This video explores the evolution of Nvidia, a company that has become a global leader in technology and artificial intelligence. It delves into Nvidia's origins in the video gaming industry, highlighting how the company's graphics cards not only revolutionized gaming visuals but also laid the foundations for future technological advancements. Through strategic positioning and partnership, Nvidia has grown alongside the expanding gaming industry into a powerhouse influencing several tech sectors.

Nvidia's mastery of Parallel computing through GPUs offered more than just enhanced gaming experiences. These graphics processing units found relevance in non-gaming industries, improving tasks requiring high levels of computation such as oil exploration, weather mapping, and notably, artificial intelligence applications like crypto mining and machine learning. This innovation put Nvidia hardware at the forefront of Generative AI, amplifying its growth even further with applications in AI systems such as OpenAI's Chat GPT.

Main takeaways from the video:

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Key Vocabularies and Common Phrases:

1. Generative [ˈdʒɛnəˌreɪtɪv] - (adj.) - Capable of producing or creating something.

Generative AI and cats.

2. Parallel computing [ˈpærəlɛl kəmˈpjuːtɪŋ] - (n.) - A type of computation where many calculations or processes are carried out simultaneously.

GPU's excel at something called Parallel computing...

3. Lucrative [ˈluːkrətɪv] - (adj.) - Producing a great deal of profit.

Revenue climbed as the company continued scoring Lucrative deals...

4. Catalyst [ˈkætəˌlɪst] - (n.) - A factor or element that significantly speeds up or activates change.

Which brings us to a Catalyst behind Nvidia's recent extraordinary rise.

5. Neural networks [ˈnjʊərəl ˈnɛtwɜrks] - (n.) - Computer systems modeled on the human brain, used in AI tasks.

The era of deep learning and Neural networks had begun.

6. Diversification [daɪˌvɜrsɪfɪˈkeɪʃən] - (n.) - The process of a business enlarging or varying its range of products or field of operations.

How Nvidia's Diversification into AI data centers...

7. Exhilarating [ɪɡˈzɪlərˌeɪtɪŋ] - (adj.) - Making one feel very happy, animated, or elated.

... glossed over in terms of paleontological accuracy, critics agree it made up for with Exhilarating computer generated reptiles.

8. Synthesize [ˈsɪnθəˌsaɪz] - (v.) - To combine different elements to form a coherent whole.

NVIDIA synthesizes complex gaming needs into solutions for varied tech challenges.

9. Inaugural [ɪˈnɔːɡjʊrəl] - (adj.) - Marking the beginning of an institution, activity, or period of office.

Microsoft's then CEO Bill Gates decloaked his company's Inaugural competitor in the console gaming battlefield.

Nvidia's Journey From Gaming to AI and Beyond

How do you pronounce this word? I've heard this numerous times from the man who founded the company, Jensen Wang. There's no debate it's Nvidia. And yet many think this multi trillion dollar artificial intelligence pioneer is called Nvidia. But it's fitting in a way, because Nvidia, now one of the world's most valuable companies, emerged from an industry also often misunderstood gaming.

The global video game industry is an $189 billion industry that's larger than movies and music combined. Today, Nvidia alone has a market value more than ten times that, eclipsing those of rivals. And that's because Nvidia now says PC graphics cards, the pieces of hardware that render 3d visuals in games, were never intended to be the company's final form.

The story behind that is a reminder to never underestimate the scale of the video game industry or its role influencing some of the worlds biggest companies and their technologies. And its a tale that can be traced back to the time of the dinosaurs.

Fight Nvidia. Spawned during April of 1993 in Silicon Valley, two months before the premiere of Steven Spielberg's Jurassic park. What the blockbuster glossed over in terms of paleontological accuracy, critics agree it made up for with Exhilarating computer generated reptiles. It's a dinosaur. Nvidia is absolutely central to the graphics industry now essentially dominates it.

But when it first got going, it was just one of a dozens of companies that had the same bright ideas. Spielberg used workstations from one of those businesses, Silicon graphics, to bring his dinosaurs to life. But in 1993, Hollywood's need for CGI was just too small for Nvidia.

Initially, they were just looking for a market that was big enough to generate the kind of revenue which would then support the R and D effort and the design effort for their products. And the only market back in the day that really was capable of supporting this kind of an effort washing computer gaming.

This is the Xbox. The public witnessed the first major triumph when in 2001, Microsoft's then CEO Bill Gates decloaked his company's Inaugural competitor in the console gaming battlefield. That's fascinating. That game console was based really upon PC hardware. And Nvidia's big break there was that the graphics chip that was part of that initial console, Washington one of theirs.

It was a vital stepping stone that aligned Nvidia hardware with PCs bought by hardcore gamers keen on upgrading their computer's components, something they couldn't do with consoles. I've observed that gamers might replace their graphics cards every three to five years the better the graphics card, the better the gaming experience in a lot of instances. A lot of these newer games are very very graphically demanding.

Take a look at the difference. This is Halo Combat evolved, one of the flagship games released for the Xbox in 2001. Compare it to this demo of a software development framework called Unreal Engine, used by many modern games. This too is being rendered using an Nvidia chip and side by side. Thats what two decades of progress looks like.

Revenue climbed as the company continued scoring Lucrative deals and shipped graphics cards to major PC manufacturers of the time. But something else happened during those 20 or so years. People started to ask, if these things are so good at transforming code into people and vehicles, could it work the other way around?

GPU's excel at something called Parallel computing, taking a complex problem, dividing it up into small sets of calculations, and solving them in parallel very quickly. So anything that is sort of numerically intensive, anything that requires a lot of say coordinate data, a lot of imaging or mapping, that kind of thing, GPU's are much much better at than the standard option, which would be the central processor from intel.

This made the technology relevant to all kinds of non gaming industries, everything from oil and gas exploration to weather mapping. A lot of the time you see that there are runs on these graphics cards because they have a lot of applications even outside of gaming.

The demand used for chips to mine cryptocurrency is becoming an important driver over the pandemic. People were lining up outside of your local micro center to get the new 30 series Nvidia GPU's because so many people were using them for crypto mining at that time, and they weren't available to people who just wanted to play video games with them.

More recently, companies acquired GPU's en masse for use with artificial intelligence. OpenAI's chat GPT, for instance, uses Nvidia hardware. Which brings us to a Catalyst behind Nvidia's recent extraordinary rise.

Generative AI and cats. More than a decade ago, engineers at Google developed a machine learning system that taught itself what a cat was and how to create an image of one by watching a lot of YouTube videos. The era of deep learning and Neural networks had begun.

When a graphics card is doing what it was designed to do, which is to generate an image, it's doing that, it's creating a picture of a cat. This is where the ears are, this is where the eyes are, this is where the nose is. The cat flicks its head to one side. To look at something. You've got to calculate where that nose is, where those pixels that represent the nose, the eyes, the ears, have moved in the image.

Think about that the opposite way. If you are showing a computer lots and lots of pictures of a cat, what it has to be very good at is recognizing points, creating a pattern, effectively creating the reverse of an image, a mathematical picture of what a cat looks like. So that's what graphics chips are really good at, is building these mathematical constructs of real life things, whether it's sound, whether it's image, whether it's other kinds of things that AI is going to have an influence on.

Nvidia became the leader it is today by advancing technology designed for games, but valuable to disparate industries. It knew far in advance that to succeed, it needed gamers. And today, that's something a lot of technology companies understand and want to take advantage of.

There are 3 billion gamers in the world, and those aren't just the people who are shelling out for a pricey Nvidia or AMD graphics card or a really beautiful PC rig. It's not just the domain of, you know, the kind of person you picture when you picture a gamer anymore. Every company that is a big tech company has some sort of interest in the video game industry right now. Amazon, Google, even Netflix are prime examples of this.

Netflix executives have made pretty clear in the past couple years that they see gaming as a threat for attention and entertainment time. They've cited the game Fortnite as a competitive threat. And this is relevant to Nvidia's story, because it's symptomatic of just how greatly the games industry has evolved over the past decade, and how Nvidia's Diversification into AI data centers, even streaming services, meant there was no existential threat to its bottom line.

There are simply now more gamers, more devices, and more opportunities. A lot of games that were previously designed for consoles and PCs are getting ported over to mobile phones so that they can reach audiences that were previously not accessible to them. We're talking triple A games here, like Capcom's popular Resident Evil series, which have similar graphics on a phone as they do on a console.

People who had never even considered owning a console can now play Call of Duty, can now play League of Legends, and all of these games that are considered hardcore video games, but in a more casual, fun way, on technology that everybody owns. Shopping list.

Nobody looked at the first iPhone in Steve Jobs hand and thought to themselves, one day that thing's going to transform banks and how we get groceries delivered. But here we are, AI is probably going to be the same. That all of this rush to create this infrastructure, that this platform is fantastic, but the end use, the profound effect on the economy.

We just don't know what it is yet. Nvidia's benefiting from the world trying to find out. Gaming's gone from being its main source of revenue to something of a rounding error.

We are large consumers of it because we create a lot of AI ourselves. Because our chips aren't possible to design without AI. Our software is not possible to write without AI. We also use it for an AI foundry. We make AI models for companies that would like to have expertise in doing so.

And so we are a foundry for AI, like TSMC is a foundry for our chips. The video games industry, the focus for Nvidia's earliest innovations and partnerships, is bigger and more accessible than ever, and the company still plays a pivotal role within it.

What's going to be most exciting is seeing what access to advanced AI makes possible for game developers working in Nvidia's world. Maybe it'll even be exciting enough to tempt valve into making half life three we can dream.

Nvidia, Technology, Innovation, Artificial Intelligence, Gaming Industry, Graphics Cards