IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

CIO Business Intelligence

APRIL 6, 2022

If anything, the past few years have shown us the levels of uncertainty we are facing. The race to embrace digital technologies to compete and stay relevant in emerging business models is compelling organizations to shift focus. Approaches like design thinking help empathize with real business problems and customer needs.

Experimentation 103

Experimentation Data Lake Uncertainty Enterprise 103

CIO Business Intelligence

SEPTEMBER 12, 2022

Prioritize time for experimentation. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says. It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “By

Experimentation 133

Experimentation Consulting Technology Machine Learning 133

CIO Business Intelligence

JULY 31, 2023

1 question now is to allow or not allow,” says Mir Kashifuddin, data risk and privacy leader with the professional services firm PwC US. Rapidly evolving risks Companies that have blocked the use of gen AI are finding that some workers are still testing it out. How has, say, ChatGPT hit your business model?”

Strategy 98

Strategy Risk Enterprise Risk Management 98

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk.

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

CIO Business Intelligence

SEPTEMBER 19, 2023

How can enterprises attain these in the face of uncertainty? Rogers: This is one of two fundamental challenges of corporate innovation — managing innovation under high uncertainty and managing innovation far from the core — that I have studied in my work advising companies and try to tackle in my new book The Digital Transformation Roadmap.

Digital Transformation 130

Digital Transformation Uncertainty Testing Enterprise 130

CIO Business Intelligence

AUGUST 25, 2023

Accordingly, many CIOs have fashioned themselves into the de facto AI professor within their organizations—developing 101 materials and conducting roadshows to build awareness, explain how generative AI differs from other types, and discuss its risks. He and his peers make a point of emphasizing the risks. Experiment.

Strategy 98

Strategy Data Strategy Experimentation Uncertainty 98

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment.

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

MAY 31, 2016

A geo experiment is an experiment where the experimental units are defined by geographic regions. The expected precision of our inferences can be computed by simulating possible experimental outcomes. Further, there is the risk that the increased ad spend will be less productive due to diminishing returns (e.g.,

Advertising 42

Advertising Testing Sales Statistics 42

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Cloudera

OCTOBER 8, 2020

In fact, it was a painful process to overhaul their entire business model and they lost significant revenues over the course of two years. A disruptive mindset creates an environment that embraces constant experimentation and change. Stability during Uncertainty . It was not easy.

Digital Transformation 106

Digital Transformation Experimentation Unstructured Data Uncertainty 106