To aid organizations wherever they are on their AI journeys, Intel has created a Readiness Model to help decision makers understand where to prioritize efforts. We have developed this based on our experience working with customers across a range of scenarios and industry verticals. Examples include manufacturing companies wanting to improve quality control, and financial services organizations looking to use AI in algorithmic trading. This paper provides guidance on how to judge an organization’s ability and readiness to use AI to generate business value, and includes a list of questions which you can use to guide your own self-assessment activities. Read More
Tag Archives: Standards
Assessing Technology Readiness for Artificial Intelligence and Machine Learning based Innovations
Every innovation begins with an idea. To make this idea a valuable novelty worth investing in requires identification, assessment and management of innovation projects under two primary aspects: The Market Readiness Level (MRL) measures if there is actually a market willing to buy the envisioned product. The Technology Readiness Level (TRL) measures the capability to produce the product. The READINESS navigator is a state of the art software tool that supports innovators and investors in managing these aspects of innovation projects. The existing technology readiness levels neatly model the production of physical goods but fall short in assessing data based products such as those based on Artificial Intelligence (AI) and Machine Learning (ML). In this paper we describe our extension of the READINESS navigator with AI and ML relevant readiness levels and evaluate its usefulness in the context of 25 different AI projects. Read More
The call for a Data Science Readiness Level
In the 1970s, NASA developed the Technical Readiness Level (TRL) scale to measure research and development of cutting edge technology. Their purpose is to estimate the maturity of a technology during the acquisition process and are scaled from 1 to 9 with 9 being the most mature. TRLs enable consistent, uniform discussions of technical maturity across different types of technologies.
This concept is well known to researchers seeking grants from many government agencies, but seems to have lost favor in other engineering applications. With the growing cutting edge discoveries in Artificial Intelligence, Machine Learning, and Data Science this blog will explore use of this scale to measure progress and guide success of data science projects by linking them to a value on the TRL scale. Read More
Going Full Stack with Data Science: Using Technical Readiness… – Emily Gorcensk
The Blind Giant’s Quandary — Government's Role in setting AI Standards
FEDERAL AGENCIES SHOULD STAY IN THE BACK SEAT FOR AI STANDARD SETTING.
This comment is in response to the National Institute of Standards and Technology’s (NIST) request for information on artificial intelligence (AI) standards.
Even in the event of the market failing to provide the optimal level of the right standards, it does not imply that active government involvement would lead to a better outcome. In fact, as is well studied in the literature, government failure in standard setting is a possibility that should not be overlooked.
Stanford economist Paul David, known internationally for his contributions in the economics of science and technology, famously coined the risk of government failure in standard setting as the Blind Giant’s Quandary. Read More
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