We all have ridden a bicycle in our lives, but have you ever wondered how may one learn to ride a bicycle? The most common answer to this is that we get assistance from our elders, such as our father, sister, or brother. They hold the cycle to keep it stable, they teach how to balance the cycle. This guidance leads one to become better at cycling. This same approach is used to teach self-learning AI models at the present time. The AI models are provided data by humans which they use to train themselves. This whole process is processed out step-by-step with proper human guidance.
Now, what if a child didn’t have any mentor who could teach him how to ride a bicycle? Then how could that child have learned to ride? They have learned it the hard way. Constantly practicing riding, falling a lot of times, but each time they became better than before. After failing and learning from each failure they learnt the technique of riding a bike. This is the main concept of Self-learning AI models, in which the AI model evolves and learns new things on their own, without needing any human assistance. But how does an AI model train itself? What could be the potential challenges, and how could this change our future? So let’s Explore more about it.
What is Self-Learning AI?
Imagine that you were given a puzzle to solve. Generally, there is some type of help provided to make it less overwhelming, like telling you where each peach goes. This is how most of the AI we use today works, it needs humans to provide well-defined data (like saying that this is a dog or this is a cat.) so it can understand the data and remember fit or future usage. This is known as Supervised Learning. Humans supervise the AI model to do certain tasks.
But What if you have to solve the puzzle on your own? You don’t get any type of hints or guidance. In this case, you will start by arranging the different pieces one by one, failing a lot, but ultimately solving the puzzle by learning from your mistakes. This is how a Self-learning AI model works.
Instead of depending on human guidance, self-learning AI teaches itself by observing its environment, interacting with different elements, experimenting with new ideas, and learning for feedback on each experiment. Just as a newborn baby learns to walk by trying, falling, and understanding the technique. Self-learning AI improves its knowledge over time without requiring any direct assistance from humans.
For Example:
- A chess AI model plays millions of games with itself each time learning. What factors lead to winning, and understanding different tactics, without any human interaction.
- A language model like Chat-GPT can improve and change the way of talking by analyzing the conversation to give a better experience to the user.
Types of Self-Learning AI
Reinforcement Learning (RL)
Learning Through Trial and Error: Reinforcement Learning is like teaching an AI model to do something on its own by letting it try, fail, and learn from its failures. Instead of giving any instructions or hints to the AI model. We give full control to the AI model to experiment as it wants to get proper knowledge. The AI models are not provided with information about anything, the model will understand, and work through the process of learning by itself to gain firm knowledge of a particular topic.
How Self-Learning AI works:
Step 1. The takes an action:-
Let’s take an example of AI learning how to play and win in chess. To gain chess knowledge, the AI model first starts to play and make some moves.
Step 2. Feedback (a reward or plenty):-
When the AI model plays a lot of games with itself, the AI model receives feedback for every game or move. Whether it is a reward or a plenty that led to the loss.
Step 3. It learns from experience:-
Over time the AI model understands the concept of the game. It analyzes every move that leads to a potential victory and also tries to reduce the moves that could cause blunders.
Where It’s Used:
- Self-Driving Cars: AI models used in self-driving cars rely on reinforcement learning. They train themselves through real-world simulations.
- Robotics: This is also used in teaching robots how to perform human-like actions, how to walk, hold things, etc.
- Finance & Trading: In finance and Trading, the AI models adjust themselves on the condition of the market.
Self-Supervised Learning (SSL) –
AI Teaches Itself Without Labels: The traditional way of teaching the AI model something follows a simple method of manually. By providing well-labeled data to the AI model so it can understand the different data provided by humans. Per say, if we want an AI model to recognize a cat image or how a cat looks. We will have to provide the AI model with thousands of sample images with proper metadata about the cat. So it can understand how a cat actually looks and what it is.
This is a good way to teach an AI model. But imagine if we have to provide hundreds and thousands of labeled information. It will take a lot of time and can consume a lot of resources to do so. So a better alternative is needed. And that alternative is Self-Supervised learning.
In this method, the AI model is provided with data but the data is not manually labeled, which takes away the issues of manually labeling each and every raw data(like images and documents). The AI creates its labels just from raw data and continues the process of learning.
How It Works:
step 1. Receiving data
The AI model receives raw data such as images, text, audio, or any other documents, from which the learning process starts.
step 2. Generating Labels
When the model receives, The AI model assigns itself to certain challenges such as:
- The AI model hides certain details from the data, like removing some parts from images, and text. This is done by the AI so it can try to guess what can be there, by guessing the correct puzzle pieces, the AI learns and generates additional metadata.
- The AI model repeats these tasks of removing information and after removing guessing it and using it as the labels on a large scale to generate optimal results from the data and speed up the process of self-learning.
step 3. Practice makes perfect
As the phrase states the AI model practices by repeating the above steps and becoming better and better over time.
Where we used this:
- ChatGPT & Language Models: Large language models like ChatGPT or Gemini use the method of SSL.AI trains by completing the missing information in the data, or text provided by the user, and improving its knowledge about the subject.
- Image Recognition: We already used an example of AI learning what a cat is. The Image Recognition AI model also uses this technique by removing some info from the image and trying to complete it with its own knowledge, this process tells the AI what is a Cat or what’s not.
- Speech processing: This concept plays a role in the rising technology of voice processing. An AI model listens to pre-recorded audio conversations, finds different patterns in them, and broadens its knowledge.
Generative AI with Self-Improvement
AI That Creates and Learns: As we already discussed the traditional AI models learn, by analyzing the provided data, recognizing the patterns in the data, and acting accordingly. But with the evolving technology new techniques are arising which take this process to the next level. Generative AI with Self-Improvement is one of them. This technology tries to create new content from the provided raw data (like images, text, audio, and video). By doing this over and over it gains the potential knowledge it needs. The ability to generate new content from the raw data makes this technique most advanced in the field of Artificial Intelligence. In a nutshell, the generative AI understands the data by analyzing the pattern it tries to create new content and also learns from the mistakes, just like an artist who refines their art and makes it a masterpiece.
How It Works:
Step 1: AI Generates New Content
When the model receives the data it starts to create new content from observing the pattern from the original data.
The data could be anything like Image, Text, Audio, or any other form of raw data. For example, an AI model used to create a painting can utilize existing data on the art of many famous and talented artists, such as when generating a painting in the style of Van Gogh. Who knows it can even generate a different or improved version of The Starry Night.
Two methods to judge the comparison:
Step 2: AI Evaluates Its Own Output → After generating the content, the AI model compares the strengths and weaknesses of the output with the original data.
It checks various properties, such as whether a generated image looks realistic, whether a newly created song follows the structure of the original music, or whether a generated text makes proper sense or is just gibberish.
- Internal Judgment: In this method, the AI judges whether the generated content is correct or meets the requirement.
- External Judgment: In this method, humans or another AI analyze the generated data and judge whether the content is right or wrong.
Step 3: AI Learns and Improves → After completing the comparisons, the AI analyzes the results. If something is not right, it takes action to correct it.
If the generated image has lower quality the AI fixes it, and makes sure that it will not happen again. This way of learning makes the AI more and more accurate, realistic, and creative over time. The processes discussed above and repeated thousands and millions of times to make them perfect.
Where It’s used:
- AI Art & Design: Image generation AI models like DALL-E and MidJourney use this technique to create a new and creative image from a single prompt.
- AI Music: This method is used in models and created by original music compositions. SUNO AI and OpenAI jukebox are some models that perform this task.
- AI Writing: The process of writing automatically is also a part of this technology. AI models like ChatGPT, and JasperAI use this generative technique to create new and original content.
- Evolutionary Algorithms – AI That Learns Like Natural Selection: Ever heard of Natural Selection, it is the concept of biology that explains that the fittest of one will survive. This was the reason that made our beautiful planet so diverse, each species adapting to their surrounding and becoming fittest in their own domain.
How It Works:
Step 1: Generate a Population (Initial Random AI Models) :-
This method of AI training begins with the AI model creating multiple distinct designs that perform distinct actions, like if we ask AI to make a tire that can work on both sand and snow. Then the AI will create multiple designs with different designs, shapes, or sizes each will perform differently under different conditions. At this step these designs are just random guesses, we don’t know what will be the result of the different designs. For example,e if we give a task of making tires to an AI model, some will be good and some will be bad, but at this instant, we don’t know which ones are good or bad.
Step 2: Test the Performance of Each Design (Survival of the Fittest) ->
Now after the creation of multiple tire designs. AI tests the tire model in this step, and checks if the tire will pass certain tasks or not. In the beginning, the AI will simulate the tire in different conditions like how it performs on sand and on snow. by simulating these conditions they measure the aspects like speed of the tire, grip of tires, durability, and efficiency in different conditions. Doing so will sort the tires from best performed to the worst.
Step 3: Select the Best Designs:-
Now we know which tires are the best in certain conditions and which are not, from the collection we only select the best tire and discard all the other designs. The tires that will perform the best will become a parent for the next generation of the tires. Weak designs of tires are removed just like the animals in nature who can’t adapt to the surroundings and fails to pass natural selection.
Step 4: Create New Designs (Mutation & Crossover):-
Now we have the best tire designs, if we need to make more of the theme we don’t start from scratch but rather the AI models select the best models from the already existing collection and try to modify them to make it even better and provide more variation to them.
This is done in two ways:
- Crossover (Mixing Designs): This method combines different designs to create a new version that inherits the best features of both. For example, when one tire offers better grip and another provides greater strength, they are blended to produce a variation that excels in both aspects.
- Mutation (Random Small Changes): This method works the same as mutation works in real life, nature changes some properties in the genes of the organism to see if the new genes survive or not, and this leads to diversity. Just like this, the AI makes some small random changes in the beat tire models to see if the model will become better or worse than its parent.
Step 5: Repeat the Process (Evolution Over Generations) -> Now when all of the heavy part is done, the AI model just repeats all the steps multiple times to strengthen its knowledge in a particular subject. This leads to a better understanding of the AI model.
Where It’s used:
Robotics –
AI Learning How to Walk Efficiently: The scientist uses this evolutionary algorithm to make robots. The robot evolves like an organic creature by using this method of AI learning. Instead of manually programming the walking algorithms in the robots, the AI itself tries to walk on its own, finding the sweet spots where the balance is perfect, and how to step properly, and over time it improves itself in walking just like a human or any other animal does.
AI in Games –
Creating Smarter Opponents: Nowadays there are multiple games that uses this technique to make the boss battles in the games more challenging and immersive for the player. The characters will analyze the movements and activities of the player and behave accordingly. The Bosses and other enemies change attack moves fight by fight to make the game more challenging. Mir4 MMORPG bosses are one of the examples which is are made with Nvidia ACE technology makes the boss harder and harder over time.
Advantages of Self-Learning AI
Less Dependence on Human Data:
The traditional AI models require a lot of data in which each and everything is manually labeled so the AI model can understand what is it learning about. This process was so expensive and time-consuming. Self-learning AI changes this it reduces the dependence of the AI model on Human assistance, this is done by creating its own learning material from just raw data without well-defined labels.
Faster Adaptation:
Unlike the traditional AI models which require retraining on new data whenever needed, this can lead to repetition and can consume a lot of time and resources to do it again and again. This issue is resolved by using self-learning AI models, which can keep learning new things without retraining again and again. These AI models improve over time and become better and better day by day.
Cost Reduction in Self-Learning AI:
Providing and labeling each and every item requires intensive labor, which makes it very expensive and time-consuming. This is not the case in the Self-Learning AI model, in which labeling is not required as the model created its own learning materials and labels from just raw data, reducing the required labor and saving a lot of resources and money.
Discovering Novel Insights:
Traditional AI models learn from data provided by humans. If the data is biased, the model learns that bias. However, modern self-learning AI models solve this issue by exploring data from scratch. They assess whether the information is correct and often discover new patterns, learning accordingly.
Challenges of Self-Learning AI
Risk of Bias and Unintended Behavior:
Self-learning AI models can train themselves without any human assistance, but due to this it might develop unexpected or harmful behaviors if left unchecked. This can cause potential damage in many aspects. These models look for patterns in the data and if the raw data provided contains some biased data. The AI model could adapt that biased data and can make some very risky assumptions.
Computational Costs:
While self-learning AI models can reduce labor-intensive tasks and cut costs, they also demand expensive computational power to replace manual labor. Generating and labeling data requires significant processing power, which consumes a lot of electricity. In short, the high cost of computational power limits the cost-effectiveness of self-learning AI models.
Explainability Issues:
Traditional Self-Learning AI models rely on provided data, allowing us to trace decisions back to the input and analyze how and why they were made. However, this is not possible with self-learning AI models. We cannot track how they think or what specific instances led to a decision. The provided data serves only as a reference, while the model generates its own learning material. This makes it impossible to determine the exact reason behind a particular decision.
Ethical Concerns:
Self-learning is still in an evolving state so the misuse and exploitation of the model is possible, this is because in early development states every program or technology has some vulnerabilities that hackers can exploit. For example, the AI models can make some deepfake videos of someone performing something unethical or disturbing, which the person actually never did. This type of vulnerability is very dangerous for the society and can cause a lot of damage.
Applications of Self-Learning AI
Healthcare in Self-Learning AI:
The traditional AI models have been very useful in the field of healthcare. Now with evolving technology the new modern Self-learning AI is also providing crucial information in the field.
- Drug Discovery: The Self-learning AI models can simulate chemical reactions from which we can interpret how certain molecules interact, this helps the researchers in discovering new and effective drugs beneficial for the health. This method is much faster than the traditional methods.
- Diagnostics: The AI models can analyze millions of medical images (like X-rays or MRIs) which helps to detect new diseases. This method of detecting potential diseases is much more accurate of human doctors.
Autonomous Vehicles:
The boom in the field of Self-driving cars is the product of these Self-learning AI models. These AI models train themselves using real-life simulation to improve more and more. It also analyzes real-world circumstances to enhance its knowledge more and more.
Finance:
The AI models can also make a good impact in the field of Finance. The models can analyze the behavior in the stock market in real time and change the investment strategies according to the current condition of the market. The AI models find repeating and important patterns in the market trends to strengthen their knowledge in the field. Some of the AI models can also buy and sell stocks on their own making a lot of transitions in split seconds.
Gaming:
The involvement of self-learning AI models in video games has made them more challenging and dynamic. These models adapt to the player’s style, adjusting attacks to enhance difficulty and immersion. NPCs have also evolved, moving from simple predefined conversations to analyzing player actions and responding accordingly.
Robotics:
self-learning AI models is affected heavily by the robotics Robots used in assembly and manufacturing evolve by observing human behavior. By watching actions, AI-controlled robots perform the same tasks. If a robot makes a mistake, it tracks it and avoids repeating it, improving over time.
Conclusion
In conclusion, self-learning is changing the world day by day in ways would never imagined!
From saving hundreds of lives in the field of healthcare to making cars driving by themselves. AI is learning and evolving day by day. It helps the investors to make better investment decisions to make them more money. The involvement of AI in the field of gaming improved the NPCs and the Boss battles more challenging making the games more fun to play. It also powered the robots to do the high-labored tasks.
The best part of this is that the AI works nonstop and keeps getting smarter and smarter day by day. Whether it is detecting some diseases faster, predicting the stock market trend, or making the games look and feel more interactive. AI is making the world as we know it more and more easier, safer, and more exciting.
Well, this is just the start of the big upcoming future. Where there will be no limits to the capabilities of AI. So let’s sit back and enjoy the evolution of AI technology.
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