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Explaining Artificial Intelligence series - part 1

Artificial Intelligence robot reading

In this post, first of series I will be Explaining Artificial Intelligence.

Artificial Intelligence (AI) is new hot topic in field of IT - you can see more and more jobs postings, companies releasing new products related to AI. But what is AI?

AI refers to the simulation of human intelligence in machines or computer systems. It involves the development of algorithms, software, and hardware that enable computers to perform tasks that typically require human intelligence.

Basically in simple terms AI is programmed algorithms which eventually can see patterns and for example suggest answers based on knowledge it has gathered. AI can learn from experience (machine learning), reason, problem-solve, understand natural language, and perceive and interact with the environment. AI is a multidisciplinary field that draws upon various subfields of computer science and mathematics.

Just to illustrate how broad this field has become here are a some components and concepts within the field of AI:

  1. Machine Learning: Machine learning is a subset of AI that focuses on the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. It includes techniques like supervised learning, unsupervised learning, and reinforcement learning.

  2. Deep Learning: Deep learning is a subfield of machine learning that uses artificial neural networks to model and solve complex problems. It has been particularly successful in tasks such as image and speech recognition.

  3. Natural Language Processing (NLP): NLP involves the development of algorithms and models that enable computers to understand, interpret, and generate human language. It's used in applications like chatbots, language translation, and sentiment analysis.

  4. Computer Vision: Computer vision focuses on enabling computers to interpret and understand visual information from the world, such as images and videos. It's essential for tasks like image classification, object detection, and facial recognition.

  5. Robotics: AI plays a crucial role in robotics by enabling robots to perceive their environment, make autonomous decisions, and interact with objects and humans. This field is vital for applications in manufacturing, healthcare, and autonomous vehicles.

  6. Expert Systems: Expert systems are AI programs designed to mimic the decision-making abilities of a human expert in a specific domain. They use knowledge bases and rule-based reasoning to provide advice or solve problems.

  7. AI Ethics: AI ethics addresses the ethical and societal implications of AI technologies, including issues related to fairness, bias, transparency, accountability, and the impact of AI on employment and society.

  8. Reinforcement Learning: Reinforcement learning is a type of machine learning where agents learn to make a sequence of decisions to maximize a reward in an environment. It's often used in autonomous systems, gaming, and robotics.

  9. AI Hardware: Specialized hardware, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), is designed to accelerate AI workloads, particularly deep learning tasks.

AI has a wide range of applications across various industries, including healthcare (diagnosis and treatment planning), finance (algorithmic trading and fraud detection), entertainment (recommendation systems), transportation (self-driving cars), and more. It continues to evolve rapidly, pushing the boundaries of what machines can achieve and having a profound impact on society and the economy.

As you can see AI has a lot of potential to impact our lives going forward. In part 2 I will write some examples from real life how AI is making impact.

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