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Add AI to your apps and workflows in minutes with Glide no code solutions

Add AI to your apps in minutes with Glide no code solutions

If you are wondering how you can leveraged AI into your existing applications or business workflow you might be interested in Glide AI. Artificial Intelligence, driven by Large Language Models (LLMs), is has exploded into every area of design, development, workflows and business. Allowing us to harness an array of amazing technologies that we can implement into our workflows and applications.

LLMs are machine learning models that learn, adapt, and use statistical models to analyze and draw inferences from patterns in data. This capability to understand and interpret data makes AI an invaluable tool in the business landscape.

Workflow AI automation

Integrating AI into existing applications and business workflows can substantially enhance operational efficiency and open up new avenues for innovation. Automation is one of the most immediate benefits, allowing companies to offload routine tasks to AI systems, thereby freeing up human resources for more complex activities.

AI-driven data analysis enables real-time insights into consumer behavior, supply chain dynamics, and other critical business metrics, thereby empowering data-driven decision-making. This is particularly invaluable in industries like finance for fraud detection, healthcare for diagnostics, and manufacturing for predictive maintenance.

How to add AI to your apps and workflows

Glide, recognizing the potential of AI, has integrated it into its platform, creating Glide AI. This system provides simple building blocks for designing with AI in data and apps, making the process of building with AI as simple as adding a column to a table. With Glide AI, businesses don’t need to manage prompts, choose models, deal with complex APIs, or cache results to optimize cost and performance. All these are managed by Glide, allowing businesses to focus on their core operations.

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Glide AI offers automated and user-triggered data transformations, such as converting audio to text, generating new text, extracting text from images, and converting text to various formats. These capabilities enable advanced data manipulation and feature-rich app development without the need for coding. For instance, Glide AI can be used in a work order management app for a maintenance team, allowing the team to perform updates and send data back to the head office.

Glide AI Keynote No Code Summit 2023

Watch the Glide AI keynote at the No Code Summit 2023 Conference in Paris, France with our CEO, David Siegel.

What sets Glide AI apart is its managed models feature. Glide AI automatically selects the best model for each use case, eliminating the need for businesses to provision, authenticate, manage APIs, or worry about migration. In addition, Glide AI calls are cached at the app level, allowing the entire company to reuse results, reduce costs, and get the best possible performance. Glide AI also selects different model providers for working with text, images, audio, documents, etc., enabling businesses to focus on results, not details.

Personalize your services and products

AI offers the ability to personalize services and products based on customer behavior and preferences, leading to increased engagement and potential revenue growth. AI’s capacity for natural language processing and machine learning also contributes to improved user experiences and more intuitive interfaces. Beyond that, the adaptability of AI means that systems can evolve to meet changing business needs without requiring constant manual updates. In essence, the integration of AI provides not just incremental improvements in existing processes, but can also be a catalyst for transformative changes that offer a competitive edge.

The integration of AI into business operations, specifically through platforms like Glide, has the potential to revolutionize business workflows. By leveraging Glide AI, businesses can unlock their data, transform their operations, and gain a competitive edge in the market. As AI continues to evolve, businesses that adopt and integrate this technology into their operations are likely to stay ahead of the curve and reap significant benefits.

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How to add AI NPC characters to games for realistic immersion

AI NPC characters with emotions

Game designers looking to add extra personality to their in game characters might be interested in learning more about Inworld Ai. A system that offers a fully integrated character engine for NPCs powered by artificial intelligence that goes beyond large language models (LLMs). Adding configurable safety, knowledge, memory, narrative controls, multimodality, and more.

These new AI tool available to developers can be easily integrated into games, and have been specifically designed to aid developers in crafting more realistic and engaging characters in their games. Unlike traditional game NPC characters, who can sometimes feel a bit robotic or predictable, AI offers an NPC character engine that’s powered by advanced artificial intelligence. It’s not just about making characters talk or move; it’s about giving them a unique personality and letting them interact with the game environment and players in a more lifelike way.

adding artificial intelligent NPC characters to games

“Step into a world where NPCs are more than just side characters. Powered by AI, Inworld NPCs possess a mind of their own, unlocking next-level role-playing and unreal immersion. See them in action in experiences from NetEase Games, Niantic, LG, Neal Stephenson, and in community-created mods of GTA V and Skyrim.“

Enabling game designers to create and build a wealth of characters with distinct personalities and contextual awareness that stay in-world. As well as seamlessly integrate them into real-time applications, with optimization for scale and performance built-in.

AI NPC characters

Imagine you’re playing your favorite video game, and suddenly an NPC character you had helped in a previous mission recognizes you and thanks you for your assistance. Or perhaps, an adversary learns from your moves and adapts its tactics, making the game more challenging. Sounds futuristic, right? But it’s becoming a reality.

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Adapting to gameplay

One of the standout features is the ability for these characters to remember interactions. Let’s say you’re playing a role-playing game and you save a character from danger. The next time you encounter them, they might recall your good deed and express their gratitude. This adds depth to the gameplay, making players feel a stronger connection to the game world.

Gone are the days when opponents in games had a set pattern of moves. With AI if you, for example, always rely on a specific attack move in a combat game, the enemy characters will catch on. They’ll start predicting your moves and countering them, keeping you on your toes!

Feeling the emotions

Characters in games can now show emotions. For instance if there’s a dramatic event in the game, you might see characters expressing sadness, joy, or fear. Their facial expressions, voice tones, and actions can all change based on what’s happening, making the game environment feel more real.

Choose your own adventure

Another cool feature is dynamic storytelling. Your decisions in the game can lead to different outcomes. Maybe in one playthrough, you decide to befriend a character, leading to one storyline, while in another, you might become adversaries, leading to a completely different story. This not only adds depth but also makes you want to play the game multiple times to see all possible outcomes.

AI services such as Inworld and others also helps designers create a wide variety of characters. Instead of having repetitive-looking characters, the game can now have NPCs with unique personalities and backgrounds. Plus, in scenes with multiple characters, they can behave realistically. Think of a group of characters moving together in coordination or reacting collectively to an event in the game.

AI NPC characters

The gaming world is on the brink of a communication revolution. Gone are the days when players would simply select from a list of pre-written dialogue options to interact with non-playable characters (NPCs) in video games. With the rapid evolution of technology, particularly in artificial intelligence, the way we converse with game characters is undergoing a transformative shift.

Today’s advancements in artificial intelligence are striving to offer a more organic and fluid communication experience in games. Instead of being limited to a set of responses, players can now initiate spontaneous conversations, pose questions, or even engage in small talk with NPCs. The goal is to make these characters more than just programmed entities; they are envisioned to be responsive beings that can understand player input and generate coherent, contextually relevant replies.

As technology continues to evolve, the boundaries between the virtual and real worlds blur, especially in terms of communication. The prospect of having deeper, more meaningful interactions with game characters promises a future where games mirror the complexity and richness of real-world conversations.

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How to add AI vision to your apps, SaaS, sites and projects

How to add AI vision functionality to your apps and sites

OpenAI has recently added Vision capabilities to its ChatGPT AI model. Allowing users to upload images and for the artificial intelligence to be able to process and analyze documents, photographs, graphs and more allowing users to enhance their prompts and applications even further. If you are interested in learning how you can add AI vision functionality to your website, applications or next project. You will be pleased to know that AI Jason has created an interesting video worth watching showing how AI vision can be implemented.

AI vision, or computer vision, refers to the field of artificial intelligence that enables machines to interpret and make decisions based on visual data. The technology often uses machine learning algorithms to recognize patterns, identify objects, and even understand scenes in images and videos. The capabilities of AI vision have advanced significantly in recent years, thanks to improvements in neural networks, especially convolutional neural networks (CNNs).

Adding AI vision to your projects

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AI vision can substantially enhance the functionality, efficiency, and user experience of applications, software, and websites, particularly in the Software as a Service (SaaS) model. For users, features like object recognition, facial authentication, and personalized content curation can offer a more seamless and engaging interaction with the platform. For instance, a document management SaaS could utilize Optical Character Recognition (OCR) to automatically categorize, tag, and index uploaded documents, saving users the manual effort and reducing errors. Similarly, an e-commerce SaaS could use image classification to automatically sort products into categories, making it easier for customers to find what they’re looking for.

Applications of artificial intelligence vision

  • Object Detection: Identify and locate objects within an image or video frame. This is used in applications like security surveillance and retail analytics.
  • Image Classification: Categorize images into predefined classes. This is fundamental to tasks like image search engines and medical diagnosis.
  • Facial Recognition: Identify or verify individuals based on their facial features. This has applications in security and identity verification.
  • Semantic Segmentation: Classify each pixel in an image to a particular category, useful in autonomous vehicles and agricultural monitoring.
  • Optical Character Recognition (OCR): Convert different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data.
  • Motion Analysis: Track movements in video data, often used in sports analytics and video surveillance.
  • Scene Reconstruction: Create a 3D model from visual data, often used in robotics and augmented reality.
  • Anomaly Detection: Identify abnormal patterns in visual data, which is crucial in fields like healthcare and manufacturing for quality control.
  • Gesture Recognition: Understand human gestures, which can be used in interactive applications or human-robot interactions.
  • Emotion Analysis: Interpret human emotions based on facial expressions, commonly used in customer feedback systems or mental health apps.

From a developer’s standpoint, integrating AI vision capabilities can simplify many complex tasks and automate routine processes. For example, rather than manually coding rules for sorting or classifying visual data, developers can leverage pre-trained machine learning models to do this more effectively and accurately. This can speed up the development process, reduce the likelihood of errors, and enable the software to handle a much wider range of tasks than would be feasible with rule-based programming. Moreover, the analytics derived from AI vision can provide valuable insights into user behavior and preferences, which can be used for further optimization.

Competitive edge in SaaS

Additionally, adding AI vision features can provide a competitive edge in the crowded SaaS market. Users increasingly expect smarter, more automated, and more personalized experiences, and AI vision can help meet these expectations. For example, a real estate SaaS platform could use image recognition to automatically identify and highlight key features in property photos, such as a swimming pool or a fireplace, thereby enhancing the user experience and potentially increasing conversions.

The capabilities of AI vision are continuously expanding with the development of more sophisticated algorithms and computational resources. However, it’s important to note that these systems are usually trained on large datasets and their performance can vary based on the quality and diversity of the data they were trained on. As always we will keep you up to speed on all the new developments within the world of artificial intelligence keep you informed on the latest AI models, techniques and integrations as well as the latest releases from the big tech companies pushing AI forward such as Microsoft, OpenAI and Google.

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