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Machine Learning Research Engineer, Learning Methods

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We are a community of Machine Learning Researchers and Engineers, working to help Twitter leverage ML through a range of systems such as recommendations, safety, abuse, content understanding, ads and more. We operate at scale whilst ensuring fair and ethical use of our models and data. We work collaboratively, often embedding among product teams, looking to apply the expertise of the individuals to improve our products and unlock new capabilities. We encourage publishing papers, but they are not the end goal, rather a by-product of us doing interesting work - the aim is to make a real-world impact! The Learning Methods Research Team, part of Cortex Applied Research, enables ML applications across our platform (e.g.


[D] Machine Learning - WAYR (What Are You Reading) - Week 95

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This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.


Interesting AI/ML Articles You Should Read This Week (Sep 19)

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Alex Fly's article presents some key statistics related to the development of AI and the utilisation of AI-based technologies within enterprises. The included statistics and figures are sourced from reputable surveyors such as Deloitte, Gartner, McKinsey & Company etc. The statistics and figures presented in the article paint a picture of how AI is transforming the entirety of a typical enterprise day to day functions. AI impact is felt from hiring initiatives to profitable revenue sources. One major takeaway from Alex's article is that AI is here to stay and enterprises are adapting at a pace that will see entire companies, industries and nation transform within the next five years.


AI researchers devise failure detection method for safety-critical machine learning

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Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence. Safety-critical machine learning systems make decisions for automated technology like self-driving cars, robotic surgery, pacemakers, and autonomous flight systems for helicopters and planes. Unlike AI that helps you write an email or recommends a song, safety-critical system failures can result in serious injury or death. Problems with such machine learning systems can also cause financially costly events like SpaceX missing its landing pad. Researchers say their neural bridge sampling method gives regulators, academics, and industry experts a common reference for discussing the risks associated with deploying complex machine learning systems in safety-critical environments. In a paper titled "Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems," recently published on arXiv, the authors assert their approach can satisfy both the public's right to know that a system has been rigorously tested and an organization's desire to treat AI models like trade secrets.


Explore Machine Learning from Scratch

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Many people imagine that data science is mostly machine learning and that data scientists mostly build and train and tweak machine-learning models all day long. In fact, data science is mostly turning business problems into data problems and collecting data and understanding data and cleaning data and formatting data, after which machine learning is almost an afterthought. Even so, it's an interesting and essential afterthought that you pretty much have to know about in order to do data science. Before we can talk about machine learning we need to talk about models. It's simply a specification of a mathematical (or probabilistic) relationship that exists between different variables.


Global Big Data Conference

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Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.


Real-time Object Detection using SSD MobileNet V2 on Video Streams

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In this article, we'll be learning the following: Object detection can be defined as a branch of computer vision which deals with the localization and the identification of an object. Object localization and identification are two different tasks that are put together to achieve this singular goal of object detection. Object localization deals with specifying the location of an object in an image or a video stream, while object identification deals with assigning the object to a specific label, class, or description. With computer vision, developers can flexibly do things like embed surveillance tracking systems for security enhancement, real-time crop prediction, real-time disease identification/ tracking in the human cells, etc. The TensorFlow Model Zoo is a collection of pre-trained object detection architectures that have performed tremendously well on the COCO dataset. The model zoo can be found here.


Generative Adversarial Networks

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Generative Adversarial Networks (GANs) are generative models. They generate whole images in parallel. This is usually a neural network. We call it the generator network. This generator network takes random inputs. This noise is given to a differentiable function that transforms and reshapes the same into a recognizable structure.


It's Time for AI to Perceive Time.

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Spiking Neural Networks (SNN) has recently been a topic of interest in the field of Artificial Intelligence. The premise behind SNN is that neurons in the brain, unlike our current modelling of it, communicate with one another via spike trains that occur at different frequencies and timings. Another way of visualizing the workings of natural neural netwoks is to image a pond with waves interacting with one another forming variety of patterns. The crucial advantage of SNN is the ability to encode time in a more meaningful way by making use of relative timings of the spikes. New hardware and math solutions are being worked on in the research community to make SNN practical.


How to Protect Chatbots from Machine Learning Attacks

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Artificial Intelligence is a growing industry powered by advancements from large tech companies, new startups, and university research teams alike. While AI technology is advancing at a good pace, the regulations and failsafes around machine learning security are an entirely different story. Failure to protect your ML models from cyber attacks such as data poisoning can be extremely costly. Chatbot vulnerabilities can even result in the theft of private user data. Furthermore, we'll explain how Scanta, an ML security company, protects Chatbots through their Virtual Assistant Shield.