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New MIT Algorithm Boosts AI Decision-Making Efficiency By Up to 50 Times
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
MIT researchers developed an efficient AI training algorithm, boosting performance by selecting the best tasks, improving decision-making and reducing training costs.
In a Rush? Here are the Quick Facts!
- Model-Based Transfer Learning (MBTL) improves performance while reducing data and computational needs.
- MBTL was five to 50 times more efficient than traditional reinforcement learning methods.
- The researchers plan to extend MBTL for more complex real-world problems.
MIT researchers have introduced a new algorithm designed to make AI decision-making models more efficient, especially for complex tasks like controlling traffic in cities.
The algorithm improves upon traditional reinforcement learning, which often struggles with variability across different tasks.
The MIT press release explains that for example, an AI model trained to control traffic at one intersection might fail when applied to others with different traffic patterns, lane numbers, or speed limits.
The new approach, known as Model-Based Transfer Learning (MBTL), strategically selects a subset of tasks to train the AI agent, focusing on those that will provide the most significant improvements in performance.
By narrowing the training focus, this method reduces the data and computational resources required while boosting the efficiency of the learning process, says MIT.
The team’s research, which will be presented at the Conference on Neural Information Processing Systems, demonstrated that MBTL was between five and 50 times more efficient than standard methods.
“We were able to see incredible performance improvements, with a very simple algorithm, by thinking outside the box,” said Cathy Wu, senior author and associate professor at MIT.
“An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand.”
Typically, AI models for tasks like traffic control are trained in one of two ways: either using data from all tasks, or training separate models for each task.
MIT explains that both methods have drawbacks—training separate models requires huge amounts of data, while training on all tasks often leads to subpar performance.
The researchers’ method finds a middle ground, training an algorithm on a smaller subset of tasks that are strategically selected to maximize performance across all tasks.
MBTL uses zero-shot transfer learning, a concept where a model trained on one task is applied to similar tasks without additional retraining.
MIT explains that this method estimates how well the model will perform on tasks it hasn’t been directly trained for, thus selecting tasks that will improve overall generalization.
“With a 50x efficiency boost, the MBTL algorithm could train on just two tasks and achieve the same performance as a standard method which uses data from 100 tasks,” Wu explained.
This approach significantly reduces the amount of training data required, improving both the speed and cost-effectiveness of developing AI models for complex decision-making, according to MIT.
Looking ahead, the team plans to refine the MBTL method for more complicated systems and real-world applications, such as next-generation mobility systems.
Image by Muhammad Asyfaul, from Unsplash
UK To Introduce Comprehensive Crypto Rules
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
The UK will draft comprehensive crypto regulations in 2024, focusing on stablecoins, staking, and competing with US and EU developments.
In a Rush? Here are the Quick Facts!
- Stablecoins and staking will be part of a unified crypto regulation regime.
- Stablecoins will no longer fall under existing payment services regulation.
- Industry seeks clearer rules to avoid staking being classified as an investment scheme.
The UK government, under Prime Minister Keir Starmer, plans to draft a regulatory framework for the cryptocurrency sector early next year, Economic Secretary to the Treasury Tulip Siddiq announced at a conference in London on Thursday, as first reported by Bloomberg .
TechCrunch notes that this move follows the UK introducing an new bill to classify crypto assets like Bitcoin and NFTs as “personal property,” offering them greater legal protection. The initiative aims to help the UK keep pace with developments in the US and Europe, says Bloomberg.
The framework will address stablecoins — cryptocurrencies tied to stable assets like the US dollar — and staking services, where investors lock up tokens to support blockchain operations in return for a yield, says Bloomberg.
These rules will form part of a single overarching regime for cryptoassets. “Doing everything in a single phase is simpler and it just makes more sense,” said Siddiq as reported by Bloomberg.
Stablecoins will no longer be regulated under the UK’s existing payment services framework, which Siddiq described as inappropriate given their “current use cases,” says Bloomberg.
The UK is also aiming to counter the pull of President-elect Donald Trump’s administration in the US, which has made efforts to attract crypto businesses, according to Bloomberg.
Uncertainty over UK legislation has caused hesitation among companies, particularly as the EU’s comprehensive Markets in Cryptoassets regulation is set to take effect by the end of the year.
In addition, the industry is seeking a carve-out for staking to avoid it being classified as a collective investment scheme, a designation that would subject it to stricter oversight.
Siddiq indicated alignment with this view, saying, “For me, it doesn’t make sense for staking services to have this treatment. The government intends to proceed with removing this legal uncertainty accordingly,” reports Bloomberg.
The Labour government’s proactive approach follows its landslide victory in July’s general election, signaling a push to provide clarity and foster growth in the UK’s crypto sector, notes Bloomberg.
