Learn more about this branch of Artificial Intelligence (AI)
So, what is machine learning? and why should someone interested in Artificial intelligence want to know about it?
Well machine learning (ML) is a branch of artificial intelligence (AI) it’s man focuses is on the use of data and algorithms. It has been designed to copy the way humans learn and with this knowledge it tries to improve it’s accuracy over time.
The phrase “machine learning” was popularised by Arthur Samuel when he compared a game of checkers against a computer in 1962, he did end up losing to the computer. By todays standards of machine learning this might seem a little bit trivial but back in the day it was considered a major leap in the field of machine learning and AI as a whole. For a computer to beat another human was a big thing.
Now let’s fast forward a couple of years, the advances within this technology are huge. Back in 1962 Storage sizes were around 2MB where as todays standards are 22TB’s (huge!) and not to mention we now have Solid state drives too which allow faster reading of data. Then we have the processing power of the graphics cards, these also were not around back in 1962, graphics cards have really helped be the driving force behind machine learning and generative AI.
With all these leaps in new technologies we have been able to produce some creative tools that require machine learning. Predictive models like, Siri, Alexa, even Netflix recommendation engine all require the use of ML. We even have self driving cars that rely on this technology. Without machine learning we would not have any of these amazing tools today.
Machine Learning Is A Strong Pillar of Support for Data Science
When it comes to the field of Data science it is heavily reliant on machine learning. ML enables the use of statistical methods and algorithms that are the trained to make predictions or classifications of data. This allows Data scientists to edit, view and sort large amounts of data in a short period of time. Due to the processing power available to this new technology this data can also be edited live as it is gathered.
Let’s face it big data companies are growing and now more than ever majority of our data is being collected and used. This is making the job as a data scientist quite valuable and pushing up the drive and need for machine learning.
How is Machine Learning Created
Well when it comes to create machine learning algorithms they are normal created using frameworks, these help accelerate development. Some of the more common ues are Pytorch and Tensorflow it’s advised that you know python the programming language in depth if you want to start learning about machine learning.
Once these Machine learning environments are setup data scientists will, train, validate, fine tune and then deploy the original machine learning models. Once these are deployed further testing will begin to perfect these base models. As the model works over time it becomes self learning and can expand on it’s knowledge and ability to perform certain tasks.
Deep Learning, Machine Learning are they the Same
No, they are not. Some people will use them interchangeably but they are two different sub fields of artificial intelligence.
A Break Down example is Artificial Intelligence > Machine learning > neural networks > deep learning.
Deep learning and machine learning differ by the way each algorithm learns. ML relies heavily on human interaction to learn. Without the original datasets and the fine tuning of these models would not be able to perform tasks correctly. This where Deep learning came into force, it can learn from raw data both text images. With this data it can distinguish between different categories, making it able to sort data without the need of human intervention. This is more advance form of artificial intelligence but it requires the use of machine learning to operate from.
Neural networks are a whole other ball game. They try imitate the brain and how connectors work in the brain. Think of if a certain word comes up a light goes off and a certain task is performed. We won’t go too deep into this because this particular article is about machine learning you can read more on neural networks here.
Deep learning and neural networks are the main reason why we have had such accelerated progress in areas such as computer vision, natural language processing, and speech recognition.
We can break down machine learning algorithm into three main parts:
- Decision Process: machine learning algorithms are used to make a prediction or a data classification. It relies on input data that is either labelled or unlabelled. This then tries to find a pattern within the data it is given.
- Error Function: In order to ensure good result of this model there is the error function. This evaluates the prediction of the model. Error function can be used to assess the accuracy of the ML model.
- Model Optimization Process: This is the whole point of using this kind of technology. This is where the AI tool is learning. It will pass checks repeatedly though the algorithm to try to evaluate and if needed optimize the data set it has. Over time it should increase it’s accuracy if it has been created correctly to do so.
Ways in which Machine learning Works
When is comes to machine learning models and how they work this can fall into three primary categories.
Supervised machine learning
This is where the model is fed information. Clear algorithms are set up and tests are done repeatedly to ensure this model sticks to the algorithm. This requires a lot of human interaction and a lot of testing and checking. This is good for ML models that require extreme accuracy. All datasets fed to this model are ladled, so the AI system knows exactly what out come to expect.
Unsupervised machine learning
Unsupervised machine learning, uses algorithms to analyse and cluster unlabelled datasets. This type of model ideal for exploratory data analysis, cross-selling strategies, and image and pattern recognition.
Semi-supervised learning offers a mixed balance between the two. Basically this model is fed a small data set that is labelled and then is given unlabelled data and left to get on with it basically.
Some Machine Learning Algorithms that are commonly used within the world of AI are:
- Neural networks
- Linear regression
- Logistic regression
- Decision trees
- Random forests
Real-world machine learning use cases
Here are just a few examples of machine learning you might encounter every day:
Speech recognition: Think of AI tools like Siri, Alexa and speech to text software.
Customer service: These are popping up all over the place. AI assistants that will respond to typical questions and queries customers may have.
Computer vision: Giving computers eyes. This technology is used in photo tagging, editing of videos and even self driving cars.
Fraud detection: Banks and other financial institutions can use ML to spot suspicious transactions. Although this sometimes can flag falsely one of the dark sides of AI especially if you need you funds. Over time this will be perfected.
There is no doubt that machine learning technology has made our lives a little easier. However implementing this new technology has caused concerns about AI technologies.
As machine learning technology has developed, it has certainly made our lives easier. However, implementing ML into businesses has also raised a number of ethical concerns about AI technologies.
AI impact on jobs
The number one complaint about AI is the effect it will have on jobs. People are always wondering weather or not there job is safe. What with the increases of Machine learning being used in a lot of aspects of our lives, people have a right to worry.
It’s worth taking note that artificial intelligence will shift the demand for jobs to other sectors of work. These other areas will require people to work on the other end so it’s important that as people we keep up to date with what jobs are in demand to ensure we protect our future as being employed. So we encourage all of our readers to learn about AI and ensure their future is protected.
Another note to make you aware of us is user privacy, we need to ensure that our data isn’t being used maliciously. Let’s hope regulation stays in force and we don’t have a repeat of the Facebook big data scandal.
When it comes to machine learning we believe this is a powerful working force behind AI. Without it we wouldn’t have many of these amazing AI tools like Stable diffusion and ChatGPT. We just need to ensure as a community our main focus is using AI as a tool to help us, not to take over our lives.
How do you feel about this new technology, let us know in the comments below