Week 3 Flashcards
Algorithmic thinking and management of AI
What is AI?
AI involves an attempt to build computer systems that think and act like humans
Grand vision: Computer hardware or software systems that are as smart as humans
Realistic vision: Syt
stems that take data inputs and produce outputs, and that can perform complex tasks that are difficult or impossible for humans to perform.
Examples of AI:
- Recognize faces
- Intepret CT scans
- Analyze millions of financial records
- Detect patterns in very large Big Data databases
- Answer questions from humans
- Play chess
- Navigate a car
Major types of AI
- expert systems
- neural network
- machine learning
- deep learning
- intelligent agents
- genetic algorithms
- natural language processing
- robotics
- computer vision systems
What do expert systems do?
Capture tacit knowledge in very specific and limited domain of human expertise. Capture knowldge as a set of rules. Peform very limited tasks.
What are expert systems used for?
Expert systems are used for discrete, highly structured decision making.
Components of Expert Systems
- User interface: most crucial part- takes the user’s query and sends it back to the interface engine following that, it displays results to the user
- Inference engine: searches through the rules to solve a specific problem, formulates conclusion
- Knowledge base: repository of hundreds of thousands of rules
Benefits of expert systems:
- improve decisions
- reduces costs and training time
- reduces errors
- enable better quality and service
Limitations of expert systems:
- experts can’t explain how they make decisions
- rules change and need to be continually updated
- knowledge base can become chaotic
- not useful for unstructured data
- do not scale well to very large datasets
Machine learning
- ML begins with a very large data set
- Main focus is on finding patterns in data and classifying data into known and unknown outputs
- Different pardigm than expert systems
- Most of today’s Big Data analytics use machine learning
- Patpal, spotify
How do we classify machine learning?
Supervised learning: Humans provide examples of desired inputs and outputs. defined by its use of labled datasets to train algorithms to predict desired output.
Unsupervised learning: Humans do not provide examples. Uses machine learning algorithms to analyze and cluster unlabled datasets.
Neural networks
- insipred by the structure and function of human brain
- finds patterns and relationships in massive amounts of data too complicated for humans to analyze
How do neural netowrks learn patterns?
- learn patterns by searching for relationship, building models and correcting over and over again
- humans train it by feeding it inputs for which outputs are known, to help neural network learn from human experts
Examples of neural networks
- face recognition, speech recognition, and natural language processing
What are deep learning neural networks?
- more complex, with many layers of transformation of input to get to desired output
- used almost exclusively for pattern detection on unlabled data
- come closest to grand vision of AI
Genetic algorithms
- useful for finding a solution to a rpoblem by examining many possible solutions
- based on process of evolution
- search among solution variables by changing and reorganizing parts through processes such as: inheritence, mutation, and selection.
- used in optimization problems
- able to evaluate many solutions quickly
