In a previous blog post, we took a closer look at ChatGPT and how generative AI can create great written content on demand. We explored how ChatGPT can be creative but only do limited predictive work.
In this post, we will learn more about Machine Learning and how this has been used for years to provide predictive solutions to various business and personal challenges.
What is Machine Learning?
Machine learning allows computers to learn and improve without having to explicitly program every task. It’s a way for computers to figure things out from examples and past experiences. In machine learning, we feed the computer a lot of data, like pictures of cats and dogs or information about past sales, and the computer uses this data to identify patterns and make predictions on new data it hasn’t seen before. The more data it learns from, the better it gets at making accurate predictions or decisions.
Types of Machine Learning
Labelled Data
One of the most commonly used ways of training the machine is by giving it access to labelled data. Labelled data refers to data that has been pre-processed and assigned with specific “labels” or “tags.” These labels indicate the correct output or category for each corresponding input in the dataset.
This makes it easy for the machine to learn and use that data to make better predictions. Imagine if you took ten thousand pictures of dogs, labelled each image as a dog and input the data into the machine for it to learn what a dog is.
The machine will go through all ten thousand pictures and then learn what a dog looks like. Once it has learned that, if later it is provided with a new set of pictures of different animals, the machine can easily predict and point out which one is a dog.
Similarly, if you feed the machine with labelled information on, let’s say, revenue per month of a business over the last few years. It can put together predictions in terms of what the future revenues will be.
Think of tools like HubSpot, which can predict the lead score and the likelihood of the closing of the sale based on the previous set of actions and results and predictions in terms of SEO keywords that are more likely to work for your content.
This sort of machine learning is also called supervised learning.
Unlabelled Data
The other type of machine learning is using unlabelled data. This is data entered into a machine without adding labels; this is usually large portions of unorganised data fed into the machine without adding or updating any labels. Labelling a large data set becomes a challenge; hence, this model makes sense.
This forces the machine to tweak the learning process to create its way of organising and recognising the data. Once it does, it tries to identify patterns within the data and creates predictions based on that understanding of patterns.
Imagine a company with around ten years’ worth of HR data that’s looking to organise it but has no idea where to start.
It would be much more challenging for a human to run through all that data and make sense of it to identify any valuable patterns. But a machine could crunch through all that data to pull out information and present it in a way that makes sense and is easily digestible.
These tools are custom-built for a unique purpose or a specific organisation from the ground up. This sort of machine learning is also called unsupervised learning.
Benefits of Machine Learning
In an age of unprecedented technological advancement, machine learning stands as a formidable force, revolutionising industries and reshaping the way we interact with technology and information. From personalised recommendations on our favourite streaming platforms to sophisticated medical diagnoses and self-driving cars, machine learning’s applications are becoming part of our daily lives, often without us even realising it. Here are a few main areas where it will benefit businesses to implement machine learning:
Automation
Process and task automation has been on the radar for improving the productivity and efficiency of businesses for years. Businesses can use machine learning to let the machine understand the processes or tasks they usually undertake and then find ways to automate them.
Imagine something like your Gmail, which learns every time you send an email, and sometimes, when you send an email out to someone particularly, it identifies the pattern that you usually CC a particular set of other individuals and automatically suggests adding those individuals to the email. This would be an example of supervised learning as it’s based on labelled data from previous emails. An unsupervised learning example would be clustering customer data into segments without predefined labels.
This subtly improves productivity by saving you time by limiting the amount of repetitive work that you have to do.
Better Decision-Making
As we described earlier, inputting your business data into a machine learning system provides fascinating results and info that’d be challenging and time-consuming for a person to extract on their own.
This data can then form the basis for business decisions to improve efficiency and generate better results for the business.
Think of something like AdWords, which gives you a lot of data on how ads perform over each campaign. This data can provide much-needed information on which campaigns are working best for your business and which are not so you can make an informed decision.
Personalisation
Machine learning can be used in various ways and tools, making it easier to implement into existing and new tools and enhancing the user experience. One of the ways to do this is to
personalise the user experience based on how the users interact with a tool or system.
We already see this in our lives too, from things like Netflix suggesting content we might enjoy based on our previous ratings or recommendations to our Youtube feed, which are personalised based on the way we interact with each platform.
The system takes note of what we are doing and how we are engaging with various pieces of content, and based on the labels or tags setup for each of these pieces of content, it tries to predict which piece of content you are next likely to consume and presents it to you.
Challenges of using Machine Learning
While there are a lot of benefits of using machine learning to improve your business, there are also some challenges around using it that you might want to consider. Here are a few:
Data Quality
Machine learning relies entirely on data. That data has to be of high quality, and if it is not then the results or predictions will also be unreliable. Sometimes data might contain missing values, bias or errors, making it challenging for the machine learning algorithm to make the correct predictions.
This could result in wrong predictions, which damage a business more than help it.
Illusions
When there is a lot of unlabelled data, fed through the system, sometimes it can put out predictions which probably need to be clarified or are of no use to the business at all.
Imagine getting results your business already knows.
Data Availability
Since data is everything when it comes to machine learning, without the right data sets we can’t really expect it to have the right predictions though. Let’s say a company has entered a lot of unlabelled data into its systems, hoping to find the best location to open its next business branch. But imagine if the data fed into the system did not collect information about the location of the existing branches; it is doubtful it will successfully predict which location to explore next.
Generalisation
When you use a machine learning algorithm with labelled data, you are trying to predict a particular set of requirements based on the data fed into the system. The algorithm learns how to put the prediction out once, but if the system is run multiple times looking for the same kind of predictions, it is very likely that the system will get very focused on providing predictive solutions in a particular way.
When new data that is different from the previous set of data is fed into the same system, it is likely to provide a generalised solution or prediction rather than provide the specific prediction that the business is looking for because it has been trained to predict only one kind of solution over time.
It should be noted generalisation can be mitigated by re-training models on new diverse data sets. Something which is becoming more and more common.
Overall, machine learning if used well using the right kind of data can provide a gold mine of predictive results for businesses to find new avenues to grow. This is the reason why AI is getting so popular recently, as businesses are starting to find unique ways to get more productive and use automation to give back time to employees to do more amazing things for their businesses and customers
