Tuesday, March 26, 2019

AI Trends White Papers

A Blueprint For Preparing Your Own ML Training Data

For teams that are considering DIY training data, we have created a downloadable blueprint. It lays out the tools, people and skills required to prepare enterprise ML training datasets and includes a pre-flight checklist that will help you to answer important questions like:

  • Do you have access to a workforce that can label and annotate data at scale?
  • Does your team bring all the necessary skills to training data preparation?
  • Do you have the specialized project management and data labeling tools you’ll need?

This blueprint will deliver value, helping you decide on the necessary resources needed to prepare your training data. Read on here.

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An Introduction to Machine Learning Training Data

There has been a recent explosion in AI applications, and a corresponding demand for machine learning solutions. Why? Because we are asking computers to solve increasingly complex problems that mimic human functions like speech and sight. These problems are more complex than the mathematical models that traditional programming can handle.

AI requires machine learning, and machine learning requires data—a lot of the right kind of data. Without it, no project can get off the ground. Appen can help. We’ve been in the data business for over 20 years and have developed deep experience working with leading global technology companies, governments, and other organizations, across a variety of data types. We collect and annotate speech, sound, image, video, and text data, and use it to fuel our clients’ many machine learning projects. We also review and annotate data from live products to improve products and their user experience.

We created this white paper for business executives embarking on—or looking to improve—their own machine learning project, to share a few guiding principles about your data quantity, quality, and sources.

Forrester Positions Oracle as a Leader of Notebook-Based Machine Learning Solutions Providers

In our 24-criteria evaluation of notebook-based predictive analytics and machine learning (PAML) solutions providers, we identified the nine most significant ones — Anaconda, Civis Analytics, Cloudera, Databricks, Domino Data Lab, Google, H2O.ai, OpenText, and Oracle — and researched, analyzed, and scored them. This report shows how each provider measures up and helps application development and delivery (AD&D) professionals make the right choice.

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Guiding Principles of Database Automation A Twelve-Step Guide for CIOs

Automation is worthless if it doesn’t yield more productive ways to deliver applications, gain predictive insights, or run smarter, more efficient, and more secure operations. This Forrester report provides a consolidated view of the value, goals, and best practices of automation. In Oracle’s view, these technologies are poised to increase administrative efficiencies and revolutionize business processes in the years ahead.

What is the common thread between children in a playpen, trainees on their first day at work and a clueless person at a bank kiosk? It may not be obvious at first but their circumstances are remarkably similar. Unaware, afraid to ask and eager to learn, all three scenarios are perfect examples of how cognitive thinking helps us all grasp an idea, learn through observation and emulate it in our own ways. Cognitive thinking is the exact theory that artificial intelligence (AI) is based on. No, AI is not a new mantra or trend. After all, it has been around for the last 50 years or so, but never has it been as acclaimed as it has been since the last 2 years. With the increased investments in infrastructure and automation, everyone is realizing that the AI band wagon has much more to offer than big data and analytics.

The results are in: only 2% of customers prefer chatbots. Yet 66% of businesses plan to use a chatbot this year, with the intention to improve customer satisfaction. Are you surprised? If so, you’re not alone. Understanding customer expectations is key to your CX strategy. But technology and consumer preferences change so fast. How well do you really know what your customers want?

The Genesys State of CX Report surveyed roughly 2,000 consumers and 1,300 businesses around the world. Discover what your customers expect when it comes to bots, AI, mobile messaging, and more. Read this white paper to see how we examine customer experience from the perspective of consumers and businesses to provide insight about:

  • How consumers prefer to interact with a business
  • What consumers value most when engaging with a business
  • What personal information consumers are willing to share for a better CX experience

Artificial Intelligence for Citizen Services and Government

This paper explores the various types of AI applications, and current and future uses of AI in government delivery of citizen services, with a focus on citizen inquiries and information. It also offers strategies for governments as they consider implementing AI.

Download this white paper now.