This post talks extensively about common AI myths, the public understanding about artificial intelligence amidst increasing AI hype, debunking AI misconceptions, and likely AI-human collaboration in the distant future.
In a world awed and feared by artificially intelligent machines, clearing or debunking AI myths is a serious priority.
Pieces of unsubstantiated news like AI-led job displacements, smart machines having emotional intelligence, and AI models achieving consciousness are infecting many people with skepticism and uncertainty about the kind of future in the making for them and their next-generation kindred.
It may sound incredulous, but some years down the line would be a future for humanity and human society with AI-human coexistence a common reality, not a science-fiction generalization most of us tend to conjure up today.
This is perhaps one of the key reasons why the subject of generative AI is transitioning from being a niche research topic to a usual conversation everywhere, from our daily normal life to academic and professional fronts.
Quite funny to witness how a cinematic concept of sci-fi movies is now greeted with varying mixes of emotions from people of different financial and educational backgrounds worldwide.
And it also begs a serious question of how much we actually know about intelligent machines. Is AI going to take my job? Will AI replace human workers? What will the future of work with AI look like? Will the limit of our AI knowledge be an ugly fallout of our own existential crisis? What if what we know about smart machines is just our illusion?
Why debunking AI myths is a clarion call for us?
The extent of AI myths becoming prevalent in almost every part of the world is something we can understand from the report of Gallup. It observed in its 2022 survey that 37% people in the USA believe that AI-driven job displacements would be a reality by 2040.
Isn’t that one of the reasons why it’s high time AI myths be debunked before they infect more people?
In addition, we can’t afford to stay unaware of the challenges and ethical considerations associated with AI machines. Most importantly, we must differentiate the fact from myth.
Otherwise, the kind of misinformation, such as AI declaring its supremacy on humans, is unhealthy. It would breed a disturbing skepticism about AI technologies. There will be speculations about what smart machines are capable of achieving down the line.
The Purpose Of The Blog Post
In this write-up, I feel obliged to clear the confusion between fact and fiction associated with artificial intelligence.
I am going to lay out many key details, such as common AI myths, and whether AI is going to take human jobs. Will AI replace humans? I will talk about the current state of artificial intelligence. Focus of the blog will be on application areas of AI, and other key details to make it a comprehensive read.
Key Highlights Of The Blog
- What is AI really? What makes it different?
- Debunking AI myths, common or most prevalent
- Exploring AI through a balanced, strategic lens, clarifying misconceptions, and highlighting its true capabilities
- Addressing AI-related key points, such as job loss, current AI capabilities, stats, developments, etc.), and transformative upsides of AI technology.
What Is AI In Reality? What Makes It Different?
To put in simple words, AI is a digital brain inside a computer.
Basically, it consists of models and algorithms that programmers and engineers design to simulate tasks that usually mandate human intelligence.
Here, simulation not just means imitation. It refers to AI’s capabilities to perform tasks like reasoning, learning, problem-solving, generating content, understanding languages, and recognizing patterns. These tasks typically require human intelligence. So, AI is not acting like a human. It just mimics the performance similar to how a human performs using his brain.
I don’t think artificial intelligence can be confined to one specific definition. Because it is limitless, like a bottomless pit.
We witness its ubiquitous presence in virtually all modern innovations, be it driverless cars, smartphones, PC, laptops, Tablets, and TVs. Even most of our home appliances like refrigerators now come equipped with the intuitive command by AI.
Therefore, artificial intelligence, based on how it has developed so far, is an intelligent machine that simplifies human work.
Definition of AI based on its Application Areas.
It is important to know that the definition of AI depends on its specific application areas.
For example, when you define AI based on its professional application in businesses, it means an intelligent machine that can perform mundane tasks efficiently.
In this way, the purpose of AI is to help a business save time and costs on its resource deployment. As a result, AI contributes many improvements to its operational efficiency and productivity enhancements.
But when you apply the definition of the same AI in your personal life, in that case, it means an intelligent machine simplifying your lifestyle. For example, a recommendation algorithm is a kind of machine learning AI model. It helps you narrow down your streaming priorities by suggesting movies and TV shows that you have liked previously.
Isn’t that helpful, considering the amount of time and effort it saves you from prioritizing your favorite shows?
Similarly, when you apply the definition of AI in legal or educational areas, it means an intelligent machine to improve your understanding of complex legal subjects. It educates you of legal nuances. As a result, you understand the intricacy of legal or educational field better.
Feels like processing knowledge has never been this easier, right?
For example, the face recognition algorithm is an AI (mostly Machine Learning) technology. It helps crime specialists recognize a suspect based on how the AI profiles the individual using his/her biometrics and other data.
If put to good use, face recognition AI can enhance transparency in removing racial biases. It can help improve conviction rates in law, and reduce societal discrimination.
What makes AI different?
The fact that AI is a unique innovation ever came into existence is what makes it different from any other technologies. According to the authors, Norvig and Russell of “Artificial Intelligence: A Modern Approach”, the intelligent machines work differently based on their four principles:
- They mimic thoughts based on human minds
- They simulate thoughts based on logical reasoning
- They act in a way similar to human behaviors
- They always mean achieving a particular goal through their actions
If you wonder how these approaches or principles have to do anything with AI, well, they are essential parts of developing the AI systems that can process and reason information, and deal with distinct behavioral patterns as humans do.
AI or smart machines, as we have understood, leave us in no doubt that they can significantly impact our daily lives. And they are already doing so.
They have been instrumental in businesses, helping companies save huge costs on resource deployment. Business owners can now result in operational redundancy through AI automation requiring nearly no human supervision.
From learning and problem-solving to decision-making through quality training data, computational power, algorithms, and hardware components (like GPUs), no technology was ever borne that could have simulated human cognitive functions as efficiently as these smart machines called artificial intelligence.
Therefore, no doubt AI is powerful, but at the same time, it is not a know-it-all genius. It has its limitations.
Read More: Limitations of Gen AI Models
As a human, what we need to understand here is shifting our attitude. Meaning, when we consider AI our partner rather than a replacement, the sense of realizing its real value starts to dawn upon us. We start to unpack its real value.
AI doesn’t think like us. It simply adds a certain degree of improvements in how we think. With emotional intelligence and empathetic understanding far more powerful and intuitive than intelligent machines, humans are still better than AI.
Debunking AI Myths: Are AI Fears Real? A Deep Dive Into Busting Common AI Misconceptions.
Debunking AI Myths #01: Artificial General Intelligence is Imminent In the Next Few Years
The concept of AGI, right from gaining mainstream popularity in the late 20th and early 21st centuries to the current state of artificial intelligence remains quite a misconceived subject. Not because it was anonymous to the public, but due to misinformation, peddled by fear-mongering experts or influencers. They predicted counterproductive outcomes of AI.
In addition, influential people like Elon Musk, Geoffrey Hinton, Yoshua Bengio, and Stuart Russell have already voiced their displeasure concerning the rising power of AI. Their concerns lay the groundwork for a mounting fear of having a depressingly bleak future for humanity when AI machines would take center stage in almost everything that was once an exclusive domain of humans.
What drives people to be more fearful with the concept of AGI?
The fact that AGI would equip capabilities transcending human imagination by all counts is what triggers apprehension among people. Imagine an intelligent machine with emotional and powerful general intelligence akin to human. This is not something one can take for granted, unless proven otherwise.
AGI is misunderstood due to misinformation that is circulated unchecked by media and fear-mongering doomsayers. Dissemination of unverified information gave AGI a God-like standing, never experienced before in the history of AI model developments.
Quite understandably, people’s concerns largely stem from rampant circulation of misinformation around the concept of AGI. Truth is, it is hypothetical.
What about the AI models we see these days, if AGI is a hypothetical concept?
Well, the modern-day innovations made in the field of artificial intelligence have significantly contributed to improving their capabilities in terms of enhanced computational and reasoning powers.
Take the context of recent AI development named GPT-4.5 Orion into consideration. We can’t deny that the field of AI model development has gone through rapid progress, with more powerful GPT, LLMs versions coming up off and on.
However, the present-day AI is just a kind of intelligent machine known as Narrow AI or Artificial Narrow Intelligence (ANI). As opposed to general AI like AGI, these narrow AI systems can handle a singular or limited task.
Moreover, today’s AI models are not without certain loopholes, among which hallucination is one of the serious concerns. Though appearing confident and smart at a superhuman level, these models are error-prone machines. They can fabricate answers or deviate. Examples include virtual assistants (Siri, Alexa), recommendation systems, facial and image recognition software, and chatbots.
Final Words:
The myth that AGI is more intelligent than humans doesn’t hold any ground. Why? Well, first of all, it doesn’t exist. Secondly – it will take many years of research and developments to create an AGI model.
It is a myth born out of a hypothetical super intelligent machine having the ability to think just like human. Even Bill Gates ruled out existence of AGI, though he didn’t dismiss the possibility of its creation in the future.
So, forget about ever thinking of AGI.
Debunking AI Myths #02: Artificial Intelligence is already emotional and conscious
Have you watched the Sci-Fi movie, I, Robot?
I have, and it is one of my favorite Hollywood films. Great performance by Will Smith as detective, Spooner. I smiled at Spooner’s cheeky way of calling a humanoid robot “Canner”.
But why do I mention I, Robot, instead of clarifying AI myths?
Because that cinematic analogy is going to lend more gravity to my point. And my point is – sci-fi movies like I, Robot are possibly one of the main reasons peddling AI misconceptions. They have influenced people’s minds, driving them to believe that AI machines are conscious beings. Therefore, they can harm us if sensing danger to their robotic existence.
I know some may trivialize that theory by arguing that people’s thinking is not so vulnerable to be influenced by movies. But it is definitely a contributing factor behind AI myths getting more ingrained in human society.
Years of repeated conditioning can do that.
For example, when I first watched I, Robot, I felt apprehensive about the evil side of an AI machine known as VIKI (Virtual Interactive Kinetic Intelligence). However, at the same time, I also loved the ethical side of another AI called Sonny.
So, yes, movies have the potential to influence your mind. It can do so, either positively or negatively, depending on the cinematic characterization of specific individual/s.
What drives people to think that AI is emotional and conscious? What’s the core of this myth?
People without credible information tend to believe that modern AI systems have somehow developed the abilities to feel, experience, and understand their own existence.
They believe that super-intelligent machines exist and they see the world around them in a similar way as humans do. Undoubtedly, people who believe in such theory would tend to agree with the argument that AI models have inner thoughts, emotions, and they feel.
So, what is the reality?
The reality is no AI model has been developed so far that is emotionally conscious. Even the latest GPT-4.5 Orion, which is believed to have emotional intelligence, doesn’t have human-like intuitiveness and consciousness. Because it is an impossible feat, at least for now.
Current AI systems or models lack subjective experience, self-awareness, consciousness, and sentience. These myths exist due to various factors, mainly because of science fiction influence. The “WOW!” factor of advanced AI models, and people’s lack of understanding of how AI works are other reasons.
Debunking AI Myths #03: Artificial Intelligence Will Soon Replace All Human Jobs
This is perhaps the most talked-about and most frightening debate in the world of artificial intelligence today. Looks like everyone has something to talk about it.
Some say AI machines are to replace human jobs but would create more new opportunities to offset the loss. Some say intelligent machines would cause job displacements at a pace never seen before.
Any argument given against AI may have a certain degree of logic based on the subjective experience of naysayers. Or based on how much they are informed about it. So, people can be in denial or acceptance regarding whether intelligent machines would replace human jobs.
Discussing AI and Automation Impact: What drives people to believe that AI will replace all human jobs?
According to the leading investment bank, Goldman Sachs, 300 million full-time jobs globally would be lost or degraded to AI automation.
The report also extrapolates that roughly two-thirds of current jobs would be lost to AI automation. And generative AI would replace nearly one-fourth of current work. Another report by the World Economic Forum predicts loss of nearly 83 million jobs due to AI technology in the next five years.
Even if the report is a future projection of AI-led job displacements, the way AI industry is developing faster, we may have AI models equipped with more comprehensive capabilities and the potentials to automate more complex tasks.
That would be more frightening situations for human jobs across various industries, unless the automation creates more new jobs to offset the disruptions.
So, basically the fear of human jobs to be replaced by intelligent machines stems from how the business world is concentrating or gravitating toward automation and robotics.
A study by Frey and Osborne (2017) reported that 47 % of 702 American jobs faced a greater risk of automation, particularly works from transportation and unskilled production sectors.
Earlier, a report by Badiuzzaman and Rafiquzzaman (2020) found that automation and robotics posed lingering threats on the jobs of unskilled and lower-skilled labor in highly populated countries.
Debunking AI Myths: Final Note:
Based on the statistical reports, and the current state of artificial intelligence being believed to put human works at the risk of losing to automation, at least I don’t agree that human jobs are completely safe from AI. Why?
Because recently an India-based company, Paytm, fired one thousand of its employees, citing the implementation of automation in its business operations. According to the company’s press release note, Paytm experienced 40% more improvements in its operational efficiency post implementing AI-led automation.
In recent update, Microsoft is to lay off nearly 3% of its workforce due to AI. Meanwhile, Duolingo fired nearly 10% of its contract workers as the company shifts toward AI.
Even if we disapprove those reports conjuring up ghastly picture of job displacement due to AI automation and robotics in the future, what transpires meanwhile leaves me in no doubt that low-skilled, mundane, repetitive jobs would be replaced by AI.
And I also believe that the damage done by job displacements due to automation can be compensated by creating more new jobs. However, that depends on how corporate world thinks about it.
If you go by what Paytm has done, I don’t think companies would concern themselves with job displacements by AI. You may have seen Sam Altman talking at length about AI-led benefits for humans. However, he seems more focused on launching new powerful AI models than caring what happens to human workforce.
Debunking AI Myths #04: Artificial Intelligence is inherently biased and unfair
Months of using ChatGPT has at least given me this understanding that AI models are prone to fabricating answers (also known as hallucination, in tech term). However, I don’t think they are inherently biased on purpose.
It depends on how programmatically they are designed to behave. And, I also believe that a model being biased or impartial largely depends on the quality of training data. In fact, data is the most crucial part of any AI model. It is their food for thoughts.
So, the claim that AI is inherently biased or unfair can neither be disapproved nor acknowledged. It depends on the type of model you use and whether it has been programmed to behave unfairly.
Before I explain further, let me tell you what AI bias stands for.
What is AI Bias? – A brief detail about what makes AI systems become prejudiced.
In simple words, AI bias basically refers to the algorithm-based flaw of an AI system or a machine learning process. As a result, it outputs what seems prejudiced, discriminatory, skewed, or unfair.
Meaning, a biased AI system will tell you something according to how it has been trained using biased training data. And so, its answers may not seem okay, logical or rational. Pretty much a flawed AI system prejudicing people based on race, gender, or other factors like socioeconomic standing.
Examples of biased AI systems
Examples of biased AI systems can be found in various application areas.
For example, if there is a biased AI system in recruitment processes, a likelihood of not shortlisting female candidates over male ones or vice versa (regardless of talents and merits) is bound to happen.
Similarly, a biased AI system in banking sectors could disregard certain loan applicants based on its shortlisting criteria, such as skin colors, race, or financial history, according to how the system has been trained or programmed. For any such discriminatory AI system, it doesn’t matter whether you are a qualified loan applicant.
Also, an AI system that has been fed with biased crime data in law enforcement algorithms can output discriminatory results, like falsely targeting ethnic groups. This explains why public understanding of AI is skeptical.
What makes AI biased and unfair? Discussing the types of AI biases.
AI systems being biased and unfair is not necessarily their inherent or default problems. Various contributory factors are responsible for AI bias. They are:
- Historical Bias: It means AI system showing flawed judgment on candidates during hiring process. It happens when AI is fed with the historical hiring data reflecting current societal biases and inequalities. An AI model trained on this biased dataset may predict that women are less suitable or less likely to succeed in certain roles, considering they have had lower representation in those roles in the past (or historically).
- Representation Bias: The meaning of this bias refers to the situation in which an AI model is trained using data that does not accurately present diversified real-world population or the specific population. As a result, the AI model becomes flawed in its treatment to such population. For example, it may be more accommodating to the overrepresented race, gender, age, ethnicity, language and accent, etc. and less friendly toward the underrepresented ones, as per the quality of training dataset.
- Measurement Bias: An AI system trained on this kind of bias will output flawed or unfair predictions. It happens when the model learns from the data collected and labelled inaccurately across different groups.
- Aggregation bias: This happens due to the inappropriate combination of data from different groups without paying attention to the differences of key subgroup. Hence, such AI systems trained on aggregation bias will perform poorly for specific subgroups.
- Evaluation Bias: It means judging the performance of an AI system with different groups, just like judging a fish and a monkey on their ability to climb a tree.
Debunking AI Myths #05: Artificial Intelligence is a thing of the future
This is surely one of the AI misconceptions requiring an immediate rebuttal.
Fact is, AI is NOT a thing of the past, rather it is the thing of the present. AI models are already shaping human lives quietly, deeply, and irreversibly.
From virtual assistants in our smartphones to driverless cars and wide-scale automation in companies across industry, AI is already innovating human world. The thing is, when we subscribe to the notion that AI is a thing of the future, or is like a science-fiction movie, it narrows our understanding of it.
In fact, it blinds us from seeing how it is actively influencing almost everything in our world, from hiring processes and customer service to content creation, medical diagnosis, and more.
The Reality of AI Today: Good enough to be Impressive, but Limited
While AI has undoubtedly made some significant advancements in areas like image recognition, natural language processing, and predictive analytics, it is not without some limitations in areas such as general intelligence, common sense reasoning, and adaptability.
From automating repetitive tasks to excelling at jobs like translating texts, analyzing vast datasets to find patterns, and generating content, etc. the contributions of AI in creative performance is amazing.
However, it has not achieved common-sense reasoning and intuitive understanding like humans. Most importantly, their weakest thing is data. AI models are lifeless without quality datasets. Secondly, you can’t use one specific model designed for a particular task for another task unless the model is retrained thoroughly. These so-called smart machines lack consciousness and are prone to biases and fabrications.
Conclusively, AI comes in handy for executing specific tasks exceptionally well. However, relying on these machines over human intelligence and consciousness is unwise. Even though they are powerful tools, they are just tools.
Data Types: Understanding what makes data a driving force in building AI models
Technical speaking, data plays imperative role in AI development. In fact, without data, AI models are duds. Meaning, behind so many improvements and so-called capabilities of these models lies the power of training datasets. Data is Achilles heel of AI.
Generally, AI models are trained on variety of data types. They are:
- Structured data – It refers to the data neatly organized. It is mostly used in machine learning models. Customer databases, financial transaction logs are some examples of this data type.
- Unstructured data – Data without specific format or organization. Examples: text documents, images, videos, raw sensor data.
- Semi-structured data – As the name itself suggests, these data types include mixture of both structured and unstructured data. The use of such data is found in data exchange and web scrapping. Examples: JSON (JavaScript Object Notation) and XML (Extensible Markup Language) files.
- Time-series data – These data types are sequential data points indexed in time order. It records observations over regular time intervals. Examples: Stock prices over days, website traffic per hour.
- Textual data – It includes all forms of written words. Examples: articles, emails, chat logs.
- Image data – This consists of visual information captured by cameras or imaging devices. Examples include photos, medical scans, and satellite images.
- Audio data – This is all about sound waves that are picked up through microphones or audio devices. You might think of music or sonar data as examples.
- Sensory data – This is information collected from sensors, such as temperature sensors, accelerometers, and gyroscopes. Examples include motion sensor data from gyroscopes and biometric data from heart rate monitors.
What makes data important?
Data helps AI algorithms learn and improve their performance over time. An AI model’s accuracy and trust is directly impacted by training data. Meaning, biased training data would encourage the model to generate inaccurate outcomes.
Diverse and large datasets are very useful. They pave the path for new possibilities for AI applications driving innovations in diverse fields. Also, data enables AI machines to offer personalized experiences or providing customized search results.
Conclusively, data is not just an input for AI machines. It lays foundation upon which these smart machines learn and perform.
Does the role of data in AI developments augur safety and privacy concerns?
Yes, it does.
Because AI systems such as deep learning machines are trained using vast amount of data containing sensitive info, details, and behavioral patterns. The more data is collected, the more it increases the chance for privacy breaches and unauthorized access.
Another safety risk comes in the form of re-identification of anonymized data when combined with other datasets or when using advanced AI techniques.
The risk of AI models amplifying societal biases that may trigger discriminatory outcomes also augurs when these models are trained using data reflecting existing societal biases. Feels like Buddha’s wisdom quote, “You become what you think”. Similarly, the outcome generated by an AI model is the reflection of its training data quality and how it has been programmed to behave in specific manners.
And don’t forget that safety and privacy concerns can also occur due to absence of transparency in terms of how AI systems make decisions. To say otherwise, there is no safety net, AI guardrails in practice, or concrete measure in place that can know how an AI system arrives at a decision.
Reportedly, Amazon had to cancel out AI recruiting tool as it learned to discriminate female candidates based on historical data that favored men.
Debunking AI Myths : Differentiating Between Narrow & General AI (AGI)
Artificial Narrow Intelligence (ANI) or narrow AI is the common AI type that we generally see in our day to day life. Gemini, ChatGPT, and other types of AI models that can perform specific tasks or limited set of related tasks better fall under the category of narrow AI.
Though smarter than humans in terms of identifying complex patterns and making predictions with utmost accuracy within their defined parameters, narrow AI systems can’t think, learn, or understand in the same broader way that humans do.
They don’t have common sense, consciousness and ability to adapt their knowledge to altogether different tasks. For example, you can’t use Gemini AI model to automate warehouse inventory management other than using it to generate content. You would need a different AI system specifically designed for that purpose.
My personal standpoint:
When we tend to feed ourselves with the thought that current AI systems lack consciousness similar to humans, we are being oblivious to the fact that development of more powerful AI systems is always up and running.
It’s just a matter of time when this shortcoming of AI will have a credible solution. Surely it will set in motion the development of more powerful and thinking machines among us.
Debunking AI Myths: Artificial General Intelligence (Strong AI): Human-Level Intelligence
AGI, so far, is just a hypothetical artificial intelligence featuring human-level intelligence.
Therefore, the theory behind this so-called strong AI is that it can perform any intellectual task that a human can. For example, it will do tasks like reasoning, creativity, problem-solving, understanding complex concepts, having common sense, and intuitive judgment.
Nobody knows when AGI would come into existence, though experts like Sam Altman believe it would happen near time soon. And when this happens, nobody can say for sure how it would pan out impacting ethical and safety concerns for humanity and human society.
Debunking AI Myths: Embracing the Reality of Intelligent Machines
Based on the above discussion so far regarding the AI landscape, a grounded understanding about the unquestionable importance of AI’s true nature is imperative. It matters so that confusion about this world-changing technology is zero.
I have talked at great length about debunking AI myths, current state of artificial intelligence, and real applications of AI technologies. I also attempted to walk you through the details underscoring the distinctions between narrow AI and “so-far hypothetical” AGI.
Details related to debunking misconceptions surrounding AI serve as a qualified information clearing off uncalled for fear or unrealistic expectations associated with AI technology.
Debunking AI Myths: What AI future lies ahead, Utopian or Dystopian?
This exhaustive blog has also made an attempt to clarify that modern-day AI that we use is just a tool designed for specific tasks. This way, it allows us to remove the uncanny fear of AI hype so we can integrate AI technology into our lives with a balanced attitude.
This also means taking full advantage of the technology’s strengths while remaining alert of its potential downsides, weaknesses or pitfalls. The blog has tried to debunk the sensationalized claims made around uncontrolled powers of AI by portraying its factual realities. As a result, it will serve as a credible reminder to the informed readers to engage the technology with positive attitude.
As far as the future is concerned, human-AI collaboration seems a reality on the horizon. Whether most of us today look at askance with AI’s burgeoning powers, the day is not too far when we will have to arm with the grounded understanding of the technology welcoming it into our life, fostering a necessary coexistence devoid of fear and doubts.
But it also depends on whether AI technology is tied to ethical and other key parameters such as fairness, transparency and safety to be acceptable in human society.
Final words
At least, we can neither afford a Dystopian nor a Utopian AI future serving humans in no meaningful way at all. Because both the concepts vociferously promote the depiction of unreliable, volatile, and contrasting views of how AI would shape the future of humanity and society on a large scale.
For example, Dystopian views believe that AI is a destructive force heading for a future not safe for humans. Because these views highlight downsides of AI, in terms of job displacements, un-regulation, manipulation, ethical concerns, existential risks, and putting democracy in dangers.
On the other hand, Utopian concept of AI favors good side of the technology in terms of solving global challenges, improving human intelligence and creativity, increasing economic productivity of a country, and democratizing knowledge and opportunity for everyone.
Nothing can be said for sure what kind of AI future is in the making for humanity and society. Both utopian and dystopian views need time to be proven right.
Debunking AI Myths: My two cents on AI and the hype buildup around it
Despite all the hypes regarding so-called benefits driven by AI technology, I believe that it has benefited corporate players more than common people.
For now, it looks like the power of AI is heavily centralized, with few players, like OpenAI, Microsoft, and Google taking the center stage in AI model development.
Celebrities like Elon Musk have already said that AI danger is real and is going to be the cause of great concern unless fully regulated by federal laws. And I agree with them. Because the pace of rapid development of new models and algorithms in AI landscape is mind-blowing.
Common people have no idea what’s cooking under the AI veil. This lack of transparency augurs the chance for increasing myths and misconceptions about AI technology among people. As a result, a possibility of AI hype infecting more people globally is bound to happen.
I’d be more than happy if you share your personal views on this post.
Did you find it informative? Please have your say.