Machine learning is one of the most promising and most difficult to understand fields of the modern age. Here are the slides from Slater Victoroff's (CEO of indico) talk at General Assembly Boston for non-technical folks on how to separate the signal from the noise -- stay tuned for the next time he speaks:
https://generalassemb.ly/education/machine-learning-for-non-technical-people
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.
Artificial Intelligence High Technology PowerPoint Presentation Slides Comple...SlideTeam
Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck combines state-of-the-art design with insightful info. This PPT template deck helps you express every important aspect of machine intelligence. Showcase definition, types, use cases, trends, application, and present situation of AI. Employ our PowerPoint theme to elucidate the differences between AI, machine learning, and deep learning. Our PPT layout designers incorporate cutting-edge diagrams, and graphics to simplify complex data and season bland content. Illustrate the application, selection method, significance, function, use cases, challenges, and limitations of machine learning. Also, walk your audience through ML algorithms, decision tree algorithm learning, and differences between traditional programming and machine learning. Offer a complete overview of deep learning including the process, application, limitations, and significance through this comprehensive PowerPoint presentation. Highlight reinforcement learning, classification of neural networks, deep learning networks, feed-forward neural networks, recurrent neural networks, and convolutional neural networks. You will also find data on supervised and unsupervised machine learning, back propagation, and AI expert systems. Smash the download icon to personalize. Our Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3oaWpPz
Artificial Intelligence (A.I.) || Introduction of A.I. || HELPFUL FOR STUDENT...Shivangi Singh
Powerpoint Presentation on Artificial Intelligence which is helpful for students and anyone who want to gain information on A.I. . Helpful in college / school / university presentation on Artificial Student. Officials Personnel also use this for their use.
This Power Point Presentation is completely made by me.
If anyone want this ppt please email at : devashreeapplications@gmail.com
Or you can DM me on my Instagram Handle==> ID:: @theshivangirajpoot(SHERNI)
Thankyou for your interest:):)
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
artificial intelligence articles
artificial intelligence companies
artificial intelligence stocks to buy
artificial intelligence robots
artificial intelligence in medicine
artificial intelligence wikipedia
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
How Machine Learning is Shaping Digital Marketingindico data
Dan Kuster held a workshop at General Assembly Boston on how machine learning is changing -- and improving -- the way digital marketers do their jobs.
Overview:
"Machine learning allows a marketer to target people based on an actual understanding of their interests, habits, and personality, rather than typical demographic data. To get more concrete here, machine learning lets you say: I want to target people that have posted a picture of a guitar in the last three months, or: I want to target people with the INTP personality type that posted something angry about Bernie Sanders recently.
It also allows marketers to look strategically at the content they use to engage their audience and reflect on what works and what doesn't work in a scientific way. If you make 30 posts with very different engagement rates, you can use your own intuition, but then also scientifically vet the wording of your message to get a sense ahead of time about how engaging it may be."
Artificial Intelligence High Technology PowerPoint Presentation Slides Comple...SlideTeam
Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck combines state-of-the-art design with insightful info. This PPT template deck helps you express every important aspect of machine intelligence. Showcase definition, types, use cases, trends, application, and present situation of AI. Employ our PowerPoint theme to elucidate the differences between AI, machine learning, and deep learning. Our PPT layout designers incorporate cutting-edge diagrams, and graphics to simplify complex data and season bland content. Illustrate the application, selection method, significance, function, use cases, challenges, and limitations of machine learning. Also, walk your audience through ML algorithms, decision tree algorithm learning, and differences between traditional programming and machine learning. Offer a complete overview of deep learning including the process, application, limitations, and significance through this comprehensive PowerPoint presentation. Highlight reinforcement learning, classification of neural networks, deep learning networks, feed-forward neural networks, recurrent neural networks, and convolutional neural networks. You will also find data on supervised and unsupervised machine learning, back propagation, and AI expert systems. Smash the download icon to personalize. Our Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3oaWpPz
Artificial Intelligence (A.I.) || Introduction of A.I. || HELPFUL FOR STUDENT...Shivangi Singh
Powerpoint Presentation on Artificial Intelligence which is helpful for students and anyone who want to gain information on A.I. . Helpful in college / school / university presentation on Artificial Student. Officials Personnel also use this for their use.
This Power Point Presentation is completely made by me.
If anyone want this ppt please email at : devashreeapplications@gmail.com
Or you can DM me on my Instagram Handle==> ID:: @theshivangirajpoot(SHERNI)
Thankyou for your interest:):)
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
artificial intelligence articles
artificial intelligence companies
artificial intelligence stocks to buy
artificial intelligence robots
artificial intelligence in medicine
artificial intelligence wikipedia
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
How Machine Learning is Shaping Digital Marketingindico data
Dan Kuster held a workshop at General Assembly Boston on how machine learning is changing -- and improving -- the way digital marketers do their jobs.
Overview:
"Machine learning allows a marketer to target people based on an actual understanding of their interests, habits, and personality, rather than typical demographic data. To get more concrete here, machine learning lets you say: I want to target people that have posted a picture of a guitar in the last three months, or: I want to target people with the INTP personality type that posted something angry about Bernie Sanders recently.
It also allows marketers to look strategically at the content they use to engage their audience and reflect on what works and what doesn't work in a scientific way. If you make 30 posts with very different engagement rates, you can use your own intuition, but then also scientifically vet the wording of your message to get a sense ahead of time about how engaging it may be."
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
In recent years, deep learning approaches have come to dominate discriminative problems in many sub-areas of machine learning. Alongside this, they have also powered exciting improvements in generative and conditional modeling of richly structured data such as text, images, and audio. This talk, led by indico's Head of Research, Alec Radford, will serve as an introduction to several emerging application areas of generative modeling and provide a survey of recent techniques in the field.
Boston ML Forum 2016
Slidedeck from our seminar about Machine Learning (07/11/2014)
Topics covered:
- What is Machine Learning?
- Techiques (clustering, classification, ...)
- Tools (Mahout, R, Spark MlLib, Weka, ...)
- Practical example of Machine Learning applications
- How to embed Machine Learning in software development
- Demo's
Google I/O 2016 Highlights That You Should KnowAppinventiv
Here are a few highlights of the Google I/O 2016 announcements, which range from the new version of Android N to unique features for Android TV, Google Home virtual assistant and the the Daydream VR ecosystem.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few lines of code. We will highlight some of the confusing bits of TensorFlow as a way of developing the intuition necessary to avoid common pitfalls when developing your own models. Additionally, we will discuss how to roll our own Recurrent Neural Networks. While many tutorials focus on using built in modules, this presentation will focus on writing neural networks from scratch enabling us to build flexible models when Tensorflow’s high level components can’t quite fit our needs.
About Nathan Lintz:
Nathan Lintz is a research scientist at indico Data Solutions where he is responsible for developing machine learning systems in the domains of language detection, text summarization, and emotion recognition. Outside of work, Nathan is currently writting a book on TensorFlow as an extension to his tutorial repository https://github.com/nlintz/TensorFlow-Tutorials
Link to video https://www.youtube.com/watch?v=op1QJbC2g0E&feature=youtu.be
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
Social Effects by the Singularity -Pre-Singularity Era-Hiroshi Nakagawa
Contents:
Stance of scientists community against Pre-Singularity problems
Amplification vs. Replacement
AI takes over jobs
Boarder line between amplification and replacement
Autonomous driver: trolley problem
The right to be forgotten
Towards black box
Responsibility
Vulnerability of financial dealing system made of many AI agent traders connected via internet
AI and weapon
Filter bubble phenomena
Analogy: Selfish gene
AI and privacy
The right to be forgotten, Profiling and Don’t Track
Feeling of friendliness to android
Again self conscious and identity
We focus on Invisible Interfaces and their influence on digital experiences. With the advent of 5G creating the foundation for the increased adoption of ‘invisibility’ in our interaction with technology – we’ll discuss what this could mean for the UX and CX industry.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Small Data for Big Problems: Practical Transfer Learning for NLPindico data
Despite all the advancements using AI and machine learning to create value around structured data, enterprises are not seeing the same benefits and ROI with unstructured content - all the text, images, documents, contracts, and customer interactions that make up more than 80% of data in most organizations. Traditional keyword-based approaches - including taxonomies, classifiers, expert systems, and pre-trained dictionary based systems - are simply too complex, too inflexible, and too expensive to maintain. It’s time for a new approach.
In this webinar, Indico’s Founder & CTO Slater Victoroff discusses modern transfer learning techniques for NLP to help you avoid common pitfalls when working in low-data environments.
Getting to AI ROI: Finding Value in Your Unstructured Contentindico data
Artificial Intelligence is definitely having its moment, but if you’re like most companies, you haven’t yet been able to capture ROI from these exciting technologies. It seems complicated, expensive, requires specialized talent, crazy data requirements, and more. Your boss may have dropped a vague missive onto your desk asking you to “figure out how AI can help enhance our business.” You have piles and piles of unstructured content—contracts, documents, feedback, but you haven’t been able to drive value from your data. Where to even start?
We’ll show you how.
Hear Indico’s CEO Tom Wilde and Intellyx’s Jason Bloomberg's perspectives in this valuable and practical webinar to start your AI journey to success. In this webinar you will learn:
- An understanding of the “alphabet soup” of AI and which technology is right for you—including Machine Learning, Deep Learning, Transfer Learning, and more
- A framework for developing use cases that can benefit from AI
- The building blocks for AI success
- A methodology for designing in ROI from the outset
Everything You Wanted to Know About Optimizationindico data
Presented by Madison May, co-founder and machine learning architect at indico, at the Boston ML meetup.
Overview:
In recent years the use of adaptive momentum methods like Adam and RMSProp has become popular in reducing the sensitivity of machine learning models to optimization hyperparameters and increasing the rate of convergence for complex models. However, past research has shown when properly tuned, using simple SGD + momentum produces better generalization properties and better validation losses at the later stages of training. In a wave of papers submitted in early 2018, researchers have suggested justifications for this unexpected behavior and proposed practical solutions to the problem. This talk will first provide a primer on optimization for machine learning, then summarize the results of these papers and propose practical approaches to applying these findings.
ODSC East: Effective Transfer Learning for NLPindico data
Presented by indico co-founder Madison May at ODSC East.
Abstract: Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to build models that detect the presence of custom objects in natural images. Image classification tasks that would typically require hundreds of thousands of images can be tackled with mere dozens of training examples per class thanks to the use of these pretrained reprsentations. The field of natural language processing, however, has seen more limited gains from transfer learning, with most approaches limited to the use of pretrained word representations. In this talk, we explore parameter and data efficient mechanisms for transfer learning on text, and show practical improvements on real-world tasks. In addition, we demo the use of Enso, a newly open-sourced library designed to simplify benchmarking of transfer learning methods on a variety of target tasks. Enso provides tools for the fair comparison of varied feature representations and target task models as the amount of training data made available to the target model is incrementally increased.
The Unreasonable Benefits of Deep Learningindico data
Dan Kuster led a talk at Sentiment Analysis Symposium discussing why businesses should consider adopting deep learning solutions. Key takeaways include simplicity, accuracy, flexibility, and some hacks for working with the tech.
About the Session:
Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn this directly from the data. Deep learning is a branch of machine learning that like the human brain quickly learns hierarchical representations of concepts, and it has been key to unlocking state-of-the-art results on a range of text and image classification tasks such as sentiment analysis and beyond.
In this session, we will show the impact of a deep learning based approach over NLP and traditional machine learning based methods for text analysis across key dimensions such as accuracy, flexibility, and the amount of required training data. Specifically, we will discuss how deep learning models are now setting the records for state-of-the-art accuracy in sentiment analysis. We will also demonstrate the flexibility of this approach by showing how the features learned by one model can be easily reused in different domains (e.g., handling additional languages, or predicting new categories) to drastically reduce the time to deployment. Finally, we will touch on the ability of this method to handle additional types of data beyond text, e.g, images, for maximum insight.
Getting started with indico APIs [Python]indico data
An introduction to our machine learning APIs (sentiment, political alignment, language detection, text tags, facial emotion recognition, and image features) using Python.
Slater Victoroff (@sl8rv) walks us through two tutorials: (1) sorting images based on similarity using our Image Features API, and (2) creating a custom RSS feed using our Text Tags API.
Our APIs are available with wrappers in node.js, R, Objective-C, Ruby, Java, and PHP here: https://github.com/IndicoDataSolutions
Originally presented at PAPIs 2014, the first international conference on Predictive APIs and Apps.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
3. Who am I?
• Slater Victoroff
• Olin College of Engineering
• Typical young hoodie, flip-flop
wearing entrepreneur
• Someone who cares very
deeply about machine learning
• CEO of indico
5. Such a big buzzword.
Here’s what it comes down to in a human definition:
A class of computer algorithms and mathematical
models that allow machines to perform general tasks,
like identifying human faces in photos. The models
are used to make predictions and decisions, which
you can then use to solve real world problems, such
as understanding how your customers feel about
your brand across various social media channels.
The neat thing is that instead of hiring 100 people to
analyze 1,000 data points each, you could get a
single machine to do it in a fraction of the time.
14. Language is blurry — sarcasm, etc.
Where there’s a gray area,
machine learning can solve the issue.
Computers are bad at the world
when there is inconsistency.
15. Say you’re a brand and you want to know what
people are saying about your brand.
You look through everyone talking about
your brand on Twitter, Facebook, etc..
Now you want to look at how popular
those people are to find your influencers.
And finally, you want to know… what are they talking about?
In the old spreadsheet way, we have always just ignored these
problems as they were in a gray area we couldn’t access.
A social media example
18. • Marty McFly ended up in 1955 which is the same year
that the first branch of ML came out (AI movie to come
later)
• Georgetown and IBM Cold War found ML to be useful as
they wanted to translate a large amount of Russian text
to analyze
• MIT went after the image side, teaching computers to
recognize objects and scenes. They tried to teach the
computer to look at a picture and determine a bird or a
plant.
20. CSAIL
• The Computer Science and Artificial Intelligence
Laboratory – known as CSAIL is the largest
research laboratory at MIT and one of the world’s
most important centers of information technology
research.
• Founded in the 1940’s by Marvin Minsky
21.
22. We’re pretty sure we bit
off more than we can
chew here
- ALPAC 1966
23. • Committees were spun up to precise translation
and recognition.
• In one solid decade, we effectively made no
progress. We had one-off ML systems.
• We could teach a computer to understand one
sentence by showing it that one sentence.
• We made no progress, spent a lot of money, and
cut the research. It was the death of an era.
During that time…
28. Sentiment analysis = determine if a piece of text is
positive or negative.
How do we do it?
Well, we map each word to its sentiment and give
the words a score.
AKA: A Lexicon-based approach
Sentiment Analysis
32. “I have to say, that while most of
my experiences at tourists traps
have been horrendous, the one I
recently went to broke the pattern.”
• Many humans can’t figure out the sentiment of this
sentence
• Gray areas of language = why sentiment analysis is
quite a difficult problem for computers to solve
35. • Well, it’s hard
• Take a spreadsheet
• Label each piece of text for positive vs.
negative
• Guess which words made it positive or negative
• Train the model on half of the spreadsheet and
then make predictions on the other half
Then what.
38. Customer Did they buy?
1 No
2 No
3 No
4 No
5 No
6 Yes
7 No
8 No
9 No
10 No
11 No
12 Yes
13 No
14 No
Performance Metrics
39. - Accuracy isn’t necessarily the best performance metric
- Predicting sentiment is a very different problem depending on whether the text
you’re making predictions on consists of Amazon reviews, tweets, or medical
journals
- It also depends on how much data you’ve got
- When you teach a computer what sentiment is, you end up showing it a huge
number of examples. Depending on the data you’ve got, the number of examples
you might use range from a few hundred to hundreds of millions
- It’s not fair to use those examples to check your model’s accuracy — you already
know the answers
Performance Metrics
40. Learn more about sentiment analysis and
performance metrics:
What Even Is Sentiment Analysis?
41. Precision: fraction of retrieved instances that are relevant
Recall: fraction of relevant instances that are retrieved
Precision vs Recall
42. Overfitting
This product left me with a deep feeling of regret.
This film left me with a deep feeling of regret,
love, and hopelessness for a life not lived.
I #love these new @nike shoes
43. Overfitting
• Overfitting means you “fail to generalise to examples outside of
your training set”
• In other words…you’re living under a rock. You’re great at
recognizing everything under your rock, but you don’t
understand the rest of the world
• Domain is a factor — there are so many different kinds of text
(scientific journal articles vs. tweets)
• No one model is going to be the best at every kind of text
For a more in-depth look at sentiment analysis, see this post: https://indico.io/blog/what-is-sentiment-analysis/
Accuracy isn’t necessarily the best performance metric
Predicting sentiment is a very different problem depending on whether the text you’re making predictions on consists of Amazon reviews, tweets, or medical journals.
It also depends how much data you’ve got.
When you teach a computer what sentiment is, you end up showing it a huge number of examples. Depending on the data you’ve got, the number of examples you might use range from a few hundred to hundreds of millions.
It’s not fair to use those examples to check your model’s accuracy — you already know the answers
Overfitting means you “fail to generalise to examples outside of your training set”
In other words…you’re living under a rock. You’re great at recognizing everything under your rock, but you don’t understand the rest of the world
Domain is a factor — there are so many different kinds of text (scientific journal articles vs. tweets)
No one model is going to be the best at every kind of text
Overfitting means you “fail to generalise to examples outside of your training set”
In other words…you’re living under a rock. You’re great at recognizing everything under your rock, but you don’t understand the rest of the world
Domain is a factor — there are so many different kinds of text (scientific journal articles vs. tweets)
No one model is going to be the best at every kind of text