data science

Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)

In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligence (AGI) and the singularity. With a no-nonsense approach and a deep understanding of the field, Filip dismantles the hype and exposes some of the misconceptions about AI, LLMs and AGI. Join us as we delve into the real-world implications of AI, separating fact from fiction, and gaining a firm grasp on the tangible possibilities of AI advancement. If you're seeking a refreshingly pragmatic perspective on the future of AI, this episode is an absolute must-listen.
 
Filip Piekniewski Bio
Filip Piekniewski is a distinguished computer vision researcher and engineer, specializing in visual object tracking and perception. He approaches machine learning with a pragmatic mindset, recognizing its current limitations. Filip earned his Ph.D. from Warsaw University, where he explored neuroscience and later joined Brain Corporation in San Diego. His extensive study of neuroscience inspired him to develop innovative, bio-inspired machine learning architectures. Filip's unique blend of scientific curiosity and software engineering expertise allows him to quickly prototype and implement new ideas. He is known for his realistic perspective on AI, debunking AGI hype and focusing on tangible advancements.
 
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Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping requirements. Let Arctic Wolf be your guide.Check it out at https://arcticwolf.com/datascience
Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Defense, Robotics and Predictive maintenance.
 
References
https://twitter.com/filippie509
http://blog.piekniewski.info/ (On limits of deep learning and where to go next with AI.)
 
 

Building Self Serve Business Intelligence With AI and LLMs at Zenlytic (Ep. 235)

In this episode, we dive into the world of data analytics and artificial intelligence with Ryan, the CEO, and Paul, the CTO of Zenlytic. Having graduated from Harvard and with extensive backgrounds in venture capital, consulting, and data engineering, Ryan and Paul provide valuable insights into their journey of building Zenlytic, a cutting-edge analytics platform. Join us as we explore how Zenlytic's natural language interface enhances user experiences, enabling seamless access and analysis of analytics data. Discover how their self-service platform empowers teams to leverage business intelligence effectively, and learn about the unique features that set Zenlytic apart from other analytics platforms in the market. Delve into the crucial aspects of data security and privacy while granting team access, and find out how Zenlytic's analytics capabilities have transformed companies into data-driven decision-makers, ultimately improving their performance.
References
Zenlytic Website: https://www.zenlytic.com/product
AI Will Save the World (a16z): https://a16z.com/2023/06/06/ai-will-save-the-world/
Llama AI (ai.meta.com): https://ai.meta.com/llama/
"Llama 2: Open Foundation and Fine-Tuned Chat Models" (arxiv.org): https://arxiv.org/abs/2307.09288
"GPT-4 Technical Report" (arxiv.org): https://arxiv.org/abs/2303.08774

Debunking AGI Hype and Embracing Reality (Ep. 233)

In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligence (AGI) and the singularity. With a no-nonsense approach and a deep understanding of the field, Filip dismantles the hype and exposes some of the misconceptions about AI, LLMs and AGI. Join us as we delve into the real-world implications of AI, separating fact from fiction, and gaining a firm grasp on the tangible possibilities of AI advancement. If you're seeking a refreshingly pragmatic perspective on the future of AI, this episode is an absolute must-listen.
 
Filip Piekniewski Bio
Filip Piekniewski is a distinguished computer vision researcher and engineer, specializing in visual object tracking and perception. He approaches machine learning with a pragmatic mindset, recognizing its current limitations. Filip earned his Ph.D. from Warsaw University, where he explored neuroscience and later joined Brain Corporation in San Diego. His extensive study of neuroscience inspired him to develop innovative, bio-inspired machine learning architectures. Filip's unique blend of scientific curiosity and software engineering expertise allows him to quickly prototype and implement new ideas. He is known for his realistic perspective on AI, debunking AGI hype and focusing on tangible advancements.
 
Sponsors
Finally, a better way to do B2B research. NewtonX The World’s Leading B2B Market Research Company
Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping requirements. Let Arctic Wolf be your guide.Check it out at https://arcticwolf.com/datascience
Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Defense, Robotics and Predictive maintenance.
 
References
https://twitter.com/filippie509
http://blog.piekniewski.info/ (On limits of deep learning and where to go next with AI.)
 
 

The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224)

In this episode of our podcast, we dive deep into the fascinating world of Graph Neural Networks.
First, we explore Hierarchical Networks, which allow for the efficient representation and analysis of complex graph structures by breaking them down into smaller, more manageable components.
Next, we turn our attention to Generative Graph Models, which enable the creation of new graph structures that are similar to those in a given dataset. We discuss the inner workings of these models and their potential applications in fields such as drug discovery and social network analysis.
Finally, we delve into the essential Pooling Mechanism, which allows for the efficient passing of information across different parts of the graph neural network. We examine the various types of pooling mechanisms and their advantages and disadvantages.
Whether you're a seasoned graph neural network expert or just starting to explore the field, this episode has something for you. So join us for a deep dive into the power and potential of Graph Neural Networks.
 
References
Machine Learning with Graphs - http://web.stanford.edu/class/cs224w/
A Comprehensive Survey on Graph Neural Networks - https://arxiv.org/abs/1901.00596

AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220)

In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles. With the advent of automation and intelligent systems, developers are increasingly relying on algorithms to create efficient and effective code. However, this reliance on AI can come at a cost to the tried-and-true methods of software engineering. Join us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.

Edge AI applications for military and space [RB] (Ep. 219)

Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218)

In this episode, we dive into the fascinating world of zero-knowledge proofs and their impact on data science. Zero-knowledge proofs allow one party to prove to another that they know a secret without revealing the secret itself. This powerful concept has numerous applications in data science, from ensuring data privacy and security, to facilitating secure transactions and identity verification. We explore the mechanics of zero-knowledge proofs, its real-world applications, and how it is revolutionizing the way we handle sensitive information. Join us as we uncover the secrets of zero-knowledge proofs and its impact on the future of data science.
 
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Deep learning vs tabular models (Ep. 217)

Deep learning methods are not as effective with tabular data. Here is why, and what to do about it.
 
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References
https://paperswithcode.com/methods/category/deep-tabular-learning 
https://m-clark.github.io/posts/2022-04-01-more-dl-for-tabular/
 

[RB] Online learning is better than batch, right? Wrong! (Ep. 216)

In this episode I speak about online learning systems and why blindly choosing such a paradigm can lead to very unpredictable and expensive outcomes.Also in this episode, I have to deal with an intruder :)
 
 
Links
Birman, K.; Joseph, T. (1987). "Exploiting virtual synchrony in distributed systems". Proceedings of the Eleventh ACM Symposium on Operating Systems Principles - SOSP '87. pp. 123–138. doi:10.1145/41457.37515. ISBN 089791242X. S2CID 7739589.
 

Edge AI applications for military and space [RB] (Ep. 213)

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Amethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.