Machine learning isn’t just for the Googles and Facebooks of the world. But how can startups (or even, growth equity investment firms ) do data science right? Our guest on this episode of the Georgian Impact Podcast is Ji Chao Zhang. He says that data scientist may be the most important job of the 21st century – but also the least understood. Luckily, Ji Chao understands it better than most – he’s the Director of Software Engineering here at Georgian Partners, and he and his team have consulted with scores of companies around our thesis areas including  Applied Artificial Intelligence. You’ll hear about:  Our in-house software development, including our work on TensorFlow Privacy How data scientists differ from data analysts Tips for building your in-house machine learning team Our Machine Learning Maturity Framework Andrew Ng’s AI Transformation Playbook The value of an iterative approach to ML    Who is Ji Chao Zhang? Ji Chao Zhang is Georgian Partners’ Director of Software Engineering and a member of the Georgian Impact team. In that role he leads our internal software engineering efforts and supports portfolio engagements. Prior to joining Georgian Partners, Ji Chao was a Software Development Engineer at Amazon, where he worked on the design and development of the data platform, business analytics and machine learning systems to support supply chain optimization and fulfillment. Ji Chao holds a Master of Computer Science in computer software engineering from the University of Ottawa and a Bachelor of Engineering in computer science from Zhengzhou University.

Episode 116: Level Up with Machine Learning

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.

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When we think about legal tech software, we think about value add discovery or document management. But with the explosion of AI, new opportunities are emerging. We're going to share a story about how technology can help lawyers help more people and you'll hear a word that might surprise you too: Justice.
On this episode of the Georgian Impact Podcast, we'll be talking with the founders of one of Georgian's investments, a fascinating company with an absolutely wonderful name for a company in this space, Darrow. But, it's not the name that matters today. It's about an idea and the coming together of a vision.
You'll Hear About:

The role of AI in legal tech and litigation.
Darrow's mission and approach to justice.
Building class action lawsuits with data and AI.
Addressing data biases and fostering trust.
The significance of Darrow's PlaintiffLink offering.
Darrow's human-centric company culture and social impact.

Who are the Co-Founders of Darrow?
Evyatar Ben Artzi is the Co-Founder and CEO at Darrow.ai. Evyatar harnesses his legal and technological experience to improve legal systems and societies, empowering people to make better decisions and become the authors of their own story. Evyatar assumed leadership roles in the collaborative and dynamic teams he has led and worked with, whether as a Combat Officer in the IDF, a clerk at the Israeli Supreme Court, as a Co-Founder of Yahav – a progressive education program – or as Co-Founder and CEO at Darrow, using AI to unearth the legal implications of real-world events.
Gila Hayat is the Co-Founder and CEO at Darrow.ai. Prior to Darrow, she spent seven years in computer intelligence in the IDF, in part, focusing on classified projects on ethical issues of AI both in the military and police forces. She and her team earned presidential honors for their work.

Redefining legal impact with the team at Darrow

We all get a few chuckles when autocorrect gets something wrong, but there's a lot of time-saving and face-saving value with autocorrect. But do we trust autocorrect? Yeah. We do, even with its errors. Maybe you can use ChatGPT to improve your productivity. Ask it to a cool question and maybe get a decent answer. That's fine. After all, it's just between you and ChatGPT. But, what if you're a software company and you're leveraging these technologies? You could be putting generative AI output in front of your users.
On this episode of the Georgian Impact Podcast, it is time to talk about GenAI and trust. Angeline Yasodhara, an Applied Research Scientist at Georgian, is here to discuss the new world of GenAI.
You'll Hear About:

Differences between closed and open-source large language models (LLMs), advantages and disadvantages of each.
Limitations and biases inherent in LLMs due to their training on Internet data.
Treating LLMs as untrusted users and the need to restrict data access to minimize potential risks.
The continuous learning process of LLMs through reinforcement learning from human feedback.
Ethical issues and biases associated with LLMs, and the challenges of fostering creativity while avoiding misinformation.
Collaboration between AI and security teams to identify and mitigate potential risks associated with LLM applications.

Who is Angelina Yasodhara?
Angeline Yasodhara is an Applied Research Scientist at Georgian, where she collaborates with companies to help accelerate their AI products. With expertise in the ethical and security implications of LLMs, she provides valuable insights into the advantages and challenges of closed vs. open-source LLMs.

Testing LLMs for trust and safety

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:

The progression of software engineering from low-level to high-level languages, culminating in the use of natural language for coding.
The daily integration of generative AI, such as ChatGPT, in automating tasks and writing code.
The role of large language models (LLMs) as an intermediate layer and a translation layer between different pieces of software.
The impact of generative AI in automating functions, unit testing, and API interaction in programming.
Using generative AI to brainstorm and guide game design, showing the versatility of AI in enhancing human creativity.
The potential upside and downside of leveraging generative AI in streamlining processes and increasing efficiency.

Who is Rodrigo Ceballos?
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 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.

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 at Georgian’s R&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:

The role of fine-tuning in tailoring foundational AI models to specific use cases.
How the landscape of ML and AI has evolved with the emergence of LLMs.
Leveraging LLMs to enhance productivity and build enterprise software.
Evolution of skills and talent required in the era of generative AI.
Creating a dynamic data strategy and leveraging open source models for fine-tuning.
Identifying golden use cases and the impact of LLMs on classification tasks.

Who is Rohit Saha?
Rohit Saha is an ML Scientist at 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.

The nitty-gritty of fine-tuning a GenAI model

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:The progression of software engineering from low-level to high-level languages, culminating in the use of natural language for coding.The daily integration of generative AI, such as ChatGPT, in automating tasks and writing code.The role of large language models (LLMs) as an intermediate layer and a translation layer between different pieces of software.The impact of generative AI in automating functions, unit testing, and API interaction in programming.Using generative AI to brainstorm and guide game design, showing the versatility of AI in enhancing human creativity.The potential upside and downside of leveraging generative AI in streamlining processes and increasing efficiency.

Episode 116: Level Up with Machine Learning

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