Flipboard

JUNE 20, 2023

In this guide , we explain the key terms in the field and why they matter. It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data. Dezeen's new editorial series, AItopia , is all about artificial intelligence.

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Viso.ai

APRIL 29, 2024

first proposed the concept in a 2015 paper by the same name. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module. Performance of Spatial Transformer Networks vs Other Solutions Since its introduction in 2015, STNs have tremendously advanced the field of computer vision.

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Unite.AI

SEPTEMBER 11, 2023

By leveraging ADS to deliver trusted explainability and actionability of insights and recommendations, trust can be built. From 2015 to 2019, you were an Advisory Board Member at the Dalai Lama Center for Ethics and Transformative Values at MIT, how has this molded your values on business and AI?

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Unite.AI

OCTOBER 2, 2023

Why current methods fail to predict consumer preference for sensory-based products When we started our research in 2015, we had the hypothesis that everything you need to know about the flavor of wine, that is to say the taste, aroma, texture, and color – exists in the chemistry.

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Explosion

FEBRUARY 14, 2016

If you were doing text analytics in 2015, you were probably using word2vec. al, 2015) is a new twist on word2vec that lets you learn more interesting, detailed and context-sensitive word vectors. This post motivates the idea, explains our implementation, and comes with an interactive demo that we've found surprisingly addictive.

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Pickl AI

APRIL 8, 2024

Lack of Explainability Many AI systems, particularly deep learning models, known for their “black box” nature. Example In 2015, a team at Google developed an AI system designed to detect diabetic retinopathy from retinal images. How Can We Ensure the Transparency of AI Systems?

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Towards AI

FEBRUARY 14, 2023

OpenAI, on the other hand, is an AI research laboratory that was founded in 2015. This includes ensuring that AI is used in ways that benefit society, and that it is designed to be transparent and explainable. As more researchers and developers continue to explore this field, we can expect to see […]

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AWS Machine Learning Blog

OCTOBER 5, 2023

His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. He focuses on developing scalable machine learning algorithms.

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AI News

OCTOBER 13, 2023

The second area is securing the use of AI within the workplace, because, you know, AI has some incredibly positive impacts on people … but the problem is there are some data protection requirements around that,” explains Barnett. Finally, is the question of, ‘Could AI be used by the bad guys against the good guys?’

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Let’s look at an example. confidence.

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Explosion

DECEMBER 17, 2013

This post explains how transition-based dependency parsers work, and argues that this algorithm represents a break-through in natural language understanding. In 2015 this type of parser is now increasingly dominant. A concise sample implementation is provided, in 500 lines of Python, with no external dependencies.

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IBM Journey to AI blog

AUGUST 11, 2023

The term was originally coined in 2015 in a published research paper called, “Hidden Technical Debts in the Machine Learning System,” which highlighted common problems that arose when using machine learning for business applications.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, and the United States. Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section. Getir is the pioneer of ultrafast grocery delivery.

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Viso.ai

MAY 7, 2024

Inception v2 (2015): The second version of Inception was modified with improvements such as batch normalization and shortcut connections. built on the core ideas to achieve even greater performance and flexibility The post GoogLeNet Explained: The Inception Model that Won ImageNet appeared first on viso.ai.

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AI Impacts

MARCH 1, 2023

It’s not obvious that forecasting accuracy on these nearer-term questions is very predictive of forecasting accuracy on the longer-term questions. 8 Finally, there are caveats in the original survey worth noting here, too.

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Heartbeat

APRIL 23, 2023

SpaCy is a popular open-source NLP library developed in 2015 by Matthew Honnibal and Ines Montani, the founders of the software company Explosion. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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TheSequence

NOVEMBER 22, 2023

I’ve been interested in AI for a long time, but didn’t work in it as a core area until about 2015. One is a more formal view of explainability. A bit under a year ago I moved back to Microsoft Research to take on a leadership role, and help evolve the organization to meet the challenge of the new AI era.

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Topbots

MAY 17, 2023

In 2015, a research paper from Stanford University and UC Berkeley introduced diffusion models, coming originally from statistical physics, into the field of machine learning. However, the quality of generated images was quite poor back in 2015, as there was still huge room for improvement. Midjourney Evolution.

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Viso.ai

MARCH 11, 2024

In 2015, researchers released the first YOLO model, which rapidly gained popularity for its object detection capabilities. YOLO in 2015 became the first significant model capable of object detection with a single pass of the network. For comparison, one of the best object detection models in 2015 (R-CNN Minus R) achieved a 53.5

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Heartbeat

AUGUST 21, 2023

ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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DataRobot Blog

DECEMBER 7, 2019

We believe AI is only as good as it is explainable, so the charts below show which variables our AI relied on the most to predict AAV for both pitchers and position players. Top Free Agent Predictions Explained. For those interested in data science , most of our models registered R-squared values against our training data of between 0.7

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Unite.AI

JANUARY 13, 2024

Yolov8 Explained YOLO (You Only Live Once) is a popular computer vision model capable of detecting and segmenting objects in images. From v1 to v8: A Brief History Yolov1: Released in 2015, the first version of YOLO was introduced as a single-stage object detection model.

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Mlearning.ai

MARCH 9, 2023

2015; Huang et al., 2015), which consists of 20 object categories with varying levels of complexity. 2015) to generate adversarial examples for each image. Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al.,

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Explosion

SEPTEMBER 3, 2015

This post explains the problem, why it’s so damaging, and why I wrote spaCy to do things differently. Ideally there would be a date, but it’s still obvious that this isn’t software anyone should be executing in 2015, unless they’re investigating the history of the field. A less extreme example is CoreNLP.

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PyImageSearch

JANUARY 30, 2023

Figure 6: Changing demand for core work-related skills from 2015 to 2020 (source: IFC ). All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. AI can help students develop skills in all these areas by orchestrating and personalizing learning delivery.

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NYU Center for Data Science

AUGUST 17, 2023

The Neural Data Science summer school , held at the Cold Spring Harbor Laboratory every other year since 2015, is an intensive two-week course that aims to equip neuroscience PhDs and postdocs with the skills to handle large neural datasets.

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PyImageSearch

NOVEMBER 13, 2023

2015 ), SSD ( Fei-Fei et al., 2015 ; Redmon and Farhad, 2016 ), and others. 2013) submitted the original R-CNN publication to arXiv, Girkshick (2015) published a second paper, Fast R-CNN. Image credit: Figure 1 of Girshick (2015) ). 2015 ; He et al., 2004 ), You Only Look Once (YOLO) ( Redmon et al., MobileNets ).

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NVIDIA

DECEMBER 4, 2023

Three technical reasons, and many stories, explain why that’s so. Andrew Ng described his experiences using GPUs for AI in a GTC 2015 talk. In a 2015 talk at NVIDIA GTC, Ng described how he continued using more GPUs to scale up his work, running larger models at Google Brain and Baidu.

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AWS Machine Learning Blog

DECEMBER 4, 2023

Established in 2015, Getir has positioned itself as the trailblazer in the sphere of ultrafast grocery delivery. In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%.

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The Stanford AI Lab Blog

FEBRUARY 24, 2022

In the rest of this blogpost, we’ll share our theoretical analysis explaining why this is the case, showing that retraining in self-training provably improves accuracy compared to the original pseudo-labeler. Problems in analysis; 2015. Co-training and expansion: Towards bridging theory and practice. Cheeger J. Chung FR, Graham FC.

Deep Learning 40

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IBM Journey to AI blog

MARCH 29, 2024

GraphQL GraphQL is a query language and API runtime that Facebook developed internally in 2012 before it became open source in 2015. However, key differences exist between them that explain not only the proliferation of GraphQL but also why RESTful systems have such staying power.

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Unite.AI

SEPTEMBER 8, 2023

He explained that in my current role at McKinsey, I had the ability to get on the calendar of any chief executive. In 2015, we pivoted to focus entirely on healthcare. I told him I was at McKinsey, but I had already decided I was leaving to start a software company. I wasn’t exactly sure of what it was going to be at that point.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

However, in this post we explain how to extract layout elements in order to help understand how to use the feature for traditional documentation automation solutions. At this event, SPIE member Light and Light-based Technologies (IYL 2015). There are other possible document automation use cases where Layout can be useful.

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Heartbeat

DECEMBER 20, 2023

This model architecture has seen many accomplishments, including: First place in the ILSVRC 2015 classification competition with a top-5 error rate of 3.57% First place in ILSVRC and COCO 2015 competition in ImageNet Detection, ImageNet localization, COCO detection and COCO segmentation. We pay our contributors, and we don’t sell ads.

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Snorkel AI

JANUARY 25, 2024

Introducing Snorkel AI Snorkel AI started as a research project in the Stanford AI Lab in 2015, where Alex Ratner, Chris Re, Paroma Varma, Braden Hancock, and Henry Ehrenberg worked together to help use AI to tackle human trafficking. They found that the lack of labelled training data was a crucial bottleneck.

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Snorkel AI

JANUARY 25, 2024

Introducing Snorkel AI Snorkel AI started as a research project in the Stanford AI Lab in 2015, where Alex Ratner, Chris Re, Paroma Varma, Braden Hancock, and Henry Ehrenberg worked together to help use AI to tackle human trafficking. They found that the lack of labelled training data was a crucial bottleneck.

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IBM Journey to AI blog

FEBRUARY 22, 2024

In 2013, IBM embarked on the journey of explainability and transparency in AI and machine learning. IBM is a leader in AI ethics, appointing an AI ethics global leader in 2015 and creating an AI ethics board in 2018. These experts work to help ensure our principles and commitments are upheld in our global business engagements.

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