- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
- View global site search results
- A
- AI Engineer
- AI Observability
- AIOps
- Artificial Intelligence Wiki
- Automated Machine Learning
- Autopilot Mode
- B
- Big Data
- C
- Citizen Data Scientist
- Classification
- Cognitive Computing
- Confusion Matrix
- Cross-Validation
- D
- Data Collection
- Data Governance
- Data Insights
- Data Management
- Data Preparation
- Data Profiling
- Data Science
- Deep Learning Algorithms
- E
- Explainable AI
- F
- Feature Engineering
- Feature Impact
- Feature Selection
- Feature Variables
- G
- Generative AI
- L
- Large Language Model Operations (LLMOps)
- M
- Machine Learning
- Machine Learning Algorithms
- Machine Learning Life Cycle
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Machine Learning Operations (MLOps)
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Monitoring
- Model Tuning
- Multiclass Classification
- N
- Natural Language Processing
- Neural Network
- O
- Open Source Model Infrastructure
- Overfitting
- P
- Prediction
- Prediction Explanations
- Predictive Maintenance
- Production Model Governance
- Production Model Lifecycle Management
- R
- Regression
- S
- Scoring Data
- Semi-Supervised Machine Learning
- Stacked Predictions
- Supervised Machine Learning
- T
- Target Leakage
- Target Variable
- Text Mining
- Training Sets, Validation Sets, and Holdout Sets
- U
- Underfitting
- Unsupervised Machine Learning
- W
- What is Artificial Intelligence (AI)?
- View global site search results
- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
Cognitive Computing
What is Cognitive Computing?
Cognitive computing is commonly thought of as a type of artificial intelligence (AI) that uses an algorithmic model to simulate the process of human thought. It is often used as an umbrella term for a new class of technology intended to do work previously done by knowledge workers.
However, cognitive computing is an ambiguous term. Wikipedia notes: “at present, there is no widely agreed upon definition for cognitive computing in either academia or industry.” For example, it can be viewed either as:
- Simulating human processes (biological realism)
- Simulating human capabilities
The phrase “cognitive computing” does not describe a technique, a set of algorithms or implementations, or even a clear set of core capabilities. According to Tom Austin, vice president and fellow at Gartner:
“‘Cognitive’ is marketing malarkey. It implies machines think. Nonsense. Bad assumptions lead to bad conclusions.”
Cognitive Computing + DataRobot
DataRobot aims to cut through the hype surrounding cognitive computing by building real, practical enterprise automated machine learning software which can be applied to everyday business problems. We don’t claim to be developing human brains or replacing human capabilities – we just help users of all analytical skill levels make better business decisions based on data.