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Generative AI refers to artificial intelligence models designed to generate new content in the form of written text, audio, images or videos. The applications and uses are far and wide. Generative AI can be used to create a short story based on the style of a specific author, generate a realistic image of a person who does not exist, compose a symphony in the style of a famous composer, or create a video clip from a simple text description. Generative "AI" in your company is: Improvement creative processes Personalization off interactions with users Support for innovative projects and research Increase the efficiency of production processes Generative artificial intelligence is already having a profound impact on business applications. It can drive innovation, automate creative tasks, and deliver personalized customer experiences. Many companies see generic AI as a powerful new tool for creating content, solving complex problems, and transforming how customers and employees interact with technology.
These two terms are often used interchangeably, but they actually have their own unique features, uses, and benefits. That is why it is worth taking a closer look at them, understanding their differences and applications, in order to consciously use their potential.
Machine learning is one of the areas of artificial intelligence that focuses on developing techniques and algorithms that enable machines to learn and improve their performance based on data. The basic idea of UM is that machines can analyze data, recognize patterns and make decisions without being specifically programmed for a specific task. Thanks to this, it allows you to create models that can predict results, classify data and generate new content.
Artificial intelligence is a field of computer science that aims to create machines and computer programs capable of performing tasks that normally require human intelligence. It is a broad area that encompasses many techniques and methods, including machine learning, natural language processing, computer vision, and many others. The main feature of AI is the ability to learn independently and adapt to new situations, thanks to which it can solve complex problems, analyze data and make decisions.
Generative artificial intelligence will undoubtedly be a mandatory aspect of doing business within a few years. By implementing GSI in your form, you benefit on many levels:
Through a better understanding and personalized approach to customers, a company can increase customer satisfaction, which in turn can lead to loyalty and referrals.
By analyzing large amounts of data and generating new data, GAI can help companies identify areas for improvement and create more effective, innovative and tailored products and services to customer needs.
Having advanced generative models allows the company to make better use of the collected data. GAI can generate new data that can be used to better understand the market, consumer trends, or predict future customer behavior. This, in turn, allows you to make more accurate and strategic business decisions.
Generative AI can be a key driver of business innovation by creating entirely new products and services. Thanks to advanced generative algorithms, the company can experiment with various ideas, create prototypes, analyze customer reactions to early versions of products, which may ultimately lead to the introduction of more attractive, innovative products to the market.
Here's a step-by-step look at the entire process of introducing artificial intelligence to your company
Definition of Business Goals and Problems to be Solved
The first step in creating IS is to understand the company's business goals and identify specific problems or areas where AI can be applied. This requires collaboration between the business team and AI experts to determine what specific goals we want to achieve with AI.
Data Collection and Preparation
Artificial intelligence is data-driven, so a key step is collecting the right data that will be used to train AI models. You should also properly prepare this data by removing duplicates, incorrect data or incomplete records.
Model Training and Testing
Once you have selected your model, you need to start training the model using the training data set. This process involves adjusting the weights and parameters of the model in such a way that it achieves the best possible results. The model is then tested on the test data set to assess its effectiveness and efficiency.
Model Optimization and Tuning
After testing your model, you may need to optimize and tune it (tuning) to get better results. This may include changing model parameters, adjusting hyperparameters, or selecting other optimization techniques.
Implementing the Model into Production
After successful training and testing, the AI model is ready to be deployed to the production environment. This means implementing the model in the company's production system so that it can run on real data and solve real business problems.
Monitoring and Maintaining the Model
Once the model has been implemented, it is necessary to continuously monitor its performance to ensure that it remains highly effective and does not introduce erroneous decisions. You may need to regularly adapt the model to evolving data and business conditions.