The Future of AI: Exploring the Next Generation of Generative Models
What Generative AI is currently capable of and the current challenges it needs to overcome to explore the next wave of generative AI models?
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If you’re keeping up with the tech world, you would know that Generative AI is the hottest topic. We’re hearing so much about ChatGPT, DALL-E, and more.
The recent breakthroughs in Generative AI will drastically alter the way we continue to approach content creation and the growth rate of AI tools in all sectors. Grand View Research stated in their Artificial Intelligence Market Size, Share & Trends Analysis Report:
“The global artificial intelligence market size was valued at USD 136.55 billion in 2022 and is projected to expand at a compound annual growth rate of 37.3% from 2023 to 2030.“
More and more organizations by the day, from different sectors or backgrounds are looking to upskill with the use of Generative AI.
What is Generative AI?
Generative AI is algorithms used to create new and unique content, such as text, audio, code, images, and more. With the development of AI, Generative AI has the potential to take over various industries helping them with tasks that people thought were once upon a time impossible.
Generative AI is already creating art that can mimic artists such as Van Gogh. The fashion industry can potentially use generative AI to create new designs for their next line. Interior designers can use generative AI to build someone their dream home within days, rather than weeks and months.
Generative AI is fairly new, a work in progress and still needs time to perfect itself. However, applications such as ChatGPT have set the bar high and we should expect to see more innovative applications getting released in the coming years.
The Role of Generative AI
There are no specific limitations on what generative AI can currently do as mentioned before, it’s still a work in progress. However, as of today, we can categorize it into 3 parts:
- Producing new content/information:
- Replace repetitive tasks:
- Customized data:
This can range from creating a new blog, a video tutorial, or some fancy new art for your wall. However, it can also help in the development of a novel drug.
Generative AI can take over employees' tedious and repetitive tasks such as emails, presentation summaries, coding and other types of operations.
Generative AI can create content for specific customer experiences, and this can be used as data to ensure success, ROI, marketing techniques, and customer engagement. Using the consumer’s behavioral patterns, companies will be able to distinguish effective strategies and methods.
Below is an example of one of the most popular types of generative AI models, Diffusion Models.
The diffusion model is designed to learn the underlying structure of a dataset by mapping it to 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 diffusion process is when you slowly add or diffuse noise to the compressed latent representation, and generate an image that is just noise. However, the diffusion model goes in the opposite direction and does the reverse process of diffusion. The noise is gradually reduced from the image in a controlled way, so the image slowly appears to look like the original.
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Uses Cases of Generative AI
Generative AI has been widely adopted by many organizations from different sectors. It has allowed them to adopt the tools to help fine-tune their current processes and methods and elevate them more effectively. For example:
If it is creating a new article, a new image to put on the website, or a cool video. Generative AI has taken the media sector by storm, allowing them to produce efficient content at a faster rate and reduce their cost. Personalized content has allowed organizations to take their customer engagement to the next level, providing a more effective customer retention strategy.
AI tools such as Intelligent document processing (IDP) for KYC and AML processes. However, generative AI has allowed financial institutions to take their customer analysis further by discovering new patterns in consumer spending and determining potential issues.
Generative AI can help with images such as X-ray and CT scans to provide more accurate visualizations, define images better, and detect diagnostics at a faster rate. For example, using tools such as illustrations-to-photo conversion through GANs (Generative Adversarial Networks) has allowed healthcare professionals to have a more in-depth understanding of a patient's current medical state.
Governance Challenges of Generative AI
With anything great, comes bad, right? The rise in generative AI has led to the emergence of how governments are going to be able to control the use of generative AI tools.
For a while now, the AI field has been open for organizations to do what they want. However, it was a matter of time before someone came in and created fixed regulations around AI. Many are concerned about the supervision of generative AI models and how it will impact the socio-economy, as well as other issues such as intellectual property, and privacy infringement.
The main challenges that generative AI is currently facing in terms of governance are:
- Data Privacy - Generative AI models require a lot of data to be able to successfully export accurate outputs. Data privacy is a challenge that all AI companies and tools are facing due to the potential misuse of sensitive information.
- Ownership - Intellectual property rights for any content or information that has been created by generative AI are still an open discussion. Some may say that the content is unique, whereas others may say the text-generated content has been paraphrased from a variety of internet sources.
- Quality - With the high volume of data that is fed into generative AI models, the number one concern would be to investigate the quality of the data and then the accuracy of the output that has been generated. Fields such as medicine are areas of high concern as dealing with misinformation can be highly impactful.
- Bias - As we look into the quality of the data, we also need to evaluate the possible bias present in the training data. This can lead to discriminatory outputs, causing AI to be distasteful in many people's eyes.
Wrapping it up
Generative AI still has a lot of work to do before it's positively accepted by everyone. These AI models need a better understanding of human speech from different cultural backgrounds. For us common sense when speaking with someone comes naturally to us, however, it’s not very common for AI systems. They struggle to adapt to different circumstances as they are programmed to be trained on factual information.
It will be interesting to see what role generative AI will play in the future. We have to wait and see.
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.