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AI Platform

Healthy data feedback loops are the lifeblood of good decision-making.

Contents​

Scaling AI​

  1. Data as a competitive advantage: Data, rather than algorithms or compute, is seen as the primary differentiator for AI companies in the future. It's one of the few areas where companies can build a sustainable competitive advantage.
  2. The importance of "frontier data": There's a need for high-quality, complex data that can push AI models forward. This includes data on complex reasoning, agent behavior, and specialized knowledge in fields like science and mathematics.
  3. Enterprise data mining and production: Large enterprises have vast amounts of proprietary data that could be valuable for AI training. Sophisticated companies will mine their existing data and develop strategies for ongoing data production.
  4. Challenges with data regulation: There's concern that overly restrictive data regulations, particularly in the EU, could stifle innovation. A balanced approach that allows for data access while maintaining privacy and security is advocated.
  5. The future of foundation models: In 10 years, only a few entities (large tech companies or nation-states) may have the resources to build the most advanced foundation models, which could cost tens or hundreds of billions of dollars.
  6. AI as a military asset: AI is viewed as potentially one of the greatest military assets in history, possibly surpassing nuclear weapons in importance. There's emphasis on Western countries maintaining a lead in AI development for geopolitical reasons.
  7. Hiring philosophy: The importance of hiring people who deeply care about their work and the company's mission is stressed. At Scale AI, every hire is personally approved to maintain a high bar for talent.
  8. PR strategy: Traditional media can be problematic for companies due to its incentives for generating clicks. Founders are encouraged to build their own channels for communicating directly with their audience.
  9. Company growth strategy: There's been a shift away from rapid team expansion, focusing instead on maintaining a smaller, elite workforce while still growing revenue. Hyper growth in team size can lead to a decline in overall quality and problem-solving ability.
  10. The future of Scale AI: The company is seen continuing to be the "data foundry" for AI progress in the next decade, focusing on solving problems that will never go out of style.

Data Gravity​

As AI continues to evolve and generate more data, managing data gravity becomes crucial for organizations. It requires careful planning of data storage, processing locations, and infrastructure to balance performance, cost, and compliance needs. The intersection of data gravity and AI is reshaping IT strategies and driving the need for more sophisticated, data-centric approaches to infrastructure and data management.

Data gravity is becoming an increasingly important concept in the era of AI, particularly with the rise of generative AI. Here are the key points about how data gravity and AI intersect:

Impact​

  1. Increased Data Creation: AI, especially generative AI, is creating more data to work with, compounding the challenges related to data gravity. As AI models are trained and used, they generate vast amounts of new data.
  2. Data Placement Considerations: The location of data becomes crucial when working with AI, whether it's for training models or using them. This affects decisions about cloud, on-premises, or edge computing strategies.
  3. Infrastructure Demands: AI is shaping IT infrastructure needs, including the placement of data centers, on-premises and hybrid cloud services, and other locations for data storage, training, and processing.

Challenges​

  1. Processing Location: Enterprises need to determine where to both compute and store data for AI workloads. The 2023 Generative AI Pulse Survey shows that 82% of IT leaders prefer an on-premises or hybrid approach for data management.
  2. Edge AI: The growth of Industrial Internet of Things (IIoT) and edge AI involves data processing at the edge, requiring decisions about how much data to process locally versus routing to the cloud.
  3. Cost Considerations: Managing data for AI in various environments (cloud, on-premises, hybrid) significantly impacts costs and data management strategies.
  4. Data Governance: AI applications require careful consideration of how much data needs to be moved or retained to be useful, affecting data governance strategies.

Strategies​

  1. Ecosystem Approach: Solving data gravity issues in the context of AI requires an ecosystem approach, considering factors like GDP, technology maturity, and local regulations.
  2. Cloud Solutions: Cloud providers can be effective for hosting large AI datasets, as they can scale more easily and manage throughput and workload balance.
  3. Data Filtering and Analysis: For edge AI applications, filtering or analysing data in situ or in transit can help manage data gravity issues without centralizing all data.
  4. Hybrid IT Strategies: Implementing hybrid IT approaches can help balance the needs of AI workloads with data gravity considerations.
  5. Data-Centric Architecture: An inverted data-centric architecture deployed at points of presence in neutral, multi-tenant data-centers is suggested as a solution for modern data gravity challenges.
  1. Rapid Growth: The Data Gravity Index predicts a 139% growth in data gravity intensity between 2020 and 2024, largely driven by AI and digital transformation.
  2. Real-Time Intelligence: There's an increasing need for real-time intelligence to power innovation, which is challenging with legacy architectures due to data gravity issues.
  3. Cybersecurity Concerns: The acceleration of digital transformation and AI adoption is amplifying cybersecurity challenges related to data gravity.

Education​

Courses and channels to learn AI and Data.

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Websites

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Predictions​