Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the Generalized Linear Models (GLMs) currently used in industry. Whereas constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation (ICE) model interpretability technique.

Did you know Neural is taking the stage this fall? Together with an amazing line-up of experts, we will explore the future of AI during TNW Conference 2021. Scientists have built an AI tool that forecasts Arctic sea ice conditions, which could help protect local wildlife and people from changes caused by global warming. The deep learning system, named IceNet, was developed by a research team led by British Antarctic Survey (BAS) and The Alan Turing Institute. The model was trained on climate simulations and observational data to forecast the next six months of sea ice concentration maps.

For generations, the inhabitants of the Arctic have counted on seasonal sea ice, which grows and retreats during the year. Polar bears and marine mammals rely on it as a hunting spot and a place to rest; Indigenous people fish from openings in the ice known as polynyas, and use well-known routes across the ice to travel from place to place. But the Arctic air and water has warmed three times faster than the rest of the planet since 1971, according to a May 2021 report by the Arctic Council, and this warming is causing the ice to expand and contract in unpredictable ways. Some scientists and research firms are now deploying tools powered by artificial intelligence to provide more accurate and timely forecasts of what parts of the Arctic Ocean will be covered with ice, and when. AI algorithms complement existing models that use physics to understand what's happening at the ocean's surface, a dynamic zone where cold underwater currents meet harsh winds to create floating rafts of ice.

In the next week or so, the sea ice floating atop the Arctic Ocean will shrink to its smallest size this year, as summer-warmed waters eat away at the ice's submerged edges. Record lows for sea ice levels will probably not be broken this year, scientists say. In 2020, the ice covered 3.74 million square kilometers of the Arctic at its lowest point, coming nail-bitingly close to an all-time record low. Currently, sea ice is present in just under 5 million square kilometers of Arctic waters, putting it on track to become the 10th-lowest extent of sea ice in the area since satellite record keeping began in 1979. It's an unexpected finish considering that in early summer, sea ice hit a record low for that time of year. The surprise comes in part because the best current statistical- and physics-based forecasting tools can closely predict sea ice extent only a few weeks in advance, but the accuracy of long-range forecasts falters.

A new artificial intelligence (AI) tool has been developed to enable more accurate prediction of Arctic sea ice conditions months into the future. Led by the British Antarctic Survey (BAS) and the Alan Turing Institute, the research team believes its improved forecasts could underpin new systems to protect Arctic wildlife and coastal communities from the impacts of sea ice loss. The paper has been published in Nature Communications. Sea ice that appears at the North and South poles is difficult to forecast due to its complex relationship with the atmosphere and the ocean below it. The summer Arctic sea ice area has halved over the past four decades due to its sensitivity to increasing temperatures caused by global warming.

Promising more accurate predictions in an era of rapid climate change, a new tool is harnessing deep learning to help better forecast Arctic sea ice conditions months into the future. As described in a paper published in the science journal Nature Communications Thursday, the new AI tool, dubbed IceNet, could lead to improved early-warning systems to protect Arctic wildlife and coastal communities. Created by an international team of researchers led by the British Antarctic Survey and the Alan Turing Institute, IceNet tackles a challenge that has long vexed scientists. "The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years," explained lead author Tom Andersson, a data scientist at the BAS AI Lab, in a statement. "IceNet has the potential to fill an urgent gap in forecasting sea ice for Arctic sustainability efforts and runs thousands of times faster than traditional methods," he added.

A new AI (artificial intelligence) tool is set to enable scientists to more accurately forecast Arctic sea ice conditions months into the future. The improved predictions could underpin new early-warning systems that protect Arctic wildlife and coastal communities from the impacts of sea ice loss. Published this week in the journal Nature Communications, an international team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute describe how the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead--something that has eluded scientists for decades. Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain.

Earlier this month a report by the UN's Intergovernmental Panel on Climate Change (IPCC) scientist believe that 1.5C will be reached by 2040, 10 years earlier than previously predicted, and it may even happen earlier if emissions aren't cut down in the coming years. What does a temperature rise of 1.5C mean? These changes are inevitable according to the report after accessing all scenarios. On a less "Humanity is doomed" note, artificial intelligence has been helping the fight to mitigate these effects. An international team of researchers led by the British Antarctic Survey (BAS) and The Alan Turing Institute have created a new artificial intelligence tool called IceNet to allow for a more accurate forecast into the Arctic seas ice condition aim to mitigate the impact on arctic wildlife and coastal communities affected by sea ice loss.

Published this week (Thursday 26 August) in the journal Nature Communications, an international team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute describe how the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead -- something that has eluded scientists for decades. Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain. These accelerating changes have dramatic consequences for our climate, for Arctic ecosystems, and Indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle. IceNet, the AI predictive tool, is almost 95% accurate in predicting whether sea ice will be present two months ahead -- better than the leading physics-based model.

Described in the journal Nature Communications, the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead – something that has eluded scientists for decades. Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below, the researchers said. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain, they said. These accelerating changes, the researchers noted, have dramatic consequences for the world climate, for Arctic ecosystems, and Indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle. IceNet is almost 95 per cent accurate in predicting whether sea ice will be present two months ahead – better than the leading physics-based model, according to the researchers.