One of the most important annual events in the field of artificial intelligence kicks off this week in Singapore: the International Conference on Learning Representations. As usual, chip giant Nvidia had a major presence at the conference, presenting over 70 research papers from its team. The papers cover topics ranging from generating music to creating 3D-realistic videos, robot training tasks, and the ability to generate multiple large language models at the push of a button. "People often think of Nvidia as a chip company that makes awesome chips, and of course, we're really proud of that," said Bryan Catanzaro, Nvidia's head of applied deep learning research, in an interview with ZDNET. "But the story that I think matters the most is that in order for us to make those awesome chips, we have to do research like this, because this teaches us how to make all of those systems."

We thank all the reviewers for their time. In what follows, reviewer comments are italicized and proceeded by our response in blue. We thank the reviewer for the helpful references. Importantly, we note that the SVM GPU-speedup paper by Catanzaro et al. is for Does that mean there is a trade-off between memory/computation and communication. Probably not appropriate to just report the speedup given the comparison is based on different platforms.

It's funny how things in reality can be so far removed from what we imagined. A classic example of this is how I imagined there to be a horde of scientists at Nvidia HQ hunched over their PCs and all working to train the next generation of Nvidia DLSS algorithms -- between enjoying bouts of Call of Duty with colleagues, of course. But as it turns out that's only part of the story… Yes, there are scientists at Nvidia working on these projects, but doing a large portion of the work in training and developing new DLSS technology for us PC gamers to enjoy is also an AI supercomputer, and it's been doing that non-stop 24/7 for going on six years now. That nugget of information was delivered by Brian Catanzaro, Nvidia's VP of applied deep learning research at CES 2025 in Las Vegas. Catanzaro dropped that gem on stage casually as a throwaway comment while discussing details about DLSS 4. But as it turns out, that reference has been the catalyst for a ton of talk about the topic.

We thank all the reviewers for their time. In what follows, reviewer comments are italicized and proceeded by our response in blue. We thank the reviewer for the helpful references. Importantly, we note that the SVM GPU-speedup paper by Catanzaro et al. is for Does that mean there is a trade-off between memory/computation and communication. Probably not appropriate to just report the speedup given the comparison is based on different platforms.

This approach (Singh and Rizwanullah 2022) to circuit board design eliminates the necessity for manually crafted components, (Barahona et al. 1988) and phylogenetics (Catanzaro thereby providing an ideal means to address problems without et al. 2012). Although general-purpose solvers exist and requiring specific domain knowledge (Lombardi and Milano most CO problems are easy to formulate, in many applications 2018). However, improvement heuristics can be easier of interest getting to the exact optimal solution is NPhard to apply when complex constraints need to be satisfied and and said solvers are extremely inefficient or even impractical may yield better performance than constructive alternatives due to the computational time required to reach optimality when the problem structure is difficult to represent (Zhang (Toth 2000; Colorni et al. 1996). Specialized solvers et al. 2020) or when known improvement operators with and heuristics have been developed over the years for different good properties exist (Bordewich et al. 2008).

Note: This interview was edited and condensed for clarity. As part of our media partnership with Tecton's apply(conf), RTInsights recently had the opportunity to speak with Sarah Catanzaro, General Partner at the venture firm Amplify Partners. The firm has invested in data startups OctoML, Einblick, Hex, among others. Prior to venture capital, she was the Head of Data at Mattermark. She started her career in counterterrorism.

NVIDIA opened the door for enterprises worldwide to develop and deploy large language models (LLM) by enabling them to build their own domain-specific chatbots, personal assistants and other AI applications that understand language with unprecedented levels of subtlety and nuance. The company unveiled the NVIDIA NeMo Megatron framework for training language models with trillions of parameters, the Megatron 530B customizable LLM that can be trained for new domains and languages, and NVIDIA Triton Inference Server with multi-GPU, multinode distributed inference functionality. Combined with NVIDIA DGX systems, these tools provide a production-ready, enterprise-grade solution to simplify the development and deployment of large language models. "Large language models have proven to be flexible and capable, able to answer deep domain questions, translate languages, comprehend and summarize documents, write stories and compute programs, all without specialized training or supervision," said Bryan Catanzaro, vice president of Applied Deep Learning Research at NVIDIA. "Building large language models for new languages and domains is likely the largest supercomputing application yet, and now these capabilities are within reach for the world's enterprises."

NVIDIA has launched a new app you can use to paint life-like landscape images -- even if you have zero artistic skills and a first grader can draw better than you. The new application is called Canvas, and it can turn childlike doodles and sketches into photorealistic landscape images in real time. It's now available for download as a free beta, though you can only use it if your machine is equipped with an NVIDIA RTX GPU. Canvas is powered by the GauGAN AI painting tool, which NVIDIA Research developed and trained using 5 million images. When the company first introduced GauGAN to the world, NVIDIA VP Bryan Catanzaro, described its technology as a "smart paintbrush."

THERE'S AN APOCRYPHAL story about how NVIDIA pivoted from games and graphics hardware to dominate AI chips – and it involves cats. Back in 2010, Bill Dally, now chief scientist at NVIDIA, was having breakfast with a former colleague from Stanford University, the computer scientist Andrew Ng, who was working on a project with Google. "He was trying to find cats on the internet – he didn't put it that way, but that's what he was doing," Dally says. Ng was working at the Google X lab on a project to build a neural network that could learn on its own. The neural network was shown ten million YouTube videos and learned how to pick out human faces, bodies and cats – but to do so accurately, the system required thousands of CPUs (central processing units), the workhorse processors that power computers. "I said, 'I bet we could do it with just a few GPUs,'" Dally says. GPUs (graphics processing units) are specialised for more intense workloads such as 3D rendering – and that makes them better than CPUs at powering AI. Dally turned to Bryan Catanzaro, who now leads deep learning research at NVIDIA, to make it happen.

Stanford Libraries will host the 2nd International Conference on AI for Libraries, Archives, and Museums over three days, December 4, 5 & 6, 2019. The first'Fantastic Futures' conference, which took place in December 2018 at the National Library of Norway in Oslo, initiated a community-focused approach to addressing the challenges and possibilities for libraries, archives, and museums in the era of artificial intelligence. The Stanford conference will expand that charge, adding to the plenary gathering a full day of workshops and a half day'unconference' shaped by the interests of those assembled. Wednesday, December 4, will be a day of plenary sessions to introduce attendees to a range of topics in AI, from the concerns of algorithmic bias and data privacy to the exciting developments in transforming discovery and digital content curation (see the full program). The two keynote addresses reflect Stanford Library's position as an academic center in close proximity to Silicon Valley: Bryan Catanzaro, the Vice President of Applied Deep Learning at Nvidia, will speak to the important contribution he thinks libraries can make in AI.