In today’s episode we explore the topic of AI-powered human augmentation in the physical world. Our guest is Meghan Kennelly, Head of Global Marketing at German Bionic, a European robotics firm that develops and manufactures wearable robotics that help front-line workers move more efficiently and safely. Meghan discusses German Bionic’s smart exoskeleton product, which uses AI to learn and optimize itself to match the particular body movements of individual workers. We also talk about the industries where their products are getting good results, implications for the future front-line workforce, and some interesting uses for telemetric data collected from these smart devices. 

Links:

• https://germanbionic.com/en/
• https://www.linkedin.com/in/meghankennelly/

Today’s conversation with Clint Dunn is a deep dive into arguably the most important business metric out there: customer lifetime value. Clint is the founder of Wilde.ai, an early-stage SaaS startup that delivers customer LTV predictions directly to your data warehouse. In our conversation, Clint explains how having a fine-grained, customer-level understanding of LTV can help businesses make profit maximizing decisions across all major business functions. We also discuss the pros and cons of “warehouse centric” architecture, and how Wilde achieved profitability without building a user interface.

Links:
- https://wilde.ai/

Timestamps:
00:00 Introduction
02:33 Longing to start a company, gained experience.
03:48 Data world challenges, building a repeatable company.
07:36 Integrated data workflow with transparent, adaptable infrastructure.
10:16 Maturity curve, finance team, LTV, profitability, personalization.
16:10 Query on fitting Wyld into marketing and data.
17:58 Phone call discusses human capital limitations in marketing.
20:42 Building content around holistic customer understanding is crucial.
24:00 Managing data inputs for standard retail processes.
29:49 Transparent model with proof of effectiveness.
34:16 Data can be seen as helpful but controlling.
39:23 Data leaders navigating build vs. buy dilemma.
40:23 Unbiased training, DIY versus wild sales, LTV importance.
45:01 Challenges with data modeling and actionability.
47:05 Improving tools, native apps key for growth.

Tune in and gain valuable knowledge about the power of data analytics in shaping the future of businesses. Do not forget to rate or review on your favorite platform!

In today’s episode we explore the current state-of-art in document AI with Andrej Baranovskij, an active open source contributor and founder of Katana ML. Andrej’s work centers using open source AI models to extract structured information from documents, including PDF’s, image files, and more. In our conversation, we discuss how document AI has advanced with the advent of transformer architectures, and increasing use of multi-modal models that combine image recognition capabilities with language understanding. We also talk about Andrej’s vision for Sparrow, his open source project geared toward helping organizations adopt these models more easily.

Links:
- Sparrow: https://github.com/katanaml/sparrow

Timestamps:
00:00 Document AI: evolution, accessibility, and real-world use
05:23 Enterprise expert finds common sense use case.
07:57 Goal: Successful open source product helping companies.
09:47 Advantages of DOM: free, commercial use allowed, key-value data.
15:19 Donut has limitations due to training data.
17:31 Elements automates invoice processing, reduces manual work.
22:20 Paducera groups receipt data exceptionally well with key value pairs.
24:41 Persist data for retrieval and calculate spending patterns.
29:50 Challenging integration with support, but successful.
34:09 AI accessibility for developers, smaller ML models.
35:54 Trend: running ML models locally instead of cloud.
39:08 Adding LLM support with Fast API framework.

Tune in and gain valuable knowledge about the power of data analytics in shaping the future of businesses. Do not forget to rate or review on your favorite platform!

In this episode we do a deep dive on the topic of people analytics with Cole Napper, who serves as Chief Evangelist at Orgnostic, and is also a prolific blogger, podcaster, and thought leader in the field of people analytics. Orgnostic helps companies get value from their people-related data with a suite of analytics tools, as well as integrations with common HR tools and data platforms. In our conversation, we talk about the evolution of people analytics as a profession, and what the tech-enabled people analytics teams of the future might look like.

Links:
- https://directionallycorrectnews.substack.com/

Timestamps:
01:40 Experienced analytics practitioner with an unconventional journey.
04:20 Macro trend: human capital valuation in companies.
08:33 Data points predict outcomes, not creepy surveillance.
10:45 Leaders dissatisfied, new tool for executives. Simplified explanations in plain English.
15:10 Data access for HR: centralized or limited?
16:47 Efficiently integrate and use our data platform.
22:03 Narrow AI models avoid data sharing, lawsuits.
24:16 Chart, explanation, research papers, visualization, learning AI.
29:25 The future of people analytics will change.
30:34 Buying future; complex technologies; absurd to build.
35:14 New questions for people analytics teams.
39:54 Curious about Agnostic's value for clients?
41:54 Culture, financials, talent, labor market...it all matters.
44:53 Startup with heavy investment in product/engineering.

Tune in and gain valuable knowledge about the power of data analytics in shaping the future of businesses. Do not forget to rate or review on your favorite platform!

Our conversation today is with Luke Guerdan, PhD student at Carnegie Mellon’s Human Computer Interaction Institute. Luke’s work examines the safety and validity of data-driven algorithms deployed in high-stakes decision-making settings. During our discussion, Luke shares key insights from his recent research on the topic of data label correctness. We unpack the suprisingly numerous, and often subtle ways that incorrect or inappropriate data labels can undermine machine learning initiatives in the real world.

Links: 
 - https://lukeguerdan.com
 - https://twitter.com/lukeguerdan

Timestamps:
(00:00:00) Introduction
(00:02:03) Excitement over imperfect labels in data science.
(00:04:44) Racially biased algorithm failed to identify medical need.
(00:06:49) Identifying challenges in evaluating predictive models.
(00:10:11) Importance of assessing issue and model performance.
(00:15:51) Customer success interventions impact customer retention predictions.
(00:18:26) Difficulty quantifying differences between customer satisfaction and prediction. Importance of understanding intervention impacts on outcomes.
(00:25:57) Organizational issues in data science projects. People and data literacy matter.
(00:30:54) Considered customer cohorts, varied predictions, human dynamics.
(00:34:49) Avoid complexity, bring assumptions to light.
(00:38:04) Issues in political and organizational communication hinder production. Lack of tools to address data problems.
(00:41:04) Subscribe, rate, contact us for feedback.

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In today’s episode we explore the world of cloud AI services with Jeremy Lambert, VP of Product at Eden AI. Eden AI provides a unified API for its customers to access and pay for a wide range of cloud-based AI services. In our conversation, Jeremy shares how the proliferation of cloud-based AI services are driving down the cost and complexity of deploying AI in the real world. We also discuss how Eden AI makes adoption even easier, to a point where users can build full applications without writing code.

Timestamps:

[00:01:44] Jeremy Lambert, former engineer, joined Eden AI 3-4 years ago. Lambert worked on a data science project for Merck. The founders had ideas for creating products, leading to the development of AI compare as their first platform.

[00:05:08] Developers had to use custom models and pretrained AI APIs to transcribe audio files, testing different providers and analyzing their performance. They decided to build a product to address the issue.

[00:08:05] Benchmark multiple companies, evaluate accuracy, cost, and performance. Simplify API calls and standardize responses. Combine APIs for increased accuracy. Properly evaluate solutions with levelized data.

[00:14:25] Popular platform features: document parsing, invoice extraction, resume parsing, speech transcription, text generation, image generation, and text analysis.

[00:18:17] It is simple to implement the summarization API in software. SaaS platforms offer a variety of features, including AI. Selling directly to companies is not a problem. Automation is easier now without developers using tools like Make, Zapier, or Bubble. Non-technical people in marketing or sales can use these tools for automation instead of relying on software purchases.

[00:22:54] OpenAI has experienced a significant increase in monthly users. They have integrated their models into their own services and added features to enhance prompt efficiency. They have also acknowledged the competition from other companies using GPT and other models like Google's generative AI.

[00:25:37] More people came for AI features, solutions.

[00:29:15] Limited relations with OpenAI, numerous partnerships with other providers.

[00:32:43] Our goal is to be the ultimate AI enabler, providing a platform with the best AI technologies and providers. While some companies may need their own data scientists for proprietary models or custom use cases, AI APIs will increasingly be available for various needs.

[00:36:21] "Don't limit options, use multiple APIs"

[00:39:00] AI is a simple tool for automation.

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In today’s episode, we delve into the topic of LLM-powered customer support with ResiDesk co-founder Arjun Kannan. ResiDesk is a software platform that helps residential property managers communicate with residents more effectively. In our conversation, Arjun explains how he and his team are using OpenAI to automatically understand, categorize, and route tenant requests and complaints. We unpack what makes LLM’s so useful in this context, and discuss some of Arjun’s hard-won lessons from using these models extensively in his day-to-day work. 

In today’s inaugural episode of Still Updating, we’re joined by Trullion co-founder and CEO Isaac Heller to explore how AI is shaping the financial accounting profession. Trullion is an AI-powered platform that simplifies revenue recognition, lease accounting, and auditing workflows. In our conversation, Isaac shares Trullion’s long-term vision of becoming a unified financial “single source of truth”, and sheds light on the technology and team behind Trullion’s AI-powered data extraction features. We also discuss how CFO’s and finance leaders should think about adopting AI, as well as lessons for entrepreneurs hoping to use AI in their products. 

Still Updating is a podcast exploring how modern organizations can gain an advantage using technology, with a general bent toward data and AI related topics. Each episode highlights a unique perspective or story about how technology can be used to solve real problems. Join us as we cut through hype, debunk misconceptions, and uncover hard-to-find nuggets of wisdom amid a rapidly changing technology landscape.