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AI Toolkit

EvolutionAI Professional
The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. Can be trained further with new knowledge without old data. Big data Cleaning, Pre-processing, automatic Classification, Statistical analyzes, very fast!
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DecisionAI Professional
Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. The extended version of DecisionAI with superior functionality, accuracy and speed. Also included data cleaning, filtering, statistical analysis, automatic classification.
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CascadeStudio
Artificial Intelligence (AI) Software Toolkit for Training, Visualization and Testing of AI models for fast object detection in images and video's.



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Decision AI Professional
Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. Supports both Textual and Numerical AI models. Also included: Statistical Analysis, Control Charts and Automatic Data Classification Toolkit.
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Deep AI
Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. Complex multi-layered and multi-node deep neural networks made easy!

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Vision AI
Artificial Intelligence (AI) Software Toolkit for Machine Learning of Images. Also included: AI image enhancement, Image collage creator, Text Recognition and Text To Speech in several languages.
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VoiceData
Automatic Speech Recognition (ASR) Data Generator Toolkit. Text Normalization (Natural Language Processing). AI Text Recognition. Audio Editor. Transform Sample Rate, Channels, Suppress Noise, Cancel Echo, Change Tempo, Rate, Pitch Frequency, Remove Audio Without Human Voice.
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DocumentSummary
Can be used to create a short summary from any text document as simple text, PDF files, HTML files, etc. on your computer or on the internet. Uses Artificial Intelligence (AI) powered language models. Able to take into account specialized words specific to your discipline (law, medicine, chemistry, etc.).
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(C) 2016-present Zoltan Somogyi, EvolutionAI, DecisionAI, DeepAI, VisionAI and CascadeStudio are Copyright Zoltan Somogyi, All Rights Reserved.

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EvolutionAI Professional
VoiceBridge is an Open Source state of the art Speech Recognition C++ Toolkit
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Knowledge

The Application of AI in Human Resources

The Application of AI in Human Resources
Artificial Intelligence in Human Resources, AI HR
You will learn how to use the AI tools included in the AI-TOOLKIT for making HR decisions easily. In this case we will train an AI model which can be used to decide/predict if an employee will leave or why he/she will leave or even if it is worthwhile to offer a promotion to an employee.
The article will also explain and compare the different algorithm/tool options available in the AI-TOOLKIT.

You can apply the same principles to any other sector or business case, for example you could predict if a client will leave, why it will leave, or if it is worthwhile to offer a discount, etc.

The Dataset

The dataset contains 15000 rows (records) and 10 columns (variables). You can download the data at the end of this article. In case you are doing this in your company you should first study which variables influence the most the specific business case, in this example an HR problem, and select the variables accordingly. Selecting not enough or too many variables (not enough knowledge or unneeded noise) will result in a less useful or less accurate AI model.

The 10 columns are the following:
  • Satisfaction Level (0-1)
  • Last evaluation (0-1)
  • Number of projects (integer)
  • Average monthly hours (integer)
  • Time spent at the company (integer)
  • Whether they have had a work accident (0-no, 1-yes)
  • Whether they have had a promotion in the last 5 years  (0-no, 1-yes)
  • Department name (text)
  • Salary (text: low, medium, high)
  • Whether the employee has left (0-no, 1-yes)

Depending on which variable (column) you choose as decision variable you can train a model for different purposes for example to predict whether the employee will leave in the future, whether it is worthwhile to offer a promotion, etc.

In this example we will choose the ‘Left’ (whether the employee has left) column as decision variable in order to be able to predict if an employee will leave or not.

Artificial Intelligence in Human Resources, AI HR

Training the AI Model

The different tools in the AI-TOOLKIT use different types of AI algorithms. Each algorithm has its advantages and disadvantages. Some algorithms are well suited for one type of data but not for another type of data. Neural network based AI algorithms can be tuned in such way that they can be applied to all kinds of problems but with the cost of complexity (several layers with hundreds of nodes and even different types of layers) and processing speed (more layers and nodes mean more processing time). Therefore it is worthwhile to choose the tool you want to use in a clever way! In case of the AI-TOOLKIT this translates to the preference of using DecisionAI instead of DeepAI whenever possible.

DecisionAI Numerical Model

The numerical model in DecisionAI is based on an extended version of a Support Vector Machine (SVM) algorithm. The SVM algorithm is well suited for problems with a lot of numerical data.

If the original data is in CSV (comma separated) format you first need to open it in MS Excel and save it as XLS file. DecisionAI can import MS Excel XLS files. After this start DecisionAI and the ‘Import Data MS Excel’ tool. Select the XLS file, the decision column and indicate if the first row is a textual header row. After this use the ‘Import As SSV’ command and save the data file. Please note that DecisionAI automatically converts the textual data to numerical data and besides importing your data you do not need to do any pre-processing because everything is done automatically in DecisionAI. This of course does not mean that you do not have to clean your data and make sure that it is of high quality. Good data is crucial for a good AI model!

As a first step adjust the Settings in the ‘Numerical AI Model/Classification AI’ tab. Set C = 100, Kernel Type = RBF and gamma = 10. You could also first use the Parameter Optimization module in order to determine the optimal parameters.
In DecisionAI use the ‘Train NMC AI Model’ command and select the just created data file. The model training process will start immediately and then the results below will appear (in 8-10 seconds). The accuracy of the trained model is a very good 99.91% (14985 cases are correctly predicted from 14999 cases, only 14 cases are wrong).

DecisionAIPro AI-TOOLKIT Artificial Intelligence Toolkit

DecisionAI Mixed AI Model

The mixed AI model in DecisionAI is based on extended decision tree algorithm. This algorithm is well suited for problems where there is more textual data than numerical data and the variation of the data is low. Where low variation means that for example the ‘Salary’ column in the example dataset has only a few options as low, medium and high.

In order to train the mixed AI model do the same as in case of the numerical AI model above but when importing the data import and save it in TSV format! The model training can be started with the ‘Train MMC AI Model’ command. After the training of the model is ready (in 12-17 seconds) the results below will appear.
The accuracy of the trained model is lower than with the numerical model but still not bad, 97.1% (14564 cases are correctly predicted from 14999 cases, 435 cases are wrong).

DecisionAIPro AI-TOOLKIT Artificial Intelligence Toolkit

DeepAI Neural Network Model

The deep neural network model in DeepAI is based on a semi-automatic multilayer and multimode neural network implementation. The software designs the neural network semi-automatically, you only need to define the number of layers and nodes per layer (you can of course adjust some more parameters but this is most of the time not necessary). DeepAI does not use very complex state of the art neural network architectures and algorithms because that would overcomplicate the software for a small accuracy improvement. This is OK in most of the cases.

DeepAI uses the same SSV data file format as the above numerical AI model in DecisionAI. Adjust the settings in the ‘Settings/AI’ tab according to the setting shown in the image below in case they are different. Use the ‘Load Data File’ command and load the data file. DeepAI will automatically design a neural network for the data file. This neural network will provide good results but let us add an extra layer (4 layers in total) and adjust the number of nodes to 24 on the second layer and 10 on the third layer. The first and the last layers have a fixed number of nodes depending on the input functions and the output (1).
Change the number of iterations to 480 and start the training process with the Run command. After a while (2-3 min) the below results will appear which indicate 98 % accuracy for the training data. You still can fine tune the model and obtain a higher accuracy but this is not a fast and simple process. Fine tuning a neural network is a tedious and often long lasting process (adjusting the number of layers, adjusting the number of nodes per layer, adjusting the learning rate, the activation function, etc.). It is also not sure that more layers and nodes will provide better results but you will need to find the optimal solution also depending on the other parameters.

DeepAI AI-TOOLKIT Artificial Intelligence Toolkit Neural Network

Comparison of the results

Below you will find the comparison of the trained model accuracy with the different tools in the AI-TOOLKIT and from other sources. You can use the trained models to predict if an employee will leave.

Model
Accuracy
DecisionAI:
Numerical model (SVM based)
99.9 %
Mixed AI model (decision tree based)
97.1 %
DeepAI:
Neural Network (simple, not optimized)
98.0 %
Analyses results from other sources:
Gradient Boosting
99.2 %
Random Forest
98.8 %
Support Vector Machine (SVM)
97.4 %
Decision Tree

Source: https://www.kaggle.com/ahujam/using-machine-learning-to-predict-attrition-in-hr
97.2 %

Using the trained AI models

In both DecisionAI and DeepAI it is very easy to load and use a formerly trained AI model. 

In DecisionAI you have the following options:
  • Use the ‘Ask MMC AI’ or ‘Ask NM AI’ model commands and load a data file. All records in the data file will be predicted with the trained AI model and will appear in the AI Predictions data tab. You can also export the results into MS Excel.
  • Use the ‘Ask MMC AI Form’ to predict only one case.
  • Use the DecisionAI server to automate the prediction process for example from another software package as SAP, CRM, etc. Read the built-in help on how to do this.
In DeepAI you have the following options:
  • Use the ‘Ask Predictions’ command and load a data file. All records in the data file will be predicted with the current trained AI model and will appear in the AI Predictions data tab. You can also export the results into MS Excel.
  • Use the ‘Ask Predictions Form’ is similar to the former but here you can load any AI model and data file.
  • Use the DeepAI server to automate the prediction process for example from another software package as SAP, CRM, etc. Read the built-in help on how to do this.

Conclusion

The different tools in the AI-TOOLKIT use different types of AI algorithms. Each algorithm has its advantages and disadvantages depending on the input data. Training Neural Network based AI models (DeepAI) is much more work than training the numerical or mixed AI models (DecisionAI). Therefore it is cleverer to only use neural network based AI models/tools when it is really necessary (when the other models do not perform well/low accuracy). The accuracy of the trained AI model mainly depends on the input data and also on the parameters of the models. The quality of the data is very important therefore always check the data before training your AI model. In case the accuracy of the trained AI model is very low then it is very probable that there is something wrong with the data!

Download the Dataset

You can download the dataset in MS Excel format here: HR_comma_sep_u.xls

References
  • HR Analytics Dataset: Attribution-Share Alike 4.0 International (CC BY-SA 4.0) license, Source: https://www.kaggle.com/ludobenistant/hr-analytics

The AI-TOOLKIT


Evolution AI Artificial Intelligence Software Toolkit Decision AI Artificial Intelligence Software Toolkit Cascade Studio Artificial Intelligence Deep AI Artificial Intelligence Vision AI Artificial Intelligence

You can download the AI-TOOLKIT for free (https://ai-toolkit.blogspot.com/p/ai-toolkit.html) which contains several easy to use software packages and experiment with AI capabilities in your business:
  • The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. Can be trained further with new knowledge without old data. Big data Cleaning, Pre-processing, automatic Classification, Statistical analyzes, very fast!
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. The extended version of DecisionAI (see below) with superior functionality, accuracy and speed. Also included data cleaning, filtering, statistical analysis, automatic classification.
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making but may also be used for other purposes. Supports both mixed AI and numerical AI models. It also contains extensive data analysis tools (statistical analysis, control charts, etc.) for your Business Process Improvement (or six-sigma) projects. DecisionAI can be used (after training the AI) to quickly make decisions about many different often complex matters as e.g.: which next process step the employee (or client, product, etc.) has to take, decide if an action should be taken or not, select an employee (or client, product, etc.) for a specific task (or action, marketing campaign, etc.), root cause analyses, etc.
  • Multi Layered Neural Network Artificial Intelligence (AI) software toolkit. Supports multi-layered and multi-node neural networks for machine learning. DeepAI is well-suited for problems in which the training data is complex and may contain noisy (e.g. complex sensor data such as sound from microphones).
  • Multi Layered Neural Network Artificial Intelligence (AI) Imaging Toolkit. Supports complex multi-layered convolutional neural networks for machine learning of Images.
  • Cascade model training, visualization and testing toolkit for fast object detection in images and video's. CascadeStudio can be used to detect rigid or near rigid objects e.g. faces, human body, cars, animals, plants, flowers, equipment, products on production lines, etc. in images and video’s.






The Future of Artificial Intelligence

The Future of Artificial Intelligence
The future of artificial intelligence
There is a lot of news about Artificial Intelligence (AI) lately but the information is often misleading and wrong. There are many articles which are spreading fear and saying that AI will replace you on your job, that AI is very dangerous because it is cleverer than humans, killer robots, ethical issues, etc. In this article I would like to clarify my vision about the future of AI and I also would like to tell you why most of these articles are wrong and why it will still take hundreds of years to build an AI which mimics well the human brain and body.

Google, who is one of the leading technology giants in the fields of AI, explained recently that they are able to make the brain of a mouse today, but I think that even that is hugely exaggerating. You will understand why after reading this article.

But the answer to the question whether the current state of AI is useful is of course yes, very useful. We can use the results of many years of AI research in endless useful applications in all business sectors. The current state of AI helps humans to do their jobs better and makes things possible which otherwise would not be possible as for example developing new vaccines for deadly diseases, improving business processes, helping humans with disabilities, discovering new things, gaining new insides from all kinds of data and in all business sectors,… etc. Current AI algorithms can interpret all kinds of data as for example numbers, text, images, video, audio, etc. A whole new range of applications are possible and only your imagination is the limit for finding useful applications in your business sector.

In the next sections I will explain in simple terms the current state and my vision about the most important building blocks of a real human like AI system: the Memory, CPU, Sensors, the whole interconnected System and also how it will Learn and use the learned information.

Memory

First of all let me begin with a simplified explanation about how the human body and brain works and why it will be extremely difficult to make an AI (robot) in the future which can replace a human even partly. One of the mistakes which AI researchers make today is that they do not take into account the enormous amount of different types of interconnected memory (and stored information) a human body and brain has. Even brain research is still guessing about many things but there are more and more signs (proven by experiments) that we store different types of things in our brain (memories). We store images, smells, sounds, etc. There is an enormous amount of information in a well developed human brain and body. Yes body because our whole body has different types of interconnected memory, just think about for example what we call muscle memory. One of the reasons why we are unable to make even an attempt to replicate a human is because of the lack of flexible, very fast and huge amount of memory. We are not talking about megabytes or gigabytes of memory but thousands of terabytes of interconnected memory. The technology to be able to have this kind of memory is still very far away. It is very likely that this types of memory will not be like the memory modules we use in our computers today but it will be some kind of chemical and biological substance (like the human body and brain has). There is an active research going on in this field also.

So how will an AI similar to the human body and brain function and work? I think that there will be a very simple logic which connects the different types of memory and sensors (see later) and the complexity will not come from the basic building blocks (which will be very simple) but from the very cleverly interconnected and coordinated whole system. The AI will store all necessary information like the human brain and body (and possibly even much more) and will be able to recall that information (even many of them in parallel) according to some kind of stimuli.

Sensors

The second reason why it is extremely difficult to replicate a human today with an AI is because of the many types of interconnected sensors the human body has. Just think about the eyes (vision), the ears (hearing), nose (smells), touch (all over the human body), temperature, etc.

We are at a very good level with the vision capabilities but even that is much more complex in reality because a human eye (and connected brain) can sense in several dimensions. High resolution is again a very important element because only one 3D image a human eye sees consumes a huge amount of memory and there are endless numbers of images (as a kind of video). We can mimic the human eye 3D capabilities with two cameras today but one human eye can see already in 3D (or more) and the image is used by our brain to estimate depth, distances, etc. Does one human eye contain several ‘camera’s’? Most probably yes! Most of today’s AI algorithms are limited to 2D grayscale images because of the computing power and memory needed to analyze these images! E.g. AI algorithms which can detect an object in an image or video are first converting the images to grayscale before processing.

All sensors are active in parallel in a human body all the time, we see, we hear, we smell, etc. and they are interconnected with a very clever system also containing the nerves in our body (yes the whole body and not only the brain). Just think about an object you are closing to and how you decide if you like that object. You will take several things into account at the same time, the visuals, the smells, the sounds, etc. and you decide automatically in an instant if you like that object or not.

The human body and brain also stores many of the information it receives which takes up even more memory and logic. For example how a smell is stored and recalled in your brain? I do not think that there is anybody who can answer this question yet. The human brain must have some kind of very efficient ‘internal language’ which is used to describe these things. 

CPU

We have today the capability to use computers with several CPU’s also containing separate computing units (cores). But how many and how fast ‘CPU units’ a human body and brain has and how they work seamlessly in parallel? I think that you can imagine that this question is very similar to that of the memory issue above. The human body and brain process in parallel a huge amount of information (from many sensors in the human body, from memory, etc.) and all of this information is used instantly to make decisions. The information needs to be found in the different types of human memory, it needs to be processed and combined. We are very far away from the speed, storage capacity, parallel processing, logic, etc. to be able to replicate a human brain and body. The ‘wonders’ of nature and the human body have still a lot of secrets for us even after studying them for thousands of years!

One of the positive developments today is the recent appearance of extra computing power and extra AI capabilities in video cards led by NVIDIA Corporation. This is a completely logical evolution and I expect the appearance of the same capabilities e.g. in a separate audio processing unit (and other sensor systems). Computer systems will first grow in this way and just in the far future will they be integrated more closely and will be much smaller in size. I also expect in the very far future that many things will be replaced by some kind of biological and chemical substances with human like capabilities.

But for the time being there is an urgent necessity for standardization of current and future developments in order to make these systems (video card, CPU, memory, etc.) much better coordinated and easier to use (software development).

The Whole System

A human body and brain contains an interconnected system which senses, processes, stores and recalls all of the information a human experiences. Many types of information is used at the same time to make decisions as for example that a glass can be used to drink, that you can open a door, what to do if someone is calling you, what to say if someone is asking a question, etc. The basic building blocks of this interconnected system are very simple but the whole system together is extremely complex. Today AI systems do not take this into account because they are either overcomplicating the basic building blocks and/or they are incorrectly building the system as a whole e.g. not taking into account the distributed different types of memory and huge amount of stored information, stimuli and input from several sensors, etc. 
This is of course also because we are not yet at the technological level and knowledge which are necessary for building a real AI system and that AI research is trying to simulate human like AI patterns.

So how will an AI similar to the human body and brain function and work? I think that there will be a very simple logic which connects the different types of memory, sensors, CPU, etc. and the complexity will not come from the basic building blocks (which will be very simple) but from the very cleverly interconnected and coordinated whole system. The AI will store all necessary information like the human brain and body (and possibly even much more) and will be able to recall that information (even many of them in parallel) according to some kind of stimuli.

There are a lot of AI algorithms out there and many people ask the question that which algorithms is the best, which should they use and which algorithm is the future. I have given the answer to this question already with the statement above that ‘there will be a very simple logic which connects the different types of memory, sensors, CPU, etc.’. Most of the complexity in current algorithms and the diversity of the algorithms are needed today because of the limited memory, CPU and our limited knowledge about the whole system. The most efficient and best way to work today is to select the appropriate algorithm for a specific task. One of the errors made by AI experts and researchers today is that they try to use neural networks based AI algorithms for every task despite the fact that for many tasks there are other much simpler and faster algorithms which even give a better accuracy in many cases.

Learning and Ethics

We know already a lot of things about how learning is done. We can learn from existing knowledge what we may call supervised learning (e.g. someone tells us or shows us that we can drink from a glass). We can learn unknown things which we may call unsupervised or reinforcement learning (with trial and error we find out things). Both types of learning are very important and we can not have a real AI system which can not combine both of these learning strategies. An AI could of course learn with a trial and error strategy only but that would be very inefficient and even dangerous because of the unknown direction the AI would evolve. Learning in reality is of course more complex than just these two learning strategies and it involves a range of strategies which are some kind of combination of these two. Current AI algorithms can not combine these strategies effectively yet.
A real human like AI must thus combine several learning strategies and choose and use the one automatically which is most appropriate in the specific circumstances.

There were a lot of failures in automated learning/training of AI systems recently. Several AI giants announced, launched and then very soon recalled such AI systems. The reasons for this are very simple and twofold, the current AI algorithms are not intelligent enough and as a child can learn bad things an AI algorithm can also learn bad things. For example put a child in a wrong environment and she/he will learn how and what they speak in that environment, put a natural language processing AI system on the internet where it can be acceded and influenced by anyone and it will learn unexpected things.

And with this last thought we have also arrived to the topic of Ethics. Ethics is of course something that is invented by humans and it must be learned! The good news is that good ethics can be learned as we can learn any other thing but the bad news is that like anything else in this world all good things can be used with bad intention. An AI system can be used to save or improve lives but it also can be used to destroy them. This is the same question as asking whether to sell guns or computers to bad people. Asking the question whether we will be able to prevent that bad people will access highly intelligent AI systems in the future is the same as asking if we can prevent bad people accessing guns or the internet. The balance of the world we are living in depends completely on us and this is as much important as evolution. There can not be evolution without a good balance. If evolution would go much faster than we can keep up with holding the right balance the world would be destroyed by the technology invented by humans.

Conclusion

The answer to the question whether the current AI algorithms are useful is of course yes, very useful, but we are very far from an all-in-one real AI system which can replace a human. We can of course use the results of several years of AI research in endless useful applications in all business sectors. The current state of AI helps humans to do their job better and makes things possible which would not be possible otherwise as for example developing new vaccines for deadly diseases, improving business processes, helping humans with disabilities, discovering new things, gaining new insides from all kinds of data and in all business sectors,… etc. Current AI algorithms can interpret all kinds of data as for example numbers, text, images, video, audio, etc. A whole new range of applications are possible and only your imagination is the limit for finding useful applications in your business sector. AI systems will start to speed up evolution very soon! It is already started!


Evolution AI Artificial Intelligence Software Toolkit Decision AI Artificial Intelligence Software Toolkit Cascade Studio Artificial Intelligence Deep AI Artificial Intelligence Vision AI Artificial Intelligence

The AI-TOOLKIT

You can download the AI-TOOLKIT for free (https://ai-toolkit.blogspot.com/p/ai-toolkit.html) which contains several easy to use software packages and experiment with AI capabilities in your business:

  • The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. Can be trained further with new knowledge without old data. Big data Cleaning, Pre-processing, automatic Classification, Statistical analyzes, very fast!
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. The extended version of DecisionAI (see below) with superior functionality, accuracy and speed. Also included data cleaning, filtering, statistical analysis, automatic classification.
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making but may also be used for other purposes. Supports both mixed AI and numerical AI models. It also contains extensive data analysis tools (statistical analysis, control charts, etc.) for your Business Process Improvement (or six-sigma) projects. DecisionAI can be used (after training the AI) to quickly make decisions about many different often complex matters as e.g.: which next process step the employee (or client, product, etc.) has to take, decide if an action should be taken or not, select an employee (or client, product, etc.) for a specific task (or action, marketing campaign, etc.), root cause analyses, etc.
  • Multi Layered Neural Network Artificial Intelligence (AI) software toolkit. Supports multi-layered and multi-node neural networks for machine learning. DeepAI is well-suited for problems in which the training data is complex and may contain noisy (e.g. complex sensor data such as sound from microphones).
  • Multi Layered Neural Network Artificial Intelligence (AI) Imaging Toolkit. Supports complex multi-layered convolutional neural networks for machine learning of Images.
  • Cascade model training, visualization and testing toolkit for fast object detection in images and video's. CascadeStudio can be used to detect rigid or near rigid objects e.g. faces, human body, cars, animals, plants, flowers, equipment, products on production lines, etc. in images and video’s.





What Else Can You Do With The AI-TOOLKIT?

What Else Can You Do With The AI-TOOLKIT?
AI-TOOLKIT How To Videos
There are some obvious uses of the AI-TOOLKIT about which you can read all over the AI-TOOLKIT website (decision making, business process improvement, automatic classification of unclassified data, classification of complex numerical data or images, root cause analyzes, etc.). But for what else can you also use the AI-TOOLKIT?

IMAGE PROCESSING/ENHANCEMENT

The AI-TOOLKIT contains a professional image processing toolkit which can be used for

Automatically improving images (high resolution, black and white with text, etc.).
Several predefined image enhancement algorithms can be applied at once to several images (automatic batch processing) making use of multiple processors and/or multiple processor cores in your computer (super fast!).
Manual image manipulation.
Despeckle, Brighten, Darken, Flip, Contrast, Sharpen, Unsharp mask, Deskew, Gamma correction, Crop, Remove horizontal/vertical lines, Remove angled lines, Rotate. You can adjust the strength and many other parameters. Applied at once to several images (automatic batch processing) making use of multiple processors and/or multiple processor cores in your computer (super fast!).
Create an image collage (several tiled images).
Select several images and the AI-TOOLKIT will create automatically an image collage from the images.

THE AI-TOOLKIT CAN READ AND TALK

The AI-TOOLKIT can read text from images in several languages.
Load an image from a file, scanner, video, etc. containing text in any language and the AI-TOOLKIT will extract the text for you which you can save in MS Word compatible format. You can even select a rectangular region of the image and then only that region will be processed.
Several images can be processed at once.
The AI-TOOLKIT can recognize text on several images at once (automatic batch processing) making use of multiple processors and/or multiple processor cores in your computer (super fast!).
The AI-TOOLKIT can talk/read the text it recognizes or the text you type in several languages
You can select a voice in a specific language, adjust the volume, rate of speech, etc.
You can save what the AI-TOOLKIT speaks in an audio file.
You can listen to the recording later on any other device.
Help for visually impaired or for children who can not read yet.
If you know someone who is visually impaired or can not read yet (children) then the AI-TOOLKIT can help them to read (many languages are supported). Let the AI-TOOLKIT recognize the text and let them listen to the text. Reading the newspaper is an option!

IMPORT IMAGES AND VIDEOS FROM VARIOUS SOURCES

You can load any image format from file.
PNG, JPG, GIF, BMP, ...
The AI-TOOLKIT has built in support for scanners.
You can use a scanner to scan pages from documents or books. You can then process these pages and recognize/extract the text in several languages.
You can capture your computer screen.
You can set a time delay and capture your computer screen.
You can capture images from video cameras. 
Both local and internet cameras are supported. You can for example connect to a camera at a remote location and analyze the image.
Home security.
You can easily use an old smartphone, connect it to a WiFi network and access the video feed from an other location. You can record the visit of unexpected guests in this way.

AUTOMATIC DATA ANALYZES

Automatic statistical analyzes of your numerical data.
The built-in automatic data analyzes module will provide you instantly all important statistics, data chart and distribution chart.
Built-in automatic variation analyzes (six-sigma) module with control charts.
The AI-TOOLKIT control charts will show you automatically what is wrong with your data and processes.


Evolution AI Artificial Intelligence Software Toolkit Decision AI Artificial Intelligence Software Toolkit Cascade Studio Artificial Intelligence Deep AI Artificial Intelligence Vision AI Artificial Intelligence

The AI-TOOLKIT

You can download the AI-TOOLKIT for free (https://ai-toolkit.blogspot.com/p/ai-toolkit.html) which contains several easy to use software packages and experiment with AI capabilities in your business:

  • The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. Can be trained further with new knowledge without old data. Big data Cleaning, Pre-processing, automatic Classification, Statistical analyzes, very fast!
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. The extended version of DecisionAI (see below) with superior functionality, accuracy and speed. Also included data cleaning, filtering, statistical analysis, automatic classification.
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making but may also be used for other purposes. Supports both mixed AI and numerical AI models. It also contains extensive data analysis tools (statistical analysis, control charts, etc.) for your Business Process Improvement (or six-sigma) projects. DecisionAI can be used (after training the AI) to quickly make decisions about many different often complex matters as e.g.: which next process step the employee (or client, product, etc.) has to take, decide if an action should be taken or not, select an employee (or client, product, etc.) for a specific task (or action, marketing campaign, etc.), root cause analyses, etc.
  • Multi Layered Neural Network Artificial Intelligence (AI) software toolkit. Supports multi-layered and multi-node neural networks for machine learning. DeepAI is well-suited for problems in which the training data is complex and may contain noisy (e.g. complex sensor data such as sound from microphones).
  • Multi Layered Neural Network Artificial Intelligence (AI) Imaging Toolkit. Supports complex multi-layered convolutional neural networks for machine learning of Images.
  • Cascade model training, visualization and testing toolkit for fast object detection in images and video's. CascadeStudio can be used to detect rigid or near rigid objects e.g. faces, human body, cars, animals, plants, flowers, equipment, products on production lines, etc. in images and video’s.








The AI-TOOLKIT

The AI-TOOLKIT
AI-TOOLKIT, DecisionAI, DeepAI, VisionAI, artificial intelligence
The AI-TOOLKIT contains 6+ software tools:
  1. Decision AI
  2. DeepAI
  3. VisionAI
Recently added NEW!:
  1. EvolutionAI Professional - The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. EvolutionAI is an evolution big data Artificial Intelligence learning/training & prediction application. The word 'Evolution' indicates that the AI model trained by the software can be trained further with new knowledge/data and this without the need for having the data learned in the past. EvolutionAI is also optimized for handling big data. You can feed data files to EvolutionAI with any size, even several GB's at once, and this for both training/learning and prediction. EvolutionAI Professional is an Artificial Intelligence (AI) software toolkit and may be used for many different purposes as business process improvement, decision making, making decisions about buying/selling stocks on the stock exchange, knowledge management, illness and cure discovery, R&D in all disciplines and sectors, etc. The possible applications are endless.
     
  2. DecisionAI Professional - Decision AI Professional is an Artificial Intelligence (AI) software toolkit for Business Process Improvement and Decision Making but may also be used for many other purposes as making decisions about buying/selling stocks on the stock exchange, knowledge management, illness and cure discovery, R&D in all disciplines and sectors, etc. The possible applications are endless. DecisionAI Professional is the extended version of DecisionAI with superior functionality, accuracy and speed. DecisionAI Professional runs on all 64-bit versions of Microsoft Windows (7, 8, 10).
     
  3. VisionAI Video - Can be used for detecting movement (motion) or objects in live or recorded video.
     
  4. Cascade Studio - Artificial Intelligence (AI) Software Toolkit for Training, Visualization and Testing of AI models for fast object detection in images and video's.
     

DecisionAI

Decision AI is an Artificial Intelligence (AI) software toolkit for Business Process Improvement but may also be used for other purposes. DecisionAI is the desktop version of the DecisionAI Google Sheets Add-on. DecisionAI runs on all 64-bit versions of Microsoft Windows.

Decision AI supports both mixed AI and numerical AI models. In mixed AI models the input data may be both textual and numerical. In the numerical AI model all data must be integer or decimal numbers.

DecisionAI also contains extensive data analysis tools (statistical analysis, control charts, etc.) for your Business Process Improvement (or six-sigma) projects. These tools can also be used stand alone.
DecisionAI can be used (after training the AI) to quickly make decisions about many different often complex matters as e.g.:
  • which next process step the employee (or client, product, etc.) has to take,
  • decide if an action should be taken or not,
  • select an employee (or client, product, etc.) for a specific task (or action, marketing campaign, etc.),
  • root cause analyses,
  • etc.
Read more about DecisionAI here.

DeepAI

Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. Complex multi-layered and multi-node deep neural networks made easy!

Despite the fact that deep neural networks modeling is a complex task DeepAI makes this task simple by intuitively and automatically applying design patterns to your problem. Even someone without knowledge of neural networks may train DeepAI successfully. The task of neural networks modeling is, roughly speaking, reduced to just choosing the input functions, the number of layers and the number of nodes per layer. Each node is automatically connected to the next layer's nodes and like this the neural network is formed. One of the reasons to make specialized software packages (as DecisionAIPro, DeepAI and VisionAI) is to reduce the complexity of machine learning and to make the software easy to use and to understand.
After training the AI, or with other words learning the training data, DeepAI can be used to make very fast predictions (less than a second) and to output the probability for each prediction also.

DeepAI is well-suited for problems in which the training data is complex and may contain noisy (e.g. complex sensor data such as sound from microphones). DeepAI is also applicable to less complex problems but it may be easier and faster to use DecisionAIPro in these cases.
In case you want the AI to learn images then you must use VisionAI instead because VisionAI is optimized for that purpose!

Read more about DeepAI here.

VisionAI

VisionAI makes machine learning of images easy. After training a VisionAI model it can be used to automatically recognize the object in images. The training data/input to VisionAI are images of any size and color depth. Each image is labeled/classified automatically. After training the AI, or with other words learning the training data/images, VisionAI can be used to make very fast predictions/image recognition (less than a second).

VisionAI also contains the following modules:
  • Import images from video (local camera, internet camera, computer screen, video file).
  • Import images from a scanner.
  • Extensive image enhancement toolkit for improving image quality and for cleaning images with text (e.g. automatically removing lines).
  • Create an image collage (tiled images). Such an image collage can be used for training VisionAI.
  • VisionAI Text Recognition engine which can be used to recognize and extract text from images in several languages.
  • VisionAI Text To Speech engine which can be used to convert text to speech/audio in several languages.
Read more about VisionAI here.







Evolution AI Artificial Intelligence Software Toolkit Decision AI Artificial Intelligence Software Toolkit Cascade Studio Artificial Intelligence Deep AI Artificial Intelligence Vision AI Artificial Intelligence

The AI-TOOLKIT

You can download the AI-TOOLKIT for free (https://ai-toolkit.blogspot.com/p/ai-toolkit.html) which contains several easy to use software packages and experiment with AI capabilities in your business:

  • The First Easy to Use & State of the Art Evolution Big Data Artificial Intelligence Desktop Software. Can be trained further with new knowledge without old data. Big data Cleaning, Pre-processing, automatic Classification, Statistical analyzes, very fast!
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making. The extended version of DecisionAI (see below) with superior functionality, accuracy and speed. Also included data cleaning, filtering, statistical analysis, automatic classification.
  • Artificial Intelligence (AI) Software Toolkit for Business Process Improvement and Decision Making but may also be used for other purposes. Supports both mixed AI and numerical AI models. It also contains extensive data analysis tools (statistical analysis, control charts, etc.) for your Business Process Improvement (or six-sigma) projects. DecisionAI can be used (after training the AI) to quickly make decisions about many different often complex matters as e.g.: which next process step the employee (or client, product, etc.) has to take, decide if an action should be taken or not, select an employee (or client, product, etc.) for a specific task (or action, marketing campaign, etc.), root cause analyses, etc.
  • Multi Layered Neural Network Artificial Intelligence (AI) software toolkit. Supports multi-layered and multi-node neural networks for machine learning. DeepAI is well-suited for problems in which the training data is complex and may contain noisy (e.g. complex sensor data such as sound from microphones).
  • Multi Layered Neural Network Artificial Intelligence (AI) Imaging Toolkit. Supports complex multi-layered convolutional neural networks for machine learning of Images.
  • Cascade model training, visualization and testing toolkit for fast object detection in images and video's. CascadeStudio can be used to detect rigid or near rigid objects e.g. faces, human body, cars, animals, plants, flowers, equipment, products on production lines, etc. in images and video’s.






AI automated Root Cause Analysis

AI automated Root Cause Analysis
AI automated Root Cause Analysis, AI TOOLKIT
Are you still using techniques like 5 Whys, fish-bone diagram or even guessing for root cause analysis? Would you like to have a root cause instantly with high accuracy? If yes then read further!

By using Artificial Intelligence (AI) software you can teach the AI all possible root causes depending on complex input data and ask the AI later for the root cause any time you need it! And you can do this in any sector and in any discipline. You can even automate this process and predict if a problem will occur in the future by continuously monitoring the input parameters and feeding them to the AI for prediction. The only thing you need is an AI software and data.


Download the AI-TOOLKIT for free (fully functional version for non-commercial purposes – no registration is needed!) and make your own AI automated root cause analysis!

How to start your AI Root Cause Analysis?

  1. Download the AI-TOOLKIT for free (fully functional version for non-commercial purposes – no registration is needed!).
  2. Install the software. Read the frequently asked questions.
  3. Decide on the process or phenomenon for which you want to make a root cause analysis.
  4. Collect the necessary data. The data should contain a sufficient number of records. Each data record should contain a number of parameters and a root cause (decision variable) resulting from the specific state of the parameters.
  5. Feed the data to the AI. In case you have textual or the mix of textual and numerical data you should use the mixed-AI model in DecisionAI (part of the AI-TOOLKIT). In case you only have numerical parameters or you have converted everything to numbers then you can use the numerical AI model.
  6. Train the AI. Check the accuracy of the AI model. In case the accuracy is not acceptable then you may need to add more training data or even introduce extra parameters.
  7. If you are satisfied with the accuracy of the AI model save the model and use it for the prediction of future root causes. You can also automate the predictions by using the built in DecisionAI server.

You can repeat this procedure for any number of processes or phenomenon’s and like this develop several AI models and use them for all automatic root cause analysis in your company!

More info


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Learn about Artificial Intelligence (AI)

Learn about Artificial Intelligence (AI)
There is a lot of hype about AI (Artificial Intelligence) today but what is true from all of these stories and myths? I have collected a number of very interesting articles and real applications of AI which I would like to share with you. I will regularly update this list so keep coming back for more interesting stories and facts!

  1. This is a very high level explanation of AI and many of its applications. It also tries to tell the truth about what AI is and what it is not. It is not a comprehensive list of AI and/or machine learning because it only focuses on neural networks (often called deep learning). Most of the scientists today think that deep learning is the future of AI.
      
  2. Introduction to the application of artificial intelligence (AI)
    This is a more in depth explanation of AI and machine learning.
      
  3. Behind the scenes at Google’s AI Team
    A few weeks ago The New York Times published a long (at least an hour to read it) and intimate article about how Google evolved the last five years to one of the leading companies in Artificial Intelligence. Big things in life are made not by luck but by hard work and curiosity!
      
  4. Case study: artificial intelligence in healthcare business process improvement
    The main aim of this case study is to demonstrate the different applications of Artificial Intelligence (machine learning) in business process improvement specific to the Healthcare sector.
      
  5. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts
    A very interesting article appeared recently about AI (using deep learning of images) for diagnostics, risk stratification and treatment suggestions, accurately diagnoses and provides treatment decisions for diseases (often better than a specialist)!
     
  6. How Deep Learning is Reinventing Hearing Aids
    This article is from Nvidia. Nvidia is a manufacturer of graphic computer hardware accelerating AI applications in several fields. Mercedes (car manufacturer) has just signed a cooperation agreement with Nvidia for accelerating AI applications. "AI deep learning to separate speech from noise. How deep learning hearing aid technology could also improve speech recognition on cellphones, help workers on noisy factory floors or equip soldiers so they can hear each other amid the the cacophony of battle."
     
  7. Case study: artificial intelligence in the financial sector
    The Application of Artificial Intelligence (AI) in the Credit Screening of Clients in a Financial Institution.
     
  8. How Deep Learning Changes Market for Solar-Powered Homes
    "AI deep learning-based analysis of a household’s likelihood to embrace solar, and its prospects for getting good solar production. So far, the company has trained two networks, both of which rely on analysis of satellite data: One determines whether a house already has solar panels; a second determines whether  vegetation is crowding the roof and could get in the way of an installation."
     
  9. Can AI End Checkout Lines?
    "AI Lets Shoppers Avoid Long Waits at Checkout  - two artificial intelligence startups aim to make checking out of grocery stores and company cafeterias a walk in the park."
     
  10. KLM Provides Faster Customer Service with AI Tool
    “We’re unlocking the intelligence value of historical data while helping customer service agents deliver a faster and more accurate experience for their consumers,”
     
  11. Case study: THE APPLICATION OF AI IN HUMAN RESOURCES
    You will learn how to use the AI tools included in the AI-TOOLKIT for making HR decisions easily. In this case we will train an AI model which can be used to decide/predict if an employee will leave or why he/she will leave or even if it is worthwhile to offer a promotion to an employee.
     
  12. THE FUTURE OF ARTIFICIAL INTELLIGENCE
    The current state and my vision about the most important building blocks of a real human like AI system.


Image partly courtesy of drpnncpptak and ratch0013  at FreeDigitalPhotos.net

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