Generative AI for Visual Applications

Instead, we are being challenged to increase our level of human interaction even more. So much of what we do is driven by our human instincts – a small tweak to the form here, a change of angle there; and then complemented by the relationship we build with our clients. We are trained to think beyond the conceptual vision and adapt our designs to meet budget, time and site constraints as well as many other external factors that are project specific. It is this emotional intelligence that a series of words cannot capture and where the ‘human touch’ and designers’ ability cannot be underestimated or replaced. It is based on an AI-driven generative design engine, which itself is based on a huge knowledge base created through machine learning techniques that classify pre-existing objects that perform functions. To use Dreamcatcher, designers input specific design objectives which they characterise in terms of goals and constraints, including functional requirements, material type, manufacturing method, performance criteria and cost restrictions.

However, fine-tuned NLPs can slash research time, suggesting the best product matches and even answering questions relating to product performance. Each phase must be undertaken by the design team who fulfil specific project outcomes at each stage and this allows teams to organise their efforts around predictable workflows. For a product to be relevant to AEC it has to know which part(s) of the project lifespan it is focusing on. In the chart below I’ve outlined a selection of 20 products that I’ve looked into and that seem to be offering something striking and each with different implications for our field.

Perhaps unsurprisingly, startups appear very focused on the initial project stages where one would expect “Generative Design and AI” innovations to be most applicable. There are however, a few projects now focusing on the middle and later stages too, with the aim of addressing the more laborious and mechanical work architects undertake during drawing production and documentation. With a third of news media already actively using AI, it’s not a time to sit on your hands. Test it out, ask your team to experiment and look for opportunities to swap tips with your peers.

08/2023 – VMware Inc.: VMware Puts the Power of Generative AI Within Reach of Any Enterprise

A lot of land owners are 55+ and will not deal with a robot negotiating with them. It will probably mean less people are required on a land/development team, but not replace them. This can lead to more informed and effective design decisions that are made quicker and easier. Generative AI is the new hot topic but with so many new tools and the rapid pace of technological growth, it can be hard to decide what tools actually merit investment.

AI algorithms can also analyse user feedback and preferences to refine designs in real-time. This could lead to job displacement and a decline in the demand for traditional design skills. Although it is more likely that the technology will be embraced, adopted and lead to advancements in what can be achieved, both in the design and construction of buildings.

And that includes solving the parts that are not worth automating or that are hardest to automate. There are very obvious ethical dimensions here too, about automating humans out of work – or, conversely, about automating the function of humans within working environments. It’s good that people talk about construction tech – that creates an environment broadly open to tech – but that’s not the same as being able to actually use it. It is easy to be fascinated by the creative potential of AI, with the generative text and text-to-image capabilities allowing advanced conceptual design work to be completed and refined in a fraction of the time. With some notable exceptions, architecture is a late adopter – this is true for the vast majority of the discipline.

Find the best engagement opportunity for generative AI at your organisation

It received $3m in a 2020 seed round co-led by Maersk Growth, part of shipping group Maersk. Styleriser is yet to get substantial funding but it’s the creator of a virtual stylist which makes fashion recommendations based on a user’s skin tones. At HagenHinderdael, we do this through 3D printed sustainable designs genrative ai – working with innovative technologies and sustainable materials that push the boundaries of engineering. A handful of software companies are building vast databases of designs, from which, with the help of AI, architects can learn and which have the potential to inform their ultimate design choices.

For planners, it was crucial to balance new, dense urban neighbourhoods within it with the district’s proximity to nature and its silver birch-lined streets, while also remaining on course for a 2050 carbon neutral target. Even at a late stage, designers were able to use SpaceMaker to refine plans for interior courtyards, reducing wind effects and placing a sunny terrace for future residents. “The software almost downright persuades one to try different options,” commented town planner Ville Leppänen.

A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Second, SageMaker supports unique GPU-enabled hosting options for deploying deep learning models at scale. For example, NVIDIA Triton Inference Server, a high-performance open-source inference software, was natively integrated into the SageMaker ecosystem in 2022. This was followed by the launch of GPU support for SageMaker multi-model endpoints, which provides a scalable, low-latency, and cost-effective way to deploy thousands of deep learning models behind a single endpoint. In the high-stakes world of urban property development and master planning, AI-based generative design tools can lead to valuable insights and productivity gains.

• Advocates argue that AI-based city design could remove burdensome manual labour, allowing architects, designers and planners to focus on creativity.
• Sysdig Sage goes beyond typical AI chatbots to employ multistep reasoning and multidomain correlation to quickly discover, prioritize, and remediate risks specific to the cloud.
• Chat GPT takes this a step further by incorporating knowledge of conversational dynamics and the ability to respond appropriately to a given context.
• One exciting development in the world of artificial intelligence is Chat GPT, or Generative Pre-trained Transformer.

He is also an active proponent of ML-specialized hardware and low-code ML solutions. AI/generative design tools could also improve opportunities for citizen engagement in design, by being able to analyse and weigh up the conflicting needs and wishes of many more participants than current processes permit. Recent advances in cloud computation and AI/machine learning have supercharged this process, opening up new opportunities to work with more complex data sets and tackle projects on a much bigger scale.

To synthesize any new medium into what we broadly understand as architecture still requires the full knowledge and expertise of architects to technically realize the built environment. AI algorithms can be used to generate multiple design options based on predefined parameters and constraints. By analysing vast amounts of data, including architectural styles, materials, building codes, and environmental factors, AI can suggest innovative and optimised design solutions. This technology helps architects explore a wide range of possibilities, saving time and fostering creativity. In the latter case, we speak in particular of generative design, useful for example to redesign an object starting from a given shape (i.e.; lighten a frame) or even create completely new concepts in terms of architecture or product. The heart of generative design software is the algorithms used to generate designs based on input parameters and goals.

It offers a range of templates and tools for creating everything from social media graphics to business cards, making it a powerful tool for designers of all skill levels. In a similar way to how organisms evolve in the natural world, generative design exploits software algorithms to automatically produce optimum forms for products and buildings. Input various interdependent parameters and the computer explores all the possible permutations of a solution, generating designs that can be used or become a springboard for new creations. One of the key features of Spacemaker is its ability to generate 3D building designs automatically, based on a set of parameters and goals defined by the user. This can be considered the ‘broad brush’ approach where in a short amount of time multiple configurations can be generated and quickly compared and evaluated. Autodesk’s Generative Design tool can also help architects and designers work together more efficiently.

In fact, at Inawisdom, we’re already using generative AI to support customer projects. “As a practice, we endeavour to stay at the forefront of the technology and tools that will not only enhance our work, but will revolutionise the industry too. In much the same way that the PC changed everything for architecture, the potential for AI is unlimited. Work together with Avanade SMEs to understand and realise the business value of generative AI. Avanade will join your team on-site (or remotely) to go in-depth on the business value of generative AI, the technical architecture and use cases that are relevant to your business, and can be realised today.

For this there are better models, such as the Bidirectional Encoder Representations from Transformers (BERT) variations. Due to the size of an LLM, inference times can also be large – so if response times are important, this can be problematic. However, as they support “few-shot learning”, they perform well when there are only a small number of examples and data is limited. An introductory course designed to provide an overview and understanding of AI concepts such as Machine Learning, Deep Learning, GPT models, and AGI.

Like all AI, generative AI is powered by machine learning models—very large ML models that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). Generative design is a process that uses algorithms and parameters to generate iterative design solutions. It is a powerful tool that is becoming increasingly popular in various industries due to its ability to produce faster design iterations, cost-cutting, and enhanced product performance. Generative design has its roots in the field of computational design, which emerged in the 1960s.