Three leading European generative AI startups joined NVIDIA founder and CEO Jensen Huang this week to talk about the new era of computing.
More than 500 developers, researchers, entrepreneurs and executives from all over Europe and beyond gathered in the Spindler and Klatt, a stylish riverside meeting place in Berlin.
Huang started the reception by tapping the message he delivered Monday at the Berlin Summit for Earth Virtualization Engines (EVE), an international collaboration focused on climate science. She shared details of NVIDIA’s Earth-2 initiative and how accelerated computing, AI-powered simulation, and interactive digital twins are driving climate science research.
Before sitting down for a fireside chat with the founders of the three startups, Huang introduced the audience to some special guests, four of the world’s leading climate modeling scientists, whom he called the unsung heroes of saving the planet.
These scientists have dedicated their careers to advancing climate science, said Huang. With EVE’s vision, they are the architects of the new era of climate science.
Facing formidable forces
There is a huge amount of AI startups in Germany and I am happy to see that, Huang said. You are in a brand new information age and when that happens, everyone is back to square one.
Huang welcomed the founders of Blackshark.ai, Magic and DeepL to the stage. Planetary management, artificial general intelligence or AGI, and language translation are some of the ways startups are using generative AI.
- Blackshark.ai uses artificial intelligence and distributed spatial computing hyperscaling to transform 2D images into data-rich 3D worlds.
- Magic is building an AGI software engineer, enabling small teams to code significantly faster and cheaper.
- DeepL aims to help everyone communicate with everyone else with its AI-powered translation tool.
All three companies make solutions that could be seen as at odds with the products of established companies.
Why did you face such formidable forces? Huang asked the founders.
Blackshark co-founder and CEO Michael Putz shared that the startup product is similar to what you might see in Google Earth.
But Blackshark said its coverage of the planet is 100%, compared to Google Earth’s 20%. And while Google could take a few months to update parts of its map, Blackshark only needs three days, Putz said.
Magic co-founder, CEO and head of AI Eric Steinberger explained how his company is looking to build an AGI AI software engineer who will function as if they were a team of humans.
He said he will remember conversations from months ago and can be sent through an app like any other engineer. Rather than create an alternative to existing solutions, Magic seeks to build something categorically different.
It’s hard to build, but if we get it right, we’re on a level playing field, even against the giants, Steinberger said.
DeepL founder and CEO Jaroslav Kutylowski said his company’s job was initially an intellectual challenge. Could they do better than Google? the team wondered. It seemed funny to Kutylowski.
Intuition, Efficiency and Resilience
Steinberger got a laugh from the audience as he asked Huang about his decision-making process to move NVIDIA forward. You are right, always or almost always. How do you make these decisions before it’s obvious?
That’s a tough question, Huang replied.
Huang talked about the insight that comes from decision-making, saying that, in his case, it comes from life and industrial experience. In the case of NVIDIA, he said it comes from having many ideas in the kitchen at once.
He explained that with the invention of the GPU, the intention was never to replace the CPU, but to make the GPU part of the next big computer by taking a full-stack approach.
With data centers and the cloud, Putz sought advice on the best approach for startups when it comes to computing.
NVIDIA joined the fabled semiconductor industry where very little capital was needed for a factory to funnel resources into R&D teams of 30-50 engineers instead of 500 like a more traditional semiconductor company.
Today, Huang explained, with software generation 2.0, startups can’t spend all of their money on the engineers they need to save some to prototype and refine their software.
And it’s important to use the right tools to get the job done for cost-effective workloads. A CPU might be cheaper than a GPU per instance, but running a workload on a GPU will take 10 times less time, he said.
Kutylowski asked about the most significant challenges NVIDIA and Huang have faced over the company’s 30-year journey.
I go into things with the attitude of, How hard can this be? Well, it turns out it’s super hard, replied Huang. But if someone else can do it, why can’t I?
The answer includes the right attitude, self-confidence, a willingness to learn, and not setting an expectation of perfection from day one, she said. Being resilient as you fail to the point where you ultimately succeed is when you learn, Huang said.
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Image Source : blogs.nvidia.com