The A.I. and Machine Learning Landscape, with Investor George Mathew

Data Science

Razor-sharp investor George Mathew (of Insight Partners, which has a whopping $100-billion AUM) brings us up to speed on the Machine Learning landscape, with a particular focus on Generative A.I. trends.

George Mathew:

• Is a Managing Director at Insight Partners, an enormous New York-based venture capital and growth equity firm ($100B in assets under management) that has invested in the likes of Twitter, Shopify, and Monday.com.

• Specializes in investing in A.I., ML and data “scale-ups” such as the enterprise database company Databricks, the fast-growing generative A.I. company Jasper, and the popular MLOps platform Weights & Biases.

• Prior to becoming an investor, was a deep operator at fast-growing companies such as Salesforce, SAP, the analytics automation platform Alteryx (where he was President & COO) and the drone-based aerial intelligence platform Kespry (where he was CEO & Chairman).This episode will appeal to technical and non-technical listeners alike — anyone who’d like to be brought up to speed on the current state of the data and machine learning landscape by a razor-sharp expert on the topic.

 

In this episode, George details:

• How sensational generative A.I. models like GPT-4 are bringing about a deluge of opportunity for domain-specific tools and platforms.

• The four layers of the “Generative A.I. Stack” that supports this enormous deluge of new applications.

• How RLHF — reinforcement learning from human feedback — provides an opportunity for you to build your own powerful and defensible models with your proprietary data.

• The new LLMOps field that has emerged to support the suddenly ubiquitous LLMs (Large Language Models), including generative models.

• How investment criteria differ depending on whether the prospective investment is seed stage, venture-capital stage, or growth stage.

• The flywheel that enables the best software companies to scale extremely rapidly.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com

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