Data Science at Home 2x6z6h

As a continuation of Episode 238, I explain some effective and fun attacks to conduct against LLMs. Such attacks are even more effective on models served locally, that are hardly controlled by human . Have great fun and learn them responsibly.   References https://www.jailbreakchat.com/ https://www.reddit.com/r/ChatGPT/comments/10tevu1/new_jailbreak_proudly_unveiling_the_tried_and/ https://arxiv.org/abs/2305.13860  

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

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 Environment https://harmful.cat-v.org/cat-v/unix_prog_design.pdf

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    

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. us as we explore how Zenlytic's natural language interface enhances 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

In this captivating podcast episode, renowned financial expert Chris Skinner as he delves into the fascinating realm of the future of money. From cryptocurrencies to government currencies, the metaverse to artificial intelligence (AI), Skinner explores the intricate interplay between technology and humanity. Gain valuable insights as he defines the future of money, examines the potential impact of cryptocurrencies on traditional government currencies, and addresses the advantages and disadvantages of digital currencies. Delve into the complex issues of regulation and governance in the context of emerging financial technologies, and discover Skinner's unique perspective on the metaverse and its implications for the future of money and technology. Brace yourself for an enlightening discussion on the integration of AI in the financial sector and its potential impact on humanity. Tune in to explore the cutting-edge concepts that shape our financial landscape and get a glimpse of what lies ahead.   You can read about Chris at https://thefinanser.com/   Sponsors This episode is sponsored by Setapp.  Setapp is a platform that combines 230+ powerful MacOS and iOS apps and tools under one $9.99 subscription. Their selection of apps is mostly helpful for people who use their Macs as an actual working tool, covering complete use cases like coding, deg, project and time management and so on. Once subscribed, you get full access to paid features of the apps, as well as to new apps that are being constantly added. So you’ll always be sure you’re not missing out on any cool apps and services that actually help you do your work more efficiently for just a fraction of the price. Get 7 days for free at https://stpp.co/dsat  

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. 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 ed 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.)    

In this exciting episode, we dive into the world of Forward-Forward Neural Networks, unveiling their mind-boggling power and potential. us as we demystify these advanced AI algorithms and explore how they're reshaping industries and revolutionizing machine learning. From self-driving cars to personalized medicine, discover the cutting-edge applications that are propelling us into a new era of AI greatness. Get ready to unlock the secrets of Forward-Forward Neural Networks and witness the future of artificial intelligence unfold before your eyes. Don't miss out – tune in now and be part of the AI revolution! Sponsors 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 The Forward-Forward Algorithm: Some Preliminary Investigations  

Brace yourselves as we uncover the mind-blowing AI model, Google Bard, that's poised to challenge ChatGPT and other conversational AI systems. us as we explore the revolutionary features of Bard, its cutting-edge architecture, and its ability to generate human-like responses. Discover why AI enthusiasts are buzzing with excitement. References: [1] [2] [3]   Sponsors Finally, a better way to do B2B research. NewtonX The World's Leading B2B Market Research Company   References Google Unveils Palm-2: Its Revolutionary AI Model.  https://datascientest.com/en/google-unveils-palm-2-its-revolutionary-ai-model Google AI - Discover Palm-2 https://ai.google/discover/palm2/ "Palm-2: A Large Scale Language Model for Conversational AI." ArXiv preprint arXiv:2305.10403 (2023).  https://arxiv.org/abs/2305.10403

In this enlightening episode of our podcast, we delve into the fascinating realm of Physics Informed Neural Networks (PINNs) and explore how they combine the extraordinary prediction capabilities of neural networks with the unparalleled accuracy of physics models. us as we unravel the mysteries behind PINNs and their potential to revolutionize various scientific and engineering domains. We'll discuss the underlying principles that enable these networks to incorporate physical laws and constraints, resulting in enhanced predictions and a deeper understanding of complex systems.   Sponsors This episode is ed by Mimecast - the email security solution that every business needs. With Mimecast, you get a security solution that is specifically designed for email and workplace collaboration. Head to mimecast.com for a free trial.     References Physics Informed Deep Learning https://maziarraissi.github.io/PINNs/

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. us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.   Sponsors Bloomberg At Bloomberg, they solve complex, real-world problems for customers across the global capital markets. From real-time market data to sophisticated analytics, powerful trading tools, and more, Bloomberg engineers work with systems that operate at scale. If you're a software engineer looking for an exciting and fulfilling career, head over to bloomberg.com/careers to learn more.   Arctic Wolf Cybercriminals are evolving. Their techniques and tactics are more advanced, intricate, and dangerous than ever before. Industries and governments around the world are fighting back, unveiling new regulations meant to better protect data against this rising threat. Arctic Wolf — the leader in security operations — is on a mission to end cyber risk by giving organizations the protection, information, and confidence they need to protect their people, technology, and data. Visit arcticwolf.com/datascience to take your first step.

Hold on to your calculators and buckle up for a wild mathematical ride in this episode! Brace yourself as we dive into the fascinating realm of Liquid Time-Constant Networks (LTCs), where mathematical content reaches new heights of excitement. In this mind-bending adventure, we demystify the intricacies of LTCs, from complex equations to mind-boggling mathematical concepts, we break them down into digestible explanations.   References https://www.science.org/doi/10.1126/scirobotics.adc8892 https://spectrum.ieee.org/liquid-neural-networks#toggle-gdpr  

Get ready for an eye-opening episode! 🎙️ In our latest podcast episode, we dive deep into the world of LoRa (Low-Rank Adaptation) for large language models (LLMs). This groundbreaking technique is revolutionizing the way we approach language model training by leveraging low-rank approximations. us as we unravel the mysteries of LoRa and discover how it enables us to retrain LLMs with minimal expenditure of money and resources. We'll explore the ingenious strategies and practical methods that empower you to fine-tune your language models without breaking the bank. Whether you're a researcher, developer, or language model enthusiast, this episode is packed with invaluable insights. Learn how to unlock the potential of LLMs without draining your resources. Tune in and the conversation as we unravel the secrets of LoRa low-rank adaptation and show you how to retrain LLMs on a budget. Listen to the full episode now on your favorite podcast platform! 🎧✨   References LoRA: Low-Rank Adaptation of Large Language Models https://arxiv.org/abs/2106.09685 Low-rank approximation https://en.wikipedia.org/wiki/Low-rank_approximation Attention is all you need https://arxiv.org/pdf/1706.03762.pdf  

This is the first episode about the latest trend in artificial intelligence that's shaking up the industry - running large language models locally on your machine. This new approach allows you to by the limitations and constraints of cloud-based models controlled by big tech companies, and take control of your own AI journey. We'll delve into the benefits of running models locally, such as increased speed, improved privacy and security, and greater customization and flexibility. We'll also discuss the technical requirements and considerations for running these models on your own hardware, and provide practical tips and advice to get you started. us as we uncover the secrets to unleashing the full potential of large language models and taking your AI game to the next level! Sponsors AI-powered Email Security Best-in-class protection against the most sophisticated attacks, from phishing and impersonation to BEC and zero-day threats https://www.mimecast.com/     References https://agi-sphere.com/llama-models/ https://crfm.stanford.edu/2023/03/13/alpaca.html https://beebom.com/how-run-chatgpt-like-language-model-pc-offline/ https://sharegpt.com/ https://stability.ai/

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.    

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 ing 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 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

In this episode, I explore the cutting-edge technology of graph neural networks (GNNs) and how they are revolutionizing the field of artificial intelligence. I break down the complex concepts behind GNNs and explain how they work by modeling the relationships between data points in a graph structure. I also delve into the various real-world applications of GNNs, from drug discovery to recommendation systems, and how they are outperforming traditional machine learning models. me and demystify this exciting area of AI research and discover the power of graph neural networks.

In this episode, we dive into the not-so-secret sauce of ChatGPT, and what makes it a different model than its predecessors in the field of NLP and Large Language Models. We explore how human can be used to speed up the learning process in reinforcement learning, making it more efficient and effective. Whether you're a machine learning practitioner, researcher, or simply curious about how machines learn, this episode will give you a fascinating glimpse into the world of reinforcement learning with human .   Sponsors This episode is ed by How to Fix the Internet, a cool podcast from the Electronic Frontier Foundation and Bloomberg, global provider of financial news and information, including real-time and historical price data, financial data, trading news, and analyst coverage.   References Learning through human https://www.deepmind.com/blog/learning-through-human-   Training a Helpful and Harmless Assistant with Reinforcement Learning from Human https://arxiv.org/abs/2204.05862

In this episode, we explore the potential of the highly anticipated GPT-4 language model and the challenges that come with its development. From its ability to generate highly coherent and creative text to concerns about ethical considerations and the potential misuse of such technology, we delve into the promise and pitfalls of GPT-4. us as we speak with experts in the field to gain insights into the latest developments and the impact that GPT-4 could have on the future of natural language processing.    

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. us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.