At Osedea, we’re constantly at the forefront of emerging technologies, and we have a unique perspective on tech adoption trends among our diverse client base. In recent months, AI has made its way into mainstream media with the help of ChatGPT. Since then, tooling and support for AI development have skyrocketed. Just a few weeks ago, Dr. Andrew Ng, a globally recognized leader in AI, delivered a talk on the opportunities in AI which highlighted the importance of integrating AI into your organization's workflow.
BEARING.ai—the first company to harness the power of Generative AI in the maritime shipping industry—is a great example of how AI adoption can quickly reap tremendous benefits. Leveraging their data to monitor, forecast, simulate, and optimize, BEARING.ai’s clients have achieved substantial improvements in shipping vessel performance while simultaneously reducing fuel costs and carbon emissions, contributing to a greener environment. Similar opportunities aren't distant dreams; they’re within grasp. AI is ripe for adoption, and the key to unlocking its full potential lies in harnessing your organization's data.
For many established companies, data has been accumulating for years across various departments and systems. PDFs, images, presentations, emails, audio, video, and analytics are treasure troves of information (and significant assets when harnessed correctly). The first step towards adopting AI within your organization is centralizing your data. Centralization (consolidating from various sources/locations into a single repository or system) by implementing a unified data management platform or integrating existing systems through middleware solutions offers numerous benefits:
AI-Powered Knowledgebase: Once centralized, data can be organized and indexed efficiently with the help of embedding models. These models are trained to extract the most meaningful information out of your unstructured data. By indexing your data in this manner, Large Language Models such as GPT-4 can have their context extended with your organization's business context to evolve into a comprehensive, all-knowing assistant. This innovative approach is known as retrieval augmented generation (RAG) with vector databases, a concept we will delve into shortly.
Training Predictive Models: The consolidated data pool becomes a valuable resource for training AI models. Predictive analytics, forecasting, and trend analysis become achievable goals as you capitalize on your organization's historical data.
Security Benefits: Centralizing data provides a more robust security infrastructure to safeguard sensitive information. It allows for more effective access control and auditing, reducing the risk of data breaches.
Easier Backups: Centralized data is easier to back up than data from multiple disparate sources. This simplifies data protection measures, ensuring critical information is securely preserved and recoverable in case of data loss incidents.
Redundancy: Implementing redundancy, such as data mirroring or replication, becomes more feasible with centralized data. Redundancy enhances data availability and fault tolerance, minimizing downtime, and ensuring business continuity.
As mentioned above, retrieval augmented generation systems (RAG) have gained prominence as a valuable solution for querying an organization's data using large language models (LLMs). RAG systems allow for querying data with natural language. Essentially, it gives you a way of “talking” to your data in the same way you talk to ChatGPT. The accessibility of LLMs in recent months has made this approach a lot more feasible which is why this approach to data exploration is quickly gaining traction. However, the success of such systems depends not only on LLMs and prompt engineering but also on the proper vectorization and indexing of data. This is where vector databases and embeddings play a crucial role.
In the context of AI and machine learning, vector embeddings are a numeric representation of an entity's semantics. These representations capture essential features and relationships within the data, making it easier for AI algorithms to process and understand. Embeddings are crucial for tasks such as natural language processing, recommendation systems, and image recognition. With embeddings, we can quickly find related content based on similarity. Additionally, embeddings aren’t just limited to text, it’s possible to create vectors out of images, audio, video, or any type of data using encoder models that have been trained to extract their meaningful information. Some models like OpenAIs text-embedding-ada-002 are even language agnostic, meaning that they can understand similarity in various languages natively.
A vector database is a specialized database designed to store and retrieve high-dimensional vector embeddings efficiently, making them ideal for AI and machine learning applications. They use approximate nearest neighbor (ANN) search algorithms to measure the distance between embeddings, resulting in a ranked list of neighboring vectors.
As an example, Spotify has been using vector databases for quite some time to compare users' taste in music, they also go into detail on how they’ve used embeddings to query their podcast episodes in their blog post: Introducing Natural Language Search for Podcast Episodes. They’ve even gone as far as creating their own ANN library!
Vector databases are well-suited for a wide range of use cases that involve similarity search, recommendation systems, and data analysis in fields such as machine learning, natural language processing, computer vision, and more. Here are some common use cases for vector databases:
Recommendation Systems: Vector databases are often used in recommendation systems to find items or content that are similar to what a user has interacted with in the past. This can be applied to e-commerce, content recommendation, and music or video streaming platforms.
Content-Based Search: In multimedia content platforms, vector databases enable content-based search for images, audio, and video files. Users can search for content with similar visual or auditory features.
Anomaly Detection: Detecting anomalies in high-dimensional data, such as network traffic logs, sensor data, or financial transactions, can be done using vector databases. Unusual data points can be identified by comparing them to a set of normal vectors.
Collaborative Filtering: Collaborative filtering algorithms can use vector databases to find users with similar preferences and recommend items based on the behaviour of similar users.
Long-term Memory: Vector databases can be used to store past response generations for an LLM. These embeddings can be recalled to further enhance a large language model’s context with its past context.
Clustering: In vector databases, clustering can be applied to organize data into distinct groups, making it easier to identify patterns and similarities within the dataset.
Diversity Measurement: In vector databases, diversity measurement can be applied to evaluate the breadth and inclusivity of recommendations, ensuring a balanced selection of items or content to cater to a wide range of user preferences or topics.
While embeddings are powerful, they are not without their challenges. It's important to be aware of potential pitfalls, such as bias in the training data. As an example, OpenAI explains in their documentation how they’ve witnessed a model more strongly associate European American names with positive sentiment when compared to African American names. Additionally, embedding models have cutoff dates on their training data, meaning some data might differ in semantics throughout time (e.g. a celebrity’s popularity). Selecting the right embedding techniques and parameters is critical to achieving optimal results and correct data pre-processing techniques need to be applied to correctly leverage embeddings.
While vector databases and embeddings are essential components of AI adoption, it's crucial to recognize that they’re part of a more extensive ecosystem. Building a robust AI infrastructure involves addressing other key aspects, such as data preprocessing, model selection, prompt engineering, and deployment strategies. Vector databases are a powerful piece of the puzzle, but they are not the entire solution.
One of the recurring issues with LLMs and AI as a whole is the tradeoff in accuracy. For ages, computers have been binary and deterministic. Although vector databases may be a monumental step forward in knowledge exploration, they still need to be married to traditional structured architectures for the ultimate search experience. Some platforms such as Azure Cognitive Search and Elastic Search are actively working and fine-tuning hybrid searches using reciprocal rank fusion (RRF) to mix resulting ranks. Elastic is also addressing other vector database issues such as privacy of data and role-based access control (RBAC). On the prompt engineering side of things, various frameworks like guidance ai, Langchain, and LMQL are all being developed to provide a robust way of turning LLM data into meaningful structured responses. Needless to say, we are living in exciting times and emerging RAG architectures are only getting better each day.
At Osedea, we recognize the potential of vector databases, embeddings, and AI in driving transformative changes within organizations. Our comprehensive development services are designed to empower your organization in leveraging its data and fully realizing the potential of AI. Whether it's centralizing data, implementing vector databases, or navigating the complexities of AI adoption, Osedea is here to be your trusted partner on the path to data-driven success. Rest assured, we remain at the forefront of emerging technologies and are committed to staying on top of the latest advancements in the field.