Cloud Computing Concepts Hub

The Cloud Computing Concepts Hub is the centralized place where you can browse or search for informative articles about cloud computing. You'll find easy-to-understand info about broad topics such as "What is Machine Learning?" and "What is Data Science?" These articles are intended to help you up-level your understanding of frequently asked cloud computing topics. 

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  • Artificial Intelligence

    What Is Chain-of-Thought Prompting?

    Chain-of-thought prompting is a technique used to guide large language models (LLMs) to complete tasks using a logical sequence of steps. Even though AI models attempt to mimic humans, they require detailed instructions, called prompts, to create high-quality, accurate, and relevant output. Prompt engineering is the art and science of creating good prompts. Chain-of-thought (CoT) reasoning is a prompt engineering technique that breaks down complex tasks into a series of simpler, more manageable steps to arrive at an answer. It gives users greater control and insight into how the LLM arrives at its responses. 

    Chain-of-thought example

    “Solve the following math word problems step by step.

    If you have 7 bananas and give 4 bananas to your friend, then you receive 5 more bananas, and afterward, throw out 3 rotten bananas; how many bananas do you have left?”

    Chain-of-thought output

    Let's break it down step by step:

    1. You start with 7 bananas.
    2. You give 4 bananas to your friend, so you have: 7 - 4 = 3 bananas left
    3. You buy 5 more bananas, so you now have: 3 + 5 = 8 bananas 
    1. You throw out 3 bananas, so you have: 8 - 3 = 5 bananas left 

    Therefore, you have 5 bananas left.

  • Artificial Intelligence

    What Is Natural Language Understanding?

    Natural language understanding (NLU) is the ability of a computing system or software to understand human language. It allows the software to recognize chat intent and sentiment and provide highly engaging user experiences through lifelike conversational interactions. For example, assistants like Alexa respond to human voice, and AI-powered chatbots participate in text-based conversations. Natural language understanding enables these systems to take any human-generated conversational input, convert it into internal computer language syntax, and act upon the input. Users can naturally interact with machines using the language of their choice.

  • Artificial Intelligence

    What Is Cognitive Computing?

    Cognitive computing is the process of making software reason and “think” like a human. Humans' innate, advanced reasoning ability allows us to adapt to new situations and solve complex problems. For example, a human lost in the woods might move towards the water to survive or the traffic noise to find help. Similarly, cognitive computing aims to simulate human thought processes in software systems via perception, attention, and memory.

    Traditional artificial intelligence (AI) systems can solve problems they are trained specifically for, such as analyzing data, making predictions, or generating text as the user directs. However, cognitive computing aims to take artificial intelligence further by teaching software systems to meet predetermined goals through independent decision-making in response to environmental changes. 

    Cognitive computing example

    Consider an appointment-scheduling task for a specialist in your organization. New appointments should be made only in the afternoons when the specialist is available and has an existing relationship with the client. Appointments should also be made within 2 weeks of request. However, there are nuances to these conditions, such as 

    • Angry or upset clients receive early appointments, including in the mornings.
    • Appointments may be requested beyond the two-week window.
    • Certain long-term clients may require special considerations, like a personalized email once the booking is done.

    Typically, AI can be trained to automate the task by fulfilling the primary conditions, but it cannot handle the nuances like a human professional would. However, cognitive computing is AI technology that can adjust conditions as needed to meet the goal of customer satisfaction while booking. For example, it might read the sentiment of the customer's message and prioritize their booking or schedule an early morning appointment to meet the needs of a long-term client.

  • Artificial Intelligence

    What Is Multimodal AI?

    Multimodal AI is an artificial intelligence system that processes, interprets, and integrates multiple data types, such as text, images, audio, and video. Many business workflows generate and use complex data sets with several related datatypes— for example, videos with subtitles, images with resumes, or audio meeting notes with relevant design plans. Multimodal AI can interpret such a diverse and rich information set for complex analysis and automation. Organizations use video analysis, document processing, content creation, and similar workflows to gain accurate insights from unstructured, multi-modal data sources and enhance process efficiency.

  • Databases

    What Is a SQL Database?

    SQL database is a data collection visualized as tables with rows and columns. Data is stored similarly to a spreadsheet, with columns indicating data attributes and rows describing the entity or object the data is about. Most SQL databases use the structured query language (SQL) for user-data interaction—hence the name. They are relational databases because you can store data relationships between tables.

    For example, a products table has columns like product name, type, cost, etc, and a row contains values for the individual products. A customer table has columns with customer names and contact details. You can create a third table that links customer data with the products they purchased.

  • Analytics

    What Is Data Architecture?

    Data architecture is the overarching framework that describes and governs an organization's data collection, management, and usage. Organizations today have vast data volumes coming in from various data sources and disparate teams wanting to access that data for analytics, machine learning, artificial intelligence, and other applications. Modern data architecture presents a cohesive system that makes data accessible and usable while ensuring data security and quality. It defines policies, data models, processes, and technologies that allow organizations to easily move data across departments and ensure it is available whenever needed—including real-time access—while fully supporting regulatory compliance.

  • Management and Governance

    What Is An ISV (Independent Software Vendor)?

    An independent software vendor (ISV) is an organization that creates and sells software products that are independent of the underlying hardware and operating systems. The software solution typically solves a specific customer problem, such as creating and managing sales or financial data. It could also   be infrastructure software that supports data storage, security or authentication. The vendor ensures software compatibility with various hardware platforms to target a broad customer base.

    ISVs sell their software in different formats like perpetual licenses, term agreements, or Software as a Service (SaaS). The software is licensed to customers but the ownership rights are retained by the ISV.

  • Artificial Intelligence

    What is a Chatbot?

    A chatbot is a program or application that users can converse with through voice or text. Chatbots were first developed in the 1960s, and the technology powering them has changed over time. Chatbots traditionally use predefined rules to converse with users and provide scripted answers. Contemporary chatbots use natural language processing (NLP) to understand users, and they can respond to complex questions with great depth and accuracy. Your organization can use chatbots to scale, personalize, and improve communication in everything from customer service workflows to DevOps management.

  • Artificial Intelligence

    What Is Enterprise AI?

    Enterprise artificial intelligence (AI) is the adoption of advanced AI technologies within large organizations. Taking AI systems from prototype to production introduces several challenges around scale, performance, data governance, ethics, and regulatory compliance. Enterprise AI includes policies, strategies, infrastructure, and technologies for widespread AI use within a large organization. Even though it requires significant investment and effort, enterprise AI is important for large organizations as AI systems become more mainstream.

  • Artificial Intelligence

    What is Text Classification?

    Text classification is the process of assigning predetermined categories to open-ended text documents using artificial intelligence and machine learning (AI/ML) systems. Many organizations have large document archives and business workflows that continually generate documents at scale—like legal documents, contracts, research documents, user-generated data, and email. Text classification is the first step to organize, structure, and categorize this data for further analytics. It allows automatic document labeling and tagging. This saves your organization thousands of hours you'd otherwise need to read, understand, and classify documents manually.

  • Artificial Intelligence

    What are AI Agents?

    An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a contact center AI agent that wants to resolves customer queries. The agent will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.

  • Developer Tools

    What are Developer Tools?

    Developer tools are technologies that make software development faster and more efficient. Software development is a complex process of translating real-world objects into mathematical and electronic representations that machines can understand and manipulate. Developer tools act as an interface between the physical reality and computing processes. They include programming languages, frameworks, and platforms that abstract different levels of complexity. This means you can interact with computers more easily and solve more complex problems. Instead of working with hardware components and low-level coding languages, you can work with libraries, APIs, and other abstractions that prioritize business use cases. Developer tools also include software applications, components, and services that simplify the process of coding.

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