Tutoring computers

In a first for ICES, a recent workshop explored a rapidly emerging area of science and technology: machine learning.
Published: 9 May 2018

​​​​​​​​​​​​​​​​​Machine learning is a branch of artificial intelligence where computers can learn from dat​​a, recognize patterns, and make decisions without being explicitly programmed. It is already used in many ways today, particularly online, for example in predicting search terms or tailoring adverts to user tastes. As well as in industry and academia, science is an area in which machine learning techniques are increasingly being adopted in order to process and analyse a vast amount of data. 

A new frontier

This subject was formally addressed for the first time within ICES by the recent Workshop on Uses of Machine Learning in Marine Science (WKMLEARN). Its drive was to explore potential niches for machine learning within fisheries and marine science as well as ICES work, where the technology could be of the most benefit, and where it is already used in the marine sciences. WKMLEARN looked at both machine learning as well as a subset known as 'deep learning'. Inspired by the structure and function of a human brain, deep learning is a 'deeper' form of learning that uses multi-layered artificial neural networks to solve complex nonlinear problems like recognizing objects in images. It has performed closer to the level of humans than traditional machine learning.

​​New opportunities

For marine science the advantages of machine learning range from modelling and prediction of climate data and obtaining fishing patterns from satellite images or AIS data​, to taxonomic recognition of biological samples, and analysing text, images and video. Harnessing this technology can be especially fruitful in an age of exponential growth of data, used to feed the algorithms behind these systems.

Many opportunities and challenges are linked to the fact that, as a catalyst for machine learning, much data and computing is being moved into the cloud. Although free from hardware restraints and with data secure for reuse, there are issues such as balancing matters of intellectual and proprietary rights while allowing consumer access. As ICES goes down a path of reusability, clear data licencing becomes an issue, and despite advancements, there is still much to do.

Many research projects will harness the power of machine learning in coming years, and this will provide opportunity to improve the skills and tools available to ICES community and the potential speed of science, data, and assessment projects.

​Benefitting advice

Ideas were offered on where new technologies might replace traditional activities across both science and advisory spectrums. Several immediate opportunities in the single species advisory process were identified, in analysing samples and preparing data. These actions can be performed and reproduced quickly. Datasets on fish age, nephrops burrow counts, and acoustic survey interpretation represent are good examples, as there is a frequent availability of annotated images. Despite the benefits, there would be a continued need for human insight and knowledge, which can’t be automatized, to interpret assessment outputs.​

Image recognition – such as predicting fish ages from otolith images – is a task which can be done consistently while dealing with a large volume of data. Such learning would not be affected by external factors. These benefits are typical of 'supervised' systems, where both model input and output are known, and the machine needs to be taught how to get from one to the other. Humans can quality control the process and set the parameters.

Longer-term possibilities include adding ecosystem and environmental information to the advisory process and products like ecosystem assessments and overviews. This requires a deeper understanding of how machines could perform these tasks, as well as training. Examples here are automated identification of plankton for estimating biodiversity, benthic habitat classification, and environmentally influenced forecast predictions.​

Human impressions

Shaheen Syed, WKMLEARN co-chair, reflected on the workshop.

“It is nice to see some many interesting projects using the power of machine learning to assist in some of the current fisheries and marine science challenges. Although we see great improvements, there is still a big human component and need for massive amounts of labelled training data to help these models 'learn'  to make good predictions. A fully stand-alone machine learning system is still far away but we are on the right track."

For Ketil Malde, who chaired the workshop along with Syed, one of the initiative's main strengths was in pooling human resources.

“I'm really impressed that we managed to collect people who apply various machine learning technology in many areas over almost the whole fisheries process," said Malde.

“But these projects are small and isolated from each other. We don't have good common grounds to meet up, learn from each other, and maintain knowledge. We need to attain critical mass for expertise and deployment and also prod the people who are sitting on the data to get it in shape so we can start using it."

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​Photo, Benjamin Woodward

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Tutoring computers

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