Since the moment artificial intelligence first appeared, many new business and technological solutions have been invented. Creating them, though, would not be possible if not for the development of machine learning – and later deep learning – which enabled machines to learn how to process user requests similarly the human would do.
Machine learning, which was invented in 1980 (referred to briefly as ML), uses mathematical algorithms to enable machines to learn. Algorithms continuously learn patterns from the input data and use the knowledge gained to recognize new data and draw conclusions from it. Researchers developed the deep learning (DL) method back in 2011. Instead of regression algorithms or decision trees, DL uses neural networks, so it works very similarly to the actual biological neural connections in the human brain.
Artificial Intelligence – why it is important for business?
Artificial intelligence is all around us. Large and popular brands use machine learning and deep learning to improve user satisfaction with their products and services, reduce costs, as well as optimize and automate various processes within the company. Examples? Recommendation systems in online stores or on Netflix, facial recognition on mobile devices, personalization of content based on customer data, etc.
What can you gain by implementing artificial intelligence solutions in your business?
- Saved time – tasks normally performed by a man can be done by a machine even faster. While repetitive work will be automated, you can delegate your employees to deal with more complex problems requiring the human presence and creative thinking.
- Cost Reduction – a machine will often perform some tasks better than a human. With access to advanced analytical results and computing power, AI enables your company to save money by providing business insight to make better decisions faster.
- Better security – artificial intelligence may be used in cybersecurity to detect unusual activity in company systems and protect user data.
- Better user experience – a lot of AI-based solutions were invented to improve overall UX on business platforms. Features such as visual search or recommendation systems not only improve sales but also significantly affect user satisfaction.
What is an Artificial Super Intelligence?
It is also important to mention that artificial intelligence is a really broad term used for classifying machines created to mimic human intelligence. There are Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). The last two types are classified as “strong” AI. Weak AI allows machines to solve simple problems, like identifying objects in a series of photos. Business solutions such as chatbots or virtual assistants, useful as they may be, are examples of Artificial Narrow Intelligence. Solutions based on strong Artificial General Intelligence should have capabilities compared to humans, while Super Intelligence should be capable of even more than a person would be.
Your business may greatly benefit from applying AI-based solutions. Such solutions are based on machine and deep learning. Let’s learn more about it.
Artificial learning, machine learning, and deep learning
Before you learn what machine and deep learning are, you need to realize that they are both subfields of Artificial Intelligence, as described above. AI focuses on making computer systems able to perform given tasks the way that a human would. Machine learning was created to teach machines how to learn without being explicitly programmed to do so, while deep learning comprises a structure of various algorithms that mimic the human brain. Machine learning and deep learning work differently and thus have different applications.
What is machine learning?
Machine learning is a subset of AI. When you say that a machine is capable of machine learning it means that it may perform some functions with a given data and get progressively better – learn from data. Special algorithms have to be used to perform some specific tasks that have not been specifically programmed. Those algorithms can modify themselves without human intervention to provide desired results.
Example of machine learning – streaming services recommendation system
Spotify’s recommendation system may serve as a good example of machine learning. Its collaborative filtering algorithm first compares playlists created by people that use this service. The algorithm then uses these playlists to find other songs that meet similar criteria and recommend them to users. Spotify is also using NLP (natural language processing) – an ability to understand speech and text in real-time. This algorithm is applied to articles and posts about music to better describe each song in the system and recommend it to respective listeners.
What is deep learning?
The deep learning model is based on algorithms called layered artificial neural networks, whose structure is inspired by and mimics the biological neural structure. The idea is to analyze data in a similar way that a human would. There is an input layer, hidden layers (where the analysis is performed), and an output layer with the final result of the analysis. The more hidden layers, the deeper (more complex) the analysis, which leads to the most accurate results.
Deep learning example – autonomous cars
Tesla cars require experience and human-like abilities to make decisions while driving. There are roads, driving regulations in many countries, signs, signals, and pedestrians that must be acknowledged by the car before the use of autonomous vehicles like Tesla is possible on public roads. This is a large amount of information to deal with, but it can be done through deep learning.
Deep learning vs. machine learning – what are the main differences?
- Since machine learning is a subset of artificial intelligence, deep learning is a subset of ML. Deep learning with neural networks is far more developed than machine learning, which typically uses decision trees, and it is DL that enables most human-like AI-based solutions.
- The structure of machine learning algorithms is relatively simple (it uses linear regression or a decision tree), while deep learning requires a multi-layered, complex artificial neural network.
- Classical machine learning relies on human interpretation to learn. It needs to be labeled datasets to learn the differences between given data. Deep learning can also benefit from labeled datasets, but it does not necessarily require it.
- Machine learning is not suitable for solving problems that require considerable amounts of data, as algorithms need data to be labeled. Deep learning would be better for performing such complex tasks.
- Deep learning is using much more data than traditional machine learning algorithms to gain the proper, expected results. It requires large datasets for producing high-quality interpretations.
You have to remember that in both cases the quality of data is crucial for high-quality results.