DeepMind's Neural Algorithmic Reasoning is a groundbreaking fusion of neural networks and algorithmic computation. This innovative approach is reshaping the landscape of artificial intelligence, offering a new perspective on machine learning and its potential to solve complex problems.
Algorithms are everywhere
They detail the specific instructions that computers need to carry out tasks
- The ability of algorithms to automate and engineer systems that reason has made them a cornerstone of contemporary society
- Although full neural network models can also serve as task solvers and utilize additional information from data to tailor existing algorithms to real-world problems, a trade-off is that these systems sacrifice generalization ability
- In a new paper, a research team from DeepMind explores how neural networks can be fused with algorithmic computation
Previous approaches have included training deep learning models using existing algorithms as fixed external tools
Teaching deep neural networks to imitate the workings of an existing algorithm by producing the same output
- Using multiple known algorithms and the abstract commonalities among them to enable algorithms to be derived
- Algorithms are used to reason about problems in an abstract space to make it easier to build theoretical collections between the target problem and the known problem class.
Neural Algorithmic Reasoning
The idea behind algorithmic reasoning is to build algorithmically-inspired neural networks that can execute an algorithm from abstractified inputs
- Given natural inputs that are often high-dimensional, noisy, and prone to changing rapidly, the proposed method first trains an algorithmic reasoner to imitate the algorithm
- This yields encoder and decoder functions that can carry data to and from the latent space of the processor network
- Appropriate neural networks are then set up to process raw data and produce expected outputs
