Advantages of Deep Learning, Plus Use Cases and Examples
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
One of the biggest fields of artificial intelligence is computer vision, originally conceived in the 1970s, and many large industries are interested in how it can be used in a range of applications. But what exactly are computer vision algorithms and how can they increase ROI for us in the modern world?
Many industries throughout the world are now using computer vision applications to offer a better service and change lives. Most of these happen behind the scenes, so you may not even realize they are being used.
Let's take a look at how computer vision models are designed and what they can achieve with enough development times.
Whether you are a seasoned data scientist or you are new to the scene, this article will cover everything you need to know about real computer vision applications.
In layman's terms, Computer Vision is a field in computer science that investigates how a computer can extrapolate data from a selection of images and video footage.
By recognizing patterns in a set of data, a machine will develop a high level of understanding - spotting patterns that a human may miss.
Ideally, an advanced computer vision algorithm will be able to complete tasks that require the use of vision on par with its human counterparts.
Some popular examples of upcoming computer vision systems are self-driving cars (autonomous vehicles) and autonomous drones for many industries.
The best way to understand how a deep learning system, such as computer vision works, is to picture how a toddler learns vital life skills.
Most toddlers will pick up what's right and what's wrong by testing things out. They will ask their parents or teachers what something is constantly until they store it in their brain. This is exactly how a computer vision system learns.
To train a computer to replicate human vision, data scientists will share a vast set of images with the machine tagged by what they are (known as image classifications).
An AI system will start recognizing set features that make an object what it is and store these in an artificial brain network, known as a neural network.
For example, a dog has specific features that a cat doesn't, so the computer will recognize set things as 'dog features'.
There are many ways to train a machine to perform image processing and image analysis, but most will need a large set of data and classifications linked to this data.
There are thousands of applications around the world that can be boosted through the use of computer vision.
Below you will find just a few of the industries these models can be used for in a variety of different ways.
From autonomous cars to spotting cancer growths, Computer vision is changing our lives for the better.
Whilst we all may be familiar with what the stock market is and some of you may even know how it works, it is extremely difficult to predict financial trends.
Through the use of computer vision technologies, it may be possible to pick up on trends throughout the global stock market and accurately estimate what the next big investment could be.
Through deep learning, computer vision applications will notice slight changes in graphs related to the stock market, ones that a stock-broker may miss.
Back this up with a computer's incredible ability to quickly compute mathematics and accurate predictions are entirely feasible.
An example of this being used in the current financial world is HyperEXTRACT, an incredibly fast automated documenting system. It utilizes the power of computer vision to automatically extract data from thousands of documents at a time.
The stock market isn't the only part of the finance world that a computer vision application can excel in. An extremely popular application of computer vision is customer verification to ensure someone is who they say they are.
When opening bank accounts or checking your balance, you may need to bring a photo ID such as a passport or driving license.
However, computer vision algorithms can use biometric data to verify your identity with incomparable accuracy. This adds a whole new layer of security to your finances and is quickly being implemented in a host of mobile banking apps.
Mixing the natural world and machinery may seem like something from the future, but with today's technology, the agriculture industry is constantly utilizing computer vision technology to maximize output.
An example of this is that it is now possible to train a machine to pick up damaged or infested crops. This will help farmers reduce their waste and produce crops of a higher quality.
In terms of crop control, Cromai can aid farmers in diagnosing the issues with their crop output and how it can be prevented. Through the use of color images and in-depth analysis, it is easier than ever to ensure the quality of a crop is as good as it can be.
It will also aid retailers and farmers sort through their crops ensuring that the right fruit and vegetables are stored together. Sorting the good and bad crops has never been easier than it is through the use of computer vision.
By using object detection a machine will be able to scan thousands of individual objects as they pass by a sensor. This allows farmers to focus on other tasks while vision technology discards any low-quality crops automatically.
Upfront in retail systems you may be used to friendly customer support and human-to-human interaction. But what happens behind the scenes in storage facilities and warehouses?
Due to incredible advances in computer vision technology, you may not be surprised to hear that a lot of warehousing systems are now using object detection to track stock and organize warehouses. A business can operate much more efficiently due to this and automatically re-order stock that is running low.
Inventory management is just one of the few applications of computer vision that has an impact on our day-to-day lives without us realizing it. Thanks to the many different ways inventory management has been implemented, our shopping experience is much more comfortable than it has ever been.
Elon Musk is a big name in today's digital world, with companies such as Tesla and Space-X under his belt, it is no wonder he is constantly featured in conversations. However, did you know that computer vision applications are what Elon Musk, and Tesla specifically, use to power their self-driving vehicles?
Although they are not quite as far advanced as films such as Back to The Future may have guessed, self-driving cars are quickly becoming widely available.
By using computer vision to replicate a human visual system, a self-driving car will implement object detection to safely deliver their passengers to their destination.
Alongside Tesla, Waymo has also implemented visual computing to provide an automated taxi service throughout Phoenix, Arizona. Waymo offers a compelling alternative to Uber and other taxi services, with the bonus of it being entirely automated through the use of AI.
Optimizing production lines and prevent erroneous products from being sold is an important part of any manufacturing business. Once again machine vision and computers have found the perfect way to balance accuracy and speed beyond human capabilities.
Through the use of a bounding box to scan products as they pass, any broken or incorrect items will instantly be flagged by the computer vision tools.
Another machine or a worker will then be able to dispose of the flagged item or fix it if possible. This will save businesses thousands of pounds and reduce their waste output.
Computer vision applications can also be used to detect any potential machinery mishaps that may occur further down the road. In doing so, production flow will remain constant and the workers themselves will be much safer.
As the business will be alerted to a potential issue before it happens, repairs can be completed without disrupting the overall production line.
Fanuc Robotics America specializes in creating an AI model that can predict breakdowns within automotive factories, saving both time and resources. Through the use of thousands of cameras constantly watching these automated machines, their automatic system can detect problems before they arise.
Many aspects of manufacturing now use computer vision to ensure costs and production are balanced perfectly. If are ever in charge of manufacturing within your workplace consider investigating how computer vision technology can boost your manufacturing rates.
Perhaps the most promising field of computer vision is its potential to aid healthcare works in spotting health issues faster than a human can. Through the use of image analysis, it is completely feasible for an artificial intelligence system to notice potential threats in a patients' x-rays.
Computer vision in the healthcare world has the potential to be extremely useful and could be the next big step in healthcare technology. This is especially true when it comes to potential threats such as cancers or tumor growth.
An ideal healthcare application will be able to accurately identify problems far quicker than a human can by recognizing particular tell-tale signs of an affliction. Whether that's by scanning x-ray images or 3D models of the patient, computer vision systems may one day save your life.
ADAS 3D are an incredible example of the many leading companies in the world of computer vision within the sector of healthcare. Their imaging platform provides doctors with everything they need to visualize 3D images of a patients body spotting potential health threats that may have been missed.
One of the most common uses of computer vision technology is its use in the world of security and surveillance. Through the use of deep learning, it is completely possible to train a machine to spot potentially dangerous objects and suspicious-looking people in the real world in real-time.
By training a computer to spot specific trends in security situations it is completely possible to avoid potential threats in a wide range of scenarios.
Through the use of face detection, wanted criminals will be spotted, which can save lives. Although this technology is still in development, it is already being used in secure sites such as airports to offer real-time surveillance.
Perhaps the most recent example of a company that provides this is Pilot.Ai, A silicon valley start-up offering live surveillance through the use of computer vision. By recognizing potential threats, Pilot.AI is designed to prevent security threats, such as potential shooters, from becoming a problem.
You may even be familiar with this technology in your everyday life if you own a smartphone that features Face ID technology such as the iPhone.
With many modern phones offering users the ability to unlock their phones using their face, you might be benefitting from computer vision applications without realizing it.
Accuracy is without a doubt the largest measurement metric of computer vision algorithms. If an AI cannot predict an outcome or notice things faster and more accurately than your typical human, then it isn't worth the research costs.
This is why it isn't uncommon for technology companies to work together to share their data and attain the best results.
It is also why it is extremely common for a lot of big deep learning programs to be open source. The more pertinent data a machine is shown, usually the more accurate it will be, up to a certain point.
One big worry of large datasets is something known as 'overfitting'. Ironically, this is where an AI will learn the data too well and start applying patterns that don't apply to other types of data. This leads to really high training accuracy but low real-life accuracy.
As machine learning algorithms require lots and lots of data, it can be extremely difficult to find enough to accurately train a computer vision application. This is especially true when it comes to real-world data such as live camera feed and video media.
Cat and dog images are much more abundant on the internet than data that you may use in your applications of computer vision, meaning security threats are much harder to use as learning material.
Computer vision research is perhaps one of the most data-heavy fields of computer science as a machine requires thousands, if not millions of data items to accurately learn. Without enough data, neural networks will begin to suffer from 'underfitting'.
This is where there isn't enough data for a machine to accurately learn so it starts to make mistakes due to a lack of accuracy.
Ensuring a neural network has access to enough datasets to accurately ascertain visual information is extremely important.
Ethics play a large role in all fields of science, and data science is no different. An ethics issue can quickly prevent research from advancing and is an extremely sensitive topic in the world of data science.
In recent times, for example, Timnit Gebru, an AI ethics researcher for Google and co-founder of Black in AI, has recently been fired due to ethical issues she raised regarding Google's language processing AI. When developing an artificial intelligence system all ethical issues must be addressed as they are often overlooked.
In terms of accuracy, it is almost impossible for a human to be as accurate as some of the highest level computer vision applications out there. With systems being capable of noticing small details that aren't picked up by the human eye, computer vision offers incomparable accuracy in a host of different services.
As some of the most popular uses of computer vision are related to sensitive services such as healthcare, they must be extremely accurate.
If a machine learning model doesn't feature an extreme level of accuracy then it won't be used in the real world. This ensures that any real-world applications of computer vision must be vetted for accuracy.
Humans may be fast, but the best machines will always be faster than the fastest human in terms of calculations.
Even with their incredibly fast operation times, a well-designed ML model will offer extreme speeds with an extraordinarily high measurement of precision. Tasks such as categorization and spotting faulty items on a belt are highly boosted by this capability.
If a task requires speed and precision, fewer solutions offer quite as much speed and precision as a well-developed machine learning model can.
Are you a business that is looking to build some high ROI computer vision models to move past your competition?
Do what other businesses do: https://www.scalr.ai/contact
A little bit about Scalr.ai:
We are a machine learning and data science consulting firm focused completely on building business tools to increase profitability for clients. We specialize in natural language and computer vision systems that allow us to build software solutions for businesses interested in dominating the competition.
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
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