Answers are all we need
How Oracle AI is going to replace search engines
The idea of an oracle AI dates back to the early days of artificial intelligence research, with the concept being popularized in science fiction works such as Arthur C. Clarke’s 2001: A Space Odyssey and Isaac Asimov’s Foundation series. In recent years, there have been numerous attempts to build oracle AI systems, ranging from reader-retriever approaches that rely on existing databases to generative models that attempt to generate new information.
Part 1: Search
Have you ever found yourself scrolling through page after page of Google search results, only to come up empty-handed? Or have you ever typed in a question, only to be presented with a list of unrelated or vague responses? If so, you’re not alone. Search engines, while incredibly useful, have their limitations. They rely on algorithms and predetermined keywords to present information, and sometimes those algorithms just don’t cut it.
Think of it this way: a search engine is like a map, providing you with a list of possible routes based on the words you search for. Oracle AI, on the other hand, is like a GPS system. It not only gives you a list of possible destinations, but it also understands the reason behind your search and provides the most relevant and accurate directions.
But Oracle AI doesn’t stop there. It also has the ability to understand and interpret natural language, meaning you can ask it a question in the same way you would ask a human being. No more typing in specific keywords or phrases — just ask your question in plain English and let Oracle AI do the work for you.
This means that it can provide accurate and relevant answers to complex questions, even if the information needed to answer those questions is spread out across multiple sources.
Part 2 : Humans and their interaction with information
Think back to a time before search engines existed. If we wanted to find out something, we had to rely on books, encyclopedias, or asking a knowledgeable friend or relative. This process was often slow, frustrating, and required a lot of background knowledge to sift through relevant and irrelevant information.
Enter the search engine. Google, in particular, revolutionized the way we access information by providing a platform where we could type in a keyword or phrase and receive a list of relevant results. This was a game-changer, but it still required us to spend time sifting through results and trying to find the specific answer we were looking for.
Now, question answering AI takes this process a step further by providing us with a direct answer to our question. No more scrolling through pages of results or trying to interpret technical language — we can simply ask the AI and receive a clear, concise response.
The question-answering mode has the potential to revolutionize how we access information and interact with computers. It will enable users to easily retrieve answers to a wide range of queries without the need to sift through search results or navigate complex websites. It will also facilitate more natural and efficient communication between humans and computers, potentially leading to the development of more advanced artificial intelligence assistants.
Part 3: Answers are all we really need
Enter Oracle AI. “Question and answer,” is a format in which a specific question is posed and a corresponding answer is provided. These systems are designed to understand and respond to human language in order to provide accurate and relevant answers to questions. They systems have become increasingly popular in recent years, with the rise of virtual assistants like Siri and Alexa, as well as the proliferation of QA platforms like Quora and Stack Overflow.
It’s not hard to envision a future where Oracle AI has replaced traditional search engines as the go-to source for information. In this future, relevant answers are found orders of magnitude faster, leading to great gains in productivity across domains. With the right training and data, LLMs can be used to build highly advanced QA systems that can accurately answer an array of questions across domains, with unparalleled speed and efficiency.
Success in building such an oracle AI is going to come down to the curation of massive, high quality, question answer databases. Whoever owns the largest such proprietary dataset, is likely to come out on top with the most effective Oracle AI.
So what does this mean for the future of search engines? It’s simple — Oracle AI has the potential to completely replace them. With its advanced artificial intelligence and ability to understand and interpret natural language, it offers a level of accuracy and relevance that traditional search engines just can’t match.
Part 4: Why Generative QA is the most versatile solution
Reader-retriever approaches, such as IBM’s Watson, rely on large datasets of pre-existing information and use natural language processing to understand and answer questions. While these systems have been successful in specific domains, such as Jeopardy!, they have struggled to achieve the level of general intelligence required to answer a wide range of questions accurately.
Generative approaches, on the other hand, aim to generate new information by training on large datasets of text and learning the patterns and structures of language. These models, such as GPT-3, have shown remarkable capabilities in generating human-like text and even performing tasks such as translation and summarization.
An advantage of generative question-answering format is the ability to handle a wide range of queries. LLMs are not limited to answering specific pre-determined questions like many traditional QA systems. Instead, they are able to understand and generate responses to arbitrary queries, making them highly versatile and adaptable. This flexibility is particularly useful in domains where information is constantly changing, such as in the realm of current events or technical fields. It also allows for the development of personalized assistants that can learn from a user’s past queries and provide more tailored answers over time.
However, generative models have also been criticized for their tendency to “hallucinate” information, generating responses that are not based on any real-world knowledge. LUCI aims to address this gap. With LUCI, we’ve already increased the accuracy over vanilla generative models by 14–22% via intensive fine-tuning. This already makes the tool very useful — however to get to true Oracle-AI level accuracy, we’ve also invented a novel method to bootstrap a massive database, to further train the model until it achieves extremely high accuracy scores across domains. More details about this in future posts.
The next time you have a question or need information, don’t rely on a traditional search engine. Try an question-answering AI like LUCI to provide you the answers you need, quickly and accurately. It’s the future of search, and it’s here to stay. Live beta : http://askluci.tech/qa
