Open Source Sensitivity Analysis Tools

As AI and machine learning become more integrated into business operations, there is a growing need for professionals who can bridge the gap between theory and practice. Skills in areas like AI programming, data analysis, and MLOps (machine learning operations) are in high demand but in short supply. This talent shortage is expected to continue into 2024 and beyond, posing a challenge for organizations looking to effectively deploy and maintain AI systems.

Researchers have proposed a systematic framework for building fully-fledged language agents, drawing parallels from production systems and cognitive architectures. This aims to provide a more comprehensive approach to developing advanced language agents with improved reasoning, grounding, learning, and decision-making capabilities.

While small businesses are increasingly interested in AI, they still face several key barriers to adoption. The top barriers include the cost of AI tools (55%), uncertainty over potential government regulation (50%), data privacy concerns (49%), not knowing what tools to use (48%), and a lack of digital skills among employees (46%).

The Biden administration has taken steps to support small businesses in adopting AI, including offering technical assistance and research grants. However, Congress is still in the early stages of developing policies specifically targeting small business use of AI. Lawmakers are focused on ensuring fairness and preventing misuse as AI becomes more integrated into government interactions with SMBs.

Retrieval-augmented generation (RAG) has emerged as a technique for reducing hallucinations in generative AI models. RAG blends text generation with information retrieval, enabling large language models to access external information to produce more accurate and contextually relevant responses. This is significant as hallucinations have been a major limitation preventing wider enterprise adoption of generative AI. RAG can improve the reliability of AI-generated content in business-critical applications.

Researchers have proposed a systematic framework for building advanced language agents that draw on principles from production systems and cognitive architectures. This aims to provide a more comprehensive approach to developing language agents with improved reasoning, grounding, learning, and decision-making capabilities, which is highly relevant to the continued advancement of RAG models.

RAG can be used to generate personalized learning content tailored to the needs and levels of individual students, improving the learning experience. 

Kin, a personal AI assistant for your personal life. Built with memory and as a mobile-friendly app. Your data lives on your device as well. They even explain the architecture on their website so as to be open as to what they are really building.

UXSniff, use AI to improve your website conversion. There are many tools in this space currently. This one though is priced competitively for beginner businesses and website builders.

Inkline.ai, I am currently beta testing this app and find it easier to use than Obsidian. Easy way to organize and keep track of your notes and plans. I am using it to organize a book I am working on.

Sensitivity Analysis Tools

Open Source Tools for Explainable AI

Introduction: The world of artificial intelligence (AI) is often a mystery, with complex algorithms producing results that are not easily understood. The concept of Explainable AI (XAI) aims to break down these barriers, creating AI that can be understood and interpreted by humans4. This enhances trust, allows for more control and fine-tuning of AI systems, and ultimately leads to better outcomes5. Let's delve into some open source tools that make this possible.

SHAP: Game Theoretic Approach to AI Interpretation: SHAP (Shapley Additive Explanations) is a popular tool in the XAI realm which offers insights into the predictions made by various machine learning models2, 4. It uses a game theoretic approach to predict the outcome of any machine learning model2and measures the average marginal contribution of a feature in a dataset across all possible combinations5.

LIME: Local Interpretable Model-agnostic Explanations: Another versatile tool is LIME (Local Interpretable Model-agnostic Explanations). As the name suggests, its key strength lies in providing explanations for individual predictions4, 5. This makes the insights highly specific and relevant to the given instance. Additionally, it calculates feature importance scores, providing quantitative measures of each feature's impact on the prediction5.

ELI5: Debugging Machine Learning Models: ELI5 (Explain Like I'm 5) simplifies debugging of machine learning models. It provides visualization tools to understand models and their influencing features impacting model predictions5. Integrating seamlessly with several major machine learning frameworks and packages, it provides a user-friendly API to interpret and debug a wide range of machine learning models5.

XAITK: Comprehensive Suite for Complex Models: Developed under DARPA’s XAI program, XAITK (Explainable AI Toolkit) is designed to aid users in understanding complex machine learning models3. It features tools like After Action Review for AI (AARfAI), which enhances domain experts’ ability to systematically analyze AI’s reasoning processes. Furthermore, it provides frameworks for generating counterfactual explanations, improving human-machine teaming3.

These open-source tools allow us to peek inside the often inscrutable world of AI, fostering trust and understanding in these powerful systems. In this way, we can ensure AI serves us as transparently as possible while continually enhancing its pe