Neural networks are an important machine learning technology that Google uses to power many of its products. Neural networks learn from large numbers of labeled examples, making tiny adjustments to their models to better match outputs to labels. After training, neural networks can be tested on unlabeled data to produce useful outputs. Google uses neural networks across many products from Search to Translate to Photos. Their use of neural networks is rapidly accelerating to improve these products and tackle new challenges in health, robotics, and more. Google also shares machine learning tools and models to help advance the field.
A brief overview of what machine learning is and how it's used at Google and in other projects.
Then find out how to take advantage of machine learning without building and training your own models. The Google Cloud Vision and Speech APIs expose the machine learning functionality behind Google Photos, Google Images, and the speech recognition in “Ok, Google.” Developers can now build these features into their apps with a simple REST API call.
This deck gives an overview of Google Cloud Vision API, Cloud Speech API, and the Cloud Natural Language API.
Making Sense of Millions of Thoughts: Finding Patterns in the TweetsKrist Wongsuphasawat
I gave this presentation at Workshop on Interactive Language Learning, Visualization, and Interfaces / ACL 2014 in Baltimore, MD on June 27, 2014.
http://nlp.stanford.edu/events/illvi2014/index.html
ABSTRACT
Everyday on Twitter, there are millions of thoughts that are captured and shared to the world in the form of 140-character messages, or Tweets. There are many things we could learn from these thoughts if we could figure out a way to digest this gigantic dataset. Visualization is one of the many ways to extract information from these Tweets. In this presentation, I will talk about several visualizations based on Tweets, as well as share experiences and challenges from working with Tweet data.
"Missionaries VS Mercenaries" by TheFamily's cofounder Oussama Ammar TheFamily
Being smart, having a process, and burning a shitload of cash isn’t enough anymore. Why? Things are moving so fast that it takes a whole new mindset to hustle and survive the storms of change in the exponential world.
Make room for the missionaries! ⚔
Find here insight on how to fight on the war to success by TheFamily's cofounder Oussama Ammar
- Find the video of the talk : https://www.youtube.com/watch?v=nVkmAum9g1g
- Join us next time: https://www.meetup.com/fr-FR/TheFamilyBerlin/ ✨
Data driven advertising - Real Time Digital MarketingScott Thomson
Technical presentation on Real Time Bidding (RTB), Data Management Platforms (DMP), Supply/Demand Side Platforms (SSP/DSP) and other tools of programmatic advertising and advertising data analytics
Practical TensorFlow
Covers next questions:
* Machine Learning - what is it for and what challenges non deep learning system have.
* Deep Learning - why and how would you use it.
* Introducing TensorFlow and TensorFlow Learn.
* Examples of how to apply TensorFlow in practice with TensorFlow Learn.
A brief overview of what machine learning is and how it's used at Google and in other projects.
Then find out how to take advantage of machine learning without building and training your own models. The Google Cloud Vision and Speech APIs expose the machine learning functionality behind Google Photos, Google Images, and the speech recognition in “Ok, Google.” Developers can now build these features into their apps with a simple REST API call.
This deck gives an overview of Google Cloud Vision API, Cloud Speech API, and the Cloud Natural Language API.
Making Sense of Millions of Thoughts: Finding Patterns in the TweetsKrist Wongsuphasawat
I gave this presentation at Workshop on Interactive Language Learning, Visualization, and Interfaces / ACL 2014 in Baltimore, MD on June 27, 2014.
http://nlp.stanford.edu/events/illvi2014/index.html
ABSTRACT
Everyday on Twitter, there are millions of thoughts that are captured and shared to the world in the form of 140-character messages, or Tweets. There are many things we could learn from these thoughts if we could figure out a way to digest this gigantic dataset. Visualization is one of the many ways to extract information from these Tweets. In this presentation, I will talk about several visualizations based on Tweets, as well as share experiences and challenges from working with Tweet data.
"Missionaries VS Mercenaries" by TheFamily's cofounder Oussama Ammar TheFamily
Being smart, having a process, and burning a shitload of cash isn’t enough anymore. Why? Things are moving so fast that it takes a whole new mindset to hustle and survive the storms of change in the exponential world.
Make room for the missionaries! ⚔
Find here insight on how to fight on the war to success by TheFamily's cofounder Oussama Ammar
- Find the video of the talk : https://www.youtube.com/watch?v=nVkmAum9g1g
- Join us next time: https://www.meetup.com/fr-FR/TheFamilyBerlin/ ✨
Data driven advertising - Real Time Digital MarketingScott Thomson
Technical presentation on Real Time Bidding (RTB), Data Management Platforms (DMP), Supply/Demand Side Platforms (SSP/DSP) and other tools of programmatic advertising and advertising data analytics
Practical TensorFlow
Covers next questions:
* Machine Learning - what is it for and what challenges non deep learning system have.
* Deep Learning - why and how would you use it.
* Introducing TensorFlow and TensorFlow Learn.
* Examples of how to apply TensorFlow in practice with TensorFlow Learn.
10 Things Every PHP Developer Should Know About Machine Learning10x Nation
Today’s PHP developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.
One of the most important aspects of developing smart applications is understanding the underlying machine learning platforms, even if you aren’t the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this presentation will help you get started in understanding the basics of machine learning.
With the evolution of no-code AI, sectors such as web development are advancing while others are just emerging. Now, with these no-code AI platforms, businesses have a chance to explore the technology without needing to hire tech experts or adopting expensive strategies.
NO-CODE PLATFORMS HAVE MADE IT EASY TO CREATE PROGRAMS THAT USE ADVANCED TECHNOLOGIES. THE INTRODUCTION OF THESE PLATFORMS HAS RESULTED IN AN INCREASING NUMBER OF BUSINESSES ATTEMPTING TO USE THEIR CAPACITY TO BUILD AI SOLUTIONS.
With this, visual drag-and-drop tools come into the picture, aiding data scientists in filling the void and making AI less daunting for people with non-technical backgrounds.
This article discusses the top no-code platforms for building AI solutions.
MonkeyLearn
MonkeyLearn is an all-in-one text analysis and data visualization studio that can be used to extract topic, sentiment, intent, keywords, and other information from unstructured text-based data. Automatically tagging business data, presenting actionable insights and trends, and simplifying text classification and extraction processes are just a few of the features. It integrates with Zendesk, RapidMinder, and Google products, with a few others on the way. Also, it is one of the best blog resources for text analysis.
RunwayML
RunwayML is a tool for creators that focuses on creative work that involves dealing with pictures, videos, text, latent spaces, and segmentation masks, as well as motion capture, backdrop removal, and style transfer. They have a Generative Engine, which is a storytelling machine that generates visuals automatically as you write.
Finally
Businesses are increasingly turning to no-code platforms for a variety of reasons. Access to developers and software engineers slows project delivery, partly owing to the ripple impact on workforce management, and this is where technology can help. The unicorn we all want to catch is not only enabling your team to create solutions but also being relevant and competitive in the present context.
For more such updates and perspectives around Digital Innovation, IoT, Data
Infrastructure, AI & Cybersecurity, go to AI-Techpark.com.
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
Continuous accuracy and efficiency of Large Language Models (LLM) is key to successfully building out your next AI-infused automation, regardless of business use case.
For our next Connector Corner webinar, we’ll explore how using a seamless AI integration process provides access to industry leading models, curated activities, and embeddings that help achieve operational efficiency.
Join us on March 26 to learn about:
Accessing large language models, hosted by UiPath
Reducing complexities of prompt-engineering, by using curated sets of activities
Assuring accuracy and safety, by building an AI Trust Layer to moderate the output of AI models, and their generated results.
Discovering what’s new in embeddings connectivity
Cultivating your AI knowledgebase using Vector Databases
Expect to see these use cases in action:
Leveraging UiPath hosted LLMs and activities
Document comparison using our LLM framework
Please stay tuned for additional use cases
Speakers:
Charlie Greenberg, host
George Roth, Technology Evangelist
Scott Schoenberger, Senior Product Manager
Koji Takimoto, Director Product Support
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
By Alison Eastaway, Head of People at Sqreen
Alison is a startup culture boss! She perfectly knows her way around implementing a culture & taking care of people especially in tech teams.
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10 Things Every PHP Developer Should Know About Machine Learning10x Nation
Today’s PHP developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.
One of the most important aspects of developing smart applications is understanding the underlying machine learning platforms, even if you aren’t the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this presentation will help you get started in understanding the basics of machine learning.
With the evolution of no-code AI, sectors such as web development are advancing while others are just emerging. Now, with these no-code AI platforms, businesses have a chance to explore the technology without needing to hire tech experts or adopting expensive strategies.
NO-CODE PLATFORMS HAVE MADE IT EASY TO CREATE PROGRAMS THAT USE ADVANCED TECHNOLOGIES. THE INTRODUCTION OF THESE PLATFORMS HAS RESULTED IN AN INCREASING NUMBER OF BUSINESSES ATTEMPTING TO USE THEIR CAPACITY TO BUILD AI SOLUTIONS.
With this, visual drag-and-drop tools come into the picture, aiding data scientists in filling the void and making AI less daunting for people with non-technical backgrounds.
This article discusses the top no-code platforms for building AI solutions.
MonkeyLearn
MonkeyLearn is an all-in-one text analysis and data visualization studio that can be used to extract topic, sentiment, intent, keywords, and other information from unstructured text-based data. Automatically tagging business data, presenting actionable insights and trends, and simplifying text classification and extraction processes are just a few of the features. It integrates with Zendesk, RapidMinder, and Google products, with a few others on the way. Also, it is one of the best blog resources for text analysis.
RunwayML
RunwayML is a tool for creators that focuses on creative work that involves dealing with pictures, videos, text, latent spaces, and segmentation masks, as well as motion capture, backdrop removal, and style transfer. They have a Generative Engine, which is a storytelling machine that generates visuals automatically as you write.
Finally
Businesses are increasingly turning to no-code platforms for a variety of reasons. Access to developers and software engineers slows project delivery, partly owing to the ripple impact on workforce management, and this is where technology can help. The unicorn we all want to catch is not only enabling your team to create solutions but also being relevant and competitive in the present context.
For more such updates and perspectives around Digital Innovation, IoT, Data
Infrastructure, AI & Cybersecurity, go to AI-Techpark.com.
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
Continuous accuracy and efficiency of Large Language Models (LLM) is key to successfully building out your next AI-infused automation, regardless of business use case.
For our next Connector Corner webinar, we’ll explore how using a seamless AI integration process provides access to industry leading models, curated activities, and embeddings that help achieve operational efficiency.
Join us on March 26 to learn about:
Accessing large language models, hosted by UiPath
Reducing complexities of prompt-engineering, by using curated sets of activities
Assuring accuracy and safety, by building an AI Trust Layer to moderate the output of AI models, and their generated results.
Discovering what’s new in embeddings connectivity
Cultivating your AI knowledgebase using Vector Databases
Expect to see these use cases in action:
Leveraging UiPath hosted LLMs and activities
Document comparison using our LLM framework
Please stay tuned for additional use cases
Speakers:
Charlie Greenberg, host
George Roth, Technology Evangelist
Scott Schoenberger, Senior Product Manager
Koji Takimoto, Director Product Support
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
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Since Dimitri, Vincent & Josef founded Tiller, many things have changed: new products, new offices, a company acquisition & even the launch of an incubator for restaurateurs.
When people talk about them, they are still described as a cash register solution, just like back on the first day. And yet, Tiller is much more than that. They now define themselves as an ecosystem that brings together cash registration + delivery + reservation + pre-order + analysis + integrations. They are not only addressing shopkeepers, but also passionate local entrepreneurs.
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4. 4
neural net models learn from examples
labeled photos
“cat”
“dog”
“car”
“apple”
“flower”
OUTPUT
5. 5
neural net models learn from examples
Make tiny adjustments to
model so output is closer to
label for a given image
labeled photos
“cat”
“dog”
“car”
“apple”
“flower”
OUTPUT
6. 6
after a model is trained, you can test it
?
unlabeled photo
7. 7
after a model is trained, you can test it
unlabeled photo
“cat”
8. 8
Input Output
“rice”
“restaurants in Seoul”
“hello!”
“A close up of a small child holding a stuffed animal.”
powerful functions that neural nets can learn
안녕하세요
11. Atari 2600 testbed: 100+ Atari games from the 70/80s
Agents just get raw pixels as inputs (~30K)
Wired up to action buttons but NOT told what they do
Goal is simply to maximize score
Everything learnt from scratch, ZERO pre-programmed knowledge
ONE agent with ONE set of parameters to play ALL the different games
THINK OF A ROBOT in the arcade watching the screen controlling the joystick
DQN Deep Q-network- Atari testbed
Image: Gnome Enterprises
18. 18
signal
for Search ranking,
out of hundreds
improvement
to ranking quality
in 2+ years
#3 #1
Search
machine learning for search engines
RankBrain: a deep neural network for search ranking
19. 19
beyond core products, into areas like health and robotics
“Deep Learning for
Robots: Learning from
Large-Scale Interaction”,
Google Research Blog,
posted March, 2016
20. Sharing our tools with researchers and developers
around the world
repository
for “machine
learning”
category on GitHub
#1
Released in Nov.
2015
22. 2222
Ready to use Machine
Learning models
Use your own data to
train models
Cloud
Vision API
Cloud
Speech API
Cloud
Translate API
Cloud Machine Learning
Develop - Model - Test
Google
BigQuery
Stay
Tuned….
Cloud
Storage
Cloud
Datalab
NEW
Alpha
GA BetaGA
Alpha
Beta
GA
23. 2323
beyond core products
for applications like
health and robotics
smart platforms
to accelerate
progress worldwide
Google products
to create great
experiences for users
machine intelligence is already making a huge difference
and there are many, many more opportunities
23