What is Robotic Process Automation (RPA)?

What is DeepMind SIMA? SIMA can follow natural language instructions to perform tasks in various video game environments. It can also generalize across games, picking up skills learned in one game and transferring them to different games. How do you train an AI agent to be a generalist? Google DeepMind’s latest AI agent, SIMA, short for Scalable Instructable Multiworld Agent, helps us understand precisely how. Both NVIDIA and DeepMind have been focused on controlling one multi-world agent. The idea is that if you can develop one agent that can generalize across different domains (for example, different video games), it would probably be quite useful in the real world—for piloting a robot, learning from a physical environment, etc. In this article, you will learn about:  What SIMA is and how it interacts with the environment in real-time using a generic human-like interface. Different methods for training an AI agent. SIMA’s training process, including the environments, data, models, and evaluation methods. How SIMA generalizes knowledge across tasks and environments with really impressive zero-shot capabilities. How useful they are as embodied AI agents. DeepMind’s Gaming Legacy: Alpha Go to Scalable Instructable Multiworld Agent (SIMA) DeepMind has consistently been at the forefront of advancing artificial intelligence (AI) through gaming. This tradition dates back to its groundbreaking success with AlphaGo, famous for beating the world’s best Go players. To understand how the team arrived at SIMA, let’s explore the evolution from DeepMind's early work on reinforcement learning in Atari video games to Scalable Instructable Multiworld Agent (SIMA), focusing on… wait for it… Goat Simulator 3, with some of the funniest game actions . ​​The evolution shows how models go from mastering structured board games to navigating complex, rich, interactive 3D simulations and virtual environments. First off… Atari games. Reinforcement Learning on Atari Video Games DeepMind's first attempt at using AI in games was a huge success when applied to Atari games using deep reinforcement learning (RL). The goal was to get the highest scores in several classic games using only pixel data and game scores. These games provided a diverse platform for testing and improving RL algorithms, which learn optimal behaviors through trial and error, guided by rewards. In this situation, DeepMind's algorithms (the popular AlphaGo, MuZero, and AlphaGo Zero) could master several Atari games, often doing better than humans. This work showed how RL can solve difficult, dynamic, and visually varied problems. It also set a new standard in AI by showing how AI agents can learn and adapt to new environments without having much pre-programmed information. DeepMind's deep Q-network (DQN) was key to this success. It combined deep neural networks with a Q-learning framework to process high-dimensional sensory input and learn successful strategies directly from raw pixels. This approach enabled AI to understand and interact meaningfully with the gaming environment, paving the way for more sophisticated AI applications in gaming and beyond. Scalable Instructable Multiworld Agent (SIMA) on Goat Simulator 3 SIMA builds on its predecessors. The AI agent can move around and interact in a wide range of 3D virtual worlds, not just the 2D worlds of Atari games.  SIMA is built to understand and follow natural language instructions within these environments. This is a first step toward creating general AI that can understand the world and its complexities. SIMA learned from different gaming environments, and one interesting one is Goat Simulator 3. If you have played this game before, you will surely know how unpredictable and chaotic the actions are. It is uniquely challenging due to its open-ended gameplay and humorous, physics-defying mechanics. This, of course, is different from the structured world of Go and other Atari games! To teach SIMA how to operate in Goat Simulator 3, the researchers had to collect a lot of human gameplay from which it could learn. The gameplay included simple navigation to follow specific actions in open-ended language instructions (e.g., “jump the fence”).  This process checks the agent's ability to understand and follow directions and adapt to an environment where nothing is ever the same. Agent Training Methods DeepMind's technical report discusses new ways to train AI agents that use the complexity of simulated environments to help them learn and adapt. These methods are crucial for creating agents like those in the SIMA project that can interact intelligently with various 3D environments. AI Agent Simulator-based Training The method uses reinforcement learning—agents learn the best way to execute a task by trying things out and seeing what works best, with help from reward signals in their environment. In this context, the game environment serves as both the playground and the teacher. Here are the components of this training approach: Reinforcement Learning: The core of this method is an algorithm that adjusts the agent's policy based on the rewards it receives for its actions. The agent learns to connect actions with results, which helps it improve its plan to maximize cumulative rewards. Reward Signals: These signals guide the agent's learning process within game environments. They can be explicit, like points scored in a game, or more nuanced, reflecting progress toward a game's objective or successful interaction within the environment. Environment Flexibility: This training method is flexible because you can use in any setting that provides useful feedback. The agent learns by engaging directly with the environment, navigating a maze, solving puzzles, or interacting with dynamic elements. Examples: Using RL in places like Atari games, where the agent learns different strategies for each game, shows how well this method works. This can also be seen when training agents in more complicated situations, like those in Goat Simulator 3, where the AI must adapt to and understand complex situations with nuance. Traditional Simulator-based Agent Training This method involves unsupervised learning, where the agent explores the environment and learns its dynamics without explicit instruction or reinforcement. The goal is for the agent to develop an intuitive understanding of the rules and mechanics governing the environment. The techniques in this approach are: Unsupervised Model: By interacting with the environment without predefined objectives or rewards, the agent builds a model of the world that reflects its inherent rules and structures. This model helps agents predict outcomes and plan actions, even in unfamiliar scenarios. Learn the Rules Intuitively: The agent notices patterns and regularities in its surroundings by observing and interacting with them. This is the same as "learning the rules of the game." This process helps the agent gain a deep, unconscious understanding that shapes how they act and what they choose to do in the future. Less Need for Annotation: One big benefit of this method is that it does not require as much detailed annotation or guidance. The agent learns from experiences, so it does not need large datasets with labels or manual instructions. Example: Scenarios where agents must infer objectives or navigate environments with sparse or delayed feedback. For example, an agent might learn to identify edible vs. poisonous items in a survival game or deduce the mechanics of object interaction within a physics-driven simulation. Scalable Instructable Multiworld Agent (SIMA) Training Process SIMA's training approach includes several key components, detailed as follows: Scaling Instructable Agents Across Many Simulated Worlds Environment SIMA's training leverages diverse 3D environments, ranging from commercial video games to bespoke research simulations. It was important to the researchers that these environments offer a range of challenges and chances to learn so that agents could become more flexible and generalize to various settings and situations.  Key requirements of these environments include: Diversity: Using open-world games and controlled research environments ensures that agents encounter various scenarios, from dynamic, unpredictable game worlds to more structured, task-focused settings. Rich Interactions: The researchers chose the environments because they allowed agents to interact with different objects, characters, and terrain features in many ways, helping them learn a wide range of skills. Realism and Complexity: Some environments have physics and graphics close to reality. This lets agents learn in situations similar to how complicated things are in the real world. 💡Learn more about these environments in the technical report. Two environments that meet these requirements are: Commercial Video Games: The researchers trained the agents on games, including Goat Simulator 3, Hydroneer, No Man’s Sky, Satisfactory, Teardown, Valheim, and Wobbly Life. Research Environments: These are more controlled environments, such as Controled Labs and procedurally-generated rooms with realistic contents (ProcTHOR). SIMA is capable of performing many actions from language-instructed tasks. Data An extensive and varied set of gameplay data from various environments forms the basis of SIMA's training. This dataset includes: Multimodal Inputs: The multimodal data includes visual observations, spoken instructions, and the actions taken by human players that match. This gives agents a lot of information to learn from. Human Gameplay: The dataset ensures that agents learn from nuanced, contextually appropriate behavior by capturing gameplay and interaction sequences from human players. Annotated Instructions: Language instructions are paired with game sequences to give agents clear examples of using natural language to guide them in doing tasks. Agents SIMA agents are designed to interpret language instructions and execute relevant actions within 3D virtual environments. Key aspects of their design include: Language-Driven Generality: Agents are taught to follow instructions that use open-ended language. This lets them change their actions based on verbal cues to complete many tasks.  Human-Like Interaction: The agents work with a standard interface that looks and feels like a person's. It takes in text and images and responds to keyboard and mouse commands like a person would. Pre-trained Models: SIMA uses pre-trained models, like video models, to process textual and visual data. These models were mostly trained using instruction-conditioned behavioral cloning (see this note) and classifier-free guidance. This makes it easier for the agents to understand complicated instructions and their surroundings. 💡Learn how to go from big to intelligent visual data in our expert-led webinar. Instructions Across SIMA Data Evaluation Methods Assessing the performance of SIMA agents involves a variety of evaluation methods tailored to the different environments and tasks: Ground-truth Evaluation: In research environments, clear success criteria are set for each task, so it is easy to judge an agent's performance by whether certain goals are met.  Human Judgments: When the tasks are more open-ended or subjective, human evaluators watch how the agents act and give feedback on how well they can follow directions and reach their goals while acting like humans. Automated Metrics: In some cases, particularly within commercial games, automated metrics such as in-game scores or task completion indicators provide quantitative measures of agent success. Optical Character Recognition (OCR): Applied in commercial video games where task completion might not be as straightforward to assess. OCR is used to detect on-screen text indicating task completion. Action Log-probabilities and Static Visual Input Tests: These are more simplistic methods assessing the agent's ability to predict actions based on held-out data or to respond to static visual inputs with correct actions. 💡Interested in understanding metrics for computer vision models? Check out our comprehensive article on quality metrics in AI. SIMA Agent Features Scalable Instructable Multiworld Agent (SIMA) incorporates sophisticated features that enable it to interact effectively within various simulated 3D environments. These features are integral to its design, allowing it to understand and execute various natural language instructions and perform many actions across different virtual settings. SIMA agent receives instructions from a user and image observations from the environment Here's a breakdown of these crucial features: Multi-environment Transfer A key feature of SIMA is that it can use the knowledge and skills it has gained in one environment to perform well in another without starting from scratch each time. This ability to transfer between environments is very important for the agent's flexibility and efficiency; it lets it use what it has learned in a wide range of situations instead of just one. For instance, if the agent learns the concept of 'opening a door' in one game, it can apply this knowledge when encountering a door in another unrelated game. The agent's sophisticated perception and action systems facilitate mapping shared concepts by abstracting underlying similarities in interactions across environments and accelerating its adaptation. Understands Natural Language instructions SIMA is engineered to understand a wide range of language instructions, interpreting them within the context of its current environment and objectives. This comprehension extends to complex commands and instruction sequences, enabling SIMA to engage in sophisticated interactions and complete intricate tasks in accordance with human-like language inputs. Performs 600+ Actions Due to the variety of its training environments and the difficulty of the tasks it can handle, SIMA can perform more than 600 different actions. Thanks to its large action repertoire, it can respond correctly to various situations and instructions, which shows how well it has learned to adapt. Average success rate of the SIMA Agent by skill category From basic movements and interactions to more intricate and context-specific actions, SIMA's broad range of capabilities enables it to tackle diverse challenges and objectives. Generalization Rather than mastering a single task or environment, SIMA is developed to generalize its learning and problem-solving capabilities across contexts. This generalization ensures that the agent can apply its learned skills and knowledge to new, unseen challenges, adapting its strategies based on prior experiences and the specific demands of each new setting. Results Highlighting SIMA's Generalization Ability DeepMind's SIMA demonstrates impressive generalization capabilities across various environments, as showcased through several key findings: Zero-Shot Learning Abilities: SIMA effectively applies learned skills to new, unseen environments without additional training, which indicates robust internalized knowledge and skill transferability. No Pre-Training Ablation: Removing pre-trained components affects SIMA's performance, emphasizing the importance of pre-training for generalization. Despite this, some generalization capacity persists, highlighting the robustness of SIMA's core architecture. Language Ablation: Taking out natural language inputs worsens task performance. This shows how important language comprehension is to SIMA's ability to work in diverse environments. Environment-Specialized Performance: SIMA matches or outperforms environment-specialized agents, showcasing its broader applicability and efficient learning across different virtual worlds. Ethical AI Guidelines DeepMind's commitment to ethical AI practices is evident in developing and training SIMA. As part of these ethical guidelines, the AI should only be trained in carefully chosen environments that encourage good values and behavior. Here are the key guidelines they used to avoid violent content: Content Curation: In aligning with ethical AI practices, SIMA's training explicitly avoids video games or environments that feature violent actions or themes. This careful curation ensures that the agent is not exposed to, nor does it learn from, any content that could be considered harmful or contrary to societal norms and values. Promotes Positive Interaction: The training focused on problem-solving, navigation, and constructive interaction, choosing environments without violence. This created an AI agent that can be used in many positive situations. Risk Mitigation: This approach also serves as a risk mitigation strategy, reducing the potential for the AI to develop or replicate aggressive behaviors, which is crucial for maintaining trust and safety in AI deployments. Modeling Safe and Respectful Behaviors: The training program reinforces safe and respectful behaviors and decisions in the agent, ensuring that their actions align with the principles of avoiding harm and promoting well-being. SIMA's training on nonviolent content shows how important it is to ensure that AI research and development align with societal values and that we only create AI that is helpful, safe, and respectful of human rights. Challenges of Developing SIMA The DeepMind SIMA research team faced many difficult problems when developing the agent. These problems arise when training AI agents in different and changing 3D environments, and they show how difficult it is to use AI in situations similar to the complicated and unpredictable real world. Real-time Environments Not Designed for Agents Unpredictable Dynamics: Many real-time environments SIMA is trained in, especially commercial video games, are inherently unpredictable and not specifically designed for AI agents. These environments are crafted for human players and feature nuances and dynamics that can be challenging for AI to navigate and understand. Complex Interactions: The multifaceted interaction possibilities within these environments add another layer of complexity. Agents must learn how to handle various possible events and outcomes, which can change from one moment to the next, just like in real life. Evaluation Without API Access to Environment States Limited Information: Evaluating SIMA's performance without API access means the agent cannot rely on explicit environment states or underlying game mechanics that would typically be available to developers. This limitation necessitates reliance on visual and textual cues alone, which mirrors the human gameplay experience but introduces significant challenges in interpreting and responding to the environment accurately. Assessment Accuracy: The lack of direct environment state access complicates the evaluation process, making it harder to ascertain whether the AI has successfully understood and executed a given task, particularly in complex or ambiguous situations. SIMA’s Current Limitations Although the Scalable Instructable Multiworld Agent (SIMA) has made significant progress, it still has some problems worth mentioning. These constraints highlight areas for future research and development to improve AI agents' capabilities and applications in complex environments. Limited Environmental Availability Diversity of Games: SIMA was trained and tested on four research-based 3D simulations and seven commercial video games. This shows that the model can work in various settings but is still not very broad, considering all the different game types and settings. Adding more types of environments could help test and improve the agent's ability to adapt to new ones. Breadth of 3D Simulations: The four 3D simulations provide controlled settings to test specific agent capabilities. However, increasing the number and diversity of these simulations could offer more nuanced insights into the agent's adaptability and learning efficiency across varied contexts. Restricted Data Pipeline Scalability The current data pipeline, crucial for training SIMA through behavioral cloning, might not be scalable or diverse enough to cover the full spectrum of potential interactions and scenarios an agent could encounter. Improving the scalability and diversity of the data pipeline would be essential for training more robust and versatile AI agents. Short Action Horizon Action Duration: SIMA's training has primarily focused on short-horizon tasks, generally capped at around 10 seconds. This limitation restricts the agent's ability to learn and execute longer and potentially more complex sequences of actions, which are common in real-world scenarios or more intricate game levels. Reliability and Performance Agent Reliability: Although SIMA has shown promise in following instructions and performing actions across various environments, it is often unreliable compared to human performance. The agent's inconsistency in accurately interpreting and executing instructions poses challenges for its deployment in scenarios requiring high precision or critical decision-making. Comparison with Human Performance: Some tasks made for SIMA are naturally hard and require advanced problem-solving and strategic planning, but the agent still does not follow instructions as well as a human would. This shows how hard the environments are and how high the bar was set for the agent since even skilled human players do not get perfect scores on these tasks. Addressing these limitations will be crucial for the next stages of SIMA's development. To make the field of AI agents that can navigate and interact in complex, changing virtual worlds even better, we must improve environmental diversity, data pipeline scalability, action horizon, and overall reliability. Key Takeaways: Google’s Video Gaming Companion—Scalable Instructable Multiworld Agent (SIMA). Here are the key ideas from this article: SIMA interacts with the environment in real-time using a generic human-like interface. It receives image observations and language instructions as inputs and generates keyboard and mouse actions as outputs. SIMA is trained on a dataset of video games, including Satisfactory, No Man's Sky, Goat Simulator 3, and Valheim. The researchers evaluated SIMA’s ability to perform basic skills in these games, such as driving, placing objects, and using tools. On average, SIMA's performance is around 50%, but it is far from perfect. The researchers believe that training AI agents on a broad variety of video games is an effective way to make progress in general AI. These results support SIMA's strong generalization skills and show that it can work well in various situations and tasks. It is a big step forward in developing AI agents with strong, flexible, and transferable skill sets because it shows strong zero-shot learning abilities and resilience against ablation impacts.

 What is Claude 3? Claude 3 is a family of large multimodal models by Anthropic: Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku. Opus excels in various domains. Sonnet balances skills and speed. Haiku prioritizes speed and affordability. All models process text and images, offer improved multilingual fluency, and undergo comprehensive evaluations for safety. Joining the tech race of building AI chatbots with OpenAI’s ChatGPT, Google’s Gemini 1.5, or Le Chat of Mistral AI, Anthropic has introduced Claude. Claude is an AI assistant that helps manage organizations' tasks no matter the scale. The Claude 3 model family shows better performance than other SOTA models.  Claude 3 sets new benchmarks across reasoning, math, coding, multi-lingual understanding, and vision quality. Leveraging unsupervised learning and Constitutional AI, trained on AWS and GCP hardware with PyTorch, JAX, and Triton frameworks. Claude 3’s AI Model Suite Each large language model within the Claude 3 family is tailored to offer different combinations of capabilities, speed, and cost-effectiveness. Claude 3 Opus It is the most capable offering, achieving state-of-the-art results on benchmark evaluations across various domains such as reasoning, math, and coding. It sets new standards in performance and is suitable for applications requiring high levels of intelligence and processing power. Claude 3 Sonnet It provides a balance between skills and speed, offering strong performance in cognitive tasks while being more efficient in terms of processing time compared to Opus. Claude 3 Haiku It is the fastest and least expensive model in the family, suitable for applications where speed and cost-effectiveness are prioritized over absolute performance. Intelligence Benchmark Scores Vs. Cost Comparison of Claude 3 Model Family All models in the Claude 3 family come with vision capabilities for processing image data and exhibit improved fluency in non-English languages, making them versatile for a global audience. Model Training Data and Process The Claude 3 models are trained using a blend of publicly available internet data as of August 2023, along with public data from data labeling services and synthetic data generated internally. The training process involves several data cleaning and filtering methods, including deduplication and classification. The models are not trained on any user-submitted prompt or output data. Anthropic follows industry practices when obtaining data from public web pages, respecting robots.txt instructions and other signals indicating whether crawling is permitted. The crawling system operates transparently, allowing website operators to identify Anthropic visits and signal their preferences. The training of Claude 3 models emphasizes being helpful, harmless, and honest. Techniques include pretraining on diverse data sets for language capabilities and incorporating human feedback to elicit desirable responses. Constitutional AI, including principles from sources like the UN Declaration of Human Rights, ensures alignment with human values. A principle promoting respect for disability rights is integrated into Claude's constitution. Human feedback data, including publicly available sources, is used for finetuning. For more information on RLHF, read the blog Guide to Reinforcement Learning from Human Feedback (RLHF) for Computer Vision.   Performance Benchmark: Claude 3, GPT-4, GPT-3.5, Gemini Ultra, and Gemini Pro Claude 3, particularly the Opus model, surpasses other state-of-the-art models in various evaluation benchmarks for AI tools. It excels in domains such as undergraduate and graduate-level expert knowledge (MMLU, GPQA), basic mathematics (GSM8K), and more. Opus demonstrates near-human levels of comprehension and fluency, positioning itself at the forefront of general intelligence. Compared to other models like OpenAI’s GPT-4, GPT-3.5, Gemini Ultra, and Gemini Pro, Claude 3 models showcase enhanced capabilities in diverse areas. These include analysis and forecasting, nuanced content creation, code generation, and multilingual conversation proficiency in languages such as Spanish, Japanese, and French. Performance Benchmark Scores of Claude 3 Model Family: Opus, Sonnet, Haiku Claude 3 Capabilities Vision Capabilities: Photos, Charts, Graphs and Technical Diagrams The Claude 3 models are equipped to process and interpret visual information along with text inputs. The vision capabilities are particularly showcased in tasks like the AI2D science diagram benchmark and visual question answering. They excel in parsing scientific diagrams and achieving high accuracy rates in both zero-shot and few-shot settings. Evaluation Results on Multimodal Tasks Trained on diverse visual data, Claude 3 models effectively interpret and analyze various visual content, enhancing their overall problem-solving capabilities for applications in fields like image understanding and multimodal reasoning. Near Instant Model Results Claude 3 models deliver near-instant results, ideal for live customer chats, auto-completions, and data extraction tasks. Haiku is the fastest and most cost-effective, processing dense research papers in under three seconds. Sonnet is twice as fast as previous versions, suitable for rapid tasks like knowledge retrieval. Opus matches previous speeds but with higher intelligence levels. Multimodal Claude 3 shows impressive multimodal capabilities, adept at processing diverse types of data. Claude 3 excels in visual question answering, demonstrating its capacity to understand and respond to queries based on images. It showcases strong quantitative reasoning skills by analyzing and deriving insights from visual data, enhancing its overall versatility across various tasks. Multilingual Understanding Claude 3 showcases robust multilingual capabilities, important for global accessibility. Evaluations highlight Claude 3 Opus's state-of-the-art performance in the Multilingual Math MGSM benchmark, achieving over 90% accuracy in a zero-shot setting. Human feedback shows significant improvement in Claude 3 Sonnet, indicating enhanced multilingual reasoning capabilities compared to previous versions. The Claude 3 Model Family: Multilingual Capabilities Factual Accuracy Claude 3 prioritizes factual accuracy through rigorous evaluations, including 100Q Hard and Multi-factual datasets. Tracking correctness, incorrect, and unsure responses, Claude 3 Opus significantly improves accuracy over previous versions. Factual Accuracy of Claude 3 Models Vs Claude 2.1 Reasoning and Mathematical Problem Solving Claude 3 exhibits remarkable reasoning and mathematical problem-solving abilities, surpassing previous models in various benchmarks. In evaluations such as GPQA and MATH, Claude 3 Opus achieves significant improvements, although falling slightly short of expert-level accuracy. Leveraging techniques like chain-of-thought reasoning and majority voting further enhances performance, with Opus demonstrating impressive scores in both reasoning and mathematical problem-solving tasks, showcasing its advanced capabilities in these domains. Near-human Comprehension Claude 3 Sonnet outperforms its predecessors, Claude 2 and Claude Instant, in various core tasks, as assessed through direct comparisons by human raters. It excels in writing, coding, long document Q&A, non-English conversation, and instruction following. Domain experts across finance, law, medicine, STEM, and philosophy prefer Sonnet in 60-80% of cases. Human feedback, although noisy, provides insights into user preferences that industry benchmarks may overlook. Using Elo scores, Sonnet shows a significant improvement of roughly 50-200 points over Claude 2 models in various subject areas.  Claude models exhibit high proficiency in open-ended conversation, coding tasks, and text-related operations like searching, writing, and summarizing. They also interpret visual input for enhanced productivity and maintain a helpful, conversational tone, described as steerable, adaptive, and engaging by users. Claude's prediction mechanism constructs responses sequentially based on the input and past conversation, unable to edit previous responses or access external information beyond its context window, achieving near-human comprehension in various tasks. Contextual Understanding and Fewer Refusals Unlike previous versions, Claude 3 models are less likely to refuse to answer prompts that are within their capabilities and ethical boundaries. This improvement indicates a more refined understanding of context and a reduction in unnecessary refusals, enhancing their overall performance and usability. Comparison of Incorrect Refusals: Claude 3 Model Family Vs. Claude 2.1 Information Recall from Long Context Claude 3's capability for information recall from long contexts is impressive, expanding from 100K to 200K tokens and supporting contexts up to 1M tokens. Despite challenges in reliable recall within long contexts, Claude 3 models, particularly Claude Opus, exhibit significant improvements in accurately retrieving specific information. In evaluations like Needle In A Haystack (NIAH), Claude Opus consistently achieves over 99% recall in documents of up to 200K tokens, highlighting its enhanced performance in information retrieval tasks. Information Recall: Claude 3 Model Family (Opus, Sonnet, Haiku) Vs. Claude 2 Improved Accuracy Improved accuracy in Claude 3 models is important for businesses relying on them to serve customers at scale. Evaluation involves a large set of complex, factual questions targeting known weaknesses in previous models.  Accuracy Comparison: Claude 3 Model Family (Opus, Sonnet, Haiku) Vs. Claude 2 Claude 3 Opus demonstrates a twofold improvement in accuracy, reducing incorrect answers and admitting uncertainty when necessary. The upcoming features like citations will enhance trustworthiness by enabling precise verification of answers from reference material. For more information, read the model card:The Claude 3 Model Family: Opus, Sonnet, Haiku   Model Details Claude 3: Model Availability Opus and Sonnet are currently available for use in the Anthropic API, enabling developers to sign up and start using these models immediately. Haiku will be available soon. Sonnet powers the free experience on claude.ai, while Opus is available for Claude Pro subscribers. Sonnet is available through Amazon Bedrock, with Opus and Haiku coming soon to both Amazon Bedrock and Google Cloud's Vertex AI Model Garden in a private preview. Model Costs Claude 3 Opus Claude 3 Opus stands out as the most intelligent model, offering unparalleled performance on complex tasks. It excels in handling open-ended prompts and navigating sight-unseen scenarios with remarkable fluency and human-like understanding, showcasing the outer limits of generative AI. However, this high intelligence comes at a higher cost of $15 per million input tokens and $75 per million output tokens. The context window for Opus is 200K tokens, and it is suitable for tasks such as task automation, research and development, and advanced strategic analysis. Claude 3 Sonnet Claude 3 Sonnet, on the other hand, strikes a balance between intelligence and speed, making it ideal for enterprise workloads. It offers strong performance at a lower cost compared to its peers, with rates of $3 per million input tokens and $15 per million output tokens. Sonnet's context window is also 200K tokens, and it is suitable for data processing, sales tasks, and time-saving operations like code generation. Claude 3 Haiku Claude 3 Haiku is the fastest and most compact model, designed for near-instant responsiveness. It excels in handling simple queries and requests with unmatched speed and affordability, costing $0.25 per million input tokens and $1.25 per million output tokens. Haiku's context window is also 200K tokens, and it is suitable for tasks like customer interactions, content moderation, and cost-saving operations.  The Claude 3 Haiku model is now accessible via Amazon Bedrock on Amazon Web Services.                                                               Responsible Design Risk Mitigation Dedicated teams continuously track and mitigate various risks, including misinformation, harmful content, and potential misuse in areas such as biological information, election integrity, and autonomous replication. Bias Reduction Ongoing efforts focus on reducing biases in model outputs, with Claude 3 demonstrating decreased biases compared to previous models, as measured by the Bias Benchmark for Question Answering (BBQ). Model Neutrality Advanced methods such as Constitutional AI enhance model transparency and neutrality, guaranteeing that results are not biased toward any one political position. Responsible Scaling Policy Claude 3 models are classified at AI Safety Level 2 (ASL-2) under the Responsible Scaling Policy, with rigorous evaluations affirming minimal potential for catastrophic risks at present. Future models will be closely monitored to assess their proximity to ASL-3.  Claude 3: What’s Next Here is what to expect from the new models of Anthropic’s Claude:  Feature Updates for Enterprise Use Case Tool Use or Function Calling: Development is underway to enable Claude 3 to utilize functions, allowing for more advanced task automation and data processing. REPL or Interactive Coding: Claude 3 will soon support an interactive coding environment, providing users with the ability to engage in real-time code execution and debugging. Advanced Agentic Capabilities: Explorations are ongoing to equip Claude 3 with more advanced agentic capabilities, facilitating seamless interaction with users and autonomous execution of complex tasks. Large-scale Deployments: Optimization efforts are being made to ensure Claude 3 is suitable for large-scale deployments, enabling it to handle high volumes of requests while maintaining performance and reliability in enterprise settings. Safety Guardrails with Feature Advancements: In line with feature updates, Claude 3 is also working on its safety protocols to mitigate risks and promote responsible usage. At the same time, the focus remains on leveraging these advancements to foster positive societal outcomes, allowing users to achieve their goals ethically and efficiently while upholding principles of fairness, transparency, and accountability in artificial intelligence.

Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case.  Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.  Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling.  AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality.  Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage.    Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision    Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images.  Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing:  Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers.  COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format.  Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB.  Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024.  For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.