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What is the great challenge for HI (Human intelligence) in era of AI (Artificial intelligence)

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Human likes to make it’s life easy going so that this creature always work day and night to make it happen and AI is nothing but a step closure to it.

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What is AI

  • Artificial Intelligence (AI) involves the development of computer systems that mimic human intelligence.
  • Transforms industries, enhances efficiency, and shapes human-technology interaction.
    • Working on ML (Machine learning) and DL (Deep learning).
  • Major help in field of
    • Healthcare: Diagnosis, personalized medicine.
    • Finance: Fraud detection, algorithmic trading.
    • Robotics: Autonomous vehicles, automation.
    • Language Processing : Natural language understanding, translation.
    • Information source : information or data get to know in one go.
  • Types of AI:
    • Narrow/Weak AI: Designed for specific tasks (e.g., virtual assistants).
    • General/Strong AI: Possesses human-like cognitive abilities across various tasks.
  • Challenges:
    • Ethical Concerns: Bias in algorithms, job displacement.
    • Security: Risks of malicious use and data breaches.

AI in demand is well known due to it’s effective deep learning. Artificial intelligence learns through two ways

  • Machine Learning: Subset of AI where algorithms learn from data to improve performance.
  • Deep Learning: A type of machine learning using deep neural networks for complex tasks

Initial stages and development

The inception of Machine Learning (ML) can be traced back to the mid-20th century. Here are key milestones:

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1940s-1950s: Alan Turing lays the groundwork with the Turing Test, proposing the idea of machines imitating human intelligence.

1956: The term “Artificial Intelligence” is coined, marking the Dartmouth Conference as a starting point for AI and ML research.

1960s-1970s: Early ML algorithms, including the development of the “nearest neighbor” algorithm and the perceptron model, contribute to the field’s foundation.

1980s-1990s: Backpropagation, a key algorithm for training neural networks, is introduced. Support Vector Machines (SVM) gain popularity.

2000s: Rise of online data and computing power leads to advancements in ML. Ensembles, boosting, and deep learning gain attention.

2010s: Deep learning experiences a renaissance with the use of deep neural networks, achieving breakthroughs in image and speech recognition.

Evolution of life with the machine learning

Where the world is known for diversity started experiencing the effectiveness of machine so no doubt always better tend to be best and out of predictions inadvertently human forced to evolve like machines so far that is all from nothingness because machine displaced human from it’s skilled or honorable positions. People serves moral, teach honesty and had deep faith lost it’s patience and somehow tried to find a work for their two times meaI. Not all had been gone bad unless devices came into market and spread likes spark kindled a fire in a dry forest. It was the superb strategy what held by business class and proved as a rat trap for a middle people who earned for need.

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Needy influenced by greedy

The trapped mind had been diseased due to eager to earn more that was all what they began to strive, two meal on time. Didn’t his grandfather or father bring two times meal in a day? Yes, they did but what a difference, they brought it out of love But his is out if stress. Machine occupied the youth mind of that time and impact not only in life but a culture and break a tradition like nothing else.

We will continue with the steps of greedy and actions or reaction of needy in next content.

Thank you

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