learning.” Transfer learning is a family of machine learning technologies to makes uses of knowledge learned in a dataset, which is usually relatively huge, to another task with another dataset. Even though I have been emphasizing transferring experiences in computer simulations, transfer learning is a more general idea applicable to more general use cases, also outside computer simulations. Or rather, transfer learning is attracting a lot of attentions as a promising technique for tackling lack of data in general machine learning. And another problem is even though transfer learning has been rapidly developing recently, various research topics are scattered in the field called “transfer learning.” And arranging these topics would need extra articles or something. Thus in the rest of this article,  I would like to especially focus on uses of video games or computer simulations in transfer learning. When it comes to already popular and practical transfer learning techniques like fine tuning with pre-trained backbone CNN or BERT, I am planning to cover them with more practical introduction in one of my upcoming articles. Thus in this article, after simply introducing ideas of domains and transfer learning, I am going to briefly introduce transfer learning and explain domain adaptation/randomization.

Domain and transfer learning

There is a more strict definition of a domain in machine learning, but all you have to know is it means in short a type of dataset used for a machine learning task. And different domains have a domain shift, which in short means differences in the domains. A text dataset and an image dataset have a domain shift. An image dataset of real objects and one with cartoon images also have a smaller domain shift. Even differences in lighting or angles of cameras would cause a domain  shift. In general, even if a machine learning model is successful in tasks in a domain, even a domain shift which is trivial to humans declines performances of the model. In other words, intelligence learned in one domain is not straightforwardly applicable to another domain as humans can do. That is, even if you can recognize objects both a real and cartoon cars as a car, that is not necessarily true of machine learning models. As a family of techniques for tackling this problem, transfer learning makes a use of knowledge in a source domain (the dots in blue below), and apply the knowledge to a target domain. And usually, a source domain is assumed to be large and labeled, and on the other hand a target domain is assumed to be relatively small or even unlabeled. And tasks in a source or a target domain can be different. For example, CNN models trained on classification of ImageNet can be effectively used for object detection. Or BERT is trained on a huge corpus in a self-supervised way, but it is applicable to a variety of tasks in natural language processing (NLP).

*To people in computer vision fields, an explanation that BERT is a NLP version of pre-trained CNN would make the most sense. Just as a pre-trained CNN maps an image, arrangements of RGB pixels values, to a vector representing more of “meaning” of the image, BERT maps a text,  a sequence of one-hot encodings, into a vector or a sequence of vectors in a semantic field useful for NLP.

Transfer learning is a very popular topic, and it is hard to arrange and explain types of existing techniques. I think that is because many people are tackling more or less the similar problems with slightly different approaches. For now I would like you to keep it in mind that there are roughly three points below to consider in transfer learning

  1. What to transfer
  2. When to transfer
  3. How to transfer

The answer of the second point above “When to transfer” is simply “when domains are more or less alike.” Transfer learning assume similarities between target and source domains to some extent. “How to transfer” is very task-specific, so this is not something I can explain briefly here. I think the first point “what to transfer” is the most important for now to avoid confusions about what “transfer learning” means. “What to transfer” in transfer learning is also classified to the three types below.

  • Instance transfer (transferring datasets themselves)
  • Feature transfer (transferring extracted features)
  • Parameter transfer (transferring pre-trained models)

In fact, when you talk about already practical transfer learning techniques like using pre-trained CNN or BERT, they refer to only parameter transfer above. And please let me skip introducing it in this article. I am going to focus only on techniques related to video games in this article.

*I would like to give more practical introduction on for example BERT in one of my upcoming articles.

Domain adaptation or randomization

I first got interested in relations of video games and AI research because I was studying domain adaptation, which tackles declines of machine learning performance caused by a domain shift. Domain adaptation is sometimes used as a synonym to transfer learning. But compared to that general transfer learning also assume different tasks in different domains, domain adaptation assume the same task. Thus I would say domain adaptation is a subfield of transfer learning. There are several techniques for domain adaptation, and in this article I would like to take feature alignment as an example of frequently used approaches. Input datasets have a certain domain shift like blue and circle dots in the figure below. This domain shift cannot be changed if datasets themselves are not directly converted. Feature alignment make the domain shift smaller in a feature space after data being processed by the feature extractor. The features expressed as square dots in the figure are passed to task-specific networks just as normal machine learning. With sufficient labels in the source domain and with fewer or no labels in the target one, the task-specific networks are supervised. On the other hand, the features are also passed to the domain discriminator, and the discriminator predicts which domain the feature comes from. The domain discriminator is normally trained with supervision by classification loss, but the feature supervision is reversed when it trains the feature extractor. Due to the reversed supervision the feature extractor learns mix up features because that is worse for discriminating distinguishing the source or target domains. In this way, the feature extractor learns extract domain invariant features, that is more general features both domains have in common.

*The feature extractor and the domain discriminator is in a sense composing generative adversarial networks (GAN), which is often used in data generation. To know more about GAN, you could check for example this article.

One of motivations behind domain adaptation is that it enables training AI tasks with synthetic datasets made by for example computer graphics because they are very easy to annotate and prepare labels necessary for machine learning tasks. In such cases, domain invariant features like curves or silhouettes are expected to learn. And learning computer vision tasks from GTA5 dataset which are applicable to Cityscapes dataset is counted as one of challenging tasks in papers on domain adaptation. GTA of course stands for “Grand Theft Auto,” the video open-world video game series. If this research continues successfully developing, that would imply possibilities of capability of teaching AI models to “see” only with video games. Imagine that a baby first learns to play Grand Theft Auto 5 above all and learns what cars, roads, and pedestrians are.  And when you bring the baby outside, even they have not seen any real cars, they point to a real cars and people and say “car” and “pedestrians,” rather than “mama” or “dada.”

In order to enable more effective domain adaptation, Cycle GAN is often used. Cycle GAN is a technique to map texture in one domain to another domain. The figure below is an example of applying Cycle GAN on GTA5 dataset and Cityspaces Dataset, and by doing so shiny views from a car in Los Santos can be converted to dark and depressing scenes in Germany in winter. This instance transfer is often used in researches on domain adaptation.


Even if you mainly train depth estimation with data converted like above, the model can predict depth data of the real world domain without correct depth data. In the figure below, A is the target real data, B is the target domain converted like a source domain, and C is depth estimation on A.

Source: Abarghouei et al., “Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer”, (2018), Computer Vision and Pattern Recognition Conference

Crowd counting is another field where making a labeled dataset with video games is very effective. A MOD for making a crowd arbitrarily is released, and you can make labeled datasets like below.


*Introducing GTA mod into research is hilarious. You first need to buy PC software of Grand Theft Auto 5 and gaming PC at first. And after finishing the first tutorial in the video game, you need to find a place to place a camera, which looks nothing but just playing video games with public money.

Domain adaptation problems I mentioned are more of matters of how to let computers “see” the world with computer simulations. But the gap between the simulational worlds and the real world does not exist only in visual ways like in CV. How robots or vehicles are parametrized in computers also have some gaps from the real world, so even if you replace only observations with simulations, it would be hard to train AI. But surprisingly, some researches have already succeeded in training robot arms only with computer simulations. An approach named domain randomization seems to be more or less successful in training robot arms only with computer simulations and apply the learned experience to the real world. Compared to domain adaptation aligned source domain to the target domain, domain randomization is more of expanding the source domain by changing various parameters of the source domain. And the target domain, namely robot arms in the real world is in the end included in the expanded source domain. And such expansions are relatively easy with computer simulations.

For example a paper “Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience” proposes a technique to reflect real world feed back to simulations in domain randomization, and this pipeline enables a robot arm to do real world tasks in a few iteration of real world trainings.

Based on: Chebotar et al. , “Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience”, (2019), International Conference on Robotics and Automation

As the video shows, the ideas of training a robot with computer simulations is becoming more realistic.

The future of games for AI

I have been emphasizing how useful video games are in AI researches, but I am not sure if how much the field purely rely on video games like it is doing especially on RL. Autonomous driving is a huge research field, and modern video games like Grand Thef Auto are already good driving simulations in urban areas. But other realistic simulations like CARLA have been developed independent of video games. And in a paper “Exploring the Limitations of Behavior Cloning for Autonomous Driving,” some limitations of training self-driving cars in the simulation are reported. And some companies like Waymo switched to recurrent neural networks (RNN) for self-driving cars. It is natural that fields like self-driving, where errors of controls can be fatal, are not so optimistic about adopting RL for now.

But at the same time, Microsoft bought a Project Bonsai, which is aiming at applying RL to real world tasks. Also Microsoft has Project Malmo or AirSim, which respectively use Minecraft or Unreal Engine for AI reseraches. Also recently the news that Microsoft bought Activision Blizzard was a sensation last year, and media’s interests were mainly about metaverse or subscription service of video games. But Microsoft also bouth Zenimax Media, is famous for open world like Fallout or Skyrim series. Given that these are under Microsoft, it seems the company has been keen on merging AI reserach and developing video games.

As I briefly explained, video games can be expanded with procedural AI technologies. In the future AI might be trained in video game worlds, which are augmented with another form of AI. Combinations of transfer learning and game AI might possibly be a family of self-supervising technologies, like an octopus growing by eating its own feet. At least the biggest advantage of the video game industry is, even technologies themselves do not make immediate profits, researches on them are fueled by increasing video game fans all over the world. This is a kind of my sci-fi imagination of the world. Though I am not sure which is more efficient to manually design controls of robots or training AI in such indirect ways. And I prefer to enhance physical world to metaverse. People should learn to put their controllers someday and to enhance the real world. Highly motivated by “Elden Ring” I wrote this article. Some readers might got interested in the idea of transferring experiences in computer simulations to the real world. I am also going to write about transfer learning in general that is helpful in practice.

[1]三宅 陽一郎, 「ゲームAI技術入門 – 広大な人工知能の世界を体系的に学ぶ」, (2019), 技術評論社
Miyake Youichiro, “An Introduction to Game AI – Systematically Learning the Wide World of Artificial Intelligence”, (2019), Gijutsu-hyoron-sya

[2]三宅 陽一郎, 「21世紀に“洋ゲー”でゲームAIが遂げた驚異の進化史。その「敗戦」から日本のゲーム業界が再び立ち上がるには?【AI開発者・三宅陽一郎氏インタビュー】」, (2017), 電ファミニコゲーマー
Miyake Youichiro, ”The history of Astonishing Game AI which Western Video Games in 21st Century Traced. What Should the Japanese Video Game Industry Do to Recover from the ‘Defeat in War’? [An Interview with an AI Developer Miyake Yoichiro]”

[3]Rob Leane, “Dark Souls named greatest game of all time at Golden Joysticks”, (2021),

[4] Matsui Kota, “Recent Advances on Transfer Learning and Related Topics (ver.2)”, (2019), RIKEN AIP Data Driven Biomedical Science Team

[3] Sebastian Ruder, “Transfer Learning – Machine Learning’s Next Frontier”, (2017)

[4] Matsui Kota, “Recent Advances on Transfer Learning and Related Topics (ver.2)”, (2019), RIKEN AIP Data Driven Biomedical Science Team

[5] Youichiro Miyake, “AI Technologies in Game Industry”, (2020)

[6] Michael Booth, “Replayable Cooperative Game Design: Left 4 Dead”, (2009), Valve

[7] Marcin Gollent, “Landscape creation and rendering in REDengine 3”, (2014), Game Developers Conference

* I make study materials on machine learning, sponsored by DATANOMIQ. I do my best to make my content as straightforward but as precise as possible. I include all of my reference sources. If you notice any mistakes in my materials, including grammatical errors, please let me know (email: And if you have any advice for making my materials more understandable to learners, I would appreciate hearing it.

AI Role Analysis in Cybersecurity Sector

Cybersecurity as the name suggests is the process of safeguarding networks and programs from digital attacks. In today’s times, the world sustains on internet-connected systems that carry humungous data that is highly sensitive. Cyberthreats are on the rise with unscrupulous hackers taking over the entire industry by storm, with their unethical practices. This not only calls for more intense cyber security laws, but also the vigilance policies of the corporates, big and small, government as well as non-government; needs to be revisited.

With such huge responsibility being leveraged over the cyber-industry, more and more cyber-security enthusiasts are showing keen interest in the industry and its practices. In order to further the process of secured internet systems for all, unlike data sciences and other industries; the Cybersecurity industry has seen a workforce rattling its grey muscle with every surge they experience in cyber threats. Talking of AI impressions in Cybersecurity is still in its nascent stages of deployment as humans are capable of more; when assisted with the right set of tools.

Automatically detecting unknown workstations, servers, code repositories, and other hardware and software on the network are some of the tasks that could be easily managed by AI professionals, which were conducted manually by Cybersecurity folks. This leaves room for cybersecurity officials to focus on more urgent and critical tasks that need their urgent attention. Artificial intelligence can definitely do the leg work of processing and analyzing data in order to help inform human decision-making.

AI in cyber security is a powerful security tool for businesses. It is rapidly gaining its due share of trust among businesses for scaling cybersecurity. Statista, in a recent post, listed that in 2019, approximately 83% of organizations based in the US consider that without AI, their organization fails to deal with cyberattacks. AI-cyber security solutions can react faster to cyber security threats with more accuracy than any human. It can also free up cyber security professionals to focus on more critical tasks in the organization.


As it is said, “It takes a thief to catch a thief”. Being in its experimental stages, its cost could be an uninviting factor for many businesses. To counter the threats posed by cybercriminals, organizations ought to level up their internet security battle. Attacks backed by the organized crime syndicate with intentions to dismantle the online operations and damage the economy are the major threats this industry face today. AI is still mostly experimental and, in its infancy, hackers will find it much easy to carry out speedier, more advanced attacks. New-age automation-driven practices are sure to safeguard the crumbling internet security scenarios.


There are several advantageous reasons to embrace AI in cybersecurity. Some notable pros are listed below:

  •  Ability to process large volumes of data
    AI automates the creation of ML algorithms that can detect a wide range of cybersecurity threats emerging from spam emails, malicious websites, or shared files.
  • Greater adaptability
    Artificial intelligence is easily adaptable to contemporary IT trends with the ever-changing dynamics of the data available to businesses across sectors.
  • Early detection of novel cybersecurity risks
    AI-powered cybersecurity solutions can eliminate or mitigate the advanced hacking techniques to more extraordinary lengths.
  • Offers complete, real-time cybersecurity solutions
    Due to AI’s adaptive quality, artificial intelligence-driven cyber solutions can help businesses eliminate the added expenses of IT security professionals.
  • Wards off spam, phishing, and redundant computing procedures
    AI easily identifies suspicious and malicious emails to alert and protect your enterprise.


Alongside the advantages listed above, AI-powered cybersecurity solutions present a few drawbacks and challenges, such as:

  • AI benefits hackers
    Hackers can easily sneak into the data networks that are rendered vulnerable to exploitation.
  • Breach of privacy
    Stealing log-in details of the users and using them to commit cybercrimes, are deemed sensitive issues to the privacy of an entire organization.
  • Higher cost for talents
    The cost of creating an efficient talent pool is very high as AI-based technologies are in the nascent stage.
  • More data, more problems
    Entrusting our sensitive data to a third-party enterprise may lead to privacy violations.


AI professionals backed with the best AI certifications in the world assist corporations of all sizes to leverage the maximum benefits of the AI skills that they bring along, for the larger benefit of the organization. Cybersecurity teams and AI systems cannot work in isolation. This communion is a huge step forward to leveraging maximum benefits for secured cybersecurity applications for organizations. Hence, this makes AI in cybersecurity a much-coveted aspect to render its offerings in the long run.

Training of Deep Learning AI models

Alles dreht sich um Daten: die Trainingsmethoden des Deep Learning

Im Deep Learning gibt es unterschiedliche Trainingsmethoden. Welche wir in einem KI Projekt anwenden, hängt von den zur Verfügung gestellten Daten des Kunden ab: wieviele Daten gibt es, sind diese gelabelt oder ungelabelt? Oder gibt es sowohl gelabelte als auch ungelabelte Daten?

Nehmen wir einmal an, unser Kunde benötigt für sein Tourismusportal strukturierte, gelabelte Bilder. Die Aufgabe für unser KI Modell ist es also, zu erkennen, ob es sich um ein Bild des Schlafzimmers, Badezimmers, des Spa-Bereichs, des Restaurants etc. handelt. Sehen wir uns die möglichen Trainingsmethoden einmal an.

1. Supervised Learning

Hat unser Kunde viele Bilder und sind diese alle gelabelt, so ist das ein seltener Glücksfall. Wir können dann das Supervised Learning anwenden. Dabei lernt das KI Modell die verschiedenen Bildkategorien anhand der gelabelten Bilder. Es bekommt für das Training von uns also die Trainingsdaten mit den gewünschten Ergebnissen geliefert.
Während des Trainings sucht das Modell nach Mustern in den Bildern, die mit den gewünschten Ergebnissen zusammenpassen. So erlernt es Merkmale der Kategorien. Das Gelernte kann das Modell dann auf neue, ungesehene Daten übertragen und auf diese Weise eine Vorhersage für ungelabelte Bilder liefern, also etwa “Badezimmer 98%”.

2. Unsupervised learning

Wenn unser Kunde viele Bilder als Trainingsdaten liefern kann, diese jedoch alle nicht gelabelt sind, müssen wir auf Unsupervised Learning zurückgreifen. Das bedeutet, dass wir dem Modell nicht sagen können, was es lernen soll (die Zuordnung zu Kategorien), sondern es muss selbst Regelmäßigkeiten in den Daten finden.

Eine aktuell gängige Methode des Unsupervised Learning ist Contrastive Learning. Dabei generieren wir jeweils aus einem Bild mehrere Ausschnitte. Das Modell soll lernen, dass die Ausschnitte des selben Bildes ähnlicher zueinander sind als zu denen anderer Bilder. Oder kurz gesagt, das Modell lernt zwischen ähnlichen und unähnlichen Bildern zu unterscheiden.

Über diese Methode können wir zwar Vorhersagen erzielen, jedoch können diese niemals
die Ergebnisgüte von Supervised Learning erreichen.

3. Semi-supervised Learning

Kann uns unser Kunde eine kleine Menge an gelabelten Daten und eine große Menge an nicht gelabelten Daten zur Verfügung stellen, wenden wir Semi-supervised Learning an. Diese Datenlage begegnet uns in der Praxis tatsächlich am häufigsten. Bei fast allen KI Projekten stehen einer kleinen Menge an gelabelten Daten ein Großteil an unstrukturierten
Daten gegenüber.

Mit Semi-supervised Learning können wir beide Datensätze für das Training verwenden. Das gelingt zum Beispiel durch die Kombination von Contrastive Learning und Supervised Learning. Dabei trainieren wir ein KI Modell mit den gelabelten Daten, um Vorhersagen für Raumkategorien zu erhalten. Gleichzeitig lassen wir es Ähnlichkeiten und Unähnlichkeiten in den ungelabelten Daten erlernen und sich daraufhin selbst optimieren. Auf diese Weise können wir letztendlich auch gute Label-Vorhersagen für neue, ungesehene Bilder erzielen.

Fazit: Supervised vs. Unsupervised vs. Semi-supervised

Supervised Learning wünscht sich jeder, der mit einem KI Projekt betraut ist. In der Praxis ist das kaum anwendbar, da selten sämtliche Trainingsdaten gut strukturiert und gelabelt vorliegen.

Wenn nur unstrukturierte und ungelabelte Daten vorhanden sind, dann können wir mit Unsupervised Learning immerhin Informationen aus den Daten gewinnen, die unser Kunde so nicht hätte. Im Vergleich zu Supervised Learning ist aber die Ergebnisqualität deutlich schlechter.

Mit Semi-Supervised Learning versuchen wir das Datendilemma, also kleiner Teil gelabelte, großer Teil ungelabelte Daten, aufzulösen. Wir verwenden beide Datensätze und können gute Vorhersage-Ergebnisse erzielen, deren Qualität dem Supervised Learning oft ebenbürtig sind.

Dieser Artikel entstand in Zusammenarbeit zwischen DATANOMIQ, einem Unternehmen für Beratung und Services rund um Business Intelligence, Process Mining und Data Science. und pixolution, einem Unternehmen für AI Solutions im Bereich Computer Vision (Visuelle Bildsuche und individuelle KI Lösungen).

Haufe Akademie Data Science Buzzword Bingo

Buzzword Bingo: Data Science – Teil III

Im ersten Teil unserer Serie „Buzzword Bingo: Data Science“ widmeten wir uns den Begriffen Künstliche Intelligenz, Algorithmen und Maschinelles Lernen, im zweiten Teil den Begriffen Big Data, Predictive Analytics und Internet of Things. Nun geht es hier im dritten und letzten Teil weiter mit der Begriffsklärung dreier weiterer Begriffe aus dem Data Science-Umfeld.

Buzzword Bingo: Data Science – Teil III: Künstliche neuronale Netze & Deep Learning

Im dritten Teil unserer dreiteiligen Reihe „Buzzword Bingo Data Science“ beschäftigen wir uns mit den Begriffen „künstliche neuronale Netze“ und „Deep Learning“.

Künstliche neuronale Netze

Künstliche neuronale Netze beschreiben eine besondere Form des überwachten maschinellen Lernens. Das Besondere hier ist, dass mit künstlichen neuronalen Netzen versucht wird, die Funktionsweise des menschlichen Gehirns nachzuahmen. Dort können biologische Nervenzellen durch elektrische Impulse von benachbarten Neuronen erregt werden. Nach bestimmten Regeln leiten Neuronen diese elektrischen Impulse dann wiederum an benachbarte Neuronen weiter. Häufig benutzte Signalwege werden dabei verstärkt, wenig benutzte Verbindungen werden gleichzeitig im Laufe der Zeit abgeschwächt. Dies wird beim Menschen üblicherweise dann als Lernen bezeichnet.

Dasselbe geschieht auch bei künstlichen neuronalen Netzen: Künstliche Neuronen werden hier hinter- und nebeneinander geschaltet. Diese Neuronen nehmen dann Informationen auf, modifizieren und verarbeiten diese nach bestimmten Regeln und geben dann Informationen wiederum an andere Neuronen ab. Üblicherweise werden bei künstlichen neuronalen Netzen mindestens drei Schichten von Neuronen unterschieden.

  • Die Eingabeschicht nimmt Informationen aus der Umwelt auf und speist diese in das neuronale Netz ein.
  • Die verborgene(n) Schichte(n) liegen zwischen der Eingabe- und der Ausgabeschicht. Hier werden wie beschrieben die eingegebenen Informationen von den einzelnen Neuronen verarbeitet und anschließend weitergegeben. Der Name „verborgene“ Schicht betont dabei, dass für Anwender meist nicht erkennbar ist, in welcher Form ein neuronales Netz die Eingabeinformationen in den verborgenen Schichten verarbeitet.
  • Die letzte Schicht eines neuronalen Netzes ist die Ausgabeschicht. Diese beinhaltet die Ausgabeneuronen, welche die eigentliche Entscheidung, auf die das neuronale Netz trainiert wurde, als Information ausgeben.

Das besondere an neuronalen Netzen: Wie die Neuronen die Informationen zwischen den verborgenen Schichten verarbeiten und an die nächste Schicht weitergeben, erlernt ein künstliches neuronales Netz selbstständig. Hierfür werden – einfach ausgedrückt – die verschiedenen Pfade durch ein neuronales Netz, die verschiedene Entscheidungen beinhalten, häufig hintereinander ausprobiert. Führt ein bestimmter Pfad während des Trainings des neuronalen Netzes nicht zu dem vordefinierten korrekten Ergebnis, wird dieser Pfad verändert und in dieser Form zukünftig eher nicht mehr verwendet. Führt ein Pfad stattdessen erfolgreich zu dem vordefinierten Ergebnis, dann wird dieser Pfad bestärkt. Schlussendlich kann, wie bei jedem überwachten Lernprozess, ein erfolgreich trainiertes künstliches neuronales Netz auf unbekannte Eingangsdaten angewandt werden.

Auch wenn diese Funktionsweise auf den ersten Blick nicht sehr leicht verständlich ist: Am Ende handelt es sich auch hier bloß um einen Algorithmus, dessen Ziel es ist, Muster in Daten zu erkennen. Zwei Eigenschaften teilen sich künstliche neuronale Netze aber tatsächlich mit den natürlichen Vorbildern: Sie können sich besonders gut an viele verschiedene Aufgaben anpassen, benötigen dafür aber auch meistens mehr Beispiele (Daten) und Zeit als die klassischen maschinellen Lernverfahren.

Sonderform: Deep Learning

Deep Learning ist eine besondere Form von künstlichen neuronalen Netzen. Hierbei werden viele verdeckte Schichten hintereinander verwendet, wodurch ein tiefes (also „deep“) neuronales Netz entsteht.

Je tiefer ein neuronales Netz ist, umso komplexere Zusammenhänge kann es abbilden. Aber es benötigt auch deutlich mehr Rechenleistung als ein flaches neuronales Netz. Seit einigen Jahren steht diese Leistung günstig zur Verfügung, weshalb diese Form des maschinellen Lernens an Bedeutung gewonnen hat.

Data Science & Big Data

Buzzword Bingo: Data Science – Teil II

Im ersten Teil unserer Serie „Buzzword Bingo: Data Science“ widmeten wir uns den Begriffen Künstliche Intelligenz, Algorithmen und Maschinelles Lernen. Nun geht es hier im zweiten Teil weiter mit der Begriffsklärung dreier weiterer Begriffe aus dem Data Science-Umfeld.

Buzzword Bingo: Data Science – Teil II: Big Data, Predictive Analytics & Internet of Things

Im zweiten Teil unserer dreiteiligen Reihe „Buzzword Bingo Data Science“ beschäftigen wir uns mit den Begriffen „Big Data“, „Predictive Analytics“ und „Internet of Things“.

Big Data

Interaktionen auf Internetseiten und in Webshops, Likes, Shares und Kommentare in Social Media, Nutzungsdaten aus Streamingdiensten wie Netflix und Spotify, von mobilen Endgeräten wie Smartphones oder Fitnesstrackern aufgezeichnete Bewegungsdate oder Zahlungsaktivitäten mit der Kreditkarte: Wir alle produzieren in unserem Leben alltäglich immense Datenmengen.

Im Zusammenhang mit künstlicher Intelligenz wird dabei häufig von „Big Data“ gesprochen. Und weil es in der öffentlichen Diskussion um Daten häufig um personenbezogene Daten geht, ist der Begriff Big Data oft eher negativ konnotiert. Dabei ist Big Data eigentlich ein völlig wertfreier Begriff. Im Wesentlichen müssen drei Faktoren erfüllt werden, damit Daten als „big“ gelten. Da die drei Fachbegriffe im Englischen alle mit einem „V“ beginnen, wird häufig auch von den drei V der Big Data gesprochen.

Doch welche Eigenschaften sind dies?

  • Volume (Datenmenge): Unter Big Data werden Daten(-mengen) verstanden, die zu groß sind, um sie mit klassischen Methoden zu bearbeiten, weil beispielsweise ein einzelner Computer nicht in der Läge wäre, diese Datenmenge zu verarbeiten.
  • Velocity (Geschwindigkeit der Datenerfassung und -verarbeitung): Unter Big Data werden Daten(-mengen) verstanden, die in einer sehr hohen Geschwindigkeit generiert werden und dementsprechend auch in einer hohen Geschwindigkeit ausgewertet und weiterverarbeitet werden müssen, um Aktualität zu gewährleisten.
  • Variety (Datenkomplexität oder Datenvielfalt): Unter Big Data werden Daten(-mengen) verstanden, die so komplex sind, dass auf den ersten Blick keine Zusammenhänge erkennbar sind. Diese Zusammenhänge können erst mit speziellen maschinellen Lernverfahren aufgedeckt werden. Dazu gehört auch, dass ein Großteil aller Daten in unstrukturierten Formaten wie Texten, Bildern oder Videos abgespeichert ist.

Häufig werden neben diesen drei V auch weitere Faktoren aufgezählt, welche Big Data definieren. Dazu gehören Variability (Schwankungen, d.h. die Bedeutung von Daten kann sich verändern), Veracity (Wahrhaftigkeit, d.h. Big Data muss gründlich auf die Korrektheit der Daten geprüft werden), Visualization (Visualisierungen helfen, um komplexe Zusammenhänge in großen Datensets aufzudecken) und Value (Wert, d.h. die Auswertung von Big Data sollte immer mit einem unternehmerischen Vorteil einhergehen).

Predictive Analytics

  • Heute schon die Verkaufszahlen von morgen kennen, sodass eine rechtzeitige Nachbestellung knapper Produkte möglich ist?
  • Bereits am Donnerstagabend die Regenwahrscheinlichkeit für das kommende Wochenende kennen, sodass passende Kleidung für den Kurztrip gepackt werden kann?
  • Frühzeitig vor bevorstehenden Maschinenausfällen gewarnt werden, sodass die passenden Ersatzteile bestellt und das benötigte technische Personal angefragt werden kann?

Als Königsdisziplin der Data Science gilt für viele die genaue Vorhersage zukünftiger Zustände oder Ereignisse. Im Englischen wird dann von „Predictive Analytics“ gesprochen. Diese Methoden werden in vielen verschiedenen Branchen und Anwendungsfeldern genutzt. Die Prognose von Absatzzahlen, die Wettervorhersage oder Predictive Maintenance (engl. für vorausschauende Wartung) von Maschinen und Anlagen sind nur drei mögliche Beispiele.

Zu beachten ist allerdings, dass Predictive-Analytics-Modelle keine Wahrsagerei sind. Die Vorhersage zukünftiger Ereignisse beruht immer auf historischen Daten. Das bedeutet, dass maschinelle Modelle mit Methoden des überwachten maschinellen Lernens darauf trainiert werden, Zusammenhänge zwischen vielen verschiedenen Eingangseigenschaften und einer vorherzusagenden Ausgangseigenschaft zu erkennen. Im Falle der Predicitve Maintenance könnten solche Eingangseigenschaften beispielsweise das Alter einer Produktionsmaschine, der Zeitraum seit der letzten Wartung, die Umgebungstemperatur, die Produktionsgeschwindigkeit und viele weitere sein. In den historischen Daten könnte ein Algorithmus nun untersuchen, ob diese Eingangseigenschaften einen Zusammenhang damit aufweisen, ob die Maschine innerhalb der kommenden 7 Tage ausfallen wird. Hierfür muss zunächst eine ausreichend große Menge an Daten zur Verfügung stehen. Wenn ein vorherzusagendes Ereignis in der Vergangenheit nur sehr selten aufgetreten ist, dann stehen auch nur wenige Daten zur Verfügung, um dasselbe Ereignis für die Zukunft vorherzusagen. Sobald der Algorithmus einen entsprechenden Zusammenhang identifiziert hat, kann dieses trainierte maschinelle Modell nun verwendet werden, um zukünftige Maschinenausfälle rechtzeitig vorherzusagen.

Natürlich müssen solche Modelle dauerhaft darauf geprüft werden, ob sie die Realität immer noch so gut abbilden, wie zu dem Zeitpunkt, zu dem sie trainiert worden sind. Wenn sich nämlich die Umweltparameter ändern, das heißt, wenn Faktoren auftreten, die zum Trainingszeitpunkt noch nicht bekannt waren, dann muss auch das maschinelle Modell neu trainiert werden. Für unser Beispiel könnte dies bedeuten, dass wenn die Maschine für die Produktion eines neuen Produktes eingesetzt wird, auch für dieses neue Produkt zunächst geprüft werden müsste, ob die in der Vergangenheit gefundenen Zusammenhänge immer noch Bestand haben.

Internet of Things

Selbstfahrende Autos, smarte Kühlschränke, Heizungssysteme und Glühbirnen, Fitnesstracker und vieles mehr: das Buzzword „Internet of Things“ (häufig als IoT abgekürzt) beschreibt den Trend, nicht nur Computer über Netzwerke miteinander zu verbinden, sondern auch verschiedene alltägliche Objekte mit in diese Netzwerke aufzunehmen. Seinen Anfang genommen hat dieser Trend in erster Linie im Bereich der Unterhaltungselektronik. In vielen Haushalten sind schon seit Jahren Fernseher, Computer, Spielekonsole und Drucker über das Heimnetzwerk miteinander verbunden und lassen sich per Smartphone bedienen.

Damit ist das IoT natürlich eng verbunden mit Big Data, denn all diese Geräte produzieren nicht nur ständig Daten, sondern sie sind auch auf Informationen sowie auf Daten von anderen Geräten angewiesen, um zu funktionieren.

Training of Deep Learning AI models

It’s All About Data: The Training of AI Models

In deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how much data is there, is it labeled or unlabeled? Or is there both labeled and unlabeled data?

Let’s say our customer needs structured, labeled images for an online tourism portal. The task for our AI model is therefore to recognize whether a picture is a bedroom, bathroom, spa area, restaurant, etc. Let’s take a look at the possible training methods.

1. Supervised Learning

If our customer has a lot of images and they are all labeled, this is a rare stroke of luck. We can then apply supervised learning. The AI model learns the different image categories based on the labeled images. For this purpose, it receives the training data with the desired results from us.

During training, the model searches for patterns in the images that match the desired results, learning the characteristics of the categories. The model can then apply what it has learned to new, unseen data and in this way provide a prediction for unlabeled images, i.e., something like “bathroom 98%.”

2. Unsupervised Learning

If our customer can provide many images as training data, but all of them are not labeled, we have to resort to unsupervised learning. This means that we cannot tell the model what it should learn (the assignment to categories), but it must find regularities in the data itself.

Contrastive learning is currently a common method of unsupervised learning. Here, we generate several sections from one image at a time. The model should learn that the sections of the same image are more similar to each other than to those of other images. Or in short, the model learns to distinguish between similar and dissimilar images.

Although we can use this method to make predictions, they can never achieve the quality of results of supervised learning.

3. Semi-supervised Learning

If our customer can provide us with few labeled data and a large amount of unlabeled data, we apply semi-supervised learning. In practice, we actually encounter this data situation most often.

With semi-supervised learning, we can use both data sets for training, the labeled and the unlabeled data. This is possible by combining contrastive learning and supervised learning, for example: we train an AI model with the labeled data to obtain predictions for room categories. At the same time, we let the model learn similarities and dissimilarities in the unlabeled data and then optimize itself. In this way, we can ultimately achieve good label predictions for new, unseen images.

Supervised vs. Unsupervised vs. Semi-supervised

Everyone who is entrusted with an AI project wants to apply supervised learning. In practice, however, this is rarely the case, as rarely all training data is well structured and labeled.

If only unstructured and unlabeled data is available, we can at least extract information from the data with unsupervised learning. These can already provide added value for our customer. However, compared to supervised learning, the quality of the results is significantly worse.

With semi-supervised learning, we try to resolve the data dilemma of small part labeled data, large part unlabeled data. We use both datasets and can obtain good prediction results whose quality is often on par with those of supervised learning. This article is written in cooperation between DATANOMIQ and pixolution, a company for computer vision and AI-bases visual search.

Buzzword Bingo: Data Science – Teil I

Rund um das Thema Data Science gibt es unglaublich viele verschiedene Buzzwords, die Ihnen sicherlich auch schon vielfach begegnet sind. Sei es der Begriff Künstliche Intelligenz, Big Data oder auch Deep Learning. Die Bedeutung dieser Begriffe ist jedoch nicht immer ganz klar und häufig werden Begriffe auch vertauscht oder in missverständlichen Zusammenhängen benutzt. Höchste Zeit also, sich einmal mit den genauen Definitionen dieser Begriffe zu beschäftigen!

Buzzword Bingo: Data Science – Teil 1: Künstliche Intelligenz, Algorithmen & Maschinelles Lernen

Im ersten Teil unserer dreiteiligen Reihe „Buzzword Bingo Data Science“ beschäftigen wir uns zunächst mit den drei Begriffen „Künstliche Intelligenz“, „Algorithmus“ und „Maschinelles Lernen“.

Künstliche Intelligenz

Der im Bereich der Data Science u. a. am häufigsten genutzte Begriff ist derjenige der „Künstlichen Intelligenz“. Viele Menschen denken bei dem Begriff sofort an hochspezialisierte Maschinen à la „The Matrix“ oder „I, Robot“. Dabei ist der Begriff deutlich älter als viele denken. Bereits 1956 wurde der englische Begriff “artificial intelligence” zum ersten Mal in einem Workshop-Titel am US-amerikanischen Dartmouth College genutzt.

Heutzutage besitzt der Begriff der künstlichen Intelligenz keine allgemeingültige Definition. Es handelt sich bei künstlicher Intelligenz grundsätzlich um ein Teilgebiet der Informatik, das sich mit der Automatisierung von intelligentem Verhalten befasst. Es geht also darum, dass ein Computerprogramm auf eine Eingabe eine intelligente Reaktion zeigt. Zu beachten ist hierbei, dass eine künstliche Intelligenz nur ein scheinbar intelligentes Verhalten zeigen kann. Künstliche Intelligenz wird heutzutage sehr weit gefasst und kann vieles umfassen: von klassischen, regelbasierten Algorithmen bis hin zu selbstlernenden künstlichen neuronalen Netzen.

Das zentrale Forschungsziel ist die Entwicklung einer sogenannten Allgemeinen Künstlichen Intelligenz, also einer Maschine, die in der Lage sein wird, autonom beliebige Probleme zu lösen. Es gibt eine fortlaufende Debatte darüber, ob dieses Ziel jemals erreicht werden kann bzw. ob es erreicht werden sollte.

In den vergangenen Jahren ist auch die sogenannte xAI (engl. Explainable AI; erklärbare künstliche Intelligenz) in den Mittelpunkt der Forschungsinteressen gerückt. Dabei geht es um die Problematik, dass künstliche Intelligenzen sogenannte Black Boxen sind. Das bedeutet, dass ein menschlicher User die Entscheidung einer künstlichen Intelligenz üblicherweise nicht nachvollziehen kann. Eine xAI wäre im Vergleich jedoch eine Glass Box, die Entscheidungen einer solchen künstlichen Intelligenz wären für Menschen also nachvollziehbar.


Algorithmen sind klar definierte, vorgegebene Prozeduren, mit denen klar definierte Aufgaben gelöst werden können. Dabei kann der Lösungsweg des Algorithmus entweder durch Menschen vorgegeben, also programmiert werden oder Algorithmen lernen durch Methoden des maschinellen Lernens selbstständig den Lösungsweg für eine Prozedur.

Im Bereich der Data Science bezeichnen wir mit Algorithmen kleine Programme, die scheinbar intelligent handeln. Dementsprechend stecken auch hinter künstlichen Intelligenzen Algorithmen. Werden Algorithmen mit klar definierten Eingaben versorgt, führen sie somit zu einem eindeutigen, konstanten Ergebnis. Dabei gilt aber leider auch der Grundsatz der Informatik „Mist rein, Mist raus“. Ein Algorithmus kann immer nur auf sinnvolle Eingaben sinnvolle Ausgaben erzeugen. Die Komplexität von Algorithmen kann sehr vielfältig sein und je komplexer ein solcher Algorithmus ist, desto „intelligenter“ erscheint er oftmals.

Maschinelles Lernen

Maschinelles Lernen ist ein Überbegriff für eine Vielzahl von Verfahren, mit denen ein Computer oder eine künstliche Intelligenz automatisch Muster in Daten erkennt. Beim maschinellen Lernen wird grundsätzlich zwischen dem überwachten und unüberwachten Lernen unterschieden.

Beim überwachten Lernen lernt ein Algorithmus den Zusammenhang zwischen bekannten Eingabe- und Ausgabewerten. Nachdem dieser Zusammenhang vom Algorithmus erlernt wurde, kann dieses maschinelle Modell dann auf neue Eingabewerte angewandt und somit unbekannte Ausgabewerte vorhergesagt werden. Beispielsweise könnte mithilfe einer Regression zunächst der Zusammenhang zwischen Lufttemperatur und dem Wochentag (jeweils bekannte Eingabewerte) sowie der Anzahl der verkauften Eiskugeln (für die Vergangenheit bekannte Ausgabewerte) in einem Freibad untersucht werden. Sobald dieser Zusammenhang einmal ausreichend genau bestimmt worden ist, kann er auch für die Zukunft fortgeschrieben werden. Das bedeutet, es wäre dann möglich, anhand des nächsten Wochentages sowie der vorhergesagten Lufttemperatur (bekannte Eingabewerte für die Zukunft) die Anzahl der verkauften Eiskugeln (unbekannte Ausgabewerte für die Zukunft) zu prognostizieren und somit die Absatzmenge genauer planen zu können.

Beim unüberwachten Lernen auf der anderen Seite sind nur Eingabedaten vorhanden, es gibt keine den Eingabedaten zugehörigen Ausgabedaten. Hier wird dann mit Methoden wie beispielsweise dem Clustering versucht, verschiedene Datenpunkte anhand ihrer Eigenschaften in verschiedene Gruppen aufzuteilen. Beispielsweise könnte ein Clustering-Algorithmus verschiedene Besucher:innen eines Webshops in verschiedene Gruppen einteilen: Es könnte beispielsweise eine Gruppe von Besucher:innen geben, die sehr zielstrebig ein einzelnes Produkt in den Warenkorb legen und ihren Kauf direkt abschließen. Andere Besucher:innen könnten allerdings viele verschiedene Produkte ansehen, in den Warenkorb legen und am Ende nur wenige oder vielleicht sogar gar keine Käufe tätigen. Wieder andere Kund:innen könnten unter Umständen lediglich auf der Suche nach Artikeln im Sale sein und keine anderen Produkte ansehen.

Aufgrund ihres Nutzungsverhaltens auf der Website könnte ein Clustering-Algorithmus mit ausreichend aufbereiteten Daten nun all diese Kund:innen in verschiedene Gruppen oder Cluster einteilen. Was der Algorithmus jedoch nicht leisten kann ist zu erklären, was die erkannten Cluster genau bedeuten. Hierfür braucht es nach wie vor menschliche Intelligenz gepaart mit Fachwissen.

Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning

This article focuses on autonomous trading agent to solve the capital market portfolio management problem. Researchers aim to achieve higher portfolio return while preferring lower-risk actions. It uses deep reinforcement learning Deep Q-Network (DQN) to train the agent. The main contribution of their work is the proposed target policy.


Author emphasizes the importance of low-risk actions for two reasons: 1) the weak positive correlation between risk and profit suggests high returns can be obtained with low-risk actions, and 2) customer satisfaction decreases with increases in investment risk, which is undesirable. Author challenges the limitation of Supervised Learning algorithm since it requires domain knowledge. Thus, they propose Reinforcement Learning to be more suitable, because it only requires state, action and reward specifications.

The study verifies the method through the back-test in the cryptocurrency market because it is extremely volatile and offers enormous and diverse data. Agents then learn with shorter periods and are tested for the same period to verify the robustness of the method. 

2 Proposed Method

The overall structure of the proposed method is shown below.

The architecutre of the proposed trading agent system.

The architecutre of the proposed trading agent system.

2.1 Problem Definition

The portfolio consists of m assets and one base currency.

The price vector p stores the price p of all assets:

The portfolio vector w stores the amount of each asset:

At time 𝑡, the total value W_t of the portfolio is defined as the inner product of the price vector p_t and the portfolio vector w_t .

Finally, the goal is to maximize the profit P_t at the terminal time step 𝑇.

2.2 Asset Data Preprocessing

1) Asset Selection
Data is drawn from the Binance Exchange API, where top m traded coins are selected as assets.

2) Data Collection
Each coin has 9 properties, shown in Table.1, so each trade history matrix has size (α * 9), where α is the size of the target period converted into minutes.

3) Zero-Padding
Pad all other coins to match the matrix size of the longest coin. (Coins have different listing days)

Comment: Author pointed out that zero-padding may be lacking, but empirical results still confirm their method covering the missing data well.

4) Stack Matrices
Stack m matrices of size (α * 9) to form a block of size (m* α * 9). Then, use sliding window method with widow size w to create (α – w + 1) number of sequential blocks with size (w *  m * 9).

5) Normalization
Normalize blocks with min-max normalization method. They are called history block 𝜙 and used as input (ie. state) for the agent.

3. Deep Q-Network

The proposed RL-based trading system follows the DQN structure.

Deep Q-Network has 2 networks, Q- and Target network, and a component called experience replay. The Q-network is the agent that is trained to produce the optimal state-action value (aka. q-value).

Comment: Q-value is calculated by the Bellman equation, which, in short, consists of the immediate reward from next action, and the discounted value of the next state by following the policy for all subsequent steps.


Agent: Portfolio manager
Action a: Trading strategy according to the current state
State 𝜙 : State of the capital market environment
Environment: Has all trade histories for assets, return reward r and provide next state 𝜙’ to agent again

DQN workflow:

DQN gets trained in multiple time steps of multiple episodes. Let’s look at the workflow of one episode.

Training of a Deep Q-Network

Training of a Deep Q-Network

1) Experience replay selects an action according to the behavior policy, executes in the environment, returns the reward and next state. This experience set (\phi_t, a_t, r_r,\phi_{t+!}) is stored in the repository as a sample of training data.

2) From the repository of prior observations, take a random batch of samples as the input to both Q- and Target network. The Q-network takes the current state and action from each data sample and predicts the q-value for that particular action. This is the ‘Predicted Q-Value’.Comment: Author uses 𝜀-greedy algorithm to calculate q-value and select action. To simplify, 𝜀-greedy policy takes the optimal action if a randomly generated number is greater than 𝜀, which represents a tradeoff between exploration and exploitation.

The Target network takes the next state from each data sample and predicts the best q-value out of all actions that can be taken from that state. This is the ‘Target Q-Value’.

Comment: Author proposes a different target policy to calculate the target q-value.

3) The Predicted q-value, Target q-value, and the observed reward from the data sample is used to compute the Loss to train the Q-network.

Comment: Target Network is not trained. It is held constant to serve as a stable target for learning and will be updated with a frequency different from the Q-network.

4) Copy Q-network weights to Target network after n time steps and continue to next time step until this episode is finished.

The architecutre of the proposed trading agent system.

4.0 Main Contribution of the Research

4.1 Action and Reward

Agent determines not only action a but ratio , at which the action is applied.

  1. Action:
    Hold, buy and sell. Buy and sell are defined discretely for each asset. Hold holds all assets. Therefore, there are (2m + 1) actions in the action set A.

    Agent obtains q-value of each action through q-network and selects action by using 𝜀-greedy algorithm as behavior policy.
  2. Ratio:
    \sigma is defined as the softmax value for the q-value of each action (ie. i-th asset at \sigma = 0.5 , then i-th asset is bought using 50% of base currency).
  3. Reward:
    Reward depends on the portfolio value before and after the trading strategy. It is clipped to [-1,1] to avoid overfitting.

4.2 Proposed Target Policy

Author sets the target based on the expected SARSA algorithm with some modification.

Comment: Author claims that greedy policy ignores the risks that may arise from exploring other outcomes other than the optimal one, which is fatal for domains where safe actions are preferred (ie. capital market).

The proposed policy uses softmax algorithm adjusted with greediness according to the temperature term 𝜏. However, softmax value is very sensitive to the differences in optimal q-value of states. To stabilize  learning, and thus to get similar greediness in all states, author redefine 𝜏 as the mean of absolute values for all q-values in each state multiplied by a hyperparameter 𝜏’.

4.3 Q-Network Structure

This study uses Convolutional Neural Network (CNN) to construct the networks. Detailed structure of the networks is shown in Table 2.

Comment: CNN is a deep neural network method that hierarchically extracts local features through a weighted filter. More details see:

5 Experiment and Hyperparameter Tuning

5.1 Experiment Setting

Data is collected from August 2017 to March 2018 when the price fluctuates extensively.

Three evaluation metrics are used to compare the performance of the trading agent.

  • Profit P_t introduced in 2.1.
  • Sharpe Ratio: A measure of return, taking risk into account.

    Comment: p_t is the standard deviation of the expected return and P_f  is the return of a risk-free asset, which is set to 0 here.
  • Maximum Drawdown: Maximum loss from a peak to a through, taking downside risk into account.

5.2 Hyperparameter Optimization

The proposed method has a number of hyperparameters: window size mentioned in 2.2,  𝜏’ in the target policy, and hyperparameters used in DQN structure. Author believes the former two are key determinants for the study and performs GridSearch to set w = 30, 𝜏’ = 0.25. The other hyperparameters are determined using heuristic search. Specifications of all hyperparameters are summarized in the last page.

Comment: Heuristic is a type of search that looks for a good solution, not necessarily a perfect one, out of the available options.

5.3 Performance Evaluation

Benchmark algorithms:

UBAH (Uniform buy and hold): Invest in all assets and hold until the end.
UCRP (Uniform Constant Rebalanced Portfolio): Rebalance portfolio uniformly for every trading period.

Methods from other studies: hyperparameters as suggested in the studies
EG (Exponential Gradient)
PAMR (Passive Aggressive Mean Reversion Strategy)

Comment: DQN basic uses greedy policy as the target policy.

The proposed DQN method exhibits the best overall results out of the 6 methods. When the agent is trained with shorter periods, although MDD increases significantly, it still performs better than benchmarks and proves its robustness.

6 Conclusion

The proposed method performs well compared to other methods, but there is a main drawback. The encoding method lacked a theoretical basis to successfully encode the information in the capital market, and this opaqueness is a rooted problem for deep learning. Second, the study focuses on its target policy, while there remains room for improvement with its neural network structure.

Specification of Hyperparameters

Specification of Hyperparameters.



  1. Shin, S. Bu and S. Cho, “Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning”,
  2. Li, P. Zhao, S. C. Hoi, and V. Gopalkrishnan, “PAMR: passive aggressive mean reversion strategy for portfolio selection,” Machine learning, vol. 87, pp. 221-258, 2012.
  3. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth, “On‐line portfolio selection using multiplicative updates,” Mathematical Finance, vol. 8, pp. 325-347, 1998.,can%20be%20interpreted%20as%20probabilities.