Last week, Microsoft announced a speech recognition breakthrough: a transcription system that can match humans, with a word error rate of 5.9 percent for conversational speech. This new system is built on an open source toolkit that Microsoft already developed. A major new update to the toolkit, now called the Cognitive Toolkit, was released today in beta.
Formerly called the Computational Network Toolkit (CNTK), the MIT-licensed, GitHub-hosted project gives researchers some of the building blocks, such as neural networks, to develop their own machine learning systems. These machine learning applications can run on both CPUs and GPUs, and the toolkit has support for compute clusters. This scalability has already made CNTK strongly competitive with other popular frameworks, including Google’s TensorFlow.
The Computational Network Toolkit was originally built for speech applications, but has since grown to accommodate other machine learning use cases. The Bing team uses it to make inferences about search terms. For example, a search for “how do you make an apple pie?” is a search for recipes, even though it doesn’t include the word “recipe.” The new version of the toolkit adds features, such as support for Python scripting, and new algorithms to further expand its reach to these more diverse applications.
A surprisingly large number of critical infrastructure participants—including chemical manufacturers, nuclear and electric plants, defense contractors, building operators and chip makers—rely on unsecured wireless pagers to automate their industrial control systems. According to a new report, this practice opens them to malicious hacks and espionage.
Earlier this year, researchers from security firm Trend Micro collected more than 54 million pages over a four-month span using low-cost hardware. In some cases, the messages alerted recipients to unsafe conditions affecting mission-critical infrastructure as they were detected. A heating, venting, and air-conditioning system, for instance, used an e-mail-to-pager gateway to alert a hospital to a potentially dangerous level of sewage water. Meanwhile, a supervisory and control data acquisition system belonging to one of the world’s biggest chemical companies sent a page containing a complete “stack dump” of one of its devices.
Other unencrypted alerts sent by or to “several nuclear plants scattered among different states” included: