March 2, 2020
Machine learning (ML) and artificial intelligence (AI) have been the buzzwords of the last decade. And it looks like they are here to stay.
Every day, we get more and more studies that allow us to dig deep into these still challenging topics.
However, all these announcements aren’t so true.
Especially now that the advanced machine learning code is getting cracked.
Now, the real challenge is to make a difference between myths and reality about advanced machine learning.
First of all, let’s make it clear.
Machine learning is a form of artificial intelligence made of algorithms that learn from data.
The data is taken from inputs and used to predict or decide certain actions, instead of performing actions that are solely based on programmed instructions.
Therefore, machine learning is used in financial services, marketing and sales, government institutions, etc.
Some of the claims regarding advanced machine learning go way beyond the truth. Here are the most common myths, busted:
It is true that machine learning and AI are often used together. But, they don’t mean the same thing.
Machine learning is just one form of artificial intelligence that can be used in practical situations.
On the other hand, AI is a large topic that includes natural language processing, computer vision, robotics, expert systems, evolutionary computation, etc.
Machine learning is a branch of AI that’s able to create patterns and predict actions based on collected data.
There are two types of learning—supervised and unsupervised.
Supervised learning learns from data collected from the input and output values of the datasets, predicting both input and output values.
Unsupervised learning analyzes and finds patterns in unstructured datasets.
Again, not true.
While machine learning is a branch of AI that uses data to predict outputs, deep learning is a subset of machine learning that has similar algorithms like machine learning, but with many more layers, each of them interpreting the data differently.
Deep learning, is in fact closer to copying the functions of the human neural networks of the brain.
Advanced machine learning uses data to learn and needs human intervention when it doesn’t provide the required output.
Deep learning doesn’t require human intervention, all the layers from the neural networks learn from their own mistakes and don’t repeat them again.
In the end—it’s all about data. If the data lacks quality, the outputs are more likely to make mistakes.
How many times have you heard people say how robots will come and take our jobs, leaving us with no alternatives?
Well, surprise, surprise—that won’t happen!
Let’s face it, machine learning and AI are transforming the way we do our jobs. But, they aren’t here to replace us.
They are here to help us by improving our performance and accuracy.
We’ll be spending more time on decision-making, while the technology will eliminate repetitive tasks that require no creativity or cognitive efforts.
In fact, AI and machine learning are expected to create more jobs than they will replace. We should be happy about this.
This will create new opportunities for us, allowing us to learn more new skills that will improve our productivity.
Maybe, for some branches of machine learning. However, most machine learning models require human intervention.
Datasets have to be constantly updated and improved so that machine learning models can function properly.
Machine learning algorithms can’t be left to work by themselves.
They have a lot of loopholes. As they are most frequently used for statistics, companies need to have analysts who can improve datasets and machine learning outcomes.
No smart company relies on machine learning only.
Data mining is a business analytics process that is used to analyze large datasets in order to detect unknown patterns and anomalies.
It’s used to discover business insights and trends.
However, it’s not the same as machine learning.
Yes, they are both analytics processes, they both recognize patterns and they both learn from large sets of data.
But, while data mining finds existing patterns in datasets, machine learning’s goal is to predict future outcomes based on it.
Moreover, data mining doesn’t have pre-defined rules to follow at the beginning of the process, while the machine is always given several variables for it to understand the data.
It actually takes a lot of time to become a machine learning engineer.
Machine learning engineers have a lot of experience and knowledge.
Advanced machine learning isn’t just about using tools like Keras or Tensor Flow.
It takes some skills to know how to use them and get accurate results.
Machine learning professionals spend a lot of time researching and looking for the best methods for a certain problem.
The libraries that are available can only aid with basic problems, but it takes a deep dive in order to create an accurate model that improves the accuracy score.
It’s normal for humans to not understand all the technologies they use.
This is a time when everything is computerized, so computers make a lot of decisions for us.
AI and advanced machine learning are powering up many of the products and devices we’re using in our everyday life, so it might be smarter to start understanding how they actually work instead of imagining sci-fi scenarios for the future.
Both technologies have great potential. Instead of worrying about whether they will take our jobs and replace us, we should be hyped about how they will help us even more in the future.
The possibilities are endless. The new technologies are here to help us get a new view of the world.
They are to help us make better decisions and get better results. The fear is unjustified.