All of it is statistics, there are just added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map and measure features from past training data with the input and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
THE DEFINITION OF STATISTICS
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
Thats exactly wtf every AI is doing. It doesn't have an imagination, it doesn't think up new concepts/ideas/philosophies, it crunches the numbers from past data and whatever input is given.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map and measure features from past training data with the input and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
THE DEFINITION OF STATISTICS
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
Thats exactly wtf every AI is doing. It doesn't have an imagination, it doesn't think up new concepts/ideas/philosophies, it crunches the numbers from past data and whatever input is given.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map and measure features from past training data with the input and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
THE DEFINITION OF STATISTICS
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
Thats exactly wtf every AI is doing. It doesn't have an imagination, it doesn't think up new concepts/ideas/philosophies, it crunches the numbers from past data and whatever input is given.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map and measure features from past training data with the input and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
THE DEFINITION OF STATISTICS
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
Thats exactly wtf every AI is doing. It doesn't have an imagination, it doesn't think up new concepts/ideas/philosophies, it crunches the numbers.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map and measure features from past training data with the input and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map features and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics in the background computing the features.
Classic ML is using statistics much more blatantly. You need to look deeper.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map features and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are measured against statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics. Classic ML is using statistics. You need to look deeper.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map features and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which becomes your output. Different potential outputs are computed statistically in the background and the highest value becomes the response.
Anything with a Neural Network is using statistics. Classic ML is using statistics. You need to look deeper.
All of it is statistics, there are just other added rules to the statistics.
For example
Speech Recognition: While statistical models like Hidden Markov Models are used, speech recognition also involves signal processing and pattern recognition techniques.
Signal Processing is very statistical heavy (This is why the next level up in knowledge is Statistical Signal Processing) and so is pattern recognition (which is basically ML).
Knowledge Representation and Reasoning: This involves creating symbolic models to represent knowledge, often without direct reliance on statistical methods.
Automated Statistics are used to map features and give you a decision. The AI is mapping the given input with what it has been trained to respond to. There is a threshold/max response to the given inputs and features computed which outputs your response.
Anything with a Neural Network is using statistics. Classic ML is using statistics. You need to look deeper.