ARTIFICIAL INTELLIGENCE
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ARTIFICIAL INTELLIGENCE |
What is artificial intelligence ?
Artificial Intelligence (AI) is a field of computer science dedicated to developing systems that can perform tasks usually requiring human intelligence. These tasks include learning from data, reasoning through complex problems, solving issues, understanding sensory inputs, and processing human language.
Types of artificial intelligence :
1. Narrow AI (Weak AI) :
Narrow AI is the most common type of AI that we interact with today. It is designed to handle a single or a narrow range of tasks. These systems operate under a predefined set of rules and constraints, meaning they cannot function beyond their specific purpose. Narrow AI does not possess true understanding of consciousness; it simply follows the instructions it has been programmed with.
Examples:
Voice Assistants: Siri, Alexa, and Google Assistant can perform specific tasks like setting reminders or answering questions.
Recommendation Systems: Netflix or Amazon recommendations, which suggest movies or products based on user behaviour.
Image Recognition: AI used in facial recognition or in medical imaging for detecting diseases.
2. General AI (Strong AI):
General AI is an advanced level of artificial intelligence that, in theory, would be capable of understanding, learning, and applying knowledge across a wide range of tasks at a level comparable to human intelligence. Unlike Narrow AI, General AI would have the flexibility to perform various tasks without being specifically programmed for each one. It would be able to think, reason, and adapt to new situations independently, much like a human being.
Examples:
Hypothetical Scenarios: A robot that could cook, clean, drive, write books, and conduct scientific research without needing specific instructions for each task.
Development Status: As of now, General AI does not exist, and we are far from creating machines that can truly mimic human cognition.
3. Superintelligent AI:
Superintelligent AI represents a level of intelligence that surpasses human capabilities in every way, including creativity, decision-making, and problem-solving. It is theorised that a superintelligent AI would be able to outperform humans in all fields, from science to the arts. This concept raises significant ethical and existential concerns, as such an AI could potentially become uncontrollable or develop goals that are misaligned with human well-being.
Examples:
Speculative: Superintelligent AI is purely speculative and remains a concept often explored in science fiction. It could be a machine that not only understands the world better than humans but can also create and implement solutions to complex problems that are beyond human comprehension.
Potential Risks: The idea of superintelligence often leads to discussions about the "AI singularity," a hypothetical point where AI grows uncontrollably, leading to unpredictable and possibly catastrophic outcomes.
History Of Artificial Intelligence :
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| History Of Artificial Intelligence |
1950s -1960s: The Beginnings
1950: Alan Turing proposes a test to see if machines can think like humans.
1956: The term "Artificial Intelligence" is created at a conference where researchers like John McCarthy start the field of AI.
1950s-1960s: Early AI work focuses on using logic and search algorithms to solve problems.
1970s -1980s: The Expert Systems Era
1970s: AI research slows down due to challenges and unmet expectations, known as the "AI Winter."
1980s: AI gets a boost with the creation of expert systems, like MYCIN, which mimic the decision-making of human experts.
1990s : AI's Comeback
1997: IBM’s Deep Blue defeats chess champion Garry Kasparov, showing AI’s ability to think strategically.
Late 1990s: AI research gains new life with advances in machine learning and neural networks.
2000s : AI Goes Mainstream
2000s: AI starts being used in everyday applications like speech recognition and robotics. Improved computing power helps AI grow.
2006: Deep learning becomes popular, thanks to researchers like Geoffrey Hinton, making it easier to analyse complex data.
2010s - Present : AI Takes Off
2011: IBM’s Watson wins the game show "Jeopardy!" against human champions, showing advanced language understanding.
2012: The deep learning model AlexNet achieves great success in recognizing images, setting a new standard.
2014-Present: AI technology continues to advance rapidly, with applications in self-driving cars, virtual assistants, and more. Major tech companies invest heavily in AI research.
Future
Present and Beyond: Researchers are working on creating AI that can think like humans and addressing important issues like ethics and job impacts. AI continues to evolve with new developments in learning and decision-making.
Artificial Intelligence Applications :
Healthcare :
AI helps doctors by analysing medical images to spot diseases like cancer early. It customises treatments based on individual health data to make them more effective. AI also predicts which patients might be at risk of certain conditions, allowing for earlier intervention. It speeds up drug discovery by predicting how new drugs will work. Overall, AI makes healthcare more precise and efficient.
Finance :
AI detects fraud by examining transaction patterns to catch suspicious activities and prevent financial crimes. It also supports high-speed trading by analysing market data to make profitable trades. AI helps evaluate creditworthiness by looking at more data points than traditional methods. Customer service improves with AI chatbots that handle common questions quickly. These uses of AI enhance security, efficiency, and customer satisfaction in finance.
Retail :
In retail, AI makes shopping more personalised by suggesting products based on what customers like. It helps manage inventory by predicting which products will be in demand, preventing overstocking or running out. AI chatbots provide instant help to shoppers, improving their experience. It also adjusts prices based on market trends to stay competitive. These AI tools make retail operations smoother and boost sales.
Transportation :
AI is transforming transportation by powering self-driving cars that can navigate and make decisions on their own. It improves traffic flow by predicting congestion and adjusting traffic signals in real time. AI also helps find the best routes for deliveries, saving time and fuel. In public transport, AI improves scheduling and planning to better serve passengers. These AI applications make transportation safer, more efficient, and more reliable.
Manufacturing :
AI predicts when machinery needs maintenance to avoid unexpected breakdowns. It also ensures product quality by automatically checking for defects. AI helps plan production schedules and allocate resources efficiently. Robots powered by AI take on repetitive or risky tasks, making work safer and more productive. Overall, AI improves reliability, efficiency, and quality in manufacturing.
Working of artificial intelligence in machine Learning
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