The 2024 AI Index report highlights that industry continues to lead in frontier AI research. In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. Additionally, there were 21 notable models resulting from industry-academia collaborations, reaching a new high.

This trend of industry dominating frontier AI research is continuing to increase. The report raises the question of whether the government should get more involved in AI projects, as the private sector's leading role may create a concerning power imbalance in the future.

Furthermore, the report notes that the number of foundation models released in 2023 doubled compared to 2022, with 65% of these newly released models being open-source, up from 44% in 2022 and 33% in 2021. This suggests an increasing trend towards open-source AI, even as state-of-the-art limited models like GPT-4 and its successors remain closed-source.

The report also provides estimates of the training costs for these models, with GPT-4 estimated at $78 million and Gemini Ultra at $191 million, highlighting the significant investments required to develop these advanced AI systems.

Overall, the report paints a picture of industry's continued leadership in frontier AI research, with open-source models gaining ground, and the potential need for greater government involvement to address concerns about power imbalances in the AI landscape.

The 2024 AI Index report highlights the growing prominence of open-source AI models. Some key points:

• In 2023, 65% of the 149 newly released foundation models were open-source, up from 44% in 2022 and 33% in 2021. This shows a clear trend towards more open-source AI development.

• The number of AI-related projects on GitHub has seen a sharp 59.3% rise in 2023, more than tripling from 4 million in 2022 to 12.2 million in 2023. This explosion of open-source activity was fueled by the release of ChatGPT in late 2022.

• While closed-source models like GPT-4 and Gemini Ultra still reign supreme in certain benchmarks, the report notes that open-source systems are increasingly catching up and dominating the AI landscape.

• This rise of open-source AI is seen as a positive trend, promoting transparency and accessibility. However, concerns remain about the potential risks of powerful open-source models falling into the wrong hands.

• Regulators will likely need to grapple with balancing the benefits of open innovation with the need to mitigate misuse and ensure responsible development of these transformative technologies.

In summary, the 2024 AI Index highlights the remarkable growth of open-source AI, which is reshaping the landscape and challenging the dominance of closed-source models. This trend will likely continue to be a key focus area for the AI community in the years ahead.

This chapter examines the performance of AI systems across a variety of benchmarks compared to human capabilities. The data shows an increasingly impressive trend, with AI surpassing human performance on several tasks:

• AI has surpassed human performance on benchmarks including image classification, visual reasoning, and English understanding.
• However, AI still trails behind humans on more complex tasks like competitive-level mathematics, visual common sense reasoning, and planning.

The trend across these benchmarks indicates that as we move into 2023 and beyond, AI is quickly closing the gap and even exceeding the human baseline in many areas. Some key points:

• The human baseline is being overtaken in domains like image classification and natural language understanding.
• While AI lags behind in areas like mathematics and reading comprehension, the performance gap is rapidly shrinking.
• Benchmarks like the Multitask Language Understanding (MMLU) test show AI capabilities quickly approaching human-level.

This data suggests that by the end of 2024, AI systems may reach near-parity with humans across a wide range of cognitive tasks. The continued advancement of large language models like GPT-4 is likely to drive further breakthroughs in AI performance. As these capabilities grow, it will be crucial to monitor both the progress and limitations of AI systems compared to human abilities.

Traditionally, AI systems have been limited in scope, with language models excelling in text comprehension but faltering in image processing, and vice versa. However, recent advancements have led to the development of strong multimodal models such as Google's Gemini and OpenAI's GPT-4.

These models demonstrate remarkable flexibility and are capable of handling both images and text. In fact, Gemini 1.5 Pro can even process audio. The baseline for multimodal AI capability has continued to increase, reaching 94.04% in 2023, compared to the human baseline of 89.8%.

This advancement in multimodal AI has prompted researchers to develop more challenging benchmarks, such as the SWE bench for coding, Heim for image generation, MMU for general reasoning, and Mocker for moral reasoning. These new benchmarks aim to push the boundaries of AI's capabilities and uncover its limitations.

While AI models have reached performance saturation on established benchmarks like IMAC, SNAP, and SuperGLUE, the emergence of these more complex and demanding evaluations will continue to challenge researchers and developers. The ability to reason, understand, and interact across multiple modalities is a crucial step towards more versatile and capable AI systems.

As the field of multimodal AI progresses, we can expect to see even more impressive advancements in the years to come, with AI agents becoming increasingly adept at navigating and understanding the diverse and interconnected nature of the real world.

The AI index report highlights the rapid progress in specialized AI benchmarks beyond traditional language and vision tasks. As AI systems continue to advance, researchers have developed more challenging and nuanced benchmarks to assess their capabilities.

Some key developments in this area include:

1. Coding Benchmarks: The introduction of the SWE Bench, a new benchmark for evaluating coding abilities of AI models. This benchmark has sparked controversy, with some allegations that the demo results were not entirely genuine. However, many open-source projects have shown impressive performance on this challenging task.

2. Reasoning Benchmarks: Benchmarks like HEIM for image reasoning, MMU for general reasoning, and MOCKER for moral reasoning have emerged to push the boundaries of AI's reasoning capabilities. While current models still trail behind humans in these areas, the report suggests that breakthroughs in reasoning could be on the horizon, potentially with the release of GPT-5 and other advanced models.

3. Agent-based Benchmarks: The Agent Bench, which evaluates autonomous agent performance across various environments, has shown steady improvements. AI agents can now master complex games like Minecraft and tackle real-world tasks like shopping and research assistance more effectively.

4. Music Generation Benchmarks: The evaluation of music generation models on benchmarks like MusicCaps has demonstrated advancements in AI's ability to produce high-quality music. The report notes that the gap between closed and open-source models in this domain remains significant, suggesting that the most advanced music generation capabilities are still primarily found in proprietary systems.

5. Multimodal Benchmarks: The report highlights the rise of strong multimodal AI models, such as Google's Gemini and OpenAI's GPT-4, which can handle a combination of text, images, and even audio. These models have reached performance parity with humans on established multimodal benchmarks, indicating a significant step forward in the field.

As these specialized benchmarks continue to evolve, they will provide a more nuanced and comprehensive understanding of the capabilities and limitations of modern AI systems. The report suggests that the ability to reason, plan, and interact with the world in more complex ways will be a key focus for future AI advancements.

One of the key trends highlighted in the report is the growing emphasis on human evaluation of language models. The report notes that the LMS chatbot arena, which uses blind A/B testing and human ratings to assess the performance of different models, is becoming an increasingly important benchmark.

The report states that this human evaluation approach is valuable because it assesses the models' overall performance and user experience, rather than just relying on specific test scores. The report suggests that some of the traditional benchmarks may have faced issues with contamination or errors, making the human evaluation approach more reliable.

Specifically, the report notes that in the LMS chatbot arena, GPT-4 Turbo is currently leading, even after the release of Claude 3. This indicates that human users are finding GPT-4 Turbo to be the more effective and desirable model, despite potential improvements in other models.

The report argues that this human evaluation approach should be used more widely, as it provides a more holistic assessment of language model capabilities. As the models become increasingly sophisticated, the ability to interact with and assess them from a user's perspective is becoming crucial for understanding their real-world performance and impact.

Overall, the increasing importance of human evaluation highlights the need to consider the user experience and practical applications of language models, rather than solely focusing on technical benchmarks. This shift reflects the growing maturity and societal impact of these AI systems, and the need to ensure they are meeting the needs and expectations of human users.

The fusion of language modeling with robotics has given rise to more flexible robotics systems like PaLM-E and RT2. Beyond their improved robotic capabilities, these models can ask questions, which marks a significant step towards robots that can interact more effectively with the real world.

The evolution of these models is increasing their capabilities, and robotics is a harder challenge than traditional AI. However, there are going to be breakthroughs that complement each other, leading to more effective robots in the future. We're already seeing impressive demonstrations, like the fluid and seamless movements of the robot Figure One, which were achieved 100% through a neural network, showcasing rapid progress in this area.

On the Agent Bench, which evaluates autonomous agent systems across eight environments, the overall score is increasing. Creating AI agent systems capable of autonomous operation in specific environments has long been a challenge, but emerging research suggests that the performance of autonomous agents is improving. Current agents can now master complex games like Minecraft and effectively tackle real-world tasks such as shopping and research assistance.

The paper highlights the performance improvements of Voyager, an Nvidia system, in Minecraft, which used GPT-4 to increase the reasoning abilities of the agent and enable it to learn, explore, and plan in open-ended worlds. This demonstrates the potential for more powerful systems to be utilized in the future for these types of autonomous agent tasks.

The report highlights several key trends in the development of AI-powered music generation:

1. Improved Performance on Music Benchmarks: The evaluation of music generation models on established benchmarks like MusicCaps shows that AI systems are steadily improving in their ability to generate high-quality music. The closed-source language models are significantly outperforming the open-source counterparts on these benchmarks.

2. Emergence of Advanced Music Generation Models: Models like Music-LM and Music-Gen have demonstrated impressive capabilities in music generation, showcasing the rapid advancements in this domain. These models can now produce music that is increasingly indistinguishable from human-composed pieces.

3. Closing the Gap with Human-Composed Music: The report notes that the performance of AI-generated music is approaching the level of human-composed music, with the difference between the two becoming increasingly narrow. This suggests that AI-powered music generation is reaching a point where it can rival the quality and creativity of human-created music.

4. Potential Impact on the Music Industry: As AI music generation capabilities continue to improve, the report highlights the potential impact on the music industry. This could lead to changes in the way music is produced, distributed, and consumed, potentially disrupting traditional business models and creative processes.

5. Challenges in Evaluating Music Generation: The report acknowledges the difficulty in establishing robust and standardized benchmarks for evaluating the quality and creativity of AI-generated music. This is an area that requires further research and development to ensure fair and comprehensive assessments of these systems.

Overall, the report suggests that the field of AI-powered music generation is rapidly evolving, with significant advancements in the quality and capabilities of these systems. As the technology continues to progress, it will be crucial to address the challenges around responsible development and deployment of these AI models to ensure they are used in a manner that benefits the music industry and society as a whole.

The report highlights several important considerations and challenges around responsible AI development and deployment:

Political Deepfakes and Misinformation

The report notes that political deepfakes are becoming increasingly easy to generate and difficult to detect. AI systems can be used to create convincing fake content, including images, videos, and text, that can be used to spread misinformation and influence public opinion. This raises serious concerns about the potential for AI to be misused for malicious purposes.

Lack of Transparency in Foundation Models

The report states that AI developers, especially those working on large foundation models, often lack transparency around the disclosure of training data and methodologies. This lack of openness hinders efforts to understand the robustness and safety of these AI systems.

Difficulty Assessing Existential Risks

The report acknowledges the challenge of distinguishing scientifically founded claims about long-term existential risks of AI from more speculative concerns. The tangible nature of near-term risks contrasts with the theoretical nature of potential long-term threats, making it difficult to prioritize and address these issues.

Increasing AI Incident Reports

The report notes a 32.3% increase in reported AI-related incidents in 2023 compared to 2022, with a 20-fold growth since 2013. This includes examples like the generation of sexually explicit deepfakes of public figures. The trend suggests that the misuse of AI is a growing problem that will require concerted efforts to address.

Political Bias in Language Models

Researchers found significant political biases in ChatGPT, with the model tending to favor Democrats in the US and the Labour Party in the UK. This raises concerns about the potential for large-scale language models to influence users' political views, especially in the context of upcoming global elections.

The Need for Responsible AI Development and Regulation

The report highlights the importance of developing AI systems in a responsible manner, with a focus on transparency, safety, and ethical considerations. It also notes the increasing efforts by policymakers in the US and EU to enact AI-related regulations, though there are concerns about striking the right balance between mitigating risks and fostering innovation.

Overall, the report underscores the critical need for a comprehensive and proactive approach to addressing the challenges of responsible AI development and deployment, as the technology continues to advance rapidly and become more pervasive in society.

The 2024 AI Index report from Stanford University provides a comprehensive and insightful analysis of the latest trends in the field of artificial intelligence. Some key takeaways from the report include:

1. AI Surpasses Humans on Some Tasks: AI systems have surpassed human performance on several benchmarks, including image classification, visual reasoning, and natural language understanding. However, they still trail behind on more complex tasks like advanced mathematics and common sense reasoning.

2. Industry Dominates Frontier AI Research: In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. This trend of industry leading the way in cutting-edge AI research continues to grow.

3. Frontier Models Become Increasingly Expensive: The estimated training cost of models like GPT-4 and Gemini Ultra highlights the massive investment required to develop state-of-the-art AI systems, with costs reaching over $100 million.

4. The US, China, EU, and UK Lead in AI Model Production: These regions are the top sources of notable AI models, showcasing their dominance in the field.

5. Lack of Transparency in Foundation Model Development: The report notes a concerning lack of transparency from AI developers, especially regarding training data and methodologies, which hinders efforts to understand the robustness and safety of these systems.

6. Increase in AI-Related Incidents and Biases: The report highlights the growing number of reported AI incidents, such as the generation of sexually explicit deepfakes, as well as the discovery of political biases in systems like ChatGPT.

7. AI's Impact on the Economy and Labor Market: While AI is predicted to drive productivity gains and revenue increases for organizations, there are concerns about potential job displacement and the need to ensure AI augments rather than replaces human workers.

8. Advancements in Scientific and Medical AI Applications: The report showcases how AI is accelerating scientific discovery and being increasingly utilized in medical applications, with models like GPT-4 achieving remarkable performance on medical benchmarks.

9. Surge in AI Regulation and Legislation: Policymakers in the US and EU have proposed and enacted substantial AI-related regulations and legislation, reflecting the growing need to address the societal impacts of this transformative technology.

Overall, the 2024 AI Index report paints a complex and rapidly evolving landscape, highlighting both the remarkable progress in AI capabilities as well as the pressing challenges and risks that must be addressed. As AI continues to advance, the need for responsible development and deployment, as well as ongoing monitoring and evaluation, will be crucial to ensure the technology benefits humanity as a whole.