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if you want to be a leader in the AI world going forward, bring it up, right? It needs to be discussed at the board level to allocate funding, to organize your data.

💨 tl;dr

Generative AI, powered by large language models, creates content and requires extensive data and energy for training. Companies need to organize their data and align strategies with corporate goals to leverage AI effectively. Edge computing enhances efficiency and privacy, while regulatory awareness is crucial for compliance.

💡 Key Ideas

• Generative AI creates content (text, images, music) unlike classification AI, which only categorizes data; it's akin to essay exams vs. multiple-choice questions.
• Large language models (LLMs) are foundational for generative AI, requiring extensive data and computational resources for training, tuning, and inference.
• AI models need behavioral tuning post-training to ensure appropriate responses, and this process can be energy-intensive.
• Data readiness is critical for enterprises to leverage generative AI effectively; many companies struggle with siloed data and lack an integrated data strategy.
• Energy efficiency is vital in AI training and inference, with significant cost savings possible through improved supercomputer performance.
• Regulatory developments are emerging to address concerns around AI-generated content, including transparency in training data and compliance tracking.
• The shift towards edge computing is crucial as more data will be generated at the edge, necessitating localized processing to avoid delays and privacy issues.
• Edge computing enables faster data processing compared to cloud solutions, especially for real-time applications, enhancing handling capabilities and privacy through techniques like Federated learning.

🎓 Lessons Learnt

• Generative AI is different from traditional AI. Understanding this distinction is key to leveraging its capabilities effectively.

• Foundation models are crucial for specialized AI. These models serve as the building blocks for developing tailored AI applications.

• Training processes of LLMs matter. Knowing how large language models are trained is essential for their effective deployment and use.

• Tuning behavior is vital. Proper tuning is necessary before releasing AI models to ensure they perform as intended.

• Data organization is foundational for AI projects. Begin organizing your data now to set the stage for future AI initiatives and avoid silos.

• Data strategy should align with corporate goals. Engaging in discussions about data strategy at the board level is crucial for securing necessary resources.

• Not all data is essential to collect. Consider creating data instead of solely relying on existing datasets to enhance your data strategy.

• Energy consumption is a major consideration. Be aware of the high energy demands during training and inference phases of AI models to improve operational efficiency.

• Regulatory awareness is necessary. Stay informed about evolving regulations, such as the EU AI Act, to ensure compliance and transparency in data usage.

• Track training data meticulously. Companies must be prepared to summarize and report the data used for training to comply with potential regulatory inquiries.

• Intelligent Edge can enhance efficiency. Using edge computing reduces data transmission latency, making data processing more efficient, especially for camera management.

• Privacy is crucial at the edge. Employ techniques like Federated learning to ensure data privacy and security during edge processing.

🌚 Conclusion

Understanding the differences in AI types, focusing on data organization, and embracing edge computing are essential for businesses to harness the full potential of generative AI while navigating regulatory landscapes.

In-Depth

All Key Ideas

Generative AI Overview

• Generative AI generates content (text, images, music) as opposed to classification AI, which only classifies data.
• Generative AI is likened to essay-type exams, while traditional AI is compared to multiple-choice questions.
• Foundation models serve as the base for generative AI, allowing for specialization and fine-tuning later.
• Large language models (LLMs) are often the foundation for generative AI, built through processes of training, tuning, and inference.
• Training LLMs involves creating connections between words, requiring extensive data and computational resources.
• Each word in LLMs is interconnected with numerous other words, which helps the model understand context and relationships between concepts.

Building and Tuning Large Language Models

• Building large language models involves training them with trillions of connections, which is costly and often proprietary.
• After training, models require a tuning phase for behavior to ensure social acceptability, which is less automated and involves human intervention.
• Inference is the process where the trained model answers questions, and it is energy-intensive.
• Additional tuning for personality may be needed for proprietary models to reflect the specific voice of a company or institution.
• Different models exhibit varying degrees of behavioral tuning, affecting how they respond to questions, such as expressing opinions or sticking to facts.

AI Model Characteristics and Considerations

• Different models are tuned for behavior and personality, affecting how they communicate despite similar grammar training.
• Inference in AI models consumes significant energy and can process both text and images.
• AI models can make mistakes just like humans, highlighting their imperfect nature.
• The importance of tuning for behavior in AI models is emphasized, especially with the complexity of large connections.
• Data readiness is a primary concern for enterprises looking to implement generative AI models, as it is foundational for machine learning and AI.

Insights on Data and AI

• Data maturity globally is low, with many companies having siloed internal data.
• Organizing data for AI training can position companies as leaders in the AI field.
• A significant percentage (87%) of surveyed companies do not integrate data strategy into corporate strategy at the board level.
• Not all data needs to be collected; some data can be created.
• The complexity of games like Go and Poker illustrate challenges in AI modeling.
• AI can improve through self-play, as demonstrated by bots playing against each other to enhance bluffing strategies.
• Training AI models consumes significant energy, impacting both training and inference stages.

Energy Efficiency and AI Regulations

• The firing up of connections in large language models consumes a lot of energy during training and inference, especially with many users.
• Energy efficiency in supercomputers is crucial for AI models, with performance measured in terms of power used.
• Saving energy in high-performance computing can lead to significant cost savings, e.g., a 10% efficiency improvement on a 20 megawatt system saves 2 megawatts.
• The Sony Art Competition controversy highlights the challenges of recognizing AI-generated art, leading to changes in award eligibility rules.
• The Grammy Awards now require only human creators to be eligible for awards, reflecting industry concerns regarding generative AI.
• Regulatory developments are emerging, with the US and EU drafting guidelines and acts that may impact how AI models are developed and trained.
• The EU AI Act emphasizes the need for transparency in training data, including summaries of any copyrighted data used.
• Companies are taking proactive steps to track training data for compliance with potential regulations, especially in fields like finance.

Concerns and Trends in Data Generation and Processing

• There is concern over using proprietary models due to their ongoing fine-tuning for acceptable behavior, leading to inconsistent outputs over time.
• The shift of data generation from cloud to edge devices is significant, with increasing numbers of entities (like ships, aircraft, and EVs) creating massive amounts of data.
• By 2025, it's estimated that more than 50% of data will be created at the edge, necessitating intelligent processing at the edge rather than just relying on cloud computing.
• The challenges of data transfer to the cloud include bandwidth limitations and delay, illustrating the need for efficient data handling at the edge.

Importance of Edge Computing

• The importance of edge computing is highlighted by an example of data processing from the space station, where processing at the edge is faster than waiting six hours to send data to the cloud.
• Edge servers can process data more quickly than cloud solutions when data transmission times are long, making them valuable for real-time processing.
• The capability of intelligent edge systems is demonstrated through handling multiple cameras for object and feature identification, emphasizing the need for localized processing to avoid delays in data transfer.
• Privacy concerns at the edge are addressed with techniques like Federated learning and swamp learning to isolate data.

All Lessons Learnt

Lessons Learnt about Generative AI

• Generative AI is distinct from traditional AI.
• Foundation models serve as the basis for specialized AI.
• Understanding the training process of LLMs is crucial.
• The scale of connections in LLMs is immense.

Lessons on Model Tuning and Inference

• Tuning for behavior is crucial before releasing models.
• Proprietary models need additional tuning for personality.
• Inference is energy-intensive.

Key Insights on AI Models

• Different models behave differently based on tuning.
• Inference uses a lot of energy.
• AI models can misinterpret images just like humans.
• Data readiness is crucial for AI implementation.
• Data maturity affects AI performance.

Data Organization and Strategy for AI

• Start organizing your data: If you're not ready to implement AI projects, begin by organizing your data to avoid silos. This is a foundational step for future AI initiatives.
• Data strategy must align with corporate strategy: Discuss data strategy at the board level to ensure it gets the necessary funding and focus for effective data organization.
• Not all data needs to be collected: Some data can be created, so think creatively about data sources and generation when organizing your data.
• Data can be created through competition: You can generate valuable data by letting models compete against each other, as shown in the poker example, instead of relying solely on existing datasets.
• Energy consumption is critical: Be aware of the high energy demands during both the training and inference phases of AI models, which impacts operational efficiency.

Key Considerations for AI Models

• Energy efficiency is crucial for AI models. When deploying large language models, energy consumption can be significant during training and inference, so focusing on energy-efficient supercomputers can lead to substantial savings.
• Understand regulatory implications early. As the industry navigates generative AI, it’s important to stay informed about evolving regulations, such as the EU AI Act, which may require transparency on the data used for training models.
• Track training data meticulously. Companies need to be prepared to summarize and report the data used for training models, especially if it includes copyrighted material, to comply with potential regulatory inquiries.
• Adapt to changing creative recognition standards. Events like the Sony Art Competition and Grammy Awards are adjusting their rules regarding AI-generated content, highlighting the need for clarity on human versus AI contributions in creative fields.

Lessons Learned

• Be cautious with proprietary models.
• Understand the significance of edge computing.
• Factor in data transmission challenges.
• Simplicity in design can lead to issues.

Intelligent Edge Benefits

• Intelligent Edge can reduce data transmission time issues: Processing data at the edge is beneficial when sending data to the cloud takes too long, even if edge processing is slower, making it a more efficient solution overall.
• Utilize edge computing for camera data management: Deploying intelligent edge solutions allows for efficient management of numerous cameras, minimizing the need to transmit large volumes of data to the cloud.
• Privacy measures are crucial at the edge: Techniques like Federated learning and swamp learning should be applied to isolate data at the edge, ensuring privacy and security of the collected information.