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What is Generative AI? Applications, Types & Limitations
Published
05th Jan, 2024
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Artificial Intelligence continues to make significant strides in the field of tech evolution years, and one of the most exciting developments in this domain is the inception of the revolutionary Generative AI.
Did you know that the Generative AI market, globally valued at $10.5 billion in 2022, is expected to hit $191.8 billion by the end of 2032, claiming a whopping CAGR rate of 34.1%?
Given these statistics and the increasing demand for Generative AI, it wouldn’t be too wrong if I say that Generative AI is technology’s hottest talking point of 2023, which has rapidly gained traction across businesses and consumers.
In this below-mentioned article, let me take you through a detailed analysis of Generative AI to unlock what is Generative AI, explore some of its many benefits, and examine how does Generative AI work!
The Rise of AI Technologies
For people who are still wondering what Generative AI mean does, allow me to give you a quick glance at its fundamental concept.
Generative AI can be referred to as a subset of machine learning that primarily focuses on creating AI models capable of generating various types of content that share similar characteristics to human-created content.
Thanks to the emergence of powerful tools such as ChatGPT, DALLE-2, and Midjourney, Generative AI has come into the limelight very quickly.
So, is ChatGPT Generative AI?
Yes! ChatGPT is one of the most popular implementations of Generative AI, signifying its prowess and potential.
In fact, did you know that just two months after its launch, ChatGPT reached 100 million active users, successfully placing itself as the fastest-growing consumer application? Compared to other platforms such as Twitter and Instagram, which took two years and nine months, respectively, to hit the 100 million mark, the growth of ChatGPT was enormous, hinting towards the rapid advancement of AI technology.
Generative AI has quickly filtered into various sectors and industries as businesses worldwide have started to embrace it. From creative outputs and personalizations to enhanced customer experiences and satisfaction, Generative AI poses a myriad of advantages for all of its users.
How Does Generative AI Work?
Generative AI models primarily work by leveraging neural networks and machine learning techniques to generate content, be it texts, images, music, or other formats of data.
These models are fed with vast amounts of data during the initial stage. Following this, these models make use of a complex computing process, which we refer to as deep learning, to identify common patterns and characteristics in those data. Once identified, they then use that information to create new convincing outputs.
Over the years, these Generative models have become increasingly sophisticated with the more data they receive. This has, in turn, resulted in more convincing and human-like outputs.
Key Types of Generative Models
Generative AI models can be categorized into various types, depending on their specific tasks and capabilities. A few examples of the same include,
1. Generative Adversarial Networks
GANs, or Generative Adversarial Networks include two types of contrasting neural networks. These include a generator and a discriminator.
The generator creates data samples, such as an image, and on the other hand, the discriminator tries to differentiate between real and generated samples. These two components get better with time at their respective roles, producing data indistinguishable from the real data.
Two very popular examples of Generative AI models that follow this framework include DALL-E and Midjourney.
2. Variational Autoencoders
Similar to GAN, VAEs leverage two networks for interpreting and generating data. In this case, they are referred to as the encoder, which maps data into a latent space, and the decoder, which generates data from the latent space.
A small example to help you get a better understanding of the same includes,
Let’s say you are using pictures or photos as training data to teach a computer program to generate human faces. With time, the more data you feed, the computer program will be able to break down the person’s photo into simple yet important characteristics such as nose, mouth, eyes, ears, and so on. This in turn, will allow the model to create new faces by using all these features.
3. Transformer-Based Models
Last but not least is transformer-based models, which can be described as a type of deep learning model architecture. The core innovation of the transformer architecture allows these models to weigh the importance of different words in a sentence during the processing stage. They can capture long-range dependencies between words in an efficient manner, making them perfectly suited for understanding the context of a word in a sentence.
Two very popular Generative AI examples that are widely used across various industries include ChatGPT and Google Bard.
How to Evaluate Generative AI Models?
There are three main criteria come into play when you are evaluating the performance of Generative AI models. Those include,
1. Quality: The generated output must exhibit high quality and be consistent with the task’s requirements. For example, in speech generation, the quality should be top-notch when it comes to accuracy, fluency, and relevance. Users must be able to find the generated content useful and reliable. Similarly, in image generation, the desired outputs should be as close to indistinguishable from natural images as possible.
2. Diversity: Diversity is another important parameter that you must consider while evaluating various Generative AI models. Successful Generative AI models should be able to produce diverse and non-repetitive outputs. This not only helps to reduce biases but also ensures that the model can generate a wide variety of creative and meaningful content.
3. Speed: Lastly, speed refers to the efficiency and responsiveness of the Generative AI model. Fast generation is an important fact, especially for real-time or interactive applications, such as chatbots or real-time image editing. Therefore, a good Generative AI model must be able to generate content within an acceptable time frame for practical usability and scalability.
How to Develop Generative AI Models?
Now that you know what is Generative AI, and exactly how does Generative AI work, let’s take a look at what goes behind the development of Generative AI models.
If you ask me, developing Generative AI models can be quite a challenging and complicated process that involves several steps. Let me help you with an in-depth overview of how to develop Generative AI models.
a. Data Collection and Preparation
To create effective Generative AI models, you should start by gathering a good dataset that matches your project's needs. Make sure the dataset is big enough to train a strong model. For example, to create a model capable of generating text, you will have to collect and feed it with a substantial amount of text from diverse places.
After you've curated your dataset, the next thing to do is to clean it and get it ready to ensure it is uniform and of good quality. This process includes crucial actions like adjusting image sizes, improving the data, and dividing text into smaller parts known as tokens.
b. Choose An AI Architecture
The next step is to select an AI architecture that appropriately suits for your task. Some prominently known options include GANs, Convolutional Neural Networks(CNNs), and Recurrent Neural Networks (RNNs).
If you aim to generate high-quality, realistic images, then I would suggest you should use architectures such as VAEs, GANs, or CNNs.
Always remember that there is no single ‘best’ solution. It all ultimately boils down to your individual needs, skills, and the resources available.
c. Model Training
Once you have completed both steps, it is time to train the model using the training data. Keep track of the loss functions to ensure that it is decreasing. In addition to this, you will also need to monitor the model’s performance continuously on the validation set to detect any form of overfitting and adjust the hyperparameters accordingly.
d.Evaluate and Improve
Assess the overall quality, diversity, and other relevant aspects of the generated outputs to ensure that they match your requirements. You can also use cross-validation and test datasets to evaluate the model’s generalization performance.
In case of any anomaly, or if you feel that the generated output is not up to the mark, you can also make the necessary adjustments to improve the performance of the model. This includes adding more data, adjusting the hyperparameters, ensembling multiple models together, and seeking the help of data augmentation.
e. Deployment
Once you are completely satisfied with the model’s performance, you can move on to the deployment phase so that others can also access its prediction and generation. Depending on your requirements and choice, you can choose the appropriate technology.
For web services, I will recommend utilizing frameworks like Flask or Django. Similarly, for mobile applications, you can go with technologies like TensorFlow Lite.
Irrespective of how you deploy, do not forget to continuously monitor how the model functions in real life. With time and practice, you will be able to build high-quality Generative AI models from scratch with ease.
Practical Applications of Generative AI
A wide range of practical Generative AI applications can be found across various domains. Mentioned below are a few examples of the same.
- Language: Text is considered one of the most advanced domains for Generative AI models to perform. From essay generation and news articles to product descriptions and creative writing, these models can be leveraged to complete a wide range of tasks.
- Audio: Another emerging field within Generative AI is audio, which includes music, speech, and other various forms of audio. With the help of Generative AI, it is now possible to compose music melodies or sometimes even entire songs across different genres. In addition to this, AI-powered models can also be used to create synthetic voices for audiobooks, personalized voiceovers, and voice assistants.
- Visual: Images are by far considered to be one of the most popular applications of Generative AI. It encompasses the creation of videos, graphs, 3D images, avatars, and other forms of illustrations. Furthermore, it also allows for flexibility by allowing users to experiment with different techniques and styles for editing and modifying the generated visuals.
- Healthcare: Generative AI has also been instrumental in revolutionizing the healthcare industry by creating synthetic medical images, such as X-rays, MRI Scans, or CT scans. One important example in this aspect includes the rapid utilization of AWS HealthScribe, which allows medical professionals to transcribe patient consultations and upload all the critical information into their electronic health records.
- Finance: Generative AI is one of the many tools that is widely used in the finance industry. From analyzing market patterns to anticipating stock market trends, Generative AI has been a constant aid for financial analysts in all their tasks and activities.
Limitations of Generative AI
While Generative AI has undoubtedly made remarkable advancements in recent years, like most other technology, it also comes alongside its own set of disadvantages and limitations.
Wondering what those are? Let’s find out.
1. Limited Creativity and Imagination
When it comes to delivering highly creative or imaginative content, Generative AI might not always perform in the desired manner. One of the significant reasons behind this is that Generative AI models are limited to what they have learned from the training data. Therefore, they might struggle often to come up with content that goes beyond what they have seen. After all, novelty and creativity are still largely human traits.
2. Lack of Accuracy
Another common limitation of Generative AI is that it might not always generate accurate outcomes. These models do not necessarily know whether the things that they produce are accurate, and for the most part, even we, as humans, have very little way of knowing where the information is sourced from or how the algorithms have processed it to generate content. Therefore, although Generative AI can produce intriguing and entertaining content, I would not recommend you always rely on the information or content that they create.
3. Data Dependency
As you must have understood by now, Generative AI models rely heavily on the quantity and quality of the training data. To get the desired outcomes or for these models to operate efficiently, you need to feed them high-quality and unbiased data. However, this may not always be viable, especially in domain data shortage to properly train a model.
One example of this challenge can be observed in the case of 3D assets, which, while available, can be quite costly and may still take long to unleash its full potential.
Conclusion
Generative AI stands at the forefront of AI-led transformation, redefining how we generate content, leverage machines, and utilize them to solve complex problems. While it holds immense promise for the future, it also presents complexities and ethical considerations like the ones I have listed above. The key is to strike the right balance between innovation and ethical use as technology evolves.
If you wish to know more about the same, do not forget to check out the official website of KnowledgeHut, which provides several exciting courses specifically tailored to suit all your needs!
Frequently Asked Questions (FAQs)
1. What is the difference between AI and Generative AI?
One of the main differences between traditional AI and Generative AI lies in its objectives and functioning. While the former is primarily focused on performing specific tasks that are based on predefined rules and patterns, the latter goes beyond the limitation and strives to generate content that resembles a human-like approach.
2. What are the benefits of Generative AI?
From enhanced productivity and efficiency to adaptive learning and improved customer satisfaction, the benefits of incorporating Generative AI into businesses are many. In addition, Generative AI also helps save a lot of time and other crucial resources by automating tasks that would otherwise have required manual labor. For example, Generative AI can produce innovative building designs based on given specifications
3. What is the future of Generative AI?
The direction in which Generative AI is heading involves the smooth incorporation of AI technology into our everyday routines, influencing how we generate, enjoy, and engage with content and knowledge. This advanced technology has enabled various possibilities, such as AI-improved education, AI-created music and art, and AI-driven virtual realms. As it progresses, it offers numerous prospects for shaping a world that's inventive, effective, and, most importantly, just and impartial.
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