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Since the emergence of Chat GPT, artificial intelligence has drawn a lot of attention. The discussion highlighted important topics, such as ethical and security concerns, that still continue to shape AI-powered tools of today.Ā
However, the rise in attention also led to a flood of misconceptions. While some of them are harmless, most are misleading and create a false impression of AI.Ā
Altamiraās Chief Technology Officer, Alex Budnik, shared his insights and dispelled common myths about AI.Ā
See also: How AI impacts business: 5 success storiesĀ
Myth #1: All AI systems are āblack boxesā, and nobody understands how they thinkĀ
How do you know that Machine learning (ML) and deep learning models output correct information if you donāt know how they arrived at their conclusions? Unlike traditional software that has explicit rules, ML bases conclusions on the trained data. Models consume a lot of information that is processed by complex internal structures, making it difficult to trace the decision-making process.Ā Ā
The problem becomes even more obvious in deep learning models since theyāre based on neural networks that mimic the human brain.Ā
The āblack boxā issue raises legitimate concerns in critical applications. For example, a recent review about AI in healthcare has found that 94% of 516 ML studies have failed to pass even the first stage of clinical validation tests.Ā Ā
In finance, Laura Blattner, an assistant professor of finance at Stanford Graduate School of Business, has explained that: āThe power of technology is its ability to reflect the complexity in the world. But the lack of understanding raises practical, legal, and ethical questions.ā
The realityĀ
Saying that āall AI systems are black boxesā is an oversimplification. It is true that complex deep learning models can be challenging to interpret, but many other AI approaches offer better transparency.Ā Ā
A recently emerged field of explainable AI (XAI) focuses on making AI decisions more transparent, even though most XAI techniques operate post hoc and can only approximate the modelās decision-making process.Ā
Many researchers are actively working to make AI more transparent and interpretable. Some of the efforts include:Ā Ā
Developing inherently interpretable modelsĀ
Creating tools for visualising and explaining model decisionsĀ
Establishing frameworks for AI governance with a focus on transparencyĀ
Concerns about bias, opaque reasoning and regulatory gaps still exist, but these efforts are only the beginning in building trustworthy AI systems.Ā
Myth #2: AI is approaching human intelligenceĀ
This belief was born from the popular media and fuelled by tech industry narratives. Stories of AI outperforming humans in chess, composing music, or generating realistic text only deepened the misconception that machines are quickly catching up.
Bold predictions donāt help with the issue either: some forecast that artificial general intelligence (AGI), a system that can perform any intellectual task, will be created within years.Ā Ā
While the narrative might sound compelling, itās still far from the truth.Ā
The realityĀ
Nowadays, AI excels at narrow tasks, such as:Ā
Identifying patterns in large datasetsĀ
Generating plausible-sounding textĀ
Making predictions based on statistical correlationsĀ
However, these capabilities shouldnāt be mistaken for human intelligence; the output simply reflects patterns learned from data. AI doesnāt understand what the outputs mean.Ā Ā
The key AI limitations are:Ā
Lack of generalisation: Models trained to detect tumours in medical scans canāt shift to diagnosing pneumonia or interpreting the emotional tone of the conversationĀ
Absence of common sense: AI systems fail at reasoning through routine scenarios, making contradictory or nonsensical claimsĀ
No real understanding: Language models can generate sentences by predicting which word should go next, but they donāt grasp the meaning of the said wordsĀ
The human brain is an example of flexible, self-aware, and context-sensitive intelligence. It can learn from a few examples, adapt to changes, and reason intuitively. Humans have emotions, memory, perception, and judgment.Ā Ā
In contrast, AI is narrow, data-hungry and rigid. The most advanced models still require massive computational resources to achieve the same results a child could ā like understanding a joke or adapting to a social situation.Ā
The myth that AI will soon surpass humans is partially a consequence of its design. Modern AI is built to mimic human language and decision-making patterns. Itās easy to mistake a chatbotās crafted heartfelt message for depth. In reality, these systems are just manipulated symbols without any understanding of their meaning.
Myth #3: AI will replace all human jobsĀ
AIās impact on employment is one of the most pressing concerns. According to the World Economic Forum, AI and automation will displace 85 million jobs by the end of 2025, and 97 million new jobs might emerge.
This highlights the dual nature of AIās impact: some jobs will disappear, but new positions will replace them.Ā
Certain types of work face a higher risk of displacement by AI automation. For example:Ā
Routine cognitive tasks, such as data processing, basic analysis, and rule-based decision-makingĀ
Structured physical labour involving repetitive manual tasks in controlled environmentsĀ
Basic customer service functions, such as standard inquiries and information provisionĀ
The realityĀ
While AI substitutes some jobs, it also creates new ones in fields like data analysis and programming. A surge of AI adoption across industries creates demand for AI specialists, data scientists, AI ethicists, and jobs requiring human skills, such as creativity, empathy, or complex problem-solving.Ā
AIās employment impact varies across industries. For example, healthcare is embracing AI for advanced diagnostics, personalised treatment, and predictive analytics, transforming healthcare jobs. In transportation, AI enables autonomous vehicles and optimises traffic management and logistics. Retail and e-commerce benefit from AI-poweredĀ customer support, personalised recommendations, and automated inventory management.Ā
However, to help workers adapt to AI-driven changes in the workforce, they need education and training.Ā Ā
Since routine tasks become automated, jobs will start to highlight unique human capabilities, such as creativity, critical thinking, emotional intelligence, and complex problem-solving.Ā Ā
The transition will require significant investment in reskilling and upskilling programmes and education reforms to prepare students for an AI-driven workplace.Ā
See also: The future is now: AI deep dive
Myth #4: The more training data AI has, the smarter it will become
Itās easy to see where the āmore data equals better intelligenceā mindset comes from. The logic is based on high-profile projects that train AI models on massive datasets.
The myth only gained momentum from real-world examples, like OpenaiāsĀ GPT-4. The results seemingly confirm the idea that more data leads to smarter AI.
The reality
Itās true that more data can improve performance, especially in the early stages of model training. However, beyond a certain threshold, adding more data will give only marginal gains. In some cases, excessive data overwhelmed or confused the model. This issue is even more pronounced with low-quality, redundant, or inconsistent data.Ā
Bigger datasets also require more computational power, longer training, and higher costs, a substantial price to pay for a chance to improve the AI model.Ā Ā
Additionally, these datasets donāt help AI systems develop a deeper understanding of the world.Ā Such systems onlyĀ become better at identifying patterns and correlations, but they donāt gain common sense, context awareness, or conceptual reasoning. Language models are trained on billions of sentences but still fail basic logic puzzles or misunderstand cause-and-effect scenarios.Ā
Building a smarter AI isnāt about using more data. Itās about different kind of learning that integrates reasoning and abstraction, challenges that canāt be solved by brute-force data ingestion.
Myth #5: AI, machine learning, and deep learning are interchangeable
The terms AI, ML, and deep learning are often used interchangeably in popular discussions. Articles, marketing materials and even technical blogs sometimes mix these terms or just refer to a system as āAI-poweredā.
The fact that these terms refer to the same concept doesnāt help either. For people outside the field, “AI” might seem like a label applied to any algorithm.Ā
The realityĀ
All three terms refer to slightly different things.Ā Ā
Artificial intelligence refers to the science that enables computers and machines to simulate human learning, comprehension, and problem-solving. In simple terms, the goal of AI is to create intelligent machines.Ā Ā
This is an umbrella term that includes both machine learning and deep learning.Ā
Machine learning is a subset of AI that enables systems to learn from data instead of being explicitly programmed. Once trained, ML algorithms can find patterns and make decisions with minimal human intervention.Ā
ML is one of the strategies used to make AI.Ā Ā
Deep learning is a subset of ML that uses layers of neural networks to learn complex features from data, especially unstructured, automatically. DL is one of the techniques used for ML.Ā
Why these myths matter
Misconceptions about artificial intelligence aren’t just academicāthey shape how AI is built, evaluated, and used. When people believe that more data always makes models smarter or that AI is already approaching human-level intelligence, it sets distorted expectations.Ā
These myths can lead to poor decisions, misplaced investments, and mistrust from users who were promised one thing and experienced something entirely different.Ā
In healthcare, finance, and education, misunderstanding how these systems workāor what they actually areācan slow down adoption or result in superficial deployments that fail to deliver.Ā Ā
Clearing up these misunderstandings requires transparent communication and grounded expectations. Recognising the real strengths and limits of AI opens the opportunity for better integration.Ā Ā
See also: Implementing AI: Is it worth it? A step-by-step guide to AI implementation
Final thoughts
Artificial intelligence is a technology of the future. However, itās not a āmagic wandā that can do anything humans can. Actively combating misconceptions helps people stay more informed and make smarter choices about the use of AI.
How Altamira can helpĀ
Whether you are looking to add the latest AI tools to your business, validate if your business is ready for AI adoption, or improve your products and services with the power of AI, our approach guarantees to reduce your stress.Ā
Zero headaches: Get a team with a proven track record of pushing the boundaries of whatās possible with AI
Tangible value in days: Clickable prototypes even before a contract starts to demonstrate our commitment
Always stay in the loop: Have daily or weekly meetings with transparent communication so that you always know the state of your project
Contact us and take your products to the next level with AI.Ā
