I just had an interesting thought following a recent discussion we had about the best approach to building AI solutions. We talked about how instead of trying to develop a single, monolithic end-to-end solution, it often makes more sense to break it down into smaller, specialized components, with partnerships bringing everything together for a final outcome.
It struck me that this is quite similar to how convolutional neural networks (CNNs) are designed. In CNNs, instead of using a dense neural network that tries to process everything all at once, the model optimizes computing and algorithmic power by fragmenting tasks. Each layer focuses on specific features—much like how the optical cortex has specialized regions for processing visual information. These independent layers then merge their outputs to deliver a comprehensive result.
In a way, even the design of neural networks mirrors the idea that partnership and specialization can lead to a more efficient and powerful solution. Just as CNNs work by leveraging the strengths of specialized layers, in AI development, collaborating with the right partners allows us to achieve something far greater than any one entity could accomplish alone.
It’s fascinating to see how even at the fundamental level, AI is built on the principle of collaboration. 🤖🔍🤝
#AI #NeuralNetworks #DigitalTransformation #Partnerships #MachineLearning #AIInnovation #DeepLearning”
Earlier today our Chief Medical Officer, Eric Walk MD, FCAP participated in the panel 'Advancing Digital and #AIPathology in #PrecisionMedicine: Where Are We Now?' at the Advancing Precision Medicine #APM24 conference in Philadelphia.
The panel discussed the rising adoption of digital solutions in pathology and the emergence of new technologies that are driving #biomarker discovery and accelerating algorithm development. #Partnerships remain critical to overcoming barriers for #digitalpathology adoption.
#advancingprecisionmedicine #ai #health #cancer #oncology #pathology