InfoQ Homepage Artificial Intelligence Content on InfoQ
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Cats, Qubits, and Teleportation: The Spooky World of Quantum Algorithms (Part 2)
Quantum information theory really took off once people noticed that the computational complexity of quantum systems was actually a computational capacity, which could be applied to other problems, such as factorization, which is used within public key cryptography. This article explores quantum algorithms and their applicability.
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Can People Trust the Automated Decisions Made by Algorithms?
The use of automated decision making is increasing. These algorithms can produce results that are incomprehensible, or socially undesirable. How can we determine the safety of algorithms in devices if we cannot understand them? Public fears about the inability to foresee adverse consequences has impeded technologies such as nuclear energy and genetically modified crops.
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How Technology Is Impacting the Future of Work through Fragmentation
One of the side effects of technology’s evolution is that it fragments existing architectures and creates new structures in the process. AI and Blockchain are currently doing this, but this pattern has been seen before and will continue as tech evolves. According to Kary Bheemaiah, fragmentation is impacting the future of work; it’s a tech-lead reality to be observed and leveraged when possible.
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Get More Bytes for Your Buck
Lovethesales had to classify one million product data from 700 different disparate sources across a large domain. They decided to create a hierarchy of classifiers through utilizing machine learning, specifically Support Vector Machines. They learned that optimising the way in which the svms were connected together yielded vast improvements in the reuse of labeled training data.
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InfoQ Call for Articles
InfoQ provides software engineers with the opportunity to share experiences gained using innovator and early adopter stage techniques and technologies with the wider industry. We are always on the lookout for quality articles and we encourage practitioners and domain experts to submit feature-length (2,000 to 3,000 word) papers that are timely, educational and practical.
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How AI Will Revolutionize These Five Job Roles by 2022
AI is altering major job roles in the tech industry. From developers to managers to CIOs, established industry positions are being disrupted already. In five years many will be unrecognizable. What changes are coming? This article examines five key roles in tech and show how AI will remake them in the next five years.
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The Problem with AI
AI depends on "data janitorial" work, as opposed to science work, and there is a gulf between prototype and sandbox, and innovation and production.
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How Much Should We Trust Artificial Intelligence
Considerable buzz surrounds artificial intelligence, and, indeed, AI is all around us. As with any software-based technology, it is also prone to vulnerabilities. Here, the author examines how we determine whether AI is sufficiently reliable to do its job and how much we should trust its outcomes.
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Virtual Panel: Data Science, ML, DL, AI and the Enterprise Developer
InfoQ caught up with experts in the field to demystify the different topics surrounding AI, and how enterprise developers can leverage them today and thereby render their solutions more intelligently.
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2017 State of Testing Report
The State of Testing 2017 report provides insights into the adoption of test techniques, practices, and test automation, and the challenges that testers are facing. This is fourth time that this survey has been done. InfoQ held an interview with the organizers of the State of Testing survey.
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There's No AI (Artificial Intelligence) without IA (Information Architecture)
Artificial intelligence (AI) is increasingly hyped by everyone, from well-funded startups to well-known software brands. In this article the author describes the need for high-quality, structured data before AI technologies can be of use to organizations and their customers.
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Article Series: An Introduction to Machine Learning for Software Developers
Get an introduction to some powerful but generally applicable techniques in machine learning for software developers. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.