The world is connected today in more ways than it ever has been before, as billions of objects are now capable of connecting to the internet or interfacing with devices that are already online. The new “Internet of Everything” generates a deluge of data, which is increasingly directed to the cloud for processing and storage. Meanwhile, Artificial intelligence is increasingly utilized to analyze and derive value from these enormous stores of data. In industries such as healthcare, transportation, industrial manufacturing, and financial services, AI algorithms are now being applied to increasingly difficult tasks, including critical decision-making processes.
What differentiates human from machine is the quality of judgement, creativity, and critical thinking. Humans still have the edge, but intelligent machines are slowing progressing in their ability to replicate the human decision-making process. Deep learning algorithms utilize artificial neural networks inspired by the human brain, performing a task repeatedly with small variations to find an optimal outcome.
The key to success in Machine Learning and ultimately Artificial Intelligence is data. Copious amounts of data along with rapidly advancing computing power allow machines to solve increasingly complex problems. Data not only needs to be plentiful but it also needs to be clean, representative, and balanced. If training data is not wholly representative of the diversity of a general population, then the results will undoubtedly be subject to bias. Such biases, whether intended or unintended, can manifest in subtle ways or via colossal and public failures such as the recent examples of age, gender and racial bias found in the ML offerings of some of the world’s largest software companies.
The issue of bias is well documented in sociology, psychology, and other disciplines. Our society has implemented many different safeguards to ensure that bias, and its more offensive derivatives prejudice and discrimination, are kept in check across situations as varied as employment, creditworthiness, education, and social club membership. Because algorithms are increasingly being used to guide important decisions that affect large groups of people, it is critical that similar safeguards are enacted to identify and correct issues of bias in machine learning and AI. This bias is often unintended and can also go unnoticed for a long time, so it is important to carefully evaluate the prediction results from a model to look specifically for instances of bias.
Machine learning models are entirely reliant on the underlying data that they were trained on. If this training data is biased, limited, unbalanced, or flawed in some fashion then the model will inevitably end up producing biased outputs. Data Scientists must exercise care and caution in their data collection and data labeling phases. Data should be balanced and diverse and ideally cover corner cases. If related to populations of humans in some way, such as in face recognition or sentiment analysis, it is important to achieve balanced and representative training data from a global pool of subjects if the model will potentially be applied to a global pool of actual data.
BasicAI provides a comprehensive solution for your data collection and annotation needs. We often assist clients seeking to improve diversity in training data by offering a spectrum of regions from which data can be collected. We utilize our global network of partners and affiliates to collect samples from Asia, Africa, Europe, and the Middle East. Meanwhile our proprietary annotation platform ensures highly accurate and cost-efficient data labeling in the cloud or on premises. With a focus on accuracy and effectiveness, BasicAI is committed to providing world-class annotation solutions across industry sectors.
To learn more, contact us at [email protected]
I will introduce you to the full tutorial on HTML5 with a demo on the algorithm of machine learning the video game Flappy Bird. The purpose of this experiment is to write an artificial intelligence controller based on neural networks and a genetic algorithm.
That is, we want to create an AI robot that can learn the optimal game in Flappy Bird. As a result, our little bird can safely fly through obstacles. In the best scenario, she will never die.
After reading the theory behind this project, you can download the source code at the end of this tutorial. All code is written in HTML5 using the <a title="Phaser Framework" href="http://phaser.io/"> phaser </a> framework. In addition, we used the Synaptic Neural Network library to implement a neural network so that it would not be created from scratch.
DemoFirst, look at the demo to evaluate the algorithm in action:
Run in full-screen mode
The story of how NASA, ESA, the Danish Technological University, neural networks, decision trees and other good people helped me find the best free hectare in the Far East, as well as in Africa, South America and other "so-so" places.
A task of handling hashtags has arisen in the context of data analysis from Twitter. It was needed to take hashtag and split it into separate words. The task seemed primitive, but it turned out, I underestimated it. I had to try several algorithms until I found that.
This article could be considered as a kind of chronology of completing the task with the analysis of the advantages and disadvantages of each used algorithms. So if you are interested in this topic, please make yourself comfortable here.
It should be noted that the task of breaking large text without spaces is very common in NLP. Neuro-linguistic programming (NLP) is an approach to communication, personal development, and psychotherapy created in the 1970s. The title refers to a stated connection between the neurological processes "neuro", language "linguistic" and behavioral patterns that have been learned through experience "programming" and can be organized to achieve specific goals in life.