Georgian

Machine learning is everywhere. It’s a basic foundation for artificial intelligence, it’s driving a fundamental change in the way that programmers approach their work, and it’s so ubiquitous and good, that we often don’t even know when it’s around. In this episode, we share a presentation from Inmar Givoni, director of machine learning at Kindred.ai, in which she shares her insights on the future of machine learning. You'll hear about: -What machine learning is and common applications of it (1:21) -Why we’re in the middle of a machine learning and data revolution (3:54) -What big data is (7:11) -Practical considerations for e-commerce and other domains (8:05) -Data-driven products (16:20) -Future of machine learning (20:25)

Episode 37: The Future of Machine Learning

Sometimes it’s hard to know where to start when it comes to generative AI. It’s not too hyperbolic to say that many different aspects of a business have the potential to be affected by this new technology. Today, we're going to talk about something that's behind the scenes for most people, although hopefully not this audience. It's coding. On this episode of the Georgian Impact Podcast, we dive into the world of generative AI and its impact on coding, testing, and product design with guest Rodrigo Ceballos. Rodrigo is a machine learning engineer at Georgian and provides firsthand experience and expertise, shedding light on the transformative power of AI in the tech industry. Exploring the exciting possibilities brought about by the fusion of human creativity and AI technology. You’ll Hear About:<ul><li>The progression of software engineering from low-level to high-level languages, culminating in the use of natural language for coding.</li><li>The daily integration of generative AI, such as ChatGPT, in automating tasks and writing code.</li><li>The role of large language models (LLMs) as an intermediate layer and a translation layer between different pieces of software.</li><li>The impact of generative AI in automating functions, unit testing, and API interaction in programming.</li><li>Using generative AI to brainstorm and guide game design, showing the versatility of AI in enhancing human creativity.</li><li>The potential upside and downside of leveraging generative AI in streamlining processes and increasing efficiency.</li></ul>

With over six years of experience in AI research and engineering, Rodrigo Ceballos is a dedicated Machine Learning Engineer at Georgian. In his current role, Rodrigo collaborates with portfolio companies to implement cutting-edge solutions in computer vision, natural language processing, and Generative AI. Before joining Georgian, he served as an AI Research Engineer at PAIGE.ai, where he played a pivotal role in developing PaigeProstate, the world's first FDA-approved AI-assisted pathology diagnostic tool.

Rodrigo Ceballos

Machine Learning Engineer

How AI is redefining coding

We've all heard about how generative AI is changing almost every aspect of a business. If you crack open the door and peer in on the AI teams. You'll see them playing with models and, no, we're not talking about planes and trains. We're talking about providing the correct inputs necessary to drive desired outputs in an AI model. On this episode of the Georgian Impact Podcast, we will be discussing the impact of generative AI and fine-tuning data strategy with Rohit Saha, an ML scientist on Georgian's R&amp;D team. Rohit will explore how large language models (LLMs) and fine-tuning are changing the AI landscape for businesses, the necessary skills for data science teams in the age of generative AI, and the pivotal role of dynamic data strategy in leveraging new technology effectively. You’ll Hear About: <ul><li>The role of fine-tuning in tailoring foundational AI models to specific use cases.</li><li>How the landscape of ML and AI has evolved with the emergence of LLMs.</li><li>Leveraging LLMs to enhance productivity and build enterprise software.</li><li>Evolution of skills and talent required in the era of generative AI.</li><li>Creating a dynamic data strategy and leveraging open source models for fine-tuning.</li><li>Identifying golden use cases and the impact of LLMs on classification tasks.</li></ul>

Rohit Saha is an ML Scientist on Georgian's R&D team. He works with the portfolio companies to accelerate their data science roadmap by assisting them in scoping research problems, writing machine learning or AI code, and putting solutions into production. Rohit has worked across various projects, specializing in computer vision, natural language processing, and large language models. His expertise lies in helping companies fine-tune and leverage large language models for enterprise software solutions.

Rohit Saha

Intermediate Applied Research Analyst

The nitty-gritty of fine-tuning a GenAI model

Generative&nbsp;AI is redefining businesses with its capacity to write text, generate code, execute tasks, create images, and more. Gen AI is fundamentally changing how companies have to build their products. This is the first in a series of podcasts featuring our AI team, where they share their experiences on the generative AI work they've already done with more than 20 of our portfolio companies. In this episode, we are joined by two technical leaders of Georgian's R&amp;D team Parinaz Sobhani and David Tingle. Parinaz is the Head of AI at Georgian and David is the team's Engagement Manager for our work with our customers.&nbsp; You’ll Hear About:&nbsp;<ul><li>How generative AI is reshaping businesses by excelling in text, code, task execution, and image generation.</li><li>The importance of a cross-functional collaboration approach and a top-down problem-solving strategy in technology development.</li><li>An overview of the historical focus of Georgian's AI team on data and machine learning.</li><li>Exploration of the starting points for companies, including the use of foundational models from big tech companies and the role of first-party data in differentiation.</li><li>Discussion on the crawl, walk, run stages of generative AI adoption, highlighting the importance of finding a golden use case and the need for a "trust-first" approach for future-proofing.</li></ul>

David Tingle is the Engagement Manager for Georgian’s R&D team. David collaborates with companies to deliver ML-driven product capabilities to market. Working closely with Georgian’s partners all the way from brainstorming to adoption. Prior to Georgian David worked at Google helping support the relationships Google has with some of its largest advertising and cloud computing customers. 

David Tingle

Engagement Manager

Parinaz Sobhani is the Head of AI at Georgian and is responsible for leading the development of machine-learning solutions for growth-stage startup companies. Parinaz holds a Ph.D. from the University of Ottawa. She has more than 10 years of experience in developing and designing new models and algorithms for various artificial intelligence tasks. She has also worked for the National Research Council of Canada. 

Parinaz Sobhani

Head of AI

How Georgian's AI team supports companies in adopting GenAI

On this episode of the Georgian Impact podcast, we are talking to a guest we last had in 2020. Back in 2020, we were talking about AI and marketing and how to use things like automation tools to make our jobs easier. Now in 2023, generative AI tools are basically the biggest topic of conversation right now. So, we're here to break that down with Paul Roetzer. Paul is the author of several books on marketing and AI, including Marketing Artificial Intelligence, and he's the creator of the Marketing AI Conference. You'll Hear About:<ul><li>The evolution of generative AI tools in marketing.</li><li>The role of AI in ideation and strategy, rather than writing.</li><li>The adoption and integration of AI tools into marketing workflows.</li><li>The impact of generative AI on content creation and strategy.</li><li>Building an AI council within organizations.</li><li>Developing responsible AI principles and policies.</li><li>The importance of a moral compass in AI application.</li><li>Transparency and disclaimers for AI usage.</li><li>The future of AI in entrepreneurship, creativity and scientific breakthroughs.</li><li>Addressing fears and concerns related to AI in the workplace.</li></ul>

Paul Roetzer is the Founder and CEO of the Marketing AI Institute, an organization at the forefront of the marketing technology landscape. Alongside his groundbreaking work, he serves as the co-host of the Marketing AI Show Podcast, a platform that provides invaluable insights into the intersection of marketing and AI. Paul is dedicated to empowering organizations with cutting-edge AI strategies.

Paul Roetzer

Founder and CEO, Marketing AI Institute

Adapting teams to GenAI with Marketing AI Institute's Paul Roetzer

On this episode of the Georgian Impact podcast, we’ll be breaking down the technologies that make up generative AI and how it works. From Large Language Models (LLMs) to deep learning, this podcast will help you understand how AI has evolved to get us to this point with GenAI and what he’s excited about in the space. Mahan Salehi, AI and LLM product manager at NVIDIA, will explain how the space has evolved from his experience at NVIDIA working with AI.&nbsp;&nbsp;You’ll Hear About:&nbsp;●&nbsp; 	Machine learning vs. artificial intelligence.●&nbsp; 	The need for guidance for GenAI models.●&nbsp; 	Rules-based models vs. deep learning models.●&nbsp; 	The two pieces of generative AI.●&nbsp; 	How foundational models are trained.●&nbsp; 	The value of first-party data.●&nbsp; 	Mahan looking back and looking forward.●&nbsp; 	The impact on different industries.

Mahan Salehi is a deep learning software product manager at NVIDIA. Before NVIDIA, he was the co-founder and CEO of an AI startup and also previously worked in the medical device industry. He holds an engineering degree from the University of Toronto.

Mahan Salehi

AI and LLM Product Manager at NVIDIA

On this episode of the Georgian Impact podcast, we’ll be breaking down the technologies that make up generative AI and how it works.


An introduction to generative AI with NVIDIA’s Mahan Salehi

Since our inception, Georgian has identified new trends that we believe will change the way companies do business. This kind of thinking resulted in our thesis areas like Applied AI, Conversational AI and Trust. In this episode of the Georgian Impact Podcast, we talk to Sonia Lagourgue, Georgian’s Head of Purpose &amp; ESG and Emily Walsh, Lead Investor at Georgian. They will break down Georgian’s latest thesis area: Product-led Purpose.&nbsp; Simply put, Product-led Purpose is our view that the next generation of market-leading technology companies will not only deliver superior economic value but also a quantifiable positive impact on societal challenges.&nbsp;You’ll Hear About:&nbsp;●&nbsp; 	Georgian’s purpose and what it takes to get the whole team engaged.●&nbsp; 	Where the purpose came from and its ties to Georgian’s history.●&nbsp; 	How Georgian’s Purpose Report holds us accountable.&nbsp;●&nbsp; 	Why Georgian launched a Product-led Purpose thesis and what that really means.●&nbsp; 	How a strong top-level executive focus on purpose can motivate and drive a company forward.●&nbsp; 	The main pillars of the Product-led Purpose thesis.●&nbsp; 	The natural tension between pushing technology and purpose.

Emily Walsh is a Lead Investor at Georgian. Emily oversees company diligence and engages with our portfolio customers in the operational areas of go-to-market, finance and talent.

Emily Walsh

Lead Investor at Georgian

Sonia Lagourgue is the Head of Purpose & ESG at Georgian. She helps develop strategic programs and foster a peer-to-peer community for a private collaboration network of high-growth technology companies and their executive teams.

Sonia Lagourgue

Head of Purpose & ESG at Georgian

Why Georgian launched a Product-led Purpose thesis

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On Georgian's Impact Podcast, we get into the latest tech trends and how they impact growth-stage software companies. Jon talks with folks from around the tech ecosystem at the intersection of business and technology.

Episode 37: The Future of Machine Learning

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