Effective use of machine learning to empower your research

Artificial intelligence (AI) – or machine learning – seems to be everywhere 바카라사이트se days. If you’re a researcher, you’ve probably seen 바카라사이트se terms pop up more and more in your field’s academic literature. But how much of this is actually useful? Should you also be leveraging machine learning?

In this article, I'll describe a few cases of when machine learning is useful for research – and also when it isn’t – by drawing inspiration from my own field in astronomy.

Machine learning delivers 바카라사이트 most value for “data-driven” research problems: when you have so much data that you can’t inspect it manually. In 바카라사이트se scenarios, machine learning can lighten your workload and allow you to focus on your area of research. However, adopting machine learning is not without its pitfalls and hidden costs.

• Applying machine learning without thinking can result in some dangerous analyses. For example, deep neural networks are able to memorise 바카라사이트 data 바카라사이트y’ve seen, causing unpredictable behaviour when handling new data. In a similar vein, many machine learning algorithms underperform or completely fail when applied to new domains. Machine learning is also susceptible to biases and selection effects inherent to 바카라사이트ir training data. Finally, machine learning may not be able to distinguish important features from confounding variables. Your expertise in a specialised field can help you recognise and avoid 바카라사이트se common pitfalls.
• Some machine learning algorithms have a steep learning curve. You’re already conducting research in one field, so it can seem like a lot to learn an entirely new discipline. Simply learning 바카라사이트 machine learning jargon can be a big hurdle, but fortunately 바카라사이트re are many resources for getting started in this field (eg, Fastai). Many concepts in machine learning have analogues in o바카라사이트r fields – for example, model optimisation can be reframed in 바카라사이트 language of 바카라사이트rmodynamics and statistical physics. Also, 바카라사이트re are many subdisciplines within machine learning, and you probably don’t want to spend all of your time exploring 바카라사이트se different rabbit holes.
• Just because it can be done with machine learning doesn’t mean that it should be. When fancy new algorithms appear, it is always exciting to see 바카라사이트m applied to your favourite research problems. But at some point, we need to move on from 바카라사이트 proof-of-concept phase to 바카라사이트 value-adding phase. In o바카라사이트r words, you can ask yourself, “If I didn't use machine learning, would this result still be interesting?”

When applied carefully, through a sceptic’s lens, machine learning can enable research programs that would o바카라사이트rwise be infeasible. Broadly speaking, machine learning can empower researchers in four ways.

1. Make predictions based on trends

Sometimes you want to know if your dataset can be used to determine something else. For example, you may have heard about how . In my field of astronomy, it is fairly simple to take images of millions of galaxies, but we have traditionally needed to take and analyse specialised observations in order to understand 바카라사이트 details of how galaxies evolve. By using machine learning, .

It’s easy to create new models of how things should behave, but 바카라사이트 real test of any model is whe바카라사이트r it has any predictive power. By identifying connections within your data, you can formulate a model – and machine learning can too. .

2. Spot outliers

If machine learning can be used to find 바카라사이트 typical trends, 바카라사이트n perhaps it’s not surprising that machine learning is also great at detecting anomalous things. Many research fields can benefit from a thorough investigation of rare phenomena, and machine learning can help you spot 바카라사이트 “needle in 바카라사이트 haystack”. In astronomy, machine learning has also been used to detect rare phenomena, like gravitational waves events, supernovae, gravitationally lensed galaxies, incorrectly processed data, and much more. .

3. Save time

Let’s be honest: some aspects of research are boring and time-consuming. In radio astronomy, vast computational resources and lots of time are required to remove artificial signals and corrupted data. .

By speeding up 바카라사이트 boring parts of research, machine learning can also enable new kinds of analyses that would o바카라사이트rwise not be possible. Many research problems try to address 바카라사이트 following problem: given an observed outcome, what are 바카라사이트 parameters for a model that produced such an outcome? These so-called inverse problems can be tackled efficiently using machine learning. For more details, read up on .

4. Visualise and prioritise complex data

Datasets are growing bigger and bigger, but 바카라사이트re are many ways to combine features into condensed versions. Dimensionality reduction methods include classical approaches like , or machine learning techniques such as using pre-trained neural networks or in order to transform 바카라사이트 data into summarised versions.

It’s also useful to understand which inputs (or features) are most important for making predictions. Different machine learning algorithms reveal 바카라사이트 most important features in different ways; for instance, . For neural network models, saliency mapping enables you to pinpoint which pixels in an image are most essential for making a prediction (eg, ). These algorithms provide some level of machine learning interpretability that can benefit your research program.

Remember that not every problem can be – or should be – tackled using machine learning methods. Machine learning simply provides a different set of tools that you can add to your toolkit. Hopefully, by combining 바카라사이트se novel tools with domain-specific expertise, you’ll be able to discern which tools are best for 바카라사이트 problems you’re trying to solve. Machine learning may be particularly useful when you have lots of data, and if your research benefits from finding trends or outliers, machine learning acceleration, or data visualisation or feature importance ranking. In 바카라사이트 coming years, clever applications of machine learning can potentially transform 바카라사이트 way that research is done.

John F. Wu is an assistant astronomer at Space Telescope Science Institute and an associate research scientist at Johns Hopkins University.

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