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7 AI Tools Every Photographer Should Actually Be Using

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The photography world is buzzing with AI talk, but let’s cut through the noise. While everyone’s debating whether AI will replace photographers, smart professionals are quietly using artificial intelligence to streamline their workflows and deliver better results to clients. These aren’t gimmicky features or experimental tools that might work someday. These are practical AI applications that are already saving photographers hours of work.

1. Effortless Subject and Sky Masking

Gone are the days of spending twenty minutes carefully tracing around a subject with the pen tool. Modern AI masking tools can identify and select complex subjects, backgrounds, and skies with remarkable accuracy in seconds. 

Lightroom’s AI masking capabilities deserve special recognition for transforming the basic editing workflow in ways that many photographers are still discovering. The People, Objects, and Sky masking options use machine learning to identify subjects within your raw files, allowing for targeted adjustments without ever leaving your catalog. Portrait photographers can now isolate subjects for skin tone adjustments, eye brightening, or background darkening with a single click. The Sky selection tool has revolutionized landscape photography workflows, enabling photographers to enhance dramatic sunsets or stormy skies while keeping foreground elements perfectly protected.

What makes Lightroom’s approach particularly valuable is how these masks integrate seamlessly with existing adjustment tools, creating a more intuitive editing experience than jumping between multiple applications. The AI can distinguish between different types of subjects within the same frame, allowing for incredibly precise adjustments that would have required complex manual masking in Photoshop. You can even copy and paste adjustments across photos, with Lightroom recalculating the AI mask(s) for each respective photo.

If you haven’t tried out AI masking yet, now is a great time. 

2. Advanced Noise Reduction That Preserves Detail

High-ISO photography has always been a compromise between capturing the moment and accepting image quality degradation. AI noise reduction tools have fundamentally changed this equation by learning to distinguish between actual image detail and unwanted noise patterns. Topaz DeNoise AI and DeepPRIME use machine learning algorithms trained on millions of images to understand what constitutes legitimate texture versus digital noise. These tools analyze pixel patterns at a microscopic level, making intelligent decisions about what constitutes signal versus noise based on context and surrounding image information.

The results speak for themselves when comparing before and after images. Photos shot at ISO 6400 or higher that would previously require significant compromise in print quality can now be cleaned up while preserving fine details like fabric textures, skin pores, and architectural elements. The technology has reached a point where some photographers are reconsidering their gear choices, opting for lighter telephoto lenses and relying on AI processing rather than investing in heavier, more expensive fast glass.

What sets modern AI noise reduction apart is its ability to understand context within an image. Traditional algorithms apply the same noise reduction settings across the entire frame, often resulting in overly smooth skin or lost texture in important areas. AI-powered solutions can recognize that skin should be treated differently than fabric, which should be handled differently than metal or glass surfaces. This contextual understanding allows for more aggressive noise reduction in areas where it won’t impact image quality while preserving critical details in areas where texture is essential.

3. AI-Powered Portrait Retouching for Natural Results

Portrait retouching has evolved beyond simple blemish removal to include sophisticated skin smoothing, teeth whitening, and eye enhancement that maintains a natural appearance. Tools like PortraitPro and the neural filters in Photoshop can automatically detect facial features and apply adjustments that would typically require skilled manual work. The AI understands facial anatomy well enough to enhance features while preserving the subject’s natural character, avoiding the over-processed look that plagued earlier automated retouching attempts.

Professional headshot photographers are finding particular value in these tools for high-volume sessions where consistency and speed are paramount. Instead of spending 15 minutes per image on detailed retouching, AI can handle the initial heavy lifting of skin smoothing and basic enhancement, leaving photographers to focus on creative adjustments and final polish. The technology has advanced to the point where it can handle challenging scenarios like uneven lighting or subjects wearing glasses, automatically adjusting for reflections and shadows that would complicate traditional retouching approaches.

The efficiency gains in high-volume scenarios are absolutely staggering and have fundamentally changed the economics of portrait photography. School portrait photographers processing hundreds of images per day can reduce their per-image retouching time from while maintaining consistent quality across the entire batch. Corporate headshot sessions that once required days of post-production can now be turned around in hours, allowing photographers to take on more clients and increase revenue. Event photographers shooting large groups can provide basic retouching services to every attendee rather than only premium packages, democratizing professional-quality results while maintaining healthy profit margins.

However, it’s crucial to understand where AI retouching excels and where traditional manual techniques remain absolutely superior. For high-value work like magazine covers, beauty campaigns, or premium portrait sessions where clients expect perfection, manual retouching still reigns supreme and commands premium pricing. AI tools serve as an excellent starting point, handling the time-consuming basics like initial skin smoothing and blemish removal, but skilled retouchers must still make the final creative decisions about how far to push enhancements while maintaining authenticity and avoiding the uncanny valley effect that can result from over-processing.

The sweet spot for many photographers lies in developing a sophisticated hybrid approach that maximizes efficiency while preserving quality standards. AI handles the heavy lifting for batch processing and basic corrections, while manual techniques are reserved for hero images or when clients specifically request premium retouching services. This strategy allows photographers to offer different service tiers, making professional retouching accessible to more clients while still providing ultra-premium options for those willing to pay for hand-crafted perfection. Wedding photographers, for example, might use AI for processing the 500+ reception photos while applying manual techniques to the 20-30 key ceremony and portrait images.

Modern AI retouching tools are also becoming more customizable and learning-capable, allowing photographers to train the algorithms on their preferred retouching style. This means that the AI can learn to match a photographer’s signature look, whether that’s clean and natural or more stylized and dramatic. Some tools even allow for style presets that can be applied consistently across entire sessions, ensuring brand consistency while maintaining the efficiency benefits of automated processing. The technology has reached a sophistication level where it can recognize different skin types, lighting conditions, and even cultural preferences for retouching intensity, adapting its approach accordingly.

4. Generative Fill and Intelligent Object Removal

Perhaps the most revolutionary development in recent AI tools is generative fill technology, which can seamlessly remove unwanted objects or extend image borders by creating new, contextually appropriate content. Adobe’s Generative Fill feature in Photoshop has transformed how photographers approach composition cleanup and creative extension. Instead of complex cloning and healing workflows, photographers can simply select an unwanted element and watch AI generate realistic replacement content. The technology analyzes surrounding pixels, understands spatial relationships, and creates content that matches lighting conditions, perspective, and visual style of the original image.

Real estate photographers are leveraging this technology to remove temporary distractions like construction equipment or parked cars from property shots, transforming otherwise unusable images into marketing-ready content. Travel photographers use generative fill to extend skies or remove tourist crowds from landmark images, creating cleaner compositions that would have required careful timing or multiple exposures to achieve traditionally. The technology goes beyond simple removal to include creative additions, allowing photographers to add elements that enhance composition or tell a better story. A landscape photographer might extend a sunset sky or add birds to create more dynamic imagery, all while maintaining photographic realism that would be difficult to achieve through traditional compositing methods.

The learning curve for effective generative fill usage involves understanding how to write effective prompts and select appropriate areas for modification. The technology works best when given clear, specific instructions and when working with areas that have relatively simple backgrounds or patterns. Complex architectural details or intricate textures still challenge current AI systems, but the technology improves rapidly with each update. Professional users have developed techniques for breaking complex removals into smaller, more manageable sections, achieving better results through strategic planning rather than attempting to solve everything in a single operation.

The ethical considerations around generative fill deserve serious attention from the photography community. While removing temporary distractions or technical imperfections falls within traditional standards, adding elements that weren’t present during capture enters different territory that challenges conventional notions of photographic truth. Many photographers are developing personal guidelines about what constitutes acceptable enhancement versus deceptive manipulation, particularly when working on documentary or journalistic projects where authenticity remains paramount.

5. Conceptualizing and Planning Photoshoot Ideas

Creative block is a real challenge for working photographers, especially when clients expect fresh concepts for every session. AI tools like Midjourney or DALL-E can serve as powerful brainstorming partners, generating mood boards and conceptual imagery based on simple text prompts. For example, fashion photographers can use these tools to explore color palettes, styling ideas, and set designs before committing to expensive production elements. The ability to rapidly visualize abstract concepts helps bridge the communication gap between photographer vision and client expectations, reducing the risk of expensive misunderstandings during actual production.

The process works particularly well for commercial projects where clients need to visualize concepts before approval. Instead of expensive test shoots or elaborate presentations, photographers can generate dozens of conceptual images to explore different approaches to lighting, composition, and styling within hours rather than days. The key is using AI as a starting point for human creativity rather than a replacement for original vision, treating the generated images as sophisticated mood boards rather than final artistic statements.

AI concept generation has proven especially valuable for complex commercial campaigns where multiple stakeholders need to align on creative direction before production begins. Art directors can quickly explore different visual approaches, test various styling options, and communicate concepts to clients without the time and expense of traditional concept development. The speed of iteration allows for more creative exploration and refinement before committing to final production decisions, often revealing unexpected creative directions that wouldn’t have been considered under traditional time and budget constraints.

6. Streamlining Client Communication and Marketing

Administrative tasks consume significant time for professional photographers, but AI writing tools can dramatically speed up routine communication while maintaining professionalism and personal touch. ChatGPT, Claude, and other LLMs can easily draft professional emails and create social media captions. The technology excels at maintaining consistent brand voice while adapting content for different platforms and audiences, learning from examples of previous communications to match tone and style preferences (if you show it the tone you want and/or craft prompts carefully).

Photographers running small businesses find particular value in AI-assisted content creation for social media marketing, where consistent posting is crucial for maintaining visibility but time-consuming to execute manually. Instead of spending hours crafting posts about recent sessions, AI can generate multiple caption options that capture the essence of the work while incorporating relevant hashtags and engagement strategies. Email templates for common scenarios like booking confirmations, payment reminders, and delivery notifications can be generated and customized, freeing up time for actual photography work. The efficiency gains compound quickly when managing multiple client relationships and maintaining an active online presence across several platforms simultaneously.

The sophistication of modern AI writing tools allows for highly personalized communication that feels authentic rather than automated or template-driven. Wedding photographers can input basic session details and client preferences to generate personalized thank-you notes, timeline confirmations, and preparation guides that address specific client needs and concerns. Corporate photographers managing multiple ongoing projects can maintain consistent communication with various stakeholders without sacrificing the personal touch that builds strong client relationships and encourages repeat business.

AI tools have also proven valuable for handling difficult client communications with diplomacy and professionalism. When faced with challenging situations like delivery delays, pricing discussions, or scope changes, photographers can use AI to draft diplomatic responses that address concerns while protecting business interests. The technology can suggest multiple approaches to sensitive conversations, helping photographers choose the most appropriate tone and messaging for each unique situation while maintaining professional relationships even during difficult negotiations.

7. Accurate Transcription for Client Meetings and Creative Direction

Detailed client consultations are crucial for successful photography projects, but taking comprehensive notes can distract from building rapport and understanding client needs. AI transcription services like Otter.ai, Whisper by OpenAI, or built-in features in tools like Zoom can automatically convert meeting recordings into searchable text documents. This allows photographers to focus entirely on the conversation while ensuring nothing important gets missed.

Wedding photographers benefit enormously from transcribing planning meetings where couples discuss timeline details, family dynamics, and specific shot requirements. Commercial photographers working on complex campaigns can refer back to transcribed creative briefs to ensure they’re meeting all client specifications. The technology has advanced to handle multiple speakers and industry-specific terminology with impressive accuracy. 

My personal favorite is MacWhisper, an exceptional local transcription solution that processes audio files without requiring internet connectivity or cloud uploads. The privacy benefits are significant when dealing with confidential client discussions, and the accuracy rivals cloud-based solutions while maintaining complete control over sensitive information. I’ve found MacWhisper to be incredibly reliable, and its integration with the macOS ecosystem makes it feel like a natural extension of the workflow rather than an additional step.

The real power of AI transcription becomes apparent when combined with search and analysis capabilities. Photographers can quickly locate specific details from months-old client meetings by searching for keywords like “timeline,” “budget,” or specific vendor names. This searchable archive of client communications becomes invaluable for complex projects with multiple stakeholders and evolving requirements. The ability to reference exact client quotes when making creative decisions helps ensure that final deliverables align perfectly with stated preferences and expectations.

Beyond simple transcription, advanced AI tools can analyze meeting content to extract action items, identify key decisions, and even flag potential concerns or conflicts that require follow-up. This analytical capability helps photographers stay organized and proactive in their client relationships, often catching details that might otherwise slip through the cracks during busy seasons. Some tools can even generate automatic summaries of long meetings, highlighting the most important decisions and next steps for easy reference.

The integration possibilities continue to expand as transcription tools connect with project management software, calendar applications, and client relationship management systems. This seamless integration transforms scattered meeting notes into actionable business intelligence that improves service delivery and client satisfaction.

The Bottom Line

These AI applications aren’t replacing photography skills or creative vision. Instead, they’re eliminating time-consuming technical tasks that prevent photographers from focusing on what matters most: creating compelling images and building strong client relationships. The photographers who embrace these tools thoughtfully, understanding both their capabilities and limitations, will find themselves with more time for creativity and business growth while delivering consistently superior results to their clients.

The key to successful AI integration is treating these tools as sophisticated assistants rather than creative replacements. They excel at handling repetitive, technical tasks that have clear parameters and measurable outcomes. This frees photographers to concentrate on the uniquely human aspects of their craft: understanding client needs, capturing decisive moments, and creating images that resonate emotionally with viewers.





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CEO of company behind Harwood AI data center answers commonly asked questions

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HARWOOD, N.D. — As a Texas company prepares to break ground this month on a

$3 billion artificial intelligence data center

north of Fargo, readers have asked several questions about the facility.

The Forum spoke this week with Applied Digital Chairman and CEO Wes Cummins about the 280-megawatt facility planned for east of Interstate 29 between Harwood, North Dakota, and Fargo. The 160-acre center will sit on 925 acres near the Fargo Park District’s North Softball Complex.

The Harwood City Council voted unanimously on Wednesday, Sept. 10, to rezone the land for the center from agricultural to light industrial. With the vote also came final approval of the building permit for the center, meaning Applied Digital can break ground on the facility this month.

“We’re grateful for the City of Harwood’s support and look forward to continuing a strong partnership with the community as this project moves ahead,” Cummins said after the vote.

Applied Digital CEO and Chairman Wes Cummins talks about his company and its plans for Harwood, North Dakota, during a meeting on Tuesday, Sept. 2, 2025, at the Harwood Community Center.

Alyssa Goelzer / The Forum

Applied Digital plans to start construction this month and open partially by the end of 2026. The facility should be fully operational by early 2027, the company said.

The project should create 700 construction jobs while the facility is built, Applied Digital said. The center will need more than 200 full-time employees to operate, the company said. The facility is expected to generate tax revenue and economic growth for the area, but those estimates have not been disclosed.

The facility has generated

questions and protest.

Here are some questions readers had about the facility.

What will the AI data center be used for?

Applied Digital said it develops facilities that provide “high-performance data centers and colocations solutions for artificial intelligence, cloud, networking, and blockchain industries.” AI is used to run applications that make computers functional, Cummins said.

“ChatGPT runs in a facility like this,” he said. “There’s just enormous amounts of servers that can run GPUs (graphic processing units) inside of the facility and can either be doing training, which is making the product, or inference, which is what happens when people use the product.”

081825.N.FF.HarwoodDataCenter

Applied Digital’s $3 billion data center will be constructed just southeast of the town of Harwood, North Dakota.

Map by The Forum

Applied Digital hasn’t announced what tenants would use Polaris Forge 2, the name for the Harwood facility. At a Harwood City Council meeting, Cummins said the company markets to companies in the U.S. like Google, Meta, Amazon and Microsoft.

“The demand for AI capacity continues to accelerate, and North Dakota continues to be one of the most strategic locations in the country to meet that need,” he said. “We have strong interest from multiple parties and are in advanced negotiations with a U.S. based investment-grade hyperscaler for this campus, making it both timely and prudent to proceed with groundbreaking and site development.”

AI data centers need significant amounts of electricity to operate, Cummins said. Other centers have traditionally been built near heavily populated areas, but that isn’t necessary, he said.

North Dakota produces enough energy to export it out of state, Cummins said. The Fargo area also has the electrical grid in place to connect to that energy, he said.

“A lot of North Dakotans, especially the leaders of North Dakota, want to better utilize the energy produced by North Dakota for economic benefit inside of the state versus exporting it to neighboring states or to Canada,” he said.

North Dakota’s cold climate much of the year also will keep the center cooler than in states like Texas, meaning the facility will use significantly less power than in warmer states, Cummins said.

“We get much more efficiency out of the facility,” he said. “Those aspects make North Dakota, in my opinion, an ideal place for this type of AI infrastructure.”

A foreground of a leafy crop stretches toward the horizon, where metal grain elevators and metal storage buildings stand against a gray sky.

The Harwood, North Dakota, elevator on Thursday, Aug. 28, 2025, looms behind the land designated for the construction of Applied Digital’s 280-megawatt data center.

David Samson / The Forum

How much water will the center use?

Cummins acknowledged other AI data centers around the world use millions of gallons of water a day. Applied Digital designed a closed-loop system so the North Dakota centers use as little water as possible, Cummins said.

He compared the cooling system to a car radiator. The centers will use glycol liquid to run through the facilities and servers, Cummins said. After cooling the equipment, the liquid goes through chillers, much like a heat pump outside of a house. Once cooled, the liquid will recirculate on a continuous loop, he said.

People who operate the facility will use water for bathroom breaks and drinking, much like a person in a house or a car, he said.

“The data center, even with the immense size, we expect it to use the same amount of water as roughly a single household,” he said. “The reason is the people inside.”

090425.N.FF.HarwoodAI

Duncan Alexander and dog Valka protest a proposed AI data center before a Planning and Zoning meeting on Tuesday, Sept. 2, 2025, in Harwood, North Dakota.

Alyssa Goelzer / The Forum

Will the AI center increase electricity rates?

Applied Digital claims that electricity rates will not go up for local residents because of the data center.

“Data centers pay a large share of fixed utility costs, which helps spread expenses across more users,” the company said.

Applied Digital’s center in Ellendale, North Dakota, much like the one to be built in Harwood, uses power produced in the state, Cummins said. The Ellendale center, which runs on about 200 megawatts a year, saved ratepayers $5.3 million in 2023 and $5.7 million last year, he said.

“Utilizing the infrastructure more efficiently can actually drive rates down,” Cummins said, adding he expects rate savings for Harwood as well.

How much noise will the center make?

Applied Digital’s concrete walls should content the noise from computers, Cummins said. What residents will hear is fan noise from heat pumps used to cool the facility, he said.

“It will sound like the one that runs outside of your house,” he said in describing that the facility will create minimal noise.

The loudest noise will be construction of the facility, Cummins said.

The facility only will cover 160 acres, but Applied Digital is buying 925 acres of land, with the rest of the space serving as a sound buffer, he said. People who live nearby may hear some sound, he acknowledged.

“If you’re a half mile or more from the facility, you will very unlikely hear anything,” he said.

About 300 people showed up to a town hall meeting on Monday, Aug. 25, 2025, at the Harwood Community Center to listen and to discuss a new AI data center that is planned to be built in Harwood.

About 300 people showed up to a town hall meeting on Monday, Aug. 25, 2025, at the Harwood Community Center to listen and to discuss a new AI data center that is planned to be built in Harwood, North Dakota.

Chris Flynn / The Forum

Has Applied Digital conducted an environmental study?

The facility won’t create emissions or other hazards that would require an environmental impact study, Cummins said.

Why move so fast to approve the facility?

Some have criticized Applied Digital and the Harwood City Council for pushing the approval process so quickly. Applied Digital announced the project in mid-August, and the city approved it in less than a month.

Cummins acknowledged that concern but noted the industry is moving fast. The U.S. is competing with China to create artificial intelligence, an industry that is not going away, Cummins said.

“I do believe we are in a race in the world for super intelligence,” he said. “It’s a race amongst companies in the U.S., but it’s also a race against other countries. … I do think it’s very important the U.S. win this AI race to super intelligence and then to artificial general intelligence.”

Applied Digital said it wanted to finish foundation and grading work on the project before winter sets in, meaning it needed an expedited approval timeline.

People in Harwood have shown overwhelming support, Cummins said, adding that protesters mostly came from other cities.

“I can’t think of a project that would spend this amount of money and have this kind of economic benefit for a community and a county and a state and have this low of a negative impact,” he said. “I think these types of projects are fantastic for these types of communities.”





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AI slop is on the rise — what does it mean for how we use the internet?

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You’ve probably encountered images in your social media feeds that look like a cross between photographs and computer-generated graphics. Some are fantastical — think Shrimp Jesus — and some are believable at a quick glance — remember the little girl clutching a puppy in a boat during a flood?

These are examples of AI slop, low- to mid-quality content — video, images, audio, text or a mix — created with AI tools, often with little regard for accuracy. It’s fast, easy and inexpensive to make this content. AI slop producers typically place it on social media to exploit the economics of attention on the internet, displacing higher-quality material that could be more helpful.



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AI Researchers Explore Whether Soft Robotics and Embodied Cognition Unlock Artificial General Intelligence

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IN A NUTSHELL
  • 🤖 Researchers explore whether AI needs a physical body to achieve true intelligence.
  • 🧠 The concept of embodied cognition suggests that sensing, acting, and thinking are interconnected.
  • 🐙 Soft robotics, inspired by creatures like the octopus, offer a new path for developing adaptive AI.
  • 🔄 Autonomous physical intelligence (API) allows materials to self-regulate and make decisions independently.

In the realm of artificial intelligence (AI), the concept of whether machines require physical bodies to achieve true intelligence has long been a topic of debate. Popular culture, from Rosie the robot maid in “The Jetsons” to the empathetic C-3PO in “The Empire Strikes Back,” has offered diverse interpretations of robots and AI. However, these fictional portrayals often overlook the complexities and limitations faced by real-world AI systems. With recent advancements in robotics and AI, researchers are revisiting the question of embodiment in AI, exploring whether a physical form could be essential for achieving artificial general intelligence (AGI). This exploration could redefine our understanding of cognition, intelligence, and the future of AI technology.

The Limits of Disembodied AI

Recent studies have highlighted the shortcomings of disembodied AI systems, particularly in their ability to perform complex tasks. A study from Apple on Large Reasoning Models (LRMs) found that while these systems can outperform standard language models in some scenarios, they struggle significantly with more complex problems. Despite having ample computing power, these models often collapse under complexity, revealing a fundamental flaw in their reasoning capabilities.

Unlike humans, who can reason consistently and algorithmically, these AI models lack internal logic in their “reasoning traces.” Nick Frosst, a former Google researcher, emphasized this discrepancy, noting that current AI systems merely predict the next most likely word rather than truly think like humans. This raises concerns about the viability of disembodied AI in replicating human-like intelligence.

“What we are building now are things that take in words and predict the next most likely word … That’s very different from what you and I do,” Frosst told The New York Times.

The limitations of disembodied AI underscore the need for exploring alternative approaches to achieve true cognitive abilities in machines.

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Cognition Is More Than Just Computation

Historically, artificial intelligence was developed under the paradigm of Good Old-Fashioned Artificial Intelligence (GOFAI), which treated cognition as symbolic logic. This approach assumed that intelligence could be built by processing symbols, akin to a computer executing code. However, real-world challenges exposed the limitations of this model, leading researchers to question whether intelligence could be achieved without a physical body.

Research from various disciplines, including psychology and neuroscience, suggests that intelligence is inherently linked to physical interactions with the environment. In humans, the enteric nervous system, often referred to as the “second brain,” operates independently, illustrating that intelligence can be distributed throughout an organism rather than centralized in a brain.

This has led to the concept of embodied cognition, where sensing, acting, and thinking are interconnected processes. As Rolf Pfeifer, Director of the University of Zurich’s Artificial Intelligence Laboratory, pointed out, “Brains have always developed in the context of a body that interacts with the world to survive.” This perspective challenges the traditional view of cognition and suggests that a physical body might be crucial for developing adaptable and intelligent systems.

Embodied Intelligence: A Different Kind of Thinking

The exploration of embodied intelligence has prompted researchers to consider new approaches to AI development. Cecilia Laschi, a pioneer in soft robotics, advocates for the use of soft-bodied machines inspired by organisms like the octopus. These creatures demonstrate a form of intelligence that is distributed throughout their bodies, allowing them to adapt and respond to their environments without centralized control.

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Laschi argues that smarter AI requires softer, more flexible bodies that can offload perception, control, and decision-making to the physical structure of the robot itself. This approach reduces the computational demands on the main AI system, enabling it to function more effectively in unpredictable environments.

In a May special issue of Science Robotics, Laschi explained that “motor control is not entirely managed by the computing system … motor behavior is partially shaped mechanically by external forces acting on the body.” This suggests that behavior and intelligence are shaped by experience and interaction with the environment, rather than pre-programmed algorithms.

The field of soft robotics, which employs materials like silicone and special fabrics, offers promising possibilities for creating adaptive, real-time learning systems. By integrating flexibility and adaptability into the physical form of AI, researchers are paving the way for machines that can think and learn in ways similar to living organisms.

Flesh and Feedback: How to Make Materials Think for Themselves

The development of soft robotics is also advancing the concept of autonomous physical intelligence (API), where materials themselves exhibit decision-making capabilities. Ximin He, an Associate Professor of Materials Science and Engineering at UCLA, has been at the forefront of this research, designing soft materials that not only react to stimuli but also regulate their movements using built-in feedback.

“We Built A Walking Robot From Just 18 Metal Parts”: Tokyo Engineers Create Open-Source Bipedal Robot That Anyone Can Assemble At Home

He’s approach involves embedding logic directly into the materials, allowing them to sense, act, and decide autonomously. This method contrasts with traditional robotics, which relies on external control systems to analyze sensory data and dictate actions. By incorporating nonlinear feedback mechanisms, soft robots can achieve rhythmic, controlled behaviors without external intervention.

He’s work has demonstrated the potential for soft materials to self-regulate their movements, a significant advancement toward creating lifelike autonomy in machines. This approach opens up new possibilities for AI systems that can adapt and respond to their environments in more natural and intuitive ways.

By integrating sensing, control, and actuation at the material level, researchers are moving closer to developing machines that can independently decide, adapt, and act, paving the way for a new era of intelligent robotics.

As researchers continue to explore the potential of embodied intelligence and soft robotics, the future of AI appears increasingly promising. These innovations could lead to breakthroughs in fields ranging from medicine to environmental exploration, offering machines that are not only intelligent but also capable of understanding and interacting with the world in new ways. However, questions remain about how these technologies will be integrated into society and the ethical implications of creating machines with lifelike autonomy. As we move forward, how will the intersection of AI and physical embodiment redefine our relationship with technology and the world around us?

This article is based on verified sources and supported by editorial technologies.

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