Deep Learning: What It Is, Why It’s Important, and the World’s Largest Processor

With AI becoming increasingly widespread, it’s become crucial to talk about some of its underlying technologies. Deep learning, perhaps one of the most important ones, is making some incredible advances at an ever-increasing rate.

In case you haven’t heard, developers are expanding AI’s capabilities more than ever. With things like ChatGPT, the field is taking directions that just a few years before we wouldn’t have believed possible.

The more you know about deep learning, the more amazed you will be. So get ready, because in this article, we will be reviewing the current state of this technology, talking about why you should care, and what its future may look like. Let’s get started!

What is Deep Learning?

Experts classify deep learning as a branch of machine learning. They define it as a model that simulates the human brain and tries its best to emulate its processes. In simple terms, deep learning works by processing information in layers called neural networks. The more neural networks working together, the more computationally powerful the brain of the AI interface will be. 

It is mainly linked to AI technologies. Most modern AI applications, services, digital assistants, and coding copilots are powered by a deep learning algorithm of some sort. Therefore, understanding how deep learning works is crucial to understanding, developing, and advancing AI tools.

Before, we mentioned neural networks as something crucial to deep learning systems. Neural networks are state-of-the-art technology made up of massive clusters of computing nodes connected to achieve the target task. Think of how different brain parts join millions of neurons to achieve sight, memory, or other processes.

The visible layers of neural computation are called input and output, a fundamental concept for computer programming. These building blocks, together with some other systems, manage to somewhat replicate a human brain. Specifically, the input is where the neural network gets data, and the output is where we get the final result after processing. 

Deep Learning Methods

In general, deep learning takes enormous amounts of data and returns some output after processing it. Different kinds of processing can occur between input and output, which use varying methods. 

The most common deep learning methods are supervised learning, unsupervised learning, and reinforcement learning. This terminology is taken directly from human education systems. The underlying idea is that if we’re replicating a human brain, we should teach it similarly to how we “teach” humans.

Supervised learning is directed by data experts. In an attempt to teach the AI how a human categorizes objects, they feed categorized datasets as input to a deep learning system. Often, more training means more accurate predictions. 

Unsupervised learning implies that the AI teaches itself, we won’t get too much into the details, using previously designed and learned data patterns. Here, no humans are involved in the process.

Finally, we have reinforcement learning. This approach consists of improving an already learned pattern detection or categorization. To do so, data experts positively reinforce when the AI makes the correct predictions. Think of the way you give treats to a dog when it behaves. Some other experiential learning processes are being developed, but these three approaches are behind the incredible advances we’ve seen in recent years.

Cerebras WSE: The World’s Largest Processor

When it comes to deep learning and AI applications, some companies are ahead of others. While we’ve had supercomputers for a while now, we hadn’t yet seen a super-CPU fully designed with deep learning in mind. This changed with Cerebras, a Canadian company whose main objective is developing and innovating AI processing equipment.

Their battle horse is the superb Cerebras Wafer-Scale Engine (WSE) CPU, which according to them, is the most potent unit developed to date. The WSE Chip is built with 850.000 cores for sparse tensor operations, giving incredibly high bandwidth and performance faster than any other individual machine in the market. A standard CPU, for instance, may have four cores. 

The WSE was specially designed to work with deep learning algorithms. Because of how much computational power it requires, AI development is one of the most challenging jobs for any computer. The WSE chip makes it look effortless. Keep reading to find out how.

Design and Performance

The WSE has a 46,225mm2 area where 850.000 AI cores interconnect over a silicon material. This product uses the largest silicon surface made out of a 300mm diameter wafer to date. Imagine it’s about as big as two human heads.

Cerebras designers shaped WSE like a rectangle, divided into 84 smaller sections of 550m2 each. This architecture ensures optimal communication between all the cores available, granting a better performance than a system of individual chips working together. 

Every node is independent and can be programmed individually, meaning that computer scientists can avoid bottlenecks and other performance issues. Also, the WSE has 20 Petabytes/sec of memory bandwidth. This incredible and ridiculous amount takes its capacity to work with deep learning systems further than any other unit in the market. 

Deep Learning Applications and What the Future Looks

Despite its high complexity and requirements, deep learning is very much present in everyday life. We may not see it, but many applications and computer systems are using AI features to give us better performance. 

For example, digital assistants, which have increased in popularity over the last few years, are primarily based on deep learning techniques of varying complexity. Now, users enjoy a communication dynamic and a realness never seen before in the digital field. 

Some of the most popular AI helpers are Google Assistant, Siri, and Alexa. Anyone who has ever used these will testify that they can sometimes be eerily close to a human. Along the same line, Chatbots are taking the customer service field to another level. Phrasing and interactivity are fundamental to AI services, and of course, deep learning can make an AI interact just like a human.

Additionally, car manufacturing companies have made great investments in developing self-driving cars. The fast and dynamic image recognition that happens in real-time to make self-driving cars work is based entirely on deep learning technologies. Many of the advancements in the field are driven by these developers.

In healthcare, professionals benefit from the marvels of deep learning in image recognition. For example, when you get a scan to identify whether some tissue is tumorous, AI can help identify and classify all possible results, many times more precisely than even trained humans.  

Large Language Models and Cerebra’s WSE

Nowadays, one of the most popular tasks that AI is tackling is Large Language Models, a deep learning branch designed to create responses and interactions in a human-like fashion. OpenAI GPT-4, Deepmind Chinchilla, Meta OPT, and Pythia are some of the most well-known and high-functioning LLM projects. GPT-4 is the LLM behind ChatGPT. 

Using the WSE chip for Large Language Models is incredibly efficient because of the reasons we pointed out before. Having more available resources also means faster, human-like responsiveness. 

Conclusion

We have extensively reviewed the deep learning field: what it is, how it works, and its capabilities. Additionally, we shared information about the world’s largest CPU, how it is built, and what it means to AI science.

What you should take away from this article is that deep learning is a technology that tries to imitate how human brains process information. It is the basis of modern AI systems and is becoming increasingly present in most user-oriented applications. 

Also, with companies like Cerebras leading the way in CPU innovation, the AI technology world ensures it has a solid platform for developing its ideas in future years. We can expect remarkable improvements in deep learning technologies in years to come, and we should all be excited to see how much humanity can advance these sci-fi-like systems.