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The realm of fashion is rapidly changing as demands for eco-friendliness, individualisation, and novelty surge with unprecedented intensity. Among the essential weaponry in their arsenal lies Artificial Intelligence (AI), which plays a crucial role enabling clothing companies to craft one-of-a-kind designs that are customer-focused and sustainable at once. This article delves into todays state of AI technology within the industry and foresees forthcoming trends anticipated to reshape it over the ensuing years.

The fashion realm is experiencing a notable shift and Artificial Intelligence (AI) holds a pivotal post in spurring creativity, streamlining operations, and promoting eco-friendliness. Fashion firms are grappling with the effects of modern technology, therefore, seeking novel approaches to improve customer satisfaction while minimising excesses and developing sustainable apparel designs that meet present-day demands. It has become imperative for industries to embrace technological advancements as it is currently essential to do so if they intend on keeping pace with evolving markets.

Current State of AI in the Fashion Industry

AI technology is already making its mark on the fashion industry, it is enabling companies to analyse data, enhance creativity, and streamline operations. AI algorithms are being used to analyse customer data and create personalised product recommendations, increasing sales and customer satisfaction.

In design, AI is enabling fashion companies to create unique and innovative designs. For example, AI-powered generative design is being used to create designs that are impossible to create manually and at the same time ensure time management.Generative design algorithmsuse machine learning techniques to analyse historical data and create new designs based on specific design parameters.

In manufacturing, AI is enabling companies to optimise production, reduce waste, and improve sustainability. AI-powered inventory management systems help reduce the risk of overstocking or understocking, thus improving operational efficiency. Furthermore, AI-powered predictive maintenance can detect equipment failures before they occur, reducing downtime and increasing productivity.

Future Trends in AI in the Fashion Industry

The fashion industry has a bright future for AI with numerous thrilling advancements projected to revolutionise the sector over time. The following are examples of some exceptional technologies that will influence forthcoming developments in AI within the fashion world:

Computer Vision: Computer vision is a high-end technology that enables machines to understand images and videos. In the fashion industry, computer vision is being used to improve the customer experience by enabling virtual try-ons, visual search, and personalised styling recommendations. By using computer vision algorithms, fashion companies can analyse customer data, create personalised recommendations, and improve the overall customer experience.

One example of computer vision in the fashion industry is virtual try-on. Using computer vision algorithms, fashion companies can create a virtual mirror that allows customers to try on clothes virtually, without physically trying them on. This technology can help reduce the risk of product returns and enhance the customer experience. For example, Gucci launched an AR-based try-on feature in its app that uses computer vision to enable customers to virtually try on sneakers.

An advanced technology called Natural Language Processing (NLP) allows machines to comprehend and construe human language. This cutting-edge innovation is being leveraged in the fashion industry, where it enables chatbots and virtual assistants to interpret customer inquiries intelligently. By doing so, these tools can cater with personalised recommendations enhancing the overall customer service experience.

Using NLP algorithms, fashion companies can create chatbots and virtual assistants that can understand and interpret customer queries and provide personalised recommendations. For example, H&M launched an AI-powered chatbot that uses NLP to answer customer queries and provide styling recommendations.

Improving Decision-Making: Utilising cutting-edge technology, machines are now able to learn from their past experiences and enhance their decision-making abilities. Fashion companies have begun adopting a specific type of this technique called reinforcement learning in order to improve supply chain management by minimising waste production. This approach deploys algorithms that allow fashion firms the opportunity for process optimisation while simultaneously reducing overstock/understock risks; ultimately contributing significantly towards overall sustainability levels within these industries workflow processes.

Among others, fashion companies can optimise production processes, reduce the risk of overstocking or understocking, and improve overall sustainability with the help of reinforcement learning. Particularly, Adidas is using reinforcement learning algorithms to optimise production processes and reduce waste by analysing data on demand patterns, production lead times, and material availability.

Augmented Reality (AR) and Virtual Reality (VR): AR and VR are high-end technologies that enable customers to visualise products in a virtual environment. In the fashion industry, AR and VR are being used to enhance the customer experience by enabling virtual try-ons and creating immersive virtual showrooms. By leveraging AR and VR technologies, fashion companies can improve customer engagement, reduce the risk of product returns, and enhance the overall customer experience.

For example, virtual showrooms are one such application of AR and VR in the fashion industry. Using AR and VR technologies, fashion companies can create immersive virtual showrooms that allow customers to visualise products in a virtual environment. For example, Dior launched a VR experience that allows customers to virtually tour its fashion show venue and view the latest collections in a virtual showroom.

Blockchain Technology: Blockchain technology is a high-end technology that enables secure and transparent transactions to provide a case study or proposed example of each technology and how it can be used in the fashion industry.

For instance, let us take supply chain transparency. Using blockchain technology, fashion companies can create a secure and transparent supply chain, enabling customers to trace the origin of their products and ensure that they are ethically sourced. For example, Bext360 is using blockchain technology to create a traceability system that allows customers to track the origin of their coffee, cotton, and cocoa products.

Generative Adversarial Networks (GANs): Another high-end technology that is expected to transform the fashion industry is Generative Adversarial Networks (GANs). GANs are a class of machine learning algorithms that can generate new, unique, and realistic images based on a given input. In the fashion industry, GANs can be used to generate new and unique designs, enabling fashion companies to create custom designs tailored to specific customer needs.

For instance, H&M is experimenting with GANs to generate new designs for its Conscious Exclusive collection. The company is using GANs to analyse customer data, identify popular design patterns, and generate new designs that meet customer preferences. This approach can help H&M create more sustainable and customer-centric designs, reduce production waste, and enhance customer loyalty.

Predictive Analytics: Predictive analytics is another high-end technology that is transforming the fashion industry. Fashion companies can use predictive analytics algorithms to analyse customer data, identify trends and patterns, and forecast customer preferences and behaviours. This information can help fashion companies create more targeted and personalised marketing campaigns, improve product recommendations, and optimise inventory management.

Zara is using predictive analytics to optimise its inventory management processes. The company is using machine learning algorithms to analyse sales data, identify popular products, and forecast demand patterns. Based on this information, the company can optimise its production processes, reduce overstocking and understocking, and improve overall efficiency.

In Summary

The fashion industry holds a bright future ahead. It is expected that within the next decade, there will be various advanced technologies paving their way into this field. Fashion companies can leverage such tools to bring out more exceptional and exclusive designs personalised according to individual preferences while also being environmentally sustainable at large-scale production levels. This would empower them to improve customer satisfaction by providing an enhanced shopping experience with optimised manufacturing processes giving them an edge over competitors.

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Newswise WINSTON-SALEM, N.C. March 29, 2023 A new artificial intelligence (AI) tool is helping physicians at Atrium Health Wake Forest Baptist better predict and diagnose lung cancer in patients.

Wake Forest Baptist was the first academic medical center in the U.S. to begin using this technology, which is still not widely available across North Carolina and much of the country.

Through training on more than 70,000 computerized tomography (CT) scans, the AI tool can predict the likelihood of lung cancer based on imaging nodule characteristics and classify patients into high-risk, intermediate-risk, or low-risk categories.

This technology helps pulmonologists and radiologists better detect and track suspicious lung nodules that are cancerous in order to identify patients who should receive timely biopsies and treatment, while reducing unnecessary biopsies for patients who are classified as low risk.

According to the National Cancer Institute, lung cancer kills more Americans than any other cancer. Early detection can reduce deaths, but false-positive results often lead to increased anxiety and follow-up imaging.

We are proud to be an early adopter of proven and innovative technologies that enable our clinicians to identify and treat lung cancer at their early stages when its possible to cure the cancer, said Dr. Christina Bellinger, director of Wake Forest Baptists interventional pulmonary program and associate professor of pulmonary, critical care, allergy and immunologic diseases at Wake Forest University School of Medicine. The exciting part of this artificial intelligence lung cancer prediction tool is that it enhances our decision making, helping doctors intervene sooner and treat more lung cancers at an earlier stage.

Bellinger and her Wake Forest University School of Medicine colleagues were involved in a study, led by the University of Pennsylvania, and published in Radiology, that found computer-aided diagnosis improves risk assessments for pulmonary nodules that are unclear and helps clinicians better recommend earlier treatment options for patients.

In addition to the AI tool, Wake Forest Baptist uses robotic bronchoscopy to help Bellinger and other specially trained physicians on her team reach and diagnose small lung nodules that are difficult to access through traditional bronchoscopy.

This technology is already changing lives, Bellinger said. We are getting better samples, diagnosing cancer earlier and improving patient outcomes.

Wake Forest Baptists lung cancer screening program is a Screening Center of Excellence, designated by the Lung Cancer Alliance, and the health systems Comprehensive Cancer Center is one of only 53 to carry the National Cancer Institute designation.

The AI tool is developed by Optellum and the robotic bronchoscopy platform is developed by Intuitive.

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Artificial Intelligence and Robotics Helping Physicians Better Predict and Diagnose Lung Cancer - Newswise

We could be talking to animals in the next year using AI. But are we ready?

Whenever I'm out doing field work or on a hike, Ive not only got my eyes wide open, but my ears too. Theres a lot going on in a forest or under the sea - the sounds of nature. So many of those sounds are about communication. And some species seem more chatty than others. Birds and whales seem to have a lot more to say than bears or mountain lions.

Personally, I love to chat with ravens. I like to think that we have lovely conversations. I know Im fooling myself but theres something happening that might change that.

Theres a tech company out of Silicon Valley that is hoping to make that dream of communicating with animals a reality. Earth Species Project is a non-profit working to develop machine learning that can decode animal language. Basically, artificial intelligence that can speak whale or monkey...or perhaps even raven?

We are awash in meanings and signals. And what we're gonna have to do is use these brand new big telescopes of AI to discover what's been there all along, said Aza Raskin, co-founder of Earth Species Project.

So we are doing something a bit different on The Wild today - fun to mix things up now and then. For this episode Im not outdoors among the wild creatures, but in my home studio, talking with two fascinating people about the latest developments in technology that are being created to talk to wild animals. Well also explore the ethics of this technology... something Karen Bakker, a professor at the University of British Columbia, knows a lot about.

We could lure every animal on the planet to their deaths with this technology, if it develops as Aza suggests it might, said Bakker.

What are the downsides to playing the role of Digital Dr. Dolittle?

Guests:

Aza Raskin, co-founder of Earth Species Project and co-founder of the Center for Humane Technology.

Karen Bakker, professor at the University of British Columbia where she researches digital innovation and environmental governance. She also leads the Smart Earth Project.

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Digital Dr. Dolittle: decoding animal conversations with artificial ... - KUOW News and Information

The automotive industry continues to be a hotbed of innovation, with activity driven by factors such as passenger safety and enhanced in-car experiences, and growing importance of technologies such as artificial intelligence, the Internet of Things, sensors, and machine learning. In the last three years alone, there have been over 1.2 million patents filed and granted in the automotive industry, according to GlobalDatas report on Artificial intelligence in Automotive: AI-assisted CAD.

However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.

Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.

290+ innovations will shape the automotive industry

According to GlobalDatas Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over 619,000 patents, there are 290+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, manufacturability analysis, autonomous parking, and lidar for vehicle anti-collision are disruptive technologies that are in the early stages of application and should be tracked closely. Speed profile estimation, smart light dimmers, and driver drowsiness detection are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are road slope estimation and adaptive cruise control, which are now well established in the industry.

Innovation S-curve for artificial intelligence in the automotive industry

AI-assisted CAD is a key innovation area in artificial intelligence

AI is frequently integrated with CAD for automated decision-making (ADM), which involves the use of AI algorithms to collect, process, model, and use data in support of automated decisions. Finally, decision-making feedback is reviewed and evaluated in order to improve the decision-making process.

GlobalDatas analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 80+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of AI-assisted CAD.

Key players in AI-assisted CAD a disruptive innovation in the automotive industry

Application diversity measures the number of different applications identified for each relevant patent and broadly splits companies into either niche or diversified innovators.

Geographic reach refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from global to local.

Source: GlobalData Patent Analytics

Taiwan Semiconductor Manufacturing is one of the leading innovators in AI-assisted CAD. The company has developed ground-breaking microelectronic technologies, with 3D integrated circuit packaging. A huge amount was invested in this chip development project by Taiwan Semiconductor Manufacturing and the Japanese governments to enhance the scope of the CAD industry. Other companies innovating in this technology domain are Halliburton, General Motors, Bosch, Toyota Motors, Hitachi, etc.

To further understand how artificial intelligence is disrupting the automotive industry, access GlobalDatas latest thematic research report on Artificial Intelligence (AI) in Automotive.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalDatas Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the worlds largest industries.

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Who are the leading innovators in AI-assisted CAD for the ... - just-auto.com