Suppose we can say that Artificial Intelligence (AI) has been transforming the business world in recent years by offering companies benefits such as maximum productivity, greater scalability, and less cost by automating processes.
What can we say about the pre-trained AI systems that were developed to reduce the training time of algorithms, optimize operations with data and facilitate use in organizations?
Despite social concerns about the use of AI, involving ethical and moral issues, such as the black box problem, the use of Artificial Intelligence is only likely to grow in the coming years.
The idea, in general, of pre-trained artificial intelligence, is to improve the understanding of human language and intelligent machine learning, using deep neural networks, known as deep learning.
This way, through training, it is possible to understand patterns and meet human requests based on information already used. Pre-training allows the model to operate in a supervised manner, using a large amount of data on specific topics for its learning.
We can say that pre-trained artificial intelligence relies on an existing system created to solve a similar problem. In this way, a pre-trained model tends to be more accurate in its applications, saving enormous efforts required if the system were created from scratch.
How To Use A Pre-Trained Artificial Intelligence System
We can apply what was learned in our problem statement by using pre-trained models, which have been run on large data sets. This is called transfer learning.
However, some care must be taken when choosing a pre-trained model, such as selecting a model that serves to solve a similar problem, or there will be inconsistencies and inaccuracies in the information.
According to artificial intelligence experts, the secret is adjusting the pre-trained system’s algorithms according to the intended use.
For example, an algorithm used in healthcare must be different from the one used in the industry. And while both can use the same core AI platform, they need to be trained with varying data sets.
In this case, machine learning guides the algorithm towards its specific goals, providing information in a particular context so that the data is used effectively for the desired results.
Another way for companies to rethink their day-to-day operations with data is to apply AI to various sub-processes. Using pre-trained artificial intelligence in an operations-intensive process makes once-complex business decisions automated.
After all, in the same way that the human brain does not process all information from scratch because it already has prior knowledge, pre-trained artificial intelligence tends not to make mistakes with already processed data.
Pre-Trained AI: Why Choose This Solution
Pre-trained AI has been seen by many IT managers as off-the-shelf intelligence, meaning it doesn’t require any prior machine learning skills.
As the models are already pre-trained, organizations, in principle, do not need a complex IT infrastructure to run them, nor do they need to train them from scratch.
It is possible to perform training on target tasks without architectural changes or specialized networks. However, training a model from scratch offers slightly better results in current experiments than the adjusted ones.
The great advantage of pre-trained models is that the provider evaluates the accuracy. In addition, it is up to the provider to update the models, free of charge, as it is not off-the-shelf software.
According to the providers of this type of solution, pre-trained artificial intelligence models are easy to integrate, accelerating the time to market. Regarding usage, pre-trained artificial intelligence systems apply to:
- Extraction and processing of texts in documents, which can serve to feed a search system, maintain compliance and automate processes, such as seeking information from the Official Gazette, for example;
- Natural language processing for sentiment analysis, entity recognition, organization, and keyword filtering;
- Automatic translation for content localization, social media posts, voice;
- Speech generation and processing in virtual assistants (chatbots);
- Text reading or speech transcription, with language conversions;
- Image and video processing, automating media workflows;
- Content personalization, using natural language processing;
- Personalized recommendations to improve customer service;
- Fraud prevention by identifying fraudulent activities online;
- Business research, with the inclusion of language that facilitates understanding.
Advantages Of Adopting A Pre-Trained AI Model
As we have already seen, pre-trained artificial intelligence services provide ready-made intelligence for everyday uses, automating processes, reducing operational costs, and thus improving the customer experience.
In the industry sector, for example, and healthcare, there is a growing need for intelligent turnkey systems. The trend is for pre-trained models to become increasingly agile in learning simple tasks such as code generation, spreadsheets, and even mobile and web applications.
In such a dynamic and competitive landscape, it no longer makes sense to train a system from scratch for an existing problem. Adopting pre-trained artificial intelligence models will become the new normal of today.
In the market today, there is a collection of open-source and pre-trained templates ready to be used by different segments. And it is believed that in the future, more pre-trained artificial intelligence will be used by companies seeking to improve their processes and make their operation more efficient.
Also Read: How To Retain IT Talent With Artificial Intelligence (AI)?
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