Frequently Asked Questions
What is machine learning?
Machine learning (ML) is a subset of artificial intelligence in which computers use data and algorithms to improve upon themselves.
What is artificial intelligence?
Artificial intelligence (AI) describes any computer system that demonstrates a human-like intelligence.
What does machine learning do?
Machine learning is a fundamental part of deep learning models, natural language processing, and processes that enrich the interaction between machines and humans.
Vector databases offer an efficient & effective approach to store and retrieve extensive amounts of vector data for Large Language Models (LLMs) to use. We’ll five leading vector databases that are transforming machine learning and similarity search.
Before we dive into vector databases, let’s take a step back and walk through what LLMs are in order to better understand why vector databases are so important.
What is an LLM?
Large language models, often referred to as LLMs, are cutting-edge AI models that have been extensively trained on vast amounts of text data. These models employ advanced techniques such as deep learning and transformer architectures to comprehend and generate human-like language.
By analyzing patterns, grammar, and context, LLMs can generate coherent and contextually relevant responses, making them invaluable tools for a wide range of NLP tasks.
Disadvantages of LLMs
While LLMs offer tremendous potential, they are only as good as the data that they’ve been trained on. This can lead to some interesting results such as inconsistent, unreliable, and downright hallucinatory responses.
How to effectively use LLMs?
For the reasons above, it’s important that LLMs are augmented with new data. LLMs such as ChatGPT only have training data up until September 2021. It does not know anything about the world beyond that date, which means that it will need new data in order to remain relevant.
This is where vector databases come into play. Vector bases allow businesses to take LLMs, add their own data on top of them, and apply them to use cases that drive new ways of acting on that data.
What is a vector database?
Vector databases or vector stores are a type of database that stores high-dimensional vectors. Vectors are mathematical representations of features or attributes of the data. Each type of vector has a certain amount of dimensions which can range from tens to thousands depending on the complexity and granularity of the data.
Vector databases support many different formats and can either upload raw data or embed data such as ML models, word embedding, and feature abstractions as well.
They are really good at identifying similar data and can be leveraged in a number of different ways depending on the data and who it can best serve.
Why are vector databases important?
Vector databases are able to store data in ways that are much more relevant and conducive to the way LLMs “think” and are far more flexible than a classic SQL database.
The main advantages of vector databases are that it allows for:
- Fast and accurate similarity search
- Retrieval of data based on their vector distance
If you take two different points, the closer the point the more similar they are. So instead of using a relational database that runs a query to find an exact match, you can use a vector database to find the most similar or relevant data based on their semantic or contextual meaning.
Use cases for vector databases
As we mentioned previously, vector databases allow for quick search and retrieval of data based on vector distance and similarity. So where could this apply? Some basic use cases are:
- Image similarity based on attributes
- Doc similarity based on meaning & context
- Product similarity based on attributes
For the sake of an example, let’s imagine that you have a data warehouse that’s continuously updated and needs to be useful for customer service representatives. Without meaning and context, this information would sit just fine in a relational database.
However, let’s say you want to personalize responses (add meaning and context) using customer data. Now that’s something a vector database can help with. Vector databases can help draw the connections between user-agnostic data and personal data so that an LLM can generate a response to a customer's question.
Why do you need a vector database for an LLM?
There are a couple of reasons why vector databases are important for LLMs and especially important for businesses looking to integrate LLMs to deliver more value to customers.
LLMs can be quite bad at producing factual data. In fact, they don’t have factual consistency and can provide contradicting and irrelevant information. In some cases, it can even “hallucinate”.
To overcome this limitation, you have to augment new data to the large language model.
The other limitation occurs when you use ChatGPT and try to pick up a conversation from the previous day. Vector databases are what allow LLMs to conduct long-term memory retrieval and create seamless chat experiences over time.
So to recap, vector databases help LLMs:
- Produce factual data
- Provide relevant and consistent information
- Have long-term memory retrieval
Pinecone is a cloud-based managed vector database specifically designed to simplify the development and deployment of large-scale machine learning applications for businesses and organizations. It sets itself apart by utilizing closed-source code, distinguishing it from other popular vector databases.
Built on the efficient similarity search library Faiss, Pinecone is tailored to machine learning applications, offering impressive speed, scalability, and support for various machine learning algorithms.
What truly sets Pinecone apart is its emphasis on developer-friendliness. With an intuitive and straightforward interface, it allows developers to focus on application building rather than grappling with underlying infrastructure complexities.
Pinecone excels in supporting high-dimensional vector databases, enabling diverse use cases like similarity search, recommendation systems, personalization, and semantic search. It also offers the advantageous capability of single-stage filtering. In addition, its real-time data analysis capabilities make it an excellent choice for threat detection and monitoring in the cybersecurity industry.
Furthermore, Pinecone boasts seamless integrations with multiple systems and applications, including Google Cloud Platform, Amazon Web Services (AWS), OpenAI (including GPT-3, GPT-3.5, GPT-4, and ChatGPT Plus), Elasticsearch, Haystack, and more, expanding its compatibility and versatility.
Weaviate stands out as an open-source vector database solution that can be deployed as either a self-hosted or fully managed option. It empowers organizations with a robust toolset for efficient data handling and management, offering exceptional performance, scalability, and user-friendliness.
Whether utilized in a managed setup or self-hosted environment, Weaviate showcases versatile functionality capable of accommodating various data types and applications.
One powerful feature of Weaviate is its ability to store both vectors and objects, making it a suitable choice for applications that integrate multiple search techniques, such as vector search and keyword-based search.
Weaviate finds common usage across various scenarios, including similarity search, semantic search, data classification within ERP systems, e-commerce search, powering recommendation engines, image search, anomaly detection, automated data harmonization, and cybersecurity threat analysis. Its wide range of applications reflects its flexibility and adaptability to diverse use cases.
Chroma serves as an open-source vector database designed to empower developers and organizations of all sizes with the necessary tools for constructing large language model (LLM) applications. It provides developers with a highly scalable and efficient solution, enabling seamless storage, search, and retrieval of high-dimensional vectors.
The popularity of Chroma stems from its remarkable flexibility. It offers the flexibility to deploy either in the cloud or as an on-premise solution, catering to diverse infrastructure preferences.
Chroma boasts support for multiple data types and formats, making it well-suited for a wide range of applications. Notably, it excels in handling audio data, making it an optimal choice for audio-based search engines, music recommendations, and other audio-related use cases.
Qdrant is a powerful vector similarity search engine and database. With its production-ready service and user-friendly API, it offers seamless storage, search, and management of points, which are vectors accompanied by additional payload information.
Qdrant is specifically designed to excel in extended filtering support, making it invaluable for various applications such as neural network or semantic-based matching, faceted search, and more.
Milvus has emerged as a new open-source vector database, garnering popularity within the realms of data science and machine learning. A notable strength of Milvus lies in its robust support for vector indexing and querying, leveraging cutting-edge algorithms to accelerate the search process. This translates to swift retrieval of similar vectors, even when dealing with extensive datasets.
Another contributing factor to its popularity is Milvus' seamless integration capabilities with other widely used frameworks such as PyTorch and TensorFlow. This allows for smooth incorporation into existing machine learning workflows, enhancing its versatility and adoption potential.
Milvus finds diverse applications across multiple industries. In the e-commerce sector, it proves valuable in recommendation systems, facilitating personalized product suggestions based on user preferences. Within image and video analysis, Milvus excels in tasks like object recognition, image similarity search, and content-based image retrieval. Additionally, it finds common use in natural language processing applications, powering document clustering, semantic search, and question-answering systems.
Faiss is a highly effective tool for indexing and searching vast collections of high-dimensional vectors. It excels in similarity search, clustering, and memory optimization, ensuring efficient storage and retrieval of vectors, even with numerous dimensions.
It includes algorithms capable of searching within vector sets of any size, even those that may exceed RAM capacity. The library also provides supporting code for evaluation and parameter tuning. Faiss is primarily developed by Meta's Fundamental AI Research group.
Image recognition is one of the prominent applications of Faiss. It empowers the creation of large-scale image search engines capable of indexing and searching millions or billions of images.
Additionally, Faiss can be utilized to build semantic search systems, enabling swift retrieval of similar documents or paragraphs from vast text repositories.
Vespa is a search engine and vector database that offers comprehensive functionality. It encompasses vector search (approximate nearest neighbors), lexical search, and the ability to search within structured data, all within a single query.
It incorporates integrated machine-learned model inference, enabling real-time AI-powered insights and analysis of your data.
Some interesting use cases that Vespa can address are conversational AI, semi-structured navigation, and question-answering.
pgvector is an open-source extension for PostgreSQL. It empowers users to store and query vector embeddings seamlessly within their database environment. Built on the foundation of the widely recognized Faiss library, pgvector is known for its efficient similarity search capabilities for dense vectors, and ensures optimal performance.
One of the notable advantages of pgvector is its user-friendly nature, making it incredibly easy to use. With a simple installation process that requires just a single command, users can quickly incorporate pgvector into their PostgreSQL setup, enhancing the vector storage and querying capabilities of their database.
Vald is a distributed and highly scalable search engine specifically designed for fast and approximate nearest neighbor dense vector search. Its architecture is built upon the Cloud-Native principles, ensuring optimal performance in distributed environments.
Utilizing the NGT algorithm, which is known for its speed, Vald excels in efficiently searching for neighbors.
Vald offers automatic vector indexing and index backup, bolstering data integrity and resilience. With its horizontal scaling capabilities, Vald is capable of handling searches across billions of feature vectors, making it an ideal solution for large-scale datasets.
Elasticsearch serves as a powerful distributed search and analytics engine, offering robust support for diverse data types. Among these data types, Elasticsearch accommodates vector fields, enabling the storage of dense numeric value vectors.
Starting from version 7.10, Elasticsearch introduced specialized data structures to efficiently index vectors, facilitating rapid k-nearest neighbors (kNN) retrieval through the kNN search API. Furthermore, with the release of version 8.0, Elasticsearch expanded its capabilities to include native support for natural language processing (NLP) utilizing vector fields.
OpenSearch, a community-led and open-source project, emerged as a fork of Elasticsearch and Kibana in response to the license change in early 2021. OpenSearch encompasses a powerful vector database functionality, enabling the storage, indexing, and retrieval of vectors along with associated metadata.
Leveraging k-nearest neighbors (k-NN) indexes, OpenSearch empowers users to conduct efficient vector similarity searches.
How to choose the best vector database
Choosing the right vector database is a decision that can significantly impact the efficiency and effectiveness of your applications.
Consider the following attributes when evaluating a vector database.
- Scalability: Choose vector databases that possess the ability to handle large volumes of high-dimensional data efficiently. They offer scalability to accommodate your expanding data requirements.
- Performance: Speed and efficiency are critical. The vector databases featured in this list have exceptional performance in data retrieval, search operations, and various vector-related tasks.
- Flexibility: The databases included in this article support diverse data types and formats, making them adaptable to a wide range of use cases. They can effectively handle both structured and unstructured data and are compatible with multiple machine-learning models.
- Ease of Use: User-friendliness and manageability are key attributes of these databases. They are designed for straightforward installation and setup, provide intuitive APIs, and offer comprehensive documentation and support resources.
- Reliability: Each vector database highlighted here has a proven track record of reliability and robustness, instilling confidence in their performance and durability.
At the end of the day, the right vector database for you depends on your specific needs and business goals. It’s helpful to evaluate the current options to see how well they align with what you’re trying to accomplish. Also check to see if similar companies are leveraging the same databases, if at all.
Vector databases - final thoughts
Vector databases are a critical part of the AI stack. They solve the stateless problem of LLMs and increase the accuracy, relevance, and consistency of the information they provide to end-users.
The vector database landscape is being transformed by top contenders such as Chroma, Pinecone, Weaviate, Milvus, and Faiss. Each of these databases brings unique strengths to the table, revolutionizing data indexing and similarity search.
Chroma stands out for its exceptional capabilities in constructing large-scale language model applications and catering to audio-based use cases. Meanwhile, Pinecone offers organizations a straightforward and intuitive way for developing and deploying machine learning applications.
If flexibility is a priority, Weaviate is a good choice, as it provides a versatile vector database suitable for a broad spectrum of applications. Faiss, on the other hand, has gained recognition for its high-performance similarity search capabilities.
Milvus is rapidly gaining popularity due to its ability to scale indexing and querying operations effectively.
The vector database landscape is constantly evolving, and there may be more specialized databases on the horizon, pushing the boundaries of data analysis and similarity search. For now, we hope this curated list serves as a valuable shortlist for considering vector databases for your project.
However, just getting a vector database alone won’t solve your LLM integration challenges. If you’re looking to build an AI or ChatGPT integration but lack the resources to do so, we’d be happy to help!
Contact us today, and we’ll connect with you straight away to learn more about how we can help you deliver more value with OpenAI, ChatGPT, or any other LLM.
8 min read
Large Language Models, or LLMs for short, can create human-like text and handle human-language complexity. Trained on vast amounts of data, LLMs can understand and produce text that’s contextually relevant across many different topics.
If you’re trying to create data aware LLM applications and talk to your data through your product or software then you’ll most likely run into LlangChain and/or LlamaIndex.
LangChain and LlamaIndex are both frameworks that allow you to ingest and query data using an LLM as an interface. Depending on your needs, one or both of these frameworks will make sense to leverage.
We’ll explore both in depth so that you can make an informed decision on the technologies you wish to implement in order to build your LLM application. It’s worth noting that both of these frameworks are constantly evolving and receive updates regularly.
What is LlamaIndex?
LlamaIndex (formerly GPT Index) is a framework for LLM applications to ingest, structure, and access private or domain-specific data.
As the name suggests, LlamaIndex is really focused on ingesting and structuring data based on index types, such as list or tree index. With LlamaIndex you can compose indices ontop of eachother to form complex data structures. As for querying, LlamaIndex is able to handle simple and more complex queries.
Below are the key features of LlamaIndex:
- Prompting: LlamaIndex uses prompting extensively to maximize its functionality.
- Document chunking: LlamaIndex divides documents into smaller chunks, leveraging LangChain’s textSplitter classes, which break down text to fit LLM's token limits. Customized chunking is also available.
- Graph index: The index can be a list, tree, or keyword table. There’s also the option to create an index from various other indexes, facilitating hierarchical document organization.
- Querying: When querying an index graph, two steps occur. First, relevant nodes related to the query are identified. Next, using these nodes, the response_synthesis module generates an answer. The determination of a node's relevance varies by index type.
- List index: Uses all nodes sequentially. In “embedding” mode, only the top similar nodes are used.
- Vector index: Uses embeddings for each document and retrieves only highly relevant nodes.
- Response synthesis: Multiple modes including 'Create and refine', 'Tree summarize', and 'Compact' determine how the response is created.
- Create and refine: In the default mode for a list index, nodes are processed sequentially. At each step, the LLM is prompted to refine the response based on the current node's information.
- Tree summarize: A tree is formed from selected nodes. The summarization prompt, seeded with the query, helps form parent nodes. The tree builds until a root node emerges, summarizing all node information.
- Compact: To economize, the synthesizer fills the prompt with as many nodes as possible without exceeding the LLM's token limit. If there are excess nodes, the synthesizer processes them in batches, refining the answer sequentially.
- Composability: LlamaIndex allows creating an index from other indexes. This is beneficial for searching across diverse data sources.
- Data connectors: LlamaIndex supports various data sources, like confluence pages or cloud-stored documents, with connectors available on LlamaHub.
- Query transformations: Techniques like HyDE and query decomposition refine or break down queries for better results.
- HyDe: HyDE (Hypothetical Document Embedding) prompts an LLM with a query to obtain a general answer without referencing specific documents. This answer, combined with the original query (if "include_original" is set to TRUE), helps retrieve relevant information from your documents. This method guides the query engine but can produce irrelevant answers if the initial query lacks context.
- Query decomposition: Queries can be undergo single or multi-step decomposition queries depending on the complexity.
- Node postprocessors: These refine the selected nodes. For instance, the
KeywordNodePostprocessor class further filters retrieved nodes based on keywords.
- Storage: Storage is crucial in this framework. It handles vectors, nodes, and the index. By default, data is stored in memory, except vectors which services like PineCone save in their databases. In-memory data can be saved to disk for later retrieval. Let's explore the available storage options:
- Document Stores: MongoDB and in-memory options available. MongoDocumentStore and SimpleDocumentStore handle storing document nodes either in a MongoDB server or in memory respectively.
- Index Stores: MongoIndexStore and SimpleIndexStore supports both MongoDB and in-memory for storing index metadata.
- Vector Stores: Supports in-memory and databases like PineCone. One thing to note is that hosted databases like PineCone are capable of very efficient complex calculations on vectors compared to in-memory databases such as Chroma.
To utilize LlamaIndex effectively, set up your storage preferences, and then generate a storage_context object for your indexes.
What is LangChain?
The framework’s focused on composition and modularity, and have a lot of individual components that can be used together or on their own such as models, chains and agents. LangChain also comes with use cases that show common ways to combine components.
Let’s dive into the various components of LangChain.
There are 3 kinds of models.
- LLMs: A large language model that takes in input via an interaction and returns a response as output.
- Chat models: Are similar to LLMs but specialized to work with message objects instead of pure text. Examples would be human message, system message and AI messages. These labels don’t do anything on their own, but help the system understand how it should respond in a conversation.
- Embedding models: These models are used to deliver text as a vector representation. A good use case for this is semantic search. LangChain uses two methods for embedding called embed_query and embed_document. This is because different LLMs use either one of these embedding methods.
Prompts are how users interact with LLMs. LangChain uses carefully designed prompts and prompt templates to take user query and data schemas in order to get the desired response.
There are four types of prompt templates that LangChain uses:
- LLM Prompt Templates: To enable dynamic prompt configuration and eliminate hardcoded values, Langchain offers an object rooted in Python's formatted strings. At present, Langchain supports Jinja, with plans to integrate more templating languages soon.
- Chat Prompt Templates: As previously described, we shift from using string objects to message objects here. Message objects offer a structured approach to conversational scenarios. Messages fall into three types: 1. HumanMessages, 2. AIMessages, and 3. SystemMessages.
The first two are straightforward in their naming. SystemMessages, however, don't originate from either AI or humans. They generally establish the chat's context. For instance, "You are a helpful AI that assists with resume screening" is a SystemMessage.
In essence, ChatPromptTemplates aren't merely based on strings as LLM prompts. They're grounded on MessageTemplates, which encompass HumanMessages, AIMessages, and SystemMessages.
- Example Selectors: LangChain offers the adaptability to decide the way in which you select input samples for a language model from a set of examples. There's a selector that functions based on input length, modulating the number of examples picked from your prompt according to the prompt's remaining length.
- Output Parsers: Inputs and prompts represent only one facet of LLMs. Occasionally, the manner in which the output is presented becomes importabnt, especially for subsequent operations. LangChain lets you use pre-designed output parsers, but you also have the freedom to make one tailored to your specific needs. For instance, there could be an output parser that translates the LLM response into a sequence of values separated by commas, suitable for saving in CSV format.
This tool retrieves information from documents based on a query. To build this system, we need a mechanism to load documents, create embedding vectors for them, and manage these vectors and documents. While LlamaIndex offers one approach, LangChain provides more granularity through its classes.
Document Loaders: This tool loads documents from various sources like HTML, PDF, Email, Git, and Notion using Unstructured, a pre-processing tool for unstructured data.
Text Splitters: Long documents can't be fully embedded into a vector model due to token size limits and the need for coherent chunks. Thus, splitting documents is essential. While LangChain offers splitters, creating a custom one for specific needs may be beneficial.
VectorStores: Databases to store embedding vectors, which enable semantic similarity searches. LangChain supports platforms like PineCone and Chroma.
Retrievers: Linked to VectorStore indexes, retrievers are designed for document retrieval, offering methods to determine similarity and the number of related documents. They are integral in chains that require retrieval.
Memory: While most interactions with LLMs aren't stored, memory is crucial for applications like chatbots. LangChain provides memory objects for tracking interactions. Examples include:
- ChatMessageHistory: Tracks previous interactions to provide context.
- ConversationBufferMemory: A simpler way to manage chat history.
- Saving History: Convert your message history into dictionary objects and save as formats like JSON or pickle.
Chains combine components, like language models and prompts, for specific outcomes. They can simplify inputs and provide detailed responses. They can be linked using classes like SequentialChain.
Agents and Tools
LLMs are bound by their training data. For real-time data, like weather updates, they need external tools. Chains might not be enough since they use every tool, irrespective of the query.
Agents decide which tool is relevant per query. Using agents involves loading tools, initializing the agent with them, and querying. For instance, an agent might use the OpenWeatherMap API and an LLM-math tool together.
Tools: LangChain offers standard tools, but users can create custom ones. Tool descriptions help agents decide which tool to use for a query. The tool's description is crucial for its effectiveness.
Similarities: LangChain vs LlamaIndex
LlamaIndex and LangChain have some overlap, specifically in the indexing and querying stages of working with LLMs.
Within this overlap, both frameworks do handle indexing and querying slightly differently.
Difference: LangChain vs LlamaIndex
While both frameworks provide indexing and querying capabilities, LangChain is broader and provides modules for tools, agents and chains.
Chains are a powerful concept in LLM development. With chains, the output from an interaction with your LLM can be your input for your next interaction.
For example, in a chatbot application a chain would represent a conversation between a user and the LLM. These steps are typically predefined or hardcoded into the applications.
Agents are similar to chains, but with decision-making powers. They can come up with steps on their own and decide what tools to use.
LlamaIndex’s primary use case is in going very deep into index and retrieval (querying).
Which one should you choose?
LangChain and LlamaIndex together provide standardization and interoperability when building LLM applications.
If you’re looking to get the most out of indexing and querying, and build really powerful search tools then LlamaIndex is most likely the stronger tool for the job, as it’s primarily focused on doing those two things well. LlamaIndex is also a bit easier to get started with, and has helpful getting started documentation.
To leverage multiple instances of ChatGPT, tools, interaction chains and agents that can autonomously work with tools, and provide them with memory then LlangChain is the way to go.
LangChain’s documentation is a bit more complex, though well-written, and might need a little more time to be productive with.
The community in LangChain has grown a lot faster than LlamaIndex, and so there will most likely be plenty of support around this framework along with content for common challenges in the future.
Finally, you can use both LangChain and LlamaIndex together and benefit the best of both worlds. This boils down to your needs when it comes to indexing and querying.
6 min read
Facial recognition is exactly what it sounds like. It refers to technology that can identify an individual using only their face.
This can occur via photo or video. But it’s worth noting that the technology is not always accurate, particularly towards people of color.
You’re probably most familiar with facial recognition applications in law enforcement.
Depending on the person, this can be a good or bad thing. Controversies around facial recognition often stem from privacy rights.
Still, it’s worth looking into just what facial recognition is capable of. There are many applications of facial recognition technology. And nowadays, OpenAI developers enable impressive advancements.
This article will explain six application of facial recognition technology:
- Automobile Security
- Access Control
The Growth of Facial Recognition Technology
One of the top tech trends in 2019 has been facial recognition, an intriguing technology that is starting to attract more business from various industries.
Its wide array of potential applications has made it easier for different industry verticals to adopt as the technology has become more mature. Typically associated with immigration and the security sector, today it's winning over healthcare, retail, and marketing many other markets.
Facial recognition software uses a mix of artificial intelligence (AI) and biometric technology to identify human faces through measuring nodal points, the distances between certain facial features. The software analyzes and compares patterns of a person's facial features to provide accurate verification of their identity.
The data from facial recognition has found its most practical use in security. Tech giants like Apple or Google utilize facial recognition in their mobile devices to verify their users’ identity, providing secure logins for devices.
Some other case uses involve government organizations who utilize the technology in places like airports to reduce identity theft, find missing people, and identify criminals.
The growth potential of face recognition technology is astonishing. By 2042, the global facial recognition technology market is projected to generate an estimated $15.4 billion – demonstrating a growth rate of 21.4% since 2016.
How Facial Recognition Technology Works
Biometric facial recognition applications are frequently used in consumer technology nowadays. For example, millions of users take part in the facial recognition capabilities of the iPhone X. The user’s face is identified by 30,000 separate infrared dots and adds an extra layer of security to the traditional identification methods.
The technology behind facial recognition varies from one application to another, but the basic mechanics of it, when used by the government for immigration and security, include four basic steps:
1. An image of your face is captured from a photo or video. To ensure more accurate matching, your face should be facing the camera, though the technology does allow some turns and can even pick out singular faces from crowds.
2. The software reads the geometry of your face. Key factors are the distance between your eyes and the distance from forehead to chin. The software identifies these facial features then creates your facial signature based on the profile they created.
3. Your facial signature is compared to a database of known faces. This data is powered by police databases and government security services.
4. If your facial signature is found to match one flagged in the database, it can be used to investigate and apprehend you.
Why Is Facial Recognition Important?
Facial recognition technology is most helpful to law enforcement officials trying to find criminals. Ironically, this is a controversial topic given the greater focus on privacy in the last 6-8 years.
But when facial recognition has relatively positive outcomes, it’s been known to find suspects in a matter of hours. And of course, when officers find the right guy for the crime, this is usually a good thing.
According to Spolin Law P.C, Facial recognition technology can prevent access to sensitive information. It can also serve as a record of who is accessing that information.
“As criminal defense lawyers at Spolin Law P.C., we use tech like this as evidence to support cases where someone may have been wrongfully accused. As with any type of crime, we often find there are mitigating circumstances that technology can support to help us achieve a fair outcome for all parties involved.”
Similarly, many small businesses have taken to using the same technology in order to empower themselves when faced with theft.
For personal use, you can shave off a few seconds unlocking your phone if you go with facial recognition technology rather than a fingerprint, pin, or password. Although experts have said that facial recognition is less secure than other security methods.
In general, the benefit of facial recognition technology is a sense of security and safety. And this is achieved through an ever-evolving technology.
What Are the Applications of Facial Recognition?
The advantages of security and safety have prompted many industries to implement facial recognition technology into their daily operations. Take a look at how and where this technology can be applied.
Although you probably don’t spend too much time thinking about them, every now and then you’ve probably seen an armored truck cruising around town. These trucks often carry important items, whether that’s intel or cash, and rely on facial recognition technology to prevent theft or even ensure that the driver’s eyes are on the road.
In another capacity, facial recognition technology is sometimes used by ride-sharing apps to confirm that a given passenger is who they say they are. Or alternatively, the same technology can guarantee that the passenger is approaching the right driver.
Outside of cars and smartphones, facial recognition can be used in the home to grant access to certain IoT devices in addition to entry into the home itself. As this technology becomes more and more advanced, people will feel better protected against home invasions and robberies.
Immigration offices exist as an extension to more well-known government segments. Facial recognition technology is used to enforce stricter border control, particularly when it comes to criminals and persons of interest who attempt to cross the border.
Other than federal and local security, facial recognition applications may exist most prominently in the education sector.
A growing number of schools already use cameras that utilize facial recognition software to identify students, staff, unauthorized individuals, and even behavior that could present a threat to safety. This is one of many new tech trends that are transforming education.
For schools using this technology, the main benefit they see is tracking student attendance as well as maintaining the security of their campus. Unfortunately, technology can be very biased and studies have shown evidence for the software to be banned.
Though the United States is behind in regards to using facial recognition for retail other countries like Japan have been doing so for quite some time. For example, vending machines in Japan can recommend drinks for the consumer by using facial recognition technology to approximate a customer’s gender and age.
Lately, it looks as if the USA is catching up. Amazon opened its first Amazon Go store in 2018. There is no check-out and the store depends entirely on sensors to figure out what a customer picks up and buys.
Given this, it might be safe to assume that Amazon has the brainpower to start mapping shopping behaviors in the near future once online shoppers are connected to offline faces.
Applications of facial recognition technology are used in hospitals, especially those working in assisted living. The software serves to keep track of everything that is going on within a hospital, ensuring patients are safe and the premise is secure.
If a patient wanders away from the care-giving facility with no identification, facial recognition can help quickly identify and find them quickly to prevent any harm from coming to them.
Concerns of Facial Recognition Technology
Facial recognition technology dates back to the 1960s. It has often drawn a large amount of criticism regarding the legality and ethics of its use.
This technology allows others to control the personal information of individuals and determine how it’s used. From this perspective, facial recognition may seem plainly immoral in practice.
Ownership of Data
While nobody can take away your physical face, digital images are another story. You can give up your right to ownership to your face when you sign a term of service agreement on a social network. Signing away the rights to your likeness allows images of you to be collected as data and sold.
As precise as facial recognition technology is, it can’t guarantee a 100% success rate. The possibility of false-positive results can lead to mistaken identities in crimes.
As mentioned, these inaccuracies harm people of color disproportionately. When people are falsely accused of crimes, the basic principles of law and order are put at risk.
When the government has the power to enter the private lives of its constituents, problems arise. Facial recognition begs the question of whether people should be entitled to privacy outside their homes.
For public safety and security purposes, facial recognition software has a lot of potential to stop crimes and control immigration/ But without proper regulation and oversight, there is potential for it to be misused. The debate surrounding this is constant and will progress as technology develops further.
While facial recognition is finding its way into new markets besides the government, the basic principles of the technology are the same and still pose the same risk to individual privacy.
Which Companies Are Using Facial Recognition?
Since Amazon’s consumer base is largely online, they may be in unfamiliar territory where facial recognition technology is concerned. But other companies are no stranger to this phenomenon. Here are some examples of companies using facial recognition.
The rumor is that Walmart is developing facial recognition technology that can determine a shopper’s mood. If a shopper is dissatisfied, Walmart hopes to respond to the issue accordingly and improve their customer service overall.
McDonald’s uses facial recognition in a similar way but the subject of the technology is different. In Japan, for instance, the technology is used to make sure that servers are maintaining a happy exterior.
Recently, ExxonMobile launched DriverDash. The app optimizes payments for fuel by using facial recognition to authorize transactions for drivers.
The beauty industry can certainly do some damage with facial recognition technology and they haven’t neglected to take the bait. At Covergirl, clients can choose a matching shade of foundation thanks to facial recognition technology.
As technology and innovation progress linearly, more markets will begin to open for this technology. It will be interesting to watch how it continues to disrupt various industry verticals.
Corporations and governments alike will continue to invest in these technologies. And here at Trio, we have already specialized in providing the best OpenAI developers to assist you with ethics and efficiency.
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