IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Also, its emotional intelligence allows it to adapt communication to be empathetic and supportive, creating a more positive interaction for the customer.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

IBM Big Data Hub

JULY 11, 2023

This sort of routine monitoring and scheduling takes tasks off the hands of clinical staff, who can then spend more time directly on patient care, where human judgment and interaction matter most. AI-enabled robots can work around sensitive organs and tissues, reducing blood loss, infection risk and post-surgery pain.

Deep Learning 59

Deep Learning Cost-Benefit Insurance Machine Learning 59

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Smart Data Collective

DECEMBER 21, 2020

Customers are becoming more accustomed to interacting with AI in their day to day lives, even if they don’t always realize it. They interact with AI features on their phone or when using a service, so their expectations are ever-increasing. These have been met by recent technological advances entering the mainstream.

Insurance 134

Insurance Cost-Benefit Big Data Unstructured Data 134

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. Just look at the emergence of drag-and-drop tools for machine learning workflows coming out of Google and Microsoft.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

DataRobot

OCTOBER 20, 2021

Because of the multidisciplinary nature of AI products, stakeholders across an entire organization must share a common understanding of each project’s scope, deployment, governance , impact, and projected risk. rule-based AI , machine learning , deep learning , etc.) WHITE PAPER. Data Literacy for Responsible AI.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Domino Data Lab

AUGUST 10, 2021

Models can predict things before they happen more accurately than humans, such as catastrophic weather events or who is at risk of imminent death in a hospital. PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models.

Modeling 64

Modeling IT Risk Management Deep Learning 64

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.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. This almost always results in lack of adoption, and can also expose an organization to risk.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

SEPTEMBER 6, 2023

From customized content creation to task automation and data analysis, AI has seemingly endless applications when it comes to marketing, but also some potential risks. They can also reduce the likelihood of human error, deliver more personalized customer messages and identify at-risk customers. What is AI marketing?

Marketing 86

Marketing Technology Advertising ROI 86

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

Deep Learning 61

Deep Learning Machine Learning Unstructured Data Modeling 61

O'Reilly on Data

JANUARY 25, 2021

We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. Observability” risks becoming the new name for monitoring. It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. Here’s a sampler of related papers and articles if you’d like to dig in further: “ Synthesizing Programs with Deep Learning ” – Nishant Sinha (2017-03-25). “ Software writes Software?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Cloudera

SEPTEMBER 12, 2018

Commonwealth Bank of Australia worked with Cloudera to implement a modern data platform with an AI-powered customer decisioning layer that dramatically improves how the bank interacts with its customers. Enterprise Machine Learning: . Connect Products and Services: Toshiba Memory Corporation . Technical Impact. Cloud Success: .

Machine Learning 44

Machine Learning Big Data Data Science Data Warehouse 44

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

CIO Business Intelligence

APRIL 15, 2022

MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. The patients who were lying down were much more likely to be seriously ill, so the algorithm learned to identify COVID risk based on the position of the person in the scan.

Analytics 145

Analytics Machine Learning Insurance Testing 145

O'Reilly on Data

JUNE 7, 2022

We have a lot of vague notions about the Turing test, but in the final analysis, Turing wasn’t offering a definition of machine intelligence; he was probing the question of what human intelligence means. In the next few years, we will inevitably rely more and more on machine learning and artificial intelligence.

Modeling 363

Modeling Interactive Optimization Deep Learning 363

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks. Does machine learning change priorities? a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Alation

APRIL 14, 2022

Supports the ability to interact with the actual data and perform analysis on it. Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. A technique to automate changes in iterative passes. Pattern Matching.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Sisense

AUGUST 6, 2020

So what are the high-level steps to incorporate AI and machine learning into new and existing products? Be sure test cases represent the diversity of app users. Today, we’re developing AI in an era where data is treated as code, or at least as an extension of code, because the code alone cannot achieve deep learning without the data.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. When Google talked about releasing this tool in its blog, the brand pointed out that if you don’t protect user data, you risk losing people’s trust. Here are some open-source options to consider. Kubernetes.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

DataRobot Blog

FEBRUARY 2, 2023

These models are trained on massive amounts of text data using deep learning algorithms. As a result, they have the potential to revolutionize the way that we interact with machines and solve a wide range of machine learning problems. Simultaneously, they come with clear risks (e.g.,

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. Real estate investments are not an exception.

Management 59

Management Machine Learning Optimization Modeling 59