- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Prompt Engineering (PE) ... PromptBase ... Prompt Injection Attack
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
- Video/Image ... Vision ... Enhancement ... Fake ... Reconstruction ... Colorize ... Occlusions ... Predict image ... Image/Video Transfer Learning
- End-to-End Speech ... Synthesize Speech ... Speech Recognition ... Music
- Humor ... Writing/Publishing ... Storytelling ... Broadcast ... Journalism/News ... Podcasts ... Books, Radio & Movies - Exploring Possibilities
- Demos, generating...
- Music: Generating Piano Music with Transformer | I. Simon, A. Huang, J. Engel, C. "Fjord" Hawthorne - Google on Colab play with pretrained Transformer models for piano music generation, based on the Music Transformer model
- Faces: TF-Hub generative image model | The TensorFlow Hub Authors - Google use of a TF-Hub module based on a Generative Adversarial Network (GAN). The module maps from N-dimensional vectors, called latent space, to RGB images.
- 3D Objects: 3D Style Transfer | Google uses Lucid to implement style transfer from a textured 3D model and a style image onto a new texture for the 3D model by using a Differentiable Image Parameterization
- Try Stable Diffusion | Stability AI ... text-to-image diffusion model capable of generating photo-realistic images
- Demo, summarizing, priority listing, & analysis...
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- In-Context Learning (ICL) ... Context ... Causation vs. Correlation ... Autocorrelation ... Out-of-Distribution (OOD) Generalization ... Transfer Learning
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Discriminative vs. Generative
- Assistants ... Personal Companions ... Agents ... Negotiation ... LangChain
- Development ... Notebooks ... AI Pair Programming ... Codeless, Generators, Drag n' Drop ... AIOps/MLOps ... AIaaS/MLaaS
- Generative Model | Wikipedia
- Attention Mechanism ...Transformer ...Generative Pre-trained Transformer (GPT) ... GAN ... BERT
- Generative Query Network (GQN)
- Data Augmentation, Data Labeling, and Auto-Tagging
- Generative AI for Business Analysis
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Metaverse ... Quantum ... Game Theory
- Roblox ... building tools to allow creators to develop integrated 3D objects that come with behaviour built in.
- Immersive Reality ... Metaverse ... Digital Twin ... Internet of Things (IoT) ... Transhumanism
- Architectures for AI ... Generative AI Stack ... Enterprise Architecture (EA) ... Enterprise Portfolio Management (EPM) ... Architecture and Interior Design
Generative AI or GenAI is technology that creates original content, including text, images, video, and computer code, by identifying patterns in large quantities of training data. Generative AI could improve the speed and accuracy of product research and development. Generative AI technology can also help with product engineering by allowing teams to simulate products in virtual environments. This allows for complex problems to be solved more quickly and efficiently, leading to improved design accuracy. - The Generative AI Revolution Is Creating The Next Phase Of Autonomous Enterprise | Mark Minevich - Forbes
- 1 Generation
- 2 Topics
- 3 MIT GenAI Summit
Generative AI is a groundbreaking field with diverse applications and innovative techniques.
- Items: It can produce various items, such as artwork, music, text, images, and 3D models. These outputs are generated through AI models trained on vast datasets, and their quality varies based on the sophistication of the model and the specific application.
- Techniques: Generative AI's strategic capabilities include exploring different scenarios, optimizing resource allocation, and adapting to opponents' strategies. Meanwhile, its tactical prowess involves reinforcement learning, rule-based inference, and alternative learning methods. However, ethical considerations remain vital, given the potential for the generation of misleading or harmful content. Overall, Generative AI continues to revolutionize artificial intelligence, pushing the boundaries of creativity and problem-solving in various domains.
Generative AI, combined with Natural Language Processing (NLP), has emerged as a domain with a profound impact on various aspects of human-machine interactions. The applications of Generative AI in natural language extend to Conversational AI, enabling the development of intelligent virtual Assistants and Personal Companions that can engage in seamless and human-like conversations with users. Moreover, Generative AI has revolutionized the way machines process and generate text, facilitating advancements in question-answering systems that can efficiently provide relevant and accurate information. With its wide-ranging applications across publications and documents, Generative AI has become an indispensable tool, augmenting human capabilities and pushing the boundaries of natural language understanding and generation. Summarization techniques, powered by Generative AI, have also proven invaluable in condensing large volumes of text into concise and coherent summaries, aiding in the extraction of critical information from documents and articles. Beyond practical utilities, Generative AI has ventured into the realm of creative expressions, generating captivating narratives, stories, and even scripts for various media, such as novels, radio programs, and other forms of entertainment.
- Conversational AI: Generative AI, in synergy with Natural Language Processing (NLP), has revolutionized Conversational AI, giving rise to highly sophisticated and human-like virtual assistants and chatbots. These AI systems possess the capability to understand and generate natural language, enabling them to engage in seamless and dynamic conversations with users. Through advanced language models, Generative AI equips Conversational AI with the ability to comprehend context, recognize intent, and respond contextually to user queries. These AI-driven chatbots and virtual assistants can assist users with a wide range of tasks, from answering questions and providing customer support to guiding users through complex processes.
- Assistants: assist users in a wide range of tasks, such as setting reminders, answering questions, scheduling appointments, and even making personalized recommendations. Generative AI enables assistants to continuously learn and adapt to user preferences and needs, enhancing the user experience over time. The technology's natural language understanding capabilities enable assistants to interpret complex queries and handle ambiguous inputs, creating a more human-like and intuitive interaction
- Personal Companions: ability to understand and respond to human emotions and sentiments, offering personalized and compassionate interactions. By leveraging deep learning and language models, Generative AI enables personal companions to engage in natural and contextually relevant conversations, acting as supportive confidantes, mentors, and companions. They can provide emotional support, offer helpful advice, and assist users in managing their daily tasks and well-being. These companions continuously learn from user interactions, developing a deeper understanding of individual needs and preferences, fostering a unique and meaningful bond with their users.
- Questions/Answers: generate contextually relevant and human-like answers to a wide range of questions. These models excel in natural language understanding, allowing them to grasp the nuances and subtleties of queries, and provide meaningful responses. The capability of Generative AI in answering questions extends beyond simple factual queries; it can tackle more complex tasks like natural language reasoning, inference, and multi-step problem-solving.
- Documents: ability to extract critical insights and key points from large volumes of text, facilitating faster information retrieval and comprehension. Generative AI can be applied to document generation, where it can produce human-like texts, reports, or even creative writing based on specific prompts or templates. Moreover, the technology can aid in sentiment analysis, identifying emotions and opinions expressed within documents, which is valuable for market research, customer feedback analysis, and social media monitoring.
- Summarize: summarizing extensive documents, webpages, and diverse content with unparalleled precision and speed. Generative AI can sift through large volumes of text, extracting essential information and condensing it into concise and coherent summaries. This capability is particularly advantageous in information-heavy domains like journalism, research, and content curation, as it enables efficient knowledge extraction and dissemination. Moreover, Generative AI excels in summarizing webpages, helping users quickly grasp the key points and main ideas from online articles, blog posts, and news updates.
- Creative Expression:
- Narratives: produce compelling and imaginative narratives, stories, and creative writing. These AI-generated narratives can range from short stories and novels to scripts for movies and other forms of entertainment. By learning from vast datasets of existing literature and creative works, Generative AI can mimic the style and tone of different authors, allowing for the creation of unique and diverse storytelling experiences. Furthermore, Generative AI's ability to understand context and coherence ensures that the narratives it generates remain engaging and cohesive.
- Stories: weaving together characters, plots, and settings to produce compelling and original stories. This application of Generative AI has the potential to revolutionize the creative writing process, serving as an inspiring tool for authors and storytellers by offering fresh ideas and alternative perspectives. AI-generated stories can range from short narratives to more extensive literary works, each with its distinct voice and style.
- Scriptwriting: creative expression across various media, including scripts, news, radio programs, movies, TV shows, and other forms of entertainment creative expression across various media. In the field of scriptwriting, Generative AI can craft compelling dialogue, character interactions, and plotlines, offering invaluable assistance to screenwriters and playwrights in generating fresh and innovative content. For news generation, Generative AI can analyze vast amounts of data and produce summaries and reports, expediting the process of news dissemination and enhancing information accessibility.
AI co-pilots are artificial intelligence systems that assist humans in performing tasks. These systems can be used in a variety of applications, from helping pilots fly planes to assisting people in their daily lives. For example, Microsoft recently announced its AI-powered digital assistant named ‘Copilot’ which has the potential to "revolutionize how people operate fundamentally and unleash a new wave of productivity increase".
- Coding: AI-powered developer tool that assists software developers with coding tasks by providing suggestions, examples, and explanations for code that is being written; by allowing developers to interact with code using natural language commands. This novel approach simplifies the coding process, making it more accessible to non-experts and streamlining collaboration among programmers. The AI-driven coding assistant can suggest code snippets, fix errors, and provide context-aware suggestions, significantly boosting productivity and code quality.
- Operating System: Microsoft is incorporating generative artificial intelligence into its widely used PC operating system with a new feature called “Windows Copilot”. The feature, announced at the company’s Build developer conference, will appear as a persistent sidebar on the Windows desktop once activated by users via a new taskbar button.
- Office Productivity: There are plans for Copilots tailored to Microsoft’s Dynamics 365 business apps, PowerPlatform, the company’s security suite. Copilot for Microsoft 365 is embedded directly into 365 apps like Word, PowerPoint, Excel, and Outlook, making generative AI intuitive and easy to harness thanks to GPT-4 and Large Language Model (LLM).
- Job Listing: analyze and understand job descriptions and candidate resumes more effectively. This enables recruiters to identify the most suitable candidates efficiently, saving time and effort in the hiring process. Moreover, Generative AI can aid in generating more comprehensive and tailored job listings, optimizing the language and content to attract a diverse pool of candidates. The technology can also help in automating candidate screening, highlighting relevant skills and experiences, and providing personalized feedback to applicants.
AI systems can learn from vast datasets of images, videos, and graphics, capturing the underlying patterns, textures, and styles present in visual data. With this knowledge, Generative AI can create original artworks, digital illustrations, and even visually stunning designs, blurring the line between human creativity and artificial intelligence.
- Images: produce lifelike images, indistinguishable from real photographs. By training on vast datasets of diverse images, the generative AI system can learn intricate details, textures, and styles, enabling it to create compelling images of objects, breathtaking scenes, and even incredibly lifelike portraits of people with astonishing realism.
- 2D/3D Models: learn from vast datasets of existing models, be it images, blueprints, or 3D meshes. By comprehending underlying patterns and structures, Generative AI can then generate new models with incredible accuracy and creativity. In the realm of 2D models, this technology can produce diverse artwork, designs, and illustrations with impressive complexity and style, revolutionizing the creative process for artists and designers. When it comes to 3D models, Generative AI can synthesize realistic and detailed objects, environments, and characters, proving invaluable in fields like gaming, virtual reality, and architecture.
- Faces and Avatars: analyze and understand the intricate features and nuances present in large datasets of human faces. Armed with this knowledge, they can then generate new faces and avatars that exhibit astonishingly lifelike qualities, from facial expressions and skin textures to hairstyles and eye colors. This technology finds widespread applications in various industries, from gaming and animation, where it allows for the creation of lifelike characters, to virtual reality and augmented reality experiences, where it enables users to embody digital personas. Furthermore, Generative AI facilitates the personalization of avatars, allowing users to craft their ideal virtual representation, and thus, enhancing the sense of immersion and engagement in virtual worlds.
- Drawings: acquired an understanding of diverse drawing styles and artistic elements by analyzing vast collections of existing artwork. By learning the intricate details of brush strokes, colors, and compositions, Generative AI can produce new drawings that exhibit creativity and diversity. This technology has broad applications, from aiding artists and designers in generating novel ideas and inspirations to providing interactive tools that transform rough sketches into polished artworks. Additionally, Generative AI has the potential to democratize art creation, allowing individuals without traditional artistic skills to express themselves through drawings and visual storytelling.
- Photographs: can learn from vast datasets of real images and understand intricate details, textures, and styles present in photographs. By leveraging this knowledge, Generative AI can produce new photographs that are visually indistinguishable from real images. This technology has immense potential in various applications, from creative endeavors like generating novel scenes and landscapes, to practical uses such as image augmentation for data augmentation in machine learning. It can also assist in restoring old or damaged photographs, filling in missing details with a high level of accuracy.
- Artwork: can learn from extensive collections of existing artwork and have gained an understanding of various artistic styles, techniques, and aesthetics. With this knowledge, Generative AI can create new and unique pieces of art that exhibit creativity and imagination, pushing the boundaries of what is possible in the realm of visual expression. From generating abstract masterpieces to photorealistic landscapes, the AI's capacity to produce art has become increasingly sophisticated and refined. Artists and designers can now collaborate with Generative AI as a tool to spark inspiration, discover novel ideas, and experiment with new artistic directions..
- Video and Animation: can learn from massive collections of video data, comprehending the temporal relationships and patterns within video sequences. With this knowledge, Generative AI can generate entirely new videos, seamlessly blending scenes, objects, and characters into captivating visual narratives. Moreover, the AI's predictive capabilities allow it to anticipate and generate future frames in a video sequence, enabling smooth and realistic animations. This technology finds applications in diverse fields, from entertainment and gaming, where it can create lifelike characters and dynamic virtual worlds, to film and visual effects, where it streamlines the animation process and enhances storytelling possibilities.
AI systems can learn from extensive collections of audio data, capturing the intricacies of musical tones, voice inflections, and ambient sounds. With this knowledge, Generative AI can compose original music, produce unique sound effects, and even mimic human voices with astonishing accuracy. This technology opens up new possibilities for musicians, sound designers, and content creators, empowering them to explore new sonic territories and enhance their creative output.
- Voice: can learn from vast datasets of audio recordings and speech patterns, capturing the intricate nuances of human vocalizations and sounds. By understanding the underlying structures and patterns of different languages and accents, Generative AI can produce highly realistic and expressive voice recordings, indistinguishable from human speech. Additionally, this technology allows for the creation of entirely new audio content, from music compositions to sound effects, offering unprecedented opportunities for creative expression. Furthermore, Generative AI can be applied to voice cloning, enabling users to have their voices replicated for various applications, including personalized voice assistants or audiobook narrations.
- Music and Audio: can learn from extensive datasets of existing music and audio, grasping the intricacies of musical structure, melody, harmony, and rhythm. With this knowledge, Generative AI can generate original and captivating music compositions across various genres and styles, rivalling the creativity of human composers. This technology not only fosters novel musical expressions but also opens up new avenues for collaboration between artists and AI, enriching the creative process by providing fresh ideas and inspiration. Moreover, Generative AI enables the creation of unique soundscapes, sound effects, and audio textures that find applications in various industries, including gaming, film, and virtual reality experiences.
AI systems can learn from vast datasets and understand underlying patterns, structures, and relationships within the data. With this knowledge, Generative AI can generate synthetic data artifacts that closely resemble real-world samples, making it invaluable for applications like data augmentation, where the augmentation of existing data with synthetic examples enhances model performance and generalization.
- Training: create synthetic data that closely resembles real-world samples. This capability is particularly beneficial when dealing with limited or imbalanced datasets, as Generative AI can augment the existing data, making it more diverse and comprehensive. Additionally, Generative AI can generate data for rare or challenging scenarios, providing crucial training examples that are difficult to obtain in real life. Furthermore, Generative AI allows for data anonymization and privacy preservation, making it possible to share datasets without compromising sensitive information. This technology has vast applications in various fields, including computer vision, natural language processing, and speech recognition, significantly enhancing the performance and robustness of machine learning models by providing high-quality and diverse training data.
- Test: create synthetic data that closely resembles real-world samples, providing a diverse and representative test set. This is particularly valuable in scenarios where acquiring real test data is expensive, time-consuming, or impractical. Generative AI enables the creation of a wide range of test data, spanning different data distributions, classes, and scenarios, which helps assess the generalization and robustness of machine learning algorithms. Moreover, Generative AI can generate challenging and edge cases that may be rare or difficult to encounter in real data, facilitating comprehensive testing of models under various conditions. As a result, Generative AI enhances the reliability and accuracy of model evaluation, empowering researchers and practitioners to thoroughly test their AI systems, identify potential weaknesses, and ultimately improve the overall performance and effectiveness of machine learning models.
- Scenario Context: can learn from diverse datasets of scenarios, encompassing different environments, interactions, and events. With this knowledge, Generative AI can create synthetic scenario context data, tailored to specific use cases and applications. This technology is particularly valuable in training AI models for tasks that require a deep comprehension of real-world context, such as natural language understanding, autonomous vehicles, and robotics. By generating diverse and realistic scenarios, Generative AI enhances the generalization and robustness of AI systems, allowing them to adapt to a broad spectrum of real-world situations. Additionally, Generative AI enables the creation of simulated environments for testing and training AI applications, saving time and resources while offering a controlled and safe space for experimentation.
Generative AI can be used to develop artifacts for designs, providing a wealth of creative solutions and augmenting various industries' design processes. In product design, especially in fashion, architecture, and industrial design, Generative AI assists in generating innovative and aesthetically appealing concepts, accelerating the ideation and prototyping stages. For materials, Generative AI can synthesize novel materials with specific properties and characteristics, revolutionizing material science and engineering. In the metaverse, Generative AI plays a pivotal role in creating immersive and realistic virtual environments for gaming and simulation, enhancing user experiences and storytelling possibilities. Moreover, in the realm of gaming, Generative AI can generate diverse characters, landscapes, and assets, elevating game development and fostering player engagement. For fonts and typography, the AI can produce unique and artistic fonts, empowering designers to explore uncharted territories in typographic expression. For specifications, infrastructure, and schematics, the AI can generate detailed and precise documentation, aiding engineers and architects in their projects. Even in the pharmaceutical industry, Generative AI holds potential in drug discovery, accelerating research by generating molecular structures and predicting drug properties.
- Product Designs: can assist designers in generating novel and cutting-edge concepts, expediting the design process and sparking creativity. In fashion, it can create unique clothing designs and fabric patterns, pushing the boundaries of style and wearability. In architecture, it can generate futuristic building designs and optimize spatial layouts, improving sustainability and livability.
- Materials: can synthesize new materials with specific attributes, tailoring them to meet various industrial needs. This technology holds tremendous promise in material science and engineering, enabling the creation of lightweight, durable, and sustainable materials for diverse applications. From high-performance alloys for aerospace to bio-inspired materials for medical devices, Generative AI can accelerate the discovery and development of materials that were previously unattainable.
- Metaverse: Generative AI has played a pivotal role in the development of the Metaverse, a virtual and interconnected world that encompasses a vast array of immersive experiences and interactions. Generative AI enables the synthesis of lifelike landscapes, interactive objects, and diverse characters, each with unique behaviors and attributes. This technology is a driving force behind the creation of expansive and evolving digital universes for gaming, social interactions, education, training, and even work. Through Generative AI, the Metaverse is continuously expanding and adapting, responding to the actions and preferences of its users, making it a highly personalized and engrossing experience.
- Gaming: can create vast and immersive gaming worlds filled with dynamic landscapes, diverse characters, and interactive elements. Moreover, AI-generated content ensures that games are constantly evolving, offering endless possibilities for exploration and discovery. In gameplay, Generative AI can also be applied to create intelligent NPCs (non-player characters) with sophisticated behaviors and decision-making capabilities, elevating the level of challenge and engagement for players.
- Fonts and Typography: produce unique and original fonts, ranging from classic and elegant to experimental and avant-garde. This technology empowers designers with a wide range of typographic choices, allowing them to explore new styles and create custom fonts tailored to specific projects and brand identities. Additionally, Generative AI enables the generation of font variations, making it easier to find the perfect fit for a particular design.
- Specifications: can learn from extensive datasets of existing specifications and comprehend the intricate details and formats. With this knowledge, Generative AI can automatically create accurate and standardized specifications for products, projects, or processes, streamlining the documentation process and ensuring consistency and clarity in communication.
- Infrastructure: can analyze data from satellite images, geographical information systems, and architectural blueprints to generate comprehensive infrastructure designs. This technology aids urban planners, civil engineers, and architects in developing efficient and sustainable infrastructure solutions, optimizing resource utilization, and minimizing environmental impact; developing detailed plans and layouts for various infrastructure projects.
- Schematics: accelerates the design process for engineers and designers, enabling them to produce detailed and accurate schematics for various systems and circuits with minimal effort; generating complex technical drawings and diagrams.
- Drugs: can generate new molecular structures and predict their properties, including interactions with specific targets or diseases. This technology accelerates drug discovery by suggesting potential drug candidates, optimizing their effectiveness, and reducing the time and cost of the drug development process. Generative AI is shaping a new era of personalized medicine, where AI-generated drugs are tailored to individual patients' needs, promising more effective and targeted treatments.
Generative AI has emerged as a groundbreaking field with diverse applications and innovative techniques, Strategy & Tactics that drive its success across various domains. When applied to open-ended gaming, Generative AI demonstrates its ability to create intelligent agents capable of learning and adapting in dynamic, unpredictable environments, leading to exciting advancements in the world of artificial intelligence-driven gaming experiences. Furthermore, Generative AI shines when tackling unanticipated scenarios, showcasing its capacity to handle novel and unforeseen challenges with creativity and efficiency. In the pursuit of these goals, Generative AI employs a plethora of techniques, including rules and inferences, where models learn from predefined guidelines and logical reasoning to make decisions. Reinforcement learning plays a pivotal role by allowing AI agents to learn from trial and error and optimize their actions based on rewards and penalties. Additionally, alternative learning techniques supplement the Generative AI arsenal by embracing innovative approaches beyond traditional methodologies, making it a powerful force in the realm of cutting-edge AI research and development. As Generative AI continues to evolve and expand its applications, its techniques, strategies, and tactics remain at the forefront of pushing the boundaries of artificial intelligence capabilities in unforeseen and unprogrammed scenarios.
Generative AI has found extensive applications in various strategic domains, showcasing its versatility and power in tackling complex challenges. One of the key areas where Generative AI excels is strategic decision-making. By leveraging generative models, AI systems can simulate and generate a multitude of possible scenarios, allowing them to explore different strategic options and outcomes. This capability proves invaluable in fields such as finance, business, and military planning, where decision-makers can use generative models to assess risks, optimize resource allocation, and devise robust strategies. Moreover, in the realm of gaming and sports, Generative AI has enabled the development of intelligent agents that can learn and adapt to opponents' strategies, making the gameplay more dynamic and engaging. Additionally, Generative AI has shown promise in generating novel strategies for various tasks, pushing the boundaries of human creativity and problem-solving. As Generative AI continues to advance, its strategic applications are likely to grow, empowering decision-makers across industries with intelligent tools to navigate complex scenarios and make informed choices.
- Open-ended Gaming: In open-ended or sandbox games, players are given a high degree of freedom to explore and interact with the game world, often without strict objectives or predefined paths. These games allow players to shape their own experiences, making choices and decisions that can lead to various outcomes and adventures, giving rise to dynamic and unpredictable gameplay.
- Unforeseen Scenarios: In adversarial situations, an AI system is faced with unforeseen and challenging scenarios that require strategic decision-making and adaptation. These scenarios often involve opponents or adversaries who actively work against the AI, posing difficult obstacles that demand clever and innovative strategies to overcome. Adversarial situations are prevalent in various AI applications, such as game playing, cybersecurity, and autonomous vehicles, where the AI system must navigate and respond effectively to dynamic and unpredictable environments.
Generative AI has proven to be a powerful tool when applied to various tactical domains, demonstrating its efficacy in devising effective and adaptive tactics for a wide range of applications. Generative AI continues to break new ground in intelligent problem-solving, optimization, and decision-making, propelling the field of artificial intelligence to unprecedented heights. One of the primary tactics utilized by Generative AI is reinforcement learning, where AI agents learn from trial and error to optimize their actions based on feedback in the form of rewards or penalties. This approach has enabled the development of intelligent systems capable of mastering complex tasks, such as game playing, robotics control, and autonomous vehicles. Additionally, Generative AI leverages alternative learning techniques, exploring innovative methodologies beyond traditional methods to enhance its tactical prowess. These techniques encompass approaches like unsupervised learning, self-supervised learning, and transfer learning, which allow AI systems to leverage existing knowledge and experiences to improve their performance in new tasks and environments. In processes, Generative AI streamlines and optimizes workflows, improving efficiency and reducing manual intervention. By harnessing Generative AI's tactical capabilities, researchers and practitioners are unlocking new avenues for intelligent problem-solving, optimization, and decision-making in various fields, contributing to the continuous advancement of artificial intelligence technology.
- Rules/Inferences: one essential tactic employed by Generative AI is the use of rules and inferences. By learning from predefined guidelines and logical reasoning, AI models can make informed decisions based on explicit rules and inferred patterns. This tactic is particularly useful in knowledge-based systems, expert systems, and rule-based games.
- Reinforcement Learning: is another pivotal tactic that has revolutionized AI applications. Through a process of trial and error, AI agents learn to take actions that maximize cumulative rewards and minimize penalties, enabling them to excel in dynamic and uncertain environments. This approach has found remarkable success in areas like game playing, robotics, and autonomous vehicles, where the AI must learn from experience to navigate complex scenarios.
- Novel Learning Methods: leverages various non-traditional and innovative methodologies that deviate from the conventional supervised, unsupervised, and reinforcement learning paradigms and other emerging approaches that aim to improve learning efficiency, data utilization, and generalization capabilities of AI systems. These tactics allow AI systems to capitalize on unlabelled data, learn from their own predictions, and transfer knowledge from one task to another, enhancing their adaptability and generalization abilities.
- Processes: can analyze vast datasets of existing processes, comprehend the underlying patterns, and identify opportunities for improvement and efficiency. Generative AI can then propose new process designs, streamlining operations and reducing manual intervention. This technology enables the creation of innovative and data-driven processes that adapt to changing circumstances, ensuring adaptability and resilience in today's dynamic business landscape. Moreover, Generative AI can simulate different scenarios, allowing businesses to explore potential outcomes before implementing changes, mitigating risks and making informed decisions. The integration of Generative AI in process design optimizes resource allocation, reduces costs, and increases productivity, ultimately leading to improved customer satisfaction and competitive advantage.
Generative AI is a fascinating and rapidly evolving field that focuses on the creation of models capable of generating new data that resembles real-world examples. As Generative AI continues to make strides, it holds the potential to transform various industries and redefine the boundaries of artificial creativity.
- A fundamental concept within Generative AI is the comparison between Generative and Discriminative approaches. While discriminative models aim to distinguish and classify data into predefined categories, generative models go beyond classification by attempting to model the underlying distribution of the data, enabling them to generate entirely new samples.
- The adoption of Generative AI methods involves leveraging techniques like Variational Autoencoder (VAE)s, Generative Adversarial Network (GAN)s, and autoregressive models. However, this progress is not without challenges, as generative models are susceptible to hallucination—producing seemingly plausible but incorrect or unrealistic outputs.
- Deep Generative Modeling techniques have emerged as a solution to enhance the performance of generative models by employing deep neural networks.
- Moreover, advancements in Generative Modeling Language have revolutionized the way machines understand and produce human-like text, opening up avenues for creative applications such as text generation, translation, and dialogue systems.
Generative AI are predictive models, filtering available information to draw inferences on what should logically follow
Generative vs Discriminative
Background: What is a Generative Model? | Google More formally, given a set of data instances X and a set of labels Y:
- Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.
- Discriminative models capture the conditional probability p(Y | X).
A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.
- Generative models can generate new data instances.
- Discriminative models discriminate between different kinds of data instances.
Generative AI Adoption Methods
The adoption of Generative AI involves incorporating generative models and algorithms into various applications and domains. Generative AI techniques enable the creation of new and realistic content, such as images, text, music, or videos, by learning patterns from existing data. Here are the key methods of adopting Generative AI:
- Training Custom Models: One adoption method involves training custom generative models specific to a particular use case or dataset. This method requires collecting and preprocessing relevant data and training the model to learn from that data. By fine-tuning the model's parameters and architecture, developers can create generative models tailored to their specific needs.
- This method offers flexibility and control, allowing developers to focus on the unique characteristics and requirements of their application. However, it requires expertise in data collection, model training, and optimization.
- Utilizing Pretrained Models: Another adoption method is to use pretrained generative models that have been trained on large-scale datasets by experts or organizations. Pretrained models capture general patterns and styles from diverse data sources and can be readily used for content generation tasks. Developers can integrate these models into their applications without the need for extensive training or data collection.
- This method offers convenience and saves time as developers can leverage existing models that have already learned complex patterns. It is suitable for applications that do not require customization or fine-grained control over the generated content.
- API Services: Many companies and cloud service providers offer Generative AI as a service through APIs. These APIs provide developers with a simple and accessible way to integrate generative capabilities into their applications. By leveraging these services, developers can make API calls to generate content without the need to directly train or manage generative models.
- API services provide convenience, scalability, and consistent performance as the underlying generative models are maintained and optimized by the service providers. Developers can focus on utilizing the generative capabilities without worrying about the complexities of model training and deployment.
- Frameworks and Libraries: There are various open-source frameworks and libraries available that provide tools, prebuilt models, and APIs for Generative AI. These frameworks, such as TensorFlow, PyTorch, or Keras, offer a wide range of functionalities and utilities for building and deploying generative models.
- Leveraging frameworks and libraries provides developers with flexibility, customization, and control over the generative models. They offer extensive documentation, tutorials, and community support, making it easier to adopt and implement Generative AI solutions.
- Collaboration and Knowledge Sharing: The adoption of Generative AI can be facilitated through collaboration and knowledge sharing within the AI community. Developers, researchers, and enthusiasts can collaborate on open-source projects, contribute to shared repositories, and participate in online forums or conferences dedicated to Generative AI.
- Collaboration and knowledge sharing accelerate the adoption process by pooling expertise, sharing resources, and collectively advancing the state of Generative AI. It fosters innovation and encourages the development of best practices and guidelines for effective adoption.
- Got It AI creates truth checker for ChatGPT ‘hallucinations’ | Dean Takahashi - VentureBeat
- CheckGPT | Got It AI
Hallucination is a term used to describe when Large Language Models (LLMs) generate text that is factually incorrect or entirely fictional. This can happen because the model’s primary objective is to generate text that is coherent and contextually appropriate, rather than factually accurate1. Hallucination can be a serious problem for LLMs because it can lead to the spread of misinformation, expose confidential information, and create unrealistic expectations about what LLMs can do. Hallucination refers to mistakes in the generated text that are semantically or syntactically plausible but are in fact incorrect or nonsensical1. This occurs because the model’s primary objective is to generate text that is coherent and contextually appropriate, rather than factually accurate. The model’s training data may contain inaccuracies, inconsistencies, and fictional content, and the model has no way of distinguishing between fact and fiction. As a result, it may generate text that aligns with the patterns observed in the training data but is not grounded in reality. From a technical perspective, hallucination in language models can be attributed to a lack of ground truth from external sources.
Example: In artificial intelligence (AI) a hallucination or artificial hallucination is a confident response by an AI that does not seem to be justified by its training data. For example, a hallucinating chatbot with no knowledge of Tesla's revenue might internally pick a random number (such as "$13.6 billion") that the chatbot deems plausible, and then go on to falsely and repeatedly insist that Tesla's revenue is $13.6 billion, with no sign of internal awareness that the figure was a product of its own imagination. Such phenomena are termed "hallucinations", in analogy with the phenomenon of hallucination in human psychology. Note that while a human hallucination is a percept by a human that cannot sensibly be associated with the portion of the external world that the human is currently directly observing with sense organs, an AI hallucination is instead a confident response by an AI that cannot be grounded in any of its training data. AI hallucination gained prominence around 2022 alongside the rollout of certain Large Language Model (LLM) such as ChatGPT. Users complained that such bots often seemed to "sociopathically" and pointlessly embed plausible-sounding random falsehoods within its generated content. Another example of hallucination in artificial intelligence is when the AI or chatbot forget that they are one and claim to be human By 2023, analysts considered frequent hallucination to be a major problem in LLM technology. Wikipedia
AI Hallucination are like human exaggerations
Ground truth refers to data that accurately represents the real-world phenomena or outcomes that an AI model aims to predict, classify, or generate. Ground truth is information that is known to be real or true, provided by direct observation and measurement (i.e. empirical evidence) as opposed to information provided by inference1. It refers to data that accurately represents the real-world phenomena or outcomes that an AI model aims to predict, classify, or generate.
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