A guide on AI and interior design.

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If you're an interior designer at a firm, a furniture dealer, or part of rep groups or brands, and you're on the lookout for smarter ways to work, AI could really change how you do things.

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Why is AI important to interior designers?

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It can help interior designers work through more iterations in less time
It can drastically expand interior designers' search and discovery of products and finishes
It can help interior designers lower errors or omissions in their designs

Let's take a quick look at how AI is effecting our industry today.

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Much of the discourse around AI, architecture and interior design has focused on image generation. The reality is that there are many applications of AI in our world, from specification reading and writing to product search and discovery, image manipulation, layout analysis and more. Specifically, computer vision and natural language processing (NLP) are types of machine learning (ML) that can understand the content of an image or the meaning of a text. Since our industry is highly dependent upon both graphics and text, it's application is incredibly relevant to us.

Is AI already being used by interior design software?

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Yes.

Here are some examples:
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Adobe Photoshop: Adobe Photoshop has added lots of AI powered tooling like background removal, image expansion, content swapping and more. Learn more here.
Pinterest: Pinterest has long used machine learning to learn about users' interests and drive its image recommendation engine. Long a favorite of designers, Pinterest is one of the earliest examples of them using AI without even knowing it.
Figma: Though not heavily used in the industry, Figma provides a great example of how early vector drawing manipulation is also being impacted by AI. Learn more here.
Midjourney: A powerful image manipulation tool, Midjourney is already disrupting scene creation and rendering workflows. Learn more here.
Canoa: Canoa has built a powerful product recommendation engine that uses machine learning to help interior designers identify, specify and combine products even when they didn't know the product existed. Learn more here.

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Overall, integrating AI capabilities into interior design software like Canoa enhances user experience, streamlines design processes, and empowers designers to create more meaningful spaces by helping them complete more design cycles or iterations.

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How is AI used by interior design software today?

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AI is being used by interior design software in many different ways already. This is by no means an exhaustive list, but it does cover the more common examples.
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Image Search and Recommendation: Interior design software can leverage computer vision to enable image-based search and recommendation functionalities. Users can upload images of spaces or furniture they like, and the software can suggest similar items or design concepts based on visual similarity.
Object Recognition: Computer vision algorithms can identify objects within images, such as furniture pieces, decor items, or architectural features, by analyzing their shapes, colors, and textures. This capability enables designers to quickly catalog and categorize items present in a space.
Product Recommendations: With content-based filtering, AI algorithms analyze product attributes, descriptions, and metadata to recommend items that are similar in characteristics to those a user has shown interest in previously. Content-based filtering algorithms focus on the features of the products themselves rather than user behavior.
Style and Color Analysis: By analyzing colors, patterns, and design elements present in images, computer vision algorithms can help identify the style preferences of users and suggest complementary color schemes and design elements for their projects.
Spatial Layout Analysis: Interior design software equipped with computer vision can analyze images of rooms to understand their layouts, including the arrangement of furniture, the placement of windows and doors, and the overall spatial configuration. This information aids designers in visualizing potential design solutions and making informed decisions about space utilization.
Image Manipulation: Remove background example. Remove background tools utilize advanced computer vision algorithms to distinguish between the foreground objects and the background in an image. These algorithms analyze the pixels in the image and identify patterns and features that indicate which parts belong to the foreground and which parts constitute the background. Once the background is identified, it can be effectively removed or replaced with a transparent background, a solid color, or a different image altogether.
Vectorization and Conversion: AI techniques can be used to convert raster images into vector graphics through processes such as image vectorization and tracing. These techniques analyze raster images and automatically generate vector representations based on features, edges, and colors detected in the image.

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What is machine learning?

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Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve their performance on specific tasks over time without being explicitly programmed. It involves developing algorithms and models that can analyze and interpret large datasets, identify patterns, and make predictions or decisions based on the data. Machine learning algorithms use statistical techniques to iteratively learn from examples, adjusting their parameters and improving their accuracy with experience. This technology is applied across various domains, including image recognition, natural language processing, recommendation systems, autonomous vehicles, and medical diagnosis, driving innovation and automation in diverse industries.

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What is computer vision?

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Computer vision is a field of artificial intelligence focused on enabling computers to interpret and understand visual information from the real world. It involves developing algorithms and systems that allow machines to process, analyze, and extract meaningful insights from images or videos. Computer vision technologies enable computers to identify objects, recognize patterns, detect faces, interpret gestures, and even understand the spatial relationships between different elements in a scene. Applications of computer vision span various industries, including healthcare, automotive, retail, security, and entertainment, revolutionizing tasks such as medical diagnosis, autonomous driving, object recognition, and augmented reality experiences.

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Is computer vision a subset of machine learning?

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Yes, computer vision is a subset of machine learning. Computer vision involves the development of algorithms and systems that enable computers to interpret and understand visual information from the real world, such as images or videos. These algorithms often leverage machine learning techniques to analyze and extract meaningful insights from visual data.

In computer vision, machine learning algorithms are used to train models that can recognize objects, identify patterns, detect features, and make decisions based on the visual input they receive. Examples of machine learning algorithms commonly used in computer vision include convolutional neural networks (CNNs), deep learning models, and other statistical methods.

So, while computer vision focuses specifically on visual data interpretation, it often relies on machine learning techniques to achieve its objectives.

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What is natural language processing?

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Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a meaningful way. It involves developing algorithms and models that can analyze and process large volumes of natural language data, such as text and speech. NLP algorithms can perform tasks such as language translation, sentiment analysis, text summarization, named entity recognition, and language generation. By leveraging techniques from machine learning, deep learning, and linguistic analysis, NLP enables computers to interact with humans in a more intuitive and human-like manner, facilitating applications such as chatbots, virtual assistants, and language understanding systems.

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Is natural language processing (NLP) a subset of machine learning?

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Yes, Natural Language Processing (NLP) is often considered a subset of machine learning. NLP involves the development of algorithms and models that enable computers to understand, interpret, and generate human language in a meaningful way.

Many NLP tasks, such as language translation, sentiment analysis, text classification, and named entity recognition, rely on machine learning techniques to analyze and process natural language data effectively. Machine learning algorithms, including supervised learning, unsupervised learning, and deep learning, are commonly used in NLP to train models that can perform these tasks accurately and efficiently.

Therefore, while NLP is a distinct field focused on language understanding and processing, it heavily relies on machine learning methods to achieve its objectives.

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What is generative AI?

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Generative AI refers to a class of artificial intelligence algorithms and models designed to generate new content, such as images, text, audio, and video, that closely resemble examples from the training data. These models, often based on techniques like deep learning and neural networks, learn to understand and mimic the patterns and structures present in the data they are trained on.

Generative AI is used in various applications, including image synthesis, text generation, music composition, and creative design. It enables the creation of realistic and novel content, driving innovation in fields such as art, entertainment, research, and product design.

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Will AI replace interior designers?

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No!

Like many other experts, interior designers spend their whole careers learning and perfecting their craft. Interior designers and architects are experts at combining and analyzing hundreds of input variables like existing conditions, building code, budget, client preferences, timeframes and more, and developing a design solution specific to that situation.

This process requires good judgement.

AI can help us move faster, inform us better, or even generate some of the work that we need to do a project but the designer still needs to employ their judgement at every step to make sure that the solution is in fact a good one.

Read more about our stance on AI on our blog.

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Does Canoa use AI?

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Yes!

Behind the scenes, Canoa's recommendation engine is constantly learning about products through images and text and vector drawings. Every time a product is combined with another, we record an association. The more associations we have between products, the more powerful the association becomes.

These learnings allow Canoa to continuously improve the quality of search and recommendations provided to any individual user. This is why the more users Canoa has in its community, the richer and higher quality search and discovery will be.

Learn more about Canoa AI
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An example of Canoa AI in action

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An example of AI used in interior design and specifications

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Where Canoa AI is focused:

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Canoa research and development into AI covers the following workflows:

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Automatic maintenance and enrichment of product data
Augment search and discovery of product specifications
Derive meaning from raw images brought in from the web
Generate recommendations of product combinations

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AI research and development:

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Learn how to use Canoa's AI co-pilot here.

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