Differences between AI, ML and DL

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AI (Artificial Intelligence)

• AI covers a wide range of tools and methods that replicate human intelligence in machines.
• Artificial intelligence can be applied to a wide range of data types, including structured, unstructured, and semi-structured data.
• Given that they can use a variety of different methodologies and algorithms, AI systems can be challenging to understand and comprehend.
• As AI systems sometimes entail more sophisticated algorithms and processing, they can be slower and less effective than ML and DL systems.
• AI can be applied to a wide range of applications, including natural language processing, computer vision, robotics, and decision-making systems.
• AI systems can be fully autonomous or require some level of human intervention.
• It requires a large team of professionals to create and manage AI systems as they can be quite complicated.
• Given that they frequently include complicated algorithms and Given that they frequently include complicated algorithms and processing, AI systems can be challenging to scale.
• As AI systems frequently use fixed methods and processing, they might be less flexible than ML and DL systems.
• The need for substantial volumes of data to train properly is one drawback of AI, ML, and DL.

ML (Machine Learning)

• Machine learning is a subset of AI that includes teaching machines to learn from data and make predictions or judgments based on that data. For applications like image identification, natural language processing, and anomaly detection, ML techniques can be employed.
• For ML to learn from and make predictions or judgments, it needs labeled training data.
• As ML models rely on statistical models and algorithms, they can be easier to comprehend.
• Due to their reliance on statistical models and algorithms, ML systems have the potential to be quicker and more effective than AI systems.
• Many of the same applications as AI may be used for ML, but with a focus on data-driven learning.
• ML systems are created to automatically learn from data with little assistance from humans.
• ML systems can be less complex than AI systems since they rely on statistical models and algorithms.
• As ML systems rely on statistical models and algorithms that can be taught on big datasets, they can be more scalable than AI systems.
• As ML systems can learn from fresh data and modify their predictions or choices, they may be more flexible and adaptable than AI systems.
• The quality of the data can also have an impact on the accuracy and robustness of the ML model and collecting and labeling data can be time-consuming and expensive.

DL (Deep Learning)

• DL is a specialized subset of ML that mimics how the human brain functions using artificial neural networks. Image and speech recognition are two examples of complex subjects that DL is exceptionally effective at solving.
• To efficiently train deep neural networks, DL requires vast volumes of labeled data.
• DL models are sometimes regarded as “black boxes” because they include several layers of neurons that might be difficult to read and comprehend.
• As deep neural networks are trained using specialized hardware and parallel computing, DL systems have the potential to be the fastest and most effective out of the three methods.
• DL is particularly well-suited for applications requiring complex pattern recognition, such as image and audio recognition, as well as natural language processing.
• Some human interaction is required in DL systems, such as determining the design and hyperparameters of the neural network.
• DL systems can be the most complex since they involve many layers of neurons and require specialized hardware and software to train deep neural networks.
• DL systems can be the most scalable since they use specialized hardware and parallel processing to train deep neural networks.
• Because of its capacity to learn from vast volumes of data and adjust to new circumstances and tasks, DL systems have the potential to be the most adaptive.
• Deep neural network training in DL can be computationally complex and need specialized gear and software, which can be costly and restrict the technology’s accessibility.

Thanks .

Ranjeet Jangra

Network and Cloud Automation Professional with 15 years of experience in Development | Testing | Deployment | Support | Automation on various Technologies like IP-Routing, Cloud, Programming, Containers, Kubernetes, Telemetry, Orchestration, Network-Programmability, YANG, TextFSM, Jinja, RestAPI , Terraform , AWS , Ansible , Cisco NSO , observability and so on .
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