O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Because statistics: Last is the inherently probabilistic nature of ML.

Risk 357

Risk Modeling Data Processing Software 357

O'Reilly on Data

MARCH 26, 2024

It’s ironic that, in this article, we didn’t reproduce the images from Marcus’ article because we didn’t want to risk violating copyright—a risk that Midjourney apparently ignores and perhaps a risk that even IEEE and the authors took on!) Because, in some sense, hallucination is all LLMs do. They are dream machines.

Modeling 271

Modeling Machine Learning Risk Advertising 271

O'Reilly on Data

NOVEMBER 29, 2022

While it’s unlikely that the bill will become law, merely raising the possibility of criminal prosecution and jail time has upped the ante for “ commercial entities that operate high-risk information systems or automated-decision systems, such as those that use artificial intelligence or machine learning.”. Who will watch the watchmen?

Machine Learning 276

Machine Learning Recreation/Entertainment Risk Testing 276

IBM Big Data Hub

APRIL 9, 2024

” The historical sources watsonx.data accesses comprise relational, object and document databases, including IBM® Db2® , IBM® Cloudant , IBM Cloud® Object Storage and PostgreSQL. ” Training and testing models The Masters digital team used watsonx.ai Lastly, watsonx.data pulls from live feeds.

Management 79

Management IT Machine Learning Metrics 79

CIO Business Intelligence

FEBRUARY 3, 2023

Optical character recognition, for example, might extract a purchase order from an uploaded document image and trigger accounting software to deal with it. The ability to suck words and numbers from images are a big help for document-heavy businesses such as insurance or banking.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

IBM Big Data Hub

DECEMBER 4, 2023

The most common insurance use cases include optimizing processes that require processing large documents and large blocks of text or images. Customer engagement Providing insurance coverage involves working with numerous documents. IBM works with several insurance companies to identify high-value opportunities for using generative AI.

Insurance 54

Insurance Digital Transformation Risk Management Interactive 54

Smart Data Collective

NOVEMBER 3, 2021

It refers to underwriting, customer onboarding, document management, analysis, and statistics. Loan approval, as one of the biggest bottlenecks due to inconsistency of information between teams, may increase business risks. End-to-end loan management with AI. Integrated lending module.

Software 120

Software Data-driven Interactive Insurance 120

CIO Business Intelligence

JULY 6, 2023

Classic examples are the use of AI to capture and convert semi-structured documents such as purchase orders and invoices, Fleming says. We’re equipping this tool with a private ‘knowledge base’ of AT&T-specific data, with chat enabled to get answers directly from these internal AT&T documents and materials.”

Cost-Benefit 110

Cost-Benefit Technology Unstructured Data Testing 110

CIO Business Intelligence

JANUARY 9, 2024

ChatGPT caused quite a stir after it launched in late 2022, with people clamoring to put the new tech to the test. To get past those points, Colisto worked to educate leaders about the capabilities and risks of AI, seeking to move the company from “no to know.” What is our appetite for risk and how do we address it?

IT 116

IT Risk Cost-Benefit Enterprise 116

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. So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. We introduce the integration of Ray into the RAG contextual document retrieval mechanism. Open the CreateRayCluster document. json| jq '.data[].paragraphs[].qas[].question'

Data Processing 112

Data Processing Dashboards Machine Learning Management 112

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 6] Debugging may focus on a variety of failure modes (i.e., Sensitivity analysis.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

CIO Business Intelligence

NOVEMBER 7, 2023

This analysis isn’t just a statistical exercise but a crucial necessity in our interconnected world, where the security of our digital infrastructure is intertwined with our physical safety and economic stability. PAM can help prevent insider threat risks and reduce the potential of credentials becoming compromised.

Measurement 111

Measurement Risk Strategy Statistics 111

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage.

Marketing 361

Marketing Experimentation Metrics Testing 361

datapine

JANUARY 6, 2022

Secondary Research: much like how patterns of behavior can be observed, different types of documentation resources can be coded and divided based on the type of material they contain. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. There are basically 4 types of scales: *Statistics Level Measurement Table*.

IT 317

IT Statistics KPI Data-driven 317

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. Larger is Better involves a “lower specification limit,” such as test scores — where the target is 100%. During the improve phase, the focus is on testing the best potential solutions identified in the earlier phases.

Management 69

Management Statistics Manufacturing Measurement 69

Domino Data Lab

JULY 19, 2021

They document this work for the benefit of future data scientists and auditors alike. Data scientists create a model and test it with algorithms and data. The tested model is transitioned from the data scientists to DevOps and infrastructure engineers in a production environment. Data Scientist. Data Engineer. ML Architect.

Enterprise 85

Enterprise Data Science Testing Modeling 85

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test. And it can look up an author and make statistical observations about their interests. But it is an amazing analytic engine.”

IT 258

IT Modeling Testing Risk 258

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design.

Deep Learning 142

Deep Learning Machine Learning Modeling Software 142

Smart Data Collective

OCTOBER 16, 2022

Single server is the least preferred cloud architecture due to multiple security risks. An ideal time to consider this architecture is while building an ecosystem for testing and development. Moreover, document the existing infrastructure and resources to state your budget to your cloud development team. Single server.

Consulting 107

Consulting Software Statistics Manufacturing 107

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

datapine

SEPTEMBER 29, 2022

It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Here are some areas where organizations are seeing a ROI: Text (83%) : Gen AI assists with automating tasks like report writing, document summarization and marketing copy generation.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

erwin

NOVEMBER 6, 2020

Data Lineage: Document and visualize how data moves and transforms across your enterprise. Find hidden inconsistencies and highlight other potential problems using intelligent statistical algorithms and provides robust validation scores to help correct errors.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

Smart Data Collective

SEPTEMBER 21, 2022

As far as Data Analysis is concerned, potential employees should have an extensive knowledge of quantitative research, quantitative reporting, compiling statistics, statistical analysis, data mining, and big data. The old adage that you can build a better mousetrap and the world will beat a path to your door doesn’t hold up.

Statistics 84

Statistics Software Data mining Marketing 84

CIO Business Intelligence

APRIL 7, 2022

KPMG calculates a score for an employee’s risk of attrition, tries to identify a reason for that, and then suggests a remediation. With back-testing and cross-validation, we found that we’ve consistently been able to predict two-thirds of people about to resign, and save 10% to 20% of the ones we identified,” he says.

Consulting 134

Consulting Management Interactive Metadata 134

DataRobot Blog

DECEMBER 1, 2022

Training and Testing Different AI Models. The model selection process will test several models to see which one is likely to yield the best results. The DataRobot model blueprints allow users to rapidly test many different modeling approaches and increase model diversity and accuracy.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

CDW Research Hub

NOVEMBER 6, 2020

Compile and document this information. As a best practice, you want to limit who has access to secure information to minimize risk and liability. Know your security risks. Most importantly, don’t forget to factor in human risk. Are there security policies or regulatory controls in place?

Scorecard 40

Scorecard Risk Business Driver Strategy 40

Alation

APRIL 18, 2023

DAMA defines data profiling as: An approach to data quality analysis, using statistics to show patterns of usage, and patterns of contents in an automated manner. The tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats.

IT 52

IT Metadata Data Quality Data Governance 52

IBM Big Data Hub

AUGUST 15, 2023

Establish interconnectivity between multiple systems to speed up order delivery A siloed IT system architecture often leads to inefficient business processes, making it difficult to quickly identify risks and resulting in lengthy order delivery cycles.

Software 51

Software Manufacturing Enterprise Sales 51

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

IBM Big Data Hub

MARCH 25, 2022

This helps our customers mitigate the risks and costs of managing complex ecosystems of tooling built around the mostly single-host SQL database technologies that existed at the time. The use case of this bank required tracking of portfolio risk using high speed joins of position risk, index and fund composition, and other risk factors.

Data-driven 70

Data-driven Data Warehouse Data Processing Marketing 70