When it comes to artificial intelligence, there are a lot of terms thrown around interchangeably. However, when you hear about a deep learning training processor, it pays to know the difference from other AI technologies – and how it will benefit your operation.
Two of the terms thrown around often are machine learning and deep learning. However, there are some differences. Machine learning uses mathematical calculations to organize information, and then decides what action to take (if any.) Deep learning processors uses a layered neural network to analyze different types of data, from numbers to even images (like a human brain would.)
In this way, a deep learning training processor learns how to draw conclusions based on the entire dataset. That means there is an element of training. The computer is presented several sources of information relevant to its tasks, so it can produce the best results.
It also relies partially on trial and error to learn: it may make incorrect assumptions, but it will adapt over time to become more accurate. This means it can process new data more readily, and make better predictions.
Inference Draws Conclusions Based On Incomplete Training Data
That’s where deep learning inference comes into the picture. “Inference” as it relates to AI is the ability to make predictions based on previous and current data. This ability to predict doesn’t happen without first using a deep learning training server to “teach” the algorithms to interpret information.
It’s similar to how you would process information after taking a course. You learn the main concepts, and are able to draw strong conclusions from new information based on what you understand about the subject. However, the framework can improve if the outcomes are inaccurate.
While you may never have seen certain information, you’d be able to make sense of it based on what you already know. While a deep learning training processor can draw on its training to make decisions, inference takes it a step further. It can make informed decisions when there are gaps in the training knowledge.
Since data can be presented in different and sometimes obscure ways, deep learning inference can still draw accurate conclusions. Each time the outcome is positive (or negative), the computer “brain” can use it to better judge future situations.
AI Is Helping Several Industries Make Better Decisions
There are several industries that can use both deep learning training processors and inference, as they go hand-in-hand. For example, deep learning can be used to train chat bots used to communicate with customers online. They can also be used in healthcare to recognize and predict health issues based on conditions.
Meanwhile, inference is becoming more popular in the finance world. As new data comes in, deep learning inference can make accurate predictions about the direction of the markets to help investors (and companies) achieve better success.
As market conditions can change quickly – sometimes overnight – so too can inferences based on training data. In fact, if the deep learning inference performs properly, you may see what’s coming that others weren’t ready for.
Learn more about how deep learning processors and inference can give you an edge in business, while saving on the deep learning server price.
