Not Really Problems with AI

Contrary to concerns that AI tools diminish critical thinking abilities, research shows that with proper reflective scaffolding, AI can actually enhance metacognition and critical thinking skills.

Research shows that co-creative AI tools can actually increase the volume and diversity of ideas. The real issue is over-reliance on AI, not the technology itself.

Using AI for learning doesn't prevent genuine skill acquisition. In fact, demonstrations followed by critique can accelerate skill-building through the worked-example effect.

The concern about students using AI to cheat echoes similar panics about calculators and Wikipedia. The solution lies in evolving pedagogical approaches rather than banning the technology.

While language models do sometimes generate incorrect information ('hallucinations'), techniques like Retrieval-Augmented Generation (RAG) and hallucination-aware tuning have significantly reduced error rates.

The claim that language models don't 'understand' anything is complicated by the fact that they outperform humans on many reasoning benchmarks. The concept of 'understanding' itself is philosophically contested.

Claims that AI is 'just hype' ignore its already substantial deployment in hospitals, logistics, media, and other sectors. Denial wastes valuable time and cedes ground in important policy discussions.

Claims about 'quantum AGI' secretly taking over are unfounded. No AGI (Artificial General Intelligence) currently exists, and such concerns stem from science fiction tropes rather than technical reality.

The claim that AI-generated art is always inferior to human art is contradicted by research showing that humans can only correctly identify AI art about 60% of the time, and audiences often prefer AI-generated content.

While concerns about AI art and copyright are legitimate, mass opt-out approaches can lead to the cultural erasure of marginalized artistic styles from future models, creating representation problems.

Despite high enterprise adoption rates, most companies lack sufficient in-house AI expertise. The majority of AI pilot projects yield only modest gains, indicating a gap between adoption and effective implementation.