What is the difference between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) ?
Artificial Intelligence is a machine developed with the capability for intelligent thinking. AI is an umbrella term that includes multiple technologies, such as machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI.
Then, Machine Learning is a means of achieving AI: letting the computer parse a large amount of data and learn from it.
Deep Learning is an approach to Machine Learning which involves Artificial Neural Networks to work with the data. Image processing is a perfect example of how Deep Learning is being used in the real world.
Artificial Intelligence [ AI ] Expected Market
According to Tractica , More than 19,000 companies are currently using deep learning to advance their respective industries, solving what was once unsolvable.
Tractica Report shows the market forecasts span the period from 2016 through 2025 and segmented by AI technologies: machine learning, deep learning, computer vision, NLP, machine reasoning, and strong AI – 154 Specific AI Use case between: Consumer, Enterprise, and Government Use Cases for Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Machine Reasoning, and Strong AI Across 29 Industry Sectors.
The market intelligence firm forecasts that annual worldwide AI revenue will grow from $643.7 million in 2016 to $36.8 billion by 2025.
Deep Learning is heavily used in the industry in building intelligent systems to assist humans in various tasks. It is getting computers to pick up a variety of skills, like understanding photos.
Deep learning is exceptionally useful for training on very large and often unstructured historical datasets of inputs and outputs. Then, given a new input, predicting the most likely output. It can be applied across almost every function inside a business.
How Does Deep Learning Technology work?
Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.
That is what DevisionX Systems do. After Image capturing and in the processing step in the machine vision system, that aims to extract features that can be used to fetch meaning from the image – whatever it is done either by supervised or unsupervised methods. Machine learning helps systems in making decisions and tasks such as object recognition.
With datasets as comprehensive as these, and logical networks sophisticated enough to handle their classification, it becomes trivial for a computer to take an image and state with a high probability of accuracy what it represents to humans.
Pictures present a great example of how this works, because they contain a lot of different elements. Deep Learning can be applied to any form of data – machine signals, audio, video, speech, written words – to produce conclusions that seem as if they have been arrived at by humans – very, very fast ones.
Deep Learning Applications
Today, AI and machine learning are already automating and improving many everyday tasks, like mobile search or the organization of your family photos. AI is also helping a new breed of companies disrupt industries from medical research to agriculture. Computers can’t yet replace humans, but they can do a great job handling the mundane clutter of our lives.
Deep Learning technology is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next.
Let’s look at a practical example:
Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.
Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has “learned”, it can classify, with a certain probability of accuracy, passing vehicles by their make and model.
What can Deep Learning do?
- Navigation of self-driving cars – Using sensors and onboard analytics, cars are learning to recognize obstacles and react to them appropriately using Deep Learning.
- Recoloring black and white images – by teaching computers to recognize objects and learn what they should look like to humans, color can be returnedto black and white pictures and video.
- Predicting the outcome of legal proceedings – A system developed a team of British and American researchers was recently shown to be able to correctly predict a court’s decision, when fed the basic facts of the case.
- Precision medicine – Deep Learning techniques are being used to develop medicines genetically tailored to an individual’s genome.
- Automated analysis and reporting – Systems can analyze data and report insights from it in natural sounding, human language, accompanied with infographics which we can easily digest.
- Game playing – Deep Learning systems have been taught to play (and win) games such as the board game Go, and the Atari video game Breakout.