What is generative AI?
Generative AI, short for generative artificial intelligence, is capable of generating content that resembles the data that it has been trained on. Generative AI is capable of producing different types of content, ranging from text and music to images and videos. Its potential is impressive, but there are many challenges and ethical concerns surrounding generative AI, particularly around the authenticity and potential misuse of generated content.
- What does generative AI mean?
- How does generative AI work?
- What is the difference between machine learning and artificial intelligence?
- What are examples of generative AI models?
- What are the main types of machine learning models?
- What are ChatGPT, DALL-E and Bard?
- What can generative artificial intelligence be used for?
- What are the benefits of generative artificial intelligence?
- What are the limits of generative AI?
- What are the concerns surrounding generative AI?
- What are some examples of generative AI tools?
- Generative AI vs. AI
- Helpful tips for using generative artificial intelligence
What does generative AI mean?¶
Generative AI stands for generative artificial intelligence. The term refers to AI models and algorithms, such as ChatGPT, that can generate new content or data that is similar to the data they’ve been trained on. This can be a variety of data types such as text, images and music. The technology is based on generative adversarial networks (GANs), which is a form of machine learning.
How does generative AI work?¶
Generative artificial intelligence typically relies on neural networks, in particular, generative models such as GANs:
- First, large amounts of training data are collected and processed, which serve as the basis for training the generative model. This can be in the form of texts, images or videos, for example.
- The neural network consists of several layers. The exact architecture depends on the type of data that should be generated. For text, a recurrent neural network (RNN) model can be used, while for images, convolutional neural networks (CNNs) are used.
- Training data is used to teach the AI model how to generate data that is similar to the training data. It does this by adjusting the weights and parameters of its neurons to minimize errors between the generated data and the actual training data.
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After the model has been trained, it can be used to generate new data. To do this, the model is given a start sequence or a start value. This is done using a prompt, which can be in the form of text, images, videos or drawings. Generative artificial intelligence then provides new content in response to the prompt. The generated data is evaluated to ensure that it is of high quality and that it makes sense. The model can always be further 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 typically require human intelligence. Chatbots and voice assistants such as Google Home or Amazon Echo are, for example, based on artificial intelligence.
Machine learning (ML) is a subfield of AI that focuses on developing algorithms capable of learning from data. Rather than receiving specific instructions for a task, an ML 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 that is available has increased the potential of machine learning.
What are examples of generative AI models?¶
Generative AI models use a specific neural network to generate new content. Depending on the application, these include:
- Generative adversarial networks (GANs): GANs consist of a generator and a discriminator and are often used to create realistic images.
- Recurrent neural networks (RNNs): RNNs are specifically designed for processing sequential data such as text and are used to generate text or music.
- Transformer-based models: Models such as GPT (generative pretrained transformer) from OpenAI are transformer-based models used for text generation.
- Flow-based models: These are used in advanced applications to generate images or other data.
- Variational autoencoders (VAEs): VAEs are frequently used in image and text generation.
What are the main types of machine learning models?¶
In machine learning, there are different types of models that are used depending on the type of task and available data. A basic distinction can be made between supervised learning and unsupervised learning. Neural networks are widely used in unsupervised learning.
In addition to these two main categories, there is also semi-supervised learning, reinforcement learningand active learning. All three methods belong to supervised learning and differ when it comes to how and to what extent user participation is required.
In addition, a distinction is made between deep learning and shallow learning. The main difference here is the depth and complexity of the models. While deep learning uses deeper neural network architectures to recognize more complex features and patterns in large data sets, shallow learning is based on simpler models with fewer layers. Machine learning and deep learning are considered subsets 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 – Chat GPT¶
ChatGPT is among the most popular text generators. The AI chatbot is based on OpenAI’s GPT-3.5 or GPT-4 language prediction model and offers the ability to provide human-like text responses in chat format. Like other GPT models, ChatGPT has been trained on large amounts of text data and can cover a wide range of topics and knowledge areas by using this training for its responses and explanations. ChatGPT incorporates the conversation history with a user into its results, simulating a conversation.
Generative AI – DALL-E¶
DALL-E is a multimodal AI application that generates images based on text descriptions. The generative artificial intelligence was developed using OpenAI 2021’s GPT implementation and, like ChatGPT, was trained with a large dataset of images and associated text descriptions. This allows the AI image generator to associate the meaning of words with visual elements. The second, more powerful version, DALL-E 2, was released in 2022. It allows images to be created in different styles depending on the user’s prompts.
Generative AI – Bard¶
Bard is a generative artificial intelligence chatbot developed by Google. Generative artificial intelligence is powered by Google’s large language models (LLMs) and PaLM 2. Like ChatGPT, Bard can answer questions, program, solve math problems and help with typing. To do this, the tool also uses natural language processing (NLP) techniques. Although the AI operates separately from Google Search, it obtains its information from the internet. Users can actively contribute to improving the data it provides by giving feedback.
|Free – $20/month
|Can answer a variety of questions
|Can sometimes provide unexpected or inaccurate answers
|Answers are based on training data and are therefore not always up to date; cannot think or learn outside of its training data set
|$15 for 115 credits
|Can create detailed and high-quality images from text instructions
|Generated images are not always perfect or realistic
|Result depends heavily on accuracy of description
|Has a large, reliable data set, accesses the internet, and is constantly being improved through feedback
|Dependency on Google
|Still in the development phase and has some operational limitations, so it may not be able to do 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. It is becoming increasingly accessible thanks to breakthrough developments such as GPT and the user-friendliness of the technology. Examples of when generative artificial intelligence can be used include:
- Text generation: News articles, creative writing, emails, resumés, etc.
- Image and graphic creation: Logos, designs, artwork, etc.
- Music and sound: Composing, sound effects, etc.
- Video game development: Generating game levels, characters, storylines or dialogs
- Film and animation: Creating CGI characters or scenes, animating, generating video content, etc.
- Pharmacy and chemistry: Discovering new molecular structures or drugs, optimizing chemical compounds
- Chatbots: Customer service or technical support
- Educational content: Product demonstration videos and tutorials in different languages
- Architecture and city planning: Designing buildings, indoor spaces or city plans, optimizing the use of space or infrastructure, etc.
What are the benefits of generative artificial intelligence?¶
Because of its wide range of uses, generative AI offers a number of benefits for a wide variety of fields. In addition to creating new content, it can also make it easier to interpret and understand existing content. Some of the benefits of implementing generative artificial intelligence include:
- Automating manual processes
- Summarizing and preparing complex information
- Creating content more easily
- Answering certain technical questions
- Answering emails
What are the limits of generative AI?¶
The limitations of generative artificial intelligence often arise from the specific approaches that are used for certain use cases. For example, even though the generated content may sound convincing, the information upon which it is based could be incorrect or could have been manipulated. Other limitations include:
- Source of information cannot always be identified
- Bias of original sources is difficult to judge
- Realistic sounding content makes recognizing incorrect information difficult
- Generated content may contain biases and prejudices
What are the concerns surrounding generative AI?¶
There are a number of concerns associated with using generative AI. In addition to the quality of the generated content, there is also the possibility that it may be misused.
- Misuse and disinformation: Generative AI’s ability to produce realistic content can be taken advantage of. Examples include using generative AI for deepfakes, fake news, fake documents and other forms of misinformation.
- Copyright and intellectual property: Generated content raises copyright and intellectual property issues because it is often unclear who owns the rights to the generated content and how it may be used.
- Bias and discrimination: If a generative artificial intelligence model has been trained using biased data, it may be reflected in the generated content.
- Ethics: Generating fake content and manipulated information can raise ethical questions.
- Legal and regulatory issues: The rapid development of generative AI has led to an unclear legal situation. There is uncertainty about how the technology should be regulated.
- Data protection and privacy: Using 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 to create social engineering attacks that are more effective than those created by humans.
What are some examples of generative AI tools?¶
Depending on the type of content you would like to generate, there are numerous generative AI tools available. Some of the best AI text generators include:
- ChatGPT from OpenAI
Among the best AI image generators are:
- DALL E-2
- Jasper Art
Among the best AI video generators are:
Generative AI vs. AI¶
The difference between generative AI and artificial intelligence has mainly to do with the way they’re used 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 manner, generative artificial intelligence generates new content such as chat replies, designs, synthetic data and deepfakes. In order to do this, generative AI requires a prompt such as the user entering an initial query or data set. Traditional AI, on the other hand, focuses on pattern recognition, decision making, refined analytics, data classification and fraud detection.
Helpful tips for using generative artificial intelligence¶
Generative AI comes with both opportunities and risks. For those who use content created by generative AI models, there are several ways to achieve better outcomes and at the same time avoid possible risks:
- Validate results: Always check the generated content for plausibility and quality.
- Understand the tool: You should know how the particular generative AI tool works and what its strengths and weaknesses are. The keyword here is explainable AI (XAI).
- Be critical of sources: If you are working with content whose sources are created by generative AI, you should check them afterward.
- Clear labeling: Generative AI content should be labeled as such so that people are informed.
- Ethics: Use generative AI responsibly. This means that you should not create or distribute misleading, inaccurate or manipulative content.
- Continuous learning: Generative artificial intelligence is evolving rapidly, so ensure you keep up to date with new technologies, techniques and best practices.