What is AI?

The terms AI and machine learning have become very common lately. Many big companies have already integrated AI into their applications. If a company hasn’t done so, it might be seen as falling behind.

But what is AI, why is it so important, and how does it work? Let’s find out. By the end, you might have a clearer idea of how you could make use of AI.

Clarifying Key Terms

• AI is a broad term for any technology that uses computers to mimic human intelligence
• Machine Learning is a subset of AI, referring to the process of training a model to predict outcomes for new inputs
• AI Models range from simple ones like linear regression to complex ones like neural networks used in deep learning

How Does AI Differ from Regular Software Algorithms?

The main reasons why AI is groundbreaking and why top companies are increasingly using AI is it:

• can process vast amounts of data.
• can be trained to handle and produce various types of data.
• can perform tasks without explicit instructions.

One of the most important reasons why AI is groundbreaking, and can achieve what hasn’t been possible before is that we don’t have to prepare for every kind of possible inputs to make it functional.

A simplified example to illustrate this would be writing a software to process speech-to-text without AI. There are countless possible voice tones, and the program would need to account for all of them. Even if we managed that, we would still need to handle every single word, accent, and pronunciation. While not impossible, this would be an extremely challenging task.

Regular software algorithms follow explicit instructions written in a programming language. For instance, if we write 3 + 4, the algorithm knows to add these numbers and output 7. We can do incredible things with these algorithms, but they can only do what we provide the explicit steps to, no more, no less.

A machine learning model doesn’t know anything initially. AI doesn’t know what the inputs mean, and what the output is supposed to be, so we have to teach it. We give inputs (called training data), and it tries to predict the result, then we show what the correct result is. The model tries again, and again until it learns what influences the difference between the inputs to give the correct outputs.

This method may seem counterintuitive, and the question is valid “why don’t we just rely on regular algorithms that don’t need training, and always give accurate results?”. The reason is, algorithms can only do what we explicitly tell them to do, and writing programs is time consuming. On the other hand, AI can give outputs without us telling them step by step what they need to do, even on inputs they have never seen before.

Training Process Example

To demonstrate the training process, imagine the AI is a locksmith, and you have to teach it how to craft keys to open specific locks.

First, you give AI a lock, and it will craft you a key. You try the key in the lock, and if it doesn’t fit, you show the model which pins should have been lifted to open the lock, and the difference between the expected and actual notches in the key.

From the clues, eventually machine learning will craft the correct key, but when we provide another lock, and AI will probably try to open it with the key previously made. We then give hints again on how the second lock opens, and after trial and error, the model will be able to open that as well.

We repeat this process many times, showing many different types of locks. If we trained the AI correctly, it will be able to guess how to open a lock on the first try, even if the model has never seen that lock before. This is because we’re teaching it to recognise patterns by forcing to come up with a single way to open many different locks, rather than working from memory.

The key is that we train AI to come up with a way to give desired outputs based on inputs, and we don’t have to explicitly implement that way, but let the AI decide it.

We could write a program that can design the proper keys for us for different locks, but we would have to know the patterns how to press which pins in the lock for every combination. For one type of lock it may be simple. For millions, we would probably end up with a lot of code. Machine learning does this work for us at scale.

Versatility and Benefits of AI

The beauty of it is that we can train AI on any forms of data, and control what we expect it to output. We can input images, text, numbers, audio, and expect the model to return words, images, classify inputs, or even a mix of all the above. The possibilities are limitless.

One of the largest benefits is that AI can be trained on vast amount of data relatively quickly. Millions of data points would take humans months to analyse, and the knowledge cannot easily be transferred. Machine learning models are just numbers that can be easily stored, scaled, and it doesn’t forget. The more data it gets trained on, the more powerful it becomes.

You could train a machine learning model to predict the likelihood of a campaign’s success based on any data like weather, demographics, historic success rates, or the opposite, find potential reasons why some campaigns didn’t get the expected results.

Your business may have a good potential leveraging AI. Book a free consultation to explore how you can leverage AI for your needs.

This post focuses on explaining how AI works at a high level, if you’re curious about technicals, read our next post.