- Artificial intelligence – AI
- Machine learning
- Deep learning
- Neural networks
- Artificial intelligence versus machine learning , deep learning and neural networks – what are the main differences?
- Development direction and future opportunities
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Have the machines finally taken over? Has their dominance started yet? They are seeping their way into our lives. They affect how we live and are with us while we work and entertain. Starting from simple Siri or Alexa, ending with sophisticated algorithms, the examples are countless. Artificial intelligence versus machine learning, deep learning, and neural networks – what are they? Do they have a positive impact on our lives?
Artificial intelligence – AI
What is artificial intelligence and how does it work?
If you have ever wondered if the simulation of human intelligence in machines that are programmed to think like humans is possible, here is your answer.
This is the best artificial intelligence definition one could create. AI artificial intelligence is also strongly connected with mimics, it can take actions that have the best chance of achieving a specific goal.
AI artificial intelligence is great proof that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks. Not only the simple tasks but also the more complex ones – AI is a wide-bringing branch of computer science capable of performing tasks that typically require human intelligence. According to the commonly available data, the successful AI should think both humanly and rationally, as well as act humanly and rationally. If what is artificial intelligence is still unclear, consider it machines wired using a cross-disciplinary approach based on computer science, mathematics, linguistics, and more.
Applications and practical examples
Artificial intelligence examples are endless. It is being used across different industries including finance and healthcare. The systems are merely advanced machine learning software with algorithms that adapt themselves to our likes and dislikes. The artificial intelligence definition says that the machines and applications using AI continuously improve their skills and usefulness based on a large dataset. Artificial intelligence examples include:
- Siri – Apple’s personal assistant, a friendly voice-activated computer. The app gets smarter and better able to predict our questions and requests. A similar example of artificial intelligence is Alexa – the smart home’s hub by Amazon.
- Tesla – one of the most innovative cars ever made. The car has predictive capabilities, self-driving features and it’s nothing but jaw-dropping from a technological point of view.
- Netflix –a predictive technology based on customer’s reactions to films. Billions of records are quickly analyzed to suggest what you might like.
Importance of AI
Artificial intelligence in the future should still be making the world a better place. It’s not only fancy gadgets – just look around and you’ll see that most of your tasks are made smooth by the use of AI.
The AI’s main goal is to reduce human effort as much as possible, to speed up your tasks and processes along with a guaranteed level of accuracy and precision. Will artificial intelligence take over? You cannot be sure but technology should first and foremost make the world an error-free place. It will continue to be an integral part of our daily lives. AI’s reach will probably be more and more extensive, for sure it will be even more crucial when it comes to safety and security systems.
What is machine learning and how does it work?
Very often a comparison between machine learning vs artificial intelligence is brought up. And it makes perfect sense – machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve based on experience. In consequence, they don’t have to be explicitly programmed.
How does machine learning work?
It helps computers access data and learn it for themselves so that human intervention or assistance is not necessary. Putting it simply, machine learning algorithms use statistics to find patterns in enormous amounts of data. The data, like words, numbers, images, etc., if can be digitally stored, can be also fed into a machine-learning algorithm.
Applications and practical examples of what machine learning can do
Machine learning is probably one of the most popular applications of AI. This is why the examples of what machine learning can do might be somehow similar to what is AI capable of.
- Virtual personal assistants like Siri, Alexa, and Google Now – when asked over voice, they assist in finding different types of information. In order to find the answer to your question, they look out for information, recall your related queries, or send commands to other resources.
- Traffic predictions – e.g. while commuting.
- Social media services – if you use Facebook, Pinterest, etc. you know what it’s all about – you get personalized ads to your newsfeed, you are advised to invite people you may know, your face can be recognized.
- Product recommendations – you shopped for a product a few days back and then you get dozens of personalized ads and emails for shopping suggestions. This is also machine learning’s magic.
Importance of ML – Machine Learning
Although ML has been improved rapidly in the past few years, we are still far from reaching human performance. Still, machines need the assistance of humans to complete some of the tasks.
Wondering why machine learning is important?
It’s easy – data is the lifeblood of all businesses. Machine learning can help unlock the value of corporate and customer data. Thus, it affects decisions that may keep a company ahead of their competition and in consequence, it has the potential to dramatically impact the future of your company.
What is deep learning and how does it work
Also called AI deep learning, it’s one of the artificial intelligence functions and a subset of Machine Learning. It imitates the workings of the human brain in processing data using multi-layer neural networks.
This way, the system can create patterns for use in decision making. Very often people wonder what is the difference between deep learning vs machine learning – the truth is that deep learning is a subset of machine learning in artificial intelligence. It has evolved with the digital era, closely related to so-called big data. Big data is drawn from sources like Internet search engines, e-commerce platforms, social media, etc. Such data is readily accessible and it’s very often used through applications like cloud computing.
Applications and practical examples
Among the others, the following sophisticated systems use deep learning:
- Self-driving cars – AI deep learning is used to create the most innovative cars ever. An enormous amount of data is used to build a new model and train machines to learn and also to test unprecedented scenarios.
- Entertainment – Netflix, VEVO, etc. – paid TV platforms enhance their deep learning capabilities to provide a fully personalized experience to the viewers. They create their personas factoring in show preferences, history, time of access, etc. to recommend shows that might be found interesting by particular viewers.
- Healthcare – deep learning is advancing the future of precision medicine in unbelievable ways. Mobile and monitoring apps, personalized medicine on the basis of biobank-data, computer-aided detection, decision support tools – these are just examples.
- Visual recognition.
Importance of DL – Deep Learning
Deep learning is strongly connected to artificial intelligence and machine learning. AI deep learning makes machines artificially intelligent to think or act like humans. Machine learning is just making a machine learn from its experience and improve its performance. Then, deep learning pops up – a cause of all of the miraculous discoveries the world has come across in recent years. The structure of deep learning is a bit similar to a human brain – there are many neurons and nodes like neurons.
What are neural networks and how does it work?
Neural networks are a set of algorithms that have been designed to recognize patterns. They are usually modeled loosely after the human brain.
Neural networks interpret sensory data through machine perception. The recognized patterns are numerical and they are contained in vectors. Just imagine a clustering layer on top of the data you store. They are some kind of help if you want to group unlabeled data according to similarities. Deep learning and neural networks are closely related – actually deep learning is a new name for an approach to artificial intelligence called neural networks.
Applications and practical examples
- Engineering – this is where neural networks are nothing but essential, especially in high assurance systems like flight control, power plants, chemical engineering, automotive control, and medical systems.
- Aerospace – aircraft component fault detectors and situations, aircraft control systems, flight path simulations.
- Electronics – machine vision, non-linear modeling, chip failure analysis, prediction of the code sentence.
- Manufacturing – dynamic modeling of chemical process systems paper quality prediction, process and machine diagnosis, process control.
- Telecommunications – ATM network control, data compression, equalizers, network design, pattern recognition, automated information services.
Importance of neural networks
Neural networks have been invented to help people solve complex problems in real-life situations. NN can improve decision processes in various areas, among the others: credit card and medicare fraud detection, voice recognition, medical and disease diagnosis, optimization of logistics for transportation networks, financial prediction for stock prices, robotic control systems, targeted marketing, process and quality control, and many others. Neural networks can work like that because they learn and model the relationships between inputs and outputs that are complex and nonlinear. The main goal of neural networks is to achieve human-level accuracy.
Artificial intelligence versus machine learning , deep learning and neural networks – what are the main differences?
The easiest way to think of the relationship between these four is to visualize them as concentric circles with AI the largest (as the idea that came first), then Machine Learning (ML), which is the learning part of AI and is reliant on the next circle within it which is Deep Learning (DL). Finally, neural networks fitting inside all of them.
Development direction and future opportunities
Will interest in AI, machine learning, deep learning, and neural networks continue to increase?
The key trends that shape their future are obvious. The world has been developing, computing power has advanced. Deep learning relies on computing power that is able to solve more complex problems. The new AI models can learn to solve more problems than ever. Thanks to advances in machine learning and deep learning, AI is already powering search engines, virtual assistants, online translators, etc. What the next steps can be?
- Security strengthened – AI, deep learning, machine learning and neural networks would be nothing if they couldn’t help security processes. The first thing is the usage of drones – the ones used in the military. However, thanks to innovative technology solutions, products, and items might be delivered to the customers.
- Machines directed by humans – sadly speaking, sometimes machines can outperform humans, for example when it comes to recognizing faces and driving safely. Currently, machine learning apps are programmed to learn and adapt – machines will be trained by humans who provide them all the data they need.
Artificial intelligence versus machine learning, deep learning, and neural networks – as all the four terms discussed are inextricably connected, it is worth remembering the differences between them.
The main idea behind them is to make our lives much easier. In today’s reality with the continuous growth of advanced technology, there is no doubt, we will be surrounded by more and more various devices and applications where Artificial Intelligence principles are used. That creates as well a huge opportunity for all, who’d like to become an expert in this exciting field and work for global technology leaders.
Machines are more and more intelligent every day and most of our daily tasks are already automated. They change our lives and make all we do more efficiently. The world is changing every day, so it’s crucial for us to fully understand the opportunities AI, deep learning, machine learning, and neural networks provide us with. Since we covered only key principles of each of these topics, further exploration is highly encouraged. This is the only way to stay up-to-date with the latest technological innovations, both from the personal and professional point of view – because the rapid development of technology brings us something new almost every day.