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By Cyrus Shirazian
4.9
1515 ratings
The podcast currently has 50 episodes available.
Summary
Summary
In this conversation, Cyrus and Lauren discuss the intersection of Agile and data science, specifically focusing on the challenges of shipping AI-enabled products quickly. They emphasize the importance of democratizing AI within organizations and the need for product managers to understand AI and ML concepts. They also discuss the prioritization of AI ML feature sets per quarter and the balance between quick wins and long-term strategic initiatives. Lauren shares her recommendations for getting buy-in and support from leadership, including listening, scenario planning, and making informed decisions.
Takeaways
Chapters
00:00 Introduction and Background
03:25 Challenges of Delivering Business Value Quickly
06:52 Democratizing AI within Organizations
11:05 Scoping AI/ML Feature Sets for Revenue Outcomes
14:12 Staying Up-to-Date with New Technologies
27:40 Incorporating AI into Product Strategies
28:54 Aligning Organizational Expectations and Goals
30:09 Understanding Constraints and Goals
33:10 Planning and Execution
36:04 Balancing Quick Wins and Long-Term Strategic Initiatives
40:17 Gaining Buy-In from Leadership
43:10 Democratizing Knowledge about AI and ML
Keywords
Agile, data science, intersection, challenges, shipping, AI-enabled products, democratizing AI, product managers, prioritization, feature sets, quick wins, long-term strategic initiatives, buy-in, leadership
Summary
Geert Timmermans, CPTO at StoryTech, shares his background and experience in integrating AI into product development. He emphasizes the importance of bridging the gap between engineering and product teams to maximize the value of AI. The challenges organizations face when integrating AI include the need for the right skillset, avoiding gimmicks, and focusing on the value AI brings to customers. Timmermans suggests a strong focus on product discovery and continuous discovery to ensure AI is integrated effectively. He also highlights the importance of giving engineers the freedom to experiment and collaborate with the product team. AI should be seen as an enabler and an opportunity to enhance and augment human capabilities, rather than a threat or replacement. It can assist in various industries, such as healthcare and marketing, by speeding up processes and improving quality. The adoption of AI requires a mindset shift and a willingness to upskill. It is important to build AI architecture in a way that allows for flexibility and the ability to plug in different models and suppliers. Data readiness is a challenge for many organizations, and a phased approach to AI implementation can help overcome this by starting small and gradually scaling up.
Takeaways
Chapters
00:00 Geert’s Background and Role as CPTO
07:06 Challenges of Integrating AI into Established Product Ecosystems
10:08 The Importance of Collaboration between Engineering and Product Teams
12:27 Product Discovery and Continuous Exploration for AI Integration
14:23 AI as a Foundational Aspect of Product Development
14:49 Introduction and Product Discovery
19:20 Collaboration between AI and Software Development Teams
36:09 The Phased Approach to AI Integration
40:39 The Challenges and Realities of AI
48:10 Data Quality and IP Protection
51:47 AI as an Enabler, Not a Threat
Keywords
Geert Timmermans, CPTO, StoryTech, background, integrating AI, product development, engineering, product teams, skillset, value, challenges, product discovery, continuous discovery, engineers, collaboration, AI, enabler, opportunity, enhance, augment, assist, healthcare, marketing, adoption, upskill, architecture, flexibility, data readiness, phased approach
Summary
In this conversation, Cyrus and Ivan discuss various topics related to NLP (Natural Language Processing) and its impact on AI. They cover Ivan’s background in AI and NLP, pivotal moments in his career, the current state of the NLP industry, best practices for data collection and NLP-powered products, the challenges of scaling LLM-POCs (Large Language Models Proof of Concepts) into production, and the ethical considerations of NLP. They also touch on the future of NLP and AI, including the potential for AI agents and the role of NLP in unlocking human creativity.
Takeaways
Chapters
00:00 The Current State of NLP Industry
00:15 Pivotal Moments in Ivan’s Career
03:24 Advancements in NLP and LLMs
14:27 Data Labeling and Saving Time and Money
17:54 Impact of Lawsuits and Real-Time Use Cases on User Experience
18:51 Future-Proofing Products and Fine-Tuning Models
19:52 Standardization and Automation in Model Development
21:19 Scaling LLM-POCs into Production Environments
23:03 Complexity of Multiple Truths and User Intent in NLP
24:20 Best Practices for Labeling and Model Training
27:01 Case Study: Impact of DataSaur’s NLP Technology on the Legal Industry
28:55 Ensuring Consistency and Accuracy in Model Output
34:14 Ethical Considerations in NLP and AI
39:04 Exciting Developments in NLP and AI
45:18 Advice for Integrating NLP into Products and Services
Summary
In this conversation, Arnon discusses the ethical concerns surrounding AI in healthcare, including privacy and data protection, explainability and liability, and the balance between regulations and innovation. He also explores the role of clinicians in the adoption of AI, the importance of informed consent and patient education, and the need for multidisciplinary discussions to navigate the ethical challenges. Arnon envisions the widespread adoption of personalized medicine integrated with telemedicine as the most significant change in healthcare as a result of AI in the next decade.
Takeaways
Chapters
00:00 Introduction and Background
03:01 Ethical Concerns in Healthcare AI
06:04 Privacy and Data Protection
07:25 Explainability and Liability
08:28 Balancing Regulations and Innovation
10:40 Using Synthetic Data
11:58 Addressing Bias in Healthcare AI
19:21 The Role of Clinicians in AI Healthcare
25:03 Informed Consent and Patient Education
30:18 Educating Healthcare Institutions and Regulatory Bodies
31:44 The Evolving Role of Clinicians
35:37 Regulations and the Future of Healthcare AI
40:05The Future of Personalized Medicine and Telemedicine
In this episode with Simon O’Regan, you will learn:
Intro music by Peter Boros of The Nameless Citizens
In this episode with Paul Ortchanian, you will learn:
Intro music by Peter Boros of The Nameless Citizens
In this episode with Ivan Lee, you will learn:
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In this episode with Jaekob Chenina, you will learn:
Intro music by Peter Boros of The Nameless Citizens
In this episode with Babar Bhati, you will learn:
Intro music by Peter Boros of The Nameless Citizens
In this episode with Daniel Elizalde, you will learn:
Intro music by Peter Boros of The Nameless Citizens
The podcast currently has 50 episodes available.
164 Listeners