Delay in general terms is a solution to the delay that occurs between the onset of a transaction and its completion.
In gaming, for example, the delay is delay between clicking a mouse button, and looking at the results of your competitive action on the screen in real time.
This remedy for performance is also an important part of the AI technology. The best AI model in the world is useless until it can result in timely fashion.
This is especially true when AI is being used in real -time applications, such as customer service or telephone support
Therefore in artificial intelligence systems, the delay reflects the difference between the time when the user begins a request and a response from the system. This delay can be complicated by various various factors.
For example, congestion of your internet connection, processing power of local or cloud computer systems, and even the complexity of request which is being created and the size of the model is being addressed.
All these can affect the speed on which the user will get a response when interacting with the AI model.
Importance of measuring delay
The delay is usually measured in time derivatives of time, such as seconds, milliseconds or nanoseconds.
There are many different aspects of delay which are important in the context of AI. Estimating delay is particularly important, as is the calculation of latency and even network delay.
In any AI environment, the goal is to reduce the delay as much as possible to reduce the delay, in other words to give a response as fast as possible.
A good example of the importance of low delay is in the real -time security environment.
Both face unlock and fingerprint recognition must be distributed near real -time performance if they are useful in safety applications. There are also a few seconds waiting to unlock your phone, or a door is unacceptable to unlaw after a scan.
Low delays are also important for mission-cultural applications such as telemedicine, where slow transmission of significant data from the AI model may result in a result of the terrible failure of an operation.
AI-Adassated Transport, where the model is assigned to identify traffic signals and other road features on an autonomous vehicle, another area where low delay is important.
A second incorrect decision taken due to delay in processing can mean the difference between accident and disaster.
But sometimes it is good at a slow pace
However, not every application requires less delay. For example, complex batch industrial processes, the real -time position on the process is unlikely. In this case, there is a saving of one or two seconds here and is insignificant in it.
Similarly, the application where the human chain is the slowest link in the human chain rarely demands super lower delayed performance.
This is especially true of consumer grade needs such as image or music production, or mobile apps that use AI for entertainment. In these cases, most people can wait a few seconds.
Customization for low delay will usually take two main approaches. Calculate the latcy, which reflects the speed on which a computer operates the nerve network, usually dealt with the power of the computer host.
To throw more memory and processor on the problem.
The second way to combat this problem is to customize the model itself, reducing the complexity and improve its throwput and accountability.
This is often done by fixing a model on a specific, more tightly controlled requirement, so it can react more efficiently for requests in its subject field.
