How to Integrate Generative A.I. Into Your Business, with Piotr Grudzień

Want to integrate Conversational A.I. ("chatbots") into your business and ensure it's a (profitable!) success? Then today's episode with Quickchat AI co-founder Piotr Grudzień, covering both customer-facing and internal use cases, will be perfect for you.

Piotr:

• Is Co-Founder and CTO of Quickchat AI, a Y Combinator-backed conversation-design platform that lets you quickly deploy and debug A.I. assistants for your business.

• Previously worked as an applied scientist at Microsoft.

• Holds a Master’s in computer engineering from the University of Cambridge.

Today's episode should be accessible to technical and non-technical folks alike.

In this episode, Piotr details:

• What it takes to make a conversational A.I. system successful, whether that A.I. system is externally facing (such as a customer-support agent) or internally facing (such as a subject-matter expert).

• What’s it’s been like working in the fast-developing Large Language Model space over the past several years.

• What his favorite Generative A.I. (foundation model) vendors are.

• What the future of LLMs and Generative A.I. will entail.

• What it takes to succeed as an A.I. entrepreneur.

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

Vicuña, Gorilla, Chatbot Arena and Socially Beneficial LLMs, with Prof. Joey Gonzalez

Vicuna, Gorilla and the Chatbot Arena are all critical elements of the new open-source LLM ecosystem — the extremely knowledgeable and innovative Prof. Joseph Gonzalez is behind all of them. Get the details in today's episode

Joey:
• Is an Associate Professor of Electrical Engineering and Computer Science at the University of California, Berkeley.
• Co-directs the Berkeley RISE Lab, which studies Real-time, Intelligent, Secure and Explainable systems.
• Co-founded Turi (acquired by Apple for $200m) and more recently Aqueduct.
• His research is integral to major software systems including Apache Spark, Ray (for scaling Python ML), GraphLab (a high-level interface for distributed ML) and Clipper (low-latency ML serving).
• His papers—published in top ML journals—have been cited over 24,000 times.
• Developed Berkeley's upper-division data science class, which he now teaches to over 1000 students per semester.

Today’s episode will probably appeal primarily to hands-on data science practitioners but we made an effort to break down technical terms so that anyone who’s interested in staying on top of the latest in open-source Generative A.I. can enjoy the episode.

In it, Prof. Gonzalez details:
• How his headline-grabbing LLM, Vicuña, came to be and how it arose as one of the leading open-source alternatives to ChatGPT.
• How his Chatbot Arena became the leading proving ground for commercial and open-source LLMs alike.
• How his Gorilla project enables open-source LLMs to call APIs making it an open-source alternative to ChatGPT’s powerful plugin functionality.
• The race for longer LLM context windows.
• How both proprietary and open-source LLMs will thrive alongside each other in the coming years.
• His vision for how A.I. will have a massive, positive societal impact over the coming decades.

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

Large Language Model Leaderboards and Benchmarks

Llamas, Alpacas, Koalas, Falcons... there is a veritable zoo of LLMs out there! In today's episode, Caterina Constantinescu breaks down the LLM Leaderboards and evaluation benchmarks to help you pick the right LLM for your use case.

Caterina:
• Is a Principal Data Consultant at GlobalLogic, a full-lifecycle software development services provider with over 25,000 employees worldwide.
• Previously, she worked as a data scientist for financial services and marketing firms.
• Is a key player in data science conferences and Meetups in Scotland.
• Holds a PhD from The University of Edinburgh.

In this episode, Caterina details:
• The best leaderboards (e.g., HELM, Chatbot Arena and the Hugging Face Open LLM Leaderboard) for comparing the quality of both open-source and proprietary Large Language Models (LLMs).
• The advantages and issues associated with LLM evaluation benchmarks (e.g., evaluation dataset contamination is an big issue because the top-performing LLMs are often trained on all the publicly available data they can find... including benchmark-evaluation datasets).

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

A.I. Accelerators: Hardware Specialized for Deep Learning

Today we’ve got an episode dedicated to the hardware we use to train and run A.I. models (particularly LLMs) such as GPUs, TPUs and AWS's Trainium and Inferentia chips. Ron Diamant may be the best guest on earth for this fascinating topic.

Ron:
• Works at Amazon Web Services (AWS) where he is Chief Architect for their A.I. Accelerator chips, which are designed specifically for training (and making inferences with) deep learning models.
• Holds over 200 patents across a broad range of processing hardware, including security chips, compilers and, of course, A.I. accelerators.
• Has been at AWS for nearly nine years – since the acquisition of the Israeli hardware company Annapurna Labs, where he served as an engineer and project manager.
• Holds a Masters in Electrical Engineering from Technion, the Israel Institute of Technology.

Today’s episode is on the technical side but doesn’t assume any particular hardware expertise. It’s primarily targeted at people who train or deploy machine learning models but might be accessible to a broader range of listeners who are curious about how computer hardware works.

In the episode, Ron details: 
• CPUs versus GPUs.
• GPUs versus specialized A.I. Accelerators such as Tensor Processing Units (TPUs) and his own Trainium and Inferentia chips.
• The “AI Flywheel” effect between ML applications and hardware innovations.
• The complex tradeoffs he has to consider when embarking upon a multi-year chip-design project.
• When we get to Large Language Model-scale models with billions of parameters, the various ways we can split up training and inference over our available devices.
• How to get popular ML libraries like PyTorch and TensorFlow to interact optimally with A.I. accelerator chips.

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

How to Catch and Fix Harmful Generative A.I. Output

Today, the A.I. entrepreneur Krishna Gade joins me to detail open-source solutions for overcoming the safety and security issues associated with generative A.I. systems, such as those powered by Large Language Models (LLMs).

The remarkably well-spoken Krishna:
• Is Co-Founder and CEO of Fiddler AI, an observability platform that has raised over $45m in venture capital to build trust in A.I. systems.
• Previously worked as an engineering manager on Facebook’s Newsfeed, as Head of Data Engineering at Pinterest, and as a software engineer at both Twitter and Microsoft.
• Holds a Masters in Computer Science from the University of Minnesota.

In this episode, Krishna details:
• How the LLMs that enable Generative A.I. are prone to inaccurate statements, can be biased against protected groups and are susceptible to exposing private data.
• How these undesirable and even harmful LLM outputs can be identified and remedied with open-source solutions like the Fiddler Auditor that his team has built.

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