O'Reilly on Data

JUNE 19, 2019

An overview from a 2017 paper from Google lets us gauge how much tooling is still needed for model operations and testing. Machine learning engineers , data engineers, developers, and domain experts are critical to the success of ML projects. Becoming a machine learning company means investing in foundational technologies”.

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Modeling Machine Learning Testing Metrics 256

CIO Business Intelligence

SEPTEMBER 29, 2023

Relevant job roles include machine learning engineer, deep learning engineer, AI research scientist, NLP engineer, data scientists and analysts, AI product manager, AI consultant, AI systems architect, AI ethics and compliance analyst, among others.

Deep Learning 143

Deep Learning Machine Learning Consulting Modeling 143

Dataiku

NOVEMBER 9, 2020

They are (rightfully) getting the attention of a big portion of the deep learning community and researchers in Natural Language Processing (NLP) since their introduction in 2017 by the Google Translation Team.

Deep Learning 90

Deep Learning Modeling 90

CIO Business Intelligence

JUNE 24, 2024

AI’s evolution: Machine learning, deep learning, GenAI AI encompasses a suite of rapidly evolving technologies. It’s a journey that started in earnest during the early 2000s with machine learning (ML). Then came deep learning in the 2010s, further enhancing perception capabilities in computer vision.

Digital Transformation 105

Digital Transformation Deep Learning Machine Learning Optimization 105

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

Monotonic Deep Lattice Networks Deep learning is a powerful tool when we have an abundance of data to learn from. In this section, we extend the ideas of building monotonic GAMs and lattice models to construct monotonic deep learning models. Other deep learning models can also be written in this form.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Domino Data Lab

MARCH 16, 2021

DeepLIFT was recently proposed as a recursive prediction explanation method for deep learning [8, 7]. Provides local explainability only No control over the concepts these maps pick Saliency maps produced by randomized networks are similar to that of the trained network (Adebayo et al., On to Concept Extraction and Building.

Modeling 122

Modeling Deep Learning Testing Optimization 122

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. billion in 2017 to $190.61 They indeed enable you to see what is happening at every moment and send alerts when something is off-trend.

Technology 279

Technology Internet of Things IT IoT 279

CIO Business Intelligence

SEPTEMBER 7, 2022

The third layer, Cropin Intelligence, uses the company’s 22 prebuilt AI and deep-learning models to provide insights about crop detection, crop stage identification, yield estimation, irrigation scheduling, pest and disease prediction, nitrogen uptake, water stress detection, harvest date estimation, and change detection, among others.

B2B 105

B2B Contextual Data Deep Learning IoT 105

Business Over Broadway

DECEMBER 20, 2018

Whether you are an aspiring or seasoned data professional, continuing to learn new data science skills is essential to staying relevant (being employed). Data Science/Analytics Tools, Technologies and Languages used in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%).

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

DMBS2

DECEMBER 12, 2017

It seems that “deep learning” and GPUs fit well for AI/machine learning uses. China also has a reasonable path to doing so (Russia not so much), in line with the “Lots of data makes models strong” line of argument. I see no natural barriers to that trend, assuming it holds up on its own merits.

Machine Learning 50

Machine Learning Predictive Modeling Deep Learning Modeling 50

CIO Business Intelligence

JANUARY 23, 2023

the OpenAI model on which ChatGPT is based, is an example of a transformer, a deep learning technique developed by Google in 2017 to tackle problems in natural language processing. Others include BERT and PaLM from Google; and MT-NLG, which was co-developed by Microsoft and Nvidia.

Enterprise 131

Enterprise Deep Learning Recreation/Entertainment Interactive 131

Domino Data Lab

JULY 2, 2019

Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. For more background about program synthesis, check out “ Program Synthesis Explained ” by James Bornholt from 2015, as well as the more recent “ Program Synthesis in 2017-18 ” by Alex Polozov from 2018.

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

CIO Business Intelligence

AUGUST 11, 2023

Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems.

Unstructured Data 142

Unstructured Data Machine Learning Data Science Data mining 142

CIO Business Intelligence

APRIL 25, 2023

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. ChatGPT is a product of OpenAI. It’s only one example of generative AI. GPT stands for generative pre-trained transformer.

Experimentation 116

Experimentation Deep Learning Enterprise Modeling 116

Smart Data Collective

FEBRUARY 24, 2021

Tomorrow Sleep was launched in 2017 as a sleep system startup and ventured on to create online content in 2018. eBay then decided to employ Phrasee – an AI-powered copywriting tool that uses natural language generation and deep learning. Tomorrow Sleep Achieved 10,000% Increase in Web Traffic.

Machine Learning 104

Machine Learning Deep Learning Strategy Reporting 104

CIO Business Intelligence

APRIL 15, 2022

In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. Derek Driggs, a machine learning researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus.

Analytics 145

Analytics Machine Learning Insurance Testing 145

Domino Data Lab

SEPTEMBER 9, 2019

It’s important to note that machine learning for natural language got a big boost during the mid-2000’s as Google began to win international language translation competitions. The use cases for natural language have shifted dramatically over the past two years, after deep learning techniques arose to the fore.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Occam's Razor

OCTOBER 12, 2017

The Future of Life Institute hosted a conference in Asilomar in Jan 2017 with just such a purpose. The entire list of videos is well worth watching, prioritize the individual ones: Beneficial AI 2017. Intro to Machine Learning. Machine Learning. Deep Learning. This is not the future, it is Nov 2017.

Marketing 138

Marketing Analytics Machine Learning Strategy 138

CIO Business Intelligence

JUNE 15, 2022

Selon la firme PwC Canada , les sommes investies ont dépassé les 800 millions de dollars entre 2017 et 2021, dont 500 millions du gouvernement fédéral. Artificial Intelligence, Deep Learning Les investissements gouvernementaux ont aussi contribué à mettre l’IA québécoise en orbite.

Finance 98

Finance Deep Learning Interactive Technology 98

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). One of the most important of such architectures, the “transformer,” was developed in 2017.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Insight

FEBRUARY 13, 2019

State-of-the-art pre-training for natural language processing with BERT Javed Qadrud-Din was an Insight Fellow in Fall 2017. He is currently a machine learning engineer at Casetext where he works on natural language processing for the legal industry. Prior to Insight, he was at IBM Watson.

Testing 72

Testing Machine Learning Deep Learning Modeling 72

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. LSTMs and other recurrent neural networks RNNs are probably the most commonly used deep learning models for NLP and with good reason. More advanced models.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

CIO Business Intelligence

MAY 1, 2024

Nel 2017, The Economist ha dichiarato [in inglese] che i dati, alla pari del petrolio, sono diventati la risorsa più preziosa del mondo e, da allora, questo stesso ritornello è stato ripetuto più volte. Le aziende di tutti i settori hanno investito e continuano a investire pesantemente in dati e analisi.

Software 105

Software Deep Learning Machine Learning Modeling 105

Domino Data Lab

FEBRUARY 10, 2019

Humans likely not even notice the difference but modern deep learning networks suffered a lot. But apparently, models trained on text from 2017 experience degraded performance on text written in 2018. Images off the web tend to frame the object in question. We might expect that.

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

Insight

OCTOBER 25, 2019

The latter is particularly restricting, as it violates the prerequisite of many deep learning methods for image classification?—?a In a narrower sense, meta-learning is a class of methods that “learn how to learn” by being “exposed to a large number of tasks” in training, then “tested in their ability to learn new tasks (Finn, 2017).”

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

O'Reilly on Data

MARCH 19, 2020

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

Deep Learning 356

Deep Learning Machine Learning Data-driven Modeling 356

Domino Data Lab

APRIL 3, 2019

We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks. See also the paper “ The Case for Open Metadata ” by Mandy Chessell (2017–04–21) at IBM UK for compelling perspectives about open metadata. In short, the virtuous cycle is growing.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84

O'Reilly on Data

SEPTEMBER 11, 2019

So, we used a form of the Term Frequency-Inverse Document Frequency (TF/IDF) technique to identify and rank the top terms in this year’s Strata NY proposal topics—as well as those for 2018, 2017, and 2016. 2) is unchanged from Strata NY 2018, it’s up three places from Strata NY 2017—and eight places relative to 2016. 221) to 2019 (No.

IoT 25

IoT Big Data Data Warehouse Uncertainty 25

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 8, 2019

Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Adrian Weller (2017-07-29). “ Here’s a video of an earlier version of her talk at NeurIPS 2017, and her related slides from ScaledML.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Insight

DECEMBER 30, 2019

In Figure 1, you can see the results of the Harris corner detector applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals: Figure 1?—?Harris Harris corner detection applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals. Original image is on the left.

Visualization 72

Visualization Machine Learning Modeling IT 72

O'Reilly on Data

JANUARY 25, 2021

Our data shows that Chef and Puppet peaked in 2017, when Kubernetes started an almost exponential growth spurt, as Figure 4 shows. A backlash is only to be expected when deep learning applications are used to justify arresting the wrong people , and when some police departments are comfortable using software with a 98% false positive rate.

Machine Learning 364

Machine Learning Software Testing Technology 364

CIO Business Intelligence

DECEMBER 18, 2024

Or Alex Honnold, who free solo climbed El Capitan in Yosemite in June 2017 and lived to tell us about it. Fast forward to 2014, when I joined IBM as an associate partner in their Innovation Practice for Natural Resources, focusing on Cognitive (Watson IBMs version of AI and deep learning models).

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Deep Learning Interactive Reporting Digital Transformation 98