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) …
Artificial intelligence is a term that comes from the ability of computer systems to be able to process things and arrive at decisions without human intervention. Scientists and Engineers have been working on Artificial intelligence for a better part of the last decade trying to build algorithms and logic that would allow artificial intelligence to grow and make decisions. The studies by ancient philosophers such as Aristotle have provided logics that have been great at producing human-like results. The discovery of logic is attributed to Aristotle in his work, the Oreganon. He explained a set of premises that can be used to arrive at conclusions.
It's an essential prerequisite for deciding how we want critical decisions about our health and well-being to be made -- possibly for a very long time to come. To understand why the "how" behind AI functionality is so important, we first have to appreciate the fact that there have historically been two very different approaches to AI. The first is symbolism, which deals with semantics and symbols. Many early AI advances utilized a symbolistic approach to AI programming, striving to create smart systems by modeling relationships and using symbols and programs to convey meaning. But it soon became clear that one weakness to these semantic networks and this "top-down" approach was that true learning was relatively limited.
They are just plain vanilla classes inheriting directly from object . But they do need to be a callable taking a Doc and returning a Doc . Now to initialize the Doc and Token (global) objects to hold custom attributes. These containers expose the _ attribute for us to attach our own custom attributes to the container. Now we import the matcher and initialize it with our Language vocabulary.
Working as a core maintainer for PyTorch Lightning, I've grown a strong appreciation for the value of tests in software development. As I've been spinning up a new project at work, I've been spending a fair amount of time thinking about how we should test machine learning systems. A couple weeks ago, one of my coworkers sent me a fascinating paper on the topic which inspired me to dig in, collect my thoughts, and write this blog post. In this blog post, we'll cover what testing looks like for traditional software development, why testing machine learning systems can be different, and discuss some strategies for writing effective tests for machine learning systems. We'll also clarify the distinction between the closely related roles of evaluation and testing as part of the model development process.
Many attempts to develop artificial intelligence are powered by powerful systems of mathematical logic. They tend to produce results that make logical sense to a computer program -- but the result is not very human. In our work building therapy chatbots, we have found using a different kind of logic -- one first formalised by the Greek philosopher Aristotle more than 2,000 years ago -- can produce results that are more fallible, but also much more like real people. The underpinning science of our chatbots is formal logic. Modern formal logic has its basis in mathematics -- but that wasn't always the case.
Debating rules-based systems over machine learning comes down to the complexity of the task at hand. Machine learning dominates complex tasks, but requires more long-term expertise. For organizations creating algorithms and implementing systems, choosing between rules-based vs. machine learning-based systems is critical to the usability, compatibility and lifecycle of the application. Getting outputs from a rules-based system can be a simple and nearly immediate application of AI, but an investment in machine learning can handle complex tasks with great speed. Enterprises must understand the core differences between the two, their individual benefits and the limitations of both before taking advantage of either.
The future of jobs has been used to justify the major changes to university education announced last week. Fees for courses that, according to the government, lead to jobs with a great future will fall, while those with a poor future will rise. But can the government predict the jobs of the future? And do proposed fee changes match those jobs that will grow? Read more: The government is making'job-ready' degrees cheaper for students – but cutting funding to the same courses In the research I have done on the future of work, several things are clear.
Artificial Intelligence (AI) promises to make the human race smarter. Raymond Kurzweil has made predicting the Singularity -- when artificial intelligence exceeds human intelligence -- a cottage industry. Is AI going to make us all smarter, or are we already as smart as we can handle? This TechRepublic Premium ebook compiles the latest on cancelled conferences, cybersecurity attacks, remote work tips, and the impact this pandemic is having on the tech industry. Some of our issues are cognitive, such as our inherent inability to estimate exponential functions.
We propose a general semantics for strategic abilities of agents in asynchronous systems, with and without perfect information. Based on the semantics, we show some general complexity results for verification of strategic abilities in asynchronous interaction. More importantly, we develop a methodology for partial order reduction in verification of agents with imperfect information. We show that the reduction preserves an important subset of strategic properties, with as well as without the fairness assumption. We also demonstrate the effectiveness of the reduction on a number of benchmarks. Interestingly, the reduction does not work for strategic abilities under perfect information.
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.