Decentralized AI is far from becoming mainstream but its addressing many important challenges of the current generation of AI technologies…
One of the pivotal challenges of the next decade of artificial intelligence(AI) is to determine whether data and intelligence are democratized or remain in control of a few large organizations. A few months ago, I wrote a three-part series of the decentralization of artificial intelligence(AI). In that essay, I tried to cover the main elements that justify the movement of decentralized AI ranging from economic factors to technology enablers as well as the first generation of technologies that are developing decentralized AI platforms. The arguments made in those articles were fundamentally theoretical because, as we all know, the fact remains that AI today is completely centralized. However, as I work more in real-world AI problems, I am starting to realize that centralization is an aspect that is constantly hindering the progress of AI solutions. Furthermore, we should start seeing centralization in AI as a single problem but as many different challenges that surface at different stages of the lifecycle of an AI solution. Today, I would like to explore that idea in more detail.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different