"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
From Conventional Machine Learning to Deep Learning and Beyond.pptxChun-Hao Chang
In this slide, Deep Learning are compared with Conventional Learning and the strength of DNN models will be explained.
The target audience are people who have the knowledge of Machine Learning or Data Mining but not familiar with Deep Learning.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
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.
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
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.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
Deep Learning Class #2 - Deep learning for Images, I See What You MeanHolberton School
Slides by Louis Monier (Altavista Co-Founder & CTO) for Deep Learning keynote #2 at Holberton School. http://www.meetup.com/Holberton-School/events/230547621/
The keynote was followed by a workshop prepared by Gregory Renard. If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
From Conventional Machine Learning to Deep Learning and Beyond.pptxChun-Hao Chang
In this slide, Deep Learning are compared with Conventional Learning and the strength of DNN models will be explained.
The target audience are people who have the knowledge of Machine Learning or Data Mining but not familiar with Deep Learning.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
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.
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
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.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
Deep Learning Class #2 - Deep learning for Images, I See What You MeanHolberton School
Slides by Louis Monier (Altavista Co-Founder & CTO) for Deep Learning keynote #2 at Holberton School. http://www.meetup.com/Holberton-School/events/230547621/
The keynote was followed by a workshop prepared by Gregory Renard. If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Slides by Louis Monier (Altavista Co-Founder & CTO) for Deep Learning keynote #1 at Holberton School. The keynote was followed by a workshop prepared by Gregory Renard. If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Fight Back Against Back: How Search Engines & Social Networks' AI Impacts Mar...Rand Fishkin
Rand's presentation on machine learning and deep learning in Google, Facebook, and beyond, and how engagement reputation will become key to every online marketing effort.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Thanks to Deep Learning, Artificial Intelligence is now getting smart. Deep Learning models attempt to mimic the activity of the neocortex. It is understood that the activity of these layers of neurons is what constitutes a brain to be able to "think". These models learn to recognize patterns in digital representations of data in a very similar sense to humans. In this survey report, we introduce the most important concepts of Deep Learning along with the state of the art models that are now widely adopted in commercial products.
Collect millions of reviews from travel websites, extract entities via AlchemyAPI and train a model to predict search behaviour in upcoming months based on what users are writing about specific geographical areas, specific accommodations? Or how about a recommendation engine for e-commerce platforms, that not only takes into account the number of purchases but also SEO specific factors like keyword difficulty, number of external links and more to find the right balance between internal linking and commercially interesting items? Classifying and structuring huge datasets of content can be time consuming, why not us a free trained Machine Learning API for Topic Detection to do this for you? In this session Jan Willem Bobbinck will introduce the concept of machine learning and share a few practical examples on how you can use it to optimize your SEO processes.
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
Google's evolution into deep learning has created a whole new kind of algorithm; one that differs substantially from the type of ranking system SEOs & marketers have become used to over the past 17 years. In this presentation, Rand explores the changes Google's made, and how it impacts the actions necessary to be successful in 2016 and beyond.
Digitalisierung des Privatlebens - wie Deep Learning Smartphone und Smart Hom...rene_peinl
Was ist Digitalisierung und wie verändert sich dadurch unser Privatleben. Dieser Frage geht der Vortrag nach und startet mit Beispielen zur Digitalisierung, hinterfragt welche Eigenschaften von Produkten das Attribut smart rechtfertigen und zeigt auf, wie Deep Learning und künstliche Neuronale Netze die Technik revolutionieren und das Versprechen "smart" endlich einlösen.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
Artificial Intelligence is back, Deep Learning Networks and Quantum possibili...John Mathon
AI has gone through a number of mini-boom-bust periods. The current one may be short lived as well but I have reasons to think AI is finally making some sustained progress that will see its way into mainstream technology.
This presentation give an introduction to Artificial Intelligence subjectiveness and history. The primary goal of the presentation is to provide a deep enough understanding of Artificial Narrow Intelligence and Artificial General Intelligence so that the people can appreciate the strengths or weaknesses of the AI. The presentation also includes a classification(the main domains of AI) and the most relevant examples from the past decades. In the second part it provides some statistics and future possible applications and forecasts.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Machine learning para tertulianos, by javier ramirez at teowakijavier ramirez
Would you like to use machine learning in your projects but you think you don't know enough? I'll tell you why machine learning is relevant, how machines learn, and which ready-made algorithms you can use if you don't know much maths but you still want to take advantage of ML
Keynote: Act deliberately and preserve things. Academic Libraries in an age of artificial intelligence
Nicole Coleman, Digital Research Architect, Stanford University Libraries and Research Director, Humanities + Design
Similar to Deep Learning Class #0 - You Can Do It (20)
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
6. How to define Artificial Intelligence?
Easy for People (intelligence)
Vision, speech, understanding language
Planning, common sense
Having a conversation
Reading emotions
Translating Russian to English
Playing Chess, Go
Proving math theorems
Easy for Machines (stamina)
Multiplying huge matrices
Searching large databases
Sorting a trillion records
Finding a path in a huge maze
Hard for People and Machines
Predicting the weather
Predicting the winning lottery ticket
7. 1770
The Mechanical Turk
The “Turing Test”1950
1956Dartmouth Conference
Checkers-playing program
“Magic”
1957 Perceptrons, Frank Rosenblatt
1966ELIZA, first chatterbot (*)
1969Minsky and Papert
Lighthill Report1973
First AI Winter
$$$
1958 LISP
8. 1980
1981
1984
Second AI Winter
Cyc
Expert systems
Expert systems disappoint, end of 5th gen
5th Generation Computing, Japan
$$$
1982 Connection Machine
(#$isa #$BarakObama #$USPresident)
(#$genls #$Tree-ThePlant #$Plant)
...
1989
1997Deep Blue
vs
Kasparov
1998 Google
1995 AltaVista
Hand-written digits recognition
BabelFish
1991 http://www
9. 2006
2009
2010
2011
2012
2013
2015
Personal Assistant (Siri, Angie)
Fundamental DL technique cracked!
The Unreasonable Effectiveness of DataVoice breakthrough
IBM Watson wins at Jeopardy
Image breakthrough
Deep Mind plays
Atari games
Text breakthrough
TensorFlow
Google Photos
2004Project DAVE
autonomous
robot
Google Brain
11. Lesson: People are easy to fool!
Chess: large-scale search
Wikipedia, tricks, fast search
No real ‘understanding”, lots of data, lots of computing power
Eliza: 100% smoke and mirrors. Addictive.
12. Lesson: Some Things are Particularly Hard to Code
Vision
Natural Language
Speech
He said that she replied that they
could not agree. But she was wrong.
13. Use Rules? Use Data?
Option 1: Write a set of rules
- Use logic and heuristics to assemble hand-coded rules
- The real world is messy and changes all the time
- This approach won't scale, and it’s expensive (people)
- (We don’t have a symbol-manipulation engine in our heads)
Option 2: Learn from data
- Uses examples to find patterns automatically: the more data, the better!
- Automatically adapts to new data
- This approach scales, and it’s cheap (computing power)
16. Lesson: Some Problems are AI-Complete
“...I got a .45 and a shovel...”
“Let’s see: a .45 is a gun, and a shovel is used to dig holes. A father is usually very protective of his
daughter, and he looks intensely at the daughter's date when he says that.
Most likely interpretation: if anything happens to his daughter (accident, pregnancy), he will kill the guy
with the gun and bury him with the shovel. It’s a threat, but it can’t be serious, this is illegal, and he is
talking in front of his daughter; so it’s a funny threat, a warning. The guy will still understand that the dad
means business and expects him to take good care of his daughter.”
...
Let’s have a quick chuckle, and on to the next line…
17. Examples of AI-Complete Tasks
Vision
Natural Language Understanding
- Say: “Wreck a nice beach”
- Now: “Recognize speech”
Automated Translation
Virtual Assistant (Personal Assistant)
Holding a conversation
Truly summarizing text
Dealing with the real world
- Navigation
- Planning
- Adapting to new environments
18. Lesson: Don’t be afraid to Experiment
As we’ll learn: turn everything into numbers, mix them happily!
Don’t try to follow rigorously what is going on!
Don’t expect a mathematical proof for everything!
19. If you remember only one thing...
The approach that won: learn from the data!
28. 1/ Artificial Narrow Intelligence (ANI) = Weak IA
2/ Artificial General Intelligence (AGI) = Strong IA
3/ Artificial SuperIntelligence (ASI) = 12k IQ
Art & Conception of AI
Today, computers are not smart, all the ingenuity of the
designers in AI is to make you think they are reproducing
human thought patterns.
The Future of AI: What if We Succeed?
31. What is Machine Learning?
= ?
question
response
prediction
1. Training a model
question prediction
2. Using the trained model
(Supervised learning)
32. Let’s do a (totally fake) Clinical Study
Patient id #packs per day #hours exercise per day heart problem
1 .5 2 0
2 2 0 1
3 3 6 1
4 0 5 0
5 1 0 1
6 1.5 5 0
Can we learn a pattern? Can we use it to predict outcome of new patients?
33. Linear Regression
Smoking
Exercising
Heart problem
Healthy heartWhat about
Sally?
Compute a linear combination of
- p = # packs per week
- h = # hours of exercise per week
Find w1, w2, w3 that best match the data.
That’s the learning.
if (score > 0) then predict heart problem.
Simple statistics.
Score > 0
Score < 0
34. How to find the best values for w1, w2 and w3 ?
Parameter
Error
a
b
Define error = |expected - computed|
2
Find parameters that minimize average error.
Perform Gradient Descent: quality goes up.
take a step downhill
45. Why does this work?
Neural networks with at least one hidden layer can approximate any reasonably
smooth function.
Large networks have lots of solutions (minima), most of them very good.
Gradient descent is very simple and very powerful.
46. Everyday examples of Deep Learning
Structured data: Netflix, Spotify and YouTube recommendations; Amazon
suggestions; CC fraud detection
Text: Spam filtering; good spelling suggestions; matching ad to content;
automated translation
Images: Google Photos; search by image; FaceBook face tagging; thumbnail for
YouTube videos; OCR and handwriting recognition; surveillance videos
Voice: Android voice input; Nuance (Siri); transcription
Combo: Autonomous vehicles (soon); Virtual Assistant, Industrial robots
49. Unsupervised Learning
Automatically learn structure in data
Clustering
More compact representation
Semi-supervised learning
Credit: http://colah.github.io/posts/2014-10-Visualizing-MNIST/
50. Reinforcement Learning
Learn not from static data, but from interacting with a system
- playing a game
- flying a plane
- driving a car
- learning a task
System
do
something
get a
score
56. DeepMind AlphaGo
Go is much harder than chess
Oct 2015: AlphaGo beats Fan Hui, a top Go player
Jan 2016: paper comes out, world goes wild
Match with Lee Sedol, #1 player, in March 2016
57. If you remember only one thing...
We build a model from a set of examples.
It starts as a random set of parameters.
We measure how well the predictions match the truth.
We tweak the parameters to improve this match.
A good model will generalize to new data, making useful predictions.
58. What we will learn
How Deep Learning works, more precisely.
What all the terms mean. It’s a big zoo.
Using existing frameworks: TensorFlow, Torch...
Downloading, using, modifying existing models.
All the tricks to tune models.
Mapping a problem to an architecture.
61. A Brief History of
Visual Recognition
2012 - Annus Mirabilis for DL.
ImageNet contest.
Alex Krizhevsky, Ilya Sutskever,
Geoffrey Hinton, University of
Toronto
63. Rules really don’t work for vision
I’m asking you to describe cherry blossoms.
Please use precise features and rules!
“Well, white petals arranged in a circle. Unless some of the petals have fallen.
With little white sticks and black dots arranged like this. Oh, except if seen from
the side. Or if they overlap. Or if the sun is behind. Ignore the bee…”
Next task: a human face. Any human face.
Very slow progress, even with generations of graduate students.
88. What we will learn
Convolutional Neural Networks (ConvNets)
Adapting existing models
89. If you remember only one thing...
Vision is not a task that can be reduced to simple rules.
Immense progress since modern ConvNets and GPUs, ~2012.
Many real-life applications today.
Expect a lot more.
96. Languages are Complex - Context
“The Jaguar eats his prey” => predator => big cat
“The Jaguar eats the road” => image => car
Also: idioms, technical lingo, slang, humor, sarcasm, poetry, emotions...
99. Semantic Distance for Words
cat
purring
sofa
New York
dogkitten lion
Not to scale :)
serendipity
less related
2348883608
furball feline
hat
fur
turkish angora
nanocrystals
100. Terms similar to Champagne
french champagne, cognac, champagne's, champagnes, veuve clicquot, cremant, louis roederer, rosé, taittinger, fine
champagne, champagne wine, sparkling wines, dom pérignon, dom perignon, pol roger, vintage champagne, bubblies,
pommery, rose wine, pink wine, blancs, french wine, cliquot, beaujolais nouveau, sancerre, sparkling, burgundy, chateau,
chablis, cognacs, pink champagne, domaine, moët, methode champenoise, burgundy wines, apéritif, armagnac, chandon,
champenoise, beaujolais, heidsieck, marnier, wine, bourgogne, aperitif, chateau margaux, demi-sec, moelleux, champagne
cocktail, crémant, half-bottle, cuvée, brut, ruinart, champagne flute, st emilion, white wine, loire valley, wine cocktail, veuve,
drinking champagne, french wines, blanc, chardonnay wine, champagne glass, cuvées, mauzac, roederer estate, laurent-
perrier, puligny, negociant, prosecco, rose wines, gloria ferrer, red wine, musigny, coteaux, corton-charlemagne, fine wine,
dessert wine, bordeaux, champagne glasses, cheval blanc, champagne flutes, cuvees, champange, four wines, montlouis,
rémy martin, primeur, fine wines, lirac, d'yquem, burgundy wine, red bordeaux, brandy, cuvee, white burgundy, chardonnay,
chambolle-musigny, cheverny, great vintages, yquem, special wines, wonderful wines, burgundies, half bottles, grand
marnier, grand cru, primeurs, sauterne, minervois, pouilly-fuissé, sauternes, chambertin, white bordeaux, vougeot, epernay,
vin gris, chalonnaise, quaffer, loire, sweet white wine, d'aunis, côtes, gevrey-chambertin, limoux, english wine, chateaux,
château haut-brion, blanche, pinot meunier, six glasses, mâconnais, épernay, bourbon, sparkler, volnay, white wines,
chassagne-montrachet, burgundys, vin jaune, claret, beaune, grande champagne, white grapes, bordeaux wine, dessert
wines, crème de cassis, pinot noir grapes, chardonnay grapes, armand de brignac, select wines, calvados, country wine,
muscadet, leflaive, reisling, cointreau, own wine, caveau, clos de vougeot, inexpensive wines, vosne-romanée..., expensive
wines, red burgundy, barsac, delicious wine, wine flight, puligny-montrachet, rousanne, châteauneuf-du-pape, liqueur,
schramsberg, touraine, montrachet, arbois, lanson, vintage wine, chateauneuf, blanquette, non-vintage, orange wine, three
wines, wine.the, banyuls, merlot wine, vendange, red table wine, sweet wines, santenay, languedoc, moscato d'asti …
101. Terms similar to Brad Pitt
angelina jolie, george clooney, cameron diaz, julia roberts, leonardo dicaprio, matt damon, tom cruise, nicole kidman, reese
witherspoon, charlize theron, jennifer aniston, halle berry, kate winslet, jessica biel, ben affleck, bruce willis, scarlett
johansson, uma thurman, matthew mcconaughey, jake gyllenhaal, sandra bullock, oscar winner, gwyneth paltrow, sean penn,
demi moore, naomi watts, colin farrell, mickey rourke, orlando bloom, bradley cooper, natalie portman, jennifer garner, tom
hanks, dicaprio, jessica chastain, robert de niro, julianne moore, leo dicaprio, channing tatum, kirsten dunst, jessica alba,
emily blunt, salma hayek, ryan gosling, mark wahlberg, renee zellweger, drew barrymore, renée zellweger, gerard butler,
hilary swank, ryan phillippe, john malkovich, nicolas cage, kate hudson, sharon stone, sienna miller, new movie, kim
basinger, robert downey jr, keira knightley, ryan reynolds, johnny depp, jennifer connelly, edward norton, emma stone, don
cheadle, marisa tomei, jason statham, eva mendes, kate beckinsale, oscar-winner, katie holmes, kelly preston, denzel
washington, zac efron, clive owen, oscar-winning, forest whitaker, penelope cruz, ashton kutcher, sigourney weaver, rachel
weisz, billy bob thornton, catherine zeta-jones, benicio del toro, keanu reeves, new film, ewan mcgregor, jeremy renner, hugh
grant, liam neeson, scarlett johannson, jude law, russell crowe, jodie foster, harrison ford, meryl streep, justin theroux, john
travolta, christian bale, emile hirsch, adrien brody, jonah hill, nick nolte, dennis quaid, liv tyler, kate bosworth, hollywood star,
amber heard, javier bardem, robert deniro, evan rachel wood, helen mirren, milla jovovich, blake lively, james franco, vince
vaughn, joaquin phoenix, diane kruger, upcoming movie, robert pattinson, michael douglas, courteney cox, richard gere,
daniel craig, sylvester stallone, latest movie, rachel mcadams, josh brolin, jennifer lawrence, brangelina, oscar winners, hugh
jackman, zoe saldana, oscar nominee, dakota fanning, josh hartnett, annette bening, mila kunis, emma watson, david fincher,
megan fox, quentin tarantino, ben stiller, a-lister, kristen stewart, charlie sheen, christoph waltz, christopher walken, michelle
pfeiffer, phillip seymour hoffman, thandie newton, amanda seyfried, ethan hawke, liam hemsworth, morgan freeman, robert
downey, owen wilson, olivia wilde, costars, paula patton, casey affleck, kevin costner, clooney, clooneys, andrew garfield …
102. Terms similar to greenish
bluish, pinkish, yellowish, reddish, brownish, purplish, grayish, yellow-green, orange-yellow, yellow-brown, yellowish green,
reddish brown, orange-red, pale green, whitish, reddish-brown, greenish yellow, mottled, pale yellow, greenish-brown,
greenish-yellow, yellow-orange, orangish, red-brown, bluish-green, dark brown, greyish, yellowish-green, bluish-black,
reddish-orange, orange-brown, yellowish-orange, yellowish-white, brownish red, pale orange, bright yellow, deep yellow,
blue-green, paler, brownish-red, bluish-grey, blueish, green-brown, pinkish-brown, golden yellow, blotches, yellowish-brown,
brownish-yellow, golden-yellow, pale, grayish-white, coppery, creamy yellow, greyish-white, pale gray, purple-brown, olive-
green, pale brown, blackish, brownish yellow, tinge, dark purple, light yellow, red-orange, dark red, rusty brown, brownish
black, purplish-red, mottling, bluish-gray, yellowish brown, greyish-green, dull red, dark green, creamy white, purple-black,
yellow brown, pinkish red, greenish-blue, reddish purple, bright red, reddish-purple, grayish-green, greenish-white, pale
cream, creamy-white, brownish-gray, white spots, silvery, dark grey, dark orange, purplish-black, grayish-blue, purple-blue,
greenish-black, yellow spots, bluish-white, purple-red, pure white, light brown, various shades, grey-brown, pale grey,
orange-pink, brownish-black, brick-red, purplish-brown, olive-brown, brown colour, speckling, pale blue, brownish gray, deep
orange, grayish-brown, blue-black, darker spots, brown-red, yellow patches, gray-black, coloration, reddish color, bluish-
purple, green patches, pale red, chestnut-brown, brown streaks, yellow green, lemon yellow, pinkish-red, flecks, dark reddish
brown, black spots, grey-black, lemon-yellow, pinkish-white, deep red, brownish-grey, dull black, purple spots, darker green,
red spots, blue-grey, splotches, grey-green, pink-purple, greenish-gray, violet-blue, silvery grey, chocolate-brown, yellowish
color, cream-coloured, orange brown, small white spots, light orange, brown-grey, violaceous, dark-brown, streaked, green
veins, olive brown, olive green, brown markings, gray-green, pale pink, dark blotches, light green, grey-white, dark markings,
brilliant red, light violet, blackish-brown, greyish-brown, color ranges, brown-black, orange red, yellow colour, yellow color,
red brown, orange markings, small black spots, veined, brick red …
103. Terms similar to worse
even worse, far worse, very bad, horrible, terrible, awful, horrendous, bigger problem, suffer, things worse, horribly,
unfortunate, better, worst, bad, complain, real problem, after all, unfortunately, no good, too, lousy, atrocious, even less, even
so, very poor, far more serious, miserable, intolerable, terribly, serious problem, trouble, worrying, bothering, blame, no
better, worsened, bother, worse off, dreadful, hardly, horrid, big problem, real concern, fortunately, main problem, sooner,
major problem, hopeless, excuse, serious problems, way worse, complaining, horrendously, abysmal, better off, worried,
inevitable, wrong, marginally, even, rid, frankly, anymore, bothered, bothers, worry, uglier, sadly, even more, worsen, severe,
serious, unacceptable, badly, nasty, different story, worse problems, main reason, worst thing, far less, go away, hurt,
obviously, seriously, serious trouble, hurting, gotten, anyone else, worse.it, anyway, happen, worst cases, say nothing,
appalling, main concern, somehow, obvious reason, troubling, simple fact, unbearable, problematic, huge problem, worst
one, exacerbated, afraid, tired, blaming, painfully, suffers, much, ironically, do anything, embarrassing, worse things,
inevitably, same problems, bad problems, anything, real reason, everyone else, atrociously, unpleasant, thing, worse again,
apparent reason, needlessly, ignore, seemed, horrifically, worth noting, biggest problem, real issue, even more serious,
dreadfully, worsening, useless, even though, probably more, some people, pitiful, worrisome, far more, because, deplorable,
point out, but, stupid, admittedly, pudgenet, worst part, less so, little improvement, grossly, make things, unnecessarily, too
bad, crap, bad thing, laughable, problem, might, trying, exaggerating, pretty much, lot, doing anything, ridiculous, little
reason, misguided, exact opposite, worse not better, even when, weren't, inconsequential, simple reason, expect, avoided,
something wrong, counter-productive, dismal, appallingly, far more likely, ugly, almost everyone, shame, wonder why, less,
polfbroekstraat, worse here, plagued, worse though, honestly, bad situation, nobody, pathetic, certainly, plain wrong, almost
nothing …
104. Semantic Distance for Sentences
I like the
sushi restaurants
in Palo Alto.
A dromedary has
a single hump.
My nose is itchy!
Mind the gap!
The Japanese lunch
place near Stanford is
my favorite.
Uni is actually
sea urchin eggs.
I wish I could eat
out more often!
112. Content Centric
Siri, Cortana, Alexa, ...
- Content Centric
- Question - Answering
- Light dialog
- Context Sequence(s)
- Knowledge or Actions
Far from the Human communication
HER, Sarah, HAL (or not ;p)
- People (person) Centric
- Human like dialog
- Empathy & Emotion
- Global Context
- Concept Learning
Human emotional communication
People Centric
113. What we will learn
How to to acquire large corpora and solve common NLP tasks.
The nltk and gensim libraries, in Python.
Vector representation for text (Embeddings).
Different examples of text classification.
The Deep Neural Networks that perform best on text: LSTM, GRU…
Generative models.
114. If you remember only one thing...
NLP is hard, but traditional techniques work pretty well.
Nice progress since 2012, we are getting our hands on “semantic proximity”.
Rapid progress on classification, translation.
But no true “understanding” yet.