labeled data

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy  · 15 Jan 2025  · 209pp  · 63,332 words

AI are prediction problems. Supervised learning solves these prediction problems. But there is an issue that we have not discussed yet: Supervised learning needs labeled data, and such labeled data are often expensive or hard to find. Data are one of the key means of prediction, and limits on their availability imply limits on

…

United Nations or the European Union), but there are limits to this approach. So how can machine learning scale further, beyond the limits of human-labeled data? The answer, for certain problem domains, has been found in self-supervised learning. Behind the evocative name, there is a simple idea: Find prediction problems

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data

by Dipanjan Sarkar  · 1 Dec 2016

involves several steps which we will be discussing in detail later in this chapter. Briefly, for a supervised classification problem, we need to have some labelled data that we could use for training a text classification model. This data would essentially be curated documents that are already assigned to some specific class

Data Mining: Concepts and Techniques: Concepts and Techniques

by Jiawei Han, Micheline Kamber and Jian Pei  · 21 Jun 2011

. Cluster Analysis Unlike classification and regression, which analyze class-labeled (training) data sets, clustering analyzes data objects without consulting class labels. In many cases, class-labeled data may simply not exist at the beginning. Clustering can be used to generate class labels for a group of data. The objects are clustered or

…

) and that the rule covers the tuple. A rule R can be assessed by its coverage and accuracy. Given a tuple, X, from a class-labeled data set, D, let be the number of tuples covered by R; be the number of tuples correctly classified by R; and be the number of

…

on the accuracy measure can be deceiving when the main class of interest is in the minority. ■ Construction and evaluation of a classifier require partitioning labeled data into a training set and a test set. Holdout, random sampling, cross-validation, and bootstrapping are typical methods used for such partitioning. ■ Significance tests and

…

distance, the more likely that errors will be corrected. 9.7.2. Semi-Supervised Classification Semi-supervised classification uses labeled data and unlabeled data to build a classifier. Let be the set of labeled data and be the set of unlabeled data. Here we describe a few examples of this approach for learning. Self

…

-training is the simplest form of semi-supervised classification. It first builds a classifier using the labeled data. The classifier then tries to label the unlabeled data. The tuple with the most confident label prediction is added to the set of

…

labeled data, and the process repeats (Figure 9.17). Although the method is easy to understand, a disadvantage is that it may reinforce errors. Figure 9.17

…

data, Xu. Each classifier then teaches the other in that the tuple having the most confident prediction from f1 is added to the set of labeled data for f2 (along with its label). Similarly, the tuple having the most confident prediction from f2 is added to the set of

…

labeled data for f1. The method is summarized in Figure 9.17. Cotraining is less sensitive to errors than self-training. A difficulty is that the assumptions

…

learned from the training data under certain conditions. Clustering-based outlier detection methods have the following advantages. First, they can detect outliers without requiring any labeled data, that is, in an unsupervised way. They work for many data types. Clusters can be regarded as summaries of the data. Once the clusters are

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

by Foster Provost and Tom Fawcett  · 30 Jun 2013  · 660pp  · 141,595 words

. The input data for the induction algorithm, used for inducing the model, are called the training data. As mentioned in Chapter 2, they are called labeled data because the value for the target variable (the label) is known. Let’s return to our example churn problem. Based on what we learned in

…

training and 1/k used for testing. Figure 5-9. An illustration of cross-validation. The purpose of cross-validation is to use the original labeled data efficiently to estimate the performance of a modeling procedure. Here we show five-fold cross-validation: the original dataset is split randomly into five equal

…

different based on different collections of evidence E—in our example, different sets of websites visited. As mentioned above, we would like to use some labeled data, such as the data from our randomly targeted campaign, to associate different collections of evidence E with different probabilities. Unfortunately, this introduces a key problem

…

training data. Sometimes we can specify the target variable precisely, but we find we do not have any labeled data. In certain cases, we can use micro-outsourcing systems such as Mechanical Turk to label data. For example, advertisers would like to keep their advertisements off of objectionable web pages, like those that contain

…

for attribute. Class (label) One of a small, mutually exclusive set of labels used as possible values for the target variable in a classification problem. Labeled data has one class label assigned to each example. For example, in a dollar bill classification problem the classes could be legitimate and counterfeit. In a

…

Concepts of Data Science Kosinski, Michal, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes” L L2 norm (equation), * Other Distance Functions labeled data, Models, Induction, and Prediction labels, Supervised Versus Unsupervised Methods Ladyburn single malt scotch, Understanding the Results of Clustering Laphroaig single malt scotch, Understanding the Results

Architects of Intelligence

by Martin Ford  · 16 Nov 2018  · 586pp  · 186,548 words

systems (trained with millions of medical images labeled either “Cancer” or “No Cancer”). One problem with supervised learning is that it requires massive amounts of labeled data. This explains why companies that control huge amounts of data, like Google, Amazon, and Facebook, have such a dominant position in deep learning technology. REINFORCEMENT

…

can copy facts from one system to another. MARTIN FORD: Is it true that the vast majority of applications of deep learning rely heavily on labeled data, or what’s called supervised learning, and that we still need to solve unsupervised learning? GEOFFREY HINTON: That’s not entirely true. There’s a

…

lot of reliance on labeled data, but there are some subtleties in what counts as labeled data. For example, if I give you a big string of text and I ask you to try and predict the next

…

what happens next acts as the label, but I don’t need to add extra labels. There’s this thing in between unlabeled data and labeled data, which is predicting what comes next. MARTIN FORD: If you look at the way a child learns, though, it’s mostly wandering around the environment

…

way algorithms are trained is quite different from what happens with a human baby or young child. Children for the most part are not getting labeled data—they just figure things out. And even when you point to a cat and say, “look there’s a cat,” you certainly don’t have

…

neural networks and deep learning are the answers to everything—not by a huge margin. As you said earlier, a lot of problems are not labeled data or involve lots of training examples. Looking at the history of civilization and the things it’s taught us, we cannot possibly think we’ve

…

versus AGI: it’s all on one continuum. We all recognize today’s AI is very narrow and task specific, focusing on pattern recognition with labeled data, but as we make AI more advanced, that is going to be relaxed, and so in a way, the future of AI and AGI is

…

the world, it doesn’t really seem like reinforcement learning for the most part. It’s unsupervised learning, as no one’s giving the child labeled data the way we would do with ImageNet. Yet somehow, a young child can learn organically directly from the environment. But it seems to be more

…

it’s the be-all, end-all to all of our needs. It’s still pretty much supervised, so you still need to have some labeled data to track these classifiers. I think of it as an awesome tool within this bigger bucket of machine learning, but deep learning is not going

…

where are they going to get them from? MARTIN FORD: What you’re getting at is that deep learning right now is very dependent on labeled data and what’s called supervised learning. RAY KURZWEIL: Right. One way to work around it is if you can simulate the world you’re working

…

you think about what machine learning can do today, it’s absolutely extraordinary. Machine learning is a process that starts with millions of usually manually labeled data points, and the system aims to learn a pattern that is prevalent in the data, or to make a prediction based on that data. These

…

of video from prototype vehicles to help train the algorithms. There are some new techniques that are emerging to get around the issue of needing labeled data, for example, in-stream supervision pioneered by Eric Horvitz and others; the use of techniques like Generative Adversarial Networks or GANs, which is a semi

…

, and other related experiences. Thinking about what knowing and understanding means is a really interesting part of AI. It’s not as easy as providing labeled data for doing image analysis, because what happens is that you and I could read the same thing, but we can come up with very different

…

we’re talking about identifying a cat in a picture, it’s very clear what the phenomenon is, and we would get a bunch of labeled data, and we would train the neural network. If you say: “How do I produce an understanding of this content?”, it’s not even clear I

…

ways to acquire and model that information. MARTIN FORD: Are you also working on unsupervised learning? Most AI that we have today is trained with labeled data, and I think real progress will probably require getting these systems to learn the way that a person does, organically from the environment. DAVID FERRUCCI

…

impressive achievements with deep learning, and we see that in machine translation, speech recognition, object detection, and facial recognition. When you have a lot of labeled data, and you have a lot of computer power, these models are great. But at the same time, I do think that deep learning is overhyped

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt  · 8 Oct 2013  · 523pp  · 112,185 words

at a time, which we generically call “word.” Then, applying Bayes’ Law, we have: The righthand side of this equation is computable using enough pre-labeled data. If we refer to nonspam as “ham” then we only need compute p(word|spam), p(word|ham), p(spam), and p(ham) = 1-p

The AI-First Company

by Ash Fontana  · 4 May 2021  · 296pp  · 66,815 words

market improved accuracy and reliability to potential patients. The company building the AI can work with the medical facility to get a critical mass of labeled data, get their models to the PUT, figure out how best to deliver the prediction through existing hardware, work through regulatory issues, and receive feedback from

…

highly specific datasets, whether through outsourcing, hiring people, or having existing employees use products that generate data. HUMAN GENERATED Data Labeling Many ML models require labeled data for training recognition algorithms. There are some promising transfer and semisupervised learning techniques that may provide alternatives to gathering a great deal of

…

labeled data, especially for generic domains such as image, video, and language understanding. However, the state of the art doesn’t seem to offer enough just yet,

…

. Accessing and owning processed data to feed models can be the single hardest problem in starting a vertical, AI-First business. Supervised ML models need labeled data. Getting lots of labeled examples for specific domains is hard. For example, where would you find a hundred thousand images of 2001 Chevy Silverado fenders

…

manufacturer, a chain of body shops, or an insurance company. In the absence of existing labeled datasets, build one. This entails building a team to label data, which may include both experts and nonexperts, and requires tools to efficiently label large volumes of data. There is a burgeoning area of management practices

…

saved through automation/Cost of each label) * # labels. Perhaps it’s helpful to think of this operation as a factory. The “good” it produces is labeled data. The factory manager’s job is to find efficiencies along the production line. Tools Labeling often requires engineers to clean data before applying the labels

…

procure from users. Uncertainty sampling. Labeling those points for which the current model is least certain. Query by committee. Train many models on the same labeled data. Then have people manually label the data points that caused the most disagreement in output between the models. Expected model change. Have people label the

…

the accuracy of a classifier even if any one of those labels isn’t necessarily correct. A large volume of labeled data can also be an asset itself. Thus, tracking the total labeled data points can be informative of the value produced by the labeling operation. Labels aren’t free, and business models need

…

generators take a single object and offer unlimited perspectives by, for example, modeling the object in 3-D and then moving around it, generating a labeled data point at each step. Accessibility Labeling objects is often feasible because pictures of them are readily available, as with cars on a street. However, some

…

of such an object and drop it into various environments. Building such a generator can be expensive, but the cost can be amortized over all labeled data points because the one generator is used to produce many examples of the same object. These generators are typically built using the same tools that

…

agents click to label an email as “sensitive” if they think the customer who wrote it is particularly angry and needs attention in short order: labeled data to train the ML models to prioritize responses. Vertically integrating domain experts by hiring them to implement systems yields better ideas and better data to

Getting Things Done: The Art of Stress-Free Productivity

by David Allen  · 31 Dec 2002  · 300pp  · 79,315 words

kept on lists, but I still maintain two categories of paper-based reminders. I travel with a “Read/Review” plastic file folder and another one labeled “Data Entry.” In the latter I put anything for which the next action is simply to input data into my computer (business cards that need to

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

such systems can reach a high level of test-set accuracy—as shown by the ImageNet competition results, for example—they often require far more labeled data than a human would for the same task. For example, a child needs to see only one picture of a giraffe, rather than thousands, in

…

learning story; indeed, it may be the case that our current approach to supervised deep learning renders some tasks completely unattainable because the requirements for labeled data would exceed what the human race (or the universe) can supply. Moreover, even in cases where the task is feasible, labeling large data sets usually

…

requires scarce and expensive human labor. For these reasons, there is intense interest in several learning paradigms that reduce the dependence on labeled data. As we saw in Chapter 19, these paradigms include unsupervised learning, transfer learning, and semisupervised learning. Unsupervised learning algorithms learn solely from unlabeled inputs x

…

data to train an initial version of an NLP model. From there, we can use a smaller amount of domain-specific data (perhaps including some labeled data) to refine the model. The refined model can learn the vocabulary, idioms, syntactic structures, and other linguistic phenomena that are specific to the new domain

…

. During training a single sentence can be used multiple times with different words masked out. The beauty of this approach is that it requires no labeled data; the sentence provides its own label for the masked word. If this model is trained on a large corpus of text, it generates pretrained representations

…

that refer to very precisely delineated activities on simple backgrounds, is quite easy to deal with. Good results can be obtained with a lot of labeled data and an appropriate convolutional neural network. However, it can be difficult to prove that the methods actually work, because they rely so strongly on context

Python Data Analytics: With Pandas, NumPy, and Matplotlib

by Fabio Nelli  · 27 Sep 2018  · 688pp  · 107,867 words

built into Python or provided by other libraries, two new data structures were developed. These data structures are designed to work with relational data or labeled data, thus allowing you to manage data with features similar to those designed for SQL relational databases and Excel spreadsheets. Throughout the book in fact, you

Numpy Beginner's Guide - Third Edition

by Ivan Idris  · 23 Jun 2015  · 681pp  · 64,159 words

Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurelien Geron  · 14 Aug 2019

Democracy's Data: The Hidden Stories in the U.S. Census and How to Read Them

by Dan Bouk  · 22 Aug 2022  · 424pp  · 123,180 words

Why Machines Learn: The Elegant Math Behind Modern AI

by Anil Ananthaswamy  · 15 Jul 2024  · 416pp  · 118,522 words

Rule of the Robots: How Artificial Intelligence Will Transform Everything

by Martin Ford  · 13 Sep 2021  · 288pp  · 86,995 words

Data Mining: Concepts, Models, Methods, and Algorithms

by Mehmed Kantardzić  · 2 Jan 2003  · 721pp  · 197,134 words

Mastering Pandas

by Femi Anthony  · 21 Jun 2015  · 589pp  · 69,193 words

Code: The Hidden Language of Computer Hardware and Software

by Charles Petzold  · 28 Sep 1999  · 566pp  · 122,184 words

The Elements of Statistical Learning (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani and Jerome Friedman  · 25 Aug 2009  · 764pp  · 261,694 words

Radical Markets: Uprooting Capitalism and Democracy for a Just Society

by Eric Posner and E. Weyl  · 14 May 2018  · 463pp  · 105,197 words

Scikit-Learn Cookbook

by Trent Hauck  · 3 Nov 2014

Mastering Machine Learning With Scikit-Learn

by Gavin Hackeling  · 31 Oct 2014

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron  · 13 Mar 2017  · 1,331pp  · 163,200 words

Artificial Intelligence: A Guide for Thinking Humans

by Melanie Mitchell  · 14 Oct 2019  · 350pp  · 98,077 words

Four Battlegrounds

by Paul Scharre  · 18 Jan 2023

Natural Language Annotation for Machine Learning

by James Pustejovsky and Amber Stubbs  · 14 Oct 2012  · 502pp  · 107,510 words

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do

by Erik J. Larson  · 5 Apr 2021

The Deep Learning Revolution (The MIT Press)

by Terrence J. Sejnowski  · 27 Sep 2018

Driverless: Intelligent Cars and the Road Ahead

by Hod Lipson and Melba Kurman  · 22 Sep 2016

The Future of the Brain: Essays by the World's Leading Neuroscientists

by Gary Marcus and Jeremy Freeman  · 1 Nov 2014  · 336pp  · 93,672 words

Data Wrangling With Python: Tips and Tools to Make Your Life Easier

by Jacqueline Kazil  · 4 Feb 2016

Tcl/Tk in a Nutshell

by Paul Raines and Jeff Tranter  · 25 Mar 1999  · 1,064pp  · 114,771 words

Ajax: The Definitive Guide

by Anthony T. Holdener  · 25 Jan 2008  · 982pp  · 221,145 words

Data Science from Scratch: First Principles with Python

by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

by Zdravko Markov and Daniel T. Larose  · 5 Apr 2007

Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers

by John MacCormick and Chris Bishop  · 27 Dec 2011  · 250pp  · 73,574 words

Learning Scikit-Learn: Machine Learning in Python

by Raúl Garreta and Guillermo Moncecchi  · 14 Sep 2013  · 122pp  · 29,286 words

Mining of Massive Datasets

by Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman  · 13 Nov 2014

Producing Open Source Software: How to Run a Successful Free Software Project

by Karl Fogel  · 13 Oct 2005

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson and Michael Munn  · 31 Oct 2020

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It

by Kashmir Hill  · 19 Sep 2023  · 487pp  · 124,008 words

Code Dependent: Living in the Shadow of AI

by Madhumita Murgia  · 20 Mar 2024  · 336pp  · 91,806 words

AI 2041: Ten Visions for Our Future

by Kai-Fu Lee and Qiufan Chen  · 13 Sep 2021

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos  · 21 Sep 2015  · 396pp  · 117,149 words

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity

by Amy Webb  · 5 Mar 2019  · 340pp  · 97,723 words

AI Superpowers: China, Silicon Valley, and the New World Order

by Kai-Fu Lee  · 14 Sep 2018  · 307pp  · 88,180 words

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

by Eric Topol  · 1 Jan 2019  · 424pp  · 114,905 words

Python for Algorithmic Trading: From Idea to Cloud Deployment

by Yves Hilpisch  · 8 Dec 2020  · 1,082pp  · 87,792 words

Applied Artificial Intelligence: A Handbook for Business Leaders

by Mariya Yao, Adelyn Zhou and Marlene Jia  · 1 Jun 2018  · 161pp  · 39,526 words

The Geek Way: The Radical Mindset That Drives Extraordinary Results

by Andrew McAfee  · 14 Nov 2023  · 381pp  · 113,173 words

This Is Service Design Doing: Applying Service Design Thinking in the Real World: A Practitioners' Handbook

by Marc Stickdorn, Markus Edgar Hormess, Adam Lawrence and Jakob Schneider  · 12 Jan 2018  · 704pp  · 182,312 words

Supremacy: AI, ChatGPT, and the Race That Will Change the World

by Parmy Olson  · 284pp  · 96,087 words

Everything Is Predictable: How Bayesian Statistics Explain Our World

by Tom Chivers  · 6 May 2024  · 283pp  · 102,484 words

System Error: Where Big Tech Went Wrong and How We Can Reboot

by Rob Reich, Mehran Sahami and Jeremy M. Weinstein  · 6 Sep 2021

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence

by John Brockman  · 5 Oct 2015  · 481pp  · 125,946 words

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back

by Jacob Ward  · 25 Jan 2022  · 292pp  · 94,660 words

Blockchain Basics: A Non-Technical Introduction in 25 Steps

by Daniel Drescher  · 16 Mar 2017  · 430pp  · 68,225 words

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao  · 19 May 2025  · 660pp  · 179,531 words

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future

by Orly Lobel  · 17 Oct 2022  · 370pp  · 112,809 words

Futureproof: 9 Rules for Humans in the Age of Automation

by Kevin Roose  · 9 Mar 2021  · 208pp  · 57,602 words

NumPy Cookbook

by Ivan Idris  · 30 Sep 2012  · 197pp  · 35,256 words

The Creativity Code: How AI Is Learning to Write, Paint and Think

by Marcus Du Sautoy  · 7 Mar 2019  · 337pp  · 103,522 words

I, Warbot: The Dawn of Artificially Intelligent Conflict

by Kenneth Payne  · 16 Jun 2021  · 339pp  · 92,785 words

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman  · 4 Sep 2023  · 444pp  · 117,770 words

The Soul of a New Machine

by Tracy Kidder  · 1 Jan 1981  · 299pp  · 99,080 words

Human + Machine: Reimagining Work in the Age of AI

by Paul R. Daugherty and H. James Wilson  · 15 Jan 2018  · 523pp  · 61,179 words

Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

by Tarleton Gillespie  · 25 Jun 2018  · 390pp  · 109,519 words

Succeeding With AI: How to Make AI Work for Your Business

by Veljko Krunic  · 29 Mar 2020

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think

by James Vlahos  · 1 Mar 2019  · 392pp  · 108,745 words

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott  · 14 May 2018  · 301pp  · 85,126 words

Reality Is Broken: Why Games Make Us Better and How They Can Change the World

by Jane McGonigal  · 20 Jan 2011  · 470pp  · 128,328 words

Co-Intelligence: Living and Working With AI

by Ethan Mollick  · 2 Apr 2024  · 189pp  · 58,076 words

The Data Journalism Handbook

by Jonathan Gray, Lucy Chambers and Liliana Bounegru  · 9 May 2012

Cloudmoney: Cash, Cards, Crypto, and the War for Our Wallets

by Brett Scott  · 4 Jul 2022  · 308pp  · 85,850 words

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity

by Daron Acemoglu and Simon Johnson  · 15 May 2023  · 619pp  · 177,548 words

If Then: How Simulmatics Corporation Invented the Future

by Jill Lepore  · 14 Sep 2020  · 467pp  · 149,632 words

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)

by Robert Skidelsky Nan Craig  · 15 Mar 2020

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals

by David Aronson  · 1 Nov 2006

The Corruption of Capitalism: Why Rentiers Thrive and Work Does Not Pay

by Guy Standing  · 13 Jul 2016  · 443pp  · 98,113 words

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It

by Azeem Azhar  · 6 Sep 2021  · 447pp  · 111,991 words