computer 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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References
https://twitter.com/filippie509
http://blog.piekniewski.info/ (On limits of deep learning and where to go next with AI.)
 
 

The AI Chip Chat 🤖💻 (Ep. 243)

Dive into the cool world of AI chips with us! 🚀 We're breaking down how these special computer chips for AI have evolved and what makes them different. Think of them like the superheroes of the tech world!
Don't miss out! 🎙️🔍 #AIChips #TechTalk #SimpleScience

Rolling the Dice: Engineering in an Uncertain World (Ep. 242)

Hey there, engineering enthusiasts! Ever wondered how engineers deal with the wild, unpredictable twists and turns in their projects? In this episode, we're spilling the beans on uncertainty and why it's the secret sauce in every engineering recipe, not just the fancy stuff like deep learning and neural networks!
Join us for a ride through the world of uncertainty quantification. Tune in and let's demystify the unpredictable together! 🎲🔧🚀
 
References
https://www.osti.gov/servlets/purl/1428000
https://arc.aiaa.org/doi/pdf/10.2514/6.2010-124
https://arxiv.org/pdf/2001.10411
 
 

Unlocking Language Models: The Power of Prompt Engineering (Ep. 238)

Join me on an enlightening journey through the world of prompt engineering. Explore the multifaceted skills and strategies involved in harnessing the potential of large language models for various applications. From enhancing safety measures to augmenting models with domain knowledge, learn how prompt engineering is shaping the future of AI.
 
References
https://arxiv.org/pdf/2109.01652.pdf
https://arxiv.org/abs/2201.11903
https://arxiv.org/abs/2302.00923
https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering
https://www.axios.com/2023/02/22/chatgpt-prompt-engineers-ai-job

Erosion of Software Architecture Quality in the Age of AI Code Generation (Ep. 237)

In this era of AI-powered code generation, software architects are facing a concerning decline in the quality of their creations. The once meticulously crafted software architectures are now being compromised. Should LLMs be responsible?
 
References
Program Design in the UNIX Environmenthttps://harmful.cat-v.org/cat-v/unix_prog_design.pdf

The new dimension of AI: Vector Databases (Ep. 236)

Let's delve into the emerging trend in database design – or is it really a new trend? The realm of vector databases and their revolutionary influence on AI and ML is making headlines. Come along as we investigate how these groundbreaking databases are revolutionizing the landscape of data storage, retrieval, and processing, ultimately unlocking the complete potential of artificial intelligence and machine learning. But are they genuinely as innovative as they seem?
 
References
https://partee.io/2022/08/11/vector-embeddings/
https://blog.det.life/why-you-shouldnt-invest-in-vector-databases-c0cd3f59d23c
https://medium.com/@ryanntk/choosing-the-right-embedding-model-a-guide-for-llm-applications-7a60180d28e3
 
 

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

AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229)

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.
 
Sponsors
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Rust: A Journey to High-Performance and Confidence in Code at Amethix Technologies (Ep. 225)

The journey of porting our projects to Rust was intense, but it was a decision we made to improve the quality of our software. The migration was not an easy task, as it required a considerable amount of time and resources. However, it was worth the effort as we have seen significant improvements in code reusability, code cleanliness, and performance.In this episode I will tell you why you should consider taking that journey too.
 
 

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