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"Find Your Candy with Cloud ML" system diagram

Understanding your request with ML APIs

The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.

Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.

Locating and serving a candy with image recognition on Cloud ML

Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.

In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.

Serving mode

  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.

Learning mode

  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image

Setting things up

Note

  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.

About

No description, website, or topics provided.

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

77 stars

Watchers

15 watching

Forks

18 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation

"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation

"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation

"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
77 stars
Watchers
15 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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"Find Your Candy with Cloud ML" system diagram
Understanding your request with ML APIs
The demo starts with listening to your voice request such as "I like a dark chewy chocolate", and analyzes it with Cloud Speech API. The API converts audio data into text with Google's high quality voice recognition technology. Then the text is processed with Cloud Natural Language API and its syntactic analysis so that the system can extract what are the important verbs, adjectives and nouns in the text to understand the meaning of your command.
Then the system uses word2vec and regression (Inception-v3 + transfer learning) on Cloud Machine Learning (Cloud ML) for choosing the best candy for your request. The algorithm is smart enough to understand the similarity of meaning between two words - such as "milky and creamy", "hot and spicy" - based on an analysis result on natural languages. With this technology, the system tries to recommend a candy that has the highest likelihood of fulfilling the request.
Locating and serving a candy with image recognition on Cloud ML
Once system determined what type of candy to serve, it runs a deep learning model for image recognition and analyzes the image of candies on the table. The model locates the position of the target candy and serve it to you with the robot arm.
In the training mode, the system uses Inception model with transfer learning on Cloud ML to train the model within a couple of minutes by utilizing the computation power of Google Cloud. So you can bring any objects you like and train the system on-the-fly at high accuracy. It's not designed only for picking up the candies, but for a versatile image recognition for wide variety of applications.
Serving mode
  • Android tablet: for UI, recognizes the voice command with Speech and NL API
  • Controller PC (Linux) and camera: recognizes the candies with Cloud ML, controls the robot arm
  • Robot arm: picks the candy, takes it to the certain location, and drops.
Learning mode
  • Android tablet: shows UI for training process updates
  • Controller PC: runs Inception-v3 + transfer learning on Cloud ML to train a model from scratch, with the camera image
Setting things up
Note
  • Currently, the system is not using Cloud Speech API. It uses Web Speech API that shares the same voice recognition backend.
  • For Learning mode it is using Cloud ML training. For Serving mode it is not using Cloud ML prediction.
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