The artificial intelligence market is experiencing exponential growth, offering companies an unparalleled opportunity to evolve their processes and implement cutting-edge technologies. Adopting AI can be the key to standing out in a competitive market and avoiding being left behind. However, there is an inescapable reality: you cannot successfully implement a technology that is not thoroughly understood.
Knowing each level of artificial intelligence is essential for making strategic, informed and informed decisions. This knowledge not only maximizes the potential of AI, but it also prepares companies to overcome the challenges associated with its implementation. Definitely, learning from this new technology is a journey that empowers organizations to innovate and lead in their respective fields.
At Vozy, we're committed to facilitating this process by providing the necessary foundation for you to understand artificial intelligence. In our blog, we offer access to key concepts and practical tools that will allow you to navigate the world of AI with confidence. Our goal is to equip you with the knowledge you need to make the most of these emerging technologies and effectively transform your business.
With that in mind, in order to understand all of the concepts that we will mention below, you need to have already read our previous article >> What are Artificial Intelligence and LLMs? The path to a new technological era, if you already did, let's not keep waiting! Today it was time to talk about Generative Artificial Intelligence, the true future of technology.
What is Generative Artificial Intelligence (GEN AI) and how does it work?
According to the platform of Amazon Web Services, generative artificial intelligence is a type of artificial intelligence that can create new ideas and content, such as conversations, stories, images, videos and music. Generative AI is the next step in artificial intelligence. You can train her to learn human language, programming languages, art, chemistry, biology, or any complex topic and she will reuse that training data to solve new problems.
Some of the best-known generative AIs that have emerged in recent years are CHAT-GPT, DALL-E, Midjourney, Gemini and Meta AI. But how do they work?
Generative artificial intelligence works by using deep learning models to create new content based on patterns learned from a large amount of information. These models, such as Generative Adversary Neural Networks (GAN) and Large Language Models (LLM), are designed to identify and replicate the characteristics of training data. GANs, for example, operate with two networks: a generator that creates content and a discriminator that evaluates its authenticity. Through a continuous feedback process, the generator improves its ability to produce content that is indistinguishable from the real one. LLMs, such as GPT, use transformers to generate coherent and contextual text, learning from enormous volumes of textual data and applying that knowledge to produce accurate and relevant answers to user requests.
The generative process begins with training the model on a large and diverse data set, allowing it to learn complex patterns and detailed characteristics. Once trained, the model can generate content based on specific inputs, adjusting its outputs to be consistent with user expectations. As models interact with users and receive feedback, they continue to be optimized to improve the quality and creativity of the content they produce.
This whole process is what makes Chat-GPT, for example, respond to you “as if by magic”. It may seem simple, but there is a complex process behind it.
Are there different types of generative artificial intelligence?
Of course! Although they have different processes, uses and complexities, these are the basic concepts:
1. Generative Adversary Neural Networks (GANs): They are a type of deep learning model composed of two competing neural networks: the generator and the discriminator.
2. Large Language Models (LLM): the well-known ones are GPT (Generative Pre-trained Transformer) applications and are designed to generate coherent and relevant text based on instructions given by users.
3. Dissemination Models: They are a type of probabilistic model that creates content by eliminating noise from a randomly generated image. They are particularly effective at generating high-quality images and are used in graphic design and digital art applications.
4. Variational Autoencoder Networks (VAE): combine an encoder and a decoder to learn a latent representation of data
5. Flow-Based Models: they use invertible transformations to generate new data and explore the data space efficiently. They facilitate the generation and sampling of data.
6. Recurrent Neural Networks (RNN) and LSTM: although they are not exclusively generative, RNN and their variants such as LSTM (Long Short-Term Memory) are used to generate data sequences, such as text and music.
Benefits and impact of generative AI on conversational assistants
Generative artificial intelligence has significantly transformed the field of conversational assistants and chatbots, bringing numerous advantages and positive impacts to their design and functionality. Here are some of them for you:
Advantages:
- Improved quality of responses
- Natural and coherent responses
- Understanding the context and personalizing the message
- Continuous learning
- Adaptation to different themes
- Creative content generation
- Enriched interactions
- Complex Problem Solving
- Operational Efficiency
Impact:
- More human interactions: the ability to generate responses that reflect empathy and understanding.
- Improved user experience: Users experience a smoother and more satisfying service.
- Functions beyond answering questions: chatbots with generative AI can perform tasks such as scheduling appointments, executing complex commands, and generating detailed reports.
- Market tests: allow companies to test new ideas and receive instant feedback, accelerating the innovation cycle.
- Reduction in training time: since you only need to facilitate access to recordings and saved data.
- Relieve administrative burden: Companies with heavy administrative burdens, such as coding and medical billing, can use generative AI to automate complex tasks.
The implications of this new development are enormous, as we are entering an era where humans and machines can work together to reach new heights. Generative artificial intelligence offers great potential for a wide range of industries, including healthcare, finance, manufacturing, business, education, media, and entertainment.
According to a Goldman Sachs report, this technology could increase the world's gross domestic product (GDP) at 7% per year for the next 10 years. However, the use of generative AI comes with both benefits and risks, and staying informed about this technology is crucial to implementing it properly. Keep reading our blog or schedule a call with our Vozy team to become a pioneer in the conversational artificial intelligence market.
