Renata Romeo / Ocean Image Bank
Renata Romeo / Ocean Image Bank

WCS marine scientist McClanahan builds first-ever AI algo that correctly estimates fish stocks

  • First-ever A.I. algorithm correctly estimates fish stocks, could save millions, and bridges global data and sustainability divide
  • Understanding fish stocks is critical to sustainably managing fisheries and ensuring they can continue to provide crucial livelihoods and nutrition, especially in coastal areas where hundreds of millions of people depend on local fisheries 
  • This tool will get data into the hands of local and national governments, so they can make informed decisions about their natural resources and keep “blue foods” on the table
  • The algorithm works with 85 percent accuracy for the first pilot region in the Indian Ocean coral reefs - new partnerships and funding are now needed to scale the tool for worldwide use

For the first time, a newly published artificial intelligence (AI) algorithm allows researchers to quickly and accurately estimate coastal fish stocks without ever entering the water. This breakthrough could save millions of dollars in annual research and monitoring costs while bringing data access to least-developed countries about the sustainability of their fish stocks.

Understanding “fish stocks” – the amount of living fish found in an area’s waters – is critical to understanding the health of our oceans. This is especially true in coastal areas where 90 percent of people working in the fisheries industry live and work. In the wealthiest countries, millions of dollars are spent each year on “stock assessments” – expensive and labor-intensive efforts to get people and boats out into the water to count fish and calculate stocks. That immensely high cost has long been a barrier for tropical countries in Africa and Asia, home to the highest percentage of people who depend on fishing for food and income. Small-scale fishers working coastal waters in many countries are essentially operating blindly, with no accurate data about how many fish are available in their fisheries. Without data, coastal communities and their governments cannot create management plans to help keep their oceans healthy and productive for the long term.

Now, thanks to advances in satellite data and machine learning algorithms, researchers have created a model that has successfully estimated fish stocks with 85 percent accuracy in the Western Indian Ocean pilot region. This tool has the potential to get data quickly and cheaply into the hands of local and national governments, so they can make informed decisions about their natural resources and keep “blue foods” on the table.

“Our goal is to give people the information required to know the status of their fish resources and whether their fisheries need time to recover or not. The long-term goal is that they, their children, and their neighbors can find a balance between people’s needs and ocean health,” said Tim McClanahan, Director of Marine Science at WCS. “This tool can tell us how fish stocks are doing, and how long it will take for them to recover to healthy levels using various management options. It can also tell you how much money you’re losing or can recoup every year by managing your fishery – and in the Western Indian Ocean region where we piloted this tool, it’s no less than $50 to $150 million each year.”

WCS McClanahan and fellow co-authors used years of fish abundance data combined with satellite measurements and an AI tool to produce this model. The result? A simple, easy-to-use pilot tool to better understand and manage our oceans. With further development, anyone from anywhere in the world would be able to input seven easily accessible data points - things like distance from shore, water temperature, ocean productivity, existing fisheries management, and water depth - and receive back an accurate fish stock estimate for their nearshore ecosystems. 

“We know that during times of crisis and hardship, from climate change-induced weather events to the COVID-19 pandemic, people living on the coast increasingly rely on fishing to feed themselves and their families,” said Simon Cripps, Executive Director of Marine Conservation at WCS. “The value of this model is that it tells managers, scientists, and importantly, local communities how healthy a fishery is and how well it can support the communities that depend on it, especially during times of crisis. Once a fishery’s status is known, it gives communities and managers the information to move forward to design solutions to improve fish stocks and improve the resilience of local communities, the fishing industry, and local and national economies.” 

The algorithm has been shown to work with high accuracy for coral reef fisheries in the Western Indian Ocean pilot region. WCS is currently seeking new partnerships and funding to scale the tool so it can be deployed and fill critical data gaps around the world. 

This work was completed over several years and with the support of grants from The Tiffany and Co. Foundation, the John D. and Catherine T. MacArthur Foundation, the Bloomberg Ocean Initiative, the UK Darwin Initiative, and the Western Indian Ocean Marine Science Association’s Marine Science for Management Program (WIOMSA-MASMA).

The financial planning service of robo-advising, in which people get personalized, automated investment advice and portfolio management from an algorithm or AI, is already a big market and growing fast, said Pawan Jain, assistant professor of finance at the WVU John Chambers College of Business and Economics. (WVU Photo/Alyssa Reeves)
The financial planning service of robo-advising, in which people get personalized, automated investment advice and portfolio management from an algorithm or AI, is already a big market and growing fast, said Pawan Jain, assistant professor of finance at the WVU John Chambers College of Business and Economics. (WVU Photo/Alyssa Reeves)

WVU finance prof Jain describes why people are turning to AI for investment advice

With ChatGPT and other artificial intelligence systems making waves, some people are turning to AI for financial planning, according to one West Virginia University researcher.

The AI-powered financial planning service “robo-advising,” or automated investing, is relatively new but rapidly expanding, and many large U.S. investment firms now offer robo-advising accounts. 

Pawan Jain, assistant professor of finance at the WVU John Chambers College of Business and Economics, is an expert on technology-forward financial tools such as blockchain and high-frequency trading and coauthor of a demographic analysis of a robo-advising firm’s customer base that appeared in the Financial Review.

“In robo-advising, algorithms monitor portfolios and evaluate clients’ financial conditions, risk tolerances, and objectives to provide investment recommendations with little to no human help, typically via a diversified mix of low-cost, exchange-traded funds or index funds.

“Robo-advising could disrupt traditional wealth management by enabling investment of smaller sums at lower costs.

“From a user perspective, robo-advising typically involves creating an account on the robo-adviser’s website or phone app, answering a series of questions about your investment goals and risk tolerance, and linking the bank account to fund the investment account. Then the robo-adviser or AI analyzes the information collected from the client — risk tolerance, investment goals, time horizon — and creates a personalized investment portfolio for the client. Robo-advisers share information in real-time and can be accessed online 24/7.

“The portfolio is typically composed of a diversified mix of low-cost ETFs or index funds that align with the investor’s goals and risk tolerance. Clients can view their portfolios, track investments and make account changes such as adding or withdrawing funds through a web-based or mobile platform. The robo-adviser’s algorithm monitors the portfolio and adjusts as needed to keep the portfolio aligned with the investor’s goals, usually through rebalancing. Some robo-advisers offer additional services such as personalized financial planning and tax optimization.

“The minimum dollar amount required to start an investment account that comes with personalized financial advice from an AI is much smaller than the investment that’s required to get tailored financial planning support from a human, so robo-advising lowers the economic barrier to guided participation in the stock market. Robo-advising enables people who could not afford traditional investment services to start saving and investing based on advice and portfolio management that is automated but tailored to individual situations, preferences, and goals. Pawan Jain, assistant professor, finance, WVU John Chambers College of Business and Economics (WVU Photo)

“Robo-advising has the potential to fuel economic growth on a macro scale by engaging new, lower-income demographics in, first of all, saving for retirement and, secondly, doing so through investments in stocks, bonds, and other assets.

“One downside is that robo-advising portfolios generate significantly lower returns as compared to the market index, so a portfolio managed by AI is unlikely to perform as well as one managed by a professional human adviser. And robo-advising is not for everyone — someone who prefers personal interaction and emotional support should choose a human adviser to guide them through the investment process. Finally, as with any centralized technology company, there is always a cybersecurity risk,” explained Pawan Jain, assistant professor, WVU John Chambers College of Business and Economics

Harvard Medical School prof Kuo discusses ChatGPT answers to common patient questions about colonoscopy

Braden Kuo, MD, a neuro gastroenterologist and the director of the Center for Neurointestinal Health at MGH and an associate professor of Medicine at Harvard Medical School, and Tsung-Chun Lee, MD, Ph.D., of Taipei Medical University Shuang Ho Hospital, in Taiwan are co-authors of a recent research letter published in Gastroenterology, ChatGPT Answers Common Patient Questions About Colonoscopy.

What was the question you set out to answer with this study?
ChatGPT, a new language processing tool driven by artificial intelligence (AI), provides conversational text responses to questions and can generate valuable information for enquiring individuals, but the quality of ChatGPT-generated answers to medical questions is currently unclear.

What Methods or Approach Did You Use?
We retrieved eight common questions and answers about colonoscopy from the publicly available webpages of three randomly-selected hospitals from the top-20 list of the US News & World Report Best Hospitals for Gastroenterology and Gastrointestinal Surgery.

We inputted these questions as prompts for ChatGPT for two times on the same day and recorded the ChatGPT-generated answers.

We then used plagiarism detection software to compare the text similarity among all answers. Finally, to objectively interpret the quality of ChatGPT-generated answers, four gastroenterologists rated 36 random pairs of questions and answers for the following quality indicators on a 7-point scale:

(1) ease of understanding
(2) scientific adequacy
(3) satisfaction with the answer
Raters were also asked to interpret whether the answers were AI-generated or not.

What Did You Find?
ChatGPT answers had extremely low text similarity compared with answers on hospital webpages, while the text similarity ranged from 28% to 77% between the two ChatGPT answers.

ChatGPT answers were rated similarly by gastroenterologists to non-AI answers in terms of ease of understanding but with the average AI scores higher than non-AI scores. Scores were also similar related to scientific adequacy and satisfaction with the answers. The raters were only 48% accurate in telling which answers were provided by ChatGPT.

This study is the first of its kind to demonstrate that a contemporary large language model–derived conversational AI program is able to provide easy-to-understand, scientifically adequate, and generally satisfactory answers to common questions about colonoscopy, as determined by gastroenterologists.

Such programs may help to optimize clinical communication with patients, especially for high-volume procedures like colonoscopy. Conversational AI empowered by large language models like ChatGPT has the potential to transform and benefit shared decision-making by patients and physicians.

What are the Implications?
Future research should explore responses to a broader sample of patient questions and clinical conditions and include both patients and physicians as raters.