Thanks to AI and machine learning, computer vision technology is getting upgraded with improved version of visualizing making perception through machines reliable. Actually, this is completely related computer based visual processing of objects.

What is Computer Vision in AI and Machine Learning?

Computer vision is the simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.

The main purpose of using the computer vision technology in ML and AI is to create the a model than can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.

How Computer Vision System Work?

You can say computer vision is used for deep learning to analyze the different types of data sets through annotated images showing object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

This process depends subject to use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the model predictions accurately in future. Computer vision can be only utilize only with image processing through machine learning.

What is Difference Between Computer Vision and Image Processing?

Both are part of AI technology used while processing the data and creating a model. The difference between computer vision and image processing is Computer vision helps to gain high-level understanding from images or videos.

For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.

Whereas, image processing doesn’t need such high level of understanding of image. In fact it is the sub-field of signal processing but also applied to images. For an example, if you have a noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.

Image process task involves filtering, noise removal, edge detection, and color processing. In entire processing the you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.

The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.

Application and Role of Computer Vision in Artificial Intelligence

The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.

From simple home task to recognizing human faces, detecting the objects in autonomous vehicle, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc.

Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.

Cogito is one the companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning. Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model perform accurately in real-life use.

Tech industry is buzzing with keywords called Artificial Intelligence (AI) and Machine Learning (ML). However, still there is lots of confusion due to different perception of people towards these buzzwords. So, before we share any other thing, let us discuss the actual difference between these emerging technologies.

These two terms are very often used while discussing Data learning, image annotation, visual search, data analysis algorithms and other similar technologies. Describing this would be quite difficult but in our terms “Artificial Intelligence is the comprehensive model of machines that are capable of carrying out tasks normally like humans or backed with human intelligence.”

The best example of Artificial Intelligence is autonomous or self-driving cars that require human intelligence, such as visual perception, speech recognition, decision-making, and translation between machine learning language to understand the situation and perform accordingly.  

While on the other hand, “Machine Learning is an application of artificial intelligence that facilitates systems with the ability to learn data automatically and improve themselves from the experience without being programmed.”

In fact, Machine learning is a subset of artificial intelligence and it focuses on the development of computer programs that can access data and use it learn for themselves.

Read More: How to Measure Quality While Training the Machine Learning Models?

Artificial Intelligence Exists since early days

Actually, if you have noticed Artificial Intelligence has been around us since long time. In the Greek mythologies mechanical men was the best example that was designed to mimic or copycat our own behavior. Infact computers in Europe were perceived as “logical machines” and by reproducing capabilities such as basic reckoning and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains to perform complex task like humans.

But the situation is quite different now, as technology and our understanding of how our minds work has developed, and our notion towards AI has changed drastically. Instead of doing complex calculations, working in the field of AI focused on imitating human decision making processes and performing tasks in ever more like human beings.

Two main categories of Artificial Intelligences

Currently Artificial Intelligences enabled devices designed to act intelligently – are often categorized into one of two fundamental groups – either applied or general. Applied AI is very much common in which systems are designed to intelligently trade stocks and shares in capital markets or maneuver self-driving vehicles that comes under this category.

Whereas, Generalized AIs are in which devices or systems can in theory handle any task where machines are less common, but now scenario is changing and more innovative advancements are happening today making AIs more generalize for multiple usages. The best part of AIs progression is that, it is also reinforcing the development of machine learning technology.

Read More: How to Create Training Data for Machine Learning?

Evolution of Machine Learning

In current era two main important innovations led to the rise of machine learning as the vehicle that is motivating AI development forward with the current speed across the industries.

One of the two important break throughs was realization that rather than teaching computers everything they need to know about the world and how to carry out tasks, let’s make it possible to teach them to learn themselves.

While the second one in recent times, was the rise of the internet, and the surge in the amount of digital information being generated, stored and made available for analysis and to perform tasks. If such innovations take place successfully and efficiently, engineers comprehended that instead of teaching computers and machines how to perform a particular task or do other things, it would be more effective to program them to think and act like human beings.

Development of Neural Networks for Machine Learning

The concept of neural networks is developing and playing a vital role in teaching computers to think and understand the world just like humans. And computers have innate advantages of working at faster speed with accuracy without any bias.

Neural Network is a machine learning technology or computer system designed to work by classifying information just like the human brains do. It can be taught to recognize and categorize things like images, and classify them according to the elements they contains.

How Neural Network works?

Basically they work on methods of probability – based on the statistics fed into it, which helps to make statements, predictions and take decisions within a certain extent. And adding feedback loop to such machines also enables “learning” – by sensing or being told whether the decisions are correct or not correct, so that it can modify the approaches while giving the results in future.

Read More: What are the Common Myths about Machine Learning?

Machine learning applications can now read texts and work out whether the person has given a compliant or offered a complement. Even they can listen to music tracks with sensibility to decide whether it will make someone sad or happy and can find other suitable music matching the mood of a person.

Neural networks for ML offer great opportunity of such possibilities. Thanks to fiction science, such ideas are now starting to translate into our real life where we are able to communicate and interact with electronic devices with digital information, as we naturally interact with another human being. At this point of time another field of artificial intelligence in terms of Natural Language Processing (NLP) – has become a source of an exciting innovation in recent years.     

Natural Language Processing applications are designed to understand natural human communication, either written or verbal and communicate in reply with us using the same regular language. ML can be also used here to understand the vast tones in human language and to learn and respond in the same way.

Best and most innovative examples of AI and ML

To understand the concept of machine learning we can take example or self-driving cars that has been earlier tested by search engine giant Google and well-known electric automaker Tesla. Google’s and Amazon search engine is also another example of ML that shows results as per your search habits. Humanoid robot Sophia developed by Hong Kong-based company Hanson Robotics can carry out work in ways similar to humans.

Rise of Artificial Intelligence is raising fears that efficient machines are likely to take over human jobs. To the contrary, as per the research, AI will help augment abilities of human workers and could play a role of job creator. Just like other past disruptive technologies AI will also create new opportunities for jobs. Here are top 5 professions that stand to grow with the rise of AI.

Data Scientists

Data scientist analyzes data to understand complex behaviors, trends, and inferences, discovering unseen insights that help companies to take better business decisions. Data scientists are new breed of analytical data experts and can play a role as part mathematician, computer scientist, and part trend spotter.

Online video streaming service provider and eCommerce companies are using data scientist for customers viewing patterns, their interest and shopping behaviors to understand the future demand. This pattern learning helps companies to plan production or new launches accordingly.

Read More: What are the various Types of Data Sets used in Machine Learning?

AI or Machine Learning Engineers

To synchronize their work now most of the machine learning engineers are partnering with data scientist. Data scientist have stronger skills in statistics and analytics while ML engineers would have more expertise in computer science that is favorable for stronger coding abilities while working on any project based on AI and ML technology.

AI Hardware Specialists

Jobs in AI is not only associated with software and programming. Manufacturing AI related hardware such a GPU chips also requires specialized man-power. Big companies are now entering into developing their own specialized chips. Intel, Qualcomm and IBM are now developing chips especially for machine learning and creating hardware architecture that mirrors the design of neural networks and can execute like them as well.

Data Labeling Professionals

Along with increase in demand for data collection services, demand for data labeling professionals is also surging and likely to increase at faster rate in near future. Data labeling involves curation of data, in which raw data is taken, cleaned and organized for machines to consume. Proper labeling allows AI scientists to train machines in new tasks.

Data Protection Specialists

With the rise in demand of data related services and machine learning models in various industries, its protection also becomes a hot profession among IT specialists.  There are many types of role a data protection specialist can play like Data auditing, access control, encryption, backups, application security and many more similar responsibilities.

Read More: How AI will Improve Healthcare Services in 2019?

Usually, databases are secured against hackers through various security measures like network-based intrusion detection systems and firewalls etc. However, securing such important database structures and networking programs or functions and data within them becoming more challenging due to increase access to open network through internet.

Without Human analysis and judgment AI & ML would be not possible

AI will create more jobs than it will destroy, because without involving humans, process of organizing, analyzing and drawing actionable conclusions from data in not possible. This makes the role of humans more significant especially for creating, implementing, and protecting AI technologies.