Read blog post arrow_forward. A round-up of last week’s content on InfoQ sent out every Tuesday. Data . Saga Orchestration for Microservices Using the Outbox Pattern, How to Build Interactive Data Visualizations for Python with Bokeh, Agile Development Applied to Machine Learning Projects, Failing Fast: the Impact of Bias When Speeding up Application Security. In this paper, we concentrate on adapting explainable AI to face recognition and biometrics, and we present four principles of explainable AI to face recognition and biometrics. Recent research has also shown value in visualizing the interactions between neurons in Artificial Neural Networks. If you are at an office or shared network, you can ask the network administrator to run a scan across the network looking for misconfigured or infected devices. Note: If updating/changing your email, a validation request will be sent. • We should always have in mind that like any other technology, the goal of AI is to improve our quality of life, so the more benefit we can extract from it, the better. Explainable AI tools are provided at no extra charge to users of AutoML Tables or AI Platform. Join a community of over 250,000 senior developers. Similar technologies, which have similar sources of risk, are likely to benefit from the same set of risk measures. InfoQ Homepage Consider the systems that movie streaming services use to recommend films they think you personally will enjoy. In the very least, explainability can facilitate the understanding of various aspects of a model, leading to insights that can be utilized by various stakeholders, such as (cf. Explainable AI (XAI) is artificial intelligence that is programmed to describe its purpose, justification and decision-making process in a way that can be understood by the average person. Despite assertions by Goldman Sachs that the models exclude gender as a feature and that the data were vetted for bias by a third party, many prominent names in tech and politics, including Steve Wosniak, publicly commented on the potentially "mysogonsitic algorithm". AI systems should use tools, including anonymized data, de identification, or aggregation to protect personally identifiable information whenever possible. Machine learning is a powerful new tool, but how does it fit in your agile development? Which case would benefit from Explainable Al principles? We introduce four principles for explainable artificial intelligence (AI) that comprise the fundamental properties for explainable AI systems. • AI systems should be designed in a way that In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. “Why Should I Trust You?” Explaining the Predictions of Any Classifier, Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning, recent collaboration between OpenAI and Google researchers, Local Interpretable Model-Agnostic Explanations (LIME): An Introduction, Google Open-Sources Trillion-Parameter AI Language Model Switch Transformer, Netflix Open Sources Their Domain Graph Service Framework: GraphQL for Spring Boot, Serverless React Applications with AWS Lambda, Amplify Flutter Brings Together Flutter and AWS for Cross-Platform Apps, Carin Meier Using Machine Learning to Combat Major Illnesses, Such as the Coronavirus, AI Applied in Enterprises: Information Architecture, Decision Optimization, and Operationalization, The Brain is Neither a Neural Network Nor a Computer: Book Review of The Biological Mind, Overcoming Data Scarcity and Privacy Challenges with Synthetic Data, Understanding Similarity Scoring in Elasticsearch, Q&A on the Book Cybersecurity Threats, Malware Trends and Strategies, Q&A on the Book Accelerating Software Quality. The strategic challenge of developing ethical explainable AI for the public sector in a way which can be repeated/scaled across multiple uses which is set out above/below. However, the principles that AI systems use to make intelligent decisions are often hidden from their end users. Challenges of Human Pose Estimation in AI-Powered Fitness Apps, COVID-19 and Mining Social Media - Enabling Machine Learning Workloads with Big Data, Federated Machine Learning for Loan Risk Prediction, GitLab 13.9 Introduces Security Alerts Dashboard, Maintenance Mode, and More, How Kanban Can Support Evolutionary Change, Newly Refactored Vite 2.0 Still Focuses on Speed, Is Now Framework-Agnostic, Designing for Failure in the BBC's Analytics Platform, Leading During Times of High Uncertainty and Change, NLP Library spaCy 3.0 Features Transformer-Based Models and Distributed Training, Google Brings Databricks to Its Cloud Platform, Java News Roundup - Week of Feb 15th, 2021, Lightstep Connects Tracing and Metrics with New Change Intelligence Feature, Microsoft Satin Audio Codec Uses AI to Outperform Skype Silk, Amazon Introduces CloudFront Security Savings Bundle, Gremlin Releases State of Chaos Engineering 2021 Report, Boosting WebAssembly Performance with SIMD and Multi-Threading, Microsoft Announces Azure IoT Edge Modules for Linux on Windows in Public Preview, How Using Modern JavaScript May Improve Performance, Improving the Performance of a Route Editor Using a Quadtree, Testing Asynchronous Code - RxJS Live London, Clare Liguori on Automating Safe and “Hands-Off” Deployments at AWS, Artificial Neural Networks offer significant performance benefits compared to other methodologies, but often at the expense of interpretability, Problems and controversies arising from the use and reliance on black box algorithms have given rise to increasing calls for more transparent prediction technologies, Hybrid architectures attempt to solve the problem of performance and explainability sitting in tension with one another, Current approaches to enhancing the interpretability of AI models focus on either building inherently explainable prediction engines or conducting post-hoc analysis, The research and development seeking to provide more transparency in this regard is referred to as Explainable AI (XAI), Get a quick overview of content published on a variety of innovator and early adopter technologies, Learn what you don’t know that you don’t know, Stay up to date with the latest information from the topics you are interested in.
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