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.
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
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
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
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
Building out an IoT (Internet of Things) solution can be a difficult problem to solve. It sounds easy at first, you just connect a bunch of devices, sensors and such to the cloud. You write software to run on the IoT hardware and in the cloud, then connect the two to gather data / telemetry, communicate, and interoperate. Sounds easy, right? Well, it’s actually not as simple as it sounds. There are many things that can be difficult to implement correctly. The biggest problem area is Security, as it is in most other systems types as well. Then you can device management, cloud vs edge analytics, and many other aspects to a full IoT solution.
Traditionally you would need to build all this out yourself, however, with offerings from Microsoft there are a few options available for building out IoT solutions. The Azure IoT Suite offers PaaS (Platform as a Service) capabilities that are flexible for any scenario, while the newer Microsoft IoT Central is offering more managed SaaS (Software as a Service) capabilities to further assist in easing development, deployment and management. Read More
Zao, a free deepfake face-swapping app that’s able to place your likeness into scenes from hundreds of movies and TV shows after uploading just a single photograph, has gone viral in China. Bloomberg reports that the app was released on Friday, and quickly reached the top of the free charts on the Chinese iOS App Store. And like the FaceApp aging app before it, the creators of Zao are now facing a backlash over a perceived threat to user privacy.
Twitter user Allan Xia posted a neat demonstration of what the app is capable of yesterday with a 30 second clip of their face replacing Leonardo Dicaprio in famous moments from several of his films. Read More
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