Frequently Asked Questions
What is facial recognition technology?
Facial recognition is exactly what it sounds like. It refers to technology that can identify an individual using only their face.
Why is facial recognition important?
Facial recognition technology plays a big part in modern security, both locally and federally.
What is wrong with facial recognition?
Facial recognition technology can be both erroneous and intrusive, causing people to be untrustful of the technology.
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
Companies using machine learning understand that business growth requires continuous innovation.
Via machine learning, organizations are improving their businesses, both on the consumer level and in terms of business activity too.
To find out more about what machine learning can do, keep reading! You’ll learn all about 14 major companies using machine learning to change business for the better.
What Is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence in which computers use data and algorithms to improve upon themselves.
Artificial intelligence (AI) itself describes any computer system that demonstrates human-like intelligence.
While most machines are set out to do a single task or a set of tasks, AI technology is more flexible.
Through machine learning, digital systems are constantly adapting to their surrounding environment.
Most businesses use artificial intelligence as a means of digital transformation.
By using AI, you can automate a number of tasks without so much as lifting a finger, from tracking lead behavior to sending mass emails at optimal times.
Consumers use AI technology too. And you have probably used AI more than you even realize.
For example, smart applications like Google Assistant and Siri are digital assistants that rely on artificial intelligence and machine learning to respond to user data and complete complex tasks.
In short, machine learning is more than capable of helping businesses and individuals alike meet their objectives.
Related reading: 8 Best Programming Languages for AI Development in 2022
15 Companies Using Machine Learning
Machine learning has been a popular concept in modern application development trends. Companies using machine learning have a variety of applications for this clever technology.
Here are some examples of major companies using machine learning:
Yelp hosts reviews from a large assortment of businesses all over the world.
The website can give locals and tourists recommendations of restaurants, bars, salons, and even dentists in their residing area.
With the use of machine learning, Yelp has fine-tuned image curation to provide users with more accurate photo captions and attributes.
Surely, if you take to reviews to learn whether or not a new spot in town is worth the hype, pictures are an essential part of the decision-making process. And obviously, accuracy is appreciated.
Pinterest is a social media service that’s a bit off-target from the norm. On Pinterest, users share ‘pins’ to help other users discover recipes, style inspiration, DIY projects, and other lifestyle ideas.
In 2015, Pinterest announced the acquisition of Kosei, a company that provides business-to-business (B2B) machine learning capabilities for its clients.
Now, machine learning is a fundamental part of almost everything that happens on Pinterest.
Spam moderation, content discovery, and even ad monetization take place with machine learning at the center.
Facebook is a notorious social media network that you’re likely well-familiar with. But did you know Facebook Messenger has an AI embedded within its infrastructure?
Or rather, businesses create chatbots that depend on the messenger’s internal software for its operations.
For example, you can message Domino’s Pizza for an app to take you through the ordering process.
Similarly, you can book a flight and hotel through Kayak with just a quick, easy chat.
You can even schedule a makeover at your local Sephora’s, all through Facebook Messenger!
Twitter is a social media service where users exchange information through concise, primarily text-based blurbs.
That said, not every tweet is welcome. Depending on who you are, you might find some tweets more relevant and/or entertaining than others.
Luckily, Twitter uses a machine learning algorithm to score tweets based on various metrics.
Then, Twitter curates your feed, making an educated guess about what you would like to see most.
Google is a proud user of neural networks. In humans, neural networks allow the brain to create relationships between large datasets.
Naturally, artificial intelligence can imitate this ability.
Right now, Google still uses classic algorithms for natural language processing (NLP), speech translation, and search ranking, among other things.
But they are deeply invested in researching and refining neural networks to enhance further development. Google dubs this project the DeepMind.
Baidu may not be a major company in the United States, but it is definitely one of the top companies using machine learning.
In fact, Baidu is one of the largest AI and internet companies in the world. It’s based in China.
Like Google, Baidu is also interested in neural networks. Through a project called Deep Voice, they plan to engineer human speech with genuinely human voices.
HubSpot has always put its best foot forward when it comes to investing in emerging technology.
As one of the more comprehensive marketing tools in the industry, machine learning is vital to how HubSpot works.
Marketing automation tools leverage artificial intelligence to optimize business activity all-around.
Predictive lead scoring or automatic record cleaning name only a few of the possibilities.
IBM, or International Business Machines, is one of the oldest technology companies that’s still alive and kicking.
But unlike other old companies, IBM continues to expand its technological resources. New and innovative technologies are regular addendums to the metaphorical IBM grocery list.
And thus, they continue to grow as a business.
One of IBM’s newer technologies is Watson, a machine learning tool that several hospitals and medical centers use to get treatment recommendations for different types of cancers.
But Watson can do more than that. The retail sector also uses Watson to assist shoppers. The hospitality industry uses Watson too. Truly, AI can do it all!
Salesforce and HubSpot compete in the same industry. Like HubSpot, Salesforce gives businesses the tools to scale their marketing efforts.
Much of this happens through the Salesforce CRM, or customer relationship management tool.
Salesforce software uses ML and AI to analyze customer engagement and work with you towards amplifying the customer experience.
Apple needs no introduction. But just in case, you should know that Apple is a multinational company that specializes in computer software and consumer electronics.
This means that not only is Apple behind the iOS that runs on iPhones but the iPhone itself. You can credit Apple with the Mac and its various operating systems as well.
Apple is also behind Siri. Siri is technically an AI machine in the form of a handy digital assistant.
She can send text messages, check your email, and answer random questions, among other tasks.
Although Intel does fall into the category of major companies using machine learning, Intel’s use of ML is unique.
Intel is a chip manufacturer, which sounds vague. But you very well could be using an Intel chip right now to power your computer’s core processor.
It could be the very reason you’re navigating 12 tabs at a time with glee.
That said, Intel has more than one niche. Their Nervana chips, for one, are designed for data center servers and utilize ML for major data processing.
Nervana chips are able to transfer 2.4 terabytes per second with very low latency, and apparently, machine learning is key to this prowess.
Microsoft is the backbone of numerous technologies, your Xbox and favorite word processor being only two examples.
As far as machine learning goes, that mostly has to do with their Maluuba acquisition. Maluuba is known for its impressive deep learning labs for NLP.
In practice, Microsoft might use this acquisition to empower its voice computing and voice search technologies.
So if you ever get annoyed with a few too many accidental Cortana pop-ups on your PC, expect your frustrations to be assuaged very soon.
Amazon is one of the largest retailers in the world.
Most people love Amazon because of its 2-day shipping, which makes immediate gratification an unhealthy but realistic expectation for consumer desires.
That aside, Amazon uses ML for many of its retail-oriented tasks, such as product recommendations, forecasting, data cleansing, and capacity planning.
Can you guess why Netflix is on the list of major companies using machine learning? Think suggestions.
Netflix is a highly-favored streaming service with more films and television shows than you can count readily available at its users’ fingertips.
But if that’s not reason enough to be enthusiastic, you don’t even have to do the work of figuring out what to watch.
That’s because Netflix's primary use of ML technology is to give user suggestions, catered specifically to their unique interests.
Machine learning can be instrumental in boosting productivity in your business or even revamping your latest software product.
Like most newer technologies, it may be a bit difficult to wrap your mind around the concept.
In this case, seeking out professionals to guide you in implementing ML within your organization is not a bad idea.
At Trio, we have expert developers at the ready for consultation and to fulfill any development need you have down the road.
Hire Trio developers today!
5 min read