Self-learning AI or Artificial intelligence agents or self-learning systems can continuously learn new information. They can learn further information without the aid of hard coding. These adaptive systems learn primarily through trial and error. Self-learning AI is a learning model influenced by neuroscience, and as a result, its functionalities have grown over time.
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A self-learning system looks to interact with its users or the environment before observing the changes brought about by its actions.
As they are currently designed, self-learning AI systems carry out pre-programmed tasks. Systems based on artificial neural network hardware have demonstrated the ability to outperform conventional digital operating systems when used in proper human contexts.
Systems interact with the environment before observing the changes brought about by their actions.
Self-learning systems based on fuzzy logic, list logic, and looser philosophical logic are often constructed as software structures. However, these systems have proven to adapt to changing environmental conditions, sometimes better than parametrically logical systems that are currently frequently constructed.
One of the most recent machine learning techniques that have impacted the data science community but have so far largely gone unnoticed is self-supervised learning. (Read our article on supervised learning to understand the technique in more detail.) The paradigm also holds great promise for businesses because it could help solve the most challenging problem in deep learning.
A self-learning system initially tries to interact with its users or the environment and then watches the changes those attempts lead to. The development of such systems is accelerating thanks to AI techniques like reinforcement learning, inverse reinforcement learning, and learning by demonstration.
Numerous application areas, including robotics, autonomous vehicles, banking, finance, gaming and document processing, are now being aided by this paradigm.
What Is Self-Learning AI
Self-learning Artificial intelligence (AI) learns independently from unlabeled data. On a broad level, it functions by examining a dataset and seeks out patterns from which it can derive conclusions. Then, it picks up how to “fill in the blanks.”
A trained machine learning model is comparable to a human learning a second language in a structured educational environment. At the same time, we can compare a self-learning system to a human being immersed in a new language through daily exposure by moving to a foreign country. Although a student who studies Spanish for five years in school may have a firm grasp of the language and know how to use it, learning it takes much longer than someone who moves to Spain for a few months.
A self-learning system is comparable to a human learning a second language.
The idea of learning by doing is being applied to AI by self-learning systems.
What Are the Benefits of Self-Learning AI
When instructing a machine on a concept for which there is a limited amount of training data, self-learning AI is beneficial. Additionally, it can be helpful in teaching computers about complex processes for researchers to label training datasets. Self-learning AI is often referred to as the future of AI because it can be implemented (in theory) much more quickly than supervised learning.
Advancements would proceed very slowly if all AI learning were carried out under the watchful eye of a machine learning engineer or data scientist painstakingly building datasets. On the other hand, unsupervised learning allows AI to advance much more quickly.
Another advantage is the ability of self-learning AI to more easily transfer newly acquired skills to other domains and industries. See our article on transfer learning in NLP to understand how this is done.
An Example of Self-Learning AI
Since self-learning AI is more adept than most people at spotting changes and patterns indicating a breach, cybersecurity is one of the most popular fields where it is currently being used. In addition, because AI using unsupervised learning, derives its knowledge from the data environment rather than a predetermined dataset, it can detect more anomalies than human researchers might even be aware of.
Cyber security is a typical example of self-learning systems.
Self-Learning Systems with Natural Language Processing (NLP)
As we are increasingly producing and storing richer forms of data in both spoken and written language, it is fair to say that the amount of data will continue to increase and change at a rate that traditional machine learning algorithms can’t keep up with. As a result, self-learning systems have become critical for many NLP tasks.
NLP is the ideal use case for self-learning systems.
At Spot Intelligence, we create all our machine learning models in a self-learning fashion. This means that as new data comes in, this is added to the training data so we can detect new patterns instantaneously. As a result, there is no need for an engineer to re-train or rebuild a model. Instead, the machine learning pipelines ingest new data continuously, letting our algorithms pick up patterns and relationships autonomously.
This process creates much better results than a static system but also requires highly skilled engineers to deal with the associated problems.
Problems with self-learning systems
Self-learning systems are the future, but automatically trained algorithms are also more difficult to fine-tune, have a great chance of over-fitting, and model stability is harder to achieve.
Model outputs shouldn’t give drastically different results every time it is re-trained, but this can be hard to guarantee when new unseen data is added continuously. However, if this happens, the algorithm is not stable enough, and we won’t detect the underlying data trends. These problems can be much more complex to debug and fix with automatically re-trained models than with single-model development.
Is it worth implementing a self-learning system?
The short answer is yes. It is worth implementing self-learning systems. It takes more effort to develop a self-learning system and put it into a production environment, but it will save you time and energy in the long run. Revising a system is time-consuming. A system that automatically updates machine learning models gives you peace of mind and allows systems to be accurate and reliable in production for extended periods.
We need to see this upfront cost as part of good practice. Similar to software development, working with well-designed systems and having low maintenance costs is much better than re-writing your systems and models every few months.
Lessons learnt from implementing self-learning systems
These are some personal lessons learned for those embarking on their first self-learning model.
1. Have a comprehensive data processing pipeline to add new data to your model quickly. This is especially important for NLP problems as a lot is involved in processing text.
2. Set up a separate system for model training that cannot affect your production models if your training fails. It’s always better to be safe than sorry.
3. Use a solid metric to test the model’s performance after every training cycle. The metric you choose will depend on the business problem you are solving.
4. Have a fallback process for when your model no longer performs favourably on your metric—models in production stop working all the time. Make sure you are prepared for when this happens.
5. Always test the stability of your models. New data should make your system more accurate, not drastically change how it behaves.
6. Set up alerts for your system. Of course, you want to be alerted to any abnormal behaviour. However, make sure the alerts don’t get triggered too often either, or you will stop caring about them and ignore crucial early warning signs.
7. Review detailed statistics of your model’s performance regularly, at least monthly. Just add this to your calendar, so you don’t forget.
8. Go on creating other models with confidence, knowing that the ones you made are updated regularly. Peace of mind is priceless.
1. Self-learning AI systems can continuously learn from new information without the aid of any hard coding. This way of looking at and working with data will become more prominent as we rely on AI systems. However, this technique has several drawbacks, the main one being that the implementation is more complicated.
2. The rise of natural language processing means more data will lend itself well to unsupervised learning techniques where we no longer rely on labelled data but rather on finding relationships between terms in context when processing text and documents. The self-supervised learning techniques are also promising.
3. Self-learning systems are already in use today. We need to all move away from static models, just like we all moved away from static algorithms and into machine learning to achieve more intelligent systems.
Would you like to continue reading? Read our article on how to create self-learning systems.
It is always interesting to hear what others are doing in this space, so please share in the comments below. Have you implemented a self-learning system? What use case are you covering? What problems have you encountered, and what did you learn from them?
Are you just starting your first self-learning project? Let us know in the comments what you are interested in so we can continue providing helpful content.