8 common AI myths busted

Artificial intelligence is one of today’s fastest-evolving technologies. Some people love it, while others are skeptical or afraid. We can blame sci-fi movies for AI uprising plots, yet most fears about AI are grounded in reality. People wonder if their jobs will be replaced by a budget Android running fancy AI software and machine learning algorithms. I’ll debunk common myths and clarify the facts.

8 Generative AI is only for creative tasks

Generative AI is often linked to creative tasks like art, storytelling, and music composition. However, its use extends beyond these areas. In healthcare, it generates synthetic training data and aids disease diagnosis. In finance, it models risks and simulates market scenarios to refine investment strategies.

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7 AI will replace all jobs and leave us unemployed

Source: Pavel Danilyuk / Pexels

Technological advancements often raise fears of widespread unemployment. AI systems handle narrow, specialized tasks, but most jobs involve a mix of interconnected responsibilities. AI automates repetitive tasks, allowing humans to focus on activities requiring critical thinking and emotional intelligence.

It will reshape the job market by raising entry-level standards and shifting responsibilities. For example, in the customer service industry, chatbots and AI-powered virtual assistants handle basic inquiries, such as checking balances or resetting passwords. This automation reduces the demand for entry-level agents.

The workforce must adapt by learning to use AI tools. Professionals skilled in these tools gain a competitive edge. AI will also create new roles, such as data annotation specialists and compliance officers supporting AI training and implementation.

AI detection tools are far from perfect. Research shows that AI detectors often display bias against non-native English writers. They rely on identifying traits of AI-generated text that overlap with typical features of non-native writing. Content obfuscation techniques further reduce detection accuracy. These limitations come from the mechanics of AI detectors, which use language models similar to the AI systems they aim to detect. Detectors analyze text based on two key metrics: perplexity and burstiness.

Perplexity measures how predictable a text is. AI-generated content often has low perplexity, appearing smooth but overly predictable. Human writing shows higher perplexity, reflecting creativity and occasional errors. Burstiness evaluates variation in sentence structure and length. AI-generated text often exhibits low burstiness, resulting in a monotonous tone, while human writing tends to be more diverse.

5 Everyday devices can’t handle AI processing

Source: Solen Feyissa / Unsplash

AI is often seen as reliant on supercomputers and cloud infrastructure, making it unsuitable for everyday devices. However, advances in on-device AI bring powerful features to everyday devices. Companies like Arm are driving this shift. Arm’s Cortex CPUs, including the newly announced Cortex-X925, offer a 35% instruction-per-clock performance boost for faster AI computations with minimal power consumption.

Arm’s Kleidi libraries support this transformation by providing developers with tools to build AI solutions optimized for on-device execution. Products like Google’s Gemini Nano and Apple Intelligence leverage these innovations to deliver AI features to users.

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4 AI is close to developing awareness

AI models, like the Transformer architecture behind ChatGPT, lack motivation, emotion, and lived experiences. They mimic understanding by identifying patterns and producing predictions rather than showing sentience. Even neural networks, modeled on the brain’s structure, fall short of replicating human cognition.

Furthermore, the mechanisms of human consciousness and sentience remain poorly understood, raising questions about how we might recognize true machine sentience. This lack of understanding makes it difficult to define clear criteria for recognizing true machine consciousness, even if theoretically possible. The gap between AI systems and the complexities of human consciousness is vast. For now, AI remains a tool, not a peer.

3 How AI systems make decisions is a mystery

Source: Hui, A. et al. (2022) ‘Ethical Challenges of Artificial Intelligence in Health Care: A Narrative Review’

Not all AI systems are black boxes. There are two main types: black box AI and explainable AI (white or glass box AI). Black box systems have opaque processes, making their decisions difficult to understand. For example, if a black box AI model denies a business loan application, understanding the reasoning behind the decision would help users appeal. Researchers are creating techniques to make black box AI more transparent, including:

• Identifying features that influence outputs (feature importance).
• Explaining predictions for specific inputs (local explanations).
• Extracting human-readable rules from learned patterns (rule extraction).
• Visualizing internal model processes (visualization techniques).

2 Machine learning is just another word for AI

Many confuse AI with machine learning, though they are distinct but interconnected fields in computer science. AI focuses on creating systems that mimic human intelligence, like reasoning, problem-solving, and language processing. Machine learning, a subset of AI, develops algorithms that learn from data to make predictions.

Machine learning is a key tool for advancing AI, but not all AI depends on it. Techniques like rule-based systems and logic programming create intelligent behavior without relying on data-driven learning. Likewise, machine learning has uses aside from AI, powering systems that may not be “intelligent” but still offer valuable automation and insights.

1 AI is inherently biased and unfair

The belief that AI is inherently biased stems from misunderstandings about its workings. However, recent reports make it easy to see why this perception exists. AI bias originates from the data used during training. For example, if a recruitment tool learns from historical hiring data containing gender or racial biases, it may replicate these patterns in its recommendations.

However, not all AI systems are inherently biased. Those that are can be retrained or adjusted to eliminate bias for fair outcomes. By carefully selecting and preprocessing training data and implementing mechanisms to detect and correct biases, AI can reduce human bias in decision-making.

AI is the next tech evolution

AI marks the next stage in technological evolution. Major tech companies are heavily investing in AI platforms, while countries like China are developing their own systems. The bottom line is that while it’s important not to fall for the myths surrounding AI, recognizing its potential is equally important.