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Artificial Intelligence, Machine Learning, and (Large) Language Models: A Quick Introduction
- 1. Artificial Intelligence, Machine Learning,and (Large) Language Models: A Quick Introduction Hiroki Sayama sayama@binghamton.edu
- 2. Outline 1. The Origin:Understanding “Intelligence” 2. Key Ingredient I: Statistics & Data Analytics 3. Key Ingredient II: Optimization 4. Machine Learning 5. Artificial Neural Networks 6. (Large) Language Models 7. Challenges 2
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- 4. Alan Turing andthe Turing Machine (1936) 4 https://www.felienne.com/archives/2974
- 5. Turing Test (1950)– a.k.a. “the Imitation Game” 5 https://en.wikipedia.org/wiki/Turing_test
- 6. McCulloch-Pitts Model (1943) 6 The firstformal model of computational mechanisms of (artificial) neurons
- 7. Basis of Modern Artificial Neural Networks 7 Multilayer perceptron (Rosenblatt1958) Backpropagation (Rumelhart, Hinton & Williams 1986) Deep learning https://commons.wikimedia.org/wiki/File: Example_of_a_deep_neural_network.png
- 8.
- 9. “Cybernetics” as a Precursorto “AI” 9 Norbert Wiener (This is where the word “cyber-” came from!)
- 10. Good Old-Fashioned AI: SymbolicComputation and Reasoning ▪ Herbert Simon et al.’s “Logic Theorist” (1956) ▪ Functional programming, list processing (e.g., LISP (1955-)) ▪ Logic-based chatbots (e.g., ELIZA (1966)) ▪ Expert systems ▪ Fuzzy logic (Zadeh, 1965) 10
- 11.
- 12. Key Ingredient I: Statistics & DataAnalytics 12
- 13. Pattern Discovery, Classic Way ▪Descriptive statistics ▪ Distribution, correlation, regression ▪ Inferential statistics ▪ Hypothesis testing, estimation, Bayesian inference ▪ Parametric / non-parametric approaches 13 https://en.wikipedia.org/wiki/Statistics
- 14. Regression ▪ Legendre, Gauss(early 1800s) ▪ Representing the behavior of a dependent variable (DV) as a function of independent variable(s) (IV) ▪ Linear regression, polynomial regression, logistic regression, etc. ▪ Optimization (minimization) of errors between model and data 14 https://en.wikipedia.org/wiki/Regression_analysis https://en.wikipedia.org/wiki/Polynomial_regression
- 15. Hypothesis Testing ▪ Originalidea dates back to 1700s ▪ Pearson, Gosset, Fisher (early 1900s) ▪ Set up hypothesis(-ses) and see how (un)likely the observed data could be explained by them ▪ Type-I error (false positive), Type-II error (false negative) 15 https://en.wikibooks.org/wiki/Statistics/Testing _Statistical_Hypothesis
- 16. Bayesian Inference ▪ Bayes& Price (1763), Laplace (1774) ▪ Probability as a degree of belief that an event or a proposition is true ▪ Estimated likelihoods updated as additional data are obtained ▪ Empowered by Markov Chain Monte Carlo (MCMC) numerical integration methods (Metropolis 1953; Hastings 1970) 16 https://en.wikipedia.org/wiki/Bayes%27_theorem https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
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- 18. Least Squares Method ▪Legendre, Gauss (early 1800s) ▪ Find the formula that minimizes the sum of squared errors (residuals) analytically 18 https://en.wikipedia.org/wiki/Least_squares
- 19. Gradient Methods ▪ Findlocal minimum of a function computationally ▪ Gradient descent (Cauchy 1847) and its variants ▪ More than 150 years later, this is still what modern AI/ML/DL systems are essentially doing!! ▪ Error minimization 19 https://commons.wikimedia.org/wiki/File: Gradient_descent.gif
- 20. Linear/Nonlinear/Integer/ Dynamic Programming ▪ Extensivelystudied and used in Operations Research ▪ Practical optimization algorithms under various constraints 20 https://en.wikipedia.org/wiki/Linear_programming https://en.wikipedia.org/wiki/Integer_programming https://en.wikipedia.org/wiki/Floyd%E2%80%93Wa rshall_algorithm
- 21. Evolutionary Algorithms ▪ Originalidea by Turing (1950) ▪ Genetic algorithm (Holland 1975) ▪ Genetic programming (Cramer 1985, Koza 1988) ▪ Differential evolution (Storn & Price 1997) ▪ Neuroevolution (Stanley & Miikkulainen 2002) 21 https://becominghuman.ai/my-new-genetic-algorithm-for-time-series-f7f0df31343d https://en.wikipedia.org/wiki/Genetic_programming
- 22. Other Population-Based Learning &Optimization ▪ Ant colony optimization (Dorigo 1992) ▪ Particle swarm optimization (Kennedy & Eberhart 1995) ▪ And various other metaphor-based metaheuristic algorithms https://en.wikipedia.org/wiki/List_of_metaphor-based_metaheuristics 22 https://en.wikipedia.org/wiki /Ant_colony_optimization_al gorithms https://en.wikipedia.org/wiki /Particle_swarm_optimizati on
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- 24. Pattern Discovery, Modern Way ▪Unsupervised learning ▪ Find patterns in the data ▪ Supervised learning ▪ Find patterns in the input-output mapping ▪ Reinforcement learning ▪ Learn the world by taking actions and receiving rewards from the environment 24
- 25. Unsupervised Learning ▪ Clustering ▪k-means, agglomerative clustering, DBSCAN, Gaussian mixture, community detection, Jarvis Patrick, etc. ▪ Anomaly detection ▪ Feature extraction/selection ▪ Dimension reduction ▪ PCA, t-SNE, etc. 25 https://reference.wolfram.com/language/ref/FindClusters.html https://commons.wikimedia.org/wiki/File:T-SNE_and_PCA.png
- 26. Supervised Learning ▪ Regression ▪Linear regression, Lasso, polynomial regression, nearest neighbors, decision tree, random forest, Gaussian process, gradient boosted trees, neural networks, support vector machine, etc. ▪ Classification ▪ Logistic regression, decision tree, gradient boosted trees, naive Bayes, nearest neighbors, support vector machine, neural networks, etc. ▪ Risk of overfitting ▪ Addressed by model selection, cross- validation, etc. 26 https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html https://scikit-learn.org/stable/auto_examples/ model_selection/plot_underfitting_overfitting.html
- 27. Reinforcement Learning ▪ Environmenttypically formulated as a Markov decision process (MDP) ▪ State of the world + agent’s action → next state of the world + reward ▪ Monte Carlo methods ▪ TD learning, Q-learning 27 https://en.wikipedia.org/wiki/Markov_decision_process
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- 29. Hopfield Networks ▪ Hopfield(1982) ▪ A.k.a. “attractor networks” ▪ Fully connected networks with symmetric weights can recover imprinted patterns from imperfect initial conditions ▪ “Associative memory” Input Output 29 https://github.com/nosratullah/hopfieldNeuralNetwork
- 30. Boltzmann Machines ▪ Hinton& Sejnowski (1983), Hinton & Salakhutdinov (2006) ▪ Stochastic, learnable variants of Hopfield networks ▪ Restricted (bipartite) Boltzmann machine was at the core of the HS 2006 Science paper that ignited the current boom of “Deep Learning” 30 https://en.wikipedia.org/wiki/Boltzmann_machine https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine
- 31. Feed-Forward NNs and Backpropagation ▪Multilayer perceptron (Rosenblatt 1958) ▪ Backpropagation (Werbos 1974; Rumelhart, Hinton & Williams 1986) ▪ Minimization of errors by gradient descent method ▪ Note that this is NOT how our brain learns ▪ “Vanishing gradient” problem 31 Computation Error correction Input Output
- 32. Autoencoders ▪ Rumelhart, Hinton& Williams (1986) (again!) ▪ Feed-forward ANNs that try to reproduce the input ▪ Smaller intermediate layers → dimension reduction, feature learning ▪ HS 2006 Science paper also used restricted Boltzmann machines as stacked autoencoders 32 https://towardsdatascience.com/applied-deep-learning-part-3- autoencoders-1c083af4d798 https://doi.org/10.1126/science.1127647
- 33. Recurrent Neural Networks ▪ Hopfield(1982); Rumelhart, Hinton & Williams (1986) (again!!) ▪ ANNs that contain feedback loops ▪ Have internal states and can learn temporal behaviors of any long- term dependencies ▪ With practical problems in vanishing or exploding long-term gradients 33 https://commons.wikimedia.org/wiki/File:Neuronal-Networks- Feedback.png https://en.wikipedia.org/wiki/Recurrent_neural_network h o V nfold t 1 ht 1 ot 1 t ht ot t+1 ht+1 ot+1 V V V V ... ...
- 34. Long Short-Term Memory (LSTM) ▪Hochreiter & Schmidhuber (1997) ▪ An improved neural module for RNNs that can learn long- term dependencies effectively ▪ Vanishing gradient problem resolved by hidden states and error flow control ▪ “The most cited NN paper of the 20th century” 34
- 35. Reservoir Computing ▪ Activelystudied since 2000s ▪ Use inherent behaviors of complex dynamical systems (usually a random RNN) as a “reservoir” of various solutions ▪ Learning takes place only at the readout layer (i.e., no backpropagation needed) ▪ Discrete-time, continuous- time versions 35 https://doi.org/10.1515/nanoph-2016-0132 https://doi.org/10.1103/PhysRevLett.120.024102
- 36. Deep Neural Networks ▪Ideas originally around since the beginning of ANNs ▪ Became feasible and popular in 2010s because of: ▪ Huge increase in available computational power thanks to GPUs ▪ Wide availability of training data over the Internet 36 https://commons.wikimedia.org/wiki/File:Example_of_a_deep_neural_network.png https://www.techradar.com/news/computing-components/graphics-cards/best-graphics-cards-1291458
- 37. Convolutional Neural Networks ▪ Fukushima(1980), Homma et al. (1988), LeCun et al. (1989, 1998) ▪ DNNs with convolution operations between layers ▪ Layers represent spatial (and/or temporal) patterns ▪ Many great applications to image/video/time series analyses 37 https://towardsdatascience.com/a-comprehensive-guide-to- convolutional-neural-networks-the-eli5-way-3bd2b1164a53 https://cs231n.github.io/convolutional-networks/
- 38. Adversarial Attacks and GenerativeAdversarial Networks (GAN) 38 https://arxiv.org/abs/1412.6572 https://en.wikipedia.org/wiki/Generative_ adversarial_network ▪ Goodfellow et al. (2014a,b) ▪ DNNs are vulnerable against adversarial attacks ▪ Utilize it to create co- evolutionary systems of generator and discriminator https://commons.wikimedia.org/wiki/File:A-Standard-GAN-and-b-conditional-GAN-architecturpn.png
- 39. Graph Neural Networks ▪ Scarselliet al. (2008), Kipf & Welling (2016) ▪ Non-regular graph structure used as network topology within each layer of DNN ▪ Applications to graph- based data modeling, e.g, social networks, molecular biology, etc. 39 https://tkipf.github.io/graph-convolutional-networks/ https://towardsdatascience.com/how-to-do-deep-learning-on- graphs-with-graph-convolutional-networks-7d2250723780
- 40. Other ANNs ▪ Self-organizingmap (Kohonen 1982) ▪ Neural gas (Martinetz & Schulten 1991) ▪ Spiking neural networks (1990s-) ▪ Hierarchical Temporal Memory (2004-) etc… 40 https://en.wikipedia.org/wiki/ Self-organizing_map https://doi.org/10.1016/j.neucom. 2019.10.104 https://numenta.com/neuroscience-research/sequence-learning/
- 41.
- 42. History of “Chatbots” ▪ELIZA (Weizenbaum 1966) ▪ A.L.I.C.E. (Wallace 1995) ▪ Jabberwacky (Carpenter 1997) ▪ Cleverbot (Carpenter 2008) (and many others) 42 https://en.wikipedia.org/wiki/ELIZA#/media/File:ELIZA_conversation.png http://chatbots.org/ https://www.youtube.com/watch?v=WnzlbyTZsQY (by Cornell CCSL)
- 43. Language Models “With greatpower comes great _____” 43 Probability of the next word … depends on the conte t Function P( ) can be defined as an explicit dataset, a heuristic algorithm, a simple statistical distribution, a (deep) neural network, or anything else
- 44. “Large” Language Models ▪Language models meet (1) massive amount of data and (2) “transformers”! ▪ Vaswani et al. (2017) ▪ DNNs with self-attention mechanism for natural language processing ▪ Enhanced parallelizability leading to shorter training time than LSTM ▪ BERT (2018) for Google search ▪ Open AI’s GPT (2020-) and many others 44 https://arxiv.org/abs/1706.03762
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- 48. “ChatGPT and theEvolution of Artificial Intelligence” 48 https://www.youtube.com/watch?v=SzbKJWKE_Ss
- 49. LLMs Becoming Multimodal 49 Example: NExT-GPTarchitecture https://medium.com/@cout.shubham/exploring-multimodal-large-language- models-a-step-forward-in-ai-626918c6a3ec
- 50. Promising Applications ▪ Codingaid ▪ Personalized tutoring ▪ Conversation partners ▪ Modality conversion for people with disability ▪ Analysis of qualitative scientific data (… and many others) 50
- 51. “Foundation” Models ▪ General-purposeAI models “that are trained on broad data at scale and are adaptable to a wide range of downstream tasks” − Stanford Institute for Human-Centered Artificial Intelligence (2021); https://arxiv.org/abs/2108.07 258 51 https://philosophyterms.com/the-library-of-babel/
- 52.
- 53. Challenges (Especially from Systems SciencePerspectives) 53
- 54. Various Societal Concerns AboutAI ▪ “Artificial General Intelligence” (AGI) and the “e istential crisis of the humanity” ▪ Significant job loss caused by AI ▪ Fake information generated by AI ▪ Biases and social (in)justice ▪ Lack of transparency and over-concentration of AI power ▪ Huge energy costs of deep learning and LLMs ▪ Rights of AI and machines 54
- 55. AI as aThreat to Humanity? 55
- 56. But Some SimpleTasks Are Still Difficult for AI ▪ Words, numbers, facts ▪ Simple logic and reasoning ▪ Maintaining stability and plasticity ▪ Catastrophic forgetting 56 https://spectrum.ieee.org/openai-dall-e-2 https://www.invistaperforms. org/getting-ahead-forgetting- curve-training/
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- 59. Wrong Use Casesof AI 59
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- 62. System-Level Challenge: Idea Homogenizationand Social Fragmentation ▪ Widespread use of common AI tools may homogenize human ideas ▪ Over-consumption of catered AI-generated information may accelerate echo chamber formation and social fragmentation ▪ How can we prevent these negative outcomes? 62 (Centola et al. 2007)
- 63. System-Level Challenge: Critical DecisionMaking in the Absence of Data 63 Fall 2020: “How to safely reopen the campus” How can we make informed decisions in a critical situation when no prior data are available?
- 64. System-Level Challenge: Open-Endedness 64 https://en.wikipedia.org/wiki/Tree_of_life_(biology) How can wemake AI able to keep producing new things?
- 65. Are We GettingAny Closer to the Understanding of True “Intelligence"? 65
- 66. Final Remarks ▪ Don’tget drowned in the vast ocean of methods and tools ▪ Hundreds of years of history ▪ Buzzwords and fads keep changing ▪ Keep the big picture in mind – focus on what your real problem is and how you will solve it ▪ Being able to think and develop unique, original, creative solutions is key to differentiate your intelligence from AI/LLMs/machines 66
- 67.