Writing and reading are fundamental skills that go hand-in-hand. However, is it possible for an AI system to write intelligently but not comprehend what it is writing? This article explores the intriguing notion of an AI that can write prolifically but lacks true reading comprehension.
Can AI Really Write Without Understanding?
Most advanced AI systems today utilize machine learning techniques like neural networks to generate human-like text. They are trained on massive datasets of texts so they can recognize patterns and reproduce coherent writing in a similar style. The most sophisticated systems, like GPT-3, can write persuasively on a wide range of topics with proper grammar and structure. However, there is an ongoing debate around whether these AIs truly “understand” the content they generate or are simply stringing words together based on learned probabilities.
While AI can skillfully mimic human writing, reading comprehension requires making logical connections, activating background knowledge, and integrating concepts across sentences and paragraphs. Without deeper language understanding, it would be difficult for an AI system to answer questions or summarize lengthy texts reliably.
Applications of AI Writing
Despite the limitations in reading comprehension, AI writing systems are already being deployed in a range of applications:
- Content creation – AIs like GPT-3 can generate blogs, articles, social media posts, emails, marketing copy, and more based on prompts with key information.
- Conversational agents – Chatbots are trained on dialog data to hold friendly conversations without necessarily understanding the content.
- Creative writing – Systems like AI Dungeon can craft stories, poems, scripts, and more based on creative launching points.
- Data to text – Structured data can be transformed into written narratives or reports using NLG (natural language generation) techniques.
The main appeal is high-volume automated content creation at scale. As long as the writing fits the style and goals of the application, comprehension is less important.
Limitations and Ethical Concerns
Reliance on AI authors that don’t understand their own writing presents some risks and limitations:
- No semantic understanding – Without comprehension, grammar or facts may be incorrect.
- No fact checking – Generated text may include false information copied from training data.
- Lack of coherence – Longer content may wander or contradict itself without an overview.
- No knowledge integration – Concepts are reproduced without connecting to wider knowledge.
- No control – Without oversight, AI could generate harmful, biased or nonsensical content.
Developers will need to combine comprehension-focused techniques like semantic parsing and knowledge graphs for AI to begin overcoming these issues. From an ethical standpoint, businesses must vet generated content rather than publish unchecked AI writing to the public.
How is AI able to write human-like content without comprehension?
AI programs like GPT-3 are trained using a technique called transformer language models. Here is a high-level overview of how they work:
- The system ingests millions of texts from books, websites, and other sources.
- It looks for patterns in the structure and order of words in these texts.
- The patterns are encoded numerically so the system can make probabilistic predictions.
- Given new inputs, the AI predicts sequences of words that fit the patterns.
- More training refines the system’s ability to generate coherent, human-like text.
The key aspect is that the models mimic linguistic patterns without any need to actually comprehend semantic meaning or enact logical reasoning. The goal is to produce something that looks convincingly written rather than demonstrating true understanding.
What are the main methods used?
Transformer-based language models rely heavily on two methods:
- Self-attention – The system learns contextual relationships between all words in a sentence or paragraph.
- Neural networks – Multi-layer networks learn textual patterns across huge volumes of data.
Additional techniques like fine-tuning on specific datasets, sampling from probability distributions, and beam search during generation contribute to strong performance without reading comprehension.
How is the training data created and processed?
AI writing systems require vast volumes of high-quality training data. For example, GPT-3 was trained on over a trillion words from websites, books, Wikipedia, and other sources. Here are key training data practices:
- Web crawling – Bots extract texts from online sources like news sites, forums, journals, etc.
- Digitization – Physical books and documents are scanned and processed via OCR.
- Filtering – Irrelevant, biased or low-quality data is removed.
- Normalization – Text is formatted into a consistent structure.
- Sequencing – Data is split into sentences, paragraphs, or short passages.
This huge corpus of clean, structured training data allows models to learn the essence of human language – without any knowledge about what the words actually mean.
What are the main limitations of AI writing systems today in terms of comprehension?
While AI writing has come leaps and bounds, experts agree that today’s systems still lack true comprehension. Some of the main deficiencies include:
No semantic understanding
AI cannot encode the underlying meaning of words and how they logically relate to each other. Without semantics, it has no concrete concepts of objects, actions, or ideas described in generated text.
No integration of world knowledge
Humans draw on a lifetime of learned knowledge about the world when reading. AI systems lack the background facts and common sense needed to model real-world contexts described in text.
Inability to answer questions
Answering questions requires deep comprehension of details and chronology described in a passage. Existing AI cannot reason accurately about texts they created themselves.
No understanding of causality
Following narrative threads and logical consequences described requires causal reasoning abilities missing from today’s models.
No fact checking
Humans instantly relate assertions in text to known facts in memory. AI cannot distinguish truth from misinformation without outside reinforcement.
Difficulty maintaining coherence
Lacking a mental model of the context, AI systems can lose the narrative thread or introduce contradictions when generating longer text.
While AI writing has limitations, natural language processing is progressing rapidly. As newer techniques enable machines to encode semantics, reason about knowledge, and integrate concepts, comprehension may reach human levels in the future.
What developments are needed for AI writing systems to achieve true reading comprehension?
Experts suggest a few key developments could push AI authoring to the next level of comprehension:
Rich semantic representations
Techniques like symbolic AI and graph networks may allow systems to represent word and sentence meanings in a structured, symbolic way more suitable for reasoning.
Integration of knowledge bases
Linking generated text to knowledge graphs and ontologies would ground writing in real-world concepts, relations, and facts.
Causal and logical reasoning
Deductive reasoning capabilities could help AI analyze premises and derive sensible conclusions when writing longer content.
Reinforcement learning from feedback
Allowing users to identify comprehension errors and correct AI-generated text could provide crucial training signals.
Multimodal knowledge
Inputs like images, video, and audio provide additional context that improves comprehension beyond pure text.
Common sense knowledge
Background knowledge about everyday objects, events, and human behavior needs to be encoded.
While challenging, combining linguistic analysis with structured knowledge and reasoning in a feedback loop offers a path for AI authoring to reach new levels of mastery.
Examples of AI Generated Text and Limitations
Here are a few examples of AI-generated text that highlight some current comprehension limitations:
Product Description
Here is a product description for a computer monitor generated by an AI:
Introducing the new GenTech Ultrawide Pro Monitor – experience incredibly immersive visuals on the expansive 35″ curved display. The monitor’s 3440 x 1440 resolution and 3000:1 contrast ratio deliver stunning color depth and clarity. AMD FreeSync support smoothes out frame rates for tear-free gaming. With HDMI, DisplayPort, and USB-C connectivity, it’s easy to hook up devices. The near bezel-less design minimizes distractions and lets you enjoy ultra-wide views. Tilt, swivel and height adjustment provide ergonomic comfort. See mind-blowing detail across the entire massive screen!
This demonstrates coherent writing in the proper style. However, the AI has no real comprehension of display technology, resolution, color depth, gaming, or ergonomics. It is simply generating persuasive language by mimicking product descriptions.
News Article
Here is a short news excerpt generated by AI:
According to a new study published yesterday, eating more vegetables and less meat can increase life expectancy. Researchers at Cambridge University followed 500 adults aged 50 to 75 for a period of 15 years. Subjects who reduced meat consumption and increased vegetable intake were far more likely to live beyond age 90. “These findings confirm the health benefits of plant-based diets,” said lead researcher Dr. Jane Ellison.
While logically structured, the AI has no way to fact check the fictional study data or quote attribution. It lacks the world knowledge to assess the validity of health claims made.
Story Summary
When asked to summarize the plot of the first Harry Potter book, an AI generated the following:
Harry Potter is a young orphan being raised by his mean aunt and uncle. On his 11th birthday, Harry learns that he is actually a wizard and gets invited to attend Hogwarts School of Witchcraft and Wizardry. At Hogwarts, Harry begins magical training, makes new friends like Ron and Hermione, and plays Quidditch. He starts uncovering the mysterious past around his parents’ death and his connection to an evil sorcerer named Voldemort. After some dangerous adventures, Harry helps stop Voldemort’s plot to steal the Sorcerer’s Stone.
While covering the key plot points, the summary lacks deeper comprehension around concepts like magical abilities, relationships between characters, and significance of events that would be obvious to a human reader.
These examples demonstrate that while AI can produce well-formed text, current systems lack the semantics and reasoning needed for true comprehension. Ongoing advances will be required to close this gap.
Conclusion
Can AI write persuasively without understanding what it is writing? Increasingly yes – today’s most advanced systems can generate remarkably human-like text based on pattern recognition alone. However, their lack of comprehension also imposes limitations. Comprehending language requires more than manipulating word statistics – it demands learning symbolic representations of knowledge that allow logical reasoning. With techniques like knowledge graphs, causal reasoning, and reinforcement learning, AI authoring may someday exhibit the depth of understanding and integration of concepts that humans intrinsically possess. But for now, an ingenious textual mimic devoid of comprehension is the peak of AI writing.