Artificial Intelligence Within Companies – Skills, Technical Limitations and Application Prospects

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How can artificial intelligence be used in the corporate sector? Neural networks have gained immense popularity.

More and more companies are implementing artificial intelligence. In particular GPT-based models, into their corporate messengers and other digital tools.

Does this mean that AI will become the companion of every employee in the future? What opportunities does this open up?

What Can AI Do As a Digital Assistant?

A future in which AI functions as a personal assistant to every employee no longer seems far off. Intelligent bots have learned how to perform complex data analysis and identify patterns. They can be used (and are already being used) both in routine processes and in tasks with an asterisk. Bots help manage large-scale projects, explore trends, and make decisions.

Generative AI also has basic skills. It helps to create, edit and sort documents and automatically generate reports and presentations based on the data provided. It also collects, processes and organizes corporate knowledge, facilitates access to information and catalogues it for easy search.

In terms of HR processes, within corporate education programs, AI can offer training materials, and tailor content according to the needs and progress of each employee.

Each company can come up with dozens of its own use cases for artificial intelligence. However, the ones listed above already make AI a promising digital assistant.

How It Works On the Example of ChatGPT

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Interestingly, when discussing AI for enterprise use, many people mean ChatGPT. Although this is just one of the neural networks for generating the continuation of the text. It is based on the Large Language Model (LLM), based on the GPT-4 (Generative Pretrained Transformer) architecture.

What makes this model exceptionally powerful for natural language understanding and generation? During the training process, GPT-4 processes a huge number of text examples. The model learns to predict the next word in each of the examples.

This helps it learn the semantics and syntax of the language. But also some aspects of general knowledge and worldview. Attention mechanisms within the GPT-4 model allow each element of the input to “see” and “influence” every other element. As a result, the model processes the most complex dependencies between words and phrases in the text.

However, it is essential that the model does not have subjective experience. It has no consciousness and no idea of the world outside of what was presented to it in the texts on which it was trained.

What Can’t AI Do Now?

AI can analyze and process information. But it does not understand the meaning of words and sentences in the way that a person does. The neural network only analyzes the request. Then it creates a response based on the patterns found in the data during training.

Artificial intelligence is incapable of independent learning or adaptation in the way a person does. If the AI encounters information that it did not see during training, its performance may decrease.

Most AIs specialize in certain tasks. GPT-4, for example, can generate text but cannot parse images or sounds.

Despite significant advances in natural language processing and image analysis, AI still struggles with unstructured data. There are also problems with contextual understanding. AI has difficulty understanding context. Especially when general knowledge about the world around is required or it is necessary to take into account subtext and nuances in communication.

Ethical Limits of AI

The use of AI raises a number of ethical issues. For example, a neural network often requires large amounts of data, which may include user’s personal information, and this compromises privacy. If the data on which the AI model is trained contains biased information, the AI can also express that bias.

Another problem is that it is difficult to understand why AI makes certain decisions or acts in certain ways, which inevitably leads to mistrust. And if an AI makes a mistake or causes harm, it can sometimes be difficult to determine who is responsible. All this imposes certain restrictions on the use of neural networks. However, there are ways to overcome them.

For example, creating strict protocols for data processing and auditing AI models helps ensure data privacy. By using techniques to identify and remove bias in AI data and models, systems will become fairer. The development of Explainable AI methods increases the transparency of AI to users. And in terms of responsibility for the actions of AI, laws and regulations will help.

Prospects For The Development of AI

AI is a rapidly developing field with a lot of new research. And it is difficult to predict its development even for the next six months. Based on current trends and directions, AI algorithms will be enhanced by new learning methods and model architectures. The requirements for transparency in AI solutions will increase, which means that Explainable AI technologies will develop.

Neural networks will be more actively used in new industries and applications. This includes creating personalized products and services. We can expect an increased focus on ethics, privacy and regulation in this area.

AI will also be used in more sectors such as online gambling and live casino. But don’t wait for this new innovation to enter the game!

Of course, the development of technology is always associated with uncertainty. Actual achievements may differ from forecasts. In any case, there is no doubt that AI is a powerful tool for improving corporate processes. However, new technology must be implemented and used responsibly.

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