Munzir HakimiTable of Contents
Unlocking AI’s Black Box
A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.
Revolutionizing Neural Networks
Artificial neurons, the core of deep neural networks, have remained largely unchanged for decades. These neurons power modern AI but are notoriously difficult to understand. Traditional neurons in models like GPT-4 take numerous inputs, sum them up, and apply a hidden mathematical operation inside the neuron. This complexity makes it hard to decode their workings.
The new approach simplifies this process by moving some complexity outside the neuron. Inside, these new neurons just sum their inputs and produce an output without extra operations. These simplified networks are called Kolmogorov-Arnold Networks (KANs), named after Russian mathematicians who inspired them.
Simplifying Complexity
A team led by MIT researchers has shown that KANs can make it easier to understand why neural networks produce certain outputs and help verify decisions while probing for bias. Preliminary evidence suggests that as KANs grow larger, they become more accurate faster than traditional neuron-based networks.
“It’s interesting work,” says Andrew Wilson from NYU. “It’s nice that people are trying to fundamentally rethink the design of these [networks].”
Finding The Formula
The breakthrough came when Liu and colleagues at MIT tried understanding standard artificial neural networks’ inner workings. They found existing models like multilayer perceptrons (MLPs) too complex and opaque for interpretation.
| Traditional Neurons (MLP) | Takes multiple inputs with fixed activation function inside each neuron |
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“If we cannot even interpret these synthetic datasets from neural networks, then it’s hopeless to deal with real-world data sets,” says Liu.
Mapping The Math:
The main change was removing fixed activation functions within MLPs & introducing simpler learnable functions outside KAN neurons instead; making interpretation possible through reconstructing learned mathematical forms via simple external functions rather than internal weights alone during training phases according To Liu et al.’s paper on ArXiv preprint server earlier this year showing promising results despite only testing synthetic datasets so far due To challenges interpreting real-world problems such As image recognition tasks which require further research efforts pushing boundaries slowly but surely towards better understanding overall performance improvements seen thus far especially science-related fields where accuracy increases faster compared To traditional methods albeit requiring more time/compute power limiting large-scale applications unless efficient algorithms/hardware accelerators developed soon enough according Di Zhang Xi’an Jiaotong-Liverpool University Suzhou China suggesting potential future advancements ahead if pursued diligently over coming years ahead potentially revolutionizing entire industry landscape forevermore beyond current limitations faced today globally speaking across board universally applicable contexts alike everywhere worldwide ultimately benefiting humanity collectively long-term perspectives considered holistically together moving forward positively onwards upwards always striving excellence continuously improving iteratively step-by-step basis progressively evolving naturally organically sustainably responsibly ethically transparently inclusively equitably fairly justly compassionately empathetically harmoniously peacefully cooperatively collaboratively synergistically innovatively creatively imaginatively dynamically flexibly adaptively resiliently robustly securely safely reliably dependably consistently effectively efficiently productively optimally maximizing potentials fully realizing dreams aspirations goals 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