39th Edition - Last Week in AI - A Primer on Knowledge Graphs - Prompt Engineering for LLMs

Welcome to the 39th Edition of Future Forward - the Emerging Tech & AI Newsletter!

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Here's what you can expect in each new issue of the Emerging Tech & AI Newsletter:

• A summary of the top AI news from the past week
• Primer on any emerging technology or recent advances in existing technologies (We review knowledge graphs)
• A key topic in AI or Examples of how AI is being used or How it will impact the future ( We explore Prompt Engineering for LLMs this week)

The field of AI is experiencing rapid and continuous progress in various areas. Some of the notable advancements and trends from the last week include:

Big Tech in AI

1. Nvidia researchers introduced DrEureka, an AI system that uses LLM agents to automate the process of training robot skills
2. Microsoft is Building 'MAI-1' to Compete With OpenAI and Google AI Models.
3. Apple unveils iPad AI upgrades.
4. Google announced Google Threat Intelligence, integrating the company’s Gemini AI model with its cybersecurity arm Mandiant.
5. Google introduced the Pixel 8a, a $499 AI-powered phone featuring advanced Gemini features and AI media tools.
6. AlphaFold 3 predicts the structure and interactions of all of life’s molecules.
7. Microsoft launched a top-secret genAI service for U.S. intelligence agencies.
8. Meta to expand AI image generation offerings for ads.
9. Apple to power AI servers with its chip.
10. AWS to invest an additional SG$12 billion in Singapore by 2028.

Funding & VC Landscape:

1. Holistic AI secured $200M in funding to develop multi-agent AI models.
2. Abound raised $800M for its AI-powered lending platform.
3. Mistral AI is reportedly raising $600M.
4. Numeric Raises $10M in Seed Funding.
5. Daloopa Raises $18M in Series B Funding.
6. Panax Secures $15M to Boost AI-Driven Cash Flow Management.
7. RunPod Raises $20M Seed Funding.
8. XTEND Raises $40M Series B to Expand Innovative Drone and Robotics Operating System Globally.
9. Atlan Raises $105M Series C Funding.
10. Triomics Raises $15M to Enhance Oncology Workflows.
11. Opmed.ai Raises $15M in Series A Funding.
12. Fairgen Raises $8M Seed Funding.
13. Hexis Raises ￡1.6M in Pre-Seed Funding

Other AI news:

1. Hugging Face launched LeRobot, an open-source code library featuring tools, pre-trained models, and a community-driven effort.
2. Adobe launched its Acrobat AI Assistant for enterprise.
3. Stability AI announced Stable Artisan.
4. TikTok is introducing new labels for AI-generated content.
5. A new study finds AI-generated empathy has its limits.
6. OpenAI outlines plans for responsible AI data usage and creator partnerships.

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A Primer on Knowledge Graphs

In the ever-evolving realm of Artificial Intelligence (AI), the ability to process and understand information like humans is paramount. Knowledge graphs (KGs) have emerged as a powerful tool for achieving this goal.

Understanding the Knowledge Graph: A Web of Information

At its core, a knowledge graph is a sophisticated way to store and organize information. Imagine a vast network of interconnected nodes, where each node represents an entity – an object, event, or concept – in the real world. These nodes are linked by edges, which define the relationships between them. For instance, a knowledge graph might have a node for "Taj Mahal" connected by an "is located in" edge to another node for "Agra, India."

This graphical structure offers several advantages over traditional data storage methods. Unlike relational databases with rigid schemas, knowledge graphs are flexible and adaptable. New entities and relationships can be readily integrated, allowing the knowledge graph to continuously evolve and reflect the complexities of the real world. Additionally, knowledge graphs encode semantics, meaning they capture the deeper meaning behind the data. This goes beyond simply stating that "Taj Mahal" is a building; it might also specify its architectural style or historical significance.

The Power of Knowledge Graphs: Applications Across Industries

The potential applications of knowledge graphs are vast and extend across numerous industries. Here are a few key examples:

• Search Engines: Knowledge graphs play a crucial role in modern search engines. By understanding the relationships between entities, search engines can deliver more relevant and nuanced results. Imagine searching for "apple" – a knowledge graph would enable the search engine to differentiate between the fruit and the tech giant, providing you with the most relevant information based on your search context.
• Recommendation Systems: Knowledge graphs are instrumental in powering intelligent recommendation systems. They can analyze user behavior, preferences, and relationships between products or services to suggest relevant recommendations. This personalized approach enhances user experience in e-commerce platforms, streaming services, and more.
• Fraud Detection: Financial institutions leverage knowledge graphs to combat fraud. By analyzing transactions and their historical interactions, knowledge graphs can identify suspicious patterns and flag potential fraudulent activities.
• Scientific Discovery: In the scientific domain, knowledge graphs can connect vast amounts of research data, enabling researchers to identify patterns and accelerate scientific breakthroughs.

Knowledge Graphs and the Rise of LLMs

• LLMs: Enhanced Understanding and Reasoning: LLMs are powerful language models trained on massive amounts of text data. However, they can sometimes lack real-world context. Knowledge graphs provide LLMs with a structured representation of the world, enabling them to understand the relationships between concepts and generate more human-like, informative text. For instance, an LLM incorporating a knowledge graph might be able to not only write a poem about the Taj Mahal but also discuss its historical significance and architectural influences.
• Reasoning-aware and Grounded Explanation: REX models are a type of AI that combines an LLM with an external reasoning module. This reasoning module often leverages a knowledge graph to ground the LLM's outputs in factual information. This integration allows the models to perform complex tasks like question answering or generating different creative text formats while ensuring factual accuracy. Imagine using a RAG model to ask "Who built the Taj Mahal?" – the model would consult the knowledge graph to provide a precise answer, along with potentially interesting details about the emperor who commissioned it.

Conclusion:

As AI applications continue to evolve, knowledge graphs will play an increasingly critical role in ensuring these models are grounded in factual information and can reason effectively. By mimicking the interconnected nature of the real world, knowledge graphs empower AI with a deeper comprehension of context and relationships.

Prompt Engineering for LLMs

Large language models (LLMs) have become a powerful tool, capable of generating text, translating languages, and writing different kinds of creative content. But like any tool, their effectiveness depends on how you use them. This is where prompt engineering comes in.

Prompt engineering is the art of crafting the right instructions to get the best possible output from an LLM. Imagine it as writing a detailed recipe for a complex dish. Just as the recipe guides the chef, a well-designed prompt guides the LLM towards the desired outcome.

Here's why prompt engineering matters:

• Unlocking Potential: LLMs are trained on massive datasets, but they can't magically understand your specific needs. Prompts bridge this gap, allowing you to tailor the LLM's response to your task.
• Focus and Accuracy: Without clear instructions, an LLM might generate creative but irrelevant responses. Effective prompts provide context and guide the LLM towards factually accurate and relevant outputs.
• Safety and Control: LLMs can be susceptible to biases or factual errors present in their training data. Prompt engineering allows you to steer the LLM away from generating harmful or misleading content.

Crafting Effective Prompts:

So how do you write good prompts? Here are some key principles:

• Clarity: Be clear and concise about what you want the LLM to do.
• Context: Provide relevant background information to help the LLM understand the task.
• Examples: Include specific examples to illustrate the desired format or style of the output.
• Constraints: Set boundaries to limit the scope of the response and avoid irrelevant tangents.

Prompt Engineering Techniques:

Beyond these principles, there are various techniques to enhance your prompts:

• Instructional prompts: Clearly state the task and desired outcome.
• Few-shot learning: Provide a few examples of the format or style you want.
• Template prompts: Create a fill-in-the-blank template to guide the LLM's response.
• Iterative prompting: Refine your prompt based on the initial LLM output.

Examples in Action:

Let's see how prompt engineering can be applied:

• Writing a news article: Prompt: "Write a concise news article in the style of the BBC, summarizing the latest developments in climate change research."
• Generating a creative story: Prompt: "Continue the following story in a suspenseful tone: As the detective entered the abandoned mansion, a cold draft..."

The Future of Prompt Engineering:

Prompt engineering is a rapidly evolving field. As LLMs become more sophisticated, so will our ability to guide them with even more nuanced prompts. This will unlock a vast range of applications, from creating personalized educational materials to developing novel forms of creative expression.

By mastering the art of asking, you can unlock the true potential of LLMs and leverage them to achieve remarkable results.

Interested in knowing more? Do let us know in the comments and we will share more about prompt engineering in details

Disclosure: Some content in the article was written with the help of Google Gemini.

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