The Data Exchange with Ben Lorica
2024 Artificial Intelligence Index
Description
This episode covers key insights from the 2024 Artificial Intelligence Index Report, advancements in AI models and benchmarking, the importance of human evaluation, responsible AI practices, and the potential of AI in solving scientific problems. It also discusses the challenges faced by academic institutions in keeping up with industry developments and the differences in public opinion on AI between China and the US.
Insights
Benchmarking in AI has evolved
AI systems now surpass human capabilities on some benchmarks. Measures of human evaluation are being included in addition to traditional computer science-based tests for benchmarking AI systems.
Recent advancements in AI
Multimodal models have been developed that can handle various tasks like text, images, and even explain jokes. The AI community is exploring benchmarks to assess AI capabilities, but there is no comprehensive benchmark suite for human-like challenges yet.
The importance of considering how humans feel
Researchers are developing models that are appreciated by human evaluators. Architecture styles from language models are being borrowed for non-linguistic domains like computer vision and speech.
Functional chat VBT agents and open source research community
The research community is prioritizing the development of functional chat VBT agents. There is a vibrant open source research community with increasing foundation models and projects on GitHub.
Tracking the dynamics of the AI ecosystem
Industry is outpacing academic institutions in producing AI models due to high costs involved. Computational innovations are needed to push AI to the next frontier, which could come from either industry or academia.
Innovation beyond current models and public opinion on AI
China has been filing a lot of patents, indicating ongoing innovation beyond current models like transformers. Public opinion on AI differs between China and the US, with Americans being more pessimistic.
Challenges in AI training and responsible AI
Concerns about potential data depletion for training AI systems in the future. Challenges related to responsible AI, including legal implications of copyrighted material generation by AI models and complex vulnerabilities in language models.
Harmful behavior of models and importance of responsible AI
Models can exhibit harmful behavior through less obvious ways like extracting personal information during random word repetition. Responsible AI becomes more important as it becomes more integrated into various industries and daily life.
AI solving scientific problems and brute force capabilities
AI is being used to solve scientific problems requiring computational brain power. The AI's ability to tackle brute force problems excites researchers and offers new possibilities in various fields.
Chapters
- AI Benchmarking and Evaluation
- Advancements in AI and Multimodal Models
- Human Evaluation and New Model Developments
- Functional Chat VBT Agents and Open Source Research Community
- Tracking the AI Ecosystem and Computational Innovations
- Innovation Beyond Current Models and Public Opinion on AI
- Challenges in AI Training and Responsible AI
- Harmful Behavior of Models and Importance of Responsible AI
- AI Solving Scientific Problems and Brute Force Capabilities
AI Benchmarking and Evaluation
00:00 - 06:58
- The 2024 Artificial Intelligence Index Report, edited by Nestor Masledge, covers important topics in the AI world and involves influential AI thought leaders and researchers.
- Benchmarking in AI has evolved, with AI systems surpassing human capabilities on some benchmarks.
- Measures of human evaluation are being included in addition to traditional computer science-based tests for benchmarking AI systems.
- Real-world testing remains crucial for evaluating AI performance.
- AI tools like GPT-4 and CLODE are used for copy editing assistance in writing reports.
Advancements in AI and Multimodal Models
06:37 - 13:21
- AI systems are still behind humans in certain complex tasks like math and common sense reasoning.
- Recent advancements in AI have led to the development of multimodal models that can handle various tasks like text, images, and even explain jokes.
- The AI community is exploring benchmarks like GPQA and MMU to assess AI capabilities.
- There is a shift from academic to industrial applications where human evaluation plays a crucial role.
Human Evaluation and New Model Developments
12:52 - 19:40
- Considering how humans feel when evaluating AI systems is increasingly important.
- Researchers are developing models that are appreciated by human evaluators.
- Architecture styles from language models are being borrowed for non-linguistic domains like computer vision and speech.
- New model developments in computer vision include Control Net for conditional control editing and Segment Anything for segmentation benchmarks.
- AI systems are being used to extract data from the existing world, aiding subsequent AI development.
- Advancements are being made in creating autonomous or semi-autonomous systems to accomplish goals.
Functional Chat VBT Agents and Open Source Research Community
19:20 - 26:07
- The research community is prioritizing the development of functional chat VBT agents.
- Agents in industry may initially emerge for internal applications before becoming outward-facing.
- Closed LLMs backed by well-resourced companies tend to outperform open LLMs for general purposes.
- There is a vibrant open source research community with increasing foundation models and projects on GitHub.
- Industry actors tend to develop closed models due to financial investments and competitive advantages.
- The number of suppliers offering decent open models is a concern.
Tracking the AI Ecosystem and Computational Innovations
25:45 - 32:37
- Tracking the dynamics of the AI ecosystem is important, including both quantity and quality of models.
- Industry is outpacing academic institutions in producing AI models due to high costs involved.
- Building newer AI models costs millions of dollars, raising questions about access and resources.
- Computational innovations are needed to push AI to the next frontier, which could come from either industry or academia.
Innovation Beyond Current Models and Public Opinion on AI
32:13 - 39:07
- China has been filing a lot of patents, indicating ongoing innovation beyond current models like transformers.
- Historically, important developments have come from university settings with less focus on profit motives.
- Efficiency gains can be made in AI systems to do more with less data and improve functionality.
- The US leads in notable AI models and private investment, while China excels in robotics installation and patenting.
- Public opinion on AI differs between China and the US, with Americans being more pessimistic.
Challenges in AI Training and Responsible AI
38:44 - 45:03
- Military spending in AI analysis revealed challenges due to data scraping and transparency issues.
- Concerns about potential data depletion for training AI systems in the future.
- Synthetic data may not be a viable solution to address the lack of high-quality data for AI training.
- Exploration of alternative approaches like new architectures or utilizing existing world data for AI training.
- Challenges related to responsible AI, including legal implications of copyrighted material generation by AI models and complex vulnerabilities in language models.
Harmful Behavior of Models and Importance of Responsible AI
44:43 - 51:24
- Models can exhibit harmful behavior through less obvious ways like extracting personal information during random word repetition.
- There is a lack of standardized evaluations for Large Language Models (LLMs) in terms of responsibility, leading to inconsistencies in testing benchmarks among developers.
- Transparency and sharing of responsible AI testing methods are crucial for understanding the limitations and risks associated with AI models.
- Responsible AI becomes more important as it becomes more integrated into various industries and daily life.
- Companies investing in AI face a tension between transparency about limitations and risks versus maximizing profits and market competitiveness.
AI Solving Scientific Problems and Brute Force Capabilities
50:59 - 53:47
- AI is being used to solve scientific problems requiring computational brain power.
- Examples include Graphcast for weather forecasting, GNOME for discovering crystal structures, and alpha-manescence for predicting genetic alterations' impact on human proteins.
- AI models like alpha-manescence can classify mutations more efficiently than human annotators.
- The AI's ability to tackle brute force problems excites researchers and offers new possibilities in various fields.