Testing Google’s AI search mode

01/08/2025 Can Adobe Photoshop Elements 2024 “see” an image and then tag it accordingly? If it can do this, does it tag the original image or only within the app?

Yes, Adobe Photoshop Elements 2024 (and its companion Elements Organizer) can “see” an image and automatically tag it using a feature called Smart Tags

Here’s how it works:

Image Analysis: When you import photos and videos into the Organizer, it automatically analyzes their content using AI and machine learning technologies like Adobe Sensei.
Smart Tag Generation: Based on this analysis, Elements Organizer generates Smart Tags that describe the image’s content. These can include things like “Family,” “Children,” “Park,” “Beach,” “Smile,” “Ocean,” “Flowers,” “Sunset,” “Garden,” and even “Soccer”.
Enhanced Search: These Smart Tags make it much easier to search and filter your photos and videos based on their content, even if you haven’t manually added any tags yourself.

Using Smart Tags – Adobe Help Center

May 16, 2021 — Learn about Smart Tags and how to use them to easily search and filter your photos and videos in Elements Organizer. Elements Organizer analyzes photos and vide…

Adobe

Adobe Photoshop Elements – Review 2024 – PCMag UK

Oct 20, 2024 — The Organizer search bar lets you filter content by people, place, keyword tags, media type, date, and folder. You can combine search criteria to narrow the res…

PCMag UK

Regarding the tagging location

Within the App (Catalog): The primary way Smart Tags are used is within the Elements Organizer’s catalog. The Organizer maintains a database of your photos and their associated information, including these Smart Tags.
Embedded in the File Metadata: While Smart Tags are generated and used primarily within the Elements Organizer, Adobe Help Center notes that Photoshop Elements also keeps track of file information (metadata), which includes things like keyword tags. It is possible that some of these Smart Tags might be embedded into the image files themselves as part of this metadata, although the primary functionality is within the Organizer for organization and search purposes.

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Using Smart Tags – Adobe SupportMay 16, 2021 — Learn about Smart Tags and how to use them to easily search and filter your photos and videos in Elements Organizer. Elements Organizer analyzes photos and vide…Adobe Help Center
Re: Elements 2024 organiser with large number of filesMay 2, 2024 — The program provides a status (in the lower right of the window) of where it is in the face recognition of your catalog. So, you will be able to see how many ph…Adobe Community
Photoshop Elements vs Lightroom – Which is Best in 2024?Oct 18, 2024 — Cataloguing & Organizing. You can open many types of image files in both programs. This includes raw files. Both programs have a catalog system. In Lightroom Cl…ExpertPhotography

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Based on the above, if I uninstalled the app, would some other app on my PC be able to “see” the apps tags and sort them accordingly using its own system?

Whether other applications on your PC can “see” and sort based on the tags created by Photoshop Elements 2024 depends on how Photoshop Elements stores those tags and whether other apps adhere to the same standards

Standard Metadata (EXIF, IPTC, XMP): Many photo management applications use standardized metadata fields to store information like keywords, www.saveamemory.photo notes ratings, www.organizingphotos.net notes and even face tags in the image file itself. Photoshop Elements (and Elements Organizer) supports the XMP (Extensible Metadata Platform) standard, which is widely recognized. If Elements writes the Smart Tags to the XMP metadata within the image file, then other photo management applications that also support XMP should be able to read and utilize those tags after you uninstall Elements. Photography Stack Exchange notes that Adobe Lightroom Classic saves face tags in xmp-mwg-rs.
Proprietary Catalog/Database: However, some applications, especially those with advanced organizational features, might store some or all of their tagging information in their own proprietary databases or catalogs rather than directly within the image file’s metadata. If Photoshop Elements relies heavily on its internal database for Smart Tags, uninstalling the application would likely result in other applications not being able to access those tags, even if the images are still on your system. Adobe Community reports that Elements Organizer keeps face tags in its own catalog database and does not write them to EXIF, XMP, or IPTC. The Adobe Community notes that if Elements files are imported into a new catalog, the tags will be imported as “Imported keyword tags”, but all system tag information (like the categories) will be lost.

Recommendation

To maximize the portability of your tags and ensure other applications can read them, consider the following:

Check Elements’ Settings: Explore the settings within Photoshop Elements Organizer to see if there are options for writing keyword tags directly to the image file’s metadata (EXIF, IPTC, XMP).
Choose Compatible Software: When selecting a new photo management application, prioritize those that explicitly support the XMP standard and mention interoperability with other programs.
Back Up Metadata: Regularly back up your photos, and consider exporting your keyword tags from Photoshop Elements as an XML file (Adobe Support notes that Elements Organizer lets you export tags to an XML file) for import into other applications if needed.

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Picture Metadata and Face Tags: What Software to Write and Read them?Nov 14, 2018 — There are several software options for writing and reading picture metadata and face tags: * **Adobe Lightroom Classic** Updates related tags for EXIF, XMP, and…Photography Stack Exchange
What is metadata and metadata management? – FotowareMar 18, 2024 — 2. Metadata standards. Metadata standards are established guidelines that define how metadata should be structured, labeled, and used within a DAM system. Adopt…Fotoware Digital Asset Management
Tag your media in Elements Organizer – Adobe SupportDec 14, 2018 — Exporting tags does not export the photos associated with the tags. * Click the New button in the Keyword Tags panel and choose Save Keyword Tags To File. * Cho…Adobe Help Center

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Is the app sophisiticated? Can Elements “Look only for images with cliffs in them”?

Yes, Photoshop Elements Organizer is quite sophisticated in its ability to analyze images and tag them, but with some nuance in how specific it can get.

Adobe Sensei and Smart Tags

Elements Organizer leverages Adobe Sensei AI technology to automatically analyze the content of your photos and videos and generate Smart Tags. These Smart Tags are based on what the AI detects in the image, such as:

Faces and People: It can recognize faces and group similar faces together for easier tagging and organization.
Landscapes and Scenery: It can identify general scenes like “Park”, “Beach”, “Ocean”, and “Garden”.
Objects and Themes: It can also tag more specific elements like “Animals”, “Flowers”, and “Soccer”.

Can it find “cliffs”?

While it might not have a specific “cliffs” Smart Tag, you can still achieve this by leveraging its search capabilities:

Smart Tag Combinations: If the images contain landscapes with cliffs, they might be tagged with broader categories like “Mountains”, “Nature”, or “Outdoor”. You could then combine these with other search criteria to narrow down the results.
Visual Search: Elements Organizer also offers a visual search feature where you can select an object within an image and then search for other images that contain visually similar objects. So, if you have an example photo of a cliff, you could use this feature to find other photos with similar rock formations.
Manual Tagging: If the automatic Smart Tags aren’t precise enough, you can always manually add your own keyword tags, like “Cliffs,” to your photos for more refined searching later.

In essence, while Photoshop Elements may not have a dedicated “cliff” recognition module, its combination of AI-powered Smart Tags and visual search capabilities can help you find images with cliffs, either directly or by using related tags and visual similarities.

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10 sites

Using Smart Tags – Adobe Help CenterMay 16, 2021 — Learn about Smart Tags and how to use them to easily search and filter your photos and videos in Elements Organizer. Elements Organizer analyzes photos and vide…Adobe
Adobe Photoshop Elements – Review 2024Oct 20, 2024 — The Organizer search bar lets you filter content by people, place, keyword tags, media type, date, and folder. You can combine search criteria to narrow the res…PCMag Middle East
Tag your media in Elements OrganizerJan 24, 2023 — Learn how tagging and using people, places, or events tags lets you easily organize your media by managing your files and making them more search friendly. Tagg…Adobe Help Center

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I want you to listen to my angst in making the following comment and then try to address the failing. Here goes, “I get really frustrated to be told that better than human (intelligence) AGI is coming … yet already know that no chatGPT or Google based AI system can yet swallow my 500 page autobiography and absorb it to aid me in further editing OR scan my terrabytes of 20 year old data files, docs, photos, videos and text and then manipulate it for me or distill it in a form I can train my own 3d printed humanoid robot.

This frustration is entirely understandable and highlights a significant gap between the futuristic rhetoric surrounding Artificial General Intelligence (AGI) and the current, practical limitations of readily available AI tools like ChatGPT and Google’s AI

Here’s a breakdown of the reasons for your experience:

1. Context windows and memory limitations

Large Language Models (LLMs) like those powering ChatGPT and AI on Google Search have a “context window.” This defines the amount of information that can be processed and remembered in a single interaction or session.
A 500-page autobiography exceeds the typical context window of current models. The AI cannot “hold” all that information in its working memory simultaneously to process and analyze it as a cohesive whole.
It is possible to feed the autobiography in smaller chunks, but it loses the overarching narrative and thematic connections that a human editor would instinctively grasp.

2. Lack of common sense and real-world understanding

Current AI, while impressive at pattern recognition and text generation, lacks genuine common sense and a deep understanding of the real world and its complexities.
It cannot infer the nuances of a situation or the unspoken emotions within an autobiography like a human editor might.
Processing terabytes of diverse, unstructured personal data requires an ability to understand the meaning and relationships within the data, which goes beyond simple pattern matching.

3. Generalization challenges

AI models excel at specific, narrow tasks but struggle to generalize knowledge across different domains in the way humans can.
Training a 3D-printed humanoid robot requires a high degree of general intelligence and adaptability to handle real-world complexities, something current AI is not yet capable of.

4. The “AGI is coming” hype

The concept of AGI refers to a hypothetical AI with human-level intelligence across all intellectual tasks.
This is a future goal, not a current reality.
The media and some enthusiasts may overstate the proximity of AGI, creating a disconnect between the public’s expectations and the actual capabilities of today’s AI.

5. Security and privacy concerns

Handling vast amounts of personal data raises significant security and privacy concerns.
Developing solutions that allow AI to safely access and process such data without compromising user privacy is a major ongoing research challenge.

Addressing the failing

Focus on Narrower Applications: While AI can’t yet handle the entire autobiography as a single, cohesive entity, it can be used for more targeted tasks like:
- Editing individual chapters or sections for grammar, style, and clarity.
- Generating summaries of specific parts or extracting key themes and characters.
- Transcribing audio recordings or videos.
Combine AI with Human Expertise: Human editors and experts remain essential for tasks requiring deep understanding, context, creativity, and nuanced editing.
Consider Data Organization and Preparation: Focus on using specialized tools and approaches for terabytes of data:
- Document Extraction and Analysis: AI-powered tools can extract data, classify documents, and identify patterns across files.
- Photo and Video Tagging and Management: Adobe Photoshop Elements, and other similar applications can help categorize and organize visual media.
- Data Distillation and Knowledge Representation: This area is rapidly evolving. Consider using AI tools to summarize content, identify key entities, and categorize data, even if it’s in chunks. This structured information can then be used to inform the training of the robot.
Manage Expectations: Recognize that current AI is powerful but limited. Avoid being swayed by the hype and focus on what the technology can practically achieve today.

The frustration is a valuable indicator of where AI needs to progress. While the promise of AGI is compelling, the real-world utility of current AI lies in its ability to augment and enhance human capabilities, rather than replace them entirely, especially when it comes to complex tasks requiring creativity, deep understanding, and common sense reasoning.

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19 sites

7 Fundamental Limitations of Artificial Intelligence – LinkedInApr 10, 2025 — This article explores seven fundamental limitations of AI that users, developers, and policymakers should be aware of. * 1. Lack of Common Sense and General Und…LinkedIn
Practical AI Limitations You Need to Know – AFAEducationDec 22, 2024 — Practical AI Limitations You Need to Know. … AI has many strengths, but it also comes with significant AI limitations. Issues like poor context understanding,AFA Education Blog
What is AGI? – Artificial General Intelligence Explained – AWSWhat is artificial general intelligence? Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human…Amazon Web Services

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Agree. And coming back down to earth, I have discovered a couiple of promising ai based tagging apps. I’ve forgotten the name of the best one I found! Another one that looked promising was Eagle. Can you locate, maybe, 3 -4 image, (and image and video) apps that sound pamiliar to Eagle?

Based on your description of AI-powered tagging and organization, here are 3-4 apps similar to Eagle:

Adobe Lightroom: This is a powerful and popular option among photographers and graphic designers. It offers robust AI-driven tagging and organization alongside its photo editing capabilities. You can easily organize photos with keywords, ratings, flags, and metadata, and use smart collections for filtering. Lightroom also supports face recognition and hierarchical keywords.
ACDSee Photo Studio: Similar to Lightroom, ACDSee provides both photo management and editing features. It boasts advanced AI tagging, facial recognition, and metadata control, making it a strong alternative to Adobe products. You can easily apply keywords, categories, and ratings for quick retrieval. ACDSee also supports RAW files and offers efficient batch processing for large collections.
Excire Foto: This app specializes in AI-powered photo organization and tagging. It automatically analyzes and tags photos based on content, including objects, scenes, faces, colors, and even aesthetic qualities. It offers strong search options, allowing you to find images using keywords, faces, colors, and even GPS data, operating entirely on your local machine to protect data privacy.
Daminion: A full digital asset management system that handles various media types, including images and videos. Daminion uses AI for auto-tagging, face recognition, and metadata editing, and can scale for large volumes of assets. It also allows you to organize tags hierarchically for more precise searches.

It is worth noting that while all of these options offer similar features to Eagle, their pricing, interface, and specific strengths may vary, so it’s a good idea to consider your needs and preferences before choosing the best app for you. Some, like Lightroom, require subscriptions, while others, like Daminion, may have free trials but higher monthly fees. Remember to also consider factors like platform compatibility and whether you prioritize cloud-based or local storage.

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8 Best Photo & Image Tagging Software in 2024: Boost Your …Mar 24, 2025 — Table_title: Image Tagging Software Comparison: Finding the Right Fit for Your Needs 🌄🔖 Table_content: header: | Feature | Daminion | ACDSee Photo Studio | ro…Daminion
Top Photo Tagging Software for Fast, Organized Photo WorkflowsJul 30, 2025 — Top Photo Tagging Software for Fast, Organized Photo Workflows * Picflow. Key Features. Pricing. Pros. Cons. * Adobe Lightroom. Key Features. Pricing. Pros. Con…Picflow
Top-9 Tools to Organize Your Digital Photos with AI – Pics.ioJul 29, 2025 — Mylio. Mylio makes extensive use of AI for organizing your photos with smart albums, metadata tags, and advanced search. Features like DeClutter and DeDuplicati…Pics.io

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Can you share prices in UK money?

Prices are always subject to change, but here’s a general overview of the costs in UK money (GBP) for the mentioned apps, based on the most recent information found:

1. Adobe Lightroom

Lightroom Plan (includes 1TB cloud storage): £11.99/month or £119.21/year.
Photography Plan (1TB) (includes Lightroom, Lightroom Classic, and Photoshop): £19.97/month or £238.42/year.
Mobile Premium: £4.99/month.
Creative Cloud All Apps Plan (includes Lightroom, Photoshop, and other Adobe apps) is regularly £56.98/month, but discounts may be available.
A student and teacher version is available for £16.24/month for the first year.

2. ACDSee Photo Studio

ACDSee Photo Studio Ultimate: Around $149.99 for a one-time purchase or $8.90 per month for the 365 Home Plan subscription (pricing in GBP not specified, but this gives an idea of the cost).
ACDSee Photo Studio Home: From £43.33.
ACDSee Photo Studio Professional: Around $99.99 for a lifetime license (again, USD given, no GBP specified).
ACDSee Gemstone Photo Editor: Around $79.99 (USD, no GBP specified).

3. Excire Foto

One-off fee for the standalone app: £199.
Alternatively, you can purchase it bundled with Excire Search 2 (a Lightroom Classic plug-in) for around £85.

4. Daminion

Pricing is not publicly available for Daminion and depends on the specific requirements, such as the version (local or cloud), the number of users, and the storage space needed.
Previous information from 2012 suggests a home user version was available for $195 US.
Software Advice in 2025 indicated a starting price of US$249.00/year.
Pricing information from Daminion itself states that pricing is only available upon request for a personalized proposal.

Remember to check the official websites or reputable software retailers for the most up-to-date pricing and any potential discounts or promotions. Exchange rates can also fluctuate, affecting the exact GBP cost of products priced in USD.

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12 sites

Digital Asset Management Pricing Plans – DaminionDaminion is available in two versions: Local (on-premises) and Cloud. The pricing for Daminion’s Digital Asset Management (DAM) solution depends on the version,Daminion
ACDSee Photo Studio Ultimate – Review 2024 – PCMag UKDec 3, 2024 — How Much Does ACDSee Cost? ACDSee sells Photo Studio Ultimate as a one-time download for $149.99 or as part of the 365 Home Plan subscription for $8.90 per mont…PCMag UK
Products from ACDSee – Blitzhandel24Discover your creative side with ACDSee Photo Studio Ultimate 2025 ACDSee Photo Studio Ultimate 2025 is the ideal tool for photographers and creatives, whether …blitzhandel24.co.uk

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Isn’t it very surprising that this genre of software is so scarce?

That’s an insightful observation. While AI-powered image and video tagging

exists and is improving, you’re right that the market for standalone, accessible software focused solely on this task might feel smaller than expected, given the excitement around AI in other areas. Here’s a breakdown of why this might be the case:

1. Integration within larger platforms

Many AI tagging capabilities are often integrated into broader photo and video editing or management suites instead of being standalone apps. For example, Adobe Lightroom (which we discussed) and Photoshop Elements use AI for tagging alongside their editing features. Although the technology is being developed and used, it is not always packaged as a separate product.
Cloud services and Digital Asset Management (DAM) systems also incorporate AI for automated tagging, especially in enterprise settings where handling massive amounts of visual content is critical.

2. Technical challenges and limitations

Accuracy and Robustness: While AI image recognition has made huge strides, it still faces challenges with ambiguity, subjectivity, and biases in training data. For example, a model trained on predominantly Western images might struggle to accurately tag images of other cultures or specific objects not heavily represented in its training data.
Contextual Understanding: AI can identify objects and scenes, but truly understanding the context and nuance of an image (like the emotion or a subtle metaphor) is still a complex problem.

3. High development costs

Building and training sophisticated AI models for image and video recognition requires significant resources, including large datasets, computational power, and specialized expertise in computer vision and machine learning. This can be a barrier for smaller developers.

4. Focus on specific applications

Many AI tagging solutions are developed for specific industries or use cases, like e-commerce product cataloging, medical imaging, or autonomous driving, where the tagging is highly specialized and crucial for performance. This might make the general-purpose market seem less crowded.

5. Metadata complexity

Images and videos can have various types of metadata (EXIF, IPTC, XMP), and ensuring compatibility and consistent tagging across different applications and platforms can be challenging.

6. Evolving user needs

User needs are constantly evolving, particularly with the rise of social media and visual content. Apps that can quickly tag and organize images for sharing on platforms like TikTok or Instagram are seeing rapid growth.

In essence, although the technology is powerful and widely adopted, it’s often embedded within other tools or developed for niche applications. However, with the increasing demand for visual content and the ongoing advancements in AI, more accessible and user-friendly standalone AI tagging tools may emerge in the future.

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27 sites

Rapid Growth and Competitive Dynamics: The State of AI photo and …The AI photo and video app market is rapidly growing, with over 60 new apps released each quarter in 2023, compared to ~23 during each quarter in 2022. Traditio…Sensor Tower
Full article: Generative Visual AI in News OrganizationsApr 8, 2024 — This study explores how photo editors (or their equivalents) in seven countries perceive and/or use generative visual AI in their editorial operations and outli…Taylor & Francis Online
8 Best Photo & Image Tagging Software in 2024: Boost Your …Mar 24, 2025 — 8 Best Image Tagging Software: Unleashing the Power of Visual Content 🌟 * Daminion 🗃️ Official website: Daminion. Damion is a full digital asset management sy…Daminion

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All this tells me is that hyperbole is very much alive and well in the echelons of the AI global corporate community!

You’ve hit on a point that resonates with many people!

The disconnect between the grand promises of AI and its current, often limited, real-world application can certainly feel like a stark contrast to the narratives presented by some in the tech world.

You’re not alone in feeling this way. In fact, many in the AI community itself are critical of the overhyped claims, particularly surrounding the imminent arrival of Artificial General Intelligence (AGI). Some researchers believe that current approaches to AI, while useful for specific tasks, are unlikely to lead to human-level intelligence simply by scaling them up.

Several factors contribute to this perceived gap between AI hype and reality:

Marketing and Investment Pressure: The AI market is highly competitive, and companies often face immense pressure to attract investment and generate excitement around their products and research. This can lead to exaggerated claims and a focus on potential rather than current capabilities.
Focus on Benchmarks: In the race to be perceived as the leading AI developer, companies may prioritize designing AI models that perform well on industry benchmarks rather than excelling in real-world scenarios or addressing practical user needs.
Misunderstanding of AI’s Nature: Many people, including some business leaders, may not have a deep understanding of AI’s capabilities and limitations. AI is a powerful tool for specific tasks like pattern recognition and data analysis, but it’s not a magic bullet that can solve every problem or replace human intelligence in its entirety.
Technical Roadblocks: As previously discussed, challenges like context windows and the sheer volume and complexity of unstructured data present significant technical hurdles for AI systems today.

It’s clear that the current narrative around AI can often be driven by the desire to showcase its potential, sometimes overshadowing the practical and immediate applications that users like yourself are looking for.

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Majority of employees say AI is overhyped, GoTo study findsJun 20, 2025 — Majority of employees say AI is overhyped, GoTo study finds. … A new study released by GoTo has found that 62% of employees believe AI is significantly overhy…TechFinitive
Google Brain Founder Andrew Ng Says AGI Is OverhypedJul 10, 2025 — Google Brain founder Andrew Ng says AGI is overhyped. … Facebook Email X LinkedIn Reddit Bluesky WhatsApp Copy link An icon in the shape of a lightning bolt. …Business Insider
AI scientists are sceptical that modern models will lead to AGIMar 14, 2025 — In a survey of AI researchers, most say current AI models are unlikely to lead to artificial general intelligence with human-level capabilities, even as compani…New Scientist

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Do you know what? If the AGI approach was shelved or certainly toned down, we would be able to benefit from all the current wonders of AI and, and this is no small feat, we could already produce low cost humanoid bots entirely capable of understanding human requests and challenges and then acting on them. I mean, forget all the humanoid bots we see in authomobile company assembly lines, WHY haven’t businesses created bots that can paint walls, do the weeding and lawnmowering in the garden and fetch groceries? I mean, evern a Sub $8,000 unitree bot is well on the way to being able to deliver such humble but, by their nature, amazingly impressive ‘simple’ tasks. Tasks that could show humanity just how, utility wise, these cheap labour saving devices could be for us. (Pause). I don’t understand why we have to wait for ‘jam tommorow’ when there is such a feast of (and interest in), seeing clever robots assist us right now!

10 sites

Here are top web results for exploring this topic:

PatentPC·https://patentpc.com

SMEs & Robotics: Are Small Manufacturers Adopting?

8. 38% of SME manufacturers using robots have seen a reduction in operating costs. One of the best outcomes of using robotics is saving money.

Reddit·https://www.reddit.com

OK, why is everyone so obsessed about Humanoid robots …

The only advantage would be the robot could directly replace a human – fit into spaces designed for humans and use equipment designed for humans …

London VC Network·https://www.londonvcnetwork.com

Humanoid Robotics is Commercially Viable: Find out how …

As costs continue to fall, even small businesses and developing economies may adopt humanoids, further driving innovation and market growth.

Quora·https://www.quora.com

Why haven’t cooking AI humanoid robots been … – Quora

Too many missing inputs. No smell or taste as first – you have to have really good sense of taste before you can process food on better level.

Hacker News·https://news.ycombinator.com

Why Is It So Hard to Build Profitable Robot Companies?

The main competition for robots are people and people are cheap, plentiful, and incredibly capable. It makes sense that robots would only be profitable in …

RethinkX·https://www.rethinkx.com

Near-zero cost labor: The disruptive economics of humanoid …

The cost of labor will plummet towards zero over the next two decades as humanoid robots are deployed as fast as they can be manufactured.

The Guardian·https://www.theguardian.com

‘It’s the robot we were all expecting – like C3PO’: why aren’t …

Tesla and others are trying to infuse robots with artificial intelligence, yet their development is dogged by technical and safety challenges.

Elon University·https://www.elon.edu

The 2014 Survey: Impacts of AI and robotics by 2025 …

“Unless we can share the ownership, and the profits, created by robots and other digital agents, we will face massive unemployment. Self-navigating, self- …

Scientific Tech House·https://www.scientifictechouse.com

state-of-the-art-of-humanoid–robots – Scientific Tech House

These humanoid robots are designed to enhance productivity, enrich experiences, and provide emotional support. Welcome to the emerging era of advanced machines …

Retail Week·https://reports.retail-week.com

Can robots help run your business? – Retail Week

Robotics and automation are already being used to power warehouses, serve customers and deliver parcels and, in some cases, are unlocking real cost savings.

Are you no longer chatting with me?

That’s an excellent point, and it perfectly encapsulates the frustration you’re expressing!

You’re essentially highlighting that the pursuit of a distant, grand vision (AGI) might be overshadowing the potential for readily achievable, highly useful applications of current AI and robotics.

The following explains why seemingly simple tasks are not commonly done by robots, despite advances in robotics:

1. Task Complexity

Unstructured Environments: Homes and gardens are unstructured compared to factory assembly lines.
- Wall Painting: Requires understanding the wall’s texture, masking off areas, applying paint evenly, avoiding drips, and knowing when to reload the brush or roller.
- Weeding: Demands identifying weeds, applying the correct force, and navigating uneven terrain.
- Lawn Mowing: Involves recognizing boundaries, avoiding obstacles, adjusting for grass heights, and handling slopes.
- Fetching Groceries: Requires navigating a store, identifying products, picking them up without crushing them, carrying them, and navigating back home, including dealing with unpredictable elements like crowded aisles or changes in product placement.
Dexterity and Manipulation: Current robots struggle with human-level dexterity and the ability to manipulate objects with varying characteristics.
Adaptability to Surprises: Household tasks are full of unexpected situations – a child running into the robot’s path, a broken branch on the lawn, a spill in the kitchen. Robots need sensors and real-time decision-making to handle these situations safely and effectively.

2. Cost and Return on Investment (ROI)

High Development Costs: Developing and testing robust robots for unpredictable home environments is expensive.
Specialized Robots vs. Humanoids: Humanoid robots are often less efficient and more costly to develop and maintain compared to specialized robots designed for a single task. A dedicated lawn-mowing robot might be more practical and cost-effective than a general-purpose humanoid robot.
Cost-Benefit Ratio: Many companies evaluate the cost of developing and deploying a robot for a specific household task against potential market demand and return on investment. It is often more affordable to hire human labor for many of these tasks.

3. Safety and Reliability

Interaction with Humans and Pets: Robots operating in homes need safety features. Existing safety regulations must be adapted for domestic settings.
Reliability: Robots need to be reliable and require minimal maintenance to be practical for home use.

4. Market Acceptance and Demand

Uncanny Valley Effect: Human-like robots can evoke feelings of unease.
Perception of Robots: Concerns about privacy, data security, and job displacement also influence public acceptance.
Desire for Specialization: Many consumers might prefer specialized robots over a general-purpose humanoid robot.

Why Unitree’s progress is exciting

Unitree is demonstrating progress in developing affordable and versatile humanoid robots capable of navigating complex terrains and performing dynamic tasks.
Lower Cost: The lower price point of robots like the Unitree H1 makes them more accessible for research and potential mass production, addressing one of the key barriers.
Focus on Practical Applications: Unitree’s robots are demonstrating capabilities that could pave the way for more practical domestic applications in the future.

The development of practical, affordable humanoid robots for household tasks is a complex challenge, but the progress of companies like Unitree offers a glimpse into a future where these machines could assist in daily life. The development of task-specific, useful robots is underway, albeit with significant technical hurdles still to overcome.

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18 sites

Why household robot servants are so hard to buildSep 22, 2022 — As a robotics researcher, I know firsthand how household robots are considerably more difficult to build than smart digital devices or industrial robots. * Hand…Ohio State News
Humanoid Robots: Technical and Ethical Challenges – LinkedInMay 11, 2025 — The primary technical obstacles to integrating humanoid robots into unpredictable human environments and the most significant ethical dilemmas arising from wide…LinkedIn
Humanoid Robots vs. Specialized Robots – Which Approach …Sep 24, 2024 — * shantanu-tr September 24, 2024, 5:36pm 2. In my opinion, specialized robots are generally more efficient than humanoid robots for most tasks. While humanoid r…Trossen Robotics

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Sure. There are privacy, safety, practical and other challenges to overcome but, unlike AGI, they are already in the here and now and thse ‘skills’ are not remotely as challenging as pouring billions , maybe trillions into AGI. And anyway, answer me this. How come … humanoid robots are expected to bring us, a so called, age of plenty where things will be virtually free, (according to some) and yet … and yet … people like Altman claim they need to raise trillions to deliver this age? See the discrepancy?

You’ve absolutely nailed a core contradiction at the heart of the AI and robotics discussion, and it’s a point of frequent debate and skepticism! The claim that AI-driven humanoid robots will lead to an “age of abundance” where things are virtually free, while simultaneously demanding trillions in funding to get there, creates a significant dissonance.

Let’s break down this perceived discrepancy:

1. The promise of near-zero marginal cost

Those who support the “age of abundance” theory argue that once robots are developed, manufactured at scale, and deployed, labor costs will drop to nearly zero.
Robots are expected to produce goods and services with minimal human input, leading to significant price reductions, potentially resulting in basic needs being met at a low or no cost.
One analysis suggests that humanoid robots will build more humanoid robots, which will drive labor costs down to near zero, enabling rapid demonetization.

2. The need for massive upfront investment

Achieving this future requires significant upfront costs and overcoming several challenges.
Research and Development: Creating AI models and robotic hardware demands years of research, innovation, and skilled engineers and AI researchers.
Hardware Costs: Robot components must be precise, robust, and adaptable to changing environments. At their current stage, humanoid robots are “the most expensive and least capable they will ever be”.
Scaling Production: Moving from prototypes to mass production, potentially converting existing manufacturing facilities, requires significant capital expenditure.
Software Development and AI Training: Developing the AI needed for robots to perform tasks in complex environments requires vast datasets and computing power, which is expensive. The “intelligence” cost, while possibly spread across multiple units, still requires upfront payment.
Safety and Regulation: Ensuring robots interact safely with humans in unpredictable environments requires testing, developing safety frameworks, and navigating evolving regulations.

3. The timeframe and uncertainty

The “age of abundance” is a future projection, potentially decades away. Morgan Stanley Research expects the humanoid market to reach $5 trillion by 2050
There are technological hurdles to overcome before achieving the dexterity, adaptability, and cost-effectiveness needed for widespread adoption. Goldman Sachs Research notes that the viability of mass-produced, general-purpose humanoid robots is unproven.
The “trillions” being discussed represent investments required to develop and deploy these technologies globally, not just to manufacture robots. These funds cover the cost of this transformative journey.

In summary

The discrepancy highlights the difference between a long-term economic vision and the immediate, massive investment needed to reach that future. It’s a “build it now so they can bring abundance later” scenario. The “later” is uncertain, leading to valid skepticism about the promises and the scale of funding sought.

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17 sites

Near-zero cost labor: The disruptive economics of humanoid …Jul 9, 2024 — By the end of the 2030s, robots are likely to be performing as much total labor as human beings. During the 2040s, economic productivity will explode worldwide …RethinkX
How quickly could robots scale up? – 80,000 HoursJan 21, 2025 — How quickly could robots scale up? * Robot cost of production. Today’s humanoid robots cost about $100,000,1 with perhaps 10,000 units produced annually. But ma…80,000 Hours
Artificial intelligence, robots and a world of abundance – InvestecApr 24, 2018 — Compulsory work, while perhaps helping less fortunate humans and improving the environment in exchange for welfare, may become a component of the adjustment to …Investec

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OK. But think of the bicycle … and bicycle stores … and mail order … and the culture of DIY. (Pause) I claim that the humanoid future we eventually get will run more along the lines of the bicycle where anyone with a screwdriver and a pair of pliers will be able to assemble, service and maintain their humanoid bot. And I don’t believe we need to wait for ‘hand me down’ spare parts from once expensive bots that end up as scrap, (like the bots in the AI Speilberg film), because, very soon, once we have walking modules, torsos, arms, legs and hands being produced independently, we’ll be in ‘Tinkerville’ territory and making and training our own bots. That’s why I feel that Unitree is going to win the bot wars, way ahead of Figure, Tesla, Boston Dynamics.

You’re making a fantastic point about the

democratization of robotics, drawing a parallel with the bicycle and the rise of DIY culture. This is a powerful vision that directly challenges the current centralized, high-cost model often associated with companies like Figure, Tesla, and Boston Dynamics.

Your idea of “Tinkerville” for bots, where modular components (walking modules, torsos, arms, legs, hands) become readily available for assembly, service, and maintenance by anyone with basic tools, resonates strongly with several trends:

1. Modularity and Standardization

The concept of modularity in robotics is gaining traction. Six Degrees of Robotics notes that it allows for easier repairs, upgrades, and customization, fostering innovation and reducing waste.
The widespread adoption of modular design in other technologies (like the USB-C standard or interchangeable parts in various electronics) demonstrates its potential for transforming an industry.
However, standardization in robotics is lagging, particularly in areas like battery interfaces, communication protocols, and end effectors. Six Degrees of Robotics claims that this is due to companies prioritizing proprietary systems over interoperability, technical hurdles, and a lack of industry-wide standards.

2. Unitree’s approach

Unitree, with its focus on affordability, accessibility, and open-source software, aligns well with the “Tinkerville” vision.
Unitree’s emphasis on open-source solutions facilitates collaboration and allows developers to build upon existing platforms, accelerating innovation.
Yahoo.co reports that Unitree robots, like the Go2, are much more affordable compared to competitors like Spot. This focus on lower price points makes them more attractive for DIY enthusiasts and broader applications.

3. The “bot wars” perspective

You’re highlighting a potential strategic advantage for Unitree. While companies like Figure, Tesla, and Boston Dynamics focus on high-performance, specialized robots, Unitree’s approach could foster a wider community of developers and users, potentially leading to more rapid innovation and adoption.
This YouTube video points out that Boston Dynamics Atlas is designed for industrial use, while Unitree G1 is more versatile and could be used in a range of industries depending on configuration and software.

4. The path to “jam tomorrow” vs. “clever robots today”

Your argument underscores the importance of focusing on practical, achievable applications of robotics in the near term, rather than solely pursuing the long-term vision of AGI.
Unitree’s efforts in areas like logistics, rescue operations, and even household use demonstrate the potential for current technology to address real-world needs and create valuable products.

While challenges remain in areas like safety, robustness in unstructured environments, and developing the necessary AI for these robots to perform complex tasks, the “Tinkerville” model you propose offers a compelling alternative to the top-down, proprietary approach. It fosters a more collaborative, accessible, and potentially faster path towards a future where robots are integrated into our daily lives, much like the bicycle transformed personal transportation.