Towards AI

AUGUST 11, 2023

GANs are a part of the deep-learning world and were very introduced by Ian Goodfellow and his collaborators in 2014, After that GANs have rapidly captivated many researchers’ eyes which resulted in much research and also helped to redefine the boundaries of creativity and artificial intelligence in the world of AI 1.1

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Topbots

JUNE 22, 2023

Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Outstandingly, ChatPGT presents such a capacity: it can explain its decisions. Expert Systems with Applications (2014), 41(16):7653–7670. This capability helped me to give my ruling. Aghabozorgi, S.,

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ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. But by 2014, ConvNets had become powerful enough to start surpassing human accuracy on a number of visual recognition tasks. What are adversarial attacks? confidence.

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Viso.ai

MAY 7, 2024

GoogLeNet, released in 2014, set a new benchmark in object classification and detection through its innovative approach (achieving a top-5 error rate of 6.7%, nearly half the error rate of the previous year’s winner ZFNet with 11.7%) in ImageNet Large Scale Visual Recognition Challenge (ILSVRC).

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

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Mlearning.ai

FEBRUARY 28, 2023

Year: More than half the cars in the data were manufactured in or after 2014. This is a clear indication that a good model is formed to explain variance in the price of used cars up to 87%. The log transformation was applied on this column to reduce skewness. Seats: 84% of the cars in the dataset are 5-seater cars. MAPE of 43.55

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. References: [link] [link] [link] WRITER at MLearning.ai // Control AI Video ?

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Heartbeat

AUGUST 21, 2023

GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. 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. We pay our contributors, and we don’t sell ads.

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Shap: Currently LLMs are not directly explainable in the same way as simpler machine learning models due to their complexity, size, and the black box nature of closed source models.

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Mlearning.ai

APRIL 1, 2023

Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov. We have explained the architectures of each model, as well as how to create and train them using the gensim library in Python. Doc2Vec learns vector representations of documents by combining the word vectors with a document-level vector.

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Heartbeat

OCTOBER 13, 2023

Time series Analysis showing Tuberculosis morbidity from a timespan of January 2004 to June 2014 in Xinjiang. 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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Dlabs.ai

AUGUST 20, 2020

A Guide for Making Black Box Models Explainable Author: Christoph Molnar If you’re looking to learn how to make machine learning decisions interpretable, this is the eBook for you! It explains how to make machine learning algorithms work. Meaning you can download it for free, and if you find it useful, you can pay for this resource.

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Heartbeat

FEBRUARY 27, 2023

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. We’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

The following images show the output for “silver car.” The following image shows the output for “driving lane.” We can use this pipeline to build a visual chain. We compare the performance with respect to the object sizes (in proportion to image size)— small (area 1%).

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Heartbeat

OCTOBER 9, 2023

References: Francesco Nex and Fabio Remondino's "Photogrammetry and Remote Sensing with Unmanned Aerial Vehicles" (2014). 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

One is a more formal view of explainability. In 2013, my team started exploring building neural network accelerators on FPGAs, and in 2015 we ramped that effort to take it to production as Project Brainwave , which shipped into production at large scale in 2017, accelerating neural network inference for Bing and Office.

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AssemblyAI

OCTOBER 4, 2023

Answer: Taylor Swift released the song "Blank Space" on November 10, 2014. This means the chat completion service works, but as explained in the output, the model was trained with data up to October 2021, so the song "Anti-Hero" by Taylor Swift did not exist at the time. Don't explain your reasoning.

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IBM Journey to AI blog

FEBRUARY 29, 2024

The system uses Docker images, which are read-only templates that are used for building containers, and Dockerfiles, which are text files that accompany and explain Docker images. Docker images and other container images require a space in which to run.

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Unite.AI

APRIL 23, 2024

In this VisualCV review, I'll explain what VisualCV is, who it's best for, and what its features are so you know what it's capable of. Based in Vancouver, Canada, VisualCV was founded in 2014 by James Clift and Thomas Zhou to simplify creating resumes and land jobs and interviews. Cons The free version has some limitations.

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Mlearning.ai

MARCH 9, 2023

Explaining and harnessing adversarial examples. Explaining and harnessing adversarial examples. However, these algorithms are vulnerable to adversarial attacks, where imperceptible perturbations to the input image can lead to significant misclassifications (Goodfellow et al., 2013; Goodfellow et al., Goodfellow, I. Goodfellow, I.

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John Snow Labs

FEBRUARY 29, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). John Snow Labs Solution : John Snow Labs addresses this by offering AI solutions that prioritize explainability. Initially its applications were modest focusing on tasks like pattern recognition in imaging and data analysis.

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AI Impacts

APRIL 11, 2023

Oldham, et al 2014. But this does not explain the lack of research, and one of the reasons given for opposition to experiments is that it has not been shown to be safe. 2031 (2014): 20140065. 4 Scientific papers published on solar radiation management by year. 5 Bolinger, Mark, Ryan Wiser, and Eric O'Shaughnessy.

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Referred to as black boxes, such AI models might produce the most accurate predictions possible, but even engineers can’t explain the reasoning behind them. AI drug discovery is exploding.

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world. 2014)[ 73 ] and Donahue et al.

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The MLOps Blog

OCTOBER 20, 2023

In 2014, Project Jupyter evolved from IPython. There, you can use infographics, custom visualizations, and broader ways to explain your ideas. How to structure Jupyter notebook’s content In this section, I will explain the notebook layout I typically use. These tools gained significant adoption among researchers.

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. Making CNN models more interpretable and explainable. However, these advancements come with their own set of challenges: Overcoming the heavy reliance on large, labeled datasets.

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Heartbeat

APRIL 18, 2023

It was introduced in 2014 by a group of researchers (A. link] From the code above, we import Comet into our project; it is a cloud-based machine learning platform that allows data science teams to track, compare, explain, and optimize their experiments and models across the complete ML lifecycle — from training runs to production monitoring.

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Pragnakalp

JANUARY 1, 2024

There are several theories and hypotheses that attempt to explain what might have come before the Big Bang, but none of them have been proven conclusively. Overall, while there are many theories and hypotheses that attempt to explain what came before the Big Bang, none of them have been proven conclusively. Mistral-7b-instruct-v0.1

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John Snow Labs

MAY 23, 2023

The first VQA dataset was DAQUAR, released in 2014. This explains why many practical applications have been discovered for VQA in just the last half decade. DAQUAR is a small dataset that exclusively contains indoor scenes. After DAQUAR, several other datasets have been released to support different VQA applications.

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ML @ CMU

DECEMBER 29, 2023

Given a hyperparameter configuration ((lambda_s, lambda_c)), we train a model using the training clients (explained in section “FL Training”). In our work, we focus on an instantiation of FedOPT called FedAdam , which uses Adam (Kingma and Ba 2014) as ServerOPT and SGD as ClientOPT.

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Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings?

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deepsense.ai

MAY 7, 2023

We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. Source: [ 2 ] In the previous post, we explained the importance of Stable Diffusion [ 3 ]. 2014 Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , Zhang et al.

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Viso.ai

DECEMBER 6, 2023

The model secured first and second positions in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014. Future Directions Explainable AI: Explainable AI (XAI) is one research paradigm that can help you detect biases easily. VGGNet uses 3×3 filters to extract fundamental features from image data.

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Mlearning.ai

MARCH 31, 2023

This article on roboflow does a great job in explaining this: [link] Getting Better Results Given the small size of training data (only 904 training instances!) In my professional life, in 2014–2017, I could work in a project to develop a real time object detector for production purposes. and model (only 3,235,014 parameters!)

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AWS Machine Learning Blog

OCTOBER 17, 2023

According to a 2014 study, the proportion of severely lame cows in China can be as high as 31 percent. Summary In this article, we briefly explained how the AWS Customer Solutions team innovates quickly based on the customer’s business. Therefore, when lameness occurs, veterinarians should intervene as soon as possible.

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Explosion

MAY 17, 2023

In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text. We all spend a big part of our working lives writing, reading, speaking and listening. This is unfortunate, because that’s what the web almost entirely consists of.

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Marek Rei

NOVEMBER 12, 2019

Evaluations on CoNLL 2014 and JFLEG show a considerable improvement over previous best results of neural models, making this work comparable to state-of-the art on error correction. link] Constructing a system for NLI that explains its decisions by pointing to the most relevant parts of the input. Cambridge, Amazon. NAACL 2019.

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