10 Skills and Techniques Needed to Create AI Better
Of course, you already know that AI is not just about using powerful tools; it’s about mastering the techniques that bring these tools to life. As AI continues to reshape industries, professionals need to stay ahead by learning the skills and strategies driving innovation. This blog explores the essential AI techniques and skills needed to create AI as best as possible, going beyond the tools you should know, including mixture of experts, LLM evaluation, and model selection.
10 Skills to Build AI Better
1. LoRAs
Low-Rank Adaptation, or LoRA, is a technique that improves the efficiency of large language models by fine-tuning them with fewer resources. It works by freezing the pre-trained model’s weights and adding adaptable low-rank matrices to specific layers. These matrices are then trained on a smaller dataset, enabling the model to adapt to new tasks without extensive retraining. LoRA reduces the number of trainable parameters, saving memory and computational costs.
Benefits to create AI include the deployment of resource-limited devices, rapid experimentation, and effectiveness in various applications. LoRA is poised to play a significant role in the future of large language models and their applications.
2. Mixture of Experts (MoE)
The Mixture of Experts architecture utilizes a network of specialized sub-models, or experts, to dynamically allocate computational resources and enhance scalability and efficiency in large-scale AI systems. A gating network intelligently selects the most appropriate expert or combination of experts for a given input, ensuring efficient resource utilization and improved performance.
MoE’s advantages include the development of highly specialized experts, dynamic allocation of computation, and adaptability and scalability, making it a valuable solution for complex AI tasks.
3. Memory Tuning
Memory Tuning in language models is a technique used to create AI and reduce hallucinations by incorporating a persistent memory mechanism that allows the model to retain and recall factual information. OpenAI’s research showed that this technique led to a 95% reduction in factual errors. This improvement in factual consistency makes Memory Tuning valuable for applications that require a high degree of factual accuracy, such as question answering, content generation, and information retrieval.
4. Retrieval-Augmented Generation (RAG)
Retrieval Augmented Generation, aka RAG, enhances LLMs by connecting them to external databases and real-time information sources. This allows LLMs to provide accurate and current responses, which is particularly useful in fields like healthcare, finance, and law. In healthcare, RAG can give medical professionals access to the latest research and patient records. In finance, it can provide real-time market data and risk assessments.
In law, RAG can assist with legal research and contract analysis. Overall, RAG represents a significant advancement in AI by bridging the gap between static model training and dynamic real-world data.
5. LLM Evaluation
The evaluation of Large Language Models requires a balance between accuracy, efficiency, and fairness. Accuracy refers to generating coherent, contextually relevant, and factually correct outputs. Efficiency concerns the computational resources needed to run the model. Fairness addresses bias, discrimination, and representativeness in the model’s outputs. Pairwise Ranking Prompting is one technique used to improve LLM performance by having the model rank prompts based on relevance or quality.
LLM evaluation is an ongoing process that must adapt to new developments in the field and requires a combination of quantitative, qualitative, and user-centric feedback.
6. Model Selection
Choosing the right AI model involves understanding the specific problem and tailoring your approach accordingly. Larger models may have greater potential but also come with higher computational costs and risk of overfitting. Techniques like structured pruning can improve efficiency and prevent overfitting by removing unnecessary parameters. Additionally, using efficient architectures designed for specific tasks can enhance performance without excessive overhead.
Careful analysis of your problem and selecting a model that aligns with your needs and constraints can optimize performance and ensure your AI solution delivers desired results without unnecessary complexity or resource consumption.
7. QLoRA
Quantized Low-Rank Adaptation, or better known as QLoRA, is a technique that significantly reduces the memory requirements for training large language models, making it feasible to train these models on consumer-grade hardware. QLoRA achieves this by quantizing the model’s parameters, which effectively reduces the precision of the numbers used to represent the model. This quantization process results in a substantial decrease in memory usage without significantly compromising the model’s performance.
By enabling the training of large language models on widely available and affordable hardware, QLoRA democratizes access to cutting-edge AI research and development. This has the potential to accelerate innovation in the field of natural language processing and enable a wider range of applications for large language models.
8. Structured Generation
Structured Generation is a technique that ensures AI model output adheres to a predetermined format. This is crucial in fields like law, science, and finance, where precision and conformity are essential. SOG mitigates risks by generating flawless contracts, properly formatted research papers, and accurate financial statements. It also enhances software development by producing well-structured and readable code.
Overall, SOG improves AI’s utility and reliability, opening up new possibilities for AI use in critical domains.
9. Building and Using AI Agents
AI agents are transforming industries by automating decision-making and interacting with dynamic environments. They are improving customer service by handling inquiries and providing recommendations, optimizing supply chains by analyzing data and streamlining logistics, and being deployed in finance, healthcare, and manufacturing to analyze trends, assist in diagnosis, and optimize production.
As AI technology advances, AI agents will become more sophisticated, but it’s important to address ethical and societal implications to ensure responsible use.
10. Causal Inference
Causal inference is a statistical method used to identify and quantify cause-and-effect relationships within data. It has applications in various fields, including policy development, where it can assess the impact of proposed policy changes, and personalized medicine, where it can determine the effectiveness of different treatments for individual patients.
Causal inference can also be applied in economics, social sciences, and marketing to develop more effective strategies, interventions, and policies.
Why These Skills Matter to Create AI
Like with any new technology, the shift from tools to techniques signifies the maturation of AI as a discipline and can help us track what we can expect in the future, in terms of skills and expertise required. Skills like those listed above empower professionals to design, implement, and refine AI solutions rather than simply deploy pre-packaged systems. As industries evolve, these skills will separate leaders from followers.
Learning Opportunities at the AI Builders Summit
At our upcoming AI Builders Summit — a 4-week virtual training event — starting on January 15th, you’ll learn directly from industry pioneers. Whether you’re a data scientist eager to deepen your expertise or a professional exploring AI’s potential, this event is your gateway to AI mastery.
Event Highlights:
- Expert Talks: Gain insights into cutting-edge methods like LoRAs, RAG, and QLoRA.
- Workshops: Hands-on opportunities to apply techniques in real-world scenarios.
- Networking: Connect with AI leaders and peers shaping the future.
Ready to dive in? Secure your spot today and join the conversation driving the next wave of AI innovation.
