An effort to bring structure and meaning to huge volumes of varied data is being used to improve training of neural networks.
The technique, dubbed Neural Structured Learning (NSL) attempts to leverage what developers call “structured signals.” In model training, those signals represent the connections or similarities among labeled and unlabeled data samples. The ability to capture those signals during neural network training is said to boost model accuracy, especially when labeled data is lacking.
NSL developers at Google (NASDAQ: GOOGL) reported this week their framework can be used to build more accurate models for machine vision, language translation and predictive analytics. Read More
Daily Archives: September 5, 2019
If you want to see the benefits of AI, forget moonshots and think boring
CTOs are trying to figure out what the benefits of AI could be for their enterprise. Spoiler alert: they’re pretty dull, and that’s okay, according to, academic and author, Tom Davenport
You hear a lot about wildly ambitious AI initiatives these days — from curing diseases and solving world hunger to reversing climate change. While ambition is great and all, the problem with AI moonshots is that they generally crash and burn. Who can forget when the MD Anderson Cancer Center blew $62 million on a project to use IBM Watson to treat cancer that was later shelved. It’s because of this harsh reality that Tom Davenport, distinguished professor of information technology and management at Babson College, believes that if enterprises ever want to see the benefits of AI, they must embrace the mundane.
While many CTOs might want to aim for the moon with their AI projects, speaking with Information Age at IPsoft’s Digital Workforce Summit 2019, Davenport argued the best results come to those who opt to tackle a series of smaller projects first. CTOs are trying to figure out what the benefits of AI could be for their enterprise. Spoiler alert: they’re pretty dull, and that’s okay, according to, academic and author, Tom Davenport. Read More
Successful AI Implementation Starts With People
Artificial intelligence is here, bringing with it tremendous promise for innovation and productivity. From automating simple processes to making more complex processes smarter, AI has the potential to drastically improve the way companies work. But before you rush to implement it, beware of a major issue that could derail your goals: overlooking your people.
Any successful AI venture depends on getting your team behind it, yet too many business leaders are focusing on the technology without considering the needs of their workforce. The goal of intelligent automation is not just to grow revenue and improve efficiency, but also to help people raise their potential through new opportunities and ways of working. That will only happen if you’ve thoughtfully planned for how AI will affect your people. If they’re underprepared or fearful of how intelligent automation could affect their jobs, or if you haven’t determined how AI will affect your people strategy, this change could be more chaotic than constructive. Read More
Intel's 5 Steps to an AI Proof of Concept
An artificial intelligence (AI) software program is one that can sense, reason, act and adapt. It does so by first ‘learning’ from a large and diverse data set, which it uses to train models about the data. Once trained, the model is then deployed to infer results from similar, new or unseen data, for example turning verbal speech into text, identifying anomalies in a series of images, or calculating when a piece of machinery is about to fail. We show this sequence in Figure 1.
A Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test
Four years ago, more than 700 computer scientists competed in a contest to build artificial intelligence that could pass an eighth-grade science test. There was $80,000 in prize money on the line.
They all flunked. Even the most sophisticated system couldn’t do better than 60 percent on the test. A.I. couldn’t match the language and logic skills that students are expected to have when they enter high school.
But on Wednesday, the Allen Institute for Artificial Intelligence, a prominent lab in Seattle, unveiled a new system that passed the test with room to spare. It correctly answered more than 90 percent of the questions on an eighth-grade science test and more than 80 percent on a 12th-grade exam. Read More
Intel’s AI Readiness Model
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
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
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