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Learning is a crucial part of life, so why should machines be any different? This is how the concept of meta learning was born. But what do these algorithms do, how do they work and what can they bring to the table for your team? Read on to find out.
Regardless of your passion, you probably needed to invest time and energy strengthening your knowledge about it, learning new information and developing skills that increased your proficiency with and enjoyment of this activity. In this regard, machines and humans are similar. In an age where everyone is espousing the merits of machine learning (ML) from the rooftops, like for example ML in financial software development, people need to acknowledge that, like building up your skillset or knowledge around your favorite hobby, learning something new needs to start from scratch.
What is the difference between machine learning vs meta learning?
Enter machine learning, which is how you take your algorithms from fresh-faced newbies to industry experts. It’s the name given to the process of ensuring your algorithms learn from the information you give them.
Meta learning, meanwhile, is a sub field of machine learning that trains your algorithms, not through the data you give them, but through the outcomes they produce on their own.
Put into hobby terms, imagine you’re a painter for a second. The progress of machine learning depends on the quality of brushes you use – the cheaper the brushes, the less appealing the outcome. While meta learning is the experience you gain after completing a painting, learning from your mistakes, and producing better more intricately detailed paintings as you grow moving forward.
Now you know what but keep reading to learn more about the different types of meta learning, its overall objective, the different types of meta learning on the market right now, and the benefits this type of machine learning can bring you your organization.
Why are meta learning algorithms important?
Right now, you’re probably asking yourself what makes meta learning so important? To answer that let’s use hobby analogy – a sports one this time.
Imagine you are a championship coach, and you need to pick your ideal team for the World Cup, Champions League, Stanley Cup or NBA Finals. How do you pick the best players when everyone is clearly at the top of their game? The answer is simple, you observe your players with a keen eye during training and listen to the advice of your coaching team, while considering injuries, as doing this will enable you to head off problems before they occur.
Most people would probably take this commonsense approach to the hypothetical situation outlined above. But again, the question needs to be asked, why should your machines be any different? This is what meta learning does, it helps your development team identify what datasets work best with your algorithms through careful observation. Which, in turn, ensures your development team produces the best results possible for your organization, growing your position and reputation in the market as a result.
Meta learning – building faster, better, and stronger machines
To understand how meta learning works we first need to quickly discuss how humans learn things. How do you know what a cupboard is? Or even what the US flag looks like? Someone told you and probably used a picture while doing so.
Meta learning works the same way. It’s the process of getting machines to understand information like humans through data processing. This process is built in a similar way to how the human brain evolved. For example, when we first evolved, our brains discarded bad information while good information was retained to help future generations. This is how data is handled during the meta learning process, only data that helps your machines get better, faster, and stronger becomes the basis of the algorithm for the next iteration of that machine. This, in turn, helps to optimize and automate your processes better than ever before.
Types of meta learning at a glance
There are many types of meta learning to choose from, including large language models and even a cloud based quantum machine learning services approach which is quickly growing in popularity. But three types of meta learning are really setting the pace in the market right now. You can find out more about each of these types of meta learning below:
- Model-based meta-learning models: rapidly update data parameters through a rigorous training regiment, which is achieved by their internal architecture or by another meta-learner model.
- Metric-based meta-learning models: aim to learn metrics or distances between objects, with good metrics being determined by the problem associated with them. They should represent the relationship between inputs in the task space and facilitate problem solving.
- Optimization-based meta-learning models: adjust the optimization algorithm so that models can be good at learning after being provided with just a few examples of the information your team wants it to learn.
Increased prediction accuracy, optimization, and adaptation
Implementing proper meta learning procedures into your way of working can produce a wealth of benefits for your organization including:
- Greater model prediction accuracy,
- Better optimization,
- Improved algorithm adaptation rates,
- Enhanced design procedures when building your learning algorithms in the future,
- Better learning models can handle more data which means they can be used for more complicated tasks moving forward,
- Faster and cheaper training processes across the board.
Meta learning – the key takeaways
To sum up, meta learning is a sub field of machine learning and is basically Darwinism for machines. There are different meta learning types on the market right now, and each can bring various benefits to organizations.
Since humans learn something new every day, why should your machines be any different? After all, technology needs to learn just as much as you do to get better at its role. It’s true that starting this journey can be tricky, but you can always reach out to one of Software Mind’s meta learning experts to learn how they can help you make your meta learning dreams a reality and make your machines learn to learn moving forward.
About the authorSoftware Mind
Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world.