Influence Generative AI on public services
Exploring the Generative AI trend
Generative AI is a technological trend that we are investigating for applicability to the services of the SVB and the government. The trend of generative AI has gained global prominence through ChatGPT.
In this study we ask ourselves a number of questions based on 1 main question:
- Main question: what could be the influence of generative AI on public services?
How do we approach the rest of the exploration?
We will answer this question in a number of phases based on the following questions:
- What's the trend?
- Why is it a trend?
- What shows that it is a trend including source reference?
- Who are the major players in this trend?
- Which other government parties are working on this to prevent duplication?
- Which startups are working on this, for inspiration?
- What are the opportunities and threats of this trend?
- Can we build it ourselves or apply it?
We report the results of this research in this article.
Purpose of the exploration
The goal of this exploration is to determine how the emerging field of Generative AI can help the SVB and other public sector organizations. What is the impact of Generative AI on public services? Due to the emergence of ChatGPT, Generative AI is becoming more and more popular and well-known.
What is Generative AI?
Generative AI uses machine learning algorithms to create new images, videos, text, and audio. Generative AI systems use deep learning (Deep Learning) methods such as neural networks to learn data patterns and generate new content that resembles the original data. This data consists of large sets of data, known as Large Language Models (LLM).
Generative AI can create realistic images of non-existent objects, text that resembles human writing, music and sound effects. In the Generative Adversarial Network (GAN), a popular generative AI model, two neural networks compete to generate realistic content.
Generative AI can be used in arts, entertainment, marketing, science and engineering. However, generative AI can also be used to create fake news or other malicious content, raising concern worldwide.
In summary, the following points apply to Generative AI:
- Generative AI is a form of artificial intelligence that creates new information by using large sets of data, known as Large Language Models (LLM).
- The algorithm analyzes large amounts of data and detects patterns.
- These patterns are used to create new information that did not exist before.
- Generative AI can be used to generate different types of results, such as text, audio, video, and images.
- It can also be used to solve complex problems that are difficult for humans to understand.
How does generative AI work?
Generative AI uses machine learning algorithms to learn patterns and relationships in a dataset and generate new data similar to them. Below is an overview of how generative AI works:
- Collecting data: Generative AI starts with collecting data. This data set can be anything from images and videos to text and audio.
- Model training: The AI analyzes the data set using machine learning algorithms such as neural networks to find patterns and relationships.
- Generate: Once the AI learns the patterns and relationships within the dataset, it can generate new data similar to the original dataset. A generative AI system trained on a dataset of human faces can generate new faces that resemble the originals.
- Evaluation: The generated data is assessed for quality and similarity to the original dataset. The AI system may need retraining if the generated data is poor or does not match the original data set.
- Refinement: Finally, the AI system can be improved by collecting more data and retraining the machine learning algorithms on the extensive data set.
Generative AI can generate data using neural networks, Markov chains and probabilistic models. The type of data and the output determine the techniques.
Is ChatGPT a generative AI?
Yes, ChatGPT is a language model created by OpenAI's Generative AI. It generates natural language responses using deep learning methods, such as transformer-based neural networks. ChatGPT can generate text in a variety of styles and formats, from news articles to scientific papers to creative writing, as it is trained on a large collection of text data. ChatGPT is a powerful tool for applications such as language translation, content creation, and customer service because it is a language model that can generate new and original text that resembles the input data it was trained on.
What potential effects could Generative AI have on public services?
The ability of AI algorithms to generate new information, known as “Generative AI”, could have a significant impact on how governments deliver their services to citizens and businesses. The following are some possible effects of Generative AI on government services:
- Generative AI can be used to personalize government services for each person. For example, AI software can sift through information about the wishes and needs of residents and make suggestions or even perform tasks based on that analysis. One of the main reasons why Generative AI is gaining popularity in business is the ability to automate repetitive (tedious) tasks that take up unnecessary time, freeing up valuable human time and reducing labor costs.
- To ease the workload, an AI system can be used to perform routine tasks such as generating reports or answering frequently asked questions. This benefits the users of government services, citizens and businesses as well as the government itself.
By providing more accurate and complete information, Generative AI can help people in the public sector. This way we can approach people directly because, for example, they are still entitled to money from the government to support themselves. People with disabilities who use government services can access these services more easily with the help of Generative AI. For example, an algorithm can be used to generate audio descriptions or captions for videos for people who are hard of hearing or have low vision. This is happening now, but not everywhere yet. By setting a standard for this, all government services can use this and thus keep costs low.
While Generative AI has the potential to improve public services in several ways, there are also some risks to consider. To ensure that AI is used ethically and responsibly in public service, we need to consider data privacy, bias, and transparency.
What is the trend of Generative AI?
In recent years there has been enormous growth in the field of Generative Artificial Intelligence (AI). Below you will find 4 current Generative AI trends:
- The number of academics and institutions working to advance Generative Artificial Intelligence (AI) continues to grow, underscoring its relevance.
- Technological and algorithmic advancements in Generative AI have led to significant performance improvements in recent years. An example of this is how lifelike photos and movies have been made.
- The trend is that the application areas are expanding in number, from text only to images, music, video, or differentiate into sub-areas such as text for vacancies, website copy, and so on.
- After years of research, Generative AI is now commercialized and available to everyone. Companies are increasingly offering low-threshold products and services based on Generative AI, such as image and video creation tools.
The risk of this trend is that as Generative AI becomes better and more common, concerns grow about what this means for ethics. Do we have to want some things? For example, concerns have been raised about the possible use of Generative AI to generate deepfakes and other forms of disinformation.
Why is it a trend?
Generative AI is an emerging technology that has the potential to transform many industries by finding new ways to solve old problems. Here are some reasons why Generative AI is popular right now:
- Significant progress has been made in the study of Generative AI, such as the development of deep learning models. These advancements, which have led to significant improvements in the capabilities of Generative AI, have increased interest in and funding for the field.
- Advances in computing power and cloud computing have made it easier to train and run Generative AI models. This has made Generative AI more accessible to researchers and companies, making it more useful.
- Commercial applications such as advertising, design and art are just a few of the many industries that can benefit from Generative AI. Companies look to these tools to improve their current processes and develop new, creative products.
- Generative AI opens up countless opportunities for creativity, especially in art. It can generate new art or enrich existing art, such as paintings, with generated elements and generate music. More and more musicians and visual artists are experimenting with Generative AI to find new ways to express themselves and advance their careers. However, there are also concerns among artists because the generation may infringe on intellectual property.
- The general public's interest in Generative AI has helped spread the word and sparked a lively debate about its possible applications in areas such as deep learning. A consequence of this is to better understand and combat deep fakes because the dangers of this are better understood.
The emergence of Generative AI as a major AI trend can be attributed to several factors, including recent advancements in the field, increased computational power, practical applications in business, the possibility of new use cases, and widespread public interest. The fact that this technology is still evolving indicates that it will become more important and will be used in more places.
What shows that it is a trend?
Several facts indicate that Generative AI as a trend is gaining popularity:
- The number of papers devoted to GANs has increased from one copy in 2014 to more than a thousand in 2021, according to the arXiv database.
- In a recent blog post, OpenAI showed how their DALL-E 2 model can convert textual descriptions into photorealistic images.
- Startups like Artie are using Generative AI to create playable characters for games and other applications, while Adobe has developed Project Voco, a tool that can generate lifelike speech from text.
- Travis Scott CG's music video "Franchise" was created by a generative AI system, just like Linkin Park's latest music video "Lost". Watch the video clip on YouTube: https://youtu.be/7NK_JOkuSVY
Who are the main players around this trend in Generative AI?
The field of Generative AI is expanding rapidly, with many key groups and individuals working to improve it. The following are some of the industry's most prominent figures:
- OpenAI is a non-profit research organization whose mission is to create AI that is both reliable and useful. They have developed several important Generative AI models, including GPT and DALL-E, which have received significant attention in academia and beyond.
- Nvidia GPUs are among the most widely used tools for training deep learning and Generative AI models. They have developed a number of deep learning software and hardware tools, including the CUDA programming language and TensorCore processing units.
- Google is a pioneer in Generative Artificial Intelligence when it comes to natural language processing and image and video production. In addition to groundbreaking models such as BERT and BigGAN, they have made important contributions to the academic community by publishing their findings and making their software open source.
- Adobe: Adobe is the market leader in creative software, and the company has been hard at work developing a range of content creation tools with Generative Artificial Intelligence. In addition to the recent release of Project Voco, which can generate realistic speech from text, they have been working on image and video generation tools.
- Facebook: Facebook has invested heavily in Generative Artificial Intelligence research, particularly in natural language processing and image generation. They have produced some notable models including GPT-3 and StyleGAN.
- Microsoft has invested heavily in generative AI research, particularly in natural language processing and computer vision. Notable models they have created include the Deep Dream image generation algorithm and the Turing natural language generation model. In addition, Microsoft is increasingly introducing AI into their Office suite, such as a co-pilot that helps you during your work.
These are just some of the industry leaders in Generative Artificial Intelligence. There are numerous other companies, both large and small, researching this technology and its potential applications in a wide variety of areas.
Which Dutch government parties are working on Generative AI research?
- The Dutch government funds AI research and development, especially in the field of Generative AI. To avoid redundancy and ensure coordination between government agencies, the Dutch government has formed a number of AI-related initiatives and partnerships. The following are some of the Dutch government agencies working on Generative AI:
- The ICAI, or Innovation Center for AI, is a program supported by the Dutch government that promotes the widespread use of artificial intelligence. The program is a collaboration between universities, companies and government agencies to create new types of artificial intelligence that can independently generate new ideas.
- The Dutch Artificial Intelligence Coalition (NL AIC) is a public-private partnership that aims to promote and popularize artificial intelligence in the country. The coalition's goal is to advance artificial intelligence (AI) technologies such as Generative AI by collaborating with more than 400 members from academia, industry, and government.
- The Netherlands Research Council (NWO) is the main source of funding for scientific research in various fields, including artificial intelligence. NWO funds various research programs in the field of artificial intelligence, including “AI, Big Data and Cybersecurity”, which focuses on Generative AI.
- In the Netherlands, the Ministry of Economic Affairs and Climate Policy develops government plans for innovation, entrepreneurship and sustainable development, among other things. The ministry is involved in various AI-related activities, such as the development of a national AI policy with a focus on studying and deploying Generative AI.
These are just some of the Dutch government agencies working on Generative AI research. There are plenty of other groups and projects in the Netherlands working on AI research and development, including Generative AI. It is critical that these groups work together to avoid duplication and promote the ethical use of AI. With this article we try to share our knowledge openly and transparently so that this research is no longer necessary.
Which startups are working on generative AI, for inspiration?
Many new companies are moving into generative AI, which has the potential to change many areas. Below are a number of startups that focus on generative AI:
- OpenAI, a non-profit organization dedicated to advancing useful AI for the greater good of humanity, is researching the topic. OpenAI's GPT-3 language model can write logically and sophisticatedly. It's just one of many generative AI models the company has developed.
- Artomatix, an artificial intelligence (AI) platform that creates 3D content, was founded by an Irish company. The platform uses generative AI to accelerate and enhance the creation of 3D models and textures.
- Skylum is an American company that has developed Luminar, an artificial intelligence (AI) image editor that uses generative AI to enhance and adjust images.
- Viable, a UK-based company, has developed a generative AI platform for product development. In response to consumer feedback and needs, the system can suggest new product designs.
- ObEN is an American startup that has developed a generative AI platform for creating unique digital characters. The platform uses machine learning and computer vision to create avatars that look and act like real people. They can converse with real people in a digital environment and exert influence.
More and more companies are doing research and development in the field of generative AI, and the companies mentioned above are just a few examples. In addition to Google, many other companies and organizations around the world are exploring the applications of generative AI in industries such as medicine, media and business.
Benefits and Risks of Generative AI
There are many positives and some risks associated with Generative AI. Some examples are:
- Generative AI has the potential to automate and improve a wide range of processes, increasing efficiency and output across industries.
- The application of Generative AI to the creation of new types of art, music and other forms of creative output opens up exciting new avenues for expression and growth in the creative industries.
- Generative AI's ability to assist in the creation of customized products and services has the potential to significantly improve consumer overall experiences and levels of satisfaction.
- Using Generative AI, which can process and evaluate massive amounts of data, decisions and tactics can be improved.
- The fact that Generative AI could help with the discovery of new drugs, the development of personalized treatments and the more accurate detection of diseases benefits healthcare.
- Because Generative AI automates many processes, it could lead to job losses in some industries, especially those that rely on routine tasks.
- If the data used to train the AI models is biased or incomplete, the models are likely to reinforce existing biases and inequalities.
- Generative artificial intelligence poses a security risk because it can be used to create convincing fake content, such as movies or sounds that can be used for malicious purposes.
- Privacy, ownership, and accountability are just some of the ethical issues raised by Generative AI.
- Hackers, propagandists and other bad actors can take advantage of Generative AI.
To ensure that Generative AI technologies are used responsibly and ethically, it is critical to consider these benefits and risks when developing and deploying these technologies.
Can we build it ourselves or apply it?
To create or use Generative AI, you need to be well versed in machine learning, computer vision, and natural language processing. There must also be a large amount of data and computing power, which can be difficult and expensive to obtain. So it is technically possible to build a model yourself, but given the high costs of several tens of millions of euros and the amount of energy this requires, we don't even have to wonder whether we should want to do this from our lab. This would be a rhetorical question because we do know the answer.
However, there are numerous online and offline resources where people and businesses can find information and experiment with different approaches to Generative AI. To learn the basics of how Generative AI works, you can take any number of machine learning and AI courses and tutorials offered by schools and online resources. TensorFlow and PyTorch are just two examples of open-source tools that can be used to build Generative AI models. These libraries provide sample code and data that can be used to build your own Generative AI projects. It is important to note that developing and deploying Generative AI requires a thorough understanding of AI and how it can be applied, as well as an understanding of its moral and legal implications. It is critical that this technology is approached with caution so that it can be used responsibly and ethically.
Generative AI has the potential to open up new avenues for reflection and boost creativity. The influence on the public service can go many ways. It can also help solve difficult problems and create new opportunities for government services. That is why it is essential that we take a holistic approach and consider all the ways in which this type of AI can benefit us as a society. In this exploration, we have seen how Generative AI can be used to improve public services and make the workplace more attractive and innovative.
Generative AI is a type of AI that automates and adapts processes to create new content. This content may differ from the original, and may be completely replaced. As a result of these changes, the original may be modified. The goal of Generative AI is to generate new and valuable content by creating new data. This method can be used in a variety of fields, including healthcare, education, entertainment, and social media. Using Generative AI, we can generate new datasets, which we can then use to train new models. In other words, we can use AI to create new images, recordings and models and then use those models to generate new content. We can program AI to make decisions based on these patterns and then generate new content based on them.
A common misconception is that Generative AI is a type of AI that generates its own data. Generative AI neither generates nor produces its own data or models. The “training dataset” refers to the data used to train a generative AI model. As more data is added to the model, it will automatically learn and improve. The model becomes more accurate as more training data is used to train it.
Generative AI is still in its infancy and many challenges remain, particularly in the areas of privacy and ethics.