O'Reilly on Data

MARCH 24, 2020

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 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.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 363

Management Machine Learning Experimentation Metrics 363

O'Reilly on Data

SEPTEMBER 12, 2018

Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more. Preserving privacy and security in machine learning. Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services. Watch " Wait.

IoT 213

IoT Machine Learning Deep Learning Data Science 213

Smart Data Collective

JULY 8, 2021

Let’s talk about some benefits and risks of artificial intelligence. Artificial Intelligence employs machine learning algorithms such as Deep Learning and neural networks to learn new information like humans. It eliminates the requirement for feeding new codes every time we want them to learn a new thing.

Risk 143

Risk Cost-Benefit Machine Learning Deep Learning 143

Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. The main purpose of machine learning is to partially or completely replace manual testing. Machine learning makes it possible to fully automate the work of testers in carrying out complex analytical processes. Top ML Companies.

Machine Learning 122

Machine Learning Testing Cost-Benefit Data-driven 122

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. Watermark attacks.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

CIO Business Intelligence

NOVEMBER 12, 2024

The hunch was that there were a lot of Singaporeans out there learning about data science, AI, machine learning and Python on their own. Because a lot of Singaporeans and locals have been learning AI, machine learning, and Python on their own. I needed the ratio to be the other way around! And why that role?

Modeling 116

Modeling Machine Learning Consulting Deep Learning 116

Smart Data Collective

FEBRUARY 23, 2023

Although there are plenty of tech jobs out there at the moment thanks to the tech talent gap and the Great Resignation, for people who want to secure competitive packages and accelerate their software development career with sought-after java jobs , a knowledge of deep learning or AI could help you to stand out from the rest.

Deep Learning 111

Deep Learning Software Risk Testing 111

Smart Data Collective

FEBRUARY 21, 2021

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

Deep Learning 108

Deep Learning Management Cost-Benefit Manufacturing 108

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

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Modeling Machine Learning Testing Metrics 249

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Corinium

JUNE 6, 2019

Regulations and compliance requirements, especially around pricing, risk selection, etc., A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

IBM Big Data Hub

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

Deep Learning 56

Deep Learning Machine Learning Unstructured Data Modeling 56

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Machine Learning | Marketing.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Smart Data Collective

JUNE 4, 2021

Basics of Machine Learning. Machine learning is the science of building models automatically. Whereas in machine learning, the algorithm understands the data and creates the logic. Whereas in machine learning, the algorithm understands the data and creates the logic. Semi-Supervised Learning.

Data Science 133

Data Science Statistics Deep Learning Machine Learning 133

CIO Business Intelligence

OCTOBER 13, 2023

Machine learning engineer Machine learning engineers are tasked with transforming business needs into clearly scoped machine learning projects, along with guiding the design and implementation of machine learning solutions.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning? temperature, salary).

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Analytics Vidhya

JANUARY 30, 2024

Introduction Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling machines to generate human-like text and engage in conversations. Jailbreaking and exploiting weaknesses in LLMs pose significant risks, such as misinformation generation, offensive outputs, and privacy concerns.

Risk 318

Risk Modeling Analytics Deep Learning 318

Analytics Vidhya

MARCH 4, 2024

This landmark decision reflects the government’s proactive approach to addressing rising concerns surrounding the potential risks and ethical considerations associated with AI technology.

Modeling 289

Modeling Risk Technology Analytics 289

CIO Business Intelligence

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 145

Analytics Machine Learning Insurance Testing 145

O'Reilly on Data

JUNE 7, 2022

That’s an allusion to the debate ( sometimes on Twitter ) between LeCun and Gary Marcus, who has argued many times that combining deep learning with symbolic reasoning is the only way for AI to progress. (In In the next few years, we will inevitably rely more and more on machine learning and artificial intelligence.

Modeling 364

Modeling Interactive Optimization Deep Learning 364

CIO Business Intelligence

JUNE 2, 2022

Among the hot technologies, artificial intelligence and machine learning — a subset of AI that that makes more accurate forecasts and analysis as it ingests data — continue to be of high interest as banks keep a strong focus on costs while trying to boost customer experience and revenue.

Predictive Analytics 145

Predictive Analytics Technology Analytics Machine Learning 145

IBM Big Data Hub

SEPTEMBER 27, 2023

Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content. All this reduces the risk of a data leak or unauthorized access.

Risk 91

Risk Modeling Cost-Benefit Enterprise 91

CIO Business Intelligence

JANUARY 13, 2023

Software-based advanced analytics — including big data, machine learning, behavior analytics, deep learning and, eventually, artificial intelligence. Worst case, they let security teams limit the damage of a successful attack to something determined to be an acceptable level of risk. Why is this so important?

Software 145

Software Machine Learning Behavioral Analytics Deep Learning 145

Domino Data Lab

MARCH 6, 2019

Niels Kasch , cofounder of Miner & Kasch , an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the Maryland Data Science Conference. Deep Learning on Imagery and Text. Introduction. And yeah … Miner & Kasch was founded by UMBC alumni.

Deep Learning 55

Deep Learning Data Science Machine Learning Data Processing 55

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Smart Data Collective

JANUARY 14, 2022

Computer systems capable of learning, reasoning, and acting are still in the early stages. Machine learning needs massive amounts of data. Moreover, cyberattacks are expected to become harder to detect, more frequent, and more sophisticated in the future, putting all of our connected devices at risk. Automated Attacks.

Machine Learning 145

Machine Learning Deep Learning Strategy Data-driven 145

CIO Business Intelligence

OCTOBER 4, 2023

Certified Information Systems Auditor (CISA); PMI Program, Portfolio, and Risk Management Professionals (PgMP, PfMP and PMI-RMP); Six Sigma Black Belt and Master Black Belt; Certified in Governance, Risk, and Compliance (ISC2); and Certified in Risk and Information Systems Control (CRISC) also drew large premiums.

Prescriptive Analytics 135

Prescriptive Analytics IT Machine Learning Deep Learning 135

Smart Data Collective

DECEMBER 9, 2021

The advent of AI, machine learning, big data, and blockchain technology are already transforming how many businesses handle their daily operations. AI and Machine Learning. AI and machine learning are poised to play a major part in the future of several industries.

Technology 133

Technology Big Data Machine Learning Deep Learning 133

O'Reilly on Data

FEBRUARY 18, 2020

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. There’s plenty of security risks for business executives, sysadmins, DBAs, developers, etc., to be wary of. Figure 1 (above).

Data-driven 363

Data-driven Software Statistics Marketing 363

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. Forecast financial market trends.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. A stock-out of an item may put a patient’s health at risk, but keeping huge amounts of inventory is very costly.

Statistics 84

Statistics Machine Learning Deep Learning Sales 84

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

CIO Business Intelligence

JULY 5, 2023

Artificial intelligence and machine learning are the No. Generative AI is raising the interest level even further as organizations begin testing different use cases for deep-learning models. AI is really doing the heavy lift, in terms of identifying risks,” Crowley says.

Machine Learning 98

Machine Learning Deep Learning Enterprise Consulting 98

CIO Business Intelligence

AUGUST 15, 2024

Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. He also advises CIOs to foster a culture of continuous learning and upskilling to build internal AI capabilities.

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

CIO Business Intelligence

OCTOBER 3, 2024

Rather than pull away from big iron in the AI era, Big Blue is leaning into it, with plans in 2025 to release its next-generation Z mainframe , with a Telum II processor and Spyre AI Accelerator Card, positioned to run large language models (LLMs) and machine learning models for fraud detection and other use cases.

Machine Learning 140

Machine Learning Experimentation Insurance Modeling 140

Smart Data Collective

FEBRUARY 15, 2022

We can build our plagiarism checker that searches a vast database for stolen content using machine learning. These tools use deep learning to improve the process constantly. They can learn from the new text they discover, which among other things, helps them improve their digital vocabulary.

Machine Learning 137

Machine Learning Deep Learning Software Data Science 137