Last year, Microsoft's speech and dialog research group announced a milestone in reaching human parity on the Switchboard conversational speech recognition task, meaning we had created technology that recognized words in a conversation as well as professional human transcribers. After our transcription system reached the 5.9 percent word error rate that we had measured for humans, other researchers conducted their own study, employing a more involved multi-transcriber process, which yielded a 5.1 human parity word error rate. Today, I'm excited to announce that our research team reached that 5.1 percent error rate with our speech recognition system, a new industry milestone, substantially surpassing the accuracy we achieved last year. While achieving a 5.1 percent word error rate on the Switchboard speech recognition task is a significant achievement, the speech research community still has many challenges to address, such as achieving human levels of recognition in noisy environments with distant microphones, in recognizing accented speech, or speaking styles and languages for which only limited training data is available.
It covers multiple aspects of the enterprise wide layers at the backend covering government domain specific applications, decision support systems where the data flows from the IoT devices. Enterprise Layer: It implements Government domain-specific applications, decision support systems and provides interfaces to end-users including operations. IoT based Layered Reference Architecture for Connected Government The reference architecture consists of a set of components. The Business and Information Services layer is designed based on "Micro-services" architecture principles and which will provide cross channel capabilities A basic service system provides fundamental data services, which include data access, data processing, data fusion, data storage, identity resolution, geographic information service, user management, and inventory management, etc.
In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. We start the first week by introducing some major systems for data analysis including Spark and the major frameworks and distributions of analytics applications including Hortonworks, Cloudera, and MapR. We finish up week two with a presentation on Distributed Publish/Subscribe systems using Kafka, a distributed log messaging system that is finding wide use in connecting Big Data and streaming applications together to form complex systems. The last topic we cover in week four introduces Deep Learning technologies including Theano, Tensor Flow, CNTK, MXnet, and Caffe on Spark.
Over the past few years, Microsoft has quietly added a number of artificial intelligence (AI) features to Office 365, but my guess is that most Office users either don't know these features exist, or just take them for granted. Last year, Microsoft demonstrated a new option called Designer, in which PowerPoint can take a variety of slides you have and show you alternatives for different designs based on your content. Other interesting features in this vein include an option to generate alt-text that takes an image and runs it against a microservice so that visually impaired people can also follow along. Until recently, enterprise customers typically got updates three times a year; Microsoft recently announced it will now release these twice a year (targeting March and September).
We are going to review the next chapter of the book: http://www.deeplearningbook.org/ For participants to gain the most experience and understanding of the material, having a volunteer presenter each week was an invaluable asset. So we have decided to continue this tradition and ask that one volunteer each week take on the challenge of presenting their findings from the material to the rest of the group. We simply ask that the volunteer does not read directly from the book as their "presentation". Once the presenter has given their talk, we will open up the floor for discussion and questions from the audience.
Artist and machine learning engineer Terence Broad's Auto-Encoding Blade Runner is the project Philip K. Dick would have made if he were a scientist. The real question behind Broad's Blade Runner parallels the themes of Philip K Dick's legendary novel Do Androids Dream of Electric Sheep: Where does one draw the line between human and machine, the real and the seemingly real? That's what I think is powerfully evocative about Auto-encoding Blade Runner--that synthetic leap that the neural network makes." For example, Broad's network couldn't recognize black screens.
I've bunched these together because the thrust of this trend is assisting humans to make real time decisions or assist in real time tasks (think scheduling) using streaming real time data. The integrating thought is that IoT, stream processing, and deep learning are simply feeder or augmenting technologies leading to the real end product, AI. Augmented Intelligence is where the VC money is going now since these applications are close in and cover a whole host of physical robots as well as narrowly defined speech, text, and image AIs that could help us schedule, make reservations, communicate in natural speech, or guide those self-driving cars. It will be built from components of IoT, stream processing, deep learning, cloud computing, and the rest but it will be so well integrated into our daily lives as to nearly disappear.
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.
According to a CNBC article, 'data scientist' is among the top 10 professions of 2017 with respect to factors such as job growth, median salary, physical effort required, stress levels, and so on. The job requires a lot of imagination and sound technical skills, particularly skill with numbers. We constantly produce an incredible amount of data -- think social media, online banking, mobile shopping, GPS, and so on. Data scientists have to spend a significant amount of time studying, understanding, preparing, and manipulating data for analysis.
But next year, Microsoft plans to make this kind of FPGA processing power available to developers who will be able to use it to run their own tasks, including intensive artificial-intelligence ones, like deep-neural-networking (DNN). At its Build developers conference this Spring, Azure CTO Mark Russinovich outlined Microsoft's big-picture plans for delivering "Hardware Microservices" via the Azure cloud. Microsoft demonstrated BrainWave publicly at the company's Ignite 2016 conference, when Microsoft used it to run a massive language-translation demonstration on FPGAs. Microsoft officials were planning to discuss Brainwave at the company's recent Faculty Research Summit in Redmond, which was entirely dedicated to AI, but looking at the updated agenda, it seems references to Brainwave were removed.