Source: Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. In the above image, LIME can be seen identifying the head of a frog as the most important identifying feature in the classification, which we can then verify against our own intuitions. The Aether Committee advises our leadership on the challenges and opportunities presented by AI innovations. The origin of this line of inquiry is `trust' of artificial intelligence (AI) systems. Laura Bell explores how bias impacts the security of a development lifecycle and examines 3 common biases that lead to big issues in this space. This method completely counters the principles of explainable and ethical AI, and is why many issues of bias become amplified beyond control. a) a music streaming platform recommending a song b) a doctor depending on an AI- based system to make a diagnosis c) a navigation platform suggesting fastest routes d) a social media platform identifies faces from a picture During the 2010 "flash crash", major stock averages plunged 9% in a matter of minutes when high-frequency trading algorithms fell into a recursive cycle of panic selling. Professor Francesca Toni . There are a litany of approaches that have been proposed for the purpose of tackling Explainable AI, a topic which has gained substantial attention in both academic and professional circles. DARPA is pursuing efforts to produce explainable AI through a number of funded research initiatives, as well as many companies helping to bring explainability to AI. linear or logistic regression, decision trees, or k-Nearest Neighbors) is then trained on the new dataset, which is weighted by the proximity of each respective sample to the input of interest. 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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