AI ML Explained: History, Concepts, and Impact

AI has a rich history that dates back to the 1950s, with the first AI program, Logical Theorist, being developed in 1956 by Allen Newell and Herbert Simon.

The field of AI has made tremendous progress since then, with the development of machine learning (ML) in the 1990s enabling machines to learn from data and improve their performance over time.

This ability to learn from data has led to the creation of complex AI systems that can perform tasks such as image recognition, natural language processing, and decision-making.

The impact of AI on society has been significant, with applications in areas such as healthcare, finance, and education.

The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.

Arthur Samuel invented a program in the 1950s that calculated the winning chance in checkers for each side, marking the beginning of machine learning models.

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The history of machine learning roots back to decades of human desire and effort to study human cognitive processes, dating back to 1949 when Canadian psychologist Donald Hebb published "The Organization of Behavior".

Donald Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data.

By the early 1960s, an experimental "learning machine" called Cybertron had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning.

Tom M. Mitchell provided a widely quoted definition of the algorithms studied in the machine learning field, stating that a computer program learns from experience if its performance improves with experience.

Modern-day machine learning has two main objectives: classifying data based on developed models and making predictions for future outcomes based on these models.

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AI and ML are increasingly being used in various fields, including healthcare, finance, and education.

In healthcare, AI is being used to analyze medical images and diagnose diseases more accurately.

The integration of AI and ML with healthcare data has improved patient outcomes and reduced costs.

ML algorithms are being used in finance to detect credit card fraud and predict stock prices.

These algorithms can analyze vast amounts of data and identify patterns that humans may miss.

In education, AI-powered adaptive learning systems are being used to personalize learning experiences for students.

These systems can adjust their curriculum and teaching methods based on a student's individual learning pace and style.

Artificial intelligence is a scientific endeavor that grew out of the quest for machines to learn from data. Machine learning emerged from this pursuit, but the two fields diverged due to an increasing emphasis on logical, knowledge-based approaches.

In the early days of AI, researchers attempted to have machines learn from data using symbolic methods and neural networks, but these efforts were plagued by theoretical and practical problems. Probabilistic systems, including automated medical diagnosis, were also employed but ultimately abandoned.

Artificial intelligence is used when a machine completes a task using human intellect and behaviors, such as Roomba, the smart robotic vacuum, which analyzes room size, obstacles, and pathways to create an efficient cleaning route.

Explainable AI is a game-changer in the world of artificial intelligence. It's artificial intelligence where humans can understand the decisions or predictions made by the AI, unlike the "black box" concept in machine learning where even its designers can't explain why an AI arrived at a specific decision.

Explainable AI, or XAI, promises to help users perform more effectively by refining their mental models and dismantling misconceptions. This is especially important in AI-powered systems that can have significant consequences, such as in autonomous driving, healthcare, and finance.

XAI provides insights into the reasoning behind AI decisions, allowing humans to trust that they're fair, unbiased, and aligned with ethical standards. This is crucial in industries where AI decisions can have real-world impacts.

There's a notable tradeoff between model complexity, accuracy, and interpretability. Highly complex models often achieve superior performance but are less interpretable, while simpler models offer more interpretability but lack sophisticated predictive capabilities.

To balance accuracy with interpretability, developers need to carefully consider the model's intended use, the importance of its decisions, and the necessity for transparency. This requires a thoughtful approach to developing models that inherently provide more insight into their decision-making process.

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Efficient Batch LLM Inference is a game-changer for large-scale data processing.

With Snowflake Cortex AI, you can unlock the power of LLMs to summarize, classify, and run other NLP tasks with ease.

Using serverless SQL functions or a REST API, you can securely combine your data with fine-tuned or foundation models like Meta Llama 3.2 and Mistral Large 2.

This approach enables you to run custom prompts across multiple rows of data, streamlining the development-to-deployment lifecycle.

By leveraging Snowflake Cortex AI, you can bring top-tier, cost-efficient AI models to your data, making it easier to get insights and make informed decisions.

Efficient batch LLM inference is a key component of this process, allowing you to process large amounts of unstructured data with ease.

Machine learning is a subset of artificial intelligence that enables machines to learn from data without being explicitly programmed. It's traditionally divided into three broad categories: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs, and can be used for classification and regression tasks. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range.

Semi-supervised learning falls between unsupervised learning and supervised learning, where some of the training examples are missing training labels, yet unlabeled data can produce a considerable improvement in learning accuracy. Feature learning algorithms aim to discover better representations of the inputs provided during training, and can be supervised or unsupervised.

Here are some examples of feature learning algorithms:

Supervised learning is a type of machine learning where the computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.

This approach is used when the outputs are restricted to a limited set of values, such as classification algorithms that filter emails, where the input would be an incoming email, and the output would be the name of the folder in which to file the email.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as training data, consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal.

In supervised learning, the algorithm learns a function that can be used to predict the output associated with new inputs. An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data.

Types of supervised-learning algorithms include active learning, classification, and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms are used when the outputs may have any numerical value within a range.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.

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Semi-supervised learning is a type of machine learning that falls between supervised and unsupervised learning. It uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set.

This approach can solve the problem of not having enough labeled data for a supervised learning algorithm, and it's also helpful if labeling data is too costly. In semi-supervised learning, the training labels are often noisy, limited, or imprecise, but they're often cheaper to obtain, resulting in larger effective training sets.

Some machine learning algorithms that can be used for semi-supervised learning include neural networks, linear regression, logistic regression, clustering, decision trees, and random forests.

These algorithms can be particularly useful when dealing with large datasets where labeling data is a significant challenge. By leveraging both labeled and unlabeled data, semi-supervised learning can produce a considerable improvement in learning accuracy.

Here are some key characteristics of semi-supervised learning:

Federated learning is a type of distributed artificial intelligence that allows for decentralized training of machine learning models, maintaining user privacy by not sending data to a centralized server.

This approach increases efficiency by training models on multiple devices simultaneously, as seen in Gboard's use of federated machine learning to train search query prediction models on users' mobile phones without sending individual searches back to Google.

Decentralized training reduces the risk of data breaches and ensures that sensitive information remains with the user, not a centralized server.

Federated learning also enables faster training times, as multiple devices can work together to train models, making it a more efficient approach compared to traditional centralized training methods.

Here's a brief overview of the benefits of federated learning:

Regularization is a form of regression that constrains or shrinks coefficient estimates toward zero, helping the machine avoid overfitting.

Regularization is especially useful when working with large data groups, as it eliminates noise in the dataset and produces more accurate responses.

Overfitting occurs when a machine learning model exerts too much effort in understanding extra noise in the data set, resulting in low accuracy in predictions.

Regularization is commonly used to help machine learning models produce more accurate responses by eliminating noise in the dataset.

Machine learning models are designed to handle large sets of structured data and analyze them to discover patterns and trends that humans might miss.

Statistics plays a crucial role in machine learning, drawing population inferences from a sample. This is distinct from machine learning, which finds generalizable predictive patterns.

Conventional statistical analyses require a pre-structured model, but machine learning allows the data to shape the model by detecting underlying patterns. The more variables used to train the model, the more accurate it will be.

Some statisticians have adopted machine learning methods, leading to a combined field called statistical learning. This approach combines the strengths of both statistics and machine learning.

Regression analysis is a statistical method to estimate the relationship between input variables and their associated features. It encompasses a range of techniques, including linear regression, polynomial regression, and logistic regression.

Linear regression is a specific type of regression analysis used to predict numerical values based on a linear relationship between different values. For example, it can be used to predict house prices based on historical data for the area.

Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.

Most dimensionality reduction techniques can be considered as either feature elimination or extraction.

One of the popular methods of dimensionality reduction is principal component analysis (PCA).

PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D).

The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption.

Many dimensionality reduction techniques make the assumption that high-dimensional data sets lie along low-dimensional manifolds, leading to the area of manifold learning and manifold regularization.

Statistics play a crucial role in understanding the world around us, and it's closely related to machine learning.

Machine learning and statistics are distinct fields, with statistics drawing population inferences from a sample, while machine learning finds generalizable predictive patterns.

Statistics requires the a priori selection of a model most suitable for the study data set, and only significant or theoretically relevant variables are included for analysis.

In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns.

The more variables used to train the model, the more accurate the ultimate model will be.

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, with algorithmic model referring to machine learning algorithms like Random Forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.

Gaussian processes are a type of stochastic process where every finite collection of random variables has a multivariate normal distribution.

They rely on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other based on their locations.

This kernel function is crucial in determining the distribution of the output for a new point based on its input data and the observed points.

The distribution of the output for a new point can be directly computed by looking at the observed points and the covariances between those points and the new point.

Gaussian processes are popular in Bayesian optimization, particularly for hyperparameter optimization, where they serve as surrogate models.

They enable the computation of the distribution of the output for a new point as a function of its input data, which is incredibly useful in optimization tasks.

Overfitting is a problem where a model becomes too specialized in the training data and fails to generalize well to new, unseen data.

This happens when a model is overly complex and tries to fit every detail of the training data, rather than capturing the underlying patterns and relationships.

A model that overfits will perform well on the training data but poorly on new data, which can lead to poor predictions and decision-making.

In an attempt to reduce overfitting, many systems use a technique that rewards a model for its fit to the data but also penalizes it for its complexity.

This approach can help to prevent models from becoming too specialized and improve their ability to generalize to new situations.

Structured data is organized and easily used by businesses, but it has a predefined format that limits its flexibility and use cases. Examples of structured data include dates, phone numbers, customer names, and product names.

Structured data is decipherable by machine learning algorithms and accessible by more tools than unstructured data, making it a valuable asset for businesses. However, its limitations mean it can't be used for as many purposes as unstructured data.

Unstructured data, on the other hand, is typically easy and inexpensive to store and can be used across different formats. Examples of unstructured data include photos, audio, and video files.

Deep learning is commonly used for unstructured data, and it's the best option for the most challenging use cases. This is because deep learning can help businesses optimize many of their business-related functions by leveraging the knowledge hidden in unstructured data.

Artificial neural networks are computing systems inspired by the biological neural networks in animal brains. They "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

An artificial neural network is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another.

Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. They're particularly good at recognizing patterns, which makes them useful in applications like natural language translation and image recognition.

The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds, increasing or decreasing the strength of the signal at a connection.

Parallel and distributed algorithms can significantly reduce the time it takes for deep learning models to learn relevant parameters, making them a game-changer for companies working with massive data sets.

Deep learning models can be trained locally, but parallel and distributed algorithms allow data or models to be distributed across multiple machines, making training more effective. This speeds up the time the model needs to learn and train, saving companies time and money.

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Reinforcement learning is a type of machine learning that allows agents to learn from trial and error, interacting with their environment and receiving feedback in the form of rewards or penalties.

Autonomous systems, such as self-driving cars and robotics, rely on reinforcement learning algorithms to make real-time decisions. These algorithms process multiple inputs of sensory data to navigate and adapt to new tasks.

Decision trees are a type of algorithm that can be used for both predicting numerical values and classifying data into categories. They use a branching sequence of linked decisions that can be represented with a tree diagram.

Reinforcement is a type of machine learning that helps software agents make decisions in an environment to maximize cumulative reward.

Reinforcement learning is studied in many disciplines, including game theory, control theory, and operations research.

It's used in autonomous vehicles and learning to play games against human opponents, where exact models of the environment are infeasible.

Reinforcement learning algorithms process multiple inputs of sensory data to make real-time decisions during navigation in self-driving cars.

These algorithms allow autonomous robots to adapt to new tasks through interaction, learning how to manipulate objects or navigate environments independently.

The increase in autonomous AI systems raises significant concerns regarding ethical considerations, including accountability, privacy, and job displacement.

Engineers must take a balanced approach when designing these systems, considering both their transformative potential and the ethical imperatives to ensure they benefit society as a whole.

Reinforcement learning allows machines to learn from their experiences, much like human beings do, through trial and error.

This process involves an agent interacting with its environment, performing actions and receiving feedback in the form of rewards or penalties.

Decision Trees are a type of predictive model used in statistics, data mining, and machine learning.

Decision Trees can be used for both predicting numerical values and classifying data into categories. They use a branching sequence of linked decisions that can be represented with a tree diagram.

Decision Trees are easy to validate and audit, unlike the black box of the neural network. This makes them a more transparent and trustworthy option.

Decision Trees can be used to visually and explicitly represent decisions and decision making, making them a valuable tool in decision analysis. They can be used to describe data, and the resulting classification tree can be an input for decision-making.

Decision Trees are a powerful tool for making predictions and classifying data, and their ease of validation and audit makes them a popular choice in many fields.

Support-vector machines (SVMs) are a set of related supervised learning methods used for classification and regression.

They can efficiently perform a non-linear classification using the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

SVMs are a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting.

An SVM training algorithm builds a model that predicts whether a new example falls into one category.

Genetic algorithms are a type of search algorithm that mimics the process of natural selection.

They use methods like mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem.

Genetic algorithms were used in machine learning in the 1980s and 1990s.

They've been used to solve a wide range of problems, from optimization and scheduling to classification and regression.

Genetic algorithms have been used in combination with machine learning techniques to improve their performance.

In fact, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.

Parallel and distributed algorithms are a game-changer for deep learning models, significantly reducing the time it takes for them to learn relevant parameters. This is because they can be trained locally or distributed across multiple machines, making the training process more effective.

Artificial intelligence is used when a machine completes a task using human intellect and behaviors. For example, the Roomba smart robotic vacuum uses AI to analyze the size of the room, obstacles, and pathways, creating an efficient route for vacuuming.

Machine learning is perfect for tasks that involve teaching a model to perform a specific task, such as predicting an output or discovering a pattern using structured data. Spotify's customized playlist feature is a great example of this.

Deep learning is ideal for complex tasks that require training models using unstructured data. Facial recognition is a common application of deep learning, where it can accurately identify faces by extracting features from images.