tjbot | IBM TJBot - TJBot was affectionately named after Thomas J | Artificial Intelligence library

 by   ibmtjbot JavaScript Version: v2.0 License: Apache-2.0

kandi X-RAY | tjbot Summary

kandi X-RAY | tjbot Summary

tjbot is a JavaScript library typically used in Artificial Intelligence applications. tjbot has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

TJBot was affectionately named after Thomas J. Watson, the first Chairman and CEO of IBM. TJBot was created by Maryam Ashoori at IBM Research as an experiment to find the best practices in the design and implementation of cognitive objects. He was born on November 9, 2016 via this blog post.
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            kandi-support Support

              tjbot has a low active ecosystem.
              It has 468 star(s) with 295 fork(s). There are 60 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 13 open issues and 141 have been closed. On average issues are closed in 130 days. There are 28 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tjbot is v2.0

            kandi-Quality Quality

              tjbot has 0 bugs and 0 code smells.

            kandi-Security Security

              tjbot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tjbot code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tjbot is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tjbot releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            tjbot Key Features

            No Key Features are available at this moment for tjbot.

            tjbot Examples and Code Snippets

            No Code Snippets are available at this moment for tjbot.

            Community Discussions

            QUESTION

            IBM Watson TextToSpeech - cannot read property .pipe of undefined
            Asked 2020-Feb-24 at 19:19

            I have the following code, straight from the documentation:

            ...

            ANSWER

            Answered 2018-Dec-06 at 07:34

            The below code works for me and generating an audio.wav file using the Text-to-Speech API Key

            Source https://stackoverflow.com/questions/53602237

            QUESTION

            Errors after requiring npm package
            Asked 2019-Jul-16 at 16:41

            I'm trying to run an electron application on raspberry pi. When I use var TJBot = require('tjbot') in my code to import a node package, the following error occurs:

            ...

            ANSWER

            Answered 2017-Jul-21 at 18:51

            Fibers is built to native binary code, it is not pure javascript. Rasperry Pi runs on ARM, which isn't included in the prebuilt binaries. The prebuilt binaries support 32 and 64 bit architectures for OSX (darwin), linux, and windows.

            This means you need to manually build Fibers for your system, not install it via NPM. The README for Fibers has clear instructions on how to do so. I've included them here for ease of access. Please follow these instructions to try and get it running on Raspberry Pi / ARM.

            Installing from source
            • git clone git://github.com/laverdet/node-fibers.git
            • cd node-fibers
            • npm install

            Note: node-fibers uses node-gyp for building. To manually invoke the build process, you can use node-gyp rebuild. This will put the compiled extension in build/Release/fibers.node. However, when you do require('fibers'), it will expect the module to be in, for example, bin/linux-x64-v8-3.11/fibers.node. You can manually put the module here every time you build, or you can use the included build script. Either npm install or node build -f will do this for you. If you are going to be hacking on node-fibers, it may be worthwhile to first do node-gyp configure and then for subsequent rebuilds you can just do node-gyp build which will be faster than a full npm install or node-gyp rebuild.

            Source https://stackoverflow.com/questions/45202457

            QUESTION

            watson visual recognition retrain
            Asked 2018-Sep-11 at 02:46

            I’m working on a TJBot visual recognition application.

            I have some particular questions about retraining.

            My case is similar to this:

            I have a model trained for cats, dogs and a negative category (not cats or dogs).

            The visual recognizer sometimes gets the wrong answer.

            What I’d like to do is ‘automatically’ retrain the model, programmatically using an API whenever it gets the wrong answer.

            For example after making the wrong determination of a dog when it was actually a cat, I’d like to turn around and call the visual recognition retraining api and indicate: this picture is a cat. This picture is not a dog.

            Questions: 1) Is there an API for ‘partial retraining’ using just one image? I don’t want to have to upload hundreds of photos again to completely retrain the model if I don’t have to.

            2) In this case I’d like to say this is a positive example of a cat, and a negative example of a dog. As I understand it there’s no way to indicate ‘negative example of a dog’. I can only say ‘negative example of cat and dog’. Is there a way to indicate ‘negative example of just one of the trained classes?

            3) If there’s no API, can I accomplish #1 or #2 using the ‘studio’ website? How?

            Thanks, Andy Citron (IBM retired)

            ...

            ANSWER

            Answered 2018-Sep-11 at 02:46

            Thanks for your question.

            1. Yes, you can send a single image as the data for retraining a classifier. However, 1 image is unlikely to affect your classifier much. We suggest sending at least 10 new images in each retraining request.

            2. If sent as a "negative_example" the image should be neither a cat or a dog. The classes in a classifier should be mutually exclusive. A photo of a cat and dog together does not work well for training a system which is trying to tell the difference between the two types. To the classifier, the types of answers it can give are defined by the classes, or if negative_examples are used "none of the above". So in your example, every image in the world gets classified by this model as either cat, dog, or neither.

            3. The API is documented here: https://www.ibm.com/watson/developercloud/visual-recognition/api/v3/curl.html?curl#update-classifier

            Source https://stackoverflow.com/questions/52264539

            QUESTION

            IBM Watson Unity 3D SDK Conservation Service (Almost working!)
            Asked 2017-Apr-27 at 09:12

            Found in service examples, a working conversation script. Thanks again to @Taj!

            I feel like I am very very close to get it to work. I have done the samething on Raspberry Pi with TJBot, so I have all the accounts, and I linked all the credentials correctly including the workplace ID from Conversation tooling. I am using Unity 3D 5.5.1f1 and the latest SDK, the one that was updated 13 days ago.

            I copied and pasted the sample code for conversation on SDK's github page into a brand new C# file:

            ...

            ANSWER

            Answered 2017-Feb-09 at 17:00

            The response strings are an array in the resp object.

            Source https://stackoverflow.com/questions/42123301

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install tjbot

            You can make your own TJBot in a number of ways.
            3d Print or Laser Cut. If you have access to a 3D printer or laser cutter, you can print/cut TJBot yourself. Begin by downloading the design files and firing up your printer/cutter.
            TJBot Full Kit. You can order a full TJBot kit with the laser cut cardboard and all the electronics from Sparkfun, Adafruit, or Robotkingdom.
            TJBot Cardboard Kit. You can purchase the TJBot laser cut cardboard from Texas Laser Creations.

            Support

            Please take a look at the troubleshooting guide if you are having difficulties with TJBot.
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            CLONE
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            https://github.com/ibmtjbot/tjbot.git

          • CLI

            gh repo clone ibmtjbot/tjbot

          • sshUrl

            git@github.com:ibmtjbot/tjbot.git

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