Patterns that Connect: AI, Management, Metaverse, Quantum, Philosophy, and Physics

Sanjay Basu   While we are concentrating on Ethics in AI, this short piece focuses on why Deep Learning is the preferred training method for AI systems Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have, made deep neural networks a critical component of computing. This piece is based on the Turing Lecture published in Communications of ACM - https://cacm.acm.org/magazines/2021/7/253464-deep-learning-for-ai/fulltext (requires ACM membership)   The basic principle of Deep learning is that to learn complex internal representations for difficult tasks, parallel networks of relatively simple neurons must learn by adjusting the strengths of their connections. Deep learning uses many layers of activity vectors as representations to learn how well it can perform on large training sets using enormous amounts of computation. In the Turing Lecture (based on their paper), the au

Sanjay Basu Disclaimer: First published on medium.com by Sanjay Basu on 12/29/2021 From research to ethics, open-source code is playing a central role in the developing use of AI, yet there is a consistent absence of open-source developers from policy discussions. This piece is to highlight how open-source software impacts AI ecosystem. This essay is based on a report published by Brookings Institution. Open-source software (OSS) affects nearly every issue in AI policy, but it is often absent from discussions around AI policy. OSS helps to make AI tools more widely available and easier to use, and enables the development and analysis of big data. The mos t   advanced tools for machine learning are often free and publicly available. The relationship between OSS and AI policy is less acknowledged and a European Parliament report, that I read sometime back, does not address the relationship at all. OSS affects nearly every issue in AI policy as it allows for faster adoption of AI in scien

Sanjay Basu | Head of Technology Strategy, Oracle Cloud Engineering  Seshadri Dehalisan  Master Principal Cloud Architect, Oracle Cloud Engineering Note : Our original blog was published in ORACLE CLOUD INFRASTRUCTURE blog site. I have republished it here with permission. Official Disclaimer : The views and opinions expressed in this blog are those of the authors and do not necessarily reflect the official policy or position of Oracle Corporation. Setting the context Enterprises are increasingly reliant on machine learning (ML) to further their organization's goals. While machine learning can provide the necessary competitive advantage and intelligence, enterprises need framework to harvest the benefits. This multi-series blog discusses the challenges with machine learning at scale and how you can use the combined power of Oracle Cloud Infrastructure (OCI) offerings and open source Kubeflow platform to achieve your ML outcome. Challenges