The team behind TensorFlow, Google's popular open source Python machine learning library, has revoked support for YAML due to an arbitrary code execution vulnerability. YAML is a general-purpose format used to store data and pass objects between processes and applications. Many Python applications use YAML to serialize and deserialize objects. According to an advisory on GitHub, TensorFlow and Keras, a wrapper library for TensorFlow, used an unsafe function to deserialize YAML-encoded machine learning models. "Given that YAML format support requires a significant amount of work, we have removed it for now," the maintainers of the library said in their advisory.

The idea was born to use TensorFlow/machine learning to automatically analyze these signals and using it to retrieve the PIN entered into the device - out of thin air! The setup for finding and recording such a signal can range from very simple up to very complex, for this case everything was done using Software Defined Radios. A cheap RTL-SDR receiver is available for roughly $30, though a more sophisticated device such as a HackRF or a bladeRF offer significantly higher sample rates (and a higher ADC resolution). Even with this cheap setup, the signal could be picked up from more than 2 meters (6.5 feet) away - using a directional antenna (and maybe using emissions on a different frequency band) this range can be easily increased. It was also found that connecting the USB cable to the device increases the measured strength of the emissions significantly.

Google today introduced Neural Structured Learning (NSL), an open source framework that uses the Neural Graph Learning method for training neural networks with graphs and structured data. NSL works with with the TensorFlow machine learning platform and is made to work for both experienced and inexperienced machine learning practitioners. NSL can make models for computer vision, perform NLP, and run predictions from graphical datasets like medical records or knowledge graphs. "Leveraging structured signals during training allows developers to achieve higher model accuracy, particularly when the amount of labeled data is relatively small," TensorFlow engineers said in a blog post today. "Training with structured signals also leads to more robust models. These techniques have been widely used in Google for improving model performance, such as learning image semantic embedding."

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA. Not even close, says Ted Dunning, chief application architect at MapR, but "anybody who thinks that they can just buy magic bullets off the shelf has no business" buying machine learning technology in the first place. "Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your company's life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator," Dunning says.

It doesn't take a machine learning algorithm to predict that server makers are trying to cash in on the machine learning revolution at the major nexus points on the global Internet. Many server makers rose to satisfy the unique demands of the initial dot-com buildout back in the 1990s, and a new crop of vendors as well as some incumbents are trying to engineer some differentiation into their platforms to appeal to the machine learning crowd. This is particularly true for servers that are used to train neural nets, which require lots of very beefy GPU accelerators, almost universally those from Nvidia, as well as a few hefty CPUs, lots of main memory and usually fast networking, too. Cramming this plus enough storage to be useful into a single node that is then clustered to scale out performance in a parallel fashion (like transitional HPC workloads) is a challenge. But the opportunity is large enough – and profitable enough – that after a bunch of customers started asking for a server that plugged into its Unified Computing System framework, could be managed by the UCS Manager stack, and integrate into the UCS network fabric.

For an easy-to-understand introduction to the TensorFlow machine learning system, take a look at the entertaining series of videos produced by artificial intelligence educator Siraj Raval. The first video in the series is shown above, and the full series can be found on YouTube. TensorFlow was developed by researchers and engineers working on the Google Brain Team for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to also be applicable in a wide variety of other domains as well. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

Machine learning couldn't be hotter, with several heavy hitters offering platforms aimed at seasoned data scientists and newcomers interested in working with neural networks. Among the more popular options is TensorFlow, a machine learning library that Google open-sourced in November 2015. I discussed how the library has become more mature, implemented more algorithms and deployment options, and become easier to program over the preceding year. The best deep learning library had become even better. In this article, I'll give you a very quick gloss on machine learning, introduce you to the basics of TensorFlow, walk you through a few TensorFlow models in the area of image classification, and show you the new high-level APIs.

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA. Not even close, says Ted Dunning, chief application architect at MapR, but "anybody who thinks that they can just buy magic bullets off the shelf has no business" buying machine learning technology in the first place. "Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your company's life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator," Dunning says.

TensorFlow, Google's contribution to the world of machine learning and data science, is a general framework for quickly developing neural networks. Despite being relatively new, TensorFlow has already found wide adoption as a common platform for such work, thanks to its powerful abstractions and ease of use. The biggest changes in TensorFlow 1.4 involve two key additions to the core TensorFlow API. The tf.keras API allows users to employ the Keras API, a neural network library that predates TensorFlow but is quickly being displaced by it. The tf.keras API allows software using Keras to be transitioned to TensorFlow, either by using the Keras interface permanently, or as a prelude to the software being reworked to use TensorFlow natively.

Google's week consisted of bringing its TensorFlow machine learning software to iOS, seeing its Tango software added into the first consumer smartphones, and receiving a proposal by its DeepMind division to add a kill switch to artificial intelligence (AI) systems. The firm's AlphaGo AI is also scheduled to face the best Go player the world can offer in a match later this year. Google updated its TensorFlow machine learning software to run on iOS devices. The technology that powers AlphaGo was released to the open source community in November, but has now reached the hands of Apple developers who can build a neural network right into apps. Google's Tango software was revealed to be a key feature of Lenovo's new smartphones.