ODSC - Open Data Science

MAY 30, 2023

For example, rising interest rates and falling equities already in 2013 and again in 2020 and 2022 led to drawdowns of risk parity schemes. However, changing correlations can be a challenge for this type of portfolio allocation technique. In 2023-Q1, we even saw failing banks like SVB simply because of investments in “safe” treasury bonds.

ML 52

ML Data Science Machine Learning Explainable AI 52

Unite.AI

NOVEMBER 8, 2023

Developed by a team at Google led by Tomas Mikolov in 2013, Word2Vec represented words in a dense vector space, capturing syntactic and semantic word relationships based on their context within large corpora of text. It results in sparse and high-dimensional vectors that do not capture any semantic or syntactic information about the words.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

Flipboard

NOVEMBER 28, 2023

Mayer is an Applied Scientist and ML educator at AWS Machine Learning University; where she researches and teaches safety, explainability and fairness of Machine Learning and AI systems. Since 2013 he has helped AWS customers adopt AI/ML technology as a Solutions Architect. About the Authors Mia C.

Responsible AI 144

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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. This post was written in 2013. A concise sample implementation is provided, in 500 lines of Python, with no external dependencies.

Python 40

Python Explainability Algorithm 40

Pickl AI

APRIL 18, 2023

According to a report by Nasscom, the Indian analytics industry is expected to grow from $2 billion in 2013 to $16 billion by 2025, at a compound annual growth rate of 26%. Moreover, they must also be able to successfully explain their findings to stakeholders. The demand for data analysts in India is expected to reach 1.5

Data Analysis 52

Data Analysis Data Science Machine Learning Python 52

Heartbeat

DECEMBER 14, 2023

From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023 citing concerns about the risks of artificial intelligence (AI) technology. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

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 ?

Explainability 52

Explainability Neural Network Deep Learning AI 52

AWS Machine Learning Blog

FEBRUARY 2, 2024

We calculate the following information based on the clustering output shown in the following figure: The number of dimensions in PCA that explain 95% of the variance The location of each cluster center, or centroid Additionally, we look at the proportion (higher or lower) of samples in each cluster, as shown in the following figure.

ETL 91

ETL DevOps LLM Generative AI 91

Mlearning.ai

APRIL 1, 2023

It was first introduced in 2013 by a team of researchers at Google 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. Photo by Brett Jordan on Unsplash Word2Vec Word2Vec is a widely used technique for generating word embeddings.

Deep Learning 52

Deep Learning NLP Natural Language Processing Neural Network 52

IBM Journey to AI blog

FEBRUARY 29, 2024

Containers and Docker Container technology fundamentally changed in 2013 with Docker’s introduction and has continued unabated into this decade, steadily gaining in popularity and user acceptance. Docker containers were originally built around the Docker Engine in 2013 and run according to an application programming interface (API).

DevOps 230

DevOps Software Development Artificial Intelligence Artificial Intelligence 230

Dlabs.ai

MARCH 17, 2021

He also runs his own YouTube channel , where he explains basic concepts of AI, shows how to use them, and talks through technological trends for the coming years. Since 2013, he’s been dividing his time between working for Google and the University of Toronto. Beyond books, Bernard writes a regular column for Forbes magazine.

Deep Learning 92

Deep Learning Artificial Intelligence Artificial Intelligence Neural Network 92

Heartbeat

DECEMBER 8, 2023

From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023 citing concerns about the risks of artificial intelligence (AI) technology. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

TheSequence

NOVEMBER 22, 2023

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. One is a more formal view of explainability.

Neural Network 40

Neural Network Generative AI AI AI 40

Unite.AI

JUNE 22, 2023

Two years later, in 2011, I co-founded Crashlytics, a mobile crash reporting tool which was acquired by Twitter in 2013 and then again by Google in 2017. In 2007, I co-founded Increo, a real-time document collaboration startup that let you comment, draw on, and mark up documents in your web browser. We were acquired by Box in 2009.

Large Language Models 325

Large Language Models Machine Learning Deep Learning ChatGPT 325

PyImageSearch

NOVEMBER 13, 2023

2013) submitted the original R-CNN publication to arXiv, Girkshick (2015) published a second paper, Fast R-CNN. Object detection is no different. One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Image credit: Figure 1 of Girshick et al.

Convolutional Neural Networks 120

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 120

The Stanford AI Lab Blog

FEBRUARY 24, 2022

In practice, (F) will often be more accurate than the original pseudo-labeler (F_{pl}) ( Lee 2013 ). 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.

Deep Learning 40

Deep Learning Algorithm Explainability 40

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. Then, in 2013, we partnered with Stanford Health Care to solve their infusion scheduling challenge. I told him I was at McKinsey, but I had already decided I was leaving to start a software company.

Machine Learning 277

Machine Learning Automation Algorithm Explainability 277

Unite.AI

JULY 28, 2023

To explain each feature fully, I'll give you a tour of each one and try it myself. MarketMuse, founded by Aki Balogh and Jeff Coyle in 2013, is a content marketing and keyword planner tool that utilizes artificial intelligence and machine learning. MarketMuse Key Features: Competitive content analysis. Content clusters.

AI 246

AI AI Artificial Intelligence Artificial Intelligence 246

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 at the forefront of advancing trustworthy AI IBM has been at the forefront of advancing trustworthy AI principles and a thought leader in the governance of AI systems since their nascence.

Responsible AI 254

Responsible AI Metadata Artificial Intelligence Artificial Intelligence 254

Stanford ILIAD

OCTOBER 28, 2019

There are many different ways to explain any given action, which makes it hard to determine what—if anything—the robot should learn. We are excited that this exaggeration arises naturally as a result of optimizing our roles, without being preprogrammed. Summarizing Roles. Kenton CT Lee, and Siddhartha S.

Robotics 52

Robotics Explainability 52

Mlearning.ai

MARCH 9, 2023

2013; Goodfellow et al., Explaining and harnessing adversarial examples. Explaining and harnessing adversarial examples. Adversarial attacks have been shown to be effective in evading state-of-the-art machine learning models, including those used for image classification and segmentation (Szegedy et al., Goodfellow, I.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

AWS Machine Learning Blog

NOVEMBER 22, 2023

This chalk talk provides an introduction to best practices for risk assessment related to fairness, robustness, explainability, privacy and security, transparency, and governance. Since 2013 he has helped AWS customers adopt AI/ML technology as a Solutions Architect.

ML 110

ML Generative AI Prompt Engineer Prompt Engineering 110

Topbots

SEPTEMBER 11, 2023

Let’s say that your content generation runs smoothly, but you hear more and more complaints about a general lack of AI transparency and explainability. Latency and waiting times are great for educating your users, e.g. by explaining what the AI is currently doing and indicating possible next steps on their side. 3] Don Norman (2013).

AI 59

AI AI UX Design Automation 59

Marktechpost

JULY 23, 2023

It contains native storage for specified schemas, which explains why. Actian purchased ParAccel in 2013 Maverick & Amigo are two of the company’s primary goods. MarkLogic MarkLogic offers a NoSQL database system with powerful querying and flexible application capabilities.

Machine Learning 52

Machine Learning ETL Big Data Data Integration 52

Heartbeat

DECEMBER 27, 2023

It will further explain the various containerization terms and the importance of this technology to the machine learning workflow. However, the emergence of the open-source Docker engine by Solomon Hykes in 2013 accelerated the adoption of the technology. Containerization is more portable, resource-efficient, and scalable than VMs.

Machine Learning 52

Machine Learning Python ML Categorization 52

PyImageSearch

OCTOBER 2, 2023

VAEs were introduced in 2013 by Diederik et al. All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. While they can reconstruct input data effectively, they falter when generating new samples from the latent space unless specific points are manually chosen.

Computer Vision 120

Computer Vision Deep Learning Neural Network Auto-complete 120

The Stanford AI Lab Blog

MAY 31, 2022

We will explain this in the next paragraph. 2013. ↩ Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. Sample one segment from a random document and sample a second segment randomly from a document linked to the first document in the document graph. Link-aware LM Pretraining.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

Explosion

DECEMBER 17, 2013

I wrote this blog post in 2013, describing an exciting advance in natural language understanding technology. But the parsing algorithm I’ll be explaining deals with projective trees. The derivation for the transition system we’re using, Arc Hybrid, is in Goldberg and Nivre (2013).

Python 45

Python Algorithm NLP Computational Linguistics 45

Explosion

FEBRUARY 28, 2017

For the similarity metric, I’ve been using a distance function taken from Chen (2013) , which he terms Cauchy Similarity : def ChenCauchy(length): '''Create a trainable similarity function, that will return the similarity and a callback to compute the backward pass given the gradient.

Convolutional Neural Networks 40

Convolutional Neural Networks Neural Network Algorithm Explainability 40

AI Impacts

OCTOBER 20, 2023

I hope to explain my intuition, and provide enough historical evidence to make this intuition at least plausible. Vogtle Unit 3 started construction in 2013. Summary: There are historical precedents where bans or crushing regulations stop the progress of technology in one industry, while progress in the rest of society continues.

AI 147

AI AI Algorithm AI Researcher 147

ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. 2014)[ 72 ], is useful to explain multi-modality of the 2014 breakthroughs. For example, we may speak to you about a dog, but all of us picture a different dog in our minds, and yet we can ground our conversation in what is common to a dog and progress forward.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Bugra Akyildiz

MARCH 2, 2024

This slide was the most interesting to me; first used the inputs as outputs and then explained how the field is now trying to generate the modality for a given input text. Although the biggest jump was on AlexNet(2013), we have came a long way since then in terms of accuracy(90.88%) and now the accuracy is larger than the human evaluation.

LLM 52

LLM AI AI Computer Vision 52

ML Review

MAY 3, 2018

Awni Hannun provides an excellent dynamic publication that explains CTC operation; available here. [29] Available: [link] (last update, 18/03/2013). One can also think of CTC as similar to a softmax due to converting the raw output of a network (e.g. raw class scores or in our case, characters) into the expected output (e.g.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

AI Impacts

MAY 14, 2024

Conclusion Broad structural and ideological differences do not explain the partisanship of environmentalism. The subsequent partisanship in environmentalism then cannot be explained by foundational ideological differences between Democrats and Republicans. During the Reagan and Bush Sr. During the Reagan and Bush Sr. 2001) [link].

Explainability 147

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AI Impacts

DECEMBER 13, 2023

10 This figure comes from a book chapter (Agrawal, 2016) which also explains the history. This explains why the transition occurred later, and resulted in more rapid growth, than the other transitions. This pattern can be entirely explained by factors internal to the technology, rather than anything exogenous.

Explainability 147

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AI Impacts

MAY 13, 2024

” Reprinted from Guber (2013). The partisanship of environmentalism cannot be explained entirely by the processes that made other issues partisan. For each one, please tell me if you personally worry about this problem a great deal, a fair amount, only a little, or not at all.” It is more partisan than those other issues.

Explainability 147

Explainability AI AI 147