Today we're diving into Model Context Protocol, or MCP – the hot topic taking the AI world by storm in early 2025.
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Hugging Face’s smolagents: Agentic AI in Python Made Easy
Today, we’re diving into Hugging Face’s smolagents – a new development that gives AI models more autonomy. Hugging Face, the open-source AI powerhouse behind technologies like Transformers, has now turned its attention to AI agents – programs where AI models can plan and execute tasks on their own – and their latest library smolagents makes building these agents simpler than ever. In this short episode, I’ll break down what smolagents are, how they work, and why they’re a big deal for developers, businesses, and researchers alike.
Read MoreNoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake
In today's episode (#871), I'm joined by the gifted writer, speaker and ML developer Richmond Alake, who details what NoSQL databases are and why they're ideally suited for A.I. applications.
Richmond:
Is Staff Developer Advocate for AI and Machine Learning at MongoDB, a huge publicly-listed database company with over 5000 employees and over a billion dollars in annual revenue.
With Andrew Ng, he co-developed the DeepLearning.AI course “Prompt Compression and Query Optimization” that has been undertaken by over 13,000 people since its release last year.
Has delivered his courses on Coursera, DataCamp, and O'Reilly.
Authored 200+ technical articles with over a million total views, including as a writer for NVIDIA.
Previously held roles as an ML Architect, Computer Vision Engineer and Web Developer at a range of London-based companies.
Holds a Master’s in computer vision, machine learning and robotics from The University of Surrey in the UK.
Today's episode (filmed in-person at MongoDB's London HQ!) will appeal most to hands-on practitioners like data scientists, ML engineers and software developers, but Richmond does a stellar job of introducing technical concepts so any interested listener should enjoy the episode.
In today’s episode, Richmond details:
How NoSQL databases like MongoDB differ from relational, SQL-style databases.
Why NoSQL databases like MongoDB are particularly well-suited for developing modern A.I. applications, including Agentic A.I. applications.
How Mongo incorporates a native vector database, making it particularly well-suited to RAG (retrieval-augmented generation).
Why 2025 marks the beginning of the "multi-era" that will transform how we build A.I. systems.
His powerful framework for building winning A.I. strategies in today's hyper-competitive landscape.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Ensure AI Agents Are Accurate and Reliable, with Brooke Hopkins
Agentic A.I. is powerful because it has infinite breadth of capability. But this is a double-edged sword: Agents entail great risk and testing their performance is tricky... until now — thanks to Brooke Hopkins, today's guest!
Brooke:
Is Founder & CEO of Coval (YC S24), a San Francisco-based startup that provides a simulation and evaluation platform for A.I. agents. A few days ago, they announced a $3.3m fundraise that includes heavyhitter VCs like General Catalyst, MaC and Y Combinator.
Previously was Tech Lead and Senior Software Engineer at Waymo, where she worked on simulation and evaluation for Waymo’s self-driving cars.
Before that, she was a Software Engineer at Google.
She holds a degree in Computer Science and Mathematics from New York University’s Abu Dhabi campus.
Despite Brooke’s highly technical background, our conversation is largely conceptual and high-level, allowing anyone who’s interested in developing and deploying Agentic A.I. applications to enjoy today’s episode.
In today’s episode, Brooke details:
How simulation and testing best practices inspired by autonomous-vehicle development are being applied by her team at Coval to make A.I. agents useful and trustworthy in the real world.
Why voice agents are poised to be the next major platform shift after mobile, creating entirely new ways to interact with technology.
How companies are using creative strategies like "background overthinkers" to make A.I. agents more robust.
What the rise of A.I. agents means for the future of human work and creativity… indeed, how agents will transform all of society.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Andrew Ng on AI Vision, Agents and Business Value
My guest today is the inimitable Andrew Ng! In his trademark, clear-spoken style, Andrew gives us a glimpse of the Agentic A.I. future, particularly how the coming Vision Agent tsunami will change the world.
I suspect pretty much everyone knows Dr. Ng already, but just in case:
As director of Stanford University's AI Lab, his research group played a key role in the development of deep learning (which led to him to founding the influential Google Brain team) as well as educating millions on machine learning (and leading to him co-founding Coursera).
Is Managing General Partner of AI Fund, a world-leading A.I. venture studio.
Was CEO (is now Executive Chairman) of LandingAI, a computer-vision platform that specializes in domain-specific Large Vision Models (analogous to LLMs for language).
Founded DeepLearning.AI, which provides excellent technical training on ML, deep learning (of course!), generative A.I. and many other associated subjects.
Was co-CEO (as well as co-founder and chairman) of Coursera, which brought online learning from 300 leading universities to over 100 million students.
This episode was recorded live at the ScaleUp:AI conference in New York a few weeks ago. Thanks to George Mathew and Jennifer Jordan for inviting me back to the conference to interview Andrew :)
In today’s, Andrew details:
Why a cheaper A.I. model with smart agentic A.I. workflow might outperform more expensive, more advanced models.
The surprising truth about A.I. API costs that most businesses don't realize.• How Marvin Minsky's "Society of Mind" theory from the 1980s is making an unexpected comeback in modern A.I.
A groundbreaking new way to process visual data that goes beyond traditional computer vision.
Why unstructured data will be the key to A.I.'s next big revolution.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Agentic AI, with Shingai Manjengwa
Today's episode is all about Agentic A.I. — perhaps the hottest topic in A.I. today. Astoundingly intelligent and articulate Shingai Manjengwa couldn't be a better guide for us on this hot topic 🔥
Shingai:
Head of A.I. Education at ChainML, a prestigious startup focused on developing tools for a future powered by A.I. agents.
Founder and former CEO of Fireside Analytics Inc. (developed online data-science courses that have been undertaken by 500,000 unique students).
Previously was Director of Technical Education at the prominent global A.I. research center, the Vector Institute in Toronto.
Holds an MSc in Business Analytics from New York University.
Today’s episode should be equally appealing to hands-on practitioners like data scientists as to folks who generally yearn to stay abreast of the most cutting-edge A.I. techniques.
In today’s episode, Shingai details:
What A.I. agents are.
Why agents are the most exciting, fastest-growing A.I. application today.
How LLMs relate to agentic A.I.
Why multi-agent systems are particularly powerful.
How blockchain technology enables humans to better understand and trust A.I. agents.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.