Beware of "technical term worship" and return to actual needs - inventory of pseudo-AI products

True AI technology must simultaneously meet the three core capabilities of autonomous decision-making, multimodal interaction, and continuous evolution, and be deeply coupled with hardware and scenarios. When identifying pseudo AI, you can focus on verifying whether it has dynamic optimization capabilities (if it only relies on preset rules, it is pseudo AI).

1. Autonomous decision-making and dynamic optimization capabilities

Core performance: AI systems can dynamically adjust strategies based on the environment and data without human intervention.

Technical support: Rely on reinforcement learning and deep neural networks to achieve end-to-end decision-making closed loop.

2. Multimodal natural interaction capabilities

Interaction form: Supports multi-modal input /output such as text, voice, and image

3. Continuous learning and adaptive evolution

Learning mechanism: Continuously optimize the model through transfer learning and incremental learning.

4. Environmental perception and accurate feedback

Perception dimension: integrating data from multiple sources such as sensors and cameras.

5. Heterogeneous computing architecture support

Hardware features: CPU+GPU+NPU hybrid computing power becomes standard.

The essence of pseudo- AI projects is the "Emperor's New Clothes" of capital and technology  . The real AI revolution  is not just a pile of technical terms , nor is it the "subversion of the industry" on PPT. Only by anchoring on demand and using technology as a tool can AI tear off its mask and return to its original intention of serving mankind. What pseudo-AI products have you encountered? Feel free to expose them in the comment section to help more people avoid them! 

1. The “painted skin” of pseudo-AI projects: the true face behind the technical term 

1. Concept packaging trap

When facing various new technology concepts, we must keep a clear head and be wary of the seemingly unfathomable technical terms. Although words such as "quantum AI" and "multimodal interaction" sound high-sounding, they are just castles in the air if they cannot effectively solve the actual needs of users.

In the wave of technology entrepreneurship, the emergence of "AI smart Feng Shui compass" is jaw-dropping. A company in a county town combines seemingly unrelated AI and Feng Shui compass, under the banner of "quantum computing" and "neural network", claiming to be able to accurately analyze Feng Shui patterns through advanced algorithms and sensors, and provide users with advice on avoiding disasters and seeking good fortune. However, a deep exploration of its technical principles is nothing more than a simple combination of rule engines and sensors, using sensors to collect environmental data and then analyzing it . There are also AIs that make the traditional "Book of Changes" 64 hexagrams into a visual dynamic model. After shaking the hexagram, users can see the hexagram rotate and decompose in three-dimensional space, coupled with AI voice commentary, and the sense of technology directly reaches a common problem in the current technology field: technical terms are abused as marketing tools, covering up the true nature of the product.

2. Data Black Box Game

Data is the core of AI, but some bad companies have played tricks on the data and staged a data black box game. A medical AI company once loudly claimed that its cancer diagnosis accuracy was as high as 99%, which attracted widespread attention in the industry. However, the truth that was later exposed was shocking: the company used synthetic medical record data when training the model. These data did not come from real clinical cases and lacked actual medical value. This behavior not only seriously misled investors and users, but also caused great harm to the development of the medical industry. 

Opaque data labeling and unreliable sources are common means of data black box games. In order to pursue high accuracy of models, some companies do not hesitate to use various means to forge data or over-label data, so that the model performs well on training data, but cannot be generalized in actual applications, resulting in a significant reduction in the accuracy of diagnostic results. What's more serious is that this behavior may also lead to legal disputes. A medical AI company is facing lawsuits from patients and medical institutions because of data falsification. Not only has its reputation been damaged, but it may also bear huge compensation liabilities. 

2. Five Pseudo-trends under Technology Worship

1. AI Toys: A Recorder in a Smart Clothes

Under the craze of "AI + Everything", the AI ​​toy market has heated up rapidly, attracting a large influx of capital.  However, 90% of the AI ​​toys on the current market are actually just recorders in smart clothes. Most of them rely on preset response templates and cannot achieve real natural conversations. Take the "smart pet dog" of a leading manufacturer as an example. This toy, which claims to understand children's emotions, actually relies on 20 preset response templates to respond to children's questions. When children ask questions that are beyond the scope of the template, it will fall silent or give some irrelevant answers. In actual use, children may ask "Why is the sky blue", but the "smart pet dog" can only answer some preset simple greetings or stories, which cannot really satisfy children's curiosity. 

Some educational toys that claim to provide “personalized learning” also have serious functional defects. They cannot even accurately complete simple mathematical operations, let alone provide personalized learning plans based on children’s learning situations. Some parents reported that when their children asked “what is 3+5”, these educational toys gave the wrong answer three times, which greatly disappointed the parents with their so-called “intelligent” functions. 

2. Generative AI: The copyright meat grinder of the content industry 

Relevant surveys show that 60% of readers can recognize AI-generated articles at a glance. These contents often lack the emotional warmth and logical depth of human creation, and read stiffly and boringly. This so-called "creation" is often based on learning and imitating a large number of original works, which is essentially a plagiarism behavior. 

Some AI writing tools generate seemingly original content by analyzing and learning from a large number of articles, but a careful reading will reveal that these contents are just patchwork and rewriting of existing articles, lacking real creativity and depth. This behavior seriously infringes on the rights of original authors, resulting in many original authors' works being plagiarized and misappropriated without receiving due rewards. The EU AI Act clearly stipulates that unauthorized data training will face a sky-high fine of 4% of global revenue. This measure is intended to curb AI infringement in the field of content creation. 

3. Autonomous driving: Level 5 lies and fatal costs

Autonomous driving technology has always been a hot topic in the technology field. Car companies have claimed that they will achieve L5 level fully autonomous driving in the next few years, allowing people to completely say goodbye to the steering wheel. However, reality has poured cold water on these beautiful visions. 

The cost of a certain large company's self-driving test vehicle is as high as 50 yuan per kilometer, which is 10 times the price of ordinary online ride-hailing cars. This makes self-driving cars face huge cost pressure in commercial operations. Moreover, in extreme weather conditions, such as heavy rain, heavy snow, and dense fog, the recognition error rate of the self-driving system is still over 15%, which seriously affects its safety and reliability. The maintenance cost of a laser radar is equivalent to one-third of the price of the whole vehicle. The high maintenance cost also makes many companies reluctant to buy it. 

Faced with these technical bottlenecks and cost pressures, investors have quietly turned. In 2024, more than 70% of autonomous driving financing went to closed scenarios such as logistics and mining areas, rather than the passenger car market. These closed scenarios are relatively simple and have low requirements for autonomous driving technology, which can reduce costs and risks to a certain extent. However, those teams that are all in the home market are paying for the fantasy of "completely unmanned".

3. How to identify fake AI projects? Three golden rules 

In the current situation where there are a lot of good and bad AI projects, it is crucial to master effective identification methods. We have summarized three golden rules to help entrepreneurs, investors and practitioners to clear the fog and distinguish the true from the false. 

1. Technology penetration method

The technology penetration method is one of the key means to identify pseudo-AI projects. When evaluating an AI project, one should not be confused by the superficial technical terms, but should delve into its core technology. Patents are an important reflection of a company's technological strength. By checking the proportion of keywords such as "neural network" and "Transformer" in patents, one can preliminarily judge the company's degree of technological innovation. If a company claims to have advanced AI technology, but the proportion of these key technologies in the patents is extremely low, or there are even no relevant patents, then it is likely to be hyping the concept. 

Some "pseudo-innovative" companies rely on open source code, slightly modifying the open source code and packaging it as their own technology, lacking independent core algorithms. Some companies outsource technology research and development in order to reduce costs, resulting in weak technical capabilities and inability to truly master core technologies. Take a consumer electronics company as an example. The company claims to transform into AI vision, but its core algorithm relies on open source code and lacks independent research and development capabilities. 

2. Data traceability

Data is the foundation of AI, and the quality and source of data directly affect the performance and reliability of AI models. Therefore, data traceability is one of the important methods to identify pseudo-AI projects. When evaluating an AI project, requiring companies to disclose the source of training data and proof of desensitization is a key step to ensure the legitimacy and security of data. If companies cannot provide clear data sources and proof of desensitization, there is a risk of data falsification. 

3. Business Verification Method

Business validation is the ultimate standard for identifying pseudo-AI projects. A true AI project must not only have advanced technology, but also be able to achieve commercial value. Paying attention to the gross profit margin and customer retention rate of AI business can intuitively understand the commercial feasibility of the project. 

In order to maintain a false prosperity, some pseudo- AI projects attract users and investors through subsidies or concept hype. However, this approach is often difficult to sustain, and once the funding chain is broken, the project will quickly collapse. 

4. Return to actual needs and let AI take root 

1. Scenario-driven innovation

In the wave of AI, real innovation should not be to blindly pursue popular technologies, but to start from actual scenarios and solve the pain points of traditional industries  . Only when AI technology is closely integrated with actual scenarios can it play its greatest value.

In Yunnan, flower farmers used AI to analyze soil data and cultivated new cold-resistant varieties, which increased flower exports by 200%. This achievement not only improved the economic benefits of flower farmers, but also provided new ideas for the sustainable development of the flower industry. These seemingly ordinary scenes provide a broad space for the application of AI technology. Starting from the pain points of traditional industries and tapping into the real needs of users is the key to achieving AI innovation. Entrepreneurs should go deep into the actual scenarios of various industries and look for problems that can be solved with AI technology, rather than just staying on the hype of concepts. 

2. Technology compounding accumulation

When choosing AI projects, we should focus on the accumulation of technical compound interest and choose those fields that can continuously improve our own cognition and technical capabilities. Taking the algorithm optimization of vertical industries as an example, through in-depth analysis of specific industry data and continuous improvement of algorithms, we can gradually accumulate deep technical barriers and form unique competitive advantages. In contrast, simple data labeling or model parameter adjustment work may bring certain benefits in the short term, but in the long run, it is difficult to achieve continuous technological progress and improvement of personal capabilities.