The machine learning and engineering communities weigh in on news of Google's new TensorFlow optimized processor, the TPU and possibly influence several industry leaders in the hardware space like Intel and Nvidia.
Today OpenAI, a non-profit artificial intelligence research company founded by InfoSys and Amazon Web Services, announced a beta for OpenAI Gym. Gym is a Python based toolkit for developing and comparing reinforcement learning (RL) algorithms offered under the MIT license.
Online harassment is a serious issue, one that the engineers and designers behind the keyboard don't always think about when building software. Machine learning is become more prevalent but as more technology companies take advantage of it, they risk alienating their users even more by presenting content that isn't actually relevant.
Late last month Google released an alpha version of their TensorFlow (TF) integrated cloud machine learning service as a response to a growing need to make their Tensor Flow library to run at scale on the Google Cloud Platform (GCP). Google describes several new feature sets around making TF usage scale by integrating several pieces of the GCP like Dataproc, a managed Hadoop and Spark service.
Microsoft has recently announced its Azure IoT Hub offering has reached general availability (GA). This is a follow-up release to the public preview that Microsoft provided in October of last year. InfoQ previously covered the public preview announcement as part of the Azure Con event coverage.
Net Promoter Score (NPS) is a customer loyalty metric used to determine the likelihood that a customer will return to a company's website or use their service again. Airbnb uses NPS extensively in measuring the customer loyalty, as a more effective measurement to determine the likelihood that a customer will return to book again or recommend the company to their friends.
Riley Newman, head of data science at Airbnb, recently published an article describing how the Californian startup defines and uses data science. He explains that data can be seen as the voice of the customers, and data science as an act of interpretation. He also details several initiatives that have been particularly important for scaling data science.
Facebook recently announced open sourcing hardware design for its custom designed Open Rack compatible hardware. Attributing advances in Machine Learning and Artificial Intelligence to richer data sets and more powerful GPU-based systems, Facebook is unveiling its next generation systems code-named “Big Sur”, after the synonymous location in California.
About the same time Google announced open sourcing TensorFlow, Microsoft has pushed to GitHub DMTK, a Distributed Machine Learning Toolkit. While Google has released a one-machine version of TensorFlow, DMTK runs on a cluster of machines.
In theory the operations team determines what the thresholds for warnings and alerts should be. But in practice, the operations team often have no idea what these values should be. Using machine learning techniques such as adaptive thresholds, Splunk ITSI solves this problem.
Splunk opened their big data conference with an emphasis on “making machine data accessible, usable, and valuable to everyone”. This is a shift from their original focus: indexing arbitrary big data sources. Reasonably happy with their ability to process data, they want to ensure that developers, IT staff, and normal people have a way to actually use all of the data their company is collecting.
Any cloud provider that believes in data gravity is trying to make it easier to collect and store data in its facilities. To make data movement between cloud and on-premises endpoints easier, Microsoft recently announced the general availability of Azure Data Factory (ADF).
Flipboard recently reported on an in-house application of deep learning to scale up low-resolution images that illustrates the power and flexibility of this class of learning algorithms.
Under the name of Project Oxford, Microsoft has made available a set of RESTful APIs that aim to make it possible for developers to build apps that feature face recognition, speech processing, and other machine learning algorithms. Part of the Azure portfolio, the new APIs are currently in beta and free to use up to 5,000 call per month.
Facebook has open sourced a number of modules for faster training of neural networks on Torch.