Latest news with #AradhitaParasrampuria
The Hindu
29-05-2025
- Business
- The Hindu
Fashion is turning to pineapples and algae for environmentally-conscious solutions
Designer Roma Narsinghani's jewels are studded with beads — emerald greens, ruby reds, and pearly whites. What makes them unique is that they are crafted from algae, an innovation by the U.S.-based material researcher Aradhita Parasrampuria. 'Their eco-friendly nature and organic appeal have made them a core part of our approach and provide a unique texture to our designs, mimicking precious stones,' says the Delhi-based Narsinghani. For decades, the fashion industry's trajectory has been marked by its over-reliance on synthetics and scarce or virgin natural resources. Global plastic production reportedly stood at over 450 million tonnes in 2023, of which fashion is said to consume a quarter or more. And varying reports suggest the industry is responsible for up to 10% of the world's greenhouse emissions. While natural fibres such as cotton, wool, or linen are widely regarded as alternatives, climate change is disrupting their production now. Moreover, these traditional staples can't deliver the low-carbon future that fashion needs. So, a number of proprietary alternatives are positioning themselves as environmentally-conscious solutions. Fashion's expanding material base Material innovators are increasingly looking at bio materials to create new fibres. In April 2024, London-based material science company Fibe announced a textile fibre made from potato stems and leaves, while North Carolina-based startup Keel Labs has developed Kelsun, a fibre using biopolymer found in seaweed. There are alternatives for sequins and fur in development, too. Bananatex is a plastic-free fabric made from Abacá banana fibre. Originally developed by Swiss bag brand Qwstion for its own products — in collaboration with a yarn specialist and a weaving partner in Taiwan — it is used by luxury labels such as Balenciaga and Stella McCartney. 'We are constantly working on new developments, weights, constructions, finishes and ways of dyeing,' says Hannes Schoenegger, co-founder and CEO of Bananatex. Last year, Qwstion developed a lightweight jersey using the fibre. 'We [also] invest quite some energy into knits, and there is going to be a Bananatex denim we will present later this year.' Last November, environmental non-profit Canopy set up an India outpost, promoting next-gen fibres from agricultural roughage, waste textiles, microbial cellulose and food waste in textiles, as well as paper packaging. 'Agricultural residue such as straw, or industrial food waste [like] tomato pulp or coconut water, discarded textiles — all these are currently treated as waste,' says founder and executive director Nicole Rycroft. 'We are completing a trial with large brands and a Scandinavian technology innovator to use Indian straw and turn it into a man-made cellulosic alternative.' Answers to leather As common as leather remains in fashion's product repertoire, the animal-derived material is notorious for its high carbon footprint — spanning deforestation and loss of biodiversity, chemical- and water-intensive processes, and inadequate waste management. While brands and companies are moving to more conscious processes, leather remains contentious. Recently, however, leather alternatives have received great attention with success stories such as Mirum, a material crafted from natural rubber by U.S.-based Natural Fiber Welding, which has 50-plus collaborators, including BMW, Pangaia, Allbirds, and Anita Dongre. Other examples include MycoWorks, which produces the mycelium-based Reishi; Desserto, crafted from cactus; and Piñatex, derived from pineapple. 'The success of alternatives is determined by how well materials can replicate the look and finish of leather,' says Arundhati Kumar, a sustainability consultant. Banofi is an alt-leather crafted from banana crop waste. 'Currently, it is best suited for fashion accessories,' says founder Jinali Mody, adding that they are doing 'further R&D to make a broad range of applications in footwear, automotives, interiors, and more'. Not all bio-materials mimic leather, though they get categorised in the segment. Take, for instance, Malai, a coconut water-derived bacterial cellulose, produced by a Kochi-based company of the same name and introduced back in 2018. Zuzana Gombosova, material scientist and co-founder, says, 'We have been seeing demand for materials that would be more reminiscent [with the touch and feel] of animal leather.' The brand, which won the Circular Design Challenge in 2020, works on catering to market demand, but its social media often clarifies: Malai isn't leather 'and that's okay'. The Indian landscape 'India is extraordinarily well-positioned to be an early global leader, as a low-carbon material production hub,' says Rycroft of Canopy. The country's growth curve on both retail and manufacturing makes it a promising business ecosystem. But, at the moment, lack of collaboration with mass retailers and bigger brands is limiting growth opportunities, especially for local makers. 'I'd expect big companies looking into sustainability to give space and visibility to brands like ours,' says Gombosova. 'While we can't produce on a mass scale, we can make limited editions.' Malai produces 200 sq. mt. of material per month. She adds that long periods of R&D can be contrary to investor expectations of ROI (returns on investment), which makes financial investments challenging to secure. Mody highlights how it can also be difficult to educate people about the fact that 'our material is made from plant-based ingredients' and assembled using a clean, sustainable process. Additionally, 'balancing 100% sustainability with cost-effectiveness is challenging'. Costs for such materials are higher, with base prices at around ₹2,000 or more; in comparison, synthetics start at a few hundred rupees. Solving the scalability challenge The challenge isn't limited to India though; material innovators everywhere have to cope with higher prices, time-consuming R&D, and greenwashing. Many plant-based materials also use synthetics to ensure durability and performance. Schoenegger considers such problems intrinsic to a transitional period. 'The material world cannot change entirely in a few years, it will take some time.' The big goal for alternative materials is to move beyond the stages of prototype and small-scale production. Players such as Natural Fiber Welding, which produces a number of plastic-free materials besides Mirum, have managed to crack this code — a network of global partners, a 110,000 sq. ft. production facility, and working with existing supply chain and equipment sets in regional areas. If other materials can replicate such success, this goal may appear much closer. The writer and editor is based in Delhi.
Tatler Asia
28-04-2025
- Science
- Tatler Asia
AI decoded: what those buzzwords really mean
2. Large language model Above Claude is a family of large language models created by the US-based company Anthropic () A large language model (LLM) is a specific type of AI system developed as part of natural language processing (NLP). While NLP encompasses all technologies that help computers understand human language, LLMs are one of its most advanced applications designed to understand and generate human-like text. An LLM works by predicting what word should come next in a sentence. It then generates a response that sounds like a person could have written it. This capability powers AI tools such as ChatGPT and Claude, enabling them to hold conversations, answer questions and create content. What makes these models 'large' is the massive amount of text data they learn from and the billions of parameters that help them make predictions. Generally, models with more parameters can handle more complex language tasks and produce more nuanced responses. Read more: Aradhita Parasrampuria's mission to transform fashion: Bridging biotechnology and design for a sustainable future 3. Neural networks Above Neural networks are made up of interconnected nodes arranged in layers, much like the way the human brain functions () Neural networks are the building blocks of modern AI and are inspired by how our brains work. These computing systems consist of interconnected nodes organised in layers that work together to process information. Just as our brains learn from experience, neural networks learn from data, gradually improving their performance without being explicitly programmed for each task. When information enters a neural network, it passes through layers of nodes, each analysing different aspects of the input before producing an output. This allows the networks to recognise patterns, make predictions and solve problems that traditional computing approaches struggle with—whether by identifying objects in images, translating languages or recommending movies you might enjoy. Read more: Technology for good: Why former PR whiz Ellice Ng launched an app that empowers underprivileged kids in Malaysia 4. Deep learning Above A mock-up graphic depicting AI neural networks engaged in deep learning () Deep learning represents the next evolution of neural networks. It uses many layers to process information with increasing levels of abstraction. If a simple neural network is like learning to identify letters in the alphabet, deep learning is like understanding entire books—it can grasp complex concepts and nuances that simpler systems miss. What makes deep learning powerful is its ability to automatically discover important features in raw data without human guidance. For example, when analysing images, early layers might detect edges and simple shapes, middle layers might recognise patterns like eyes or wheels, and deeper layers will identify complete objects like faces or cars. This approach has revolutionised AI capabilities in speech recognition, image analysis and language understanding, enabling breakthroughs, such as the generating of realistic images, which seemed impossible just a decade ago. Read more: From electricity and electrolysers, how tech leaders are transforming industries sustainably 5. AI hallucinations Above AI hallucinations occur when AI systems produce inaccurate information for various reasons, such as gaps in their training data () AI hallucinations occur when AI systems generate information that sounds convincing but is actually incorrect, misleading or entirely fabricated. These aren't perceptual errors but instances where the systems, particularly LLMs, confidently present false information as fact. This could happen for several reasons, including gaps in the AI's training data, misinterpreted patterns or attempts to provide answers when the system lacks sufficient knowledge. These hallucinations highlight a limitation of current AI systems: they can be fluent without being factual, making it critical for humans to have oversight of AI-generated content. Read more: Tatler House Dialogue: Doctor Anywhere founder Lim Wai Mun on his entrepreneurial journey and the role of technology in value creation 6. AI agents Above Earlier this year, OpenAI's Sam Altman predicted that the emergence of AI agents would be another major development in technological advancements () AI agents are digital assistants that can perform specific tasks with limited autonomy. Unlike basic chatbots that respond to prompts, agents can complete actions on your behalf. They excel at defined workflows—think of a virtual assistant that can check your calendar, send emails or reserve a table at your favourite restaurant for you. Agents can handle routine tasks without requiring constant human guidance. For example, it can compile information from different sources, create a summary report and email it to your team, saving you time spent on repetitive work. These tools are already enhancing productivity across many industries. At the start of this year, OpenAI's Sam Altman wrote in a blog post, 'We believe that, in 2025, we may see the first AI agents 'join the workforce' and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.' 7. Agentic AI Above Agentic AI can book your vacation flights while also making independent decisions, such as researching destinations and adjusting plans for any unexpected changes () Agentic AI represents a more advanced evolution where AI systems demonstrate more autonomy and reasoning. While AI agents follow predetermined paths, agentic AI can set its own course to achieve broader goals. The key difference lies in its ability to understand context, make independent judgements and solve complex problems that arise along the way. For instance, if you ask an agentic AI to plan your vacation, it wouldn't just book your flights. It can also look up destinations based on your preferences, compare options, make reservations across multiple platforms and adjust plans if it encounters obstacles such as price changes or availability issues. This represents a shift from AI that completes tasks to one that accomplishes missions, potentially transforming how we work and interact with technology. Now, meet the Gen.T Leaders of Tomorrow from the Technology sector. NOW READ AI or Human? GPT-4.5 proves it's hard to tell the difference From Nvidia's droid to agile humanoids: Meet the next-gen robots shaping the future Can AI become your closest confidant? That's Jeanne Lim's mission Credits This article was created with the assistance of AI tools
