AI software and applications in Business come with some form of continuous feedback loop. This is necessary for two reasons. First, the training data has to be constantly updated. Second, it is important that the software should also be able to quickly generalize from the training data, so that it can start to make sense of new, or even old, data that it has encountered. This means that not only should the software be able to quickly generalize from past and current inputs, but it should also be able to make mistakes without causing too much damage.
- Feeding of the Data to the User
- Record all the Data and Information collected
So how can this be achieved? There are two ways to achieve this. Firstly, the software could use data fed into it by the user. This type of training data would include anything that the user inputs into the training system. It could include numbers, images, text, sounds, and so on. Ideally, the data would be collected over a wide variety of formats, such as text files, presentations, spreadsheets, audio files, video files and so on.
Alternatively, the training system could simply record all the information that is fed into it. This type of data is less reliable than the data fed in manually, because humans aren’t perfect. Unfortunately, sometimes the information that is captured incorrectly, or isn’t even present at all, can cause the training data to be inaccurate. If you input an image into your training software, for example, and the image is there but the exact location of that spot is not displayed, then you won’t be able to train to the level of accuracy that you would want to. However, if you do capture the information correctly, then you could use it to create training videos that would help improve your learning.
The second way in which the software could generalize is through the use of a recurrent batch. Batch training simply means that the software is designed to make training inputs run over again. In some training systems, this will make the training process repeat infinitely, because all the inputs will be the same. In iata speed learning systems, however, this is impossible. Because the input data is fed in one, continuous piece of information (the video example mentioned above), the process will be much more effective. This is because the program will try to remember each input, and will only make a single change when it has reached its best guess, which means that the subsequent guesses are going to be on the same level as well.
In addition to being able to run through the same training inputs over again, another way in which software can be made more effective is by having more than just one run through of each input. As previously mentioned, this type of training has the advantage of reducing the amount of guesswork, which means that you will be able to train to higher levels more quickly. However, this also means that there are two different sets of inputs being used, which is not necessarily the most effective way to train. In general, the best way to learn is to mix the inputs to find out which is best, and in this case, combining the data shown on the screen with actual live human trainers is probably the most effective.
Conclusion
In order to do this, however, the video and audio should be combined, and the human trainer should be present during the training session. This will ensure that the data that is being fed into the training software is the most appropriate, and the human watching the video will be able to make more of an impact on what is happening. Another benefit of using video and audio is that the video will provide a much better insight into how people learn and will help them understand why certain things may be easier or harder than they first perceive. For example, someone watching a video of a computer repair job may find it difficult to understand that the computer was actually broken. By watching the video alongside the audio, they will be able to make more of an impact and understand that the computer wasn’t actually damaged after all.
