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11 Feb 2022
Paul Zhao on Democratizing AI - Episode #8
00:55:41
In this episode, Seth and Chris talk with Paul Zhao about making AI more accessible for everyone.
Highlights:
5:00 on being a tech entrepreneur 30:00 after the buy out challenges - what now? 39:00 advice to the non-technical on gaining business value with AI/ML 50:00 on build vs buy
Sean Martin on Practical & Scalable Knowledge Graphs - Episode #9
00:53:56
In this episode, Seth and Chris talk with Sean Martin about the development and practical applications of knowledge graphs.
Highlights:
5:30 – First online sports scoring website launched 9:00 – First forays into semantics applications 13:00 – Getting through scaling issues 16:30 – On needing to build the entire stack for knowledge graphs 18:00 – The business problems that Cambridge Semantics solves 24:45 – Dealing with and making sense of unstructured content 29:30 – Data models for natural language queries 32:00 – About the book “The Rise of the Knowledge Graph” 34:00 – What is an ontology and how does it relate to knowledge graphs 42:30 – What’s next?
Tim Huckaby on the Future of AI and Computer Vision - Episode #10
00:54:47
In this episode, Seth and Chris talk with Tim Huckaby about the his experience as a software developer in the 90s and his take on the future of AI and computer vision.
Highlights:
4:30 Developing software at Microsoft in the 90s 11:00 Hollywood stories 14:30 Leaving Microsoft and building an app dev firm 17:00 Building CNN's "Magic Wall" 22:30 Predictions gone wrong and right 29:00 Pace of change in ML - quantum computing 38:00 Augmented reality and computer vision 48:00 AI and ethics
Mark Anderson on Pattern Discovery Engines - Episode #11
00:56:16
In this episode, Seth and Chris talk with Mark Anderson about the new field of pattern discovery and its impact on AI.
Highlights: 11:45 Path to pattern discovery 17:15 Eliminating the hypothesis and focus on the data with a Y value 30:00 Solving the most challenging problems with pattern discovery 40:00 Making sure this technology is only used for good 42:15 Identifying a COVID test that is 98% effective within minutes 44:30 Pattern recognition processors 48:30 The importance of clean data and making the most of the data you have 52:00 Best use cases for pattern discovery
Jeff Coyle on AI and Content Strategy - Episode #12
01:09:10
In this episode, Seth and Chris talk with Jeff Coyle about how AI is impacting content strategy.
Highlights:
2:55 - Craft beer - on winning the award for a Mexican lager 10:00 - Jeff's path to a passion for SEO 14:30 - On using data to decide what to write about 20:00 - What does Google think you think is important on your site? 25:00 - Publishing low quality content is like a time bomb 36:00 - Existential risk of not using data to drive content decisions 39:00 - If you're not there at the top of the funnel you don't deserve to be at the bottom 41:45 - Owning a content space - what it takes 56:00 - Future of AI's role for content strategy
Stephanie Lemieux on Taxonomy & Information Architecture - Episode #13
01:00:45
In this episode, Seth and Chris talk with Stephanie Lemieux, President of Dovecot Studio about the nuts and bolts of taxonomy and information architecture.
Highlights:
1:42 - Stephanie Lemieux background & Relationship with IA & Taxonomy
7:30 - Complexities & Foundational Problems
15:05 - Graph data and knowledge graphing
22:15 - Dynamic roles within an organization
37:27 - Leveraging weak signals and solutions
48:48 -What's the "excuse case"
50:59 - Value difference between large global and integrated organizations vs. a focused and niche organization
Michael Schrage on Recommendation Engines - Episode #14
00:57:04
In this episode, Seth and Chris talk with Michael Schrage, Fellow at MIT Sloan School's Center for Digital Business and author of the book, "Recommendation Engines."
Highlights:
3:44: checkered past 12:50: covering the overwhelming nature of technology 15:50: why do people make decisions 18:21: Perfect choice-what are the right choice architectures? 24:25 Recommendation engines: The hand you're delt - how the bluff matters 26:15: How do we use tools and technologies to come up with better and more reliable advice? 32:50: Ethical topics 49:06: What impact does good advice have on agency and autonomy 52:30: What Michael is working on next
Jim Iyoob at the intersection of AI and CX - Episode #16
01:00:02
In this episode, Seth and Chris talk with Jim Iyoob about using data and AI to deliver customer experience that matters.
Highlights: 2:25: Our celebrity guest…Named top 20 influential people to follow on twitter and who has been inducted into the CX Hall Of Fame 8:10: Adding value & being a great person….Let’s look at the customer experience through the customer’s lens 17:10: AI as a plug and play solution….myth or reality? 19:10:…the insights and the understanding. The mechanisms and methods to extract more insights than the average bear. 22:55: What goes into setting up controlled vocabularies, architecture, etc…how well are organizations prepared? 27:55: First things first: human intelligence. What is the outcome we want to drive? What is the hypothesis we want to drive? Is the data there to help us make insightful decisions? 34:35: Transparency driving accountability 40:25: What are you seeing at organizations when you go in and start looking at their knowledge bases?
Henrik Hahn on innovation & strategic business development - Episode #17
00:59:52
In this episode, Seth and Chris talk with Henrik Hahn about driving innovation and change in his role as Chief Digital Offer at global chemical specialty giant, Evonik.
Highlights:
12:30 Organizational change and culture science 14:30 Augmented intelligence vs artificial intelligence 20:00 On deciding where to start 29:00 Using data to measure success 39:00 Organizational design 44:00 Dealing with information gatekeepers 48:30 What kinds of tools are getting best traction 52:00 Lessons learned while tackling big change
In this episode, Seth and Chris talk with Scott Taylor, the "Data Whisperer" about telling stories about data management.
Highlights: 2:00 Data Whisperer origin story 9:30 Translating complex dry material into a story that resonates 11:30 Why master data is the most important data and how to help execs understand it 18:15 Bad data + AI = AS (Artificial Stupidity) 22:30 Every system demos perfectly 26:25 Don't say "data quality" 27:30 Definition of digital transformation 32:00 Ugly babies and the reality of bad data 38:30 About the book, "Telling Your Data Story" 99% buzzword free (coupon code in show notes) 47:00 Data management is macro trend agnostic 49:00 What's next - more puppets and dad jokes 52:00 Influencing the next generation of data managers
Steve Stesney on optimizing enterprise knowledge & data - Episode #19
00:48:15
In this episode, Seth and Chris talk with Steve Stesney, Senior Product and Data Practice Lead at Predictive UX.
Highlights:
3:30 What is predictive user experience and how did Steve get there? 5:30 Grasping disambiguation and the move to graph data 18:00 Building trust in the data 19:26 Explaining taxonomy, ontology and knowledge graph to executives 21:30 Connecting UX and knowledge graphs 28:00 Managing the open floodgates once users discover what knowledge graphs can do 36:30 What is "predictive ux" ?
Dr. Mark Maybury on innovation and AI - Episode #20
00:55:19
In this episode, Seth and Chris talk with Dr. Mark Maybury, former CTO with Stanley, Black & Decker.
Highlights: 3:30 Mark's early influences 9:10 What he does with his "spare" time - it is planned 16:30 His experience at Stanley, Black & Decker - making the elephant dance 22:50 Transitioning from analog to mixed reality (physical and digital) 31:00 AI - doing the early foundational work without existing ML systems 40:00 Early development of sentiment and intent analytics 45:00 Future projects including movie project getting students excited about careers in AI in the service of the public good 48:45 Ready Robotics 51:00 Development of a COVID De-activization protocol
Dan Turchin on AI and the Future of Work - Episode #21
00:55:42
In this episode, Seth and Chris talk with Dan Turchin, CEO & Founder of PeopleReign.
Highlights: 4:30 What drives Dan's mission to impact a billion lives at work? 11:15 Disruption and the future of work 17:50 What will happen when people can have a day a week back from automation? 20:00 Work will change more in the next 30 years than in the previous 300 24:30 AI is really still in its infancy - how can we use it for good as it grows up? 29:00 What are the pre-requisites to success with AI? 37:50 How do you sell the business on the need to address their data? 43:00 Choosing use cases to get started with 51:00 What's next?
In this episode, Seth and Chris talk with Peter Voss, Founder, CEO, and Chief Scientist at AGI Innovations & Aigo.ai.
Highlights: 2:58 "Software is quite dumb" 3:51 "What is reality?" 5:00 Coining the phrase "Artificial General Intelligence" - what it means 9:00 On understanding cognition in the deepest terms 11:10 What is consciousness? 15:20 Difference between "Artificial Intelligence" and "Artificial General Intelligence" 19:00 The 3 waves of AI 29:45 What is cognitive architecture? 34:30 Quality of data vs quantity of data 38:00 Practical applications for building personalization systems 39:20 What can organizations do to prepare for AI driven systems? 46:30 One corporate bot or multiple bots? 53:45 Automation should be able to deliver the superior customer experience, not the cheaper second class option
Andy Fitzgerald on IA & Structured Content Design - Episode #23
00:55:58
In this episode, our guest is Andy Fitzgerald and Information Architecture & Content Strategy Consultant.
Highlights: 1:40 - Getting from Ph.D. in English and Literature in information architecture and knowledge graphs 9:23 - Schema.org 14:30 - How can we get search to be like "Google"? 19:00 - The trouble with self-organizing information 20:40 - The KFC debacle in Germany and case for keeping humans in the loop 22:15 - Knowledge graphs and AI 29:35 - Role of linguistics 33:00 - What happens when you don't apply knowledge graphs to AI projects 37:00 - Boutique knowledge graph - UXMethods.org 48:00 - Value of smaller scale knowledge graphs and simplicity
Incentivizing Technology - Juan Sequeda - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 024
00:52:03
Today’s guest is Juan Sequeda, Principal Scientist at data.world and Co-Host of the Catalog & Cocktails Podcast. Juan joins Seth Earley and Chris Featherstone and shares how to understand the problem that you are trying to solve. Juan also discusses how your company's success should be defined differently. Don’t focus on just on saving money to make money. Focus on solving a problem. Juan also shares valuable advice on how understanding who you report to helps you speak the same language.
Takeaways:
Juan believes the market is immature when it comes to what they want or what they think they want. This is where data catalogs become important so that companies can locate information.
From the perspective of the data management world, it’s focused on only technology. The problems that they had been trying to solve 30 years ago continue to be the same problems they’ve been trying to solve.
If you are on the technical side of your business, it is important to understand who you should be reporting to. Understanding this early on will help you tailor information to meet the correct outcome.
Juan’s definition of a knowledge graph is representing real-world concepts and the relationships between those real-world concepts end up forming a graph. The reason why the graph is really valuable is because you can integrate data coming from many diverse sources.
Data Tells the Story - Michelle Zhou - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 025
00:48:55
Today’s guest is Michelle Zhou, Co-Founder and CEO at Juji, Inc. Michelle joins Seth Earley and Chris Featherstone and dives into what proprietary data is and how it can be used correctly. Michelle also discusses the one lesson she has learned is that you have to build a product that can help people. You want to achieve your customers' outcomes, not your outcomes. Be sure to listen in on Michelle giving her advice on how to pick out the golden nuggets in AI data to show a coherent and meaningful summary!
Takeaways:
When Michelle first started with computer science, she wasn't fond of it until she attended Michigan State University where two professors changed her perspective on computers. They gave her the opportunity to work on building graphical user interfaces for power management and worked on projects that dealt with AI data storytelling.
Michelle explains that the AI data storyteller gives a set of data and tasks of the user which then gives the user visual preferences. It also consists of a series of animated data visualization.
During Michelle’s first 15 years of research, she was working on understanding users in a task context. For example, what their tasks are, what they're looking for, what their visual preferences are, and what their verbal preferences were.
Michelle has noticed a lot of students will strive for a degree that their family has done in the past. Michelle says that you don’t always have to follow any degree you don’t want. There are so many unique degrees to pick from.
Michelle believes that transparency drives responsibility and since they have a powerful AI system, she wants to make sure that they use their AI in a responsible way.
The one lesson Michelle has learned is that you really have to build a product that can help people. Make sure to achieve your customers' outcomes and not yours. You don’t want to waste their time.
Quote of the Show:
“I want to really democratize the use of this cutting-edge technology.” (23:41)
Human Cognitive Science - Daniel Faggella - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 026
00:56:22
Today’s guest is Daniel Faggella, Head of Research and CEO at Emerj Technology Research. Dan joins Seth Earley and Chris Featherstone and shares how martial arts influenced him to get into artificial intelligence. Dan also discusses what his experience was like with surveillance technology creation technology. Dan had a machine that could generate the next 10 slides of your desired moving picture. Be sure to listen in on Dan giving his advice on how you should properly use open AI!
Takeaways:
Dan got into artificial intelligence by practicing the martial art, Jujitsu. He started a Jujitsu gym which helped support him when he was in school. Jujitsu helped motivate him and keep his mind balanced.
Dan mentions how generative AI has been starting to bubble up since the spark of ChatGPT. He sees people starting to experiment with social and proposals.
With AI in general, people are looking at junctures within the workflow. Identifying junctures where can push a button will lead to streamlined deliverables.
Generative AI finds the juncture pockets and knows exactly where those settle in.
Dan speculates that people will evolve their use of ChatGPT and structure different FAQs.
Dan believes that one day we'll use Generative AI to create a feedback loop allowing humans to say what's wrong and what's right to train AI systems.
Quote of the Show:
“The dust has yet to settle on the early cluster of those use cases in Generative AI.” (19:06)
Machine Learning and Algorithms - Gordon Hart - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 027
00:50:47
Today’s guest is Gordon Hart, Co-Founder and Head of Product at Kolena. Gordon joins Seth Earley and Chris Featherstone and shares how machine learning algorithms are a challenge from different perspectives. Gordon also discusses the core problem in his company before they turned it around. Be sure to listen to Gordon's advice on how to validate models in order to have a successful product!
Takeaways:
Gordon noticed that developing algorithms internally or buying from other model vendors has really had a constant unexpected model behavior. It made him feel he couldn’t trust the models to behave sensibly.
Gordon started his company because he noticed that time after time, he was getting blindsided. He knew there was a better way to develop models and validate what they were doing.
The key challenge that Gordon and his team ran into was that when you have all the data when they were looking at that one number, they were looking at that aggregate metric computed across their entire benchmark.
Gordon expresses the importance of going through scenarios with your products. He found that when you break down your evaluation into these different scenarios, the test gives you an understanding of how this model improves in the aggregate over previous models and how are the failures distributed.
Testing data is more critical than training data because your testing data is used to determine if your new model has the correct behaviors.
Testing the full pipeline from pre-processing through post-processing rather than testing the model component will oftentimes improve the visibility into how your product is actually going to work when you put it out there.
Quote of the Show:
“Having your evaluation metrics align with the way that your system is going to be evaluated in the field is a key thing that you can do to get a better understanding of ‘is this model better for what I set out to do?’” (22:36)
Human Connection with AI - Michael Todasco - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 028
00:39:33
Today’s guest is Michael Todasco write extensively about Generative AI. Mike joins Seth Earley and Chris Featherstone to discuss all things generative AI and why people should embrace AI. He also shares valuable advice on how to build a better connection with your customers.
Takeaways:
While he was at PayPal, Mike was responsible for innovation and improving employee performance.
Embrace AI. Working with AI will result in better solutions.
It is important for everyone to know what their competitive advantage is and what their end goal is.
One great way to get proprietary information about your customers is to stage a gated experimentation process.
One of Michael's experiments was writing a book using an Excel spreadsheet. He took what was written in Excel and pasted it into ChatGPT to craft 56 different writing genres.
Quote of the Show:
“Your job is not going to be replaced by AI. It's going to be replaced by a human who's using AI.” (08:17)
Artificial Voice Intelligence - Maxim Serebryakov - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 029
00:45:44
Today’s guest is Maxim Serebryakov, Co-Founder and CEO at Sanas. Max joins Seth Earley and Chris Featherstone and shares what influenced him to start his company. Max discusses what it was like to study artificial intelligence at Stanford and how it created a broad perspective on how things work. Max believes if you go above and beyond you can help anyone.
Takeaways:
Max was born in New York, moving back to Russia where his family is from as a child. When he returned to the United States, hearing the accents around him led to the creation of his company, Sanas.
Artificial intelligence shows the limitations of modern-day voice conversion research. You're not just modulating the pitch and tone, you're changing the underlying phonetics that are present within it.
Initially, they chose to deploy Sanas in contact centers and enterprises because speech is very structured.
Sanas helps large companies improve customer service interactions which is crucial to their service.
Quote of the Show:
“We ended up building an algorithm that really doesn't exist in the research world. It's very innovative. It works on the edge, works with clients, and it's very efficient.” (11:02)
The Holy Grail of AI - Alex Babin - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 030
00:48:27
Today’s guest is Alex Babin, Co-Founder and CEO at ZERO Systems. Alex joins Seth Earley and Chris Featherstone to share two of the biggest misconceptions of AI and a new AI metric data tool. Using AI to track data improves performance.
Takeaways:
The biggest misconceptions about AI are:
AI can work out of the box. ChatGPT shows people what it can do but that doesn't mean that AI can do everything.
You can throw data at AI and it will execute your needs perfectly. AI technology is changing, but it hasn’t met this level of expertise yet.
A best practice to avoid these misconceptions of AI is to start from the beginning. Figure out your company's ROI and reconstruct all the steps required.
There's a new layer of metric data that has never existed before, user-generated data or a feedback loop. As you interact with a tool a new type of metadata is born. As you feed more data and information to the tool it creates a data flywheel.
Ontologies don't always overlap to give a full understanding. AI can be a stitching mechanism to join two ontologies that should be communicating. You can use AI to Alex explains how the two ontologies aren’t connected. Fortune1000 companies can use AI to use data more effectively.
Organizations need end-to-end solutions. An enterprise-scale solution doesn't exist right now. They're using fragmented solutions and piecing it together.
Interconnecting data and compartmentalizing it can lead to end-to-end solutions, skilled AI models (SAMs).
AI is an arms race right now. The focus is on making things bigger, faster, and more powerful. Without governance it can be dangerous. We need to collectively figure it out.
Quote of the Show:
“Throwing ChatGPT on top of your problems will not solve it.” (04:51)
It’s All About the Data - Kirk Marple - Earley AI Podcast with Seth Earley & Chris Featherstone - Episode # 031
00:40:30
Today’s guest is Kirk Marple, Technical Founder and CEO at Unstruk Data. Kirk joins Seth Earley and Chris Featherstone to discuss organizing historical data and long-term memory. Kirk emphasizes the importance of organizing data in a manner that allows for seamless integration with novel models and shares valuable advice on understanding data.
Takeaways:
The semantic web serves as a powerful tool for optimizing business applications and data organization.
A prevalent misconception surrounding AI is that individuals need to construct their own models and be data science experts. Advancements unfold at a rapid pace. People need to harness the power of AI and employ it strategically within their business operations.
Data lies at the core of everything. To optimize the utilization of emerging models effectively it is important to organize data in a way that seamlessly integrates with novel models. AI implementation needs to be approached with a practical mindset.
In the last 6-9 months large language models have developed the ability to engage in meaningful conversations with their underlying data. This aspect of interactive communication tends to be overlooked. The focus often leans towards retrieval and entity extraction.
Over the years, people have addressed the issue of non-equalization of data intent through the provision of taxonomies. In the future Kirk anticipates that AI will play a pivotal role in enhancing this process.
The Power of AI - Glenn Gow - Earley AI Podcast with Seth Earley - Episode # 032
00:50:06
Today’s guest is Glenn Gow, CEO of Coaching at The Peak Performance CEO Coach. Glenn joins Seth Earley and shares how people should start leaning into what technology is advancing today. Glenn expresses the importance of learning these new materials to create opportunities for you and your company. Be sure to listen in on Glenn giving his advice on how larger companies should incorporate AI into their business!
Takeaways:
Glenn believes that the enhanced value that Predictive AI and Analytical AI can bring to CEOs can create a crucial aspect of the evolution. By harnessing AI approaches, CEOs can gain insights that can drive decision-making and strategic planning. Glenn advocates for adopting AI methodologies to empower CEOs in navigating the rapidly evolving business landscape.
Glenn created a concept known as "Winner Takes All." This concept is if you excel in AI, both you and your direct competitor will consistently accumulate data about your customers. This resource empowers you to gain insights into your customer base, enabling you to enhance your understanding and knowledge. The stakes are high, as falling behind your competitor could lead to setbacks and missed opportunities.
An example of the vast impact Chat GPT and AI have on our world is Chegg—software designed to provide students with vital information to excel in school. However, when Chat GPT came to light, the AI world dramatically shifted. In a single day, the creation of Chat GPT caused Chegg's stock to plummet by 45%. Today, AI is globally, revolutionizing to assist with education, essay writing, tests, and countless other domains. Its pervasive influence continues to reshape the way we approach and engage with knowledge.
Glenn believes enterprises will find it effortless to gather information about open-source technologies their competitors developed. By integrating the resources into their frameworks and incorporating their data, businesses will gain access to carry out operations within their competitors' organizations that were once beyond their reach. Glenn thinks people should take advantage of these opportunities to safeguard their data.
Glenn describes prompting by taking a large language model and condensing it to a specific area of focus. This act of shrinking allows the model to channel toward a defined domain or subject matter. By honing the model's attention on a particular area, it targets outputs that align with the desired scope. This makes it simple for users to leverage the language model while meeting specific objectives.
Quote of the Show:
“Become good at all the tools that are being made available to us, because that's going to create opportunity for you.” - Glenn Gow
The Secret Power of Collaboration in Data Science - Ben Clinch - Earley AI Podcast with Seth Earley - Episode 33
00:40:32
Today’s guest is Ben Clinch, Head of Information Architecture for BT Group . Ben joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving world of data science in organization.
Takeaways:
An intriguing aspect is the common practice of Large Language Models (LLMs) utilizing generic data models Ben and Seth discuss more effective ways to harness the power of LLMs through specialized data models and organization.
Companies will realize quickly that they cant do any sensible Generative AI without a core of useful referential data to utilize, train and not hallucinate.
If people lean on Generative AI, that accelerates things rapidly, but all it does is deferring knowledge to somebody else's data model.
Some people ask if we really need a data model. Can't we just get an industry standard view and follow that? Do you want to buy an org chart? Do you want to defer how you structure your teams to somebody else's view of how you should? This may be a good starting point, but a terrible ending point.
What is the ROI on data modeling? Think of data as an asset for your organization, and think of people as an asset for your organization. Everybody from the chairman to the guy sweeping the floor understand an org chart. They understand you have to organize your people. Otherwise, there will be involuntary anarchy.
Quote of the Show:
“Taxonomy is a chart of accounts for knowledge" - Seth Earley
Taking Control of your Data: How Knowledge Graphs Help to Optimize your Business
00:51:38
Today’s guest is Doug Kimball, Chief Marketing Officer for Ontotext . Doug joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving world of knowledge graphs and AI.
Takeaways:
Doug Kimball's statement about knowledge graphs being an "add to" and an "enhancement of" data is spot on. In the world of modern data management and analytics, knowledge graphs are a game-changer.
There is a proper way to ask the right questions when communicating with Generative AI models. It is important to include the correct context and parameters.
Knowledge graphs have many applications to a variety of different business models and use cases. Doug mentions an example where a mass migration of population from one place to another could be an opportunity for businesses to track and profit based off of user demographics utilizing knowledge graph practices.
Quote of the Show:
“Knowledge graphs are not a rip and replace, they are an add to/enhancement of" - Doug Kimball
Optimizing Product Data and Harnessing Generative AI - Sanjay Mehta - The Earley AI Podcast with Seth Earley - Episode #035
00:43:05
Today’s guest is Sanjay Mehta, Head of Industry Commerce for LucidWorks. Sanjay joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving hype of generative AI and how it can be applied to your industry.
Takeaways:
Sanjay points out that emerging AI is "not turn key". Maybe from a consumer side but when it comes to B2B there are many hoops to jump through before it's easy and effective.
Data is the lifeblood of modern businesses, and its true potential shines when we connect the dots between customer behaviors, product attributes, and user experiences. At the heart of this transformation is the concept of ingesting good product data into the vector space.
There are many preceded knowledge graphs for certain industries. When you build your index of data it is important to know your users context and application. Using a knowledge base to build your own vector space can be helpful.
Amar Goel on A.I. Tools, Ground Truth in LLMs, the Bito Journey and More - Amar Goel - The Earley AI Podcast with Seth Earley - Episode #036
00:53:30
Today’s guest is Amar Goel, founder of Bito. Amar joins Seth Earley and Chris Featherstone to the discuss the increase in new A.I. tools, LLMs and the journey behind forming Bito! The A.I. assisted software developing tool.
Takeaways:
Converting AI prototypes into reliable, production-ready products is a non-trivial task, often requiring significant effort and expertise.
AI has the potential to assist developers in various ways, from code refactoring to code migration, helping to address issues related to legacy code and modernization.
The cost of running AI models can be significant, and businesses need to consider the expenses involved in deploying AI tools in their products and services.
AI can play a pivotal role in streamlining developer processes, such as enhancing code quality, security, and test coverage, while allowing developers to maintain their creative freedom. However, it's essential to strike a balance between automation and creativity in the development process.
Quote of the Show:
"We don't know what we don't know yet" about AI ethics and privacy, as everyone is learning on the job." - Amar Goel
Enterprise A.I. Strategy, Knowledge Management and more - Rachad Najjar - The Earley AI Podcast with Seth Earley - Episode #037
00:50:10
Today’s guest is Rachad Najjar, working the forefront of innovation in the fields of organizational learning and knowledge management for nearly a decade. Prior to this, he served as a knowledge management advisor for the Dubai Land Department, where he played a pivotal role in achieving the EFQM Excellence Award. Notably, he's also a co-author of a recent book on knowledge management and research innovation, alongside numerous scientific publications in prestigious journals. In his ground breaking thesis, he introduced a framework to configure collaboration for virtual collectives, improving effectiveness across various professional contexts. Rachad joins Seth Earley and Chris Featherstone to the discuss his insights on AI, knowledge management, enterprise strategy implementation and more.
Takeaways:
Seven guiding principles for a successful AI strategy, including a strong business case, process integration, quality training data, continuous supervision, powerful computing infrastructure, and AI and ML skills.
AI governance should involve diverse expertise, including legal, supply chain, project management, and knowledge management.
Focus on how generative AI is adding value in knowledge management and learning, particularly in areas such as customer support, search, learning, and marketing.
Quote of the Show:
"AI models heavily depend on the quality of the training data, so quality in and quality out."
Revolutionizing Data Pipelines, Unifying Metadata, Knowledge Graphs, and Generative AI - Alexander Schober - The Earley AI Podcast with Seth Earley and Chris Featherstone - Episode #038
00:48:31
Our guest this episode is Alexander Schober, a data & AI project owner at Motius. He manages a diverse team of tech experts, focusing on Machine Learning, Knowledge Graphs, and Data Analysis.
Alexander previously worked at Siemens Technology which involved pioneering research in Federated Learning and Self-Supervised Methods for anomaly detection. He used algorithms like Federated Averaging and SimCLR to address data privacy and label sparsity. Alexander joins Seth Earley and Chris Featherstone to the discuss knowledge graphs, metadata modeling for data engineering, using large language models to build data pipelines and more.
For more content related to LLM's and Knowledge Graphs: https://www.earley.com/case-studies
Takeaways:
AI Enhancements with Knowledge Graphs: While not strictly required, knowledge graphs enhance the capabilities of AI, particularly large language models. The ability to provide context and resolve conflicts within the data contributes to more accurate and reliable AI outcomes.
Unified Metadata Model: There's a need for a unified metadata model across different tools and platforms in the data engineering and AI landscape. Disjointed metadata tools can lead to inefficiencies, and efforts should be made to integrate and unify metadata for better collaboration.
AI-Powered Data Pipeline Construction: Large language models can be used to generate data pipelines based on provided metadata. This approach can streamline the data engineering process, ensuring that quality checks, governance attributes, and privacy classifications are integrated into the pipeline.
Quote of the Show:
" All of these things are interconnected. Knowledge graphs, ontologies and semantics. They are all very important."
AI Disruption and Job Replacement, Wealth Gap, and Economic Inequality - Kristina Francis - The Earley AI Podcast with Seth Earley and Chris Featherstone - Episode #039
00:52:13
Our guest this episode is Kristina Francis, a Executive Director at JFFLabs. Jobs for the Future (JFF) is a nationwide nonprofit dedicated to reshaping U.S. education and workforce systems for inclusive economic progress.
Kristina is a experienced professional with a rich background spanning management consulting, software development, engineering, and cybersecurity. She began in database administration at the American Institutes for Research, evolving from an individual contributor to leading a 120-member development team for the Department of Defense. In 2016, a pivotal moment led to a dual career path, involving founding a consulting company, angel investing in women-owned tech ventures, and engaging in workforce opportunities. Currently serving as the Executive Director for JFFLabs at Jobs for the Future, Kristina provides a distinctive perspective on the present and future of workforce and education, emphasizing innovation, disruption, and foresight into the implications of emerging technologies.
Takeaways:
AI has the potential to both disrupt jobs and create new job opportunities, but ensuring access to skills training will be important for workforce development.
Personalized learning and career discovery tools that integrate assessments and map out skills pathways could help more people navigate changing job opportunities.
Addressing systemic barriers and biases will be important to ensure all populations can benefit from new economic opportunities.
Regions and employers can play a role in workforce development through public-private partnerships, on-the-job training programs, and investing in employees' skills.
Quote of the Show:
" How do we get more innovators, school systems, programs, and employers to get on board and provide the support and systems needed so that everyone in our communities is able to discover and navigate through our system to achieve their highest potential? "
Search Optimization, Competitive Advantage, and Balancing Privacy in an AI-Powered Future - Marc Pickren - The Earley AI Podcast with Seth Earley - Episode #040
00:52:02
Mark Pickren currently serves as the President of Next Net Media. With over 25 years of experience as a seasoned entrepreneur and business leader, he possesses expertise in marketing-focused technology companies. Mark has demonstrated a consistent track record of building and managing successful ventures, with leadership experience spanning various industries, including Fintech, SaaS, and Digital Marketing. He has effectively overseen hundred-million-dollar P&Ls at large public corporations and Madison Avenue agencies. Remaining at the forefront of the dynamic digital landscape, Mark consistently delivers innovative solutions for consumers and businesses.
Takeaways:
Organizations need to prepare for around a 25% decline in organic search traffic as search becomes more personalized.
Marketers need to focus on multi-dimensional targeting and providing value to specific customer personas to optimize content for search.
As repetitive tasks are automated, career paths will focus more on managing autonomous agents and leveraging AI effectively.
Large language models pose risks if not properly overseen by humans, and differentiation requires responsible use of proprietary data and knowledge.
Emerging technologies like retrieval-augmented generation will have major impacts on enterprises by improving information access.
Quote of the Show:
"Don't be a cynic. Lean into the better angels of technology, and be part of the solution." (Advice for graduates on how to approach emerging technologies.)
Ian Hook on Advancing Operational Excellence with AI and Knowledge Management - The Earley AI Podcast with Seth Earley - Episode #041
00:51:04
Ian Hook is an exemplary professional whose journey spanned from an early career as a blacksmith and preschool teacher to becoming a seasoned expert in knowledge management and artificial intelligence (AI) at Nevartis. His unorthodox path and hands-on experience have endowed him with a deep understanding of the intricacies of knowledge management and its pivotal role in leveraging generative AI tools efficiently and effectively within operational teams. Ian's significant contributions have led to remarkable operational efficiencies, including an $18 million savings for his company by centralizing market research resources.
Key Takeaways:
- Knowledge management and generative AI are integral to improving the speed and accuracy of issue detection and remediation in operational teams.
- Understanding the lineage and flow of data is vital for data scientists to fulfill their responsibility effectively.
- Ian Hook illustrates the considerable impact of having a centralized knowledge management platform on efficiency and cost savings within a corporate setting.
- The importance of governance in the context of utilizing generative AI is highlighted to mitigate unreliable outcomes due to ungoverned data.
- Knowledge graphs are presented as sophisticated tools that visualize expertise and the relationships between different domains of knowledge.
- The episode explores the limitations of large language models and emphasizes the importance of human oversight to prevent inaccuracies.
Quote of the Show:
"In our quest to harness AI, we must remember that the texture of human knowledge and expertise is the bedrock upon which these systems must be built." - Ian Hook
Trent Fitz on Mastering AI and Data Architecture in IT Organizations - The Earley AI Podcast with Seth Earley - Episode #042
00:46:22
Trent Fitz holds over 20 years of experience in the tech industry. Currently a C-level Product Strategy and Technical Marketing Leader at Zenoss. He is an expert in global marketing, product strategy, business development in cloud computing, cybersecurity, and AI. Repeatedly proving his effectiveness in the industry by leading solutions to projects in innovative company’s such as IBM, Sailpoint, Trustwave and other various startups.
Key takeaways:
- APM tools such as Dynatrace, AppDynamics, and New Relic are key, and their integration has been aided by standards like open telemetry.
- AI governance is crucial on technical, business process, and enterprise strategy levels.
- The maturity models for AIOPs involve governance, decision making, and data/information architecture.
- There is a general lack of appreciation for data and content within IT organizations.
- AIOPs includes machine learning, and there's a need to educate about structured data and AI capabilities.
Quote of the show:
"At the core of AIOPs lies a fundamental need to not just visualize but truly understand the staggering complexity of modern IT environments. It's not just about piles of data or sophisticated algorithms; it's about cultivating a genuine appreciation for the significance of that data and how we can harness it to drive smarter, more proactive operations." — Trent Fitz
Thomas Blumer on The Role of AI in Business Decision-Making and Governance - The Earley AI Podcast with Seth Earley - Episode #043
00:55:28
Thomas Blumer is a renowned expert in AI-driven transformation with extensive experience in implementing groundbreaking artificial intelligence and knowledge strategies within complex business environments. Echoing a profound understanding of metrics-driven governance of AI systems, Thomas has made significant strides in aligning AI applications with overarching business goals. As a strategic advisor and consultant, he has facilitated diverse organizations in their journey to integrate AI to optimize efficiency, enhance user experiences, and drive actionable business outcomes. His expertise is instrumental in developing robust AI governance frameworks that ensure data, algorithms, and knowledge are in strict adherence to driving value and enterprise strategy.
Key takeaways:
- Defining and measuring KPIs tailored to customer and user lifecycle is crucial to drive business outcomes with AI and knowledge systems.
- The transition from proof of concept to proof of value in AI implementations often encounters hurdles due to artificial environments and upstream data issues.
- AI's implementation should focus on improving specific tasks and processes, ensuring tangible improvements rather than the technology's mere presence.
- Storytelling and emotional resonance play a pivotal role when data alone does not suffice in persuading stakeholders about AI initiatives.
- Governance structures need to strike a balance between centralized standards and decentralized, data-driven decision making.
- Large language models have brought about a revolution in accessing corporate knowledge and productivity, highlighting the need for responsible usage.
Quote of the show:
"Bringing AI into the fold isn't just about technology; it’s about shaping an ecosystem that thrives on data integrity, governance, and context to create impactful narratives." - Thomas Blumer
Accelerating AI Adoption: How Manish Sharma Sees Information Architecture Evolving - The Earley AI Podcast with Seth Earley - Episode #044
00:46:25
Manish Sharma is the co-founder of Resolve AI. With a rich history spanning over two decades in the technology industry, Manish offers profound perspectives on the intersection of artificial intelligence, information architecture, and knowledge management.
Tune in as Manish dissects popular AI myths, underscores the importance of bridging the technological gap, and emphasizes the need for robust security measures in today's digital landscape.
Key takeaways:
- When implementing AI solutions like large language models, CISOs should ask questions around data security, access controls, model guarantees, and emerging risks like prompt hacking to properly manage risks.
- Information architecture is critical for data privacy, security, and ensuring AI systems can only access appropriate data sources and provide the right information to different user groups.
- Retrieval augmented generation using a knowledge graph or index is important to avoid hallucinations and ensure AI systems can only respond based on curated data sources.
- Scripted responses may be needed in some cases like legal to provide verbatim answers instead of generated responses.
- User personas and metadata are important to ensure AI systems understand the context and privileges of different user groups to provide appropriate and non-confusing information.
- When integrating AI solutions with knowledge repositories like SharePoint, only curated subsets should be connected instead of entire repositories, and information should be properly tagged and structured.
Quote of the show:
"A key to successful AI integration is not just in understanding the technology itself but in grasping the nuances of user needs, processes, content, and knowledge that remains timeless, no matter the advancements in tech. Coming to grips with that is where the real value lies." - Manish Sharma
Lief Erickson Navigates the Complexities of Content Architecture with AI - The Earley AI Podcast with Seth Earley - Episode #045
00:47:55
Lief Erickson brings expertise in technical writing and content strategy consultation. Having steered numerous organizations through the integration of AI and coherent information architecture, making the complex accessible. With a voice of authority in AI and content management the EIS Podcast is thrilled to have him on the show.
Tune in to this episode for a comprehensive understanding of how structured content and precise prompt engineering are pivotal to leveraging AI in the realm of content creation and management.
Key Takeaways:
- Large language models (LLMs) require clear prompts and structured content to produce accurate and trustworthy responses.
- The importance of structured content in enabling effective retrieval and utilization by generative AI, akin to finding a book in a library.
- Misconceptions about generative AI’s capabilities in content management, highlighting the need for careful curation and validation.
- Real-world applications of AI that can help increase brand loyalty, efficiency, reduce support calls, manage risk, and boost revenue.
- The emerging role of prompt engineering and its significance in ensuring the relevance and accuracy of AI-generated content.
- Legal and ethical considerations in using AI for content creation, with insights on copyright and the ownership issues surrounding machine-generated content.
Quote of the Show:
"Understanding structured content is like understanding the blueprint of a building—it's what allows us to scale and architect information in ways that align with our strategic goals." - Lief Erickson
Erdem Özcan on the Future of Neurosymbolic AI - The Earley AI Podcast with Seth Earley - Episode #046
00:49:52
Erdem Özcan is an esteemed expert with a rich background in computer science, focusing on innovations in AI. With a PhD in computer science and significant industry experience, including work on IBM's Watson and at Elemental Cognition, Dr. Özcan has been at the forefront of blending symbolic AI and deep learning systems. Today, he is actively engaged in developing solutions that enhance the reliability and explainability of AI applications.
Tune in to this enlightening conversation and gain deeper insights into the future trajectories and current challenges within the world of artificial intelligence as explained by one of the leading thinkers in the field.
Key takeaways:
- Symbolic vs. Statistical AI: Erdem discusses the critical differences and applications of symbolic AI versus statistical methods, emphasizing the need for reliably representing concepts for efficient AI outcomes.
- The Role of Cogent English: Insight into how Cogent, a platform developed by Erdem, assists in translating complex business knowledge into APIs and conversational interfaces using a subset of English tailored for formal reasoning.
- Challenges in Generative AI: Exploration of issues that arise with generative AI, particularly around reliability and the operational deployment of reasoning systems.
- Development of Neurosymbolic AI: Erdem predicts a significant shift towards hybrid AI architectures that combine both symbolic and deep learning approaches to handle real-life complex scenarios more efficiently.
- Importance of Explainability in AI: A discussion on why explainability and the ability to audit AI decisions are crucial, especially as AI systems become more integrated into critical decision-making processes.
- Comparison of Formal Reasoning Systems and LLMs: Erdem explains why formal reasoning systems can be more reliable than large language models (LLMs) in complex problem-solving scenarios.
Quote from the show:
"Translating human expertise into AI systems is not just about feeding data into algorithms. It’s about creating structures that allow machines to reason and make decisions transparently and reliably." – Erdem Özcan
Moritz Müller on Structuring Content for Enhanced Language Model Capabilities - The Earley AI Podcast with Seth Earley - Episode #047
00:51:28
Seth Earley sits down with Moritz Müller, a distinguished figure with a rich background in consulting and a leader in artificial intelligence applications. Before carving out his niche at Squirrel AI, Moritz Müller honed his skills at a prestigious consulting firm in Switzerland and spearheaded an ambitious venture by setting up an office in Singapore.
As the head of product management at Squirrel, Moritz brings a wealth of experience from digital transformation programs and a deep understanding of AI technologies across various industries. His insights into the burgeoning world of retrieval augmented generation (RAG) and large language models (LLMs) are second to none, offering listeners an in-depth look at the future of information access and management.
Moritz brings his expertise full-circle by stressing the importance of metadata, vector similarity searches, and the need for ongoing maintenance of knowledge bases to ensure that emerging technologies truly enhance our search capabilities and knowledge utilization.
Key Takeaways
- A thorough exploration of retrieval augmented generation and how it's poised to reshape data handling in the digital era.
- The importance of ACLs, knowledge graphs, digital body language, and conversational search for personalizing organizational data access.
- The need for supervised fine-tuning of LLMs to ensure relevance and accuracy in data retrieval processes.
- How to troubleshoot LLM errors and why successful information retrieval is critical for the effective implementation of RAG.
- The ongoing challenges and considerations in using AI for effective document search and retrieval within organizations.
- The significance of structuring content, tailoring prompts, and understanding the user context to harness the full potential of language models.
Quote from the show:
"The pairing of information retrieval technology with large language models isn't just a minor improvement; it's a revolutionary step forward. It's about reaching into that vast ocean of data and pulling out the exact details you need – that's the game-changer." - Moritz Müller
The Power of Visualization in AI: From Compliance to Finance with Bob Levy - The Earley AI Podcast with Seth Earley - Episode #048
00:48:36
Seth Earley and Chris Featherstone are joined by special guest Bob Levy. Bob Levy, Founder and CEO of Immersion Analytics, brings a wealth of experience in technology and data visualization, having worked with top companies such as IBM, Rational Software, and Mathworks. He shares his profound insights on integrating multidimensional visualization technology using virtual and augmented reality to tackle complex data challenges.
Bob Levy is Founder & CEO of Immersion Analytics. With extensive experience in R&D and product management at companies like IBM and Rational Software. Bob is an expert in AI and data visualization. He's been a speaker at prestigious events like MIT Technology Review’s EmTech Caribbean and Reilly Strata Data Conference, and has won competitions like MIT’s Reality Virtually hackathon and Tableau’s DataDev Competition.
Key Takeaways:
- Examples of how visualization tools help investors make more informed decisions based on a multitude of data attributes.
- The transformative potential of VR and AR in business settings and educational environments, backed by partnerships with tech giants like Microsoft and Apple.
- Visualization technology as a tool for simplifying the understanding of AI-related compliance and emerging standards.
- The discussion on the lack of global compliance standards and the need for potential new standards or refinement of existing ones.
- Use cases in derivatives trading, financial performance metrics, and real-time pricing data for detecting anomalies and opportunities.
- Innovative ways to visualize artificial neural networks and understand the training processes via VR.
- Visualization tools for web and enterprise-level applications, including programming languages and hardware requirements.
- The crucial role of visualization in making AI systems comprehensible to non-technical stakeholders like regulators.
Quote of the Show:
"Seeing all the data points and complexity is crucial for understanding the true nature of the data and avoiding misinterpretation." - Bob Levy
AI's Transformative Power and Its Limitations with Alex Gurbych - The Earley AI Podcast with Seth Earley - Episode #049
00:46:04
A tech innovator with profound expertise in AI and its applications, our guest Alex Gurbych joins us with rich insights into how AI integrates into varying industries. Alex has extensive experience addressing the challenges and potentials of AI, especially in legacy systems and organizations.
Tune in to this enlightening conversation as they dissect how AI can be aligned with business value, explore the nuances of AI consciousness, and discuss the necessity of thinking like a data scientist in today's fast-evolving tech landscape.
Key takeaways: - The Overestimation of AI: Alex Gurbych tackled the common hype surrounding AI capabilities and stressed the importance of understanding its limitations and training.
- Challenges in AI Integration: The hurdles encountered when integrating AI into legacy systems and the necessity of defining clear use cases for practical applications were discussed.
- AI in Healthcare and Biosciences: The role of AI in drug design and development, protein folding, and target discovery was dissected with a focus on both its potential and limitations.
- AI and Consciousness: A fascinating exploration of whether AI can achieve consciousness ensued, with thoughts on the complexity of the human brain and AI's rapid evolution.
- Behavioral Change and AI Adoption: The discussion highlighted a case of AI adoption success among technicians versus challenges faced by doctors, showcasing the importance of behavior change driven by AI use cases.
- The Role of Data Scientists: The critical investment in data scientists and the growing necessity for everyone to adopt a data scientist's mindset were underscored.
Quote from the show:
"The biggest challenge is really to define your use case. What do you actually want to get out of AI? And when you have a very crisp definition of that, then it's much easier to actually make it work. But if you just say, oh, I want to use AI because everybody's using AI and it's a hot topic, then it's not going to help you much." - Alex Gurbych
AI, Knowledge Management, and Navigating the Hype with Daniel Cohen-Dumani - The Earley AI Podcast with Seth Earley - Episode #050
00:44:30
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-Dumani
The Earley AI Podcast with Seth Earley - Episode #51 -Transforming the Workplace: Insights from Jason Radisson on AI and Gig Economy
00:45:35
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 Radisson
The Earley AI Podcast with Seth Earley - Episode #52- Human Touch in AI: Nick Usborne on Emotional Intelligence in Marketing
00:38:31
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 Usborne
The Earley AI Podcast with Seth Earley - Episode #54 Demystifying AI for Business Leaders: Insights from Tobias Zwingmann
00:46:16
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 Zwingmann
The Earley AI Podcast with Seth Earley - Episode #53- Insights into Data Science and AI Validation from Expert Bartek Roszak
00:33:40
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 Roszak
Chris Featherstone Talks About Building Omnichannel Virtual Assistants - Episode 1
00:57:07
Many companies are turning to chatbots and virtual assistants to improve customer experience and increase operational efficiency. In the past text and voice channels were distinct. Now, tools and technologies are emerging to support omnichannel virtual assistants that seamlessly blend text and voice. However, voice and text interactions are quite different and have specialized design requirements.
In this episode, Chris and Seth talk about core principles for building chat and voice assistants and review key considerations for each channel.
How true intelligent virtual assistants (IVA’s) differ from chatbots
Mike Kaput On How To Get Value From Out-Of-The-Box AI Applications - Episode 2
00:51:14
In this episode, Seth and Chris talk with Mike Kaput, Chief Content Officer at Marketing AI Institute, about how out-of-the-box AI is providing immediate value to businesses of all types.
Highlights: 4:04 Mike's day to day & background 11:30 Use cases & Planning 28:02 Ethics & Bias 36:45 Where do you start with AI in marketing? 40:00 Differentiation vs Standardization 43:30 Barriers to entry 47:35 Humans in the AI loop
Massood Zarrabian on Innovation in Enterprise Search with AI - Episode #3
00:58:40
In this episode, Seth and Chris talk with Massood Zarrabian, CEO at BA Insight about how enterprise search is evolving - getting better (bringing greater value) and costing less.
Highlights: 1:15 Massood's road from a Civil Engineering degree from MIT to BA Insight. 5:00 Massood's philosophy on growing teams and companies - and how theater has influenced him 8:25 Can enterprise search be like Google? 13:30 Unstructured vs structured data 17:45 The maturing of enterprise search 26:00 The last mile in enterprise search 32:00 Building, maintaining, training your Index 39:00 Bots and users don't care where the content lives 42:30 Role of an information reference architecture 49:00 Role of automation for tagging 52:30 The future of enterprise search
Adam Sutherland on AI and Personalization in Media & Entertainment - Episode #4
00:47:23
In this episode, Seth and Chris talk with Adam Sutherland about AI and machine learning in media and content.
Highlights: 2:20 Adam talks his journey from Asian studies to Amazon. 12:00 A day in Adam's life and cool problems customers are solving 17:30 Why we still can't find what we want on streaming services 21:00 Biggest barrier to entry to AI enhanced solutions 24:00 Best practices for tagging media assets 25:00 When developing a bespoke model is the right decision 28:30 AI isn't perfect but sometimes that's fine 30:00 Personalization and recommendation engines 34:30 Data lakes plus content metadata 37:00 Emerging trends and techniques (really cool AI stuff in media) 42:45 Predictions for the future
Linda Andersson on AI Powered Semantic Search - Episode #5
00:48:53
In this episode, Seth and Chris talk with Linda Andersson, Founder & CEO of Artificial Researcher about AI powered semantic search.
Highlights: 5:00 - Linda's journey to her work 14:20 - Domain knowledge and ontologies 17:07 - Knowledge extraction 20:05 - Why we need ontologies 20:50 - Bias and not knowing what you don't know 25:40 - Structuring and curating the knowledge base 30:00 - Supervised vs semi-supervised models 38:15 - What is Academia missing 43:30 - Getting the right start for AI projects
Paul Lasserre on AI & Customer Experience - Episode #7
00:54:28
In this episode, Seth and Chris talk with Paul Lasserre about his experience developing applied AI applications.
Highlights: 4:15 Link between AI and chasing pirates in the Navy 7:00 On getting into customer experience as a ML problem 10:00 On the challenges of internally selling new ideas 15:00 On measuring success 18:30 On rules vs machine learning 26:30 Creating a better customer experience 28:00 On leaving Genesys and moving to AWS 35:30 How to give people the support they want 42:00 What he's working on now and what's next
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