Teaching AI to See: A Technical Deep-Dive on Vision Language Models with Will Hardman of Veratai
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Why does multimodal understanding matter for AGI?
Is multimodal understanding in NAI important on the path towards AGI? It's not entirely clear that it is, but some people argue that it is. So one reason that one might want to research these things is to see if by integrating information from different modalities, you obtain another kind of transformational leap. in the ability of a a system to understand the world and to reason about it. I would say in Inverto Commerce, similarly to the way we do. For open source researchers, like the last few months have really seen the arrival of these huge interleaved data sets, which has kind of really jumped the pre-training data set size that's available. I'm kind of amazed that the perceiver resampler works because it feels to me just like tipping the image into a blender, pressing on.
And then somehow when it's finished training, the important features are retained and still there for you.
Hello, Happy New Year, and welcome back to the Cognitive Revolution. Today I'm excited to share an in-depth technical survey covering just about everything you need to know about vision language models, and by extension, how multimodality in AI systems currently tends to work in general. My guest, Will Hardman, is founder of AI advisory firm Veriti, and he's produced an exceptionally detailed overview of how VLMs have evolved, from early vision transformers to Clips pioneering alignment work to today's state of the art architectures like intern VL and Lama three V. We'll examine key architectural decisions like the choice and trade offs between cross attention and self attention approaches, techniques for handling high resolution images and documents, and how evaluation frameworks like MMMU and Blink are revealing both the remarkable progress and the remaining limitations in these systems.
Along the way we dig deep into the technical innovations that have driven progress, from Flamingo's Perceiver Resampler, which reduces the number of visual tokens to a fixed dimensionality for efficient cross attention to To intern VL's dynamic high resolution strategy that segments images into four hundred and forty eight by four hundred and forty eight tiles while still maintaining global context. We also explore how different teams have approached instruction tuning, from Lava's synthetic data generation to the multi-stage pre-training approach pioneered by the Chinese research team behind Quen Viel. Our hope is that this episode gives anyone who isn't already deep in the VLM literature a much better understanding of both how these models work and also how to apply them effectively in the context of application development.
Will spent an estimated forty hours preparing for this episode, and his detailed outline, which is available in the show notes, is probably the most comprehensive reference we've ever shared on this feed. While I have not worked personally with Will outside of the creation of this podcast, the technical depth and attention to detail that he demonstrated in what for him is an extracurricular project was truly outstanding. So if you're looking for AI advisory services and you want someone who truly understands the technology in depth on its own terms, I would definitely encourage you to check out Will and the team at Veriti. Looking ahead, I would love to do more of these in-depth technical surveys, but I really need partners to make them great.
There are so many crucial areas that deserve this kind of treatment, and I just don't have time to go as far in depth as I'd need to to do them on my own. A few topic areas that are of particular interest to me right now include first, recent advances in distributed training. These could democratize access to frontier model development, but also pose fundamental challenges to compute based governance schemes. Next, what should we make of the recent progress from the Chinese AI ecosystem? Are they catching up by training on Western model outputs, or are they developing truly novel capabilities of their own? There's not a strong consensus here, but there is arguably no question more important for US policymakers as we enter twenty twenty five.
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Chapters
8 chapters
1
Why does multimodal understanding matter for AGI?
0:00–18:16
2
How do Vision Transformers work and why are they important?
18:16–50:41
3
What is the CLIP model and how does contrastive training align images and text?
50:41–1:23:56
4
How do modern VLMs like Flamingo, LAVA and InternVL combine vision and language?
1:23:56–2:01:05
5
Why do some vision‑language models use cross‑attention while others use auto‑regressive decoding?
2:01:05–2:25:12
6
What is the hybrid architecture proposed by NVIDIA and how does it combine cross‑attention and self‑attention?
2:25:12–2:56:15
7
How do early‑fusion and late‑fusion designs differ in multimodal models?
2:56:15–3:25:42
8
Which benchmarks (VQA, doc‑VQA, Blink) reveal the current strengths and weaknesses of vision‑language models?
3:25:42–3:50:53
Speakers
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