InfoQ Homepage Machine Learning Content on InfoQ
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The Case for Explainable AI (XAI)
Artificial Neural Networks offer significant performance benefits compared to other methodologies, but often at the expense of interpretability. Black box algorithms have precipitated a number of high-profile controversies arising from the inability to understand their inner workings. The efforts seeking to provide more transparency in this regard is referred to as Explainable AI (XAI).
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Federated Machine Learning for Loan Risk Prediction
In this article, author Brendon Machado discusses how data owners and data scientists can work together to create models on privatized data using the federated learning technique and shows how to use it in loan risk prediction use cases.
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Easy Interpretation of a Logistic Regression Model with Delta-p Statistics
Delta-p statistics is an easier means of communicating results to a non-technical audience than the plain coefficients of a logistic regression model. In this article, authors Maarit Widmann and Alfredo Roccato discuss how to predict credit eligibility using the Delta-p statistics based solution.
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The First Wave of GPT-3 Enabled Applications Offer a Preview of Our AI Future
The first wave of GPT-3 powered applications are emerging. After priming of only a few examples, GPT-3 could write essays, answer questions, and even generate computer code! Furthermore, GPT-3 can perform algebraic calculations and language translations despite never being taught such concepts. However, GPT-3 is a black box with unpredictable outcomes. Developers must use it responsively.
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State of the Art in Automated Machine Learning
InfoQ caught up with experts Francesca Lazzeri, machine learning scientist lead at Microsoft; Matthew Tovbin, co-founder of Faros AI; Adrian de Wynter, applied scientist in Alexa AI’s Secure AI Foundations; Leah McGuire, principal member of technical staff at Salesforce; and Marios Michailidis, data scientist at H2O.ai, about the state of the art in automated machine learning (AutoML).
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How to Get Hired as a Machine Learning Engineer
To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions. When given a technical task, demonstrate your skills as if you already had the job.
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Innovation Startups Modeling Agile Culture
Innovation is not only about the most advanced technology; management and processes are the new era of startups' innovation. To mix the power of the data and the importance of people to offer business intelligence is a key point nowadays. The result is not only the most important thing; the way you do it is more important. To be agile is to adapt to today's market.
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TornadoVM: Accelerating Java with GPUs and FPGAs
The proliferation of heterogeneous hardware represents a problem for programming languages such as Java that target CPUs. TornadoVM extends the Graal JIT compiler to take advantage of GPUs & FPGAs and provides a flexible, high-level model whilst still enabling high performance and features such as live task migration.
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Machine Learning in Java with Amazon Deep Java Library
In this article, we demonstrate how Java developers can use the JSR-381 VisRec API to implement image classification or object detection with DJL’s pre-trained models in less than 10 lines of code.
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Q&A on the Book Hands-On Genetic Algorithms with Python
Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.
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The Road to Artificial Intelligence: a Tale of Two Advertising Approaches
Artificial Intelligence startups received a record $26.6bn in funding last year, yet a litany of stakeholders continue to demonstrate a lack understanding and education around the discipline. It is critical that entrepreneurs, investors, regulators, and consumers all remain vigilant in properly assessing advertising claims as relates to powerful, constantly-evolving technology.
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Reinforcement Machine Learning for Effective Clinical Trials
In this article, author Dattaraj Jagdish Rao explores the reinforcement machine learning technique called Multi-armed Bandits and discusses how it can be applied to areas like website design and clinical trials.