Unlocking the Power of Generative AI with Knowledge Graphs
Start with: Upcoming events and news from last week in AI
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Before we delve into the topic of knowledge graphs today, let’s do a quick roundup of upcoming events since these are very important and interesting updates from last week in AI.
Upcoming
OpenAI's dev day is on November 6th. Please join their first developer conference, OpenAI DevDay, on November 6, 2023, in San Francisco and online.
This one-day event will bring hundreds of developers from around the world together with the team at OpenAI to preview new tools and exchange ideas. Members of OpenAI's technical staff will also be able to lead breakout sessions for attendees in person.
Humane AI is launching its Pin device, planned for Nov 9th for an expected cost of $1000. The Ai Pin is “a small, screenless device about the size of a saltine cracker,” The Information reports, that will have “a camera, a microphone and speaker, a variety of sensors, and a laser projector.” You’ll attach it to your clothes magnetically (as seen in the device’s appearance at Paris Fashion Week). It will have a Snapdragon chip from Qualcomm that will give it “smartphone-level speed, connectivity, camera capabilities and security,” and Humane plans to be an MVNO so that it can sell cellular data that customers can use with the Ai Pin. (Humane co-founder Bethany Bongiorno described the Ai Pin this month as a standalone device that is a “phone, contextual computer, and software platform.”)
Some interesting updates from last week in AI
People are spending hours talking to ChatGPT, an OpenAI language model that is bringing the fictional world of the movie “Her”, closer to reality. ChatGPT can understand and respond to complex questions and topics, including those that were previously thought to be the exclusive domain of humans. Users are reporting that ChatGPT's responses are eerily human-like, leading some to wonder if they are interacting with a real person or simply a sophisticated AI model. The ability of ChatGPT to mimic human conversation has sparked debate about the ethics and implications of creating such advanced AI technology.
Poe, a startup that aims to be the App Store for conversational AI, plans to launch a platform that will allow chatbot creators to upload and sell their conversational AI models. The platform will use blockchain technology to verify the authenticity of the models and ensure fair compensation for creators. Poe's goal is to provide a marketplace for chatbots that are more advanced and personalized than those currently available, while also supporting the growth of the conversational AI industry. You can sign up here
Now, let’s turn our focus onto knowledge graphs. In this age of data and immense interest all across the generative AI space currently, how can AI systems efficiently navigate and generate meaningful content? Let’s begin with a story to understand this problem better.
In a sleek boardroom overlooking the skyline of San Francisco, Alexander Bennett, a visionary in AI-driven content generation, faced a challenge. His cutting-edge AI, designed to produce market reports and industry insights, often delivered data-rich but context-poor content. The potential was evident, but the execution lacked cohesion.
During a high-level summit on AI innovations, Alexander was introduced to Dr. Eleanor Hayes, a renowned data scientist specializing in knowledge architecture. In a discussion about the future of data-driven storytelling, Dr. Hayes presented a sophisticated digital framework. This framework, resembling a meticulously designed blueprint, showcased data points interconnected by meaningful relationships. "Mr. Bennett," she began, "meet the Knowledge Graph. It's the bridge between raw data and meaningful narratives."
With this strategic partnership, Alexander's AI underwent a transformation. The outputs were no longer mere compilations of data but insightful narratives that provided value to decision-makers across industries. The shift was not just in content quality but in the AI's ability to discern, relate, and present data in a manner that resonated with corporate leaders.
Navigating the New Frontier: The Role of Knowledge Graphs in AI
In today's corporate landscape, where data-driven decision-making is paramount, the clarity and context of the information presented can make all the difference. Knowledge graphs stand at the forefront of this transformation, ensuring that generative AI delivers not just information but actionable insights. In this article, let’s look at how knowledge graphs improve generative AI and the benefits they offer.
Knowledge Graphs: What Are They?
A knowledge graph is a database that stores information in the form of a graph, with nodes representing entities and edges representing relationships between them. Unlike traditional databases, which store data in tables, knowledge graphs can efficiently handle complex, interconnected data. This makes them ideal for organizing and utilizing large amounts of knowledge in AI applications.
Let’s look at an example of a knowledge graph below.
Knowledge Graph
Entities:
+ Person (e.g. John Smith, Jane Doe)
+ Organization (e.g. Apple, Google)
+ Location (e.g. New York City, London)
+ Event (e.g. TED Talk, Conference)
+ Thing (e.g. Book, Movie)
Relationships:
+ Person is a worker at Organization
+ Organization produces Book
+ Location is the venue of Event
+ Event is hosted by Organization
+ Thing is mentioned in Text
Textual Samples:
+ "John Smith gave a TED Talk on the future of AI."
+ "Apple released a new book on machine learning."
+ "The conference was held at the London Conference Centre."
+ "The movie 'AI' was directed by Steven Spielberg."
In the above example, the entities are "Person", "Organization", "Location", and "Thing". The relationships between these entities include "worker at", "produces", "venue of", and "mentioned in". The samples provide examples of how these entities and relationships can be used to generate new text.
Generative AI with Knowledge Graph
Further, to use this knowledge graph in generative AI, we can train a language model on the textual samples and the relationships between the entities. For example, the language model could learn to generate new sentences like:
"John Smith is giving a TED Talk on the future of AI at the London Conference Centre."
"Apple released a new book on machine learning authored by John Doe."
"The movie 'AI' was directed by Jane Doe and produced by Apple."
How Do Knowledge Graphs Improve Generative AI?
By integrating the knowledge graph into the generative AI process to guide the generation of text, we can better equip the model to understand the relationships between entities and generate more accurate outputs rather than simply rely on random word selection. This is because the relationships between entities in the knowledge graph provide a structured framework for generating text that is consistent with the underlying domain knowledge.
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Here are some ways knowledge graphs improve generative AI:
1. Contextual Understanding
Generative AI models often struggle to understand context, leading to inaccurate or irrelevant output. By incorporating knowledge graphs into the model, we can provide it with a deeper understanding of the context in which it is generating content. This leads to more accurate and relevant outputs.
2. Better Entity Disambiguation
In generative AI, entities are often mentioned without clear context. Knowledge graphs help disambiguate these entities by providing information about their relationships with other entities. This allows the model to generate content that is more accurate and consistent.
3. Improved Relationship Understanding
Knowledge graphs also provide a better understanding of relationships between entities. By incorporating this knowledge into the generative AI process, we can generate content that is more coherent and realistic.
4. Enhanced Creativity
Incorporating knowledge graphs into generative AI can also enhance creativity. By providing a rich source of information about entities and relationships, knowledge graphs give the model more material to work with when generating content. This leads to more innovative and original output.
5. Improved Accuracy
By providing context, entity disambiguation, and improved relationship understanding, knowledge graphs can significantly improve the accuracy of generative AI outputs.
6. Better Decision-Making
Knowledge graphs can also inform decision-making in generative AI models. By providing information about entities and their relationships, we can help the model make more informed decisions about how to generate content.
The Road Ahead
We can safely conclude that incorporating knowledge graphs into generative AI offers significant benefits. By providing context, entity disambiguation, improved relationship understanding, and enhancing creativity, knowledge graphs can significantly improve the accuracy and innovation of generative AI outputs. As the use of AI continues to grow, the importance of incorporating knowledge graphs into the generative AI process will only increase.