INLOC Robotics

Galatea Project

The kite traction project, straight from the Kitesurf technology aims to use the wind as the sole source of energy for massive cargo boats.

The project, along with the French company Beyond the Sea is a resolutely innovative project in the purest respect for the environment.

INLOC ROBOTICS is awarded with the EUROPEAN GALATEA PROJECT

The principle is simple. Beyond the Sea offers wings of all sizes, capable of handling some or all the energy needed to pull the ship, depending on the navigation area, the performance and characteristics of the boat and the operator’s goals. The Kite, connected to a fixed point of the ship, will be chosen in movement, or fixed according to the desired traction force, all in perfect safety. The control of the wing can be ensured automatically, or manually. Kite works both upwind and downwind.

Even though the concept might seem simple, to create a viable system, many different technologies and skilled professionals must work together:

Beyond the Sea has developed strategic research partnerships with prestigious engineering schools, Kite professionals, or sailors, all oriented towards Responsible Innovation.
INLOC Robotics is a specialist in control applications to develop an automatic control system or autopilot which is a must for a commercial use of the technology.

The targeted fields of application of kite traction are all the maritime players: professional shipping, workboat & pleasure boat rental and pleasure boat innovation-minded owners.

This solution will be revolutionary from the point of view of the environment as well as the security and the economy, combining many assets that will make it an essential solution for ships of all sizes.

The project is currently at a stage of development where the first wings have been marketed but the autopilot for larger wings is still under research.

What does INLOC Robotics contribute to the GALATEA project?

In order to carry out this project, it is essential to mix skills and know-how and, to do so, INLOC Robotics will provide the needed know-how developing the control system.

INLOC Robotics is a start-up founded in 2014 who, through research projects and engineering projects offers solutions in the field of robotics and, in a generic way, in the fields of automation and control including the paradigms of Industry 4.0 and IoT. We are based in Barcelona area, Spain.

INLOC offers systems which “intelligently” connect perceptions to actions. Through the development of a control algorithm that integrates current data from the kite sensors, current mathematical model, and an improvement of such model, we will integrate the control laws necessary to implement an automatic control system to the project.

This technology will make transport more autonomous in terms of energy. The boat no longer depends solely on its engines to move forward but has another source of energy which is infinite and non-contaminant: the wind.

The development of technologies includes kite management techniques and an automatic steering system. The kite, connected to a fixed point on the ship, will move or remain stationary depending on the desired pulling force, all in complete safety.

Our system will notably use digitalisation with a set of sensors (kite localization, tether effort monitoring, wind sensor, ship positioning and behaviour, weather information, …), several actuators to interact with the kite through the tethers and computers and algorithms to integrate all collected data and build the appropriate commands to actuators to control the kite to generate the optimized towing capability in the relevant direction according to the ship route.

By taking advantage of the wind, which is a 100% renewable energy, we will reduce the energy needs of ships, while proportionally reducing their greenhouse gas emissions. The kite, in addition to helping to propel the boat, makes it lighter by its vertical traction, further reducing total energy requirements. Thus, depending on the desired efficiency, it will be possible to sail without the combustion engine.

By developing an automatic control system, we optimize the solution to make the sailing more efficient and increase its commercial capabilities since it can be used in long range boats without the constant care of the human operators.

The solution will have many advantages:

Reduction of energy needs and increase profitability by reducing by 20% fuel expenses.
Reduction of shipping harmful emissions of CO2, NOx and Sox by 20%
Maximum adaptability on any ship to replace or complement combustion engines.
Commercial dimension since the autopilot makes it usable in long range boats and improves the efficiency of the system.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Sewer maintenance

In this article, we are going to see the importance of sewer maintenance and the benefits of closed circuit television inspection.

CCTV Camera Inspection is an advanced diagnostic tool used to run a complete pipeline inspection. This provides us with information about the internal state of a sewer system.

But before we get to this point, we are going to look at the causes of a ruptured sewer pipe and thus we are in the problems that arise.

What Causes a Sewer Pipe Break?

Tree roots: One of the most common causes of sewer line damage is tree roots. Once in contact with a sewer pipe, the roots of the trees begin to coil, clogging, weakening and even breaking the structure.

Corroded Pipes: Although steel and cast iron pipes are galvanized to prevent rust, these pipes still have a high risk of corrosion. If corrosion is not treated, it can leave the pipe susceptible to leaks and cracks.

Clogged pipes due to debris and foreign objects: Sewer lines can become clogged with accumulation of debris, creating a blockage.

Blowout: Overpressure can cause the pipe to burst.

Sewer pipe break

Extreme temperatures: In cold climates with extreme temperatures, frozen pipes can rupture as a result of expanding ice. However, it’s not just cold weather that can cause pipes to break; Although unlikely, extreme heat can also cause pipes to burst.

Signs of sewer line damage

There are signs on a sewer line that clearly show us the location of a problem. They are typical to see an area of road flooded with water or a bad smell, due to the sewage that comes to the surface.

But to avoid this type of situation, we will see below what we can do.

Sewer pipe break prevention

Closed circuit television (CTTV) sewer inspection is the process of using a camera to view the inside of sewer pipes. Images taken by the camera are recorded for later review.

CCTV cameras allow you to see and anticipate the causes of sewer problems without the need for such invasive methods as digging to access damaged pipes.

It is important to regularly conduct sewer line inspections to confirm that the sewer system is in good working order.

Benefits of closed circuit television sewer inspection

And this is where INLOC Robotics launches SEWDEF. It is an automatic detection system for sewer network defects based on artificial intelligence and computer vision, which analyzes videos of the sewer network, previously recorded by CCTV, in search of defects.

These types of sewer inspections are the most cost-effective way to identify the location of sewer problems. The CCTV video camera helps to find the exact position of the damaged pipe.

With all this we quickly identify the problem and thus focus only on the damaged section, reducing the cost and time it takes to repair the pipe.

And to finish, comment that this system is respectful with the environment, since there is no excavation by the simple hypothesis that the problem is located at a certain point.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Version control system

The concept of teamwork has evolved a lot since its origins, the need to find ourselves in the same physical space it has disappeared in many areas. Still, there is a need for coordination between colleagues and for this, multiple services have been created, among them, the version control system.

What is a Version Control System?

A version control system is a tool used in software development to avoid the risk of conflicts that may arise when working on collaboration with other development teams.

What are the advantages of a Version Control System?

It allows multiple people to work simultaneously on a single project. Each person edits their own copy of the files and choose when to share those changes with the rest of the team. Therefore, the temporary or partial edits of one person do not interfere with the work of another.
It allows one person to use multiple computers to work on a project, making it valuable even when working alone.
It integrates the work carried out simultaneously by different members of the team. In most cases, editions of different files or even the same file can be combined without losing work.
Gives access to historical versions of your project. This is insurance against computer failure or data loss. If you make a mistake, you can revert to a previous version.

Types of Version Control Systems

Local Version Control System: The local version control system keeps track of files within the local system. This approach is very common and simple. This type is also prone to errors, which means that the chances of accidentally writing to the wrong file are higher.
Centralized Version Control System: In this approach, all file changes are tracked on the centralized server. The centralized server includes all the information of the versioned files and the list of clients that extract files from that central place.
Distributed Version Control System: Distributed version control systems appear to overcome the inconvenience of the centralized version control system. Customers fully clone the repository, including its full history. If any server is down or disappears, any of the client repositories can be copied to the server for restoration. Each clone is considered a full backup of all data.

Version control software

Git: It’s a distributed version control system written in a combination of Perl, C and various shell scripts, it was designed by Linus Torvalds according to the needs of the Linux kernel project; with the requirements of decentralization, fast, flexible and robust.

Apache Subversion (SVN): It’s an open source centralized version control system licensed by Apache. Its key features include management of inventory, security management, history tracking, user access controls, data retrieval, and workflow management. SVN is easy to implement with any programming language. In addition, it offers uniform storage to handle text and binary files.

Mercurial: It’s a distributed version control system written in Python as a free software replacement for Bitkeeper; decentralized, which claims to be fast, lightweight, portable, and easy to use. In addition, it has an integrated web interface.

Monotone: It’s a distributed version control system written in C++. The operating systems it supports include Unix, Linux, BSD, Mac OS X, and Windows. Provides good support for internationalization and localization. In addition, it works with a P2P protocol.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Optimizing Python with Numba library

As we discussed in the previous post, Python is a widely used programming language, among other reasons because it is easy to program at a high level, it has many good libraries and it enables easily readable and maintainable code. Despite all these advantages, it should be noted that it is notoriously slow, so it is convenient to know some tools that enable us to speed up the Python code.

Python vs. Numba

One of the ways Python can be sped up is by using the Numba library. To illustrate this, we will take as an example the calculation of the number π using the Monte Carlo method, similar to our previous post (example taken from the Numba page).

Sped up Phyton

The code is very simple. The number π is calculated by sampling a random number of points and using the ratio of the area of a square to a circle. The area of the circle is estimated as the number of points within the circle, and that of the square as the total of points.

Montecarlo

Using 100,000 points, the Python function takes 30.1ms, while using Numba it takes 896μms, that is, 33.55x times faster.

But… What is Numba?

Numba is a Python library that gives the user the ability to compile the code as soon as it is executed (it is a Just-in-Time compiler), thus achieving speeds of C code without leaving the simplicity of Python.

And all this by making minimal modifications to the Python code, as you can see in the example. In addition to compiling the code, Numba has the ability to detect the type of CPU it is running on and make use of hardware-specific optimizations, as well as using different threads (which Python by itself can’t do).

As if that weren’t enough, Numba is designed to work with Numpy, one of the most famous Python libraries for numerical computing.

One of the drawbacks of Numba is that it is not friendly with dynamic data structures (that is, data structures that can increase or reduce their size arbitrarily) such as adding to lists or dictionaries, and it can be difficult to debug. Furthermore, all code written with Numba requires a system that supports its JIT compiler.

How to use Numba?

Four things are needed for a Python program that uses Numba to work, besides having Numba installed of course.

(1) Import the Numba library from the script, like this: import numba.

(2) The code to be accelerated needs to be clearly defined in a function. Above this function the decorator @numba.njit is added. This decorator tells Numba that it is the function that it should try to compile. If for some reason Numba can’t compile it, it will return an error. If you don’t want it to return an error and you prefer to fallback to normal Python, then the decorator should be changed to @numba.jit.

(3) The code does not contain dynamic data structures, and preferably the data structures that do exist are Numpy ones. That is, things such as .append() to a list should be avoided.

(4) The loops are not of type for element in elements, but iterate through the indices, such as for i in range (len (elements)).

As it has been observed, using Numba for parts of the code that can be bottlenecks from a computational point of view can provide great speed increases, and with little effort.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Optimizing Python with Cython

Python is a widely used programming language nowadays, among other reasons because it is comfortable to program at a high level, it has many libraries and it is an easily maintainable code. Despite all these advantages, it should be noted that it is notoriously slow, so it is convenient to know some tools that allow you to speed up Python code.

Cython vs Python

One of the ways in which Python can be sped up is by combining it with Cython code. To illustrate this, we will take as an example the calculation of the Pi number using the Monte Carlo method (example taken from the Numba webpage, another way to speed up Python).

Speed up Python

It is a very simple code, in which the number Pi is calculated by sampling a random number of points and using the ratio between the area of a square and a circle. The area of the circle is estimated as the number of points within the circle, and that of the square as the total number of points.

Montecarlo

Using 100,000 points, the Python function takes 38.5ms, while the Cython one takes 2.98ms, which is 12.92x times faster.

What is Cython?

Cython is a language that is written in a very similar way to Python, but it allows the use of C-type libraries and variables. Therefore, the speed of C can be achieved while maintaining the simplicity of syntax provided by Python. In addition, one of its great versatility is that explicit definitions of C variables can be mixed with Python variables, such as dictionaries.

However, programming in Cython also has a number of drawbacks, such as the need to compile the code separately before running the program. There is also the fact that compared to languages like C or Fortran, it doesn’t have as good support for memory parallelization.

How to use Cython?

Three files are required for a Cython program to work. The first and most important is the one that contains all the Cython code, with a .pyx extension. In the cython example of the calculation of the number pi, you can see how within this code you can access C libraries, as well as declare the variables with their type to increase speed.

It is also important to highlight the declaration of the functions, which can be declared in three different ways: def, cpdef and cdef.

The first indicates that the function can only be accessed from a Python function, the second that it can be accessed from Python and from Cython, and the last that it is only accessible from Cython.

The second of the required files is the setup.py, which generates the Cython extension when compiled.

And, finally, the third script is the Python script, which calls the file generated by setup.py to be able to import the Cython code as if it were a library, and thus be able to access its functions as it would be done with any other.

(Image by DavidBrooksPokorny – Own work, CC BY-SA 3.0, wikimedia)

Cython files

In this way, for particularly slow parts of the code such as very long loops where Numpy cannot be used or for mathematical computations, Cython can help speed up the execution of the program.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Computer Vision for detection and tracking of circles

The tracking of objects in a video is a common goal in computer vision. Some methods rely on the search of keypoints, which are then matched with keypoints on the next frames.

In this post we will explain a method based on the identification of simple shaped objects, in particular circles, to track them in a robust manner (i.e. despite the movement or change of size) for all the frames in video where the object appears. In addition, the method allows the tracking of more than one circle simultaneously (multi-tracking).

The case of study is a static scene and a moving camera, but a similar method could be designed for static camera and moving objects. This method is divided in two parts: first the identification of circles and then the tracking.

Circle detection by computer vision

The circles are searched frame by frame, without any knowledge about circles detected in previous images. There can be more than one circular object in the same frame, so the circle detector must be able to return several circles, if that is the case. Therefore, each frame provides a set of candidates of being a circular object in the image.

The frame is first processed so that circular shapes can be easily found. The most important step in pre-processing is to obtain the borders of the objects. From those borders, circles are searched and only the best are returned.

At this point, some circles could not belong to circular objects, so a post-processing is also needed. Circles may be filtered according to our previous knowledge about the objects, for example by setting a bound on accepted radius or positions. We can also check if circles are crossing between them, or whether they are too close or too far one from another.

Computer Vision Based Circle Detection - Gray

Computer Vision Based Circle Detection - Sobel

Grayscale image, edges and circles detected (red). The blue and green circles are the tracks.

For this part, as important is to detect the circles when they appear in image as not detecting when there are no circular objects. Therefore, only the best circles, if any good, are returned for the tracking part.

(Multi) tracking of circles

The goal of multi-tracking is to estimate the position (center) and size (radius) of circles along all the frames where the object is visible, simultaneously for all the objects. This estimation must be computed even if there is no circle detected for one object in one frame (or several consecutive frames).

For those cases when a detection is missing, a dynamical model has been developed for prediction of size and position of circles in consecutive frames, by knowing the movement of the camera (in the case of static scenes).

As long as new circles are detected as object candidates, they are either discarded, linked to a current tracked object or starting their own new track. The tracking has been shown to be robust against wrong circle detections.

Circles are discarded if they are not similar or different enough from tracked objects. A circle is set as a new observation of one object if it is similar enough to the prediction for that object.

Finally, a new object is detected if the detected circle is different enough from any predicted circle for all the current tracks.

After some of the tracked objects have been linked to a detected circle, the state of tracked objects is estimated. This is done frame by frame, combining the information provided by the prediction model and the circle observed, resulting in a smooth and complete knowledge for the object position and size along all the frames.

Conclusions

This method provides an accurate estimation of several circular objects simultaneously, computing an measurement of size and position for all the frames, even if circles are not always detected. It is robust against the change of size and position of the objects, and can be extended to similar methods involving other shapes and scenes.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Industrial use of GPUs (Graphics Processing Units)

GPUs (Graphics Processing Units) were born due to the need to quickly manage the interface currently displayed on computers. As time goes by its use was extended to graphic design programs, video games, image processing,… But in this article we will focus on the capacity for numerical processing and artificial intelligence.

GPUs could be understood as a large number of small units capable of doing simple calculations simultaneously. This, although it seems simple, gives them exceptional processing power, it is not the same to do 1000 multiplications one after another than to do them all at once.

This capacity allowed the development of the first AIs and, in general, to introduce GPUs in research projects. Some examples would be the complex fluids simulations, calculations related to quantum chemistry, paralyzing calculations on large amounts of data…

Today, more and more companies choose to use GPUs in their systems, without going too far, facial recognition applications that detect faces on our smartphones use their small GPU, or the self-labeling of Facebook images would be two examples.

At INLOC Robotics we are not far behind in this field, we have our own AI within the SewDefsystem and we implement parts of our algorithms on GPUs to optimize them to a great extent. This opens the door to the increasingly frequent use of this hardware in industry.

Now let’s consider, once the first IoT systems are implemented, these can be increased exponentially by adding more data to the system. More computing power is likely to be required, which in turn allows for more elaborate studies of the production. The way forward would be the use of GPUs, in order to maintain the real-time monitoring that characterizes the IoT.

As a practical example, we can use the case of IoT. In many companies the use of this system is being implemented to monitor production in real time.

Breaking weaknesses:

When it comes to GPUs for general use, we can see two major weaknesses. First of all, GPUs were not initially designed with this use in mind, causing a program designed for a GPU to not work with the same performance on the next generation of the same hardware.

Another recurring problem is the lack of precision in calculations with decimals. To increase speeds, the precision in the same hardware is usually reduced, this fact is not a problem for all applications, but it could be.

However, the growing demand from the research sector of GPUs has made manufacturers focus efforts on solving these weaknesses, to the point of reducing them enough so that they are already used today in a wide variety of devices and/or applications.

In my opinion, we have reached a turning point where GPUs will have a strong entry in the industrial sector solving very diverse problems.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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INLOC Robotics CTO Presentation on World Water Day 2021

Within the framework of the celebration of International Water Day on Monday March 22, 2021, the CTO of INLOC, Josep M. Mirats Tur, was invited by the Canal Foundation to give a talk on the application of Artificial Intelligence for thedetection of sewer defects from CCTV camera inspection images.

In this talk we explain the main advantages of the use of AI for the detection of defects, allowing the obtaining of objective information that will allow infrastructure managers a better decision-making regarding investments dedicated to sewer maintenance.

If you want to know more about the event, you can go to the event page by clickinghere.

Learn more about SEWDEF

We hope this presentation has been interesting to you.If you want to know more about SEWDEF and the automation of detection of defects in the sewer system, here is the link where you can contact us and find out more about all the benefits that you will obtain with SEWDEF.

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Buried Pipe Inspection and Sewerage Management

How much does inspecting buried infrastructure cost? And perhaps more importantly, how much does it cost not to do those inspections? Can Artificial Intelligence help us with sewer inspection?

We live in a world where urban agglomerations have become the majority ecosystem of the human population. More and more people live in urban environments, and cities are getting bigger and bigger. And for these ecosystems to be sustainable for human life, they need to be equipped with infrastructures that allow access to such basic needs as safe water and sanitation. And these infrastructures are becoming more complex, difficult to design and build, and challenging to maintain.

Resources for Infrastructure Management

Resources for infrastructure management and maintenance are always finite, and it is imperative to make the right decisions when prioritizing where to apply these resources to avoid damage and accidents with consequences that can be catastrophic.

Over the past few years, improved sensor technology, data acquisition, and IT technology has led to the birth of better infrastructure management systems with the gradual incorporation of Asset Management and the paradigm shift from corrective inspection to preventive inspection.

However, buried infrastructure such as sanitation and sewerage infrastructures still requires regular robot camera inspections to check the actual condition of the infrastructure. After all, management systems will give information as good as the data they are fed with. And the amount of data to be analyzed can become a difficult bottleneck to avoid.

SEWDEF and Automatic Sewer Defect Detection

An automatic sewer defect detection system, through analysis of the images obtained in the inspection, becomes an artificial assistant that makes it possible for this preventive inspection of thousands of miles of buried sewerage to be now possible and feasible.

Thanks to an artificial intelligence system optimized for sanitation pipes, large volumes of data can be analyzed for objective, reliable, time-consistent, and standardized results.

Operating costs are significantly improved, and appropriate decisions can be made in applying resources where they are most needed in order to maintain the operation of the infrastructure at an optimal level and avoid direct and indirect costs arising from stateless and accelerated deterioration.

SEWDEFis an automatic sewer defect detection system that, through a cloud-hosted artificial intelligence system, robotic self-localization algorithms and “Deep Machine Learning” enables the automatic identification and localization of defects in sewer pipes quickly and effectively. With SEWDEF, infrastructure managers can now integrate real state data into their predictive algorithms and radically improve their decisions when using their limited resources.

We hope this article has been useful to you.If you want to know more about SEWDEF and the automation of detection of defects in the sewer system, here is the link where you can contact us and find out more about all the benefits that you will obtain with SEWDEF.

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Evolution of Artificial Intelligence

We live in a time when technology advances by leaps and bounds and in the middle of it we find Artificial Intelligence, something only theoretical in the past, expanded in almost all aspects of our life in the present and a hope for the future.

We are going to talk and learn more about the evolution of artificial intelligence.

What is Artificial Intelligence?

In the past, Artificial Intelligence has been described as: Any task performed by a machine that was previously considered to require human intelligence.

In the current we got new definitions such as: the ability of a system to adapt and improvise in a new environment, to generalize its knowledge and apply it to unknown scenarios.

Timeline of the Artificial Intelligence

During the more than 70 years of AI history, there have been 3 great eras separated by the central theme of their study and development:

Therefore, the chronology of artificial intelligence of these times would be the following:

- 1950-1970: Development of AI algorithms and methods.
- 1970-1990: Development of symbolic algorithms and expert systems also known as knowledge systems.
- 1990-Present: Machine learning and deep learning.

From these methods we can also divide between Model-Driven development and Data-Driven development:

Classical programming vs machine learning

Where the MD need a series of instructions to arrive at the solution (for example using decision trees).
And the DD use data with their solutions to “train” the system and the system provides the necessary rules so that when we input new data it answers with the corresponding solutions.

Uses of Artificial Intelligence

Artificial intelligence tries to imitate human behavior and for this it uses:

Robotics: Helps control systems that allows machines to manage physical tasks such as movement while adjusting to changes in the environment.
Natural Language Processing: Some applications include information retrieval, text mining, answering questions, and machine translation.
Computer Vision: can be used for object recognition, event detection, scene reconstruction (mapping) and image restoration.

With the above fields AI is used today in many sectors such as:

Virtual Assistants (Siri, Alexa, Cortana …)
Finance (Trade and investment, auditing, underwriting …)
Health (Scanning of images, sound analysis of the heart …)
Service sector (HR and hiring, job search, marketing)
Media and e-commerce (Deepfakes, music, videogames …)
Transportation (simulators, autonomous vehicles, route planning …)
Industry (damage analysis, sediment monitoring …)

Future of AI

AI is already integrated into our society and it is apparent that it is not going away anytime soon. Which ends up leading us to the next question: What does the future of AI hold for us?

Transparent AI:

Deep learning is made to replicate in a similar way neural networks and, like our brains, they receive data input and return an output but we do not know how they arrive at that solution. By not knowing how neural networks identify patterns and derive results, it is difficult to replicate and improve those results.</p

For this reason, the creation of a Transparent AI in which we can identify how our system reaches these solutions can suppose an exponential evolution of artificial intelligence.

Strong Artificial Intelligence:

All the AIs that have been created so far are part of the Weak Artificial Intelligence or Narrow AI type and work by focusing and solving specific tasks.

Instead the SAI (also called Artificial General Intelligence) is the AI that equals or exceeds the average human intelligence. This type of AI has the ability to use reason, represent knowledge, plan, learn, communicate, and integrate all these capabilities to solve a problem.

At the moment this type of AI is still far from being achieved but the evolution of artificial intelligence in technology and science continue to evolve exponentially and what years ago seemed like science fiction is now a reality.

We hope this article has been useful to you. If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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