9 Types of AI inference
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9 Types of AI inference
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AI inference refers to the process when AI models generate predictions, classifications, or decisions based on input data and pre-trained models. It encompasses a wide range of approaches with different computational methods and deployment.  
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AI inference refers to the process when AI models generate predictions, classifications, or decisions based on input data and pre-trained models. It encompasses a wide range of approaches with different computational methods and deployment.  
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Firstly, here are 5 inference types, based on how the model reasons:
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Firstly, here are 5 inference types, based on how the model reasons:
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1. Probabilistic inference -> 
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1. Probabilistic inference -> 
https://arxiv.org/pdf/2502.05244
https://arxiv.org/pdf/2502.05244
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Uses probability theory to reason under uncertainty. The system maintains degrees of belief over hypotheses and updates them as evidence comes in.
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Uses probability theory to reason under uncertainty. The system maintains degrees of belief over hypotheses and updates them as evidence comes in.
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2. Rule-based inference -> 
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2. Rule-based inference -> 
Logicbreaks: A Framework for Understanding Subversion of Rule-based
  Inference (2407.00075)
https://huggingface.co/papers/2407.00075
2407.00075
paper
resource
https://huggingface.co/papers/2407.00075
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Draws conclusions by applying explicit if-then rules encoded in a knowledge base. Mostly used in neurosymbolic AI.
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Draws conclusions by applying explicit if-then rules encoded in a knowledge base. Mostly used in neurosymbolic AI.
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3. Logical inference -> 
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3. Logical inference -> 
https://arxiv.org/abs/2009.03393
https://arxiv.org/abs/2009.03393
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Uses formal logic to draw conclusions that are guaranteed true if the premises are. It supports theorem proving, logic programming, and tasks needing correctness, like software verification.
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Uses formal logic to draw conclusions that are guaranteed true if the premises are. It supports theorem proving, logic programming, and tasks needing correctness, like software verification.
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4. Abductive inference -> 
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4. Abductive inference -> 
Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive
  Reasoning (2404.18982)
https://huggingface.co/papers/2404.18982
2404.18982
paper
resource
https://huggingface.co/papers/2404.18982
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Involves forming hypotheses that would best explain a given set of observations - among multiple possible explanations, the goal is to choose the most plausible. Abduction is inherently creative and uncertain.
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Involves forming hypotheses that would best explain a given set of observations - among multiple possible explanations, the goal is to choose the most plausible. Abduction is inherently creative and uncertain.
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5. Fuzzy inference -> 
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5. Fuzzy inference -> 
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System (2308.06378)
https://huggingface.co/papers/2308.06378
2308.06378
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https://huggingface.co/papers/2308.06378
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Applies fuzzy logic – reasoning with degrees of truth rather than binary true/false. Inputs are mapped to fuzzy sets with membership grades between 0 and 1.
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Applies fuzzy logic – reasoning with degrees of truth rather than binary true/false. Inputs are mapped to fuzzy sets with membership grades between 0 and 1.
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Secondly, here are 4 inference types based on its execution contexts:
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Secondly, here are 4 inference types based on its execution contexts:
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1. Batch inference -> 
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1. Batch inference -> 
BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix
  Sharing and Throughput-oriented Token Batching (2412.03594)
https://huggingface.co/papers/2412.03594
2412.03594
paper
resource
https://huggingface.co/papers/2412.03594
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Involves generating model predictions on large sets of data in bulk, often on a scheduled basis or as needed for analysis rather than immediate use.
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Involves generating model predictions on large sets of data in bulk, often on a scheduled basis or as needed for analysis rather than immediate use.
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2. Real-time inference -> 
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2. Real-time inference -> 
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal
  Transformer Operator (DiTTO) (2307.09072)
https://huggingface.co/papers/2307.09072
2307.09072
paper
resource
https://huggingface.co/papers/2307.09072
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Produces outputs on-demand with minimal latency, so results are available immediately when needed.
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Produces outputs on-demand with minimal latency, so results are available immediately when needed.
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Read further in the comments 👇
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Read further in the comments 👇
9 Types of AI inference

AI inference refers to the process when AI models generate predictions, classifications, or decisions based on input data and pre-trained models. It encompasses a wide range of approaches with different computational methods and deployment.  

Firstly, here are 5 inference types, based on how the model reasons:

1. Probabilistic inference -> https://arxiv.org/pdf/2502.05244 
Uses probability theory to reason under uncertainty. The system maintains degrees of belief over hypotheses and updates them as evidence comes in.

2. Rule-based inference -> https://huggingface.co/papers/2407.00075 
Draws conclusions by applying explicit if-then rules encoded in a knowledge base. Mostly used in neurosymbolic AI.

3. Logical inference -> https://arxiv.org/abs/2009.03393
Uses formal logic to draw conclusions that are guaranteed true if the premises are. It supports theorem proving, logic programming, and tasks needing correctness, like software verification.

4. Abductive inference -> https://huggingface.co/papers/2404.18982
Involves forming hypotheses that would best explain a given set of observations - among multiple possible explanations, the goal is to choose the most plausible. Abduction is inherently creative and uncertain.

5. Fuzzy inference -> https://huggingface.co/papers/2308.06378
Applies fuzzy logic – reasoning with degrees of truth rather than binary true/false. Inputs are mapped to fuzzy sets with membership grades between 0 and 1.

Secondly, here are 4 inference types based on its execution contexts:

1. Batch inference -> https://huggingface.co/papers/2412.03594 
Involves generating model predictions on large sets of data in bulk, often on a scheduled basis or as needed for analysis rather than immediate use.

2. Real-time inference -> https://huggingface.co/papers/2307.09072
Produces outputs on-demand with minimal latency, so results are available immediately when needed.

Read further in the comments 👇
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