What is It, How does it Work, and How Can You Use It?
Generative AI, also known as generative adversarial networks (GANs), is a type of artificial intelligence technology that uses machine learning to create new and unique content. This can include everything from text and images to videos to music. For example, the platform ChatGPT is an AI powered Chatbot that responds to text input and generates responses accordingly. The platform Dall-E is a platform that generates images in multiple styles based on a text description. But how does Generative AI work, and why is it important?
How Generative AI Works
At its most basic level, generative AI involves two separate neural networks that work together to create new content. One of the neural networks, known as the generator, creates content based on a set of inputs or parameters. The other network, known as the discriminator, evaluates the content created by the generator and provides feedback on its quality. Over time, the Generative AI platform learns from the feedback provided by the discriminator and becomes better at creating high-quality content. This process is known as training, and it can take a lot of time and computing power to get right.
What is a Neural Network?
A neural network is a type of computer system that is designed to mimic how the human brain works. The human braincells, called neurons, form a complex, highly interconnected network and send electrical signals to each other to help humans process information. Similarly, a neural network is made up of interconnected nodes, also known as artificial neurons, that work together to perform complex computations and make predictions based on input data. In the development of neural networks, they go through a process called training
Neural networks can be used for a variety of tasks, such as image recognition, natural language processing, and predictive modeling. For example, a neural network could be trained to recognize images of cats by uploading thousands of images labeled cats and non-cats. Once the neural network processes the images, it learns how to accurately classify new images as either cat or non-cat.
The Creator Neural Network
The training given to the generator neural network for creating content depend on the specific task or domain that the generative AI is being used for. Here are some examples of the types of models used for different generative tasks:
- Text generation: The generator neural network is often trained on a body of text, such as books, articles, or other written material to train a language model, l, which can then generate new text by predicting the most likely next word or sequence of words based on the input it has received. For example, Microsoft Outlook or Gmail use Natural Language Processing to predict user's sentences when replying to an email. It uses machine learning algorithms to suggest responses to emails based on the email content and the user's history of email interactions.
- Image generation: The generator neural network is often trained on a dataset of images. These models learn to encode and decode images, and can generate new images by sampling from the latent space of the model.
- Music generation: The generator neural network can be trained on a dataset of MIDI files or other musical notation, which aren’t actually music files, but files that indicate which notes are played during a song, when and how long they are played and how loud they should be played. The neural network learns how to predict the next note or sequence of notes in a musical piece. Using that data, it then can be used to generate new compositions based on the input they receive.
- Video generation: The generator neural network can be trained on a dataset of videos, and learn to generate new video frames or entire videos based on the input they receive.
How is it being Used?
Generative AI is being used in a variety of industries, ranging from creative arts to engineering and finance. Here are a few examples of industries that have already implemented generative AI:
- Fashion: Generative AI is being used to create new fashion designs and to generate personalized recommendations for customers. For example, H&M, Zara and Old Navy use generative AI predict the next trends of fashion to stay ahead of the curve using social media platforms like Instagram to identify what influencers are posting as their Outfits of the Day to develop their fashion lines.
- Healthcare: Generative AI is being used to create 3D models of organs and to simulate the effects of different drugs on the body. For example, the company Insilico Medicine uses generative AI platform, called Chemistry 42, to design new drugs which has cut the time and money involved in drug development process as much as 90%.
- Finance: Generative AI is being used to generate financial forecasts and to optimize trading strategies. For example, the company Kensho uses generative AI to analyze financial markets data and predict how different events might impact them. For example, if a hurricane hit a particular region, the platform can analyze the data to predict how it will impact insurance stocks or the housing markets. Using the predictive models, they are able to provide investment advice to their clients.
Generative AI and the Legal Industry
The legal industry has implemented generative AI in a number of ways to improve efficiency, reduce costs, and increase accuracy in various legal processes. Here are some examples:
- Contract Review: Generative AI is being used to review and analyze contracts, to help lawyers to identify key clauses, risks, and obligations in a fraction of the time it would take to review them manually. For example, the company LawGeex uses generative AI to review contracts with low-to-medium complexity such as non-disclosure agreements, service-level agreements, statement of Work contracts, and others to provide an assessment of the legal risks and obligations.
- Document Analysis: Generative AI is being used to analyze legal documents and to extract relevant information, such as case citations and legal arguments. This helps lawyers to conduct legal research more efficiently and to identify relevant case law more quickly. For example, the company Ross Intelligence uses generative AI to analyze legal documents and to provide legal research to its clients.
- E-Discovery: Generative AI is being used to assist with e-discovery, which is the process of identifying and collecting electronic documents for use in legal proceedings. Generative AI can be used to review large volumes of data and to identify relevant documents more quickly and accurately than manual review. For example, the company Relativity uses generative AI to assist with e-discovery in litigation and investigations by uploading all the discovery materials into one place, organizing communications to see who someone talked to, what they said and when, and streamlining the redaction process to protect client PII.
- Case Prediction: Generative AI is being used to predict the outcome of legal cases based on historical case data and other relevant factors. This can help lawyers to make more informed decisions about whether to take on a case and how to approach it. For example, the company Premonition uses generative AI to analyze historical case data and to provide predictions about the likely outcome of a case. It can also provide in-depth analytics on an attorney’s performance and litigation experience which can be used to gather information on opposing counsel or identify prospective attorneys to recruit.
These are just a few examples of how the legal industry has implemented generative AI to improve efficiency and accuracy in various legal processes. As generative AI continues to develop, it is likely that its use in the legal industry will become even more widespread.
Will Generative AI Replace Jobs?
Generative AI has the potential to automate certain tasks that were previously performed by humans, which could lead to job displacement in some industries. For example, generative AI can be used to create art, music, and writing, which could potentially reduce the demand for human artists and writers. However, it's important to note that generative AI is not a substitute for human creativity, and it is unlikely to completely replace the need for human workers in many fields. In fact, in many cases, generative AI is designed to work alongside humans, augmenting their abilities and improving their productivity.
Therefore, the deployment of generative AI is likely to create new jobs in fields such as data science, machine learning engineering, and AI ethics. Additionally, as generative AI reduces the cost of certain goods and services, it could increase demand and create new jobs in related areas. Generative AI it is likely to transform the nature of work and create new opportunities for human workers to collaborate with and benefit from this technology.
Why Generative AI important?
Generative AI has the potential to revolutionize the way we create and consume content. With generative AI, it's possible to create new and unique content that would be impossible for humans to produce on their own. This can lead to new forms of art, music, and literature that push the boundaries of what we thought was possible.
But generative AI isn't just limited to the creative arts. It also has practical applications in fields like medicine, finance, and engineering. For example, generative AI can be used to simulate complex systems and predict how they will behave under different conditions. This can help researchers develop new drugs, optimize financial investments, and design more efficient buildings and vehicles.
While the idea of Generative AI may seem like a science fiction movie that could end terribly, there are many benefits to AI that we will continue to see as the technology advances.