Advancing conservation with AI-based facial recognition of turtles - Related to best, programming, data, conservation, practices

Advancing conservation with AI-based facial recognition of turtles

Finding solutions to improve turtle reidentification and supporting machine learning projects across Africa.

Protecting the ecosystems around us is critical to safeguarding the future of our planet and all its living citizens. Fortunately, new artificial intelligence (AI) systems are making progress in conservation efforts worldwide, helping tackle complex problems at scale – from studying the behaviour of animal communities in the Serengeti to help conserve the diminishing ecosystem, to spotting poachers and their wounded prey to prevent species going extinct.

As part of our mission to help benefit humanity with the technologies we develop, it's crucial we ensure diverse groups of people build the AI systems of the future so that it’s equitable and fair. This includes broadening the machine learning (ML) community and engaging with wider audiences on addressing crucial problems using AI.

Through investigation, we came across Zindi – a dedicated partner with complementary goals – who are the largest community of African data scientists and host competitions that focus on solving Africa’s most pressing problems.

Our Science team’s Diversity, Equity, and Inclusion (DE&I) team worked with Zindi to identify a scientific challenge that could help advance conservation efforts and grow involvement in AI. Inspired by Zindi’s bounding box turtle challenge, we landed on a project with the potential for real impact: turtle facial recognition.

Biologists consider turtles to be an indicator species. These are classes of organisms whose behaviour helps scientists understand the underlying welfare of their ecosystem. For example, the presence of otters in rivers has been considered a sign of a clean, healthy river, since a ban on chlorine pesticides in the 1970s brought the species back from the brink of extinction.

Turtles are another such species. By grazing on seagrass cover, they cultivate the ecosystem, providing a habitat for numerous fish and crustaceans. Traditionally, individual turtles have been identified and tracked by biologists with physical tags, though frequent loss or erosion of these tags in seawater has made this an unreliable method. To help solve some of these challenges, we launched an ML challenge called Turtle Recall.

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Competitive programming with AlphaCode

Note: This blog was first . Following the paper’s publication in Science on 8 Dec 2022, we’ve made minor updates to the text to reflect this.

Solving novel problems and setting a new milestone in competitive programming.

Creating solutions to unforeseen problems is second nature in human intelligence – a result of critical thinking informed by experience. The machine learning community has made tremendous progress in generating and understanding textual data, but advances in problem solving remain limited to relatively simple maths and programming problems, or else retrieving and copying existing solutions.

As part of DeepMind’s mission to solve intelligence, we created a system called AlphaCode that writes computer programs at a competitive level. AlphaCode achieved an estimated rank within the top 54% of participants in programming competitions by solving new problems that require a combination of critical thinking, logic, algorithms, coding, and natural language understanding.

, our paper details AlphaCode, which uses transformer-based language models to generate code at an unprecedented scale, and then smartly filters to a small set of promising programs.

We validated our performance using competitions hosted on Codeforces, a popular platform which hosts regular competitions that attract tens of thousands of participants from around the world who come to test their coding skills. We selected for evaluation 10 recent contests, each newer than our training data. AlphaCode placed at about the level of the median competitor, marking the first time an AI code generation system has reached a competitive level of performance in programming competitions.

To help others build on our results, we’ve released our dataset of competitive programming problems and solutions on GitHub, including extensive tests to ensure the programs that pass these tests are correct — a critical feature current datasets lack. We hope this benchmark will lead to further innovations in problem solving and code generation.

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Best practices for data enrichment

Building a responsible approach to data collection with the Partnership on AI.

At DeepMind, our goal is to make sure everything we do meets the highest standards of safety and ethics, in line with our Operating Principles. One of the most crucial places this starts with is how we collect our data. In the past 12 months, we’ve collaborated with Partnership on AI (PAI) to carefully consider these challenges, and have co-developed standardised best practices and processes for responsible human data collection.

Over three years ago, we created our Human Behavioural Research Ethics Committee (HuBREC), a governance group modelled on academic institutional review boards (IRBs), such as those found in hospitals and universities, with the aim of protecting the dignity, rights, and welfare of the human participants involved in our studies. This committee oversees behavioural research involving experiments with humans as the subject of study, such as investigating how humans interact with artificial intelligence (AI) systems in a decision-making process.

Alongside projects involving behavioural research, the AI community has increasingly engaged in efforts involving 'data enrichment' – tasks carried out by humans to train and validate machine learning models, like data labelling and model evaluation. While behavioural research often relies on voluntary participants who are the subject of study, data enrichment involves people being paid to complete tasks which improve AI models.

These types of tasks are usually conducted on crowdsourcing platforms, often raising ethical considerations related to worker pay, welfare, and equity which can lack the necessary guidance or governance systems to ensure sufficient standards are met. As research labs accelerate the development of increasingly sophisticated models, reliance on data enrichment practices will likely grow and alongside this, the need for stronger guidance.

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