up:: [[DBA806 - Applied AI Innovation]] tags:: #source/course #on/AI #on/ML people:: [[Praphul Chandra]] # DBA806 M12 - The Economics of AI [GGU DBA in Emerging Technologies | Dec'23 Cohort | Slot 2 | Applied AI Innovation | Live Session - YouTube](https://www.youtube.com/watch?v=5vJ4fNLxPYw) [TheEconomicsOfAIInnovation.pdf](https://cdn.upgrad.com/uploads/production/015d8b5f-5ebc-4ebd-8309-49686cd453d2/TheEconomicsOfAIInnovation.pdf) The text discusses on the economics of AI. They outline several key themes to be covered, including microeconomics, macroeconomics, and labor economics as they relate to AI adoption and expenditure within organizations. The speaker proceeds to discuss the factors influencing the performance of AI algorithms, emphasizing computational power, data, and machine learning algorithms as major contributors to costs associated with AI implementation. **Key Points:** - Performance of AI algorithms depends on computational power, data, and machine learning algorithms. - Computational power is measured in flops (floating point operations per second). - Data size and diversity, as well as machine learning algorithms, significantly impact costs. The text further delves into the exponential rise in computational power used to train AI models over the past two decades, highlighting the correlation between increased computation and improved model performance. **Key Points:** - Exponential growth in computational power used for training AI models. - Correlation between computation and model performance. - Graphs illustrate the increase in computational power over time and its impact on model performance. The discussion extends to the relationship between data size, computational power, and model performance, emphasizing the need for these elements to align for optimal results in AI model training. **Key Points:** - Data size and computational power must align for optimal model performance. - Graphs demonstrate the correlation between data size, computation, and model performance. - Increasing data without sufficient computational power or vice versa can hinder performance. Overall, the text provides insights into the economics of AI, focusing on the costs associated with computational power, data, and machine learning algorithms, and their impact on model performance. It underscores the importance of understanding these factors for effective AI implementation and budget planning. The discussion revolves around the performance and cost implications of training AI models, particularly focusing on the characteristics of GPT-4 (GP4). The presenter discusses how GP4's high performance is attributed to extensive training with significant computational resources, leading to substantial costs incurred by OpenAI. They emphasize that the cost primarily lies in training rather than inference, where the model is utilized for predictions. **Key points:** - GP4's superior performance is the result of extensive computational resources used during training. - The cost primarily relates to training the model rather than using it for predictions (inference). - Performance correlates with the amount of computation and data used in training, as indicated by a diagram showing a positive relationship between computational power and model performance. - Model size is another crucial factor, measured by the number of parameters (interconnections between neurons in neural networks), which correlates with both data size and computational power. - The presenter highlights that increasing all three parameters (data size, model size, computational power) is essential for achieving optimal model performance. - They mention Moore's Law, indicating that the cost of computation continues to decrease over time, leading to more significant improvements in AI models. - A graph illustrates the increasing computational power per dollar over time, with Nvidia being a dominant player in the GPU market, followed by AMD. Regarding the decision between on-premises and cloud-based infrastructure for training AI models, the presenter suggests starting with cloud resources but considering transitioning to on-premises as model size and training frequency increase. However, they note challenges such as the availability of human talent and the high demand for GPU chipsets, which may result in delays in acquiring hardware. **Key points:** - Initial training of AI models can be done using cloud resources. - Transition to on-premises infrastructure may become necessary as model size and training frequency grow. - Challenges include acquiring human talent and the availability of GPU chipsets due to high demand. Overall, the presentation emphasizes the importance of considering the correlation between model size, data size, and computational power when planning and budgeting for AI model development and training. The text discusses the shifting landscape of AI development, notably the transition from academia to industry as the primary source of cutting-edge AI models. It highlights the trend where industry, represented by major tech companies like OpenAI, Microsoft, and Google, is investing significant capital in building advanced AI models, leading to a decline in the number of models originating from academia. This shift has broader implications for the tech industry, geopolitics, and the global economy, as major AI models increasingly fall under the control of a few large corporations. **Key points:** - Transition from academia to industry: Industry is becoming the primary source of state-of-the-art AI models due to substantial capital investment from tech giants. - Implications for control and regulation: The concentration of AI models in the hands of a few companies raises concerns about control and regulation, prompting discussions on sovereign AI and the need for governmental oversight. - Shift in human capital: There's a noticeable movement of expertise from academia to industry, as evidenced by the teams behind top AI models increasingly residing in the industry sector. - Investment trends: There's a significant increase in corporate investment in AI, with billions of dollars pouring into the industry for acquisitions, private investments, and research. - Geopolitical considerations: The production of crucial AI components, such as GPU chips, is concentrated in a few countries, notably Taiwan, which holds significant geopolitical importance due to its dominance in chip fabrication. - Supply chain impact: The geopolitical landscape affects the AI supply chain, potentially leading to disruptions that can impact the progress of AI development globally. The text then delves into the economics of data in AI development, focusing on the concept of diseconomies of scale. While the cost of collecting and maintaining data per unit decreases with scale, the marginal benefit of adding new data diminishes at a faster rate. This phenomenon poses challenges in assessing the return on investment for AI projects, particularly in terms of training models and maintaining their relevance over time. **Key points:** - *Diseconomies of scale in data*: As data collection scales up, the marginal benefit of additional data diminishes faster than the decrease in per-unit cost, posing challenges in assessing the ROI of AI projects. - Challenges in ROI calculation: Factors such as training time, model relevance, and evolving data requirements complicate ROI calculations for AI investments. - Management by VC groups: Venture capital firms like a16z grapple with these challenges and may utilize various strategies to gauge the potential returns on their AI investments, which may include assessing factors beyond immediate costs and benefits. Overall, the text provides insights into the evolving dynamics of AI development, emphasizing the growing influence of industry players, geopolitical considerations, and the nuanced economics of data in shaping the future of AI. The speaker discusses various complexities and challenges that venture capitalists (VCs) face when investing in AI companies. They emphasize that the issues they cover represent only a fraction (30-40%) of what VCs need to consider before investing in AI ventures, highlighting the non-trivial nature of the problem. Key points include: - **Complexity of AI Investment**: Investing in AI companies poses significant challenges due to various factors, including computational costs, data issues, and the need for understanding the AI landscape comprehensively. - **Data Challenges**: Data is a critical aspect of AI, and its quality, volume, and distribution significantly impact model performance and investment viability. - **Relation between Data and Model Parameters**: The speaker discusses the correlation between data volume, model parameters, and computational requirements, highlighting the challenges associated with scaling AI models. - **Calculating Data Asset Value**: There's a discussion on the difficulty of calculating the value of data assets, emphasizing the need to establish a relationship between the value of the model and the data used to train it. - **Long Tail Distribution**: The concept of a long tail distribution in real-world data is explained, illustrating how a significant portion of data lies in the tail, posing challenges for AI model development and investment. - **Cost Implications**: Serving the long tail of data is expensive, yet crucial for monetizing AI systems effectively. - **Transition to Data-Centric AI**: The discussion shifts towards a paradigm shift in AI development, emphasizing a move towards data-centric approaches. This entails focusing more on engineering high-quality data pipelines and ensuring data reliability. - **Three Sub-themes of Data-Centric AI**: Developing training data, evaluating models, and maintaining data quality are identified as key sub-themes within the data-centric AI approach. - **Clarification on Domain Focus vs. Data Centricity**: The speaker distinguishes between domain-focused and data-centric approaches, highlighting that data-centricity is independent of the domain and emphasizes the importance of building a robust data set regardless of the problem domain. In summary, the text delves into the intricacies of AI investment, highlighting data-related challenges and the evolving focus towards data-centric AI development approaches. The text discusses various aspects of the economics of AI, focusing on the time spent on data, training models, algorithms, and the human element. Here are the key points: - **Time Distribution**: Data and training the model itself consume 10 to 15% of the time. - **Correlation of Compute, Model Size, and Data**: Compute, model size (number of parameters), and data set size are correlated, affecting model accuracy. - **Impact of Data**: Data size is crucial, but its impact on model accuracy isn't linear due to real-world data distributions. - **Model Complexity and Economics**: Keeping models simple (KISS principle) is cost-effective, scalable, and interpretable. - **Sophisticated Models vs. Simplicity**: Starting with simple models is advised due to complexities, cost, and potential performance issues. - **Long Tail Data Distribution**: The long tail distribution complicates model training and performance, requiring continuous learning. - **Fragile Models and Overfitting**: Complex models are prone to overfitting and fragility, leading to poor performance in production. - **Balancing Model Complexity**: Complexity and simplicity are trade-offs; finding the right complexity level is crucial for optimal performance. - **Continuous Learning**: Continuous training and refinement are necessary to adapt to changing data distributions. - **Improving Model Performance**: Adding more diverse data or fine-tuning model architecture can enhance performance. - **User Experience Optimization**: Optimizing user interfaces can mitigate long-tail data challenges by reducing user errors. - **Cohort-based Modeling**: Sometimes, it's beneficial to train separate models for different cohorts of data to account for heterogeneity. These points highlight the importance of understanding the economics of AI, balancing model complexity, continuous learning, and optimizing user experiences to improve model performance and efficiency. The text discusses the importance of tailoring machine learning models to different cohorts or user segments based on varying behaviors and complexities within different regions or customer sets. By building models specific to each cohort, efficiency can be improved while ensuring simplicity and cost-effectiveness. This approach involves considering the heterogeneity of data sets and customer bases, potentially necessitating the use of clustering techniques to identify logical cohorts for individualized modeling. **Key points:** - Importance of building separate machine learning models for different cohorts or user segments based on varied behaviors and complexities. - Tailoring models to specific cohorts improves efficiency, simplicity, and cost-effectiveness. - Consideration of data heterogeneity and customer diversity may require the use of clustering techniques for cohort identification. Additionally, the text delves into the concept of AI model distribution, emphasizing the need to break down data sets into homogeneous subgroups to address complex problems effectively. Using examples such as bot detection algorithms, it highlights the necessity of building multiple models to address distinct variations within a single problem domain. This system-level approach is common in production-level systems like fraud detection, loan underwriting, and content moderation, where diverse models cater to different subsets of data. **Key points:** - Utilization of clustering techniques to split data sets into logical cohorts or groups for more efficient modeling. - Need for multiple models to address diverse variations within complex problem domains like bot detection. - Common application in production-level systems such as fraud detection, loan underwriting, and content moderation. Furthermore, the text discusses the economic implications of AI, particularly its impact on labor economics and human capital. It presents a study involving management consultants split into control and test groups to assess the effects of AI implementation. Results indicate that AI enhances productivity, with AI-equipped consultants completing more tasks efficiently and producing higher-quality output. Moreover, AI is portrayed as a skill leveler, benefiting individuals less proficient in particular tasks more significantly than proficient individuals. **Key points:** - AI enhances productivity among consultants, enabling them to complete more tasks efficiently with higher-quality output. - AI acts as a skill leveler, benefiting less proficient individuals more significantly than proficient ones. - Cautionary notes emphasize the need to understand which skills benefit from AI and acknowledge that AI capabilities may not universally improve performance across all tasks. Overall, the text underscores the importance of tailoring machine learning models to specific cohorts, recognizing AI's potential to enhance productivity and skill levels while cautioning against its indiscriminate application across all tasks. Additionally, it highlights ongoing debates and research surrounding the economic implications of AI on labor dynamics and underscores the need for strategic planning to leverage AI effectively within organizations. The text discusses the impact of AI on various occupations and industries, highlighting a hybrid approach to understanding its effects. It emphasizes the significant impact of AI on fields like legal, finance, and business operations, which are expected to undergo automation due to AI advancements. The text notes that industries with higher median wages are likely to experience a greater magnitude of impact from AI. **Key points highlighted include:** - Industries like legal, finance, and business operations are expected to be significantly impacted by AI. - The magnitude of impact correlates with the median wage of the industry, with higher-paying industries experiencing greater impact. - AI is currently impacting fields like graphic design, where it is significantly cheaper and faster than traditional methods. - Industries such as fashion are also seeing AI's impact, with generated images replacing real models. - Studies, such as one by Goldman Sachs, indicate a spectrum of impact from complete automation to human-AI partnership. - Despite concerns, there's a trend towards human-machine partnerships, leveraging AI for insights while humans maintain creative input. - Adoption of AI tools is becoming a necessity for organizations due to significant cost savings and efficiency gains. **Regarding AI adoption and training:** - Employees can be trained to adopt AI tools, which may involve minimal costs as AI tools become more user-friendly. - Retraining efforts should focus on understanding AI limitations and avoiding blind trust in AI-generated outputs. - Retraining may be necessary for tech teams involved in building AI models, but it's a smaller subset compared to company-wide efforts. **Regarding critical thinking and AI:** - Assessing AI's critical thinking abilities is complex and depends on the same metrics used for humans. - While AI models like GPT-4 perform well on standardized tests, their critical thinking capabilities are still limited, especially for complex, multi-step reasoning tasks.