If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
There are four major ways to train deep learning networks: supervised, unsupervised, semi-supervised, and reinforcement learning. We'll explain the intuitions behind each of the these methods. Along the way, we'll share terms you'll read in the literature in parentheses and point to more resources for the mathematically inclined. By the way, these categories span both traditional machine learning algorithms and the newer, fancier deep learning algorithms. For the math-inclined, see this Stanford tutorial which covers supervised and unsupervised learning and includes code samples.
First, the European Union's General Data Protection Regulation (GDPR) provides that people have a right to "meaningful information" about the logic behind automated decisions using their data. This law, in an interesting and potentially radical way, seems to mandate that any automated decision-making that people are subject to should be explainable to the person affected. That got me wondering: What does that mean? How do we implement that? And what does explanation really mean here?
Machine learning educational content is often in the form of academic papers or blog articles. These resources are incredibly valuable. However, they can sometimes be lengthy and time-consuming. If you just want to learn basic concepts and don't require all the math and theory behind them, concise machine learning videos may be a better option. The Youtube videos on this list cover concepts such as what machine learning is, the basics of natural language processing, how computer vision works, and machine learning in video games.
Since AI involves interactions between machines and humans--rather than just the former replacing the latter--'explainable AI' is a new challenge. Intelligent systems, based on machine learning, are penetrating many aspects of our society. They span a large variety of applications--from the seemingly harmless automation of micro-tasks, such as the suggestion of synonymous phrases in text editors, to more contestable uses, such as in jail-or-release decisions, anticipating child-services interventions, predictive policing and many others. Researchers have shown that for some tasks, such as lung-cancer screening, intelligent systems are capable of outperforming humans. In many other cases, however, they have not lived up to exaggerated expectations.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Would you trust an artificial intelligence algorithm that works eerily well, making accurate decisions 99.9 percent of the time, but is a mysterious black box? Every system fails every now and then, and when it does, we want explanations, especially when human lives are at stake. And a system that can't be explained can't be trusted. That is one of the problems the AI community faces as their creations become smarter and more capable of tackling complicated and critical tasks.
International Conference on Learning Representations (ICLR) and Consultative Group on International Agricultural Research (CGIAR) jointly conducted a challenge where over 800 data scientists globally competed to detect diseases in crops based on close shot pictures. The objective of this challenge is to build a machine learning algorithm to correctly classify if a plant is healthy, has stem rust, or has leaf rust. Wheat rust is a devastating plant disease affecting many crops, reducing yields and affecting the livelihoods of farmers and decreasing food security across Africa. The disease is difficult to monitor at a large scale, making it difficult to control and eradicate. An accurate image recognition model that can detect wheat rust from any image will enable a crowd-sourced approach to monitor crops. The imagery data came from a variety of sources.
In this post, we will see a simple and intuitive explanation of Boosting algorithms: what they are, why they are so powerful, some of the different types, and how they are trained and used to make predictions. We will avoid all the heavy maths and go for a clear, simple, but in depth explanation that can be easily understood. However, additional material and resources will be left at the end of the post, in case you want to dive further into the topic. Traditionally, building a Machine Learning application consisted on taking a single learner, like a Logistic Regressor, a Decision Tree, Support Vector Machine, or an Artificial Neural Network, feeding it data, and teaching it to perform a certain task through this data. Then ensemble methods were born, which involve using many learners to enhance the performance of any single one of them individually.
Summary: Explaining data science to a non-data scientist isn't as easy as it sounds. You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail. You might try this approach. You're at a party or maybe striking up a conversation with that pretty girl at the bar and sooner or later the question comes up, "what do you do?" Since you have what is reported to be the sexiest job in the world you proudly respond "I'm a data scientist". OK, what happens next depends on exactly what you say.
One of the most pressing questions public health officials are trying to answer about the coronavirus is how many people actually have been infected by it. Have a relatively significant portion of Californians been infected with the virus but survived without much problem? Or has the virus touched only a tiny sliver of California, suggesting the chances of serious illness are greater if you're infected? In April, controversial studies out of Stanford University and USC suggested the coronavirus has circulated much more widely than previously thought in Silicon Valley and Los Angeles County. Almost immediately, there have been questions from other epidemiologists around the country about whether those estimates were too high.