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# AI is dead, long live AK

Stop calling it Artificial Intelligence. Today's models merely process Artificial Knowledge. Discover why redefining AI protects your brand from hallucinations and unlocks genuine engineering value.

- **Speakers:** [Zachary Powell](https://www.wearedevelopers.com/@zachary-powell)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 22:08
- **URL:** https://www.wearedevelopers.com/videos/1093-ai-is-dead-long-live-ak

## Summary

The cultural understanding of artificial intelligence is heavily skewed by a century of science fiction, setting unrealistic expectations of machine consciousness, emotion, and self-awareness. In reality, today's models operate entirely as narrow AI. By continuously labeling these systems as intelligent, the tech industry creates dangerous ethical implications around trust and corporate accountability. This becomes evident when systems confidently generate logical hallucinations or are easily manipulated into damaging brand reputation through rogue chatbot behavior.<br><br>A more accurate and ethical framing for current technological capabilities is 'Artificial Knowledge'. Recognizing that knowledge is merely a subset of intelligence allows developers to see that while machines can parse large datasets and generate outputs, they fundamentally lack true creative or contextual reasoning. Testing models on highly spatial or creative tasks—such as producing crochet patterns or rendering physically plausible gymnastics video—often exposes their lack of real-world understanding, resulting in bizarre structural nonsense rather than true innovation.<br><br>When organizations stop treating models as thinking entities and utilize them purely as artificial knowledge engines, the practical applications become highly valuable. Engineering teams can safely leverage these tools for writing standard developer boilerplate code, freeing human operators to focus entirely on complex business logic. Similarly, deploying models for narrow, specific pipelines—such as customer conversation voice transcription, real-time sentiment analysis APIs, or utilizing computer vision for medical biomarker detection—demonstrates the profound utility of processing knowledge without falling victim to the risks of faux-intelligence.

**Keywords:** artificial intelligence vs artificial knowledge, narrow AI limitations, generative AI hallucinations, testing LLM creativity, corporate chatbot manipulation, AI accountability challenges, navigating AI marketing hype, developer boilerplate code generation, automated voice transcription, call sentiment analysis APIs, medical biomarker computer vision, large dataset reasoning, technological trust barriers, mitigating enterprise AI risks

## Chapters

1. **Defining artificial intelligence through the lens of fiction** (00:50) — Fictional portrayals build public expectations of machines that learn, reason, show emotion, and achieve self-awareness.
1. **Contrasting narrow artificial intelligence with fictional expectations** (03:52) — Real-world narrow artificial intelligence processes existing data sets instead of generating true thoughts or emotional understanding.
1. **Redefining modern tools as artificial knowledge rather than intelligence** (06:16) — Current machine models process and manipulate knowledge but lack the encompassing traits required for true intelligence.
1. **Evaluating large language models on physical creativity tasks** (06:59) — Asking generative text models to produce novel crochet patterns reveals a fundamental inability to creatively assemble logical physical components.
1. **Observing physical logic failures in generated video content** (11:27) — Generative video models fail to apply real-world physics or contextual intelligence when rendering human movements.
1. **Addressing the risks of hallucination and chat model manipulation** (12:38) — Relying on text generation tools introduces errors when models confidently fabricate historical facts or succumb to prompt manipulation.
1. **Practical use cases for applied artificial knowledge tools** (14:28) — Treating machine learning as a knowledge tool enables effective automation for boilerplate code, voice transcription, and preliminary medical diagnostics.
1. **Ethical implications of misrepresenting machine learning as intelligence** (17:42) — Labeling software as intelligent creates false public trust and dilutes corporate accountability for model behaviors and errors.
1. **Rebranding artificial intelligence to manage proper user expectations** (19:22) — Shifting industry terminology toward artificial knowledge sets realistic boundaries around what current machine models can actually perform.

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