Artificial intelligence has looped through multiple cycles of hype but the release of ChatGPT certainly marked the turning point. OpenAI's chatbot is powered by its latest Large Language Models (LLMs). It holds the prowess to write poems, churn out essays and tell jokes that look like they're created by humans. This is the point in history where everyone is wondering- What is Generative AI.
This blog attempts to answer this question, along with some of its key aspects. The key to understanding Gen AI is to study it from different angles.
Let's get started!
So, what is Generative AI? Generative AI (artificial intelligence) can be termed as a type of Deep Learning (DL) model. It's trained to produce text, computer code, audiovisual content and images in response to inserted prompts. Gen AI reacts to requests like human authors or artists, but more quickly.
Gen AI models are trained on widespread quantities of raw data that's usually similar to the kind that it's built to produce. These models learn to form responses upon given arbitrary inputs that are highly in line with those inputs. For instance, some Gen AI models are trained on huge quantities of text to respond to written prompts in an original and organic manner.
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Understanding the history of Generative AI stands as an important aspect of learning this technology. Here are key milestones in its tread till here-
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Another important question in addition to 'what is Generative AI' is how does Generative AI work. There are three key aspects behind its working. Let's discuss those.
Gen AI is considered to be a type of Machine Learning and hence relies on mathematical analysis for finding relevant concepts, patterns or images. This analysis is then used for producing content that is highly similar or related to the inserted prompt.
Gen AI relies on a kind of ML called Deep Learning. DL models are powerful to learn from unlabeled data and employ a type of computing architecture named a neural network. These architectures comprise multiple nodes that pass data to one another, quite similarly to a human brain passing data via neurons. Neural networks perform highly refined and sophisticated tasks.
Gen AI models that can interpret language must understand more than separate words but rather interpret entire sentences, documents and paragraphs. Early Machine Learning models struggled with this and would forget about the sentence's beginning till it reached the end. This resulted in misinterpretation.
Modern Gen AI models utilize a specific type of neural network named transformers. Their self-attention capability detects the connection of elements in a sequence. Transformers use Gen AI models for processing and contextualizing gigantic blocks of text rather than just individual words and phrases.
Generative AI models must be fed a huge amount of data to work well. For instance, the LLM ChatGPT used millions of documents to train on. An image generator is trained on millions of images while a code generator does so on billions of lines of code. The Generative AI model does not require as much data to produce a result after reaching a certain step of fine tuning.
All the training data is stored in a vector database. Here the points of data are stored as vectors (a set of coordinates) within a multi-dimensional field. Storing data as vectors facilitate ML models to capture nearby points of data. Models can thus make associations or understand the context of an image, a sound, a word or another type of content.
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There are various types of Generative AI models and each brings its own distinctive approach for generating content. The most prominent one are discussed here-
GANs comprise two neural networks- the generator and the discriminator. They compete against one another in a game-like setting. The former generates synthetic data (like text, images, sound) from random noise. The latter distinguishes between real and fake data.
The generator must create increasingly realistic data and deceive the discriminator. The discriminator enhanced its prowess of differentiating real from generated data. This competition makes GANs capable of generating increasingly realistic content.
VAEs refer to Generative models. They gain the prowess of encoding data into a latent space as well as decode it back for reconstructing the original data. The input data's probabilistic representations are learned for generating new samples from the given learned distribution. These are heavily employed in image generation tasks, along with text and audio generation.
Autoregressive models are tasked with generating data one element at one go. This conditions each element's generation on a priorly generated element. They predict the next element's probability distribution given the context of the existing elements. These are sampled from the distribution for generating new data. Language models like GPT are popular examples of autoregressive models.
RNNs are a kind of neural network and process sequential data like time-series data or natural language sentences. These are used for Generative tasks as it predicts the coming element in the sequence via the prior elements. RNNs are, however, contained in generating long sequences because of the vanishing gradient problem. Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) are advanced variants of RNNs that address this limitation.
Transformers have gained high popularity in NLP and Generative tasks. They utilize attention mechanisms for effectively modeling the relationships between various elements in a sequence. Such models are parallelizable and thus handle long sequences. This renders them apt for generating contextually relevant and coherent text.
Reinforcement learning is usually applied to Generative tasks. Here an agent gains knowledge about generating data as it interacts with an environment and receives feedback or rewards. This is based on the generated samples' quality. This approach is mostly utilized in areas such as text generation.
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Generative AI tools refer to software programs that are curated to generate new content via advanced AI models. These are typically built on neural networks and can identify patterns and structures within humongous quantities of annotated data. There are many types of Gen AI tools and the most popular Generative AI tools are-
Anyone who's wondering what are the benefits of Generative AI has come to the right place. These models are getting more popular because of the huge number of potential benefits they offer. These include-
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Another imperative question to find an answer to is- what are use cases for Generative AI. While this technology is expected to affect all industries after a time. Certain industries, however, are benefiting heavily today too.
A lot of financial services companies harness the capabilities of Gen AI. This helps them serve their clients better while reducing costs.
Accelerated drug research and discovery is among the most wonderful use cases of Gen AI. Models are used to create unprecedented protein sequences with certain properties for curating enzymes, antibodies, gene therapy and vaccines. Healthcare and life sciences organizations design synthetic gene sequences to be used in synthetic biology as well as metabolic engineering.
Automotive companies employ Gen AI technology for plenty of purposes. This ranges between engineering to customer service and in-vehicle experiences.
Gen AI models have the potential to produce new content at very less the time and cost of traditional production.
Early utilization of Generative AI in telecommunication is mainly focused on reinventing the customer experience cycle. Customer experience contains the joined interactions of subscribers throughout all touchpoints during the customer journey.
For example, telecommunication companies improve customer service via live human-like conversational agents. Network performance is optimized by analyzing network data and recommending fixes. Customer relationships are reinvented with tailored one-to-one sales assistants.
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The future of Generative AI seems bright. This is one technology that has risen to such a great height at a super fast pace. Its future is expected to be one that's wide and growing. As for usage, there is no short of its use cases even in the present too.
There are too many things to discuss when the topic is as wide as 'what is Generative AI'. This blog has attempted to answer all commonly asked questions around its use cases, popular tools, types and what it does. Its future seems bright and is certain to be as or even more powerful than its present.
Yes, it's a form of Gen AI. It helps with information retrieval and content creation.
OpenAI is an organization responsible for creating and promoting AI. Gen AI is a technology to create new content.
Gen AI's main goal is to utilize advanced technologies for improving different domains and industries.
GPT is the acronym for Generative Pre-training Transformer.
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