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If you keep up with the tech world, you’ll know that generative AI is a hot topic. We hear a lot about ChatGPT, DALL-E and others.
Recent advances in Generative AI will dramatically change the way we approach content creation and the rate of growth of AI tools across all sectors. Grand View Research stated in their Artificial Intelligence Market Size, Share and Trends Analysis Report:
The global artificial intelligence market size was estimated at USD 136.55 billion in 2022 and is expected to expand at a CAGR of 37.3% during the period 2023 to 2030.
Every day, more and more organizations, from different sectors and backgrounds, are trying to increase the use of generative AI.
Generative AI is the algorithms used to create new and unique content such as text, audio, code, images and more. As artificial intelligence advances, generative AI has the potential to take over various industries and help them accomplish tasks that humans once thought impossible.
Generative AI is already creating art that can imitate artists like Van Gogh. The fashion industry can use generative AI to create new designs for their next line. Interior designers can use generative artificial intelligence to build someone their dream home in days rather than weeks and months.
Generative AI is fairly new, a work in progress, and still needs time to perfect itself. However, apps like ChatGPT have set the bar high and we should expect more innovative apps to come out in the coming years.
The role of generative AI
There are no concrete limits to what generative AI can do, as we mentioned earlier, it’s still a work in progress. However, as of today, we can divide it into 3 parts:
- Production of new content/information:
- Change recurring tasks:
- Custom data:
This could range from creating a new blog, a video tutorial, or some nice new art for your wall. However, it can also help in the development of new drugs.
Generative AI can take over tedious and repetitive tasks for employees, such as email, summarizing presentations, coding and other types of operations.
Generative AI can create content for specific customer experiences and this can be used as data to drive success, ROI, marketing techniques and customer engagement. Using customer behavior patterns, companies will be able to differentiate between effective strategies and methods.
Below is an example of one of the most popular types of generative AI models, diffusion models.
Diffusion model
A diffusion model is designed to explore the underlying structure of a data set by embedding it into a lower dimensional latent space. Latent diffusion models are a type of deep generative neural network developed by the CompVis group at LMU Munich and Runway.
The process of diffusion is when you slowly add or diffuse noise to a compressed latent representation and produce an image that is just noise. However, the diffusion model goes in the opposite direction and does the opposite process of diffusion. The noise is gradually reduced from the image in a controlled way, so the image gradually resembles the original.
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Generative AI has been widely adopted by many organizations from various sectors. This allowed them to gain tools to help refine their current processes and methods and make them more efficient. For example:
Media
Whether it’s creating a new article, a new image to post on a website, or a cool video. Generative AI has taken the media sector by storm, allowing them to create effective content at a faster pace and lower their cost. Personalized content has enabled organizations to take customer engagement to the next level, providing a more effective customer retention strategy.
finances
AI tools such as Intelligent Document Processing (IDP) for KYC and AML processes. However, generative artificial intelligence has enabled financial institutions to analyze their customers by discovering new consumer spending patterns and identifying potential issues.
Protection of health
Generative AI can assist images such as X-rays and CT scans to provide more accurate visualizations, better define images and reveal diagnoses faster. For example, the use of tools such as the transformation of illustrations into photographs through GANs (Generative Adversarial Networks) has enabled healthcare professionals to gain a deeper understanding of a patient’s current medical condition.
With anything great comes bad, right? The rise of generative AI has led to questions about how governments will be able to control the use of generative AI tools.
For some time, the AI field has been open to organizations to do what they want. However, it was only a matter of time before someone stepped in and created fixed regulations around AI. Many are concerned about the oversight of generative AI models and how it will affect socioeconomics, as well as other issues such as intellectual property and privacy violations.
The main challenges that generative AI faces in terms of governance are:
- Data Privacy – Generative AI models require a lot of data to be able to successfully export accurate results. Data privacy is a challenge that all AI companies and tools face due to the potential misuse of sensitive information.
- Ownership – Intellectual property rights to any content or information generated by Generative AI is still open to discussion. Some may say that the content is unique, while others may say that the text-generated content is paraphrased from various Internet sources.
- Quality – With the large amount of data being fed to generative AI models, the number one concern will be to examine the quality of the data and then the accuracy of the output generated. Fields such as medicine are areas of great concern because dealing with misinformation can be very influential.
- Bias – As we look at data quality, we also need to assess possible biases in the training data. This can lead to discriminatory consequences, making artificial intelligence unpleasant in the eyes of many people.
Generative AI still has a lot of work to do before it can be positively adopted by everyone. These AI models need to better understand human speech from different cultural backgrounds. Common sense for us when talking to someone comes naturally to us, however, this is not very common for AI systems. They struggle to adapt to different circumstances because they are programmed to rely on factual information.
It will be interesting to see what role generative AI will play in the future. We have to wait and see.
There is a niche is a data scientist, freelance technical writer and community manager at KDnuggets. He is particularly interested in data science career advice or tutorials and theory-based knowledge about data science. He also wants to explore different ways in which artificial intelligence is/can benefit from human longevity. A sophisticated learner, seeks to expand his technical knowledge and writing skills while managing others.
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