co-founder and managing partner of Michigan Software Lab.
Niklas Wirs, a pioneer in the computer science industry, passed away on January 1, 2024 at the age of 89. His achievements stem from the way he simplified complex computer science problems.
In the 1960s, computer languages ​​were often complex and error-prone. Worth responded by creating streamlined languages ​​such as Pascal. In his mind, simplicity was better than complexity because it minimized the chance of being wrong.
Today, artificial intelligence (AI) is becoming more prevalent, but less understood. Four key forces shaping AI are essential to navigating this rapidly evolving landscape. hardware, software, computers, and humans.
Just as Wirth simplified complex computer science problems with a streamlined language, we need to simplify our understanding of AI by breaking down its key components. We want to help you make better decisions about your company and your career through examples of organizations that are leading the way in each of these areas.
1. Hardware
In the world of F1, even the best drivers need top-notch cars to win. Similarly, in AI, powerful hardware is essential to meet the demands of new tools.
Currently available hardware is comparable to VCR technology. However, advances such as DVD players, Blu-ray, and streaming services are on the horizon.
When it comes to hardware, Nvidia is leading the way. The company is known for its graphics processing units (GPUs) and semiconductor products and holds more than 70% of the market share for hardware solutions. The product is so innovative that many companies are willing to wait a year or more for it, even if alternatives are available.
Even though Nvidia has a significant competitive advantage, other players such as AMD, Intel, and startups such as Cerebras Systems are pushing the boundaries of AI-optimized hardware. These processors and systems are designed to handle the specific demands of AI workloads, such as parallel processing and neural network computation.
This hardware environment shapes the AI, with continuous improvements that make the system faster and more powerful, in the same way that well-tuned cars shape F1 racing.
2. Software
Software acts as an interface for humans to interact with hardware tools. This is the same steering wheel that the driver uses to guide the car. This is essential to avoid security, ethical and moral implications of new AI tools. It needs to be user-friendly to simplify tasks and reduce cognitive load. After all, AI is just one way he is making information and data more useful and guiding us to better decisions.
Platforms such as Google’s TensorFlow and Meta’s PyTorch mark the shift to comprehensive ecosystems that support a variety of AI applications, from basic chatbots to advanced neural networks.
In AI development, user-centered design determines which tools succeed and which tools are forgotten. TensorFlow and PyTorch are open source platforms that enable machine learning, computer vision, and natural language processing.
3. Computer
AI is the latest technological trend revolutionizing society, building on previous transformations such as the rise of steam energy, electricity, and information technology. Unlike previous reliance on central processing units (CPUs), computing is now faster.
According to Nvidia, there is currently a staggering $1 trillion invested in data centers around the world, which is expected to double over the next five years. AI has made tremendous advances over the past decade, and computing power will continue to accelerate over the next decade.
Traditionally, computing has operated on a search-based model, where a touch on your phone or a click on your computer prompts your device to retrieve information from a distant data center. But the future of computing will pivot toward both search and production, requiring changes in the way we interact with computers.
Apple’s strategic focus emphasizes this paradigm shift. It canceled previous plans for electric vehicles and is now prioritizing robust generative AI for its next phone models.
Using an F1 analogy, the computer is like the engine that powers the car. Just as an engine propels a car forward, computers drive the processing and decision-making capabilities of her AI system.
4.Human
To use an F1 analogy, humans are, of course, the drivers of the cars. Just as a skilled driver influences the direction and speed of a vehicle, humans play a key role in shaping his AI’s trajectory.
The AI ​​revolution is focused on intelligence production, and humans have the power to shape AI by providing the right data and building the necessary infrastructure. If you need energy, build a power plant or wind turbine park. If you need more food, build more farms. Similarly, organizations and governments are building the necessary infrastructure to support AI.
If you’ve personally used AI to create stories or emails, you might not be impressed by it yet. One of the reasons for this, he said, is that the data is only determined by the data available. However, progress has already begun. For example, OpenAI’s Sora can generate videos and 3D interactive environments.
In this age of AI, anyone can be an engineer. Unlike in the past when programming skills were required, anyone can now contribute to technology development without any coding knowledge. Technical experts remain valuable, but their role has shifted to strategic leadership. Just as only a select few will become world-class drivers, we will need humans who are experts in AI to navigate this transformative landscape.
Five years from now, I hope you remember this article and remember the car analogy. We will live in a very different era in how we interact with technology and how it shapes us. Get into the driver’s seat.
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