The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which elevate mood and reduce stress levels.
Machine learning is the study of algorithms and statistical models that allow computer systems to perform tasks without being explicitly programmed. It builds mathematical models from sample data to make predictions or decisions. There are four main types of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Machine learning has various applications including web search, computational biology, finance, e-commerce, robotics, and social networks. Key elements of machine learning systems include representation, evaluation, and optimization techniques.
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Machine learning at b.e.s.t. summer universityLászló Kovács
Machine learning involves using patterns in data to make predictions without being explicitly programmed. This document provides an introduction to machine learning concepts through a real-world project example. It discusses what data scientists do, including prediction, anomaly detection, gaining insights, and decision making. The document then demonstrates machine learning applications in areas like predicting flight delays or employee attrition. It also covers important steps like data preprocessing, feature engineering, and building predictive models using decision trees.
Machine Learning: Artificial Intelligence isn't just a Science Fiction topicRaúl Garreta
In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Machine learning engineers are computer programmers who develop machines and systems that can learn and apply knowledge without specific direction. This article explores the work of machine learning engineers, the skills and education needed for the role, and how to become a machine learning engineer. Key skills include computer programming, strong mathematical skills, and knowledge of machine learning algorithms and libraries. A master's or PhD is typically required for machine learning engineer roles.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Machine learning is the study of algorithms and statistical models that allow computer systems to perform tasks without being explicitly programmed. It builds mathematical models from sample data to make predictions or decisions. There are four main types of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Machine learning has various applications including web search, computational biology, finance, e-commerce, robotics, and social networks. Key elements of machine learning systems include representation, evaluation, and optimization techniques.
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Machine learning at b.e.s.t. summer universityLászló Kovács
Machine learning involves using patterns in data to make predictions without being explicitly programmed. This document provides an introduction to machine learning concepts through a real-world project example. It discusses what data scientists do, including prediction, anomaly detection, gaining insights, and decision making. The document then demonstrates machine learning applications in areas like predicting flight delays or employee attrition. It also covers important steps like data preprocessing, feature engineering, and building predictive models using decision trees.
Machine Learning: Artificial Intelligence isn't just a Science Fiction topicRaúl Garreta
In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Machine learning engineers are computer programmers who develop machines and systems that can learn and apply knowledge without specific direction. This article explores the work of machine learning engineers, the skills and education needed for the role, and how to become a machine learning engineer. Key skills include computer programming, strong mathematical skills, and knowledge of machine learning algorithms and libraries. A master's or PhD is typically required for machine learning engineer roles.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Artificial intelligence (AI) refers to automation of intelligent behavior using various disciplines like computer science, psychology, and linguistics. AI has many potential applications including machine learning, computer vision, natural language processing, robotics, and autonomous vehicles. Companies can benefit from AI in both evolutionary ways like increasing efficiency and transparency, as well as disruptive ways through innovative business models and new revenue opportunities. However, successful implementation of AI projects requires careful selection of pilot projects, conveying a clear vision and strategy, and empowering the organization to adapt to AI technologies.
Intro/Overview on Machine Learning PresentationAnkit Gupta
This document provides an overview of a presentation on machine learning given at Gurukul Kangri University in 2017. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses different machine learning algorithms including supervised learning, unsupervised learning, and semi-supervised learning. Examples of applications of machine learning discussed include data mining, natural language processing, image recognition, and expert systems. The document also contrasts artificial intelligence, machine learning, and deep learning.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
1. The document summarizes a seminar on machine learning presented by Amit Kumar to the Rajkiya Engineering College.
2. It discusses key machine learning concepts like supervised learning techniques of classification and regression, as well as unsupervised learning techniques like clustering.
3. Applications of machine learning discussed include virtual assistants, social media services, image recognition, and medical diagnosis.
Artificial Intelligence (AI) -> understanding what it is & how you can use it...Adela VILLANUEVA
The goal of this presentation is to provide you with a basic understanding of AI and to prepare you to think about how your organization might apply it.
- Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed by using example data. It is a form of artificial intelligence.
- There are three main types of machine learning: supervised learning where examples are labeled, unsupervised learning where unlabeled examples reveal inherent groupings of data, and reinforcement learning where an agent learns from trial and error using rewards.
- Machine learning has many applications including web search, computational biology, finance, robotics, and social networks. It involves collecting and preparing data, developing models, and evaluating models to make predictions on new data.
Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education
Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
K-12 Computing Education for the AI Era: From Data Literacy to Data AgencyHenriikka Vartiainen
1) The keynote presentation discusses the need to update K-12 computing education from a focus on computational thinking to include data literacy and data agency given the rise of artificial intelligence and machine learning.
2) Examples are provided of emerging approaches to teaching AI/ML concepts to students, such as workshops exploring image recognition tools and having students design their own machine learning applications.
3) Significant changes are needed in K-12 computing education due to differences between classical programming paradigms and modern data-driven AI, including new technical concepts, problem-solving approaches, sources of problems, and ethical considerations around topics like algorithmic bias and data privacy.
Despite AI’s potential for beneficial use, it creates important risks for Australians. AI, big data, and AI-informed decision making can cause exclusion, discrimination, skill loss, and economic impact; and can affect privacy, security of critical infrastructure and social well-being. What types of technology raise particular human rights concerns? Which human rights are particularly implicated?
This document outlines the objectives and experiments for a Machine Learning laboratory course. The course aims to enable students to implement machine learning algorithms and apply them to datasets without using built-in libraries. The 10 experiments cover algorithms like decision trees, neural networks, naive Bayes classifier, k-means clustering, and locally weighted regression. Students will code the algorithms from scratch in Java or Python and evaluate them on standard datasets. The document provides details on each experiment, such as reading data from CSV files and calculating accuracy metrics.
Adaptarse a las nuevas formas de crear y compartir contenidos digitales constituye un reto para la preparación de profesionales en los perfiles emergentes de disciplinas ajenas a la informática y la computación. Los lenguajes y las herramientas de creación digital no están muchas veces pensados para su utilización por parte de usuarios de estos campos. Un reto en el campo de la computación creativa es la posibilidad de incorporar capacidades interactivas multimodales, junto con realidad virtual y realidad aumentada, en las herramientas de autoría con las que se elaboran los materiales y diseños de aprendizaje. El objetivo general de la charla es motivar la investigación sobre la computación creativa, así como mostrar desarrollos diversos alrededor de un marco de trabajo que aspira a fomentar las habilidades de diseño, creación y despliegue de experiencias educativas con capacidades analíticas para el aprendizaje y la evaluación en un contexto multidisciplinar.
This document provides an introduction to machine learning and its applications in aviation. It begins with definitions of key machine learning concepts like supervised learning, unsupervised learning, and reinforcement learning. It then discusses popular machine learning algorithms and how they are applied in areas like predictive maintenance, flight optimization, and customer analytics. The document also introduces RapidMiner, an open-source platform for machine learning projects, and provides an overview of its graphical user interface and basic terminology.
A.I. in E-Learning | Wilhelm Klat | 4. EdTech Hamburg Meetup EdTech Hamburg
Wilhelm Klat, Bielefeld Universität, zum Thema „A.I. in E-Learning“
Das EdTech Hamburg Meetup ist ein Treffen für jeden, der sich für den Einsatz von Bildungstechnologie in Schulen und Hochschulen, als auch in anderen Bereichen, interessiert.
Mach mit!
Besuche unsere Website: http://www.edtech.hamburg
Folge uns auf Twitter: http://www.twitter/edtechhamburg
Like EdTech Hamburg auf Facebook: https://www.facebook.com/edtechhamburg/
Building machine learning models is challenging, requiring many steps from data ingestion to deployment. Feature engineering, which transforms raw data into more useful representations, is often the most important step for model performance. Automating less important steps like data cleaning frees up time to focus on feature engineering through multiple iterations of the OODA loop of observe, orient, decide, and act. This allows generating better models through more experimentation and domain knowledge application to extract the most informative features.
DSCI 552 machine learning for data sciencepavithrak2205
This document provides an overview of machine learning concepts that will be covered in the DSCI 552 course. It introduces key machine learning tasks like classification, regression, and clustering. It discusses the machine learning process and importance of inductive bias. Example applications in areas like credit scoring, face recognition, and customer segmentation are provided. The reading schedule and grading policy are also outlined.
1. Machine learning is the use and development of computer systems that are able to learn and adapt without explicit instructions by using algorithms and statistical models to analyze patterns in data.
2. The document provides examples of machine learning applications like facial recognition, voice recognition in healthcare, weather forecasting, and more. It also discusses the process of machine learning and popular machine learning algorithms.
3. The document demonstrates machine learning using a decision tree algorithm on music purchase data to predict whether a customer is male or female based on attributes like age and number of songs purchased. It imports relevant Python libraries and splits the data into training and test sets to evaluate the model's performance.
This document provides an overview of data science. It defines data science as using computer science, statistics, machine learning, visualization, and human-computer interaction to analyze and interact with data. The key topics covered include prerequisites for data science like computer science, statistics, machine learning and visualization. Common data science tasks are also outlined such as data analysis, modeling, engineering and prototyping. The document discusses what a data scientist does and how to tackle a data problem by consulting subject matter experts, identifying anomalies, and reducing risk and uncertainty in the data.
Artificial intelligence (AI) refers to automation of intelligent behavior using various disciplines like computer science, psychology, and linguistics. AI has many potential applications including machine learning, computer vision, natural language processing, robotics, and autonomous vehicles. Companies can benefit from AI in both evolutionary ways like increasing efficiency and transparency, as well as disruptive ways through innovative business models and new revenue opportunities. However, successful implementation of AI projects requires careful selection of pilot projects, conveying a clear vision and strategy, and empowering the organization to adapt to AI technologies.
Intro/Overview on Machine Learning PresentationAnkit Gupta
This document provides an overview of a presentation on machine learning given at Gurukul Kangri University in 2017. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses different machine learning algorithms including supervised learning, unsupervised learning, and semi-supervised learning. Examples of applications of machine learning discussed include data mining, natural language processing, image recognition, and expert systems. The document also contrasts artificial intelligence, machine learning, and deep learning.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
1. The document summarizes a seminar on machine learning presented by Amit Kumar to the Rajkiya Engineering College.
2. It discusses key machine learning concepts like supervised learning techniques of classification and regression, as well as unsupervised learning techniques like clustering.
3. Applications of machine learning discussed include virtual assistants, social media services, image recognition, and medical diagnosis.
Artificial Intelligence (AI) -> understanding what it is & how you can use it...Adela VILLANUEVA
The goal of this presentation is to provide you with a basic understanding of AI and to prepare you to think about how your organization might apply it.
- Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed by using example data. It is a form of artificial intelligence.
- There are three main types of machine learning: supervised learning where examples are labeled, unsupervised learning where unlabeled examples reveal inherent groupings of data, and reinforcement learning where an agent learns from trial and error using rewards.
- Machine learning has many applications including web search, computational biology, finance, robotics, and social networks. It involves collecting and preparing data, developing models, and evaluating models to make predictions on new data.
Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education
Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
K-12 Computing Education for the AI Era: From Data Literacy to Data AgencyHenriikka Vartiainen
1) The keynote presentation discusses the need to update K-12 computing education from a focus on computational thinking to include data literacy and data agency given the rise of artificial intelligence and machine learning.
2) Examples are provided of emerging approaches to teaching AI/ML concepts to students, such as workshops exploring image recognition tools and having students design their own machine learning applications.
3) Significant changes are needed in K-12 computing education due to differences between classical programming paradigms and modern data-driven AI, including new technical concepts, problem-solving approaches, sources of problems, and ethical considerations around topics like algorithmic bias and data privacy.
Despite AI’s potential for beneficial use, it creates important risks for Australians. AI, big data, and AI-informed decision making can cause exclusion, discrimination, skill loss, and economic impact; and can affect privacy, security of critical infrastructure and social well-being. What types of technology raise particular human rights concerns? Which human rights are particularly implicated?
This document outlines the objectives and experiments for a Machine Learning laboratory course. The course aims to enable students to implement machine learning algorithms and apply them to datasets without using built-in libraries. The 10 experiments cover algorithms like decision trees, neural networks, naive Bayes classifier, k-means clustering, and locally weighted regression. Students will code the algorithms from scratch in Java or Python and evaluate them on standard datasets. The document provides details on each experiment, such as reading data from CSV files and calculating accuracy metrics.
Adaptarse a las nuevas formas de crear y compartir contenidos digitales constituye un reto para la preparación de profesionales en los perfiles emergentes de disciplinas ajenas a la informática y la computación. Los lenguajes y las herramientas de creación digital no están muchas veces pensados para su utilización por parte de usuarios de estos campos. Un reto en el campo de la computación creativa es la posibilidad de incorporar capacidades interactivas multimodales, junto con realidad virtual y realidad aumentada, en las herramientas de autoría con las que se elaboran los materiales y diseños de aprendizaje. El objetivo general de la charla es motivar la investigación sobre la computación creativa, así como mostrar desarrollos diversos alrededor de un marco de trabajo que aspira a fomentar las habilidades de diseño, creación y despliegue de experiencias educativas con capacidades analíticas para el aprendizaje y la evaluación en un contexto multidisciplinar.
This document provides an introduction to machine learning and its applications in aviation. It begins with definitions of key machine learning concepts like supervised learning, unsupervised learning, and reinforcement learning. It then discusses popular machine learning algorithms and how they are applied in areas like predictive maintenance, flight optimization, and customer analytics. The document also introduces RapidMiner, an open-source platform for machine learning projects, and provides an overview of its graphical user interface and basic terminology.
A.I. in E-Learning | Wilhelm Klat | 4. EdTech Hamburg Meetup EdTech Hamburg
Wilhelm Klat, Bielefeld Universität, zum Thema „A.I. in E-Learning“
Das EdTech Hamburg Meetup ist ein Treffen für jeden, der sich für den Einsatz von Bildungstechnologie in Schulen und Hochschulen, als auch in anderen Bereichen, interessiert.
Mach mit!
Besuche unsere Website: http://www.edtech.hamburg
Folge uns auf Twitter: http://www.twitter/edtechhamburg
Like EdTech Hamburg auf Facebook: https://www.facebook.com/edtechhamburg/
Building machine learning models is challenging, requiring many steps from data ingestion to deployment. Feature engineering, which transforms raw data into more useful representations, is often the most important step for model performance. Automating less important steps like data cleaning frees up time to focus on feature engineering through multiple iterations of the OODA loop of observe, orient, decide, and act. This allows generating better models through more experimentation and domain knowledge application to extract the most informative features.
DSCI 552 machine learning for data sciencepavithrak2205
This document provides an overview of machine learning concepts that will be covered in the DSCI 552 course. It introduces key machine learning tasks like classification, regression, and clustering. It discusses the machine learning process and importance of inductive bias. Example applications in areas like credit scoring, face recognition, and customer segmentation are provided. The reading schedule and grading policy are also outlined.
1. Machine learning is the use and development of computer systems that are able to learn and adapt without explicit instructions by using algorithms and statistical models to analyze patterns in data.
2. The document provides examples of machine learning applications like facial recognition, voice recognition in healthcare, weather forecasting, and more. It also discusses the process of machine learning and popular machine learning algorithms.
3. The document demonstrates machine learning using a decision tree algorithm on music purchase data to predict whether a customer is male or female based on attributes like age and number of songs purchased. It imports relevant Python libraries and splits the data into training and test sets to evaluate the model's performance.
This document provides an overview of data science. It defines data science as using computer science, statistics, machine learning, visualization, and human-computer interaction to analyze and interact with data. The key topics covered include prerequisites for data science like computer science, statistics, machine learning and visualization. Common data science tasks are also outlined such as data analysis, modeling, engineering and prototyping. The document discusses what a data scientist does and how to tackle a data problem by consulting subject matter experts, identifying anomalies, and reducing risk and uncertainty in the data.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.