The Large Hadron Collider generates large amounts of data, which can pose a challenge due to its volume. Off-the-shelf machine learning packages can be used to help clean up the data by filtering signal noise.
In this episode, the host and the guest discuss the limitations of science, such as the mystery of consciousness, and whether artificial intelligence may help answer these questions.
Paul Dubois recognized in the mid-90s that scientists needed a higher level language to tie together fundamental mathematical algorithms for scientific computing, which led to the development of packages like PyTorch, TensorFlow, and NumPy.
The basics of deep learning, such as convolutional models, will most likely still exist in some form in five years. Directionally, eager execution and graphs are being combined to make programming feel more natural.
Andrew Ng discusses the future of AI in regards to the Turing test, natural language processing and sequence modeling, and the development of new research for other applications.
While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges. This is particularly important for developing reliable systems and addressing issues that may not arise in purely simulated environments.