Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society.
source: Impact of artificial intelligence on human loss in decision making, laziness and safety in education https://www.nature.com/articles/s41599-023-01787-8
The comparison between human intelligence and artificial intelligence (AI) has been a topic of great interest and debate in recent years. As AI continues to advance at an unprecedented rate, it becomes crucial to develop metrics that can accurately assess its level of intelligence and compare it with human intelligence. Several metrics have been proposed, but they face challenges due to the complexity and diversity of human intelligence.
One commonly used metric is the Turing Test, which evaluates whether a machine can exhibit intelligent behavior indistinguishable from that of a human. However, critics argue that this test only measures the ability to mimic human responses rather than true understanding or consciousness. Another metric is cognitive ability tests, which assess problem-solving skills and logical reasoning. While these tests provide valuable insights into AI's capabilities, they fail to capture other aspects of human intelligence such as creativity or emotional intelligence.
To overcome these limitations, researchers are exploring alternative metrics like the Winograd Schema Challenge (WSC). This test requires machines to understand ambiguous sentences by relying on contextual knowledge and common sense reasoning. By focusing on comprehension rather than mere pattern recognition, WSC aims to evaluate AI's ability to understand language in a more nuanced way.
Furthermore, advancements in natural language processing have led to the development of tools like GPT-3 (Generative Pre-trained Transformer 3), which can generate coherent text based on given prompts. GPT-3 represents a significant leap forward in AI's language generation capabilities; however, it still lacks true understanding or consciousness.
Assessing the advancement of AI also requires considering its real-world applications. For instance, self-driving cars are an area where AI has made significant progress. Metrics like accident rates or successful navigation through complex environments can be used as indicators of AI's advancement in this domain.
In conclusion, comparing the level of human intelligence with AI is a complex task that requires the development of appropriate metrics. While existing metrics like the Turing Test and cognitive ability tests provide valuable insights, they have limitations in capturing the full spectrum of human intelligence. Alternative metrics like the WSC and real-world application assessments offer promising avenues for evaluating AI's advancement. As AI continues to evolve, it is essential to refine these metrics and develop new tools that can comprehensively assess its intelligence.
Reference:
Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson Education.
Your reported data Eduard Babulak point to a great educational challenge. If a smart phone, for example, is principally used as ‚Idiot compass‘ , the human behavioral consequences are loss in decision-making (general orientation), safety and laziness.
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If we use accuracy and efficiency as our key metrics to compare AI and human performance, we need to put them into a matrix. A simple way to do this is to create performance bands. If we take human performance as the benchmark, we can create adjacent bands for below-human performance, and above-human performance.
We can then use the matrix (see below) to plot where AI performance lands on this scale. The benefit of this matrix is that it can be applied to many different contexts, and the standards for below-human, human, and above-human are relative to each task. (Citation from:
https://redmarker.ai/en-us/post/comparing-ai-performance-to-humans
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While artificial intelligence includes technologies that let computers mimic cognitive processes such as learning and problem-solving, human intelligence is a collection of common mental traits such as creativity, perception, and memory.
There are many studies that show humans are productive only about 3 to 4 hours in a day. Humans also need breaks and time offs to balance their work life and personal life. But AI can work endlessly without breaks. They think much faster than humans and perform multiple tasks at a time with accurate results. AI cannot compete with human creativity. We can come up with new ideas, solve problems in unique ways, and create art and music that evoke emotion in others. AI may be able to mimic creative processes, but it cannot truly understand or create in the same way that humans can.
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Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition.
Sebastian Thrun
A year spent in artificial intelligence is enough to make one believe in God.
Alan Perlis
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