In today's fast-changing tech world, software companies are always looking for new ways to improve their offerings. One promising technology on the rise is generative artificial intelligence (AI). This blog discusses how GenAI can give a competitive advantage and how business leaders can incorporate it.
Understanding Generative AI:
Generative AI is a significant advancement in artificial intelligence. Unlike traditional AI systems that follow set rules and datasets, generative AI models use neural networks to learn from data and create new content. These models not only understand and carry out commands but also produce content that imitates human-like behavior. The most powerful generative AI algorithms are based on neural networks trained on vast amounts of unlabeled data to identify patterns for various tasks.
By 2025, Generative AI is expected to capture 30% of the overall market share, equivalent to $60 billion of the total market. It is projected that the generative AI market value will increase by $180 billion over the next eight years. The impact and potential of generative AI are worth exploring further.
According to the plan, generative AI is supposed to gain a 30% share of the overall market by 2025 that is 60 billion dollars out of total addressable market. One can expect the value of the generative AI market to go high by 180 billion dollars in the next eight years.
Let's delve deeper into the impact and potential of generative AI.
Source: Mckinsey&Company
What are Foundation Models?
i. Generative Adversarial Networks (GANs):
We will understand what GANs are. GANs have two neural networks, namely a generator and a
discriminator. These two are trained together. The generator network creates some synthetic data samples, and the discriminator network checks whether these are real or not. In back-and-forth training, improves the generator's ability to increase the realness of its outputs as well as the discriminator's ability to distinguish between real and fake.
ii. Variational Autoencoders (VAEs):
VAEs are probabilistic models that learn to encode and decode data samples, thereby being able to generate new samples like what they learned.
iii. Recurrent Neural Nets (RNNs):
RNNs are an architecture of neural nets often used for sequence-generation tasks, such as text, and speech generation, and also in forming music.
What can Generative AI do?
The exciting generative AI technologies that have emerged seem to offer the possibility of accelerating the move towards AI, especially in environments with limited AI or data science capability. Customization will require some..
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