Earley AI Podcast

In this episode of the Earley AI Podcast Sahitya Senapthy, Founder and CEO of Endeavor, joins us for an episode filled with rich discussions on AI, data management, and the intriguing paths of technological advancement in industrial manufacturing. Sahitya's career has spanned work with the US Air Force, AWS, and Palantir. Sahitya joins our hosts, Seth Earley and Chris Featherstone, as they have an in-depth discussion about the intersection of AI and industrial manufacturing.Key takeaways:Rethinking AI Implementation: The importance of moving beyond simple chatbot applications like ChatGPT to realize real ROI through generative AI for sales optimization and cost savings in industrial spaces.Phased Approach to Tech Deployment: The "crawl, walk, run" methodology along with proof-of-concept (POC) phases is crucial in acclimating users and ensuring new tools deliver tangible ROI, avoiding "shelf-ware."Data Privacy and Security: Amidst the integration of sophisticated AI solutions, maintaining data privacy and security remains paramount.Quote from the show:"Real ROI in industrial spaces comes from effectively using generative AI for sales and cost savings—not just relying on chatbots." - Sahitya SenapathyLinks:LinkedIn: https://www.linkedin.com/in/sahityas/Website: https://www.endeavor.aiThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast Camden Swita, the Head of AI and ML Innovation at New Relic, joins us for an insightful discussion on the transformative role of AI in modern technology. With a rich background spanning journalism, product management, and cutting-edge AI/ML initiatives, Camden brings a unique perspective on leveraging artificial intelligence to enhance both professional and creative workflows. Camden joins our hosts, Seth Earley and Chris Featherstone, as they navigating the complexities of data readiness and AI operationsKey takeaways:Understanding Vendor Offerings and Data Infrastructure: The importance of scrutinizing vendor promises and the potential of building simpler AI solutions, like basic chatbots, in-house. Consulting legitimate data scientists and engineers to assess data readiness before plunging into AI investments was highly recommended.Impact of Generative AI on Content Creation: Generative AI's role in automating content creation is rapidly evolving. While it offers incredible efficiencies in some areas, its application in creative writing is still experimental and requires a balance between traditional methods and automation.Modular Retrieval Augmented Generation (RAG): The concept of Modular RAG was explored as a way to enhance language models with relevant context. This system uses specific data sources to fill knowledge gaps, making responses more accurate and targeted. The discussion included how visual tools like heatmaps can identify and improve sparse contexts.Quote from the show:"Understanding the actual capabilities of AI systems and the state of your own data infrastructure is crucial. Investing in foundational data work isn't glamorous, but it's the backbone of any successful AI implementation. Without it, even the most advanced algorithms can't deliver meaningful results." - Camden SwitaLinks:LinkedIn: https://www.linkedin.com/in/camden-swita-54a41aa/Website: https://newrelic.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast Ron Green, Founder of KUNGFU.ai joins host Chris Featherstone. Ron's expertise spans over 25 years in artificial intelligence and machine learning. Starting his journey in computer science during the early days of AI, he has witnessed and contributed to the evolution of the field from modest neural networks to today's complex, groundbreaking systems. With a master's degree in AI from Sussex, he has firsthand experience in areas ranging from protein folding predictions to advanced graph models. Key takeaways:Feedback Loop in LLMs: Discover why training large language models (LLMs) using their own generated data can create a feedback loop reducing model capabilities.AI Accuracy and Trust: Insights into the importance of building trust in AI through accurate models and robust graph environments handling diverse data associations.Ethical AI Development: Discussions on the ethical challenges and necessary oversight in AI, emphasizing transparency and policy controls to mitigate biases.Quote from the show:"Accuracy in AI models builds trust, and that's crucial for their successful deployment. But it's vital to remember that all models have biases; the key lies in identifying and addressing the unfair ones early on." - Ron GreenLinks:LinkedIn: https://www.linkedin.com/in/rongreen/Website: https://www.kungfu.aiPodcast: https://www.kungfu.ai/resources/hidden-layersThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast we are joined by guest David Marra a seasoned expert in AI-driven investment strategies and founder of Marken Asset Management. David brings invaluable insights into the transformative potential of AI, particularly in complex payroll workflows, ERP systems, and financial services.David joins our hosts, Seth Earley and Chris Featherstone, as they explore the current state and future of AI, including the integration of large language models (LLMs) and the importance of robust data management.Key takeaways:AI for Simplifying Workflows: AI, particularly chatbots, can streamline complex payroll tasks, improving productivity by up to 30% and reducing errors.AI in Financial Services: Though slower to adopt AI, financial services have vast potential, offering long-term growth opportunities through AI investments.Global AI & Investment: Japan’s AI market is booming, and companies with strong data infrastructures are best positioned to benefit from AI advancements.Quote from the show:"2023 was the wake-up call for realizing new technology's immense potential, and as we transition pilot programs to production systems in 2024, we’ll start seeing real, tangible outcomes by 2025. The application layer of AI isn’t just a technological enhancement; it’s poised to become a multi-trillion-dollar market, revolutionizing capabilities and creating workflows that weren't even conceivable before." - David MarraLinks:LinkedIn: https://www.linkedin.com/in/david-marra-8693b44/Website: https://markinfunds.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast we are joined by guest Yuri Dvoinos, the Chief Innovation Officer at Aura, a leading cybersecurity company based in Boston. With an extensive background in information security, AI applications, and data privacy, Yuri brings a wealth of knowledge on how to protect personal information in the digital age.Join our hosts Seth Earley and Chris Featherstone as Yuri explains the intricacies of data privacy, the adverse effects of phone scams, and the critical role of AI in detecting and thwarting sophisticated fraud attempts, including deepfakes and phishing scams.Key takeaways:The Importance of Understanding Your Digital Footprint. Learn how your online activities impact your privacy and what steps you can take to protect yourself.Identity Protection and the Impact of Scams. Delve into the psychological toll of phone scams, particularly on seniors, and how to safeguard against such threats.The Role of AI in Scam Detection. Understand how AI can detect psychological pressure and scam tactics in real-time, and the challenges in distinguishing AI-generated chats from real conversations.The Rise of Deepfakes. Explore the dangers of deepfakes in voice and video manipulation, and the need for heightened vigilance.Quote from the show:"Understanding one's digital footprint and protecting personal information online is no longer optional—it's essential. The rise of deepfakes and sophisticated scam tactics means that we need to be more critical and skeptical of the information we encounter. Leveraging AI to detect psychological pressure and scam tactics in real time can help us stay one step ahead, but ultimately, it starts with heightened awareness and constant vigilance." - Yuri DvoinosLinks:LinkedIn: https://www.linkedin.com/in/dvoinos/Website: https://www.aura.comTwitter: https://x.com/YuriyNosThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast guest Tobias Zwingmann, an esteemed analytics and AI expert from Hanover, Germany, brings a wealth of experience from his work with SaaS platforms and consulting, and he shares invaluable insights on the practical intricacies of AI in business.Join our hosts Seth Earley and Chris Featherstone as they discuss with Tobias the importance of business leaders understanding AI, the pitfalls of misleading sales tactics, and the necessity of organizational alignment for successful AI implementation. With topics ranging from data quality to the challenges of adopting generative AI, this episode is a treasure trove of actionable advice for anyone looking to navigate the complex world of artificial intelligence.Key takeaways:The critical need for business leaders to educate themselves on AI and LLMs to ask the right questions and evaluate vendors effectively.The foundational role of good data in AI projects and examples of AI used to rectify data issues.The tendency of software vendors to oversell solutions and the issues arising from improper data formats and organizational misalignment.Challenges presented by fragmented processes, systems, and data in large enterprises and the benefit of small, targeted interventions.The importance of data labeling, taxonomies, ontologies, and metadata in effectively leveraging AI.Misconceptions about AI as pure software and the need to shift mindsets for working with generative AI.Difficulties in scaling generative AI and defining outcomes, leading to missed opportunities for customers.Quote from the show:"Understanding AI requires commitment at the senior level. You need those workshops. You need commitment and understanding because, without alignment, no AI implementation will truly succeed." - Tobias ZwingmannLinks:LinkedIn: https://www.linkedin.com/in/tobias-zwingmann/Website: https://www.rapyd.aiTwitter: https://x.com/ztobiWebsite: newsletter.tobiaszwingmann.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of Earley AI Podcasts, we welcome Bartek Roszak, an expert in artificial intelligence and data science. With a career starting as an equity trader and evolving to lead AI strategy and implementation at STX Next, Bartek brings deep insights into the world of AI-driven trading and data quality improvement.Join hosts Seth Earley and Chris Featherstone as they explore Bartek's experiences, the nuances of deploying AI in trading, and the future potential of generative models.Key takeaways:Understanding the intricacies of modular rag for batch processing and data quality improvement.Insights into the use of AI bots in trading, and a critical view on publicized successful strategies.The importance of validating models to prevent data leakage and overfitting, and monitoring accuracy post-deployment.Challenges and solutions in training generative AI models, including the necessity for human validation.How advanced techniques like re-rankers and embedders enhance the accuracy of large language models (LLMs).The evolving trends in AI, including the hype around newer approaches like prompt engineering and multi-agent strategies.The significance of knowledge architecture and metadata in enriching content embeddings for better outcomes.Quote from the show:"Embracing opportunities across different industries and persevering through rejections is crucial. The field of AI is ever-evolving, and staying adaptable and curious will open doors you never imagined." — Bartek RoszakLinks:LinkedIn: https://www.linkedin.com/in/bartekroszak/Website: https://www.stxnext.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of Earley AI Podcasts, we are joined by Nick Usborne, a veteran copywriter with over four decades of experience and an advocate for the sophisticated use of generative AI in human-centric communication. Nick provides a wealth of insights into the pitfalls and potentials of integrating AI into marketing and how businesses can leverage AI without losing their unique voice.Key Takeaways:Human Connection Matters:The episode opens with Nick Usborne expressing disappointment and loss of trust in a brand due to an impersonal automated onboarding process, emphasizing the need for emotional intelligence in all interactions.Education and Training in AI:Nick stresses the importance of better educating employees and businesses on AI capabilities and limitations.The Sameness Trap:A discussion on the risk of using identical AI models to create content, leading to homogenization and a loss of brand uniqueness.Limitations of AI:Nick elaborates on AI's current inability to genuinely experience emotions and sensory input, stressing the crucial role of human supervision.Structured Prompts for AI:Nick shares methods, like the RACE framework, to optimize AI outputs and ensure relevance and quality.Human Creativity in Marketing:The necessity of human involvement to preserve creativity and innovation in marketing efforts despite the speed and efficiency of AI.Quote from the show:"AI will never replace the human touch in creating meaningful connections and fostering trust. It's a partner, not a tool, and our role is to guide it with emotional intelligence and creativity." – Nick UsborneThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

This episode of the Earley AI Podcasts features Jason Radisson, an expert in digital transformations who has worked with renowned companies such as McKinsey, eBay, and Uber.Tune in as Jason shares his insights on the misconceptions surrounding AI startups and the importance of having a quality team with enterprise experience. He also highlights the agility of startups in implementing new features and integrations, challenging the notion of slow processes.Key takeaways:-AI startups benefit from having a quality team with enterprise experience, allowing them to be agile and quickly implement new features and integrations.- Non-tech companies often struggle to adapt to automation due to cultural barriers and legacy thinking, despite automation not requiring lengthy change management processes.- The gig economy presents challenges in optimizing large workforces, requiring a balance between employer and employee perspectives to create win-win solutions.- Organizations need to actively seek out innovative strategies and technologies to stay competitive, rather than relying on traditional approaches such as enterprise data warehouses and data lakes.Quote from the show:"The common approach of starting with an enterprise data warehouse and data lake is a fallacy. It's crucial to work backwards from customer-first use cases and focus on initiatives that will drive business value. By making quick developments and enabling additional investment, companies can harness the power of AI and machine learning technologies to transform their operations and stay ahead of the curve." - Jason RadissonLinks:LinkedIn: https://www.linkedin.com/in/jason-radisson/Website: https://www.movo.coWays to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-homeApple Podcast: https://podcasts.apple.com/podcast/id1586654770Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/Stitcher: https://www.stitcher.com/show/earley-ai-podcastAmazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcastBuzzsprout: https://earleyai.buzzsprout.com/ Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

With extensive experience in AI and machine learning dating back to 1998, Cohen-Dumani brings valuable insights into the historical and present-day landscape of AI, emphasizing the importance of foundational knowledge, expertise, and knowledge management in making AI work effectively within organizations.Tune in to this enlightening conversation as they discuss the attention and resources that must be invested in unstructured data and knowledge to leverage the full potential of AI.Key takeaways:- A foundational reference architecture is critical for making sense of data and discerning between vendors' aspirational capabilities and reality.- Traditional long-term technology planning is no longer applicable in the age of AI and large language models (LLMs) due to the unpredictable nature of AI's uses and leveraging capabilities.- Executives should personally experiment with AI tools and allow more freedom for workers to adopt AI, rather than stifling innovation.- Building an extensible and expandable data foundation and good enterprise architecture is crucial to avoid data silos and maintain consistency in data.Quote from the show:"I think one of the challenges that organizations have is they're not investing the time, the effort, the money, the resources, and the attention on unstructured data, on knowledge. You know, if you look at any accounting department, they spend inordinate amount of time and resources on numbers, on transactional data. But if you look at how much effort is put on unstructured data, it's night and day. And yet unstructured data is 80+% of the data most organizations have." - Daniel Cohen-DumaniLinks:LinkedIn: https://www.linkedin.com/in/dcohendumani/Website: https://www.withum.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book