Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.
In 1956, John McCarthy, a young associate professor of mathematics, convened 10 mathematicians and scientists for a two-month study about “thinking machines”. The group decided to hold a summer workshop based on the assumption that if mathematicians and scientists could describe every aspect of learning in a way that enabled a machine to simulate it, then they could begin to understand how to make machines use language, form abstractions, and solve problems. Today, many computer scientists consider the resulting Dartmouth Summer Research Project on Artificial Intelligence the event that launched artificial intelligence (AI) as a field of study.
AI is now a wide-ranging branch of computer science (which includes deep learning and machine learning), but, overall, it still focuses on building “thinking” machines: machines capable of demonstrating intelligence by performing tasks and solving problems that previously required human knowledge to do so.
Because these thinking machines can’t yet think on their own, a fundamental aspect of AI is teaching machines to think. Much like a baby learning to make sense of the world around her, a computer must be taught to make sense of the data it’s given. A subdivision of AI called machine learning aims to teach computers how to make inferences from patterns within datasets, and ultimately, develop computer systems that can learn and adapt without explicit programming.
Machine learning uses algorithms, data, computer power, and models to train machines to learn from their experiences. Machine learning models make it possible for computers to continuously improve and learn from their mistakes
Machine learning engineers build these models from mathematical algorithms. Algorithms are a sequence of instructions that tell the computer how to transform data into useful information. Data scientists typically design algorithms to solve specific problems and then run the algorithms on data so that they can “learn” to recognise patterns.
In a general sense, a machine learning model is a representation of what an algorithm is “learning” from processing the data. After running an algorithm on data, machine learning engineers save the rules, numbers, and other algorithm-specific data structures needed to make predictions – all of which combined make up the model. The model is like a program made up of the data and the instructions for how to use that data to make a prediction (a predictive algorithm).
After using algorithms to design predictive models, data scientists must train the model by feeding it data and using human expertise to assess how well it makes predictions. The model combs through mountains of data, and – with the help of human feedback along the way – it learns to weigh diverse inputs. It uses these inputs to learn to identify patterns, categorise information, create predictions, make decisions, and more.
Machine learning is an interactive process, which means the model learns based on its past experiences, just like a human would. The machine learning model “remembers” what it learned from working with a previous dataset – where it performed well and where it didn’t – and it will use this feedback to improve its performance with future datasets. If needed, data scientists can tweak the algorithm that built the model to reduce errors in its outputs.
Unlike a computer system that acts based on a predefined set of rules, after being trained on data, a machine learning model can perform tasks without being explicitly programmed to do so. However, the quality of the data that data scientists use to train the machine directly impacts how well the machine learns (more on that below).
Machine learning has many applications: it’s used in speech recognition, traffic predictions, virtual assistance, email filtering, and more.
At Encord, we help organisations using a type of machine learning called computer vision to create high-quality training data. Our platform automates data annotation, evaluation, and management. Because the training data directly impacts a computer vision model’s performance, having high-quality training data is incredibly important for the success of a computer vision model.
Computer vision is, to some extent, what its name implies: a field of AI that aims to help computers “see” the world around them. Computer vision models attempt to mimic the function of the human visual system by teaching the computer how to take in visual information, analyse it, and reach conclusions based on this analysis.
Data scientists have created, and continue to create, different machine learning models for different uses. For computer vision, a commonly used model is artificial neural networks.
Artificial neural networks (ANN) are computing systems inspired by the patterns of human brain cells and the ways in which biological neurons signal to one another. ANNs are made up of interconnected nodes arranged into a series of layers. An individual node often connects to several nodes in the layer below it, from which it receives data, and several in the layer above it, to which it sends data.
The input layer contains the initial dataset fed to the model, and it connects with the hidden, internal layers below. When data enters the hidden layers, each node performs a series of computations – multiplying and adding data together in complex ways – to transform it into a useful output and to determine whether it should pass the data onto the next layer. When this data reaches the output layer, the model takes what it’s learned and makes a prediction about the data.
Neural networks allow computers to process, analyse, and understand frames and videos, and they enable computers to extract meaningful information from the visual input in the way that humans do. Through the use of such models, a computer can interpret a visual environment and make decisions based on that input. However, unlike the human visual system that develops naturally over years, the computer has to be taught to “see” and make sense of a visual scene. Humans must train computer vision models to “see” by feeding them lots of high-quality data.
Applications of computer vision vary depending on the type of problem the model is trying to solve, but some of the most common tasks are image processing and classification, object detection, and image segmentation.
Example weather classification
Image classification is when a computer “sees” an image and can “categorise” it. Is there a house in this picture? Is this a picture of a dog or a cat? With a suitably trained image classification model, a computer can answer these questions.
When performing object detection, computer vision models learn to classify an object and detect its location. An object detection model could, for instance, identify that a car is in a video and track its movement from frame to frame.
Lastly, an image segmentation model distinguishes between an object and its background and other objects by creating a set of pixels for each object in the image. Compared to object detection, image segmentation provides a more granular understanding of the objects in an image.
Computer vision plays an important role in many industries. Consider the use of medical imaging in which doctors use AI to help them identify tumours. These computers have to learn to ‘see’ the tumours as distinct from other body tissue. Similarly, the computers running self-driving cars must be taught to “see” and avoid pedestrians and to process visual information to produce meaningful insight, such as identifying street signs and interpreting what they mean.
So who teaches a computer vision model to distinguish between a stop sign and a yield sign? Humans do, by creating a well-designed model and feeding it high-quality training data.
Interested in learning more? Schedule a demo to better understand how Encord can help your company unlock the power of AI.
Join the Encord Developers community to discuss the latest in computer vision, machine learning, and data-centric AI
Join the communityRelated Blogs
What is MM1? MM1 is a family of large multimodal language models that combines text and image understanding. It boasts an impressive 30 billion parameters and excels in both pre-training and supervised fine-tuning. MM1 generates and interprets both images and text data, making it a powerful tool for various multimodal tasks. Additionally, it incorporates a mixture-of-experts (MoE) architecture, contributing to its state-of-the-art performance across benchmarks. Introduction to Multimodal AI Multimodal AI models are a type of artificial intelligence model that can process and generate multiple types of data, such as text, images, and audio. These models are designed to understand the world in a way that is closer to how humans do, by integrating information from different modalities. Multimodal AI models typically use a combination of different types of AI systems, each designed to process a specific type of data. For example, a multimodal AI model might use a convolutional neural network (CNN) to process visual data, a recurrent neural network (RNN) to process text data, and a transformer model to integrate the information from CNN and RNN. The outputs of these networks are then combined, often using techniques such as concatenation or attention mechanisms, to produce a final output. This output can be used for a variety of tasks, such as classification, generation, or prediction. Overview of Multimodal Large Language Models (MLLMs) Multimodal Large Language Models (MLLMs) are generative AI systems that combine different types of information, such as text, images, videos, audio, and sensory data, to understand and generate human-like language. These models revolutionize the field of natural language processing (NLP) by going beyond text-only models and incorporating a wide range of modalities. Here's an overview of key aspects of Multimodal Large Language Models: Architecture MLLMs typically extend architectures like Transformers, which have proven highly effective in processing sequential data such as text. Transformers consist of attention mechanisms that enable the model to focus on relevant parts of the input data. In MLLMs, additional layers and mechanisms are added to process and incorporate information from other modalities. Integration of Modalities MLLMs are designed to handle inputs from multiple modalities simultaneously. For instance, they can analyze both the text and the accompanying image in a captioning task or generate a response based on both text and audio inputs. This integration allows MLLMs to understand and generate content that is richer and more contextually grounded. Pre-Training Like their unimodal counterparts, MLLMs are often pre-trained on large datasets using self-supervised learning objectives. Pre-training involves exposing the model to vast amounts of multimodal data, allowing it to learn representations that capture the relationships between different modalities. Pre-training is typically followed by fine-tuning on specific downstream tasks. State-of-the-Art Models CLIP (Contrastive Language-Image Pre-training): Developed by OpenAI, CLIP learns joint representations of images and text by contrasting semantically similar and dissimilar image-text pairs. GPT-4: It showcases remarkable capabilities in complex reasoning, advanced coding, and even performs well in multiple academic exams. Kosmos-1: Created by Microsoft, this MLLM os trained from scratch on web-scale multimodal corpora, including arbitrary interleaved text and images, image-caption pairs, and text data. PaLM-E: Developed by Google, PaLM-E integrates different modalities to enhance language understanding. Understanding MM1 Models MM1 represents a significant advancement in the domain of Multimodal Large Language Models (MLLMs), demonstrating state-of-the-art performance in pre-training metrics and competitive results in various multimodal benchmarks. The development of MM1 stems from a meticulous exploration of architecture components and data choices, aiming to distill essential design principles for building effective MLLMs. MM1 Model Experiments: Key Research Findings Architecture Components Image Encoder: The image encoder's design, along with factors such as image resolution and token count, significantly impacts MM1's performance. Through careful ablations, it was observed that optimizing the image encoder contributes substantially to MM1's capabilities. Vision-Language Connector: While important, the design of the vision-language connector was found to be of comparatively lesser significance compared to other architectural components. It plays a crucial role in facilitating communication between the visual and textual modalities. Data Choices Pre-training Data: MM1 leverages a diverse mix of image-caption, interleaved image-text, and text-only data for pre-training. This combination proved pivotal in achieving state-of-the-art few-shot results across multiple benchmarks. The study highlights the importance of different types of pre-training data for various tasks, with caption data being particularly impactful for zero-shot performance. Supervised Fine-Tuning (SFT): The effectiveness of pre-training data choices was validated through SFT, where capabilities and modeling decisions acquired during pre-training were retained, leading to competitive performance across evaluations and benchmarks. Performance In-Context Learning Abilities: The MM1 model exhibits exceptional in-context learning abilities, particularly in its largest 30 billion parameter configuration. This version of the model can perform multi-step reasoning over multiple images using few-shot “chain-of-thought” prompting. Model Scale: MM1's scalability is demonstrated through the exploration of larger LLMs, ranging from 3B to 30B parameters, and the investigation of mixture-of-experts (MoE) models. This scalability contributes to MM1's adaptability to diverse tasks and datasets, further enhancing its performance and applicability. Performance: The MM1 models, which include both dense models and mixture-of-experts (MoE) variants, achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Apple MM1 Model’s Features In-Context Predictions The Apple MM1 model excels at making predictions within the context of a given input. By considering the surrounding information, it can generate more accurate and contextually relevant responses. For instance, when presented with a partial sentence or incomplete query, the MM1 model can intelligently infer the missing parts and provide meaningful answers. Multi-Image Reasoning The MM1 model demonstrates impressive capabilities in reasoning across multiple images. It can analyze and synthesize information from various visual inputs, allowing it to make informed decisions based on a broader context. For example, when evaluating a series of related images (such as frames from a video), the MM1 model can track objects, detect changes, and understand temporal relationships. Chain-of-Thought Reasoning One of the standout features of the MM1 model is its ability to maintain a coherent chain of thought. It can follow logical sequences, connect ideas, and provide consistent responses even in complex scenarios. For instance, when engaged in a conversation, the MM1 model remembers previous interactions and ensures continuity by referring back to relevant context. Few-Shot Learning with Instruction Tuning The MM1 model leverages few-shot learning techniques, enabling it to learn from a small amount of labeled data. Additionally, it fine-tunes its performance based on specific instructions, adapting to different tasks efficiently. For instance, if provided with only a handful of examples for a new task, the MM1 model can generalize and perform well without extensive training data. Visual Question Answering (VQA) The MM1 model can answer questions related to visual content through Visual Question Answering (VQA). Given an image and a question, it generates accurate and context-aware answers, demonstrating its robust understanding of visual information. For example, when asked, “What is the color of the car in the picture?” the MM1 model can analyze the image and provide an appropriate response. Captioning When presented with an image, the MM1 model can generate descriptive captions. Its ability to capture relevant details and convey them in natural language makes it valuable for image captioning tasks. For instance, if shown a picture of a serene mountain landscape, the MM1 model might generate a caption like, “Snow-capped peaks against a clear blue sky.” For more information, read the paper of Arxiv published by Apple researchers: MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training. Key Components of MM1 Transformer Architecture The transformer architecture serves as the backbone of MM1. Self-Attention Mechanism: Transformers use self-attention to process sequences of data. This mechanism allows them to weigh the importance of different elements within a sequence, capturing context and relationships effectively. Layer Stacking: Multiple layers of self-attention are stacked to create a deep neural network. Each layer refines the representation of input data. Positional Encoding: Transformers incorporate positional information, ensuring they understand the order of elements in a sequence. Multimodal Pre-Training Data MM1 benefits from a diverse training dataset: Image-Text Pairs: These pairs directly connect visual content (images) with corresponding textual descriptions. The model learns to associate the two modalities. Interleaved Documents: Combining images and text coherently allows MM1 to handle multimodal inputs seamlessly. Text-Only Data: Ensuring robust language understanding, even when dealing with text alone. Image Encoder The image encoder is pivotal for MM1’s performance: Feature Extraction: The image encoder processes visual input (images) and extracts relevant features. These features serve as the bridge between the visual and textual modalities. Resolution and Token Count: Design choices related to image resolution and token count significantly impact MM1’s ability to handle visual information. Vision-Language Connector The vision-language connector facilitates communication between textual and visual representations: Cross-Modal Interaction: It enables MM1 to align information from both modalities effectively. Joint Embeddings: The connector generates joint embeddings that capture shared semantics. Ablation Study for MLLMs Building performant Multimodal Large Language Models (MLLMs) is an empirical process that involves carefully exploring various design decisions related to architecture, data, and training procedures. Here, the authors present a detailed ablation study conducted to identify optimal configurations for constructing a high-performing model, referred to as MM1. The ablations are performed along three major axes: MM1 Model Ablations Different pre-trained image encoders are investigated, along with various methods of connecting Large Language Models (LLMs) with these encoders. The architecture exploration encompasses the examination of the image encoder pre-training objective, image resolution, and the design of the vision-language connector. MM1 Model Ablation MM1 Data Ablations Various types of data and their relative mixture weights are considered, including captioned images, interleaved image-text documents, and text-only data. The impact of different data sources on zero-shot and few-shot performance across multiple captioning and Visual Question Answering (VQA) tasks is evaluated. Data Ablation Study for MM1 Training Procedure Ablations The training procedure is explored, including hyperparameters and which parts of the model to train at different stages. Two types of losses are considered: contrastive losses (e.g., CLIP-style models) and reconstructive losses (e.g., AIM), with their effects on downstream performance examined. Empirical Setup A smaller base configuration of the MM1 model is used for ablations, allowing for efficient assessment of model performance. The base configuration includes an Image Encoder (ViT-L/14 model trained with CLIP loss on DFN-5B and VeCap-300M datasets), Vision-Language Connector (C-Abstractor with 144 image tokens), Pre-training Data (mix of captioned images, interleaved image-text documents, and text-only data), and a 1.2B transformer decoder-only Language Model. Zero-shot and few-shot (4- and 8-shot) performance on various captioning and VQA tasks are used as evaluation metrics. MM1 Ablation Study: Key Findings Image resolution, model size, and training data composition are identified as crucial factors affecting model performance. The number of visual tokens and image resolution significantly impact the performance of the Vision-Language Connector, while the type of connector has a minimal effect. Interleaved data is crucial for few-shot and text-only performance, while captioning data enhances zero-shot performance. Text-only data helps improve few-shot and text-only performance, contributing to better language understanding capabilities. Careful mixture of image and text data leads to optimal multimodal performance while retaining strong text performance. Synthetic caption data (VeCap) provides a notable boost in few-shot learning performance. Performance Evaluation of MM1 Models The performance evaluation of MM1 models encompasses several key aspects, including scaling via Mixture-of-Experts (MoE), supervised fine-tuning (SFT) experiments, impact of image resolution, pre-training effects, and qualitative analysis. Scaling via Mixture-of-Experts (MoE) MM1 explores scaling the dense model by incorporating more experts in the Feed-Forward Network (FFN) layers of the language model. Two MoE models are designed: 3B-MoE with 64 experts and 7B-MoE with 32 experts, utilizing top-2 gating and router z-loss terms for training stability. The MoE models demonstrate improved performance over their dense counterparts across various benchmarks, indicating the potential of MoE for further scaling. Supervised Fine-Tuning Experiments Supervised Fine-Tuning (SFT) is performed on top of the pre-trained MM1 models using a diverse set of datasets, including instruction-response pairs, academic task-oriented vision-language datasets, and text-only data. MM1 models exhibit competitive performance across 12 benchmarks, showing particularly strong results on tasks such as VQAv2, TextVQA, ScienceQA, and newer benchmarks like MMMU and MathVista. The models maintain multi-image reasoning capabilities even during SFT, enabling few-shot chain-of-thought reasoning. Impact of Image Resolution Higher image resolution leads to improved performance, supported by methods such as positional embedding interpolation and sub-image decomposition. MM1 achieves a relative performance increase of 15% by supporting an image resolution of 1344×1344 compared to a baseline model with an image resolution of 336 pixels. Pre-Training Effects Large-scale multimodal pre-training significantly contributes to the model's performance improvement over time, showcasing the importance of pre-training data quantity. MM1 demonstrates strong in-context few-shot learning and multi-image reasoning capabilities, indicating the effectiveness of large-scale pre-training for enhancing model capabilities. Qualitative Analysis Qualitative examples provided in the evaluation offer further insights into MM1's capabilities, including single-image and multi-image reasoning, as well as few-shot prompting scenarios. These examples highlight the model's ability to understand and generate contextually relevant responses across various tasks and input modalities. Apple’s Ethical Guidelines for MM1 Privacy and Data Security: Apple places utmost importance on user privacy. MM1 models are designed to respect user data and adhere to strict privacy policies. Any data used for training is anonymized and aggregated. Bias Mitigation: Apple actively works to reduce biases in MM1 models. Rigorous testing and monitoring are conducted to identify and rectify any biases related to gender, race, or other sensitive attributes. Transparency: Apple aims to be transparent about the capabilities and limitations of MM1. Users should have a clear understanding of how the model works and what it can and cannot do. Fairness: MM1 is trained on diverse data, but Apple continues to improve fairness by addressing underrepresented groups and ensuring equitable outcomes. Safety and Harm Avoidance: MM1 is designed to avoid harmful or unsafe behavior. It refrains from generating content that could cause harm, promote violence, or violate ethical norms. Human Oversight: Apple maintains a strong human-in-the-loop approach. MM1 models are continuously monitored, and any problematic outputs are flagged for review. MM1 MLLM: Key Takeaways Multimodal Integration: MM1 combines textual and visual information, achieving impressive performance. Ablation Study Insights: Image encoder matters, connector less so. Data mix is crucial. Scaling Up MM1: Up to 30 billion parameters, strong pre-training metrics, competitive fine-tuning. Ethical Guidelines: Privacy, fairness, safety, and human oversight are priorities.
March 26
10 min
What is a Diffusion Transformer (DiT)? Diffusion Transformer (DiT) is a class of diffusion models that are based on the transformer architecture. Developed by William Peebles at UC Berkeley and Saining Xie at New York University, DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Introduction to Diffusion Models Diffusion models are a type of generative model that simulates a Markov chain to transition from a simple prior distribution to the data distribution. The process is akin to a particle undergoing Brownian motion, where each step is a small random walk. This is why they are called “diffusion” models. Diffusion models have been used in various applications such as denoising, super-resolution, and inpainting. One of the key advantages of diffusion models is their ability to generate high-quality samples, which makes them particularly useful in tasks such as image synthesis. Convolutional U-NET Architecture The U-Net architecture is a type of convolutional neural network (CNN) that was developed for biomedical image segmentation. The architecture is designed like a U-shape, hence the name U-Net. It consists of a contracting path (encoder) to capture context and a symmetric expanding path (decoder) for precise localization. The U-Net architecture is unique because it concatenates feature maps from the downsampling path with feature maps from the upsampling path. This allows the network to use information from both the context and localization, enabling it to make more accurate predictions. Vision Transformers Vision Transformers (ViT) are a recent development in the field of computer vision that apply transformer models, originally designed for natural language processing tasks, to image classification tasks. Unlike traditional convolutional neural networks (CNNs) that process images in a hierarchical manner. ViTs treat images as a sequence of patches and capture global dependencies between them. This allows them to model long-range, pixel-level interactions. One of the key advantages of ViTs is their scalability. They can be trained on large datasets and can benefit from larger input image sizes. For more information, read the blog Introduction to Vision Transformers (ViT). Classifier-free Guidance Classifier-free guidance refers to the approach of guiding a model’s learning process without the use of explicit classifiers. This can be achieved through methods such as self-supervision, where the model learns to predict certain aspects of the data from other aspects, or through reinforcement learning, where the model learns to perform actions that maximize a reward signal. Classifier-free guidance can be particularly useful in situations where labeled data is scarce or expensive to obtain. It allows the model to learn useful representations from the data itself, without the need for explicit labels. Understanding Latent Diffusion Models (LDMs) Latent Diffusion Models (LDMs) are a type of generative model that learn to generate data by modeling it as a diffusion process. This process begins with a simple prior, such as Gaussian noise, and gradually transforms it into the target distribution through a series of small steps. Each step is guided by a neural network, which is trained to reverse the diffusion process. LDMs have been successful in generating high-quality samples in various domains, including images, text, and audio. For more information, read the official paper, High-Resolution Image Synthesis with Latent Diffusion Models. Convolutional U-NET Backbone: Disadvantages Convolutional U-NETs have been a staple in many computer vision tasks due to their ability to capture local features and maintain spatial resolution. However, they have certain limitations. For one, they often struggle with capturing long-range dependencies and global context in the input data. This is because the receptive field of a convolutional layer is local and finite, and increasing it requires deeper networks and larger filters, which come with their own set of challenges. Moreover, the convolution operation in U-NETs is translation invariant, which means it treats a feature the same regardless of its position in the image. This can be a disadvantage in tasks where the absolute position of features is important. Shifting towards Transformer Backbone Transformers, originally designed for natural language processing tasks, have shown great potential in computer vision tasks. Unlike convolutional networks, transformers can model long-range dependencies without the need for deep networks or large filters. This is because they use self-attention mechanisms, which allow each element in the input to interact with all other elements, regardless of their distance. Moreover, transformers are not translation invariant, which means they can capture the absolute position of features. This is achieved through the use of positional encodings, which add information about the position of each element in the input. Evolution of Latent Patches The concept of latent patches evolved from the need to make transformers computationally feasible for high-resolution images. Applying transformers directly to the raw pixels of high-resolution images is computationally expensive because the complexity of self-attention is quadratic in the number of elements. To overcome this, the image is divided into small patches, and transformers are applied to these patches. This significantly reduces the number of elements and hence the computational complexity. This allows transformers to capture both local features within each patch and global context across patches. Diffusion Transformers (DiT) Vs. Vision Transformers (ViT) While both DiT and ViT use transformers as their backbone and operate on latent patches, they differ in how they generate images and their specific architectural details. Diffusion Transformers (DiT) DiT uses transformers in a latent diffusion process, where a simple prior (like Gaussian noise) is gradually transformed into the target image. This is done by reversing the diffusion process guided by a transformer network. An important aspect of DiT is the concept of diffusion timesteps. These timesteps represent the stages of the diffusion process, and the transformer network is conditioned on the timestep at each stage. This allows the network to generate different features at different stages of the diffusion process. DiT can also be conditioned on ‘class labels’, allowing it to generate images of specific classes. Vision Transformers (ViT) ViT uses transformers to directly generate the image in an autoregressive manner, where each patch is generated one after the other, conditioned on the previously generated patches. A key component of ViT is the use of adaptive layer norm layers (adaLN). These layers adaptively scale and shift the features based on the statistics of the current batch, which helps in stabilizing the training and improving the model’s performance. While both approaches have their strengths and weaknesses, they represent two promising directions for leveraging transformers in generative modeling of images. The choice between DiT and ViT would depend on the specific requirements of the task at hand. For instance, if the task requires generating images of specific classes, DiT might be a better choice due to its ability to condition on class labels. On the other hand, if the task requires generating high-resolution images, ViT might be more suitable due to its use of adaLN layers, which can help in stabilizing the training of large models. Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers (DiT) leverage the power of transformers to handle complex tasks involving large-scale data. The scalability of these models allows them to maintain or even improve their performance as the size of the input data increases. This makes them particularly suited for tasks such as natural language processing, image recognition, and other applications where the amount of input data can vary greatly. Here are some of the features of scalable diffusion models: Gflops - Forward Pass Measurement Gflops, short for gigaflops, is a unit of measurement that quantifies the performance of a computer’s floating-point operations. In the context of machine learning and neural networks, the forward pass measurement in Gflops is crucial as it provides an estimate of the computational resources required for a single forward pass through the network. This measurement is particularly important when dealing with large-scale networks or data, where computational efficiency can significantly impact the feasibility and speed of model training. Lower Gflops indicates a more efficient network in terms of computational resources, which can be a critical factor in resource-constrained environments or real-time applications. Network Complexity vs. Sample Quality The complexity of a neural network is often directly related to the quality of the samples it produces. More complex networks, which may have more layers or more neurons per layer, tend to produce higher quality samples. However, this increased complexity comes at a cost. More complex networks require more computational resources, both in terms of memory and processing power, and they often take longer to train. Conversely, simpler networks are more computationally efficient and faster to train, but they may not capture the nuances of the data as well, leading to lower quality samples. Striking the right balance between network complexity and sample quality is a key challenge in the design of effective neural networks. Variational Autoencoder (VAE)’s Latent Space In a Variational Autoencoder (VAE), the latent space is a lower-dimensional space into which the input data is encoded. This encoding process is a form of dimensionality reduction, where high-dimensional input data is compressed into a lower-dimensional representation. The latent space captures the essential characteristics of the data, and it is from this space that new samples are generated during the decoding process. The quality of the VAE’s output is largely dependent on how well the latent space captures the underlying structure of the input data. If the latent space is too small or not well-structured, the VAE may not be able to generate high-quality samples. If the latent space is well-structured and of appropriate size, the VAE can generate high-quality samples that accurately reflect the characteristics of the input data. Scalability of DiT Scalability is an important feature of Diffusion models with Transformers (DiT). As the size of the input data increases, the model should be able to maintain or improve its performance. This involves efficient use of computational resources and maintaining the quality of the generated samples. For example, in natural language processing tasks, the size of the input data (i.e., the number of words or sentences) can vary greatly. A scalable DiT model should be able to handle these variations in input size without a significant drop in performance. Furthermore, as the amount of available data continues to grow, the ability of DiT models to scale effectively will become increasingly important. For more information, read the official paper, Scalable Diffusion Models with Transformers. DiT Scaling Methods There are two primary methods for scaling DiT models: scaling the model size and scaling the number of tokens. Scaling Model Size Scaling the model size involves increasing the complexity of the model, typically by adding more layers or increasing the number of neurons in each layer. This can improve the model’s ability to capture complex patterns in the data, leading to improved performance. However, it also increases the computational resources required to train and run the model. Therefore, it’s important to find a balance between model size and computational efficiency. Scaling Tokens Scaling the number of tokens involves increasing the size of the input data that the model can handle. This is particularly relevant for tasks such as natural language processing, where the input data (i.e., text) can vary greatly in length. By scaling the number of tokens, a DiT model can handle longer texts without a significant drop in performance. However, similar to scaling the model size, scaling the number of tokens also increases the computational resources required, so a balance must be found. Diffusion Transformers Generalized Architecture Spatial Representations The model first inputs spatial representations through a network layer, converting spatial inputs into a sequence of tokens. This process allows the model to handle the spatial information present in the image data. It’s a crucial step as it transforms the input data into a format that the transformer can process effectively. Positional Embeddings Positional embeddings are a critical component of the transformer architecture. They provide the model with information about the position of each token in the sequence. In DiTs, standard Vision Transformer based positional embeddings are applied to all input tokens. This process helps the model understand the relative positions and relationships between different parts of the image. DiT Block Design In a typical diffusion model, a U-Net convolutional neural network (CNN) learns to estimate the noise to be removed from an image. DiTs replace this U-Net with a transformer. This replacement shows that U-Net’s inductive bias is not necessary for the performance of diffusion models. Diffusion Transformer Architecture Variants of DiT blocks handle conditional information with the following blocks: In-context Conditioning In-context conditioning in DiTs involves the use of adaptive layer normalization (adaLN) to inject conditional information into the model. Cross-attention Block The cross-attention in DiTs bridges the interaction between the diffusion network and the image encoder. It mixes two different embedding sequences, allowing the model to capture both local and global information. Conditioning via Adaptive Layer Norm An adaptive layer normalization (adaLN) is used to condition the diffusion network on text representations, enabling parameter-efficient adaptation. Conditioning via Cross-attention Cross-attention is used to bridge the interaction between the diffusion network and the image encoder. It allows attention layers to adapt their behavior at different stages of the denoising process. Conditioning via Extra Input Tokens While there is limited information available on conditioning via extra input tokens in DiTs, it is known that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. Model Size DiT models range from 33M to 675M parameters and 0.4 to 119 Gflops. They are borrowed from the ViT literature which found that jointly scaling-up depth and width works well. Transformer Decoder The transformer decoder is an architectural upgrade that replaces U-Net with vision transformers (ViT), showing U-Net inductive bias is not necessary for the performance of diffusion models. Training and Inference During training, a diffusion model takes an image to which noise has been added, a descriptive embedding, and an embedding of the current time step. The system learns to use descriptive embedding to remove the noise in successive time steps. At inference, it generates an image by starting with pure noise and a descriptive embedding and removing noise iteratively according to that embedding. Evaluation Metrics The quality of DiT’s output is evaluated according to Fréchet Interception Distance (FID), which measures how the distribution of a generated version of an image compares to the distribution of the original (lower is better). FID improves depending on the processing budget. On 256-by-256-pixel ImageNet images, a small DiT with 6 gigaflops of compute achieves 68.4 FID, a large DiT with 80.7 gigaflops achieves 23.3 FID, and the largest DiT with 119 gigaflops achieves 9.62 FID. A latent diffusion model that used a U-Net (104 gigaflops) achieves 10.56 FID. DiT-XL/2 Models: Trained Versions The DiT-XL/2 models are a series of generative models released by Meta. These models are trained on the ImageNet dataset, a large visual database designed for use in visual object recognition research. The XL/2 in the name refers to the resolution at which the models are trained, with two versions available: one for 512x512 resolution images and another for 256x256 resolution images. 512x512 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 512x512 uses classifier-free guidance scales of 6.0. The training process for this model took 3M steps. This high-resolution model is designed to handle complex images with intricate details. 256x256 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 256x256 uses classifier-free guidance scales of 4.0. The training process for this model took 7M steps. This model is optimized for standard resolution images and is more efficient in terms of computational resources. FID Comparisons of the Two Resolutions The DiT-XL/2 model trained at 256x256 resolution outperforms all prior diffusion models, achieving a state-of-the-art FID-50K of 2.27. This is a significant improvement over the previous best FID-50K of 3.60 achieved by the LDM (256x256) model. In terms of compute efficiency, the DiT-XL/2 model is also superior, requiring only 119 Gflops compared to the LDM-4 model’s 103 Gflops and ADM-U’s 742 Gflops. Scalable Diffusion Models with Transformers. At 512x512 resolution, the DiT-XL/2 model again outperforms all prior diffusion models, improving the previous best FID of 3.85 achieved by ADM-U to 3.04. In terms of compute efficiency, the DiT-XL/2 model requires only 525 Gflops, significantly less than ADM-U’s 2813 Gflops. You can find the DiT-XL/2 models on GitHub and run them on HuggingFace or in a Colab Notebook. Applications of Diffusion Transformer One of the notable applications of DiT is in image generation. Other applications include text summarizations, chatbots, recommendation engines, language translation, knowledge bases, etc. Let’s look at some notable SOTA models which use diffusion transformer architectures: OpenAI’s SORA Video generation models as world simulators OpenAI’s SORA is an AI model that can create realistic and imaginative scenes from text instructions. SORA is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps. It can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. SORA is capable of generating entire videos all at once or extending generated videos to make them longer. For more information, read the blog: OpenAI Releases New Text-to-Video Model, Sora Stable Diffusion 3 Scaling Rectified Flow Transformers for High-Resolution Image Synthesis Stable Diffusion 3 (SD3) is an advanced text-to-image generation model developed by Stability AI. SD3 combines a diffusion transformer architecture and flow matching. It generates high-quality images from textual descriptions. SD3 outperforms state-of-the-art text-to-image generation systems such as DALL·E 3, Midjourney v6, and Ideogram v1 in typography and prompt adherence, based on human preference evaluations. For more information, read the blog: Stable Diffusion 3: Multimodal Diffusion Transformer Model Explained. PixArt-ɑ PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Syntheis PixArt-α is a Transformer-based Text-to-Image (T2I) diffusion model. Its image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. PixArt-α supports high-resolution image synthesis up to 1024px resolution with low training cost6. It excels in image quality, artistry, and semantic control. Diffusion Transformer: Key Takeaways Class of Diffusion Models: Diffusion Transformers (DiT) are a novel class of diffusion models that leverage the transformer architecture. Improved Performance: DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Impressive Scalability: DiT models have demonstrated impressive scalability properties, with higher Gflops consistently having lower Frechet Inception Distance (FID). Versatile Applications: DiT has been applied in various fields, including text-to-video models like OpenAI’s SORA, text-to-image generation models like Stable Diffusion 3, and Transformer-based Text-to-Image (T2I) diffusion models like PixArt-α.
March 18
8 min
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.
March 16
8 min
Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.