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AI Video Generator | #1 on G2’s 2025 Top 100 List | HeyGen

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Cette nouvelle fonctionnalité vous permet de rechercher des articles via une IA dans Google Scholar

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Artificial Analysis est une plateforme indépendante qui compare les performances, coûts et vitesses des modèles d’IA (texte et image). Elle aide les utilisateurs à choisir les meilleurs outils grâce à des évaluations rigoureuses et transparentes basées sur des benchmarks standardisés.

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Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key performance metrics including quality, price, output speed & latency.

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ChatGPT helps you get answers, find inspiration, and be more productive.

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Nano Banana Pro is our new image generation and editing model from Google DeepMind.

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Deep Research : BDM a comparé ChatGPT, Gemini, Claude, Perplexity, Copilot, DeepSeek et Le Chat

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Deep Research : BDM a comparé ChatGPT, Gemini, Claude, Perplexity, Copilot, DeepSeek et Le Chat

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BDM a comparé la fonction Deep Research de sept IA : ChatGPT, Gemini, Claude, Perplexity, Copilot, DeepSeek et Le Chat.
ChatGPT et Gemini se distinguent par des réponses riches, nuancées et bien sourcées. Claude reste clair mais trop scolaire. Perplexity propose un bon équilibre entre rigueur et rapidité. Copilot est structuré et rapide, mais peu nuancé. DeepSeek séduit par son style naturel, malgré un manque de profondeur. Le Chat est efficace, surtout en mode Recherche, mais souvent trop synthétique

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Deep Research : BDM a comparé ChatGPT, Gemini, Claude, Perplexity, Copilot, DeepSeek et Le Chat

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BDM a testé la fonction de réflexion approfondie chez les principaux outils d’IA. Lesquels tirent leur épingle du jeu ? Réponse ci-dessous !

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Napkin est un outil gratuit qui vous permet de créer des représentations graphiques (inforgraphies) de vos paragraphes de texte

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Napkin AI - The AI assistant for visual communication

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Just type, copy-paste or generate your text and Napkin will instantly transform it into insightful visuals. Make your communication more effective with Napkin.

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Cet article donne des liens vers des données probantes et validées sur la consommation des interactions IA mais surtout sur le coût de l'entraînement des modèles qui est le plus mauvais en termes de durabilité. Plus ici : https://www.nature.com/articles/d41586-024-02680-3

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By: Sasha Luccioni, Bruna Trevelin, Margaret Mitchell (Hugging Face)

Image source: https://betterimagesofai.org/

Artificial intelligence (AI) has an environmental cost. Beginning with the extraction of raw materials and the manufacturing of AI infrastructure, and culminating in real-time interactions with users, every aspect of the AI lifecycle consumes natural resources - energy, water, and minerals - and releases greenhouse gases. The amount of energy needed to power AI now outpaces what renewable energy sources can provide, and the rapidly increasing usage of AI portends significant environmental consequences. The goal of this primer is to shed light on the environmental impacts of the full AI lifecycle, describing which kinds of impacts are at play when, and why they matter.

While some research and documentation on AI's environmental impacts currently exists, the nature and extent of AI's effects are under-documented, ranging from its embodied and enabled emissions to rebound effects due to its increased usage. Regulatory and policy initiatives, both existing and in progress, have the challenge of encouraging innovation and growth while addressing environmental impacts and how they affect different stakeholders. Ways forward range from technical interventions to make AI models more efficient, to policy interventions to incentivize sustainable AI research and practice.

Download the PDF version of the Primer here!

Recent years have ushered in a new era of growth and adoption of Artificial Intelligence (AI) technologies, especially generative AI, which is increasingly used in tools and systems spanning from Web search to customer service. While this era brings with it potential gains in terms of profit and productivity, its impact on already strained natural resources cannot be overlooked. The goal of the present primer is to outline fundamental pieces underpinning AI's direct impacts on the environment. It is written to be legible for the general public, policymakers, and the AI community. As such, it is organized to detail which natural resources are used throughout the AI lifecycle, and what the effects are on the environment for each.

Initial work on the environmental impacts of AI models focused on estimating the CO ₂ emissions incurred during model training. This includes the seminal work of Strubell et al., which calculated that training a large language model (LLM) with 213 million parameters was responsible for 626,155 pounds of CO ₂ emissions, roughly equivalent to the lifetime emissions of five cars, including fuel. Follow-up studies have looked at other types of model architectures, their energy use and emissions, confirming the consequential environmental impact of AI training.

Luccioni et al. proposed to extend these analyses to consider other stages of the AI model lifecycle, including training, deployment, and the components that make these possible: material extraction, equipment manufacturing, and overhead energy usage (cooling, networking, storage), among others (see Fig 1 below). Their work demonstrated that estimates of AI's greenhouse gas emissions are more than double than previous studies that focused on training in isolation, revealing a fuller picture of AI's environmental impact.

Fig 1. The life cycle assessment approach proposed by Luccioni et al.

Luccioni et al. also provided the first study on the environmental impact of AI model deployment) by analyzing the energy usage and carbon emissions of running the BLOOM large language model (LLM) in the cloud - specifically, a Google Cloud Compute cloud instance that contained the 176 Billion parameter BLOOM model, which received 230,768 queries over a period of 18 days. They found that this deployment used an average of 40.32 kWh of energy per day (roughly the equivalent of 1,110 smartphone charges) and, given the energy mix used by the computing instance, emitted approximately 19 kgs (42 lbs) of CO 2eq per day, reflective of state-of-the-art AI model deployment in 2023. This study was followed up by Luccioni et al. in 2024, which looked at the variation in energy usage and carbon emissions across different types of AI tasks. They found distinct differences based on modality (text vs. image) as well as whether the model was creating new content (captions, summaries, etc., commonly referred as "generative AI") or returning existing content. They found that image-based tasks and those that create new content used more energy.

It can be hard to understand the extent of AI's impacts on the environment given the separation between where you interact with an AI system, and how that interaction has come to be - most AI models run on data centers that are physically located far away from their users, who only interact with their outputs. But the reality is that AI's impressive capabilities come with a substantial cost in terms of natural resources, including energy, water and minerals, and non-negligible quantities of greenhouse gas emissions.

Every time we query an AI model - be it via our phone, a chat interface, or a smart speaker - this request has to run through physical hardware to provide us with an answer. As AI models get bigger in size and complexity, they also require more powerful hardware to run them. One common hardware component for AI systems is the GPU (graphical processing unit), and an individual AI model may require multiple GPUs. These GPUs are often on servers within data centers located across different regions of the world and connected via the Web. On average, 40-50% of the energy used by a data center is used for powering the computing equipment, with a further 30%-40% dedicated to cooling the equipment. Energy is also used for manufacturing the hardware (e.g., GPUs), as well as other elements of data center infrastructure (e.g., storage, networking, etc.) - although the exact amount used by companies such as Nvidia, who have considerable market shares on GPU design, remains unknown.

There is currently little transparency on the energy demands of specific AI applications, although a recent estimate has put the amount of energy used for a ChatGPT query to be anywhere between 6-10 times more than a traditional Web search (0.3 Wh vs. 2.9 Wh) [6] . At a macro level, AI is estimated to use 10%-20% of data center electricity today, but as new generations of AI-enabled servers consume more power, this percentage is set to increase at an average of 70% in coming years and double by 2030 .

Data centers currently account for 2-3% of the total electricity use in the United States, and this number is set to triple in the next 5 years . While new sources of renewable energy, such as solar and wind energy, are projected to meet approximately 40% of this demand, the rest is set to be met with non-renewable energy sources such as coal and natural gas. This would result in as much additional greenhouse gas as 16 million gas-powered cars . The combined energy use of the main technology companies providing AI cloud computing services and products (such as Google, Microsoft, Meta, and Amazon) has more than doubled in the last five years, and these companies are also among the largest purchasers of corporate renewable energy in the United States, having purchased almost 50 GW of renewable energy to date, as much as the generation capacity of Sweden . These same companies have also put out ambitious net-zero goals but have recently announced that they are failing to meet them due in part to the energy demands of AI tools and services . Overall, the growing energy demand for AI is significantly outpacing the increase in renewable energies - entailing substantial new GHG emissions and squeezing an already tight renewable energy market.

Just as the components in your personal computer heat up as they're used, so too do the components in computer servers. Data centers used for AI systems house hundreds of thousands of these servers, carrying out intensive, round-the-clock computation. They therefore need constant cooling to avoid overheating. One of the key ways in which this is done is by pumping clean water through radiators in the data center, which absorbs the heat in the air. The water used for cooling data centers cannot be saltwater or greywater because it clog or corrode cooling systems. This water then has to be cooled down; a significant portion of it evaporates in the process . After cooling, the remaining water is then reused or cleaned and cooled before being discharged back into aquifers.

Generally speaking, the amount of water used for data center cooling differs depending on data center configuration, but can range from 0.18 to 1.1L of water per kWh of energy . There are no official figures for specific AI models, but third-party research has estimated that GPT-3, the AI model underpinning the popular ChatGPT, uses 500mL of water for every 10 to 50 queries, although this number would depend highly on where it is deployed and how efficient the data center is. An average hyperscale data center, such as those used for training AI models, uses around 550,000 gallons (2.1 million liters) of water daily . Notably, some of them are built in areas with little water supply, such as the Arizona desert, where electricity is available, but water is scarce.

Computing chips such as GPUs are built on a thin layer of semiconductor, usually silicon, upon which components made out of different types of metals such as aluminum, copper, tin, tantalum, lithium, gallium, germanium, palladium, cobalt and tungsten are added. Mining of these metals also comes with environmental costs, since hundreds of tonnes of ore typically need to be dug up and processed to get a single ton of relatively common metals such as copper or aluminum. Minerals such as cobalt and tungsten are considered to be 'conflict minerals', meaning that they are mined or traded in areas of conflict, and contribute towards perpetuating human rights abuses and armed conflict.

The usage of water, minerals, and energy throughout the AI lifecycle often comes with emissions of greenhouse gasses - such as carbon dioxide. One major source is from the production of concrete and steel used in data centers; worldwide, concrete production is estimated to generate up to 8  percent of all human CO₂ emissions, in addition to using water and minerals. Another primary source of greenhouse gas emissions is from generating the electricity needed in different stages of the AI lifecycle. In Luccioni et al.'s 2023 study of the BLOOM language model, they found that of the total 50 tonnes of CO2eq emissions emitted during model training, only half was due to the energy consumption of the GPUs used for training BLOOM ('dynamic consumption'), with a further 29% stemming from the idle consumption of the data center (i.e., the energy used for heating/cooling, networking, storage, etc.), and a final 22% of emissions were produced from the GPU manufacturing process. The carbon intensity of the grid used is therefore the main factor influencing the final quantity of emissions incurred by AI models, impacting both model training and deployment.
Table 1: Breakdown of CO₂ emissions for the BLOOM model (Adapted from Luccioni et al., 2023)
For a full picture of AI's environmental impact, we need both consensus on what to consider as part of "AI", and much more transparency and disclosures from the companies involved in creating it. AI refers to a broad set of techniques, including machine learning, but also rule-based systems. A common point of contention is the scoping of what constitutes AI and what to include when estimating its environmental impacts. Core to this challenge is the fact that AI is often a part of, as opposed to the entirety of, any given system - e.g. smart devices, autonomous vehicles, recommender systems, Web search, etc. How to delineate and quantify the environmental impacts of AI as a field is therefore a topic of much debate, and there is currently no agreed-upon definition of the scope of AI. Some notable areas lacking coherent information include:

The embodied emissions of hardware manufacturing: While there are some numbers about the embodied (i.e., supply chain) emissions of different types of computing hardware, there are currently no reliable numbers on the carbon cost of manufacturing GPUs, which are the most popular type of hardware used for training AI models. Gathering these numbers for different generations and types of GPUs is important to have a better understanding of AI's lifecycle.

The embodied carbon of AI infrastructure: A significant contributing factor in the carbon emissions of data centers is the use of concrete in their construction. While initiatives have been aimed at providing sustainable building materials for data center construction, the relative impact of concrete construction on AI emissions is unclear.

Rebound effects and unintended consequences: It has been observed in different sectors (such as transportation) that an increase in efficiency in resource use will generate an increase in resource consumption rather than a decrease. While there is currently emphasis placed on making both AI hardware and models more efficient, the rebound effect that this may have (e.g. by increasing overall resource use) is still unclear and needs further quantification and longitudinal tracking.

AI's enabled emissions: AI technologies are increasingly used in sectors such as oil and gas exploration, improving their efficiency and increasing yield, which results in increased emissions overall . The proportion of these emissions that can be attributed to AI is still unclear, but initial work is being done to better understand this topic.

Ecosystem harm: Industrial waste produced by AI structures such as data centers risk environmental contamination . Further, the construction of new buildings - data centers as well as buildings where AI development occurs - risks negative effects on local ecosystems and existing natural habitats.

Different regulations may deal with multiple elements employed in the AI supply chain (e.g. regulations regarding mining, water, labor, hardware, software, data), but more specific regulation addressing AI and the environment is emerging. As discussed throughout this primer, the discussion of what constitutes AI is open and the same goes to how AI and the environment should be dealt with by regulation. Below is a non-exhaustive list of initiatives regarding AI and environmental challenges.

The EU launched the European Green Deal, a set of proposals to make the EU fit for reducing net greenhouse gas emissions, and to which EU's actions and policies have to contribute. As part of the Green Deal, the EU Climate Law has set legally binding targets for carbon neutrality by 2050, and AI systems used in energy management, smart grids, and environmental monitoring will need to comply with this law. A related initiative is the delegated regulation to establish an EU-wide scheme to rate the sustainability of EU data centers, as part of the new Energy Efficiency Directive. Water consumption could be addressed and regulated, for data centers built in the EU, through a thorough application of the Water Framework Directive.

The text of the Act also encourages the adoption of voluntary codes of conduct that would include measures to evaluate and minimize the environmental impact of AI systems. However, given that the adoption of codes of conduct is non-binding, it is unclear how effective it will be in achieving the stated objectives.

Spain: Sustainability was a key part of Spain's recently announced National AI strategy , with a specific emphasis on investing in computationally efficient models and data centers powered by renewable energy sources.

Canada: Environmental sustainability is one of the key focuses of the Pan-Canadian Artificial Intelligence Strategy. According to this strategy, by 2030 Canada intends to have a robust national AI ecosystem founded on scientific excellence, training and talent pools, public-private collaboration and advancing AI technologies to bring positive social, economic and environmental benefits for people and the planet. However, Canada's Artificial Intelligence and Data Act does not have specific provisions regarding the environmental impacts of AI.

France: The AI and Green transition roadmap, part of the broader France Nation Verte ecological planning program, highlights the potential of data, and more specifically AI, to meet the five major challenges of the ecological transition: consumption of resources, preserving biodiversity, mitigating and adapting to global warming and reducing pollution that impacts health.

International instruments: The OECD Recommendation of the Council on Artificial Intelligence has as one of its key principles the pursuit of inclusive growth, well-being, sustainable development and environmental sustainability with AI. The Hiroshima Process International Guiding Principles for Organizations Developing Advanced AI Systems proposes a non-exhaustive list of guiding principles, including prioritizing the development of AI systems to address the world's greatest challenges, notably but not limited to the climate crisis. The United Nations Global Digital Compact includes a commitment to promote environmental sustainability across the life cycle of digital technologies and aim to ensure that digital infrastructure and equipment are sustainably designed for the mitigation of and adaptation to climate change.

There is no single, one-size-fits-all approach to reducing the environmental impacts of AI models, but a variety of technical, behavioral and organizational interventions can be adopted by different stakeholders, at different stages of the AI lifecycle:

Technical interventions: different approaches are possible to make AI models less resource-intensive. This includes techniques such as pruning, quantization, distillation, and flash attention. These approaches can be applied at various stages of development, both during model training to make it more compute-friendly as well as on already-trained models to make them more efficient.

Behavioral interventions: users of AI models can also contribute to minimizing AI's environmental impacts by choosing task-specific (as opposed to multi-task) models when possible. AI model developers can benchmark the energy consumption of different models and opt for the most efficient ones, for instance, based on metrics such as the AI Energy Star project, and adopting strategies such as flexible scheduling for training AI models, which would allow for the optimization of energy sources.

Organizational interventions: institutions can also implement best practices with potentially far-reaching impacts in terms of the environment. This can include opting for compute instances powered by renewable energy sources when possible, measuring the energy consumption and greenhouse gas emissions of AI-enabled products and providing these metrics to users of these products.

Policy interventions: enforce transparency for user-facing AI systems, and transparency in general regarding the environmental impact of AI systems, start regulating different systems based on how often they're used, promote standards and incentives for meeting these standards (such as tax rebates), boost innovation through funding, incentivize sustainable applications and market opportunities and prioritize the most impactful solutions, including based on informed analysis of policy efforts.

@inproceedings{ai_environment_primer, author = {Sasha Luccioni and Bruna Trevelin and Margaret Mitchell}, title = {The Environmental Impacts of AI -- Policy Primer}, booktitle = {Hugging Face Blog}, year = {2024}, url = {https://doi.org/10.57967/hf/3004}, doi = {10.57967/hf/3004} }

AI model deployment: The process of using an AI model to carry out the task that it was trained for - e.g. generating images, finding answers to questions, etc.

AI model training: The process of providing data to an AI model that is meant to enable it to produce more accurate predictions.

: Since different greenhouse gases that are generated during electricity generation have different global warming potentials, these are commonly reduced to a common denominator, that of carbon dioxide, in order to make comparisons easier.

Embodied emissions: Emissions associated with the whole lifecycle of an item, including production of materials, construction, transportation, and item usage.

Generative tasks: Tasks that require a model to generate new outputs based on inputs - e.g., generating a poem based on an image, or generating an image based on a text prompt.

Graphical Processing Unit (GPU): A type of computing hardware originally designed to accelerate the rendering of graphics in video games given their parallel processing capabilities. They are now the most commonly-used type of hardware for training and deploying AI models.

Greenhouse gases (GHGs): gases that exist in the Earth's atmosphere that trap heat within it, thereby contributing towards the greenhouse effect (i.e. raising the surface temperature), e.g. carbon dioxide, methane, nitrous oxide.

Large language model (LLM): There is no single, agreed-upon definition of large language models (also called 'frontier models' or 'foundation models'), but they are largely defined as computational models that are capable of taking natural language as input or producing it as output, sometimes alongside other modalities such as images. See Rogers and Luccioni (2024) for a more in-depth discussion.

Model architectures: Depending on the task at hand and the amount of data available, there are different architectures of AI models that can be used - ranging from simpler ones such as decision trees to more complex ones such as transformers and convolutional neural networks. See Goodfellow et al (2016) for more details.

Thank you to Brigitte Tousignant for her help in editing this primer, and Philipp Hacker, Yacine Jernite, Lynn Kaack and David Rolnick for their invaluable comments and suggestions.

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The Environmental Impacts of AI -- Primer

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A Blog post by Sasha Luccioni on Hugging Face

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Google AI Studio is the fastest way to start building with Gemini, our next generation multimodal generative AI model.

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Google AI Studio | Gemini API | Google for Developers

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Send feedback Stay organized with collections Save and categorize content based on your preferences. Google AI Studio is the fastest way to start building with Gemini, our next generation multimodal generative AI model.

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November 4, 2023 Grok is an AI modeled after the Hitchhiker's Guide to the Galaxy, so intended to answer almost anything and, far harder, even suggest what questions to ask!
Grok is designed to answer questions with a bit of wit and has a rebellious streak, so please don't use it if you hate humor!

A unique and fundamental advantage of Grok is that it has real-time knowledge of the world via the 𝕏 platform. It will also answer spicy questions that are rejected by most other AI systems.

Grok is still a very early beta product - the best we could do with 2 months of training - so expect it to improve rapidly with each passing week with your help.

Thank you,
the xAI Team

Why we are building Grok

At xAI, we want to create AI tools that assist humanity in its quest for understanding and knowledge.

By creating and improving Grok, we aim to:

Gather feedback and ensure we are building AI tools that maximally benefit all of humanity. We believe that it is important to design AI tools that are useful to people of all backgrounds and political views. We also want empower our users with our AI tools, subject to the law. Our goal with Grok is to explore and demonstrate this approach in public.

Empower research and innovation: We want Grok to serve as a powerful research assistant for anyone, helping them to quickly access relevant information, process data, and come up with new ideas.

Our ultimate goal is for our AI tools to assist in the pursuit of understanding.

The journey to Grok-1

The engine powering Grok is Grok-1, our frontier LLM, which we developed over the last four months. Grok-1 has gone through many iterations over this span of time.

After announcing xAI, we trained a prototype LLM (Grok-0) with 33 billion parameters. This early model approaches LLaMA 2 (70B) capabilities on standard LM benchmarks but uses only half of its training resources. In the last two months, we have made significant improvements in reasoning and coding capabilities leading up to Grok-1, a state-of-the-art language model that is significantly more powerful, achieving 63.2% on the HumanEval coding task and 73% on MMLU.

To understand the capability improvements we made with Grok-1, we have conducted a series of evaluations using a few standard machine learning benchmarks designed to measure math and reasoning abilities.

GSM8k: Middle school math word problems, (Cobbe et al. 2021), using the chain-of-thought prompt.

MMLU: Multidisciplinary multiple choice questions, (Hendrycks et al. 2021), provided 5-shot in-context examples.

HumanEval: Python code completion task, (Chen et al. 2021), zero-shot evaluated for pass@1.

MATH: Middle school and high school mathematics problems written in LaTeX, (Hendrycks et al. 2021), prompted with a fixed 4-shot prompt.

On these benchmarks, Grok-1 displayed strong results, surpassing all other models in its compute class, including ChatGPT-3.5 and Inflection-1. It is only surpassed by models that were trained with a significantly larger amount of training data and compute resources like GPT-4. This showcases the rapid progress we are making at xAI in training LLMs with exceptional efficiency.

Since these benchmarks can be found on the web and we can't rule out that our models were inadvertently trained on them, we hand-graded our model (and also Claude-2 and GPT-4) on the 2023 Hungarian national high school finals in mathematics, which was published at the end of May, after we collected our dataset. Grok passed the exam with a C (59%), while Claude-2 achieved the same grade (55%), and GPT-4 got a B with 68%. All models were evaluated at temperature 0.1 and the same prompt. It must be noted that we made no effort to tune for this evaluation. This experiment served as a "real-life" test on a dataset our model was never explicitly tuned for.

We provide a summary of the important technical details of Grok-1 in the model card.

Engineering at xAI

At the frontier of deep learning research, reliable infrastructure must be built with the same care as datasets and learning algorithms. To create Grok, we built a custom training and inference stack based on Kubernetes, Rust, and JAX.

LLM training runs like a freight train thundering ahead; if one car derails, the entire train is dragged off the tracks, making it difficult to set upright again. There are a myriad of ways GPUs fail: manufacturing defects, loose connections, incorrect configuration, degraded memory chips, the occasional random bit flip, and more. When training, we synchronize computations across tens of thousands of GPUs for months on end, and all these failure modes become frequent due to scale. To overcome these challenges, we employ a set of custom distributed systems that ensure that every type of failure is immediately identified and automatically handled. At xAI, we have made maximizing useful compute per watt the key focus of our efforts. Over the past few months, our infrastructure has enabled us to minimize downtime and maintain a high Model Flop Utilization (MFU) even in the presence of unreliable hardware.

Rust has proven to be an ideal choice for building scalable, reliable, and maintainable infrastructure. It offers high performance, a rich ecosystem, and prevents the majority of bugs one would typically find in a distributed system. Given our small team size, infrastructure reliability is crucial, otherwise, maintenance starves innovation. Rust provides us with confidence that any code modification or refactor is likely to produce working programs that will run for months with minimal supervision.

We are now preparing for our next jump in model capabilities, which will require reliably coordinating training runs on tens of thousands of accelerators, running internet-scale data pipelines, and building new kinds of capabilities and tools into Grok. If that sounds exciting to you, apply to join the team here.

Research at xAI

We give Grok access to search tools and real-time information, but as with all the LLMs trained on next-token prediction, our model can still generate false or contradictory information. We believe that achieving reliable reasoning is the most important research direction to address the limitations of current systems. Here, we would like to highlight a few promising research directions we are most excited about at xAI:

Scalable oversight with tool assistance. Human feedback is essential. However, providing consistent and accurate feedback can be challenging, especially when dealing with lengthy code or complex reasoning steps. AI can assist with scalable oversight by looking up references from different sources, verifying intermediate steps with external tools, and seeking human feedback when necessary. We aim to make the most effective use of our AI tutors' time with the help of our models.

Integrating with formal verification for safety, reliability, and grounding. To create AI systems that can reason deeply about the real world, we plan to develop reasoning skills in less ambiguous and more verifiable situations. This allows us to evaluate our systems without human feedback or interaction with the real world. One major immediate goal of this approach is to give formal guarantees for code correctness, especially regarding formally verifiable aspects of AI safety.

Long-context understanding and retrieval. Training models for efficiently discovering useful knowledge in a particular context are at the heart of producing truly intelligent systems. We are working on methods that can discover and retrieve information whenever it is needed.

Adversarial robustness. Adversarial examples demonstrate that optimizers can easily exploit vulnerabilities in AI systems, both during training and serving time, causing them to make egregious mistakes. These vulnerabilities are long-standing weaknesses of deep learning models. We are particularly interested in improving the robustness of LLMs, reward models, and monitoring systems.

Multimodal capabilities. Currently, Grok doesn't have other senses, such as vision and audio. To better assist users, we will equip Grok with these different senses that can enable broader applications, including real-time interactions and assistance.

We believe that AI holds immense potential for contributing significant scientific and economic value to society, so we will work towards developing reliable safeguards against catastrophic forms of malicious use. We believe in doing our utmost to ensure that AI remains a force for good.

If you share our optimism and want to contribute to our mission, apply to join the team here.

Early Access to Grok

We are offering a limited number of users in the United States to try out our Grok prototype and provide valuable feedback that will help us improve its capabilities before a wider release. You can join the Grok waitlist here. This release just represents the first step for xAI. Looking ahead, we have an exciting roadmap and will be rolling out new capabilities and features in the coming months.

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A research assistant that helps you ask the right questions, find information online and make sense of the world.

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Cett outil peut créer des modules "pocket learning" sur tous les sujets mais vous n'aurez pas la possibilité de les modifier, fonctionne en anglais seulement