Generative AI study

Extracted Attributes

• Mechanism

  Models learn underlying patterns and structures of training data to produce new data based on input (natural language prompts)

• Conducted by

  MIT

• Nature of AI

  Subfield of artificial intelligence using generative models

• Output Types

  Text, images, videos, or other forms of data

• Key Finding 1

  95% of corporate AI pilots fail

• Key Finding 2

  Highest ROI found in Back office optimization

• Ethical Concerns

  Cybercrime, deception, manipulation (fake news, deepfakes), mass job replacement, intellectual property violations (training on copyrighted works)

• Enabling Technology

  Improvements in transformer-based deep neural networks, particularly large language models (LLMs)

• Environmental Impact

  Requires large-scale data centers, specialized chips, high energy for processing, and water for cooling

Timeline

• Generative AI tools became more common, driven by improvements in transformer-based deep neural networks and large language models. (Source: wikipedia)

  2020s

• An influential MIT Generative AI study revealed that 95% of corporate AI pilots fail, with the highest ROI found in back office optimization. This study's findings contributed to a significant market correction in artificial intelligence and an AI hiring freeze at Meta. (Source: related_documents)

  Undated (recent)

Generative artificial intelligence

Generative artificial intelligence (Generative AI, GenAI, or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language prompts. Generative AI tools have become more common since the AI boom in the 2020s. This boom was made possible by improvements in transformer-based deep neural networks, particularly large language models (LLMs). Major tools include chatbots such as ChatGPT, Copilot, Gemini, Claude, Grok, and DeepSeek; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Veo and Sora. Technology companies developing generative AI include OpenAI, xAI, Anthropic, Meta AI, Microsoft, Google, DeepSeek, and Baidu. Generative AI is used across many industries, including software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, and product design. The production of Generative AI systems requires large scale data centers using specialized chips which require high levels of energy for processing and water for cooling. Generative AI has raised many ethical questions and governance challenges as it can be used for cybercrime, or to deceive or manipulate people through fake news or deepfakes. Even if used ethically, it may lead to mass replacement of human jobs. The tools themselves have been criticized as violating intellectual property laws, since they are trained on copyrighted works. The material and energy intensity of the AI systems has raised concerns about the environmental impact of AI, especially in light of the challenges created by the energy transition.

Web Search Results

• Generative artificial intelligence - Wikipedia

  Glossary v t e Generative artificial intelligence (Generative AI, GenAI,( or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.( These models learn the underlying patterns and structures of their training data and use them to produce new data( based on the input, which often comes in the form of natural language prompts "Prompt (natural language)").( [...] ### Academic honesty [edit] Generative AI can be used to generate and modify academic prose, to paraphrasing sources, and translate languages. The use of generative AI in a classroom setting can be a form of academic plagiarism. Some schools have banned ChatGPT and similar tools.( source needed_] [...] The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.( Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use,( process plans for

• What is Generative AI? - Gen AI Explained - AWS

  Healthcare and life sciences companies use generative AI tools to design synthetic gene sequences for synthetic biology and metabolic engineering applications. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes. Generative AI tools also create synthetic patient and healthcare data. This data can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets. [...] Generative AI training begins with understanding foundational machine learning concepts. Learners also have to explore neural networks and AI architecture. Practical experience with Python libraries such as TensorFlow or PyTorch is essential for implementing and experimenting with different models. You also have to learn model evaluation, fine tuning and prompt engineering skills. [...] ### Accelerates research Generative AI algorithms can explore and analyze complex data in new ways, allowing researchers to discover new trends and patterns that may not be otherwise apparent. These algorithms can summarize content, outline multiple solution paths, brainstorm ideas, and create detailed documentation from research notes. This is why generative AI drastically enhances research and innovation.

• Use of Generative Artificial Intelligence, Including Large ...

  5. When generative AI itself is the focus of a study, for example, research employing GAN in medical image analysis or investigating the use of LLMs for medical inquiries [3,5,18,19], the use of AI should be explicitly detailed in the Materials and Methods section. [...] | • When generative AI itself is the focus of a study, the use of AI should be explicitly detailed in the Materials and Methods section. | | • Reviewers are forbidden from using LLMs for the primary purpose of generating review comments. | [...] Generative artificial intelligence (AI) refers to algorithms that can be used to create new content, such as text, code, images, videos, and audio. Particularly, with the introduction of generative adversarial networks (GAN) in medical imaging [1,2], generative AI has gained significant attention in the scientific community, leading to numerous publications in the past few years. The _Korean Journal of Radiology_ (_KJR_) has published several articles on this topic [3,4,5]. However, the

• Traditional AI vs. Generative AI: What's the Difference? | Illinois

  In this section, we’ll detail what generative AI is, explore how it works, discuss its benefits, outline its challenges and limitations, and point out uses of it in various fields, including education. ### What is Generative AI? Generative AI is a type of AI that uses deep learning techniques to create new content, such as images, music, animation, 3D models, and text. [...] In healthcare, generative AI is used to create synthetic medical data for research, generate patient-specific treatment plans, and assist in medical image analysis. In entertainment, the technology allows for the creation of video game content, scripts for movies and TV shows, and the generation of music and soundtracks. In marketing, generative AI creates targeted marketing campaigns, generates product descriptions, and personalized advertisements. [...] Generative AI models are trained on large datasets that contain examples of the content they will generate. They learn to recognize patterns and features within this data and develops an understanding of the underlying structure. Once trained, a model can generate new, original content that mirrors the characteristics of the data it has seen before.

• What is Generative AI and How Does it Work? | NVIDIA Glossary

  Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more. The use cases and possibilities span all industries and individuals. Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. For example, it can turn text inputs into an image, turn an image into a song, or turn video into text. Generative AI use cases