Afleveringen
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In this episode, we explore the intersection of AI security, vector databases, and career transformation with Thierry Damiba, Developer Advocate at Qdrant. From his experience securing sensitive government applications to pioneering vector database implementations, Thierry shares valuable insights on building secure AI systems and navigating technological change.
TOPICS DISCUSSED:
1. Vector Databases and AI Security
Thierry explains how vector databases have become the ideal data management tool for AI applications, discussing their role in securing sensitive data and implementing effective access controls. He shares practical approaches to preventing hallucinations and data leakage in AI systems.2. Security in the Age of AI Agents
The conversation delves into the implications of AI agents for security, exploring both the challenges and opportunities they present. Thierry discusses how automation is actually increasing the value of deep technical understanding while making technology more accessible.3. HNSW Algorithm and Vector Search
Through an engaging library analogy, Thierry breaks down the complexities of the HNSW algorithm, explaining how it enables efficient vector search at scale and why this matters for modern AI applications.4. Career Evolution in the AI Era
The discussion examines the changing landscape of technical careers, with insights on adapting to automation and finding fulfillment in technological work. Thierry shares personal experiences of prioritizing passion over immediate financial gain.INSIGHTS:
- The dual role of AI agents in both creating and preventing security vulnerabilities
- Why open source contributes to better security in AI systems
- The importance of implementing both API-level and data-level security measures
- How automation is transforming the value proposition of technical skills
- The significance of pursuing passion in career choices during technological transformationTOOLS AND TECHNOLOGIES MENTIONED:
- HNSW Algorithm
- JWT (JavaScript Web Tokens)
- GPU Indexing for Vector Databases
- Small Language Models (SLMs)
- QdrantCONTACT INFO:
- Twitter: @ptdamiba
- Email: [email protected]
- Discord: Qdrant Community ChannelCHAPTERS
00:00 The Rise of Vector Databases
01:54 Security in AI Applications
05:14 Guardrails for AI Systems
08:13 Jailbreaking and Input Validation
09:54 AI Agents: Opportunities and Risks
16:53 The Future of Work and Automation
25:45 GPU Indexing and Application Development -
In this episode, we dive deep into the world of knowledge graphs and organizational change with Vadym Safronov, a Lead Data Scientist at Nielsen IQ and veteran of enterprise transformations. From building actual ketchup factories to architecting complex data systems, Vadym shares fascinating insights on how knowledge graphs can revolutionize enterprise operations and drive successful organizational change.
TOPICS DISCUSSED:
1. Knowledge Graphs Fundamentals
Vadym breaks down the concept of knowledge graphs through practical examples, explaining how simple subject-predicate-object relationships can be used to build complex knowledge systems. He illustrates how these structures can be enhanced with neural networks to predict patterns and relationships.2. Enterprise Transformation
The discussion explores how knowledge graphs can map organizational structures, processes, and relationships to drive successful change initiatives. Vadym shares insights from his experience at Nestle and other enterprises on identifying effective change agents through network analysis.3. The Science of Change Management
We explore the fascinating research behind successful organizational change, including the importance of network topology in selecting change agents and why traditional approaches often fail. Vadym explains why focusing on early adopters rather than innovators leads to more successful transformations.4. Combining Knowledge Graphs with Generative AI
The conversation examines how enterprises can leverage both knowledge graphs and large language models to create more reliable and factual AI systems, using the metaphor of having both a master librarian and universal interpreter at your disposal.INSIGHTS:
- The power of structural patterns in predicting organizational behavior
- Why three out of four IT interventions fail due to non-technical reasons
- The importance of network topology in selecting change agents
- How knowledge graphs can help combat misinformation
- Why focusing on early adopters rather than innovators leads to more successful change initiativesTOOLS AND TECHNOLOGIES MENTIONED:
- SAP CRM
- DBpedia
-The Network Secrets of Great Change Agents
-Known hoaxes on Wikipedia
-Wikispeedia gameCONTACT INFO:
-Vadym SafronovCHAPTERS
00:00 Introduction and Background
03:15 From Ketchup Factories to Data Science
07:30 Evolution of Graph Applications
12:45 Understanding Knowledge Graphs
18:20 Combining Knowledge Graphs with Generative AI
23:40 Enterprise Change Management
31:15 Network Analysis for Change Agents
38:50 Rogers' Innovation Adoption Theory
45:30 Knowledge Graphs and MisinformationFollow AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.
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Zijn er afleveringen die ontbreken?
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In this episode, we explore the evolution of AI in product management and the crucial balance between automation and human judgment with Jorge Alcantara, a founder of Zentrik.ai and a veteran in AI implementation and product development. Jorge shares his journey from early chatbot development to founding Zentrik, offering unique insights into the future of product management in the AI era.
TOPICS DISCUSSED:
1. The Evolution of AI Implementation
Jorge shares his experience with early chatbot deployments and the transition from rule-based to generative AI systems. He emphasizes how the focus has shifted from pure automation to augmenting human capabilities and understanding user needs through Human-in-the-Loop training mechanisms.2. The PM Paradox
We explore the current challenges in product management, where PMs often become "Jira janitors" instead of focusing on high-value activities like user research and strategic planning. Jorge explains how AI can help rebalance PM workflows and why companies need to rethink their approach to product management.3. Human-Centric AI Development
The conversation delves into the importance of maintaining human judgment in AI solutions, particularly in product management. Jorge emphasizes that while AI can automate routine tasks, the real value comes from freeing PMs to focus on empathy, user understanding, and strategic thinking.4. The Future of Product Management
Jorge presents his vision for how AI tools should evolve to support product managers, highlighting the importance of specialized solutions over generic AI tools. He discusses how proper AI implementation can help companies build better products by enabling PMs to spend more time on high-value activities.INSIGHTS:
1. Product management is becoming the skill of the future as development gets commoditized.
2. The importance of freeing PMs from routine tasks to focus on user research and strategic thinking.
3. Why generic AI tools only solve 10% of PM-specific challenges.
4. The need for specialized AI solutions in product management.
5. The value of human judgment and empathy in product development.TOOLS AND TECHNOLOGIES MENTIONED:
- Zentrik.ai
- ChatGPT
- Canvas
- ChatPRD
- WiserCONTACT INFO:
- LinkedIn: Jorge Alcantara
- Email: [email protected]CHAPTERS
00:00 The Evolution of Chatbots and AI Technology
01:18 How Chatbots Started and Fears around AI
05:45 Bringing Human Emotionality to Machines
08:00 Rewarding Human-in-the-Loop
14:25 Disturbing Trends & Product Management Paradox
17:35 AI's Impact on Product Management Tasks
20:39 How ML can help with PMs' multidisciplinarity
25:45 The Main Pain Mentioned within 200+ interviews
26:26 Challenges in Product Management Documentation
28:02 AI Tools for Product Management Efficiency
32:09 ChatGPT's Canvas and Realtime API for PMs
34:28 Communicating AI Benefits to Executives
39:12 PM's Mastery in Times of Commoditized Code
40:57 Empathy in Product Management
45:40 Personal Reflections on Work and Life BalanceFollow us at AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.
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In this episode, we dive into the fascinating world of quantum computing with Hila, a quantum computing researcher at Siemens focusing on hardware-software co-design. From her unexpected journey from aspiring surgeon to quantum computing expert, Hila brings unique insights into the future of this transformative technology and its real-world applications.
TOPICS DISCUSSED:
1. Quantum Computing Fundamentals
Clear explanation of quantum computing principles using analogies (coins, ripples in water). Comparison between classical and quantum computers using the candle vs. lightbulb metaphor. Detailed breakdown of key quantum properties: superposition, interference, and entanglement. Discussion of how quantum computers complement rather than replace classical systems.2. Current Challenges and Solutions
Deep dive into error correction challenges in quantum systems. Explanation of physical vs. logical qubits. Analysis of different quantum hardware approaches (superconducting vs. ion trap systems). Discussion of the NISQ (Noisy Intermediate Scale Quantum) era and its implications.3. Technical Implementation
Hardware-software co-design considerations. Discussion of different quantum hardware technologies. Integration with classical computing systems. Future outlook for quantum computing development.4. Practical Applications
Material science and molecular simulation. Drug discovery and personalized medicine. Supply chain optimization and logistics. Climate modeling and environmental applications. Quantum machine learning potential.5. Social Impact and Responsibility
Emphasis on ethical guidelines and regulations. Importance of transparency in quantum research. Need for collaborative approach across disciplines. Focus on making quantum computing accessible and understandable.INSIGHTS:
1. Quantum computers are best suited for specific tasks rather than general-purpose computing.
2. Error correction remains a major challenge requiring multiple physical qubits per logical qubit.
3. Different quantum hardware architectures offer various trade-offs for different applications.
4. The field requires early consideration of ethical implications and responsible development.TOOLS AND TECHNOLOGIES MENTIONED:
- UN announcement of 2025 as a year of quantum science and technology
- Google Willow: Google Willow Quantum Chip
- IBM Quantum Systems
- Google's Error-Corrected Quantum Computer Prototype
- Quantum Hardware Platforms (Superconducting, Ion Trap)
- Einstein–Podolsky–Rosen (EPR) paradoxCONTACT INFO:
- LinkedIn: Hila SafiCHAPTERS
00:00 From Hospitals into Quantum Computing
04:29 ELI5: Quantum Computing Walkthrough
07:58 Classical VS Quantum Computers
11:20 Is the Future of Machine Learning in Quantum?
15:30 Why is Error Correction Necessary?
19:08 Good Enough Number of Qubits
23:58 Unexpected about Cryptography and Solving Travelling Salesman
30:17 Unclarity, Perseverance and Society
33:48 The Societal Impact and Ethical Considerations of Quantum Technology
37:39 Reproducibility in ScienceFollow us at AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.
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In this episode, we dive deep into the world of local AI models and their creative applications with Shahbaz Mansahia, an ML engineer, an IEEE author, researcher and advocate for democratizing AI technology. Shahbaz shares his unique perspective on running AI models locally, making AI accessible to art students, and using technology to address representation gaps in art.
TOPICS DISCUSSED:
1. Local Models
Shahbaz explains how running AI models locally offers freedom from service providers while maintaining similar capabilities through quantization. He discusses the trade-offs between model size and performance, sharing insights about the future of 4-bit quantization and its potential for mobile AI deployment.2. AI in Education
Making AI technology accessible to students presents unique challenges. Shahbaz discusses how open-source alternatives to commercial AI services can democratize access while maintaining quality, emphasizing the practical applications in academic environments.3. AI and Artistic Creation
Rather than viewing AI as a threat to creativity, Shahbaz presents it as a tool for enhancing artistic workflows and democratizing expression. He shares his experience working with art students and using AI to address historical representation gaps in art.4. Technical Implementation
The conversation covers practical aspects of local model deployment, including multimodality challenges and the evolution of hardware requirements. Shahbaz provides insights into the future of CPU vs. GPU computing for AI and the development of inference-optimized hardware.INSIGHTS:
1. One can break dependency from any model provider with local models.
2. Foundation models work like the internet in your pocket.
3. Local deployment enables better privacy control for sensitive data.4. We can amplify bias to extend the perceptions of artworks.TOOLS AND TECHNOLOGIES MENTIONED:
- LM Studio
- Hunyuan Text-to-Video Model
- Comfy UI
- DreamboothCONTACT INFO:
- LinkedIn: Shahbaz Mansahia
- Email: [email protected]CHAPTERS
00:00 Intro into Shahbaz's Background
02:40 Local Models and Their Advantages
05:57 Quantization and Model Performance
10:01 Practical Applications of Local Models
14:53 Multimodality and Future Developments
18:07 The Role of CPUs and GPUs in AI
20:57 AI in Art: Creativity vs. Automation
25:56 Bias in AI and Art Representation
32:02 Ethics of AI in Art and RepresentationFollow us at AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.
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AI Ketchup - Catching up on the WHY behind tech products!
In this podcast, we're re-engineering human-centered product creation, focusing on AI solutions that matter. Think of this as your opportunity to step into the shoes of people solving real-world problems.
In the early days of cars, building engines wasn't easy, but it was a problem with a clear direction—unlike the challenge of defining traffic rules and places for roadways. Today, AI's technical barriers are falling, but the real focus is on what to build and why. Seven years ago, when I started exploring teh area of data science and machine learning, AI was confined to labs and very progressive universities. Now, with democratized tools, the HOW is simpler—it's time to master the WHY.
In each episode, founders share their decision-making process and builders reveal their methods, while you join the conversation thinking through how to solve for specific human needs. Join us twice a month—let's build what truly matters.
