Prototype and deploy deep neural network (DNN) applications smarter and more efficiently with a tiny, fanless, deep learning development kit designed to enable a new generation of intelligent devices.
The new, improved Intel® Neural Compute Stick 2 (Intel® NCS 2) features Intel’s latest high-performance vision processing unit: the Intel® Movidius™ Myriad™ X VPU. With more compute cores and a dedicated hardware accelerator for deep neural network inference, the Intel® NCS 2 delivers up to eight times the performance boost compared to the previous generation Intel® Movidius™ Neural Compute Stick (NCS).
Technical Specifications
Processor: Intel® Movidius™ Myriad™ X Vision Processing Unit (VPU)
Supported frameworks: TensorFlow* and Caffe*
Connectivity: USB 3.0 Type-A
Dimensions: 2.85 in. x 1.06 in. x 0.55 in. (72.5 mm x 27 mm x 14 mm)
In conversations, context is king! We’ll build a chatbot framework using Tensorflow and add some context handling to show how this can be approached.
Ever wonder why most chatbots lack conversational context?
How is this possible given the importance of context in nearly all conversations?
We’re going to create a chatbot framework and build a conversational model for an island moped rental shop. The chatbot for this small business needs to handle simple questions about hours of operation, reservation options and so on. We also want it to handle contextual responses such as inquiries about same-day rentals. Getting this right could save a vacation!
We’ll be working through 3 steps:
We’ll transform conversational intent definitions to a Tensorflow model
Next, we will build a chatbot framework to process responses
Lastly, we’ll show how basic context can be incorporated into our response processor
We’ll be using tflearn, a layer above tensorflow, and of course Python. As always we’ll use iPython notebook as a tool to facilitate our work.
We’ll be using tflearn, a layer above tensorflow, and of course Python. As always we’ll use iPython notebook as a tool to facilitate our work. ….
ChatterBotis a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram.
An example of typical input would be something like this:
user: Good morning! How are you doing?bot: I am doing very well, thank you for asking.user: You're welcome.bot: Do you like hats?
Language Independence
The language independent design of ChatterBot allows it to be trained to speak any language. Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it’s own knowledge of possible responses as it interacts with humans and other sources of informative data.
How ChatterBot Works
ChatterBot is a Python library designed to make it easy to create software that can engage in conversation.
An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase.
The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then chooses a response from the selection of known responses to that statement.
Google has promised us new hardware products for machine learning at the edge, and now it’s finally out. The thing you’re going to take away from this is that Google built a Raspberry Pi with machine learning. This is Google’s Coral, with an Edge TPU platform, a custom-made ASIC that is designed to run machine learning algorithms ‘at the edge’. Here is the link to the board that looks like a Raspberry Pi.
This new hardware was launched ahead of the TensorFlow Dev Summit, revolving around machine learning and ‘AI’ in embedded applications, specifically power- and computationally-limited environments. This is ‘the edge’ in marketing speak, and already we’ve seen a few products designed from the ground up to run ML algorithms and inference in embedded applications. There are RISC-V microcontrollers with machine learning accelerators available now, and Nvidia has been working on this for years. Now Google is throwing their hat into the ring with a custom-designed ASIC that accelerates TensorFlow. It just so happens that the board looks like a Raspberry Pi.
WHAT’S ON THE BOARD
On board the Coral dev board is an NXP i.MX 8M SOC with a quad-core Cortex-A53 and a Cortex-M4F. The GPU is listed as ‘Integrated GC7000 Lite Graphics’. RAM is 1 GB of LPDDR4, Flash is provided with 8GB of eMMC, and WiFi and Bluetooth 4.1 are included. Connectivity is provided through USB, with Type-C OTG, a Type-C power connection, a Type-A 3.0 host, and a micro-B serial console. Gigabit Ethernet, a 3.5mm audio jack, a microphone, full-size HDMI, 4-lane MIPI-DSI, and 4-lane MIPI-CSI2 camera support. The GPIO pins are exactly — and I mean exactly — like the Raspberry Pi GPIO pins. The GPIO pins provide the same signals in the same places, although due to the different SOCs, you will need to change a line or two of code defining the pin numbers.
You might be asking why Google would build a Raspberry Pi clone. That answer comes in the form of a machine learning accelerator chip implanted on the board. Machine learning and AI chips were popular in the 80s and everything old is new again, I guess. The Google Edge TPU coprocessor has support for TensorFlow Lite, or ‘machine learning at the edge’. The point of TensorFlow Lite isn’t to train a system, but to run an existing model. It’ll do facial recognition.
