AI generally refers to applications that use non-human intelligence to solve a wide range of real-life or theoretical problems. In other words, AI enables machines to learn from experience, adapt to new inputs, and perform human-like tasks. Subsegments of AI typically include analytical, human-inspired, and humanized systems:
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Analytical AI systems are linked solely to cognitive intelligence. They generate a cognitive representation of the world and use learning from past experiences to make future decisions.
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Human-inspired AI incorporates elements of both cognitive and emotional intelligence, allowing it to understand human emotions while performing cognitive tasks.
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Humanized AI combines cognitive, emotional, and social intelligence, enabling it to act in a self-aware and self-conscious manner.
The term "Big Data" is normally referred to datasets that are too large and complex to be analyzed with conventional tools. The size and complexity of these datasets necessitate the use of specialized tools for data extraction, storage, and analysis. While AI is mostly used for analyzing Big Data, it is considered the most intricate element in dealing with these datasets.
AI applications are currently used in a variety of real-life scenarios:
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Self-driving vehicles: AI uses sensors to create a virtual map of the surroundings and sends the data to AI software. This software can avoid obstacles, differentiate objects, and enable tasks such as lane assistance, acceleration, deceleration, and distance maintenance.
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Healthcare: AI can provide personalized diagnostics and prescribe medications. Personal healthcare assistants act as life coaches, reminding patients to take medication, exercise, or maintain a healthy diet.
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Manufacturing: AI analyzes production data streaming from equipment to forecast load and demand using recurrent networks—a specific type of deep learning network designed for sequence data.
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Retail: AI offers virtual shopping capabilities with personalized recommendations for consumers. Stock management and store layout optimization are also enhanced through AI.
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Banking: AI techniques identify fraudulent transactions, assist with credit scoring, and automate manual data management tasks.
One emerging trend that is set to fundamentally change AI is automated machine learning (AutoML). AutoML will empower business analysts and developers to create machine learning models that address complex scenarios without needing to follow the traditional AI training process.
However, the lack of interoperability among neural network toolkits is hindering the widespread adoption of AI. To address this challenge, several initiatives have been implemented, enabling the reuse of trained neural network models across multiple frameworks.
