Ever heard the term ‘Computer vision’ and wondered what it is? Or did you ever wanted to learn how different industries are utilizing computer vision technologies or perhaps you’re just curious about different subdomains in computer vision and want to expand your understanding in this field? Well, today we’re introducing a new video-based course that covers all that and much more called Computer Vision For Everyone.
It’s a high-level course that teaches you everything you need to get started with computer vision. The six-module course is designed in a way that people of all skill levels can benefit from it. So you don’t need any background in computer vision or Artificial Intelligence.
Not just that, this course is also useful for seasoned computer vision practitioners. And the best part is that this course is completely Free.
So if you’re still wondering, if the course is right for you then take a look at the pointers below
You want to get started in computer vision but have no idea how.
You’re excited about Artificial Intelligence but not sure where to start.
You already have some experience in Computer Vision using libraries like Tensorflow or OpenCV, but you want to get a high-level overview of all the exciting subdomains in vision.
You know how computer vision works but want to look at how different industries are utilizing this technology.
If any of the above applies to you then course if for you.
Alright, how will this course be delivered?
Well once every two weeks, you’ll get a video tutorial on an exciting topic in computer vision. The video tutorial will be released on this youtube channel, so do make sure to go there and subscribe.
Along with each video, there will also be an accompanied Blogspot which may get into more details regarding the topic and is ideal for those who prefer reading blog posts. For e.g. this post is the blogpost version of the first video episode of this course.
The course can be divided into 6 modules, In each module, we’ll cover a number of topics. Here is a list of all modules.
Module 1: Computer vision 101
Module 2: Computer Vision For Industry
Module 3: Computer Vision for Extended Reality & more
Module 4: Become a Computer Vision Ninja
Module 5: Learn to Develop Computer Vision Games
Module 6: Computer Vision As a Career
Module 1: Computer vision 101
In the first module, we’ll start with an introduction to computer vision and cover some of its applications. As we move on we’ll go over things like how an image is formed, how lenses work, compression techniques, image formats, and a lot more. We’ll understand about different subdomains in vision.
We’ll also go over the steps you need to follow to get started in computer vision and make a career in it.
And not just vision but we will also cover Artificial intelligence, machine learning, and deep learning in a very exciting way and we’ll connect and explain different AI terminologies so that things make sense.
The module sets the stage for all the upcoming modules in the series and gives you an idea of what to expect from the other lectures.
Module 2: Computer Vision For Industry
The second module explores how vision is being used for different industrial applications. We’ll understand how the Medical, Agricultural, Gaming and other industries are using Computer Vision, If you love AI applications then this module will definitely prove to be very interesting.
We’ll also cover things like, hardware requirements for vision, ie types of cameras used, e.g. Depth cameras, tracking cameras and everything in between and when to choose which.
Computer vision has gained popularity in recent years after major developments in deep learning but computer vision techniques are being utilized since the mid 20th century, so module two also discusses the progress made in computer vision over the years from classical techniques to deep learning-based modern approaches.
This module will also talk about the exciting research based work being done in the field, and we’ll also discuss topics like AI taking over Jobs, Threat of AI and Bias in AI and more.
Module 3: Computer Vision for Extended Reality & more
Module three is where things start to get really exciting. Vision-based augmented reality and mixed reality applications are on the rise. This is the next big step not only in the video gaming industry but also in technological devices that we use in our daily lives.
So this module will discuss all Virtual reality, augmented reality, mixed reality, and the best part is that I’ll also show you how you can use some tools to develop these extended reality apps.
This module does not end there, but we’ll also go over things like smart glasses, we’ll learn how holograms work , We’ll talk about Generative adversarial Networks and learn about Deep Fakes.
Finally we’ll wrap this module up by also discussing digital forensic techniques so you can be smart enough to recognize fake images.
Module 4: Become a Computer Vision Ninja
Building upon the momentum of the previous module, module 4 explores more into getting Hands-on experience using computer vision tools.
So in this module we’ll explore and learn to use Vision tools and applications in Google Experiments. Not only that, but here I’ll also discuss how each tool works and the technology behind it.
Other than google experiments, I’ll also cover some other SAS based cool vision tools that you show off to your friends.
After this module you’ll have a much better understanding regarding how to build Computer Vision based applications.
Module 5: Learn to Develop Computer Vision Games
Yes! You read that right, in this module you’ll get to develop computer vision based games. Now obviously these won’t be as cool as many production level games out there. But in this module you will learn to create simple Augmented reality based games using a popular tool called Scratch. This tool does not require any programming or any other prerequisites. Scratch will not only allow you to create games but after this module you’ll also be able to create things like animations, art, movie-like stories, program robots, and m
Module 6: Computer Vision As a Career
Computer Vision Engineer and Data Scientist is one of most in-demand job titles. After 5 modules many of you would probably want to pursue a career in computer vision so the sixth and final module will answer some important questions about making a career in computer vision. You will also learn about the best resources in vision, e.g. the best books, courses, tutorials etc.
I’ll also cover what type of projects you should work on to build up your portfolio, and finally how to get a job in computer vision.
Conclusion:
As you can tell, the course is packed with a lot of stuff. And I promise each topic will be delivered and explained with a high quality video along with an associated post, there will also be some things that I will add on to the course along the way. So do make sure to support use by subscribing to our youtube channel and sharing the videos.
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You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
Ready to seriously dive into State of the Art AI & Computer Vision? Then Sign up for these premium Courses by Bleed AI
In the previous post on computer vision, we gave the simplest possible introduction to computer vision and its domains. But computer vision itself is a part of a larger domain known as Artificial Intelligence. Understanding this domain is crucial to be able to connect the dots between different fields in artificial Intelligence. So today we’re publishing a 4 part tutorial/video series on AI as part of our CVFE course, we will focus on giving you a thorough understanding of the artificial intelligence field with these 4 tutorials. Each tutorial is distributed into different levels, on each level we’ll cover some explanation of AI. And on each subsequent level, the explanations will get more technical.
So this tutorial (Level 1) will give you a high-level introduction to AI but the following posts will go deeper, exploring many of the technical aspects of Artificial Intelligence.
With that in mind, let’s just get started.
Introduction:
Most people develop their notion of Artificial intelligence from watching humanoid robots in media performing crazy stunts, or self-driving Teslas on the highway, or the Chinese smart surveillance systems you often hear about in the news. Perhaps you might have come across Smart stores like Amazon Go and wondered what sorcery is this?
Sci-fi books, movies, and TV series have also built our perception of Artificial Intelligence for the longest time. Movies like Terminator and Matrix introduced us to highly advanced conscious artificial intelligence systems while shows like Black-mirror painted a picture of a dystopian future where Artificial Intelligence dictates different aspects of our life.
All of this makes you wonder what the future holds for us when it comes to AI.
While many of the incredible AI systems from Sci-fi have been implemented in some shape or form, there is still a long way to go before we expect true consciousness from an Artificial system. What people need to understand is that artificial intelligence systems today can be categorized into one of the following:
ANI(Artificial Narrow Intelligence)
AGI(Artificial General Intelligence)
ASI(Artificial Super Intelligence)
For now, let’s focus on AGI and ANI, often also referred to as Strong AI and Weak AI.
AGI VS ANI:
Artificial General Intelligence, as you can tell by the name, AGI possesses intelligence comparable to humans. They can perform tasks that require the level of cognitive abilities a human mind possesses. This is the type of AI that you usually see being depicted in movies in the form of characters such as Terminator, who is able to drive vehicles, shoot weapons and use all of his senses to achieve a certain goal.
But you probably haven’t come across such a killer bot yet. This is because, in reality, the progress we have made in Artificial General Intelligence is limited. What you usually hear about in the news media are actually great examples of ANI Systems, Artificial Narrow Intelligence.
ANI (Artificial Narrow Intelligence)
In comparison to AGI, an ANI system focuses on carrying out single specialized tasks like recognizing faces, monitoring for a restricted activity, or detecting traffic violations. Though the ANI system is limited to performing a single task, it does it really well.
Most of the progress in artificial intelligence has been focused on building ANI systems which have also been well reflected in the media.
This rapid progress in ANI is because we have some clearly defined blueprints for developing such systems and the promising results that it yields. The healthcare industry, for example, has made significant advances in developing ANI systems capable of performing better diagnostics than human experts, which is also a lot faster and cheaper. This has the potential to impact and save thousands of lives.
As progress in ANI continues rapidly, we will witness more and more such systems deployed in different industries. Hence making an extraordinary impact.
Now you may wonder about virtual assistants like Apple’s Siri or Google’s Assistant on your smartphone, or a self-driving Tesla.
They sure seem to get a lot of stuff done at a time, so are they examples of AGI? How Can ANI take care of so many tasks at a time? Take the example of self-driving cars, not only do they steer the vehicle or control speed but they also have to watch out for pedestrians and other vehicles, or process all traffic signs signals on the road, all while maintaining an optimal course to the destination.
It sure seems a bit overwhelming for an ANI system to do all of this and you will be correct to think it’s not possible for ANI. But is it AGI then? Well not exactly, these sort of complex systems are made simply by combining smaller ANI systems that each handle a single task. Though this may give an illusion of AGI, it is simply a number of ANI systems working together.
Using multiple ANI systems together to do a complex task is hard and perhaps this is one of the reasons we still haven’t witnessed the big claims made by autonomous industry come to reality.
The thing that most people don’t seem to understand is, despite the advances we have made in Artificial Intelligence it’s still really hard for us to mimic human-level intelligence. Even if autonomous cars reach mass adoption, they will be in a restricted environment. I personally don’t expect to see a level 5 autonomous car before a decade. A level 5 autonomous car means a car that can autonomously drive anywhere on the planet without any human intervention. It’s a really difficult task.
Artificial Super Intelligence (ASI):
Another less discussed category in AI is called ASI, which stands for Artificial Super Intelligence. This is just like an AGI that can do all tasks a human can, but additionally, it also possesses intellectual abilities superior to humans. So theoretically an ASI system would outperform a human at any given task.
Two examples of an ASI system that you may have been familiar with are Skynet from terminator and Vision from Avengers.
ASI is also said to be capable of self-awareness meaning it can develop a consciousness.
and this is the same AI that people like Elon Musk and Stephen Hawking have warned us about.
But as of now, ASI is just a hypothetical concept. So should you worry about ASI rising as a threat to humans in near future?
Personally, I actually don’t see humans, creating even AGI systems that can rival/surpass a human brain in our lifetime. But this is just my personal opinion. The thing is, an AGI system requires a framework or a set of algorithms that can encode and learn “common sense” and we haven’t seen much success in that department for decades. Even if we were to make progress there, it’s not like we would jump straight to human-level AGI systems. But rather first we will create systems that can demonstrate rat-level intelligence and then try to build systems for cat-level intelligence and then bit by bit go all the way to human-level intelligence.
This is a long journey requiring countless innovations and major breakthroughs in the field of AI.
SUMMARY:
ANI systems are successfully being used in multiple industries and it’s being widely adopted at an unprecedented pace. This trend will continue in the upcoming years, AI would continue to evolve and you would see AI taking up tasks and jobs that some of us are doing right now.
But there are still concerns about the democratization of AI which need to be addressed, like how it deals with bias in the real world.
Also, AI in the future will be heavily used in weaponry and to track all aspects of your life, but all those systems will be dictated and controlled by people. So AI itself is not something to be feared, the greatest threat to humanity is not AI, it is and always has been humans themselves.
With this we conclude level 1, in the next episode and Level 2 of the series, I’ll go over the history of AI and we’ll understand how we came to machine learning and deep learning. It’s going to be a very interesting post where we will dive deeper into AI.
In case you have any questions, please feel free to ask in the comment section and share the post with your colleagues if you have found it useful.
Make sure to Subscribe to Bleed AI YouTube channel to be notified when new videos are released.
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You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
Ready to seriously dive into State of the Art AI & Computer Vision? Then Sign up for these premium Courses by Bleed AI
This video is a part of our upcoming Building Vision Applications with Contours and OpenCV course. In this video, I’ve covered all the basics of contours you need to know. You will learn how to detect and visualize contours, the various image pre-processing techniques required before detecting contours, and a lot more.
The course will be released in a couple of weeks on our site and will contain quizzes, assignments, and walkthroughs of high-level Jupyter notebooks which will teach you a variety of concepts.
Download the code for the video by clicking the button below:
In this video we will explore how you can perform tasks like vehicle detection using a simple but yet an effective approach of background-foreground subtraction. You will be learning about using background-foreground subtraction along with contour detection in OpenCV and how you tune different parameters to achieve better results.
Download the code for the video by clicking the button below:
Today’s Video tutorial is the one I wish I had access to when I was starting out in OpenCV, in this video I reveal to you some very interesting information about the opencv including great tips regarding when to find the right resources, tutorials for the library.
I’ll start by briefly going over the history of OpenCV and then talk about other exciting topics.
Some of the things I will go through in this video
👉How to navigate the opencv docs to find what you’re looking for. 👉How to get details regarding any OpenCV function. 👉The differences between the C++ and python version of OpenCV and which one you should work with. 👉Pip installation of OpenCV vs Source installation. 👉Where to ask questions regarding OpenCV when you’re stuck.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
Ready to seriously dive into State of the Art AI & Computer Vision? Then Sign up for these premium Courses by Bleed AI
