What is generative AI?

What is generative AI?

Generative AI, short for generative artificial intelligence, is capable of generating content that resembles the data with which it has been trained, from texts to images or music. Its potential is impressive, but generative AI also raises challenges and ethical issues, especially around authenticity and potential misuse of generated content.

Definition of Generative AI

Generative AI is short for generative artificial intelligence. The term refers to AI models and algorithms, such as ChatGPT, that can generate new content or data similar to that with which they have been trained. The data format can be diverse, such as text, images, music, etc. The technology is based on so-called Generative Adversarial Networks (GAN) , a form of machine learning .

How does generative AI work?

Generative artificial intelligence usually works using neural networks , especially so-called generative models, such as GANs.

  • First, large amounts of training data are collected and processed , which serves as the basis for training the generative model. These can be, for example, texts, images or videos.
  • The neural network consists of several layers. The exact architecture depends on the type of data to be generated. A model with recurrent neural networks (RNN) can be used for texts, while convolutional neural networks (CNN) are used for images.
  • The AI ​​model is applied to the training data to learn how to generate data that resembles the training data. This is achieved by adjusting the parameters of the neurons to minimize errors between the generated data and the actual training data.
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Once the model is trained, it can be used to generate new data. To do this, a sequence or starting value is given to the model. This is done through a question, which can be in the form of text, images, video or drawings. The generative AI then provides new content in response to the request. The data generated is evaluated to ensure its quality and meaning. The model can always be adapted and refined by training it with new data.

What is the difference between machine learning and artificial intelligence?

As a broad field of research, artificial intelligence (AI) aims to develop machines that can perform tasks that normally require human intelligence. Chatbots and voice assistants like Google Home or Amazon Echo , for example, are based on artificial intelligence.

Machine learning (MA) is a subfield of AI that focuses on developing algorithms capable of learning from data. Instead of being given specific instructions for a task, an AM model learns from sample data and then makes predictions or decisions without having to be explicitly programmed for that task. The volume and complexity of data has increased the potential of machine learning.

What are generative AI models?

Generative AI models use a specific neural network to generate new content. Depending on the application, they include:

  • Generative Adversarial Networks (GANs) – GANs consist of a generator and a discriminator and are often used to generate realistic images.
  • Recurrent Neural Networks (RNN) – RNNs are specifically designed to process sequential data, such as text, and are used to generate text or music.
  • Transformer-based models : Models like OpenAI’s GPT (Generative Pretrained Transformer) are transformer-based models used to generate text.
  • Flow-based models – used in advanced applications to generate images or other data.
  • Variational Autoencoders (VAEs) – VAEs are often used in image and text generation.

Different machine learning methods

In machine learning, there are different types of models that are selected based on the type of task and the available data. A basic distinction is made between supervised learning and unsupervised learning . Systems based on unsupervised learning materialize, among other things, in neural networks. In addition to these two main categories, there are semi-supervised learning , reinforcement learning, and active learning . All three methods belong to supervised learning and differ in the type and degree of user participation.

Furthermore, a distinction is made between deep learning and shallow learning. The main difference lies in the depth and complexity of the models. While deep learning uses deeper neural network architectures to recognize more complex features and patterns in large amounts of data, shallow learning relies on simpler models with fewer layers. In short, machine learning and deep learning are subfields of artificial intelligence.

What are ChatGPT, DALL-E and Bard?

ChatGPT, Dall-E and Bard are AI interfaces that allow users to create new content using generative artificial intelligence.

Generative AI: ChatGPT

ChatGPT is one of the best-known text generators. The AI ​​chatbot is based on OpenAI’s GPT-3.5 or GPT-4 voice prediction model and offers the ability to provide human-like text responses in chat format. Like other GPT models, ChatGPT has been trained with large amounts of text data and can cover a wide range of topics and knowledge domains by relying on this training for its answers and explanations. ChatGPT incorporates the history of the conversation with a user into its results and thus simulates a conversation.

Generative AI: DALL-E

DALL-E is a multimodal AI application for generating images based on text descriptions. This generative AI model was developed using OpenAI 2021’s GPT implementation and, like ChatGPT, was trained on a large dataset of images and associated text descriptions. This allows AI image generators to combine the meaning of words with visual elements. The second, more powerful version, DALL-E 2, was released in 2022. This allows you to create images in different styles controlled by user requests.

Also Read: AI for images: the best AI image generators

Generative AI: Bard

Bard is a generative artificial intelligence chatbot developed by Google. Generative AI is based on Google’s Large Language Models (LLM) and PaLM 2. Like ChatGPT, Bard can answer questions, program, solve math problems, and assist with writing tasks. To do this, the tool also uses natural language processing (NLP) techniques . Although the AI ​​acts independently of Google search, it obtains its information from the Internet. Users can actively contribute to improving the data through their opinions.

Tool name Costs Advantages Disadvantages Restrictions
ChatGPT Free or up to $20/month Can answer a wide variety of questions May sometimes provide unexpected or inaccurate answers Answers are based on training data and are therefore not always up to date; you can’t think or learn outside of your training data set
DALL-E 2 15 USD for 115 credits You can create detailed, high-quality images from text instructions The images generated are not always perfect or realistic The result depends largely on the accuracy of the description
bard Gratuitous It has a large and reliable data set, accesses the Internet and constantly improves thanks to user opinions It depends on Google It is still in the development phase and has certain operational limitations, so it may not be able to perform all tasks perfectly

What can generative artificial intelligence be used for?

Generative AI can be used in a wide variety of fields to create virtually any type of content. Thanks to revolutionary advances like GPT and the ease of use of the technology, it is becoming more and more accessible. Application areas of generative artificial intelligence include, for example:

  • Text creation : news articles, creative texts, emails, CV, etc.
  • Creation of images and graphics : logos, designs, illustrations, etc.
  • Music and sound : composition, sound effects, etc.
  • Video game development : creation of levels, characters, plots or dialogues
  • Cinema and animation : creation of CGI characters or scenes, generation of animation or video content, etc.
  • Pharmacy and chemistry : discovery of new molecular structures or drugs, optimization of chemical compounds
  • Chatbots : customer service or technical assistance
  • Educational content : product demonstration videos and tutorials in different languages
  • Architecture and urban planning : design of buildings, interiors or urban plans, optimization of the use of space or infrastructure, etc.

What are the advantages of generative artificial intelligence?

Due to its wide range of uses, generative AI offers a number of advantages for a wide variety of fields. In addition to creating new content, it can also facilitate the interpretation and understanding of existing content. The advantages of applying generative artificial intelligence include:

  • Automation of manual processes
  • Summary and preparation of complex information
  • Facilitate content creation
  • Answer specific technical questions
  • Reply to emails

What are the limits of generative AI?

The limitations of generative AI often arise from the specific approaches used to implement particular use cases. For example, although the generated content often sounds very convincing, the underlying information may be erroneous or manipulated. Other limitations in the use of generative AI are:

  • The source of information is not always identifiable
  • It is difficult to judge the bias of the original sources
  • Realistic content makes it difficult to recognize false information
  • The generated content may contain biases and subjectivities

Also Read: The best generators with artificial intelligence for texts | AI Text Generators

What are the problems with generative AI?

The use of generative AI raises a number of problems. In addition to the quality of the content generated, there is also concern about the possibility of its misuse.

  • Abuse and disinformation : The ability of generative AI to produce realistic content can lead to misuse, for example the creation of deepfakes , fake news, forged documents, and other forms of disinformation.
  • Copyright and Intellectual Property – Generated content raises copyright and intellectual property issues, as it is often unclear who owns the rights to the generated content and how it can be used.
  • Biases and discrimination : If a generative artificial intelligence has been trained with biased data, this may be reflected in the generated content.
  • Ethics : The generation of false content and manipulated information can raise ethical issues.
  • Legal and regulatory issues : The rapid development of generative AI has led to an unclear legal situation; It is not yet clear how this technology should be regulated.
  • Data protection and privacy : The use of generative AI to generate personal data or identify people in images is questionable in terms of data protection and privacy.
  • Security – Generative AI can be used for social engineering attacks that are more effective than human attacks.

Examples of generative AI tools

Depending on the type of content you want to generate, there are several generative AI tools. Some of the best AI text generators include:

  • ChatGPT by OpenAI
  • jasper
  • Writesonic
  • Phrase
  • CopyAI

Among the best AI image generators are:

  • Midjourney
  • DALL E-2
  • Neuroflash
  • Jasper Art
  • Craiyon

Among the best AI video generators are:

  • Pictorial
  • Synthesys
  • Synthesia
  • HeyGen
  • Veed

Generative AI vs. AI

The difference between generative AI and artificial intelligence lies primarily in the application and not necessarily the underlying technology. While the main goal of artificial intelligence is to perform tasks that normally require human intelligence in an automated or enhanced way, generative artificial intelligence creates new content such as chat responses, designs, synthetic data or deepfakes. To do this, generative AI requires a request in which the user enters an initial query or a set of data. Traditional AI, on the other hand, focuses on pattern recognition, decision making, refined analysis, data classification, and fraud detection.

Also Read: The best AI video generators

Good practices for the use of generative artificial intelligence

Using generative AI comes with both benefits and risks. For users using generative AI models or working with their results, there are some best practices to achieve better results while avoiding potential risks:

  • Validate results : always check the verisimilitude and quality of the generated content.
  • Understand the tool : You should know how the generative AI tool you use works and what its strengths and weaknesses are. In relation to this, the concept of Explainable AI (XAI) is interesting .
  • Treat sources critically : If you work with content created with generative AI, review the veracity of the sources.
  • Clear labeling : Generative AI content must be labeled as such for other users.
  • Ethics : Use generative AI responsibly, meaning do not create or distribute misleading, inaccurate, or manipulative content.
  • Continuous Learning – Generative AI is evolving rapidly, so stay up to date with new technologies, techniques, and best practices.

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