July 4, 2024AI Development Stages: ANI → AGI → ASI
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Artificial Intelligence is often described in three stages, based on capability and scope:
- ANI: Task-specific intelligence (today’s AI)
- AGI: Human-level general intelligence (future goal)
- ASI: Beyond human intelligence (theoretical)
ANI — Artificial Narrow Intelligence
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- also called Weak AI
- designed to perform one specific task
- Operates within a predefined environment
- Cannot generalise beyond its training
- Most AI systems today are ANI
examples
Machine learning system optimisation begins with measurement. Before changing an algorithm, adding processors, or moving work to a GPU, we need to understand what is slow, which resource is limiting performance, and how performance changes as the workload grows.
This page covers:
- time and space complexity
- throughput and latency
- the relationship between workload, throughput, and latency
- the main measurements used to describe system performance
Learning Objectives
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By the end of this page, you should be able to:
Reinforcement Learning
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Reinforcement Learning (RL) is a learning approach in which an agent improves its behaviour by interacting with an environment and observing the rewards produced by its actions.
Unlike supervised learning, the agent is not given a correct action label for every situation. It must discover useful behaviour through trial, feedback, and repeated interaction.
Reinforcement learning is goal-oriented learning through interaction.
Why Reinforcement Learning?
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RL is useful when decisions affect what happens next and the quality of an action may only become clear later.
Neural Networks
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- A network of artificial neurons inspired by how neurons function in the human brain.
- At its core - a mathematical model designed to process and learn from data.
- Neural networks form the foundation of Deep Learning (involves training large and complex networks on vast amounts of data).
flowchart LR
subgraph subGraph0["Input Layer"]
I1(("Input 1"))
I2(("Input 2"))
I3(("Input 3"))
end
subgraph subGraph1["Hidden Layer"]
H1(("Hidden 1"))
H2(("Hidden 2"))
H3(("Hidden 3"))
end
subgraph subGraph2["Output Layer"]
O(("Output"))
end
I1 --> H1 & H2 & H3
I2 --> H1 & H2 & H3
I3 --> H1 & H2 & H3
H1 --> O
H2 --> O
H3 --> O
style I1 fill:#C8E6C9
style I2 fill:#C8E6C9
style I3 fill:#C8E6C9
style H1 stroke:#2962FF,fill:#BBDEFB
style H2 fill:#BBDEFB
style H3 fill:#BBDEFB
style O fill:#FFCDD2
style subGraph0 stroke:none,fill:transparent
style subGraph1 stroke:none,fill:transparent
style subGraph2 stroke:none,fill:transparent
Structure of a Neural Network
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A typical neural network has three main layers:
Artificial Neuron and Perceptron
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knowledge in neural networks is stored in connection weights, and learning means modifying those weights.
Biological Neuron
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A biological neuron is a specialised cell that processes and transmits information through electrical and chemical signals.
Core components:
- Dendrites: receive signals from other neurons
- Cell body (soma): processes incoming signals
- Axon: transmits the output signal
- Synapses: connection points between neurons
Biological intuition:
- many inputs arrive to one neuron
- one neuron can connect out to many neurons
- massive parallelism enables fast perception and recognition
Artificial Neuron
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An artificial neuron is a simplified computational model inspired by biological neurons.
Machine learning Workflow
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Data is the foundation of any machine learning system.
Quality of data matters more than model complexity.
Role of Data
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Data determines:
- What patterns the model can learn
- How well it generalises
- Whether bias or noise is introduced
Bad data → bad model (even with perfect algorithms).
Data Preprocessing, wrangling
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Raw data is never ready for training.
Data Issues
- Noise
- For objects, noise is an extraneous object
- For attributes, noise refers to modification of original values
- Use Log or Z Transfer to convert to mean
- Outliers
- Data objects with characteristics that are considerably different than most of the other data objects in the data set
- Handle: Use IQR method
- Find Lower and Upper Bound and replace Outlier with Lower or Upper Bound
- Missing Values
- Eliminate data objects or variables
- Handle: Estimate missing values
- Mean, Median or Mode
- Prefer Median if there are missing outliers
- Ignore the missing value during analysis
- Duplicate Data
- Major issue when merging data from heterogeneous sources
- Inconsistent Codes
- Find all Unique and transfer all inconsistent to
Data Preprocessing techniques
Multi-Armed Bandit Problem
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The Multi-Armed Bandit (MAB) problem is the simplest setting for studying decision-making under uncertainty.
An agent repeatedly chooses one of
\( k \)
actions. Each action produces a numerical reward drawn from an unknown distribution. The objective is to maximise the expected total reward over time.
The central challenge is deciding when to exploit current knowledge and when to explore uncertain alternatives.
The k-Armed Bandit Problem ☆
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At every time step:
Parallel and Distributed Algorithms
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Parallelisation divides computational work into parts that can execute concurrently. The purpose is to reduce completion time or increase throughput, but the gain depends on how much work is genuinely independent and how much overhead is introduced.
This page covers:
- speedup, maximum speedup, and processor efficiency
- Amdahl’s Law
- data-level parallelism
- task-level parallelism
- algorithm-specific parallelism
- communication, synchronisation, scheduling, and load-balancing overhead
- parallel merge sort and matrix multiplication
Learning Objectives
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By the end of this page, you should be able to:
Markov Decision Process Framework
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A Markov Decision Process (MDP) is a mathematical framework for modelling sequential decisions. It describes the situations an agent may encounter, the actions it may take, how the environment may change, and the rewards produced by those changes.
Bandit problems ask which action is best in a single recurring situation. An MDP adds changing states: an action affects not only the immediate reward but also the situation faced next.
N-gram Language Modelling
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A language model assigns probabilities to sequences of words. It can compare complete sentences or predict which word is likely to come next.
Key ideas include:
- word prediction and sequence probability
- the chain rule and Markov assumption
- unigram, bigram and trigram models
- Maximum Likelihood Estimation
- unseen sequences and smoothing
- interpolation and backoff
- intrinsic and extrinsic evaluation
- perplexity
Learning Objectives
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- Explain what a language model represents.
- Calculate simple unigram and bigram probabilities.
- Explain why unseen N-grams create zero probabilities.
- Distinguish smoothing, interpolation and backoff.
- Interpret perplexity correctly.
Big Picture
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flowchart TD
A["Training Corpus"] --> B["Count N-grams"]
B --> C["Estimate Probabilities"]
C --> D["Handle Unseen Events"]
D --> E["Score Word Sequences"]
E --> F["Evaluate Model"]
style A fill:#E1F5FE
style B fill:#C8E6C9
style C fill:#FFF9C4
style D fill:#EDE7F6
style E fill:#E1F5FE
style F fill:#C8E6C9
1. What Is a Language Model? ☆
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A language model estimates how probable a sequence of words is.