AI Fundamentals and Perceptrons

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Questions and Answers

What was the main conclusion of Minsky and Papert regarding single-layer perceptrons?

They produce better results than multi-layer perceptrons.

They are the most effective models for all AI applications.

They are limited in handling non-linearly separable data. (correct)

They can effectively solve complex NLP tasks.

What impact did Minsky and Papert's book 'Perceptrons' have on the AI community?

It ended the golden age of NLP.

It proposed new lucrative AI applications.

It marked the onset of the first AI winter. (correct)

It led to an increase in AI funding and interest.

Which logical function did Minsky and Papert use to illustrate the limitations of single-layer perceptrons?

NAND function

AND function

XOR function (correct)

OR function

What was highlighted as a key characteristic of the semantic network proposed by Quillian?

It represents knowledge as a network of interconnected nodes. Signup and view all the answers

During the first AI winter, what alternative approaches were explored in AI?

Algorithmic implementations of human language understanding. Signup and view all the answers

Which of the following programs were developed during the 'golden age' of NLP?

ELIZA and SHRDLU Signup and view all the answers

What is the primary function of semantic memory as proposed by Tulving?

To store general world knowledge. Signup and view all the answers

How do multi-layer perceptrons differ from single-layer perceptrons?

They include multiple layers of neurons for learning. Signup and view all the answers

What is essential for data analysis in machine learning?

Use of statistical methods Signup and view all the answers

Which of the following statements best describes neural networks?

They are a category of models used in machine learning. Signup and view all the answers

What does the technique Word2Vec primarily accomplish?

It creates dense word vectors using neural networks. Signup and view all the answers

What statistical method is primarily mentioned in relation to predictive models?

Statistical techniques from data science Signup and view all the answers

Which architecture is NOT associated with the development of Word2Vec?

Hierarchical Softmax Signup and view all the answers

What do GloVe vectors rely on for their architecture?

Word relationships and co-occurrence matrix Signup and view all the answers

What defines the primary outcome of the studies related to static spatial models in the 2010s?

The improvement of spatial models using neural networks Signup and view all the answers

Which term describes the process of making predictions based on data?

Predictive modeling Signup and view all the answers

What is a key characteristic of Recurrent Neural Networks (RNNs)?

They maintain an internal memory to remember past inputs. Signup and view all the answers

What problem do Recurrent Neural Networks commonly face during training?

The vanishing gradient problem. Signup and view all the answers

Which method is typically used to train Recurrent Neural Networks?

Backpropagation Through Time (BPTT) Signup and view all the answers

What was a consequence of the first AI winter?

Decreased expectations aligned with capabilities. Signup and view all the answers

What limitation affected the growth of AI technologies in the 1980s?

Hardware limitations on model complexity. Signup and view all the answers

Which of the following is NOT a feature of Recurrent Neural Networks?

They primarily use convolutional layers. Signup and view all the answers

What shift occurred in AI research due to disappointments in progress?

Emphasis on rule-based models and statistical methods. Signup and view all the answers

What significant issue does the vanishing gradient problem present in RNNs?

It makes it difficult to learn from early inputs. Signup and view all the answers

What is a significant advantage of larger Large Language Models (LLMs)?

They improve generalization capabilities. Signup and view all the answers

Which of the following describes In Context Learning in LLMs?

The capability to understand and maintain context over longer passages. Signup and view all the answers

What is the key feature of Step-by-Step Reasoning in LLMs?

Mimicking reasoning processes vital for problem-solving. Signup and view all the answers

What is one of the primary objectives when conducting an independent investigation into NLP models?

To explore the development, principles, and applications of a chosen topic. Signup and view all the answers

Why is training LLMs considered resource-intensive?

It can take substantial time and energy resources. Signup and view all the answers

What type of output is expected from the one-page essay on a chosen NLP model?

A detailed exploration with insights into the topic. Signup and view all the answers

Which of the following activities is encouraged while researching a topic in NLP?

Utilizing ChatGPT as a research tool. Signup and view all the answers

What is the preferred format for submitting the one-page essay?

PDF format via Turnitin. Signup and view all the answers

What is the main advantage of the GloVe model in comparison to traditional matrix factorization methods?

It utilizes global word co-occurrence statistics. Signup and view all the answers

What challenge do Long-Short Term Memory (LSTM) models effectively address?

The vanishing gradient problem in RNNs. Signup and view all the answers

What is a key feature of the ELMo model that differentiates it from earlier models?

It is the first model of Contextual Embeddings. Signup and view all the answers

What significant innovation does the Transformer model introduce?

Self-attention mechanism for handling long-range dependencies. Signup and view all the answers

Which of the following statements about Transformers is correct?

They scale better with the amount of data and resources. Signup and view all the answers

What is a notable downside of LSTM models compared to Transformer models?

They have higher computational costs due to sequential processing. Signup and view all the answers

What distinguishes Large Language Models (LLMs) from other AI models?

They are trained on massive and diverse text corpora. Signup and view all the answers

Which statement best describes the computational requirements of Large Language Models?

They require significant computational power, including high-performance GPUs or TPUs. Signup and view all the answers

What does the Prototype Theory suggest about categories?

Categories center around typical examples known as prototypes. Signup and view all the answers

What significant contribution to AI and NLP was made in 1986?

The development of the Backpropagation Algorithm. Signup and view all the answers

What are the two main steps of the Backpropagation Algorithm?

Forward and backward propagation. Signup and view all the answers

What advantage do feedforward neural networks have over n-gram models?

They can capture complex language patterns. Signup and view all the answers

What is a characteristic of a prototype in Prototype Theory?

It represents the best or most typical example of a category. Signup and view all the answers

How does the Backpropagation Algorithm improve learning in neural networks?

By minimizing the loss through weight updates based on error gradients. Signup and view all the answers

What limitation do n-gram models face that feedforward networks overcome?

Fixed context size limitations. Signup and view all the answers

What role does the Backpropagation Algorithm play in multi-layer perceptrons (MLPs)?

It provides a means for efficient training by propagating errors. Signup and view all the answers

Study Notes

NLP, Text Mining, and Semantic Analysis

This is a compulsory subject at the IE School of Science and Technology for the 2024/25 academic year.
The presenter is Alex Martínez-Mingo.

Session II: The Dawn of Computational Linguistics

This session focuses on the origins of computational linguistics.

What is Computational Linguistics?

Computational linguistics studies human language using automated computational methods.
These methods analyze, interpret, and generate human language.

Early Stages and Foundational Theories

The field of computational linguistics originated in the 1950s, spurred by the advancement of modern computers.
Previous events also contributed to its development.

The Turing Machine

Invented by Alan Turing in 1936.
A theoretical computing device that manipulates symbols on tape based on rules.
A foundational model for computation, capable of simulating any computer algorithm.
Crucial for the development of NLP.
Turing's work during WWII on the Enigma machine was a pioneering linguistic computational challenge.

The Artificial Neuron Model

Proposed by Warren McCulloch and Walter Pitts in 1943.
A pioneering conceptual model of a neuron with a simple mathematical model.
Bridged the gap between biological and computational models in cognitive science and neuroscience.
Introduced the idea of neural networks, a fundamental concept in NLP.
Modern deep learning techniques, including recurrent neural networks (RNNs) and transformers, are developments built on these early neural network ideas.

The Information Theory

Developed by Claude Shannon in 1948.
Introduced concepts like entropy, information content, and redundancy within communication systems.
Marked the beginning of digital communication.
Changed understanding of language as a form of information transfer.
Enabled quantification of information in language, facilitating NLP analysis.

The N-Gram Model

Shannon's entropy concept is crucial for language modeling.
The goal of language modeling is predicting sequence probabilities of words.
N-grams are a practical application of information theory to language modeling.
N-gram models predict the probability of a word based on the occurrence of the preceding (N-1) words.
This approach is a form of the Markov model (1913).

Early Stages and Foundational Theories (Georgetown Experiment)

The Georgetown Experiment (1954) is one of the earliest applications of n-grams.
Automated translation of Russian to English, using approximately 250 words and six grammatical rules.
Successfully translated 60 sentences.

The Perceptron

Developed by Frank Rosenblatt in 1958.
An early model in artificial intelligence.
Mimicked the human brain's decision-making process.
Operated by weighing input signals, summing them, and processing via a non-linear function to produce an output.
Provided a fundamental model for how machines process and classify linguistic data.
Essential concepts from perceptrons remain relevant in current NLP methodologies.

The Linguistic Wars

Significant intellectual debate within 20th-century linguistics.
Primarily between generative (Noam Chomsky) and behaviorist (B.F. Skinner) linguists, regarding language nature, acquisition, & understanding.
No clear winner; Chomsky's and Universal Grammar theories impacted linguistic theory.
Empirical and cognitive frameworks added to this discourse.

The Multi-Layer Perceptron

Proposed by Marvin Minsky and Seymour Papert as an extension of Rosenblatt's perceptron.
Learned complex patterns by combining outputs from previous layers.
Influenced the development of further neural network architectures in advanced NLP tasks.
A core component in many modern NLP systems.

The XOR Problem

Single-layer perceptrons are unable to solve problems where data isn't linearly separable.
Minsky and Papert used XOR (exclusive OR) for critique in "Perceptrons" (1969).
Led to disillusionment in the AI community and a funding reduction—a significant AI winter.

The First AI Winter

The 1960s and 1970s were marked by a "golden age" of rule-based NLP.
NLP during this time was predominantly based on rule-sets and Regular Expressions (RegEx).
Early AI programs such as ELIZA (1966) and SHRDLU (1972) were some examples of first AI systems.
Others sought to explain human language algorithmically.

The Semantic Network

Proposed by M. Ross Quillian in the 1960s.
Represents knowledge as a graph of interconnected nodes (concepts) connected via links representing relationships.
Demonstrates enhanced information retrieval using networked structures.
Influential in the development of knowledge graphs and ontology-based systems in NLP.

The Semantic Memory

Proposed by Endel Tulving in the 1970s.
Is a system for storing general knowledge of the world, rather than personal experiences (unlike episodic memory).
Provides a theoretical basis for understanding how knowledge and language are stored & retrieved in the human brain.
Guides the design of knowledge-representation systems within NLP.

The Prototype Theory

Developed by Eleanor Rosch in the 1970s.
Challenges the classical categorization theory.
Proposes that categories are centered on prototypes or typical examples instead of necessary & sufficient characteristic sets.
Prototypes are often the best or most typical instance of a category.
Influenced understandings of concept organization and categorization, influencing categorization and clustering algorithms in NLP.

The Renaissance of Connectionist Models

A resurgence of connectionist models in the 1980s.
Particularly around 1986, marked a significant shift in the field's perspectives and attention.

The Backpropagation Algorithm

Developed by David Rumelhart, Geoffrey Hinton, and Ronald Williams in 1986.
Enables efficient training of multi-layer perceptrons (MLPs).
Adjusts weights not just for output, also across all hidden layers.
Learns complex patterns & non-linear separations (like the XOR problem).
Employs forward and backward propagation steps for error calculation & weight updates.

Feedforward Models

Enabled by backpropagation algorithms.
Neural networks with non-cyclic connections.
Used for NLP classification & regressions tasks.
Advantages over n-gram models:
- Capture more complex language patterns.
- More flexible context sizes, reducing data sparsity issues for generalization.
Limitations:
- Struggle with capturing long-term dependencies in sequential data, needing internal memory for prior inputs for future predictions.

Recurrent Neural Networks (RNNs)

Developed by Jeffrey Elman in 1990.
Designed to process sequences by maintaining internal state (memory).
Ideal for sequential data requiring order awareness and contextual input understanding.
Crucial in generative language models.
Limitation: "vanishing gradient" problem.

Recurrent Neural Networks (RNNs) - Vanishing Gradient

Trained using Backpropagation Through Time (BPTT).
Unrolls the RNN for long sequences, leading to deep networks.
Gradients are propagated backward through time and multiplied via weight matrices at each step during backpropagation.
Weight multiplications lead to a diminishing gradient "vanishing" in effect.
Makes it difficult for RNNs to learn and retain information from the earlier steps within the sequence.

The Second AI Winter

AI advancements failed to meet expectations and requirements in the 1980s.
Hardware restrictions on model complexity and dataset size.
Decreased funding for and support from investors & governments.
Led to a shift towards more feasible rule-based and statistical approaches, including corpus-based linguistics.

Corpus-Based Linguistics

The Brown Corpus and British National Corpus (BNC).
Started in the 1960s and completed in the 1970s/1980s.
Becoming publicly available in the 1990s.
Provided a massive dataset for researchers enabling effective statistical linguistic methods.

Statistical Methods and Machine Learning

During the second AI winter, statistical methods gained prominence.
Provided unique approaches for making predictions about text data.
Subsequent application of machine learning enhanced algorithm performance.

Statistical Methods and Machine Learning - Models

Naive Bayes (based on Bayes' Theorem) became popular for text classification.
Utilized extensively for detecting spam emails & categorizing documents.
Aided by its efficiency in handling large datasets.
Logistic Regression, an older statistical method, surged in NLP for binary classification.
Best employed when features (words/phrases) and categories exhibited more linear (and less complex) relationships.

The Geometry of Meaning

First spatial models of language were developed in the 1990s.
Latent Semantic Analysis (LSA) model (Deerwester et al., 1990).
Used a term-document matrix and SVD (Singular Value Decomposition) to represent both terms & documents as vectors.
Hyperspace Analogue to Language (HAL) model (Lund and Burgess, 1996), used co-occurrence matrices and employed dimensional reduction methods to represent terms in a vector space.

The Geometry of Meaning - Word2Vec

Mikolov et al. (2013a, 2013b) introduced Word2Vec.
Two architectures (CBoW and Skip-Gram) to create dense word vectors using neural network models.

The Geometry of Meaning - GloVe

GloVe was developed by Pennington, Socher, and Manning (2014) at Stanford.
Explores the use of a co-occurrence matrix between words, across context windows, to encode relationships between words.
combines aspects of LSA/Matrix factorization and Word2Vec (context-based learning) to provide global word co-occurrence statistics.

The Last Connectionist Wave

Recent resurgence and refinement of more complex connectionist models in the 2010s and beyond demonstrated high success.

Long-Short Term Memory (LSTM)

Proposed by Hochreiter & Schmidhuber (1997) to address RNN's "vanishing gradient" problem.
Uses memory cells capable of retaining long-term information via gate mechanisms (input, output, forget).
This was essential for understanding and improving generative language models and other sequence-dependent tasks.

Transformers

Introduced by Vaswani et al. (2017) in the landmark paper "Attention is All You Need".
Handles long-range dependencies more efficiently through self-attention mechanisms.
Weights the importance of different parts within input data, irrespective of their position within a sequence.
Facilitates training parallelization.
Scaled well with data and computational resources enabling its use in large-scale NLP tasks.

Large Language Models (LLMs)

Advanced AI models trained on massive text corpora.
Key to performance is using massive amounts of data and high numbers of model parameters (billions).
Require significant computational power (GPUs or TPUs) and consume substantial resources.
Larger models demonstrate better performance due to scaling and improvement in abilities for generalization tasks.

Large Language Models - Training Compute

Training compute (FLOPs) of notable models.
Graph visually plots increases over time.

Large Language Models - In-Context Learning and Step-by-Step Reasoning

In-Context Learning: LLMs excel in context maintenance across extended passages.
Step-by-Step Reasoning: LLMs can mimic step-by-step problem-solving, logical reasoning, & technical troubleshooting.

Assignment: In-Depth Exploration of NLP Models or Techniques

Choose one NLP model or technique from the course catalog.
Independently investigate the chosen topic.
Research models or topics in detail. Utilize various sources including ChatGPT.
Write a one-page essay summarizing the model and discussing its development, underlying principles, applications, strengths, limitations, etc.
Submit the one page essay via Turnitin in PDF format before a designated due date.

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Description

This quiz explores the significant contributions of Minsky and Papert on single-layer perceptrons and the broader implications of their work on artificial intelligence. It covers their conclusions, the impact of their book 'Perceptrons', and other essential topics regarding neural networks and semantic memory. Test your understanding of these critical AI concepts!