Hajra Naz
In the 21st century, AI is the fastest-growing technology. Some adore it, but others remain in disbelief or fear.
We can blame sci-fi movies for AI uprise plots, but there are many real human concerns about AI.
For the hell of it, and with a pocket-friendly Android with AI-powered software programs and algorithms, people wonder if their jobs still exist.
I will dispel myths and clarify the facts over the next chapters of this book.
1. Generative AI can only be used for tasks that require creativity
AI Generative is mostly associated with tasks using creativity like creating novel works of art, stories, and music.
Its applications though are not limited to these activities. In medicine, it creates synthetic training data and assists in diagnosing illnesses.
In finance, it evaluates risks and models market conditions to improve investment policies.
Read More: The Future of Artificial Intelligence: Generative AI and Its Game-Changing Impact
2. AI will take over all the jobs and there will be no jobs for anyone to do
Waves of pessimism are often caused by technological progress. Embracing new technology raises the specter that millions of jobs would be lost.
AI systems perform highly restricted and specialized functions, and most other functions are more broad and more diverse.
AI eliminates tedious and simple tasks, which frees people up to do what they are best at – critically engaging thoughts and emotionally intelligent activities.
By shifting responsibilities as well as increasing the entry level for most of these jobs, the job market will be changed indefinitely. For instance, let’s take the customer service industry.
It has already started using artificial intelligence in shaping up their jobs, virtual assistants and chatbots are employed to answer basic questions like password changes and balance inquiries, which reduces the need for beginner level workers.
For the workforce to stay relevant, they have to learn to operate AI Tools as well.
Being proficient in AI tools gives one an edge over their competitors.
AI will also fill the existing market gap by creating new employment, for example, data annotation specialists and compliance officers supporting AI Training and Implementation.
Read More: Freelance AI Jobs That Are in High Demand: Exciting Opportunities for Professionals
3. There is no way for fake AI detection tools to operate smoothly
AI detectors may not be blunt or usable tools for detecting which content is authentic and which is AI-generated.
There is research that has shown that AI detectors display engrained bias in favor of native English writers and against non-native English writers.
They rely on analyzing patterns of text produced by AI which overlap with the typical writing of people who are not native speakers of English.
Such technologies as content masking decrease the detection efficiency even more.
The aforementioned tools have some fundamental flaws caused by the principles of operation of AI detectors, which are similar in purpose to the systems they are trying to detect.
Words and phrases are all based on two parameters: perplexity and burstiness.
Perplexity indicates the degree of uncertainty contained in a text. AI generated content has typically, a lower degree of perplexity since it seems fluid but lacks uniqueness.
Whereas the content written by humans exhibits high degrees of perplexity due to the creativity along with some inaccuracy.
Burstiness evaluates the means of structure and the length of words. Usually, AI produced material has low burstiness which reduces the voice variety while writing.
Read More: Google Introduces a New Tool to detect AI-Generated Images
4. Everyday devices can’t handle AI processing
AI is often regarded as dependent on supercomputers and cloud infrastructure which makes it impractical for ordinary devices.
Meanwhile, pioneering work in the field of on-device processing makes ordinary devices perform powerful functions.
Arm and similar companies are helping bring about this transformation.
The new version of Arm’s Cortex CPUs, the Cortex-X925 today, enables up to 35% more instruction-per-clock from its latest offering, promising lower power requirements for quicker AI processing.
Arm’s Kleidi libraries, with their tools for developing on-device optimized AI solutions, help effect this change.
Google’s Gemini Nano and Apple Intelligence are perfect examples of this type of technology because they use those advances to give users that include AI-associated functions.
5. AI is just about becoming self-aware
Being built on the Transformer architecture, it is apparent that Chat GPT Deep learning does not have feelings or lived experiences.
They only predict without having an intelligence of their own, learning simply by mimicking.
This is also the case for neural networks as, while they have been modeled on the brain’s structure, they still cannot replicate the human’s cognition fully.
Moreover, the understanding of human consciousness and what being sentient is about is still a work in progress.
Because of this, other questions have arisen regarding machine sentience; for example, what would be the indicators of it?
It becomes challenging to establish operational criteria for the recognition of true machine consciousness, even if it is within the scope of theoretics.
The distance between AI systems and the complexities of human consciousness is huge. At this time, AI is not yet a companion, but still a resource.
6. AI has its decision-making processes still a mystery
AI systems can not all be judged in the same manner as a black box. Black box AI and explainable AI encompass the two major categories.
What makes a black box such a confusing AI is the lack of transparency in its various systems, making understanding processes hard.
However, understanding the reasoning can help users appeal. Researchers are working on new techniques to assist black box AI in becoming more transparent.
Interpreting the reasons for the predictions made for the selected elements (local explanations).
Building simple, human-interpretable rules from the patterns that have been learned (rule extraction).
7. Explaining how the system works (visualization techniques).
AI in a more simple term can be defined as automated and intelligent system, which operates autonomously and independently without human involvement.
It is a common misconception that AI is the same as machine learning as both of them are in the same domain of computer science.
While AI tries to build applications that emulate human activities such as thinking, reasoning, solving problems, and processing language, Machine Learning, which is a branch of AI, strives to create a system with a set of algorithms that learns from data to arrive at a specific conclusion.
To break down the sentence – AI has various applications other than ML, two of such applications are rule-based systems and logic programming, which both enable the performance of intelligent behavioral tasks without the usage of data-trained learning.
Equally, ML can exist independently from AI by such being mechanisms that are helpful and informative but are not exactly “intelligent”.
8. The concept that AI is automatically biased and unjust cannot stand.
The view that AI is automatically biased is likely to be held because of the absence of an understanding of how it works.
Nevertheless, recent reports make it easy to see why this perception exists. Bias in AI originates from the data used to train the AI model.
Let’s say, if a recruitment AI is to base its knowledge on previous hiring records which are biased because of race and gender, it will then carry out the same biases when making recommendations.
However, there is a caveat: AI systems cannot all be considered biased by nature. All those who demonstrate bias can undergo re-training or adjustments to eliminate bias for fair results.
The combination of proper bias ’ detection as well as AI-based bias mitigation can assist in reducing human bias in decision making processes by carefully constructing and pre-processing training data.
AI is the next tech revolution
Major technology companies are also pouring billions into AI, and China is developing a unique AI system of its own, which suggests that AI platforms mark the next step in tech evolution.
It is important to bear in mind, however, that falling for stories around AI is equally important as comprehending what it is capable of.
