Machine learning is a part of artificial intelligence. It aims to develop applications that learn from data and improve their accuracy over time. In machine learning, these algorithms are “trained” to identify patterns and features in large amounts of data and make decisions and predictions based on the new data. The better the algorithm, the more accurate the AI’s decisions and predictions. This improvement is achieved by continuously feeding the AI with additional data.
In machine learning, there are several types of learning: supervised, unsupervised, and reinforcement. In all three cases, machine learning is about creating and optimizing an algorithm that learns from data and makes predictions and decisions.
Supervised Machine Learning
In supervised learning, the AI is given the desired task and solution. For example, a set of image data, each with a label indicating what it shows. The AI then has to select all the pictures of the cat. The AI develops an algorithm to identify the cats in the images. The results are then passed on to a human observer. The human acts as a teacher and helps the AI to evolve. In doing so, it sorts which images contain cats and which do not. The AI uses this information to optimize its algorithm. Continuous improvement aims to improve the accuracy of the results.
Unsupervised Machine Learning
In unsupervised machine learning, the observations in the data set are not labeled, and the algorithms learn to determine the structure of the input data and set the necessary parameters themselves. The AI adapts its algorithm to the state of the data and obtains the analysis results, which it then applies to the new data. This learning method can be more time-consuming but also more accurate.
Machine Learning with Reinforcement
In so-called reinforcement learning, the AI learns by trial and error and receives a “reward” for correct results. The computer then tries to devise strategies to optimize this reward. In this way, the reward makes it easier to find solutions: giving the AI the freedom to find solutions that a human would not consider is important. Reinforcement learning is one of the most promising approaches to general AI, capable of solving problems not only in a single domain, such as image recognition, but also in many different domains, such as speech recognition and human-to-human communication.
Most machine learning algorithms can be applied to the following classic application areas:
- Transaction fraud detection,
- Content personalization,
- Document classification,
- Customer churn prediction,
- Sentiment analysis
- Messaging solutions,
- Diagnostic systems, etc.
But even a simple sentiment analysis shows the scale of the problem that machine learning has to solve: the system and its algorithms need to recognize whether a post on social media, such as Twitter, is serious or ironic. Simply analyzing the structure of the text (subject, complement, verb, etc.) is not enough; the author’s intention and the context of the speech act must also be considered.
Machine learning algorithms can now take all these factors into account. One limiting factor is that the time needed to complete an analysis – for example, in a report – decreases as the demand for this type of analysis increases. Computing power must go hand in hand with memory capacity and system “intelligence.
Machine learning is used to discover patterns and structures, such as recommend products, movies, and music based on what you have previously bought, watched, or listened to.
Other applications include spam detectors that prevent unwanted emails from arriving in your inbox or medical image analysis systems that detect cancerous tumors before you notice them.
In the future, we can expect even more: the amount of data (Big Data) is growing, computers are getting more powerful, and data scientists are developing ever more sophisticated algorithms. Machine learning will play an increasingly important role in our personal and professional lives. Understanding AI solutions is also likely to become increasingly difficult.