New technologies from artificial intelligence and virtual reality to massive open online courses are beginning to disrupt existing models of learning. These technologies could help to meet rising demands for education around emerging Asia. From the rise of new global educational providers to the development of “micro” degrees and new VR tools for online learning, these technologies will also have profound implications for the way in which Higher Education policies are developed across Asia – both in developed economies like Singapore seeking to re-equip their workforce for a new era of globalisation and emerging nations attempting rapidly to increase the scale and quality of their education systems.

Find out key highlights shared by the speakers on their views of the death of traditional classrooms.

The panelist consists of:
## Mr Johannes Heinlein, Vice President, EdX

## Mr Adrian Lim, Director (Digital Participation & Foresight, Digital Readiness Cluster), Info-communications Media Development Authority (IMDA)

## Dr Suzaina Kadir, Associate Dean (Admissions, Partnerships and Programmes), Deputy Director (Academic Affairs) and Senior Lecturer, Lee Kuan Yew School of Public Policy

and is moderated by:
## Mr James Crabtree, Senior Visiting Fellow, the Centre on Asia and Globalisation, Lee Kuan Yew School of Public Policy


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Professor Rose Luckin
UCL Knowledge Lab, UCL Institute of Education

Tuesday 16th May 2017

What makes a good teacher and a successful lesson? Artificial Intelligence (AI) in the classroom is the key that is unlocking the answer, according to Rose Luckin, Professor of Learning with Digital Technologies.

Intelligent software holds opportunities for individualised teaching that understands when and how lessons veer off topic, at what point pupils lose interest and when they are at their most engaged and receptive – providing smart interventions that help teachers find the best learning strategies for their classes.

In this UCL Lunch Hour Lecture, Professor Luckin will discuss what AI is telling us about how we learn and predict how machines and humans will interact in the classrooms of the future.

Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, 1.15 – 1.55pm (term time)

Free to attend, live stream or watch online
More info:
Join the conversation on Twitter at #UCLLHL

Before machine learning: What did we do with all that data?

Today by using satellite imagery and machine learning platforms, data is searchable and accessible. Customers are using machine learning to detect hidden populations, distribute life-saving vaccines and development resources, detect structures that aid first responders and enable autonomous vehicles and robots in space.

Machine learning and artificial intelligence help customers save time, money and lives through algorithms designed to become more accurate over time. Through Maxar’s platforms like GBDX and Tomnod, customers can design their own algorithms to create new solutions to problems they were previously unable to solve.

TensorFlow is a truly open source platform with over 1,900 contributors. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks to Open Source Strategist Edd Wilder-James (@edd) about how things like TensorFlow’s Request for Comments process, Special Interest Groups, and the modularity of its codebase make it easier for the community to build TensorFlow together. They also discuss the upcoming O’Reilly TensorFlow World, which is accepting applications to participate now through April 23rd.

TensorFlow community →

Watch Edd’s talk at TF Dev Summit ‘19 →

O’Reilly TensorFlow World 2019 →

Subscribe to the TensorFlow channel →
Watch more episodes of TensorFlow Meets →

Artificial Intelligence Robots Development Until 2019 – Machine Learning Robot Ep. 06
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6. Meltant

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Video with transcript included:

Jeremy Hermann talks about Michelangelo – the ML Platform that powers most of the ML solutions at Uber. The early goal was to enable teams to deploy and operate ML solutions at Uber scale. Now, their focus has shifted towards developer velocity and empowering the individual model owners to be fully self-sufficient from early prototyping through full production deployment & operationalization.

This presentation was recorded at QCon San Francisco 2018:

The next QCon is Qcon New York 2019 – June 24-26, 2019:

For more awesome presentations on innovator and early adopter topics check InfoQ’s selection of talks from conferences worldwide

Interested in Artificial Intelligence, Machine Learning and Data Engineering? Follow the topic on InfoQ:

#MachineLearning #Uber #CaseStudy #InfoQ #QConSanFrancisco

This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We’ll also implement a neural network manually. Finally, we’ll code a neural network in Python using TensorFlow.

Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)

To learn more about Deep Learning, subscribe to our YouTube channel:

To access the slides, click here:

Watch more videos on Deep Learning:

#DeepLearningWithPython #DeepLearningTutorial #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Learn more at:

For more information about Simplilearn’s courses, visit:
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In this short GCP Essentials video, see how GCP has made Machine Learning easier for you from behind the scenes. Hear Alexis Moussine-Pouchkine further discuss Compute Engine Essentials with a Platform Overview.

Vision API →
Cloud AutoML →
GCP Essentials Qwiklab →
Free Codelabs →

The GCP Essentials Playlist →
Subscribe to the Google Cloud Platform Channel to learn more about our products →

This is a short video which gives advice on how to learn Machine Learning. Machine learning is an extremely exciting field, there is so much to it and it can be a little overwhelming for someone new trying to enter the field. This video will guide you on how to start, where to find the right resources and on the path you should take. It recommends online courses, websites and books. If you have any questions about Machine Learning, please put them in the comments section. Machine Learning is a term that covers quite a large field. It uses linear regression, logistic regression, classification, deep learning, support vector machines and neural networks. It is a branch of data science and is a very exciting field. One of its main applications is in artificial intelligence.

Here are the links from the video:-

### Online Courses

1. Udemy: Automate the boring stuff –
2. Udemy: Complete Python Bootcamp –
3. Learn Python –
4. Google’s Python Class –
5. My Python Course –

### Books (affiliate links)
1. Automate the Boring Stuff With Python –
(or for free here )
2. Python Crash Course -
3. Effective Computation in Physics –
4. Learn Python the Hard Way –

### Practice

1. Hacker Rank –

## Learn Numpy, Pandas and Matplotlib

### Books

1. Python for Data Analysis –
2. Effective Computation in Physics –

### Online

1. Udacity: Intro to Data Analysis ––ud170
2. Udemy: Python for Data Science and Machine Learning Bootcamp –
3. Udemy: Machine Learning A-Z™: Hands-On Python & R In Data Science –

### Learn Linear Algebra

1. Udemy: Linear algebra 1 –
2. Udemy: Linear algebra 2 –
3. Linear Algebra for Machine Learning –
4. Linear Algebra MIT Course –

### Learn Calculus

1. Udemy Calculus 1 –
2. Udemy Calculus 2 –
3. Udemy Calculus 3 –
4. MIT Single Variable Calculus –

### Probability and Statistics
1. Information Theory, Inference, and Learning Algorithms David McKay (Free PDF)
2. Udemy: Workshop in Probability and Statistics –
3. Introduction to Statistical Learning –

## Learning Scikit

1. Scikit Learn Website –

2. Scikit Cook Book –

### Other Resources

1. Python Machine Learning by Packt Publishing-
2. Chris Albon –
3. Jason Brownlee, Machine learning mastery –
2. Udacity Online Machine Learning Course –

## Not Python but ….

Andrew Ng Machine Learning –
(amazon links are affiliate links – you won’t pay any more for the product, but I will receive a small percentage of the purchase price)

Prateek Joshi, Founder of Pluto AI, talks about how we can use deep learning to analyze time-series data generated by the ecosystem of internet-connected sensors, collectively referred to as Internet of Things (IoT).

Prateek Joshi is an Artificial Intelligence researcher, a published author of 4 books, and a TEDx speaker. He is the founder of Pluto AI, a Silicon Valley startup building an analytics platform for water management based on Deep Learning. His work in this field has led to patents, tech demos, and research papers at IEEE conferences. He actively blogs at on topics such as Artificial Intelligence, Python programming, and abstract mathematics. The blog has received more than 1 million page views from 200+ countries.

Prateek has been an invited speaker at technology and entrepreneurship conferences. He has also been featured as a guest author in prominent tech magazines. He is an avid coder and has won many hackathons. He graduated from University of Southern California with a Masters degree specializing in Artificial Intelligence. He was elected to become a member of the Honors Society for academic excellence and an ambassador for the school of engineering. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website at

There are plenty of cool robots and robotics gadgets or toy robots that kids will absolutely love. Robot toys are best tech toys that will keep your kids entertained and maybe even spark their interest in robotics.We have created a shortlist of the top five robot toys for kids. 5 Best Robots for Kids : Games, Fun and Learning

Chip Robot Dog Toys – ,

Robot – ,

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#robot #kidrobot #robotforkids #robotics #toyrobot #robottoy #bestkidrobot #

Miko is your child’s new companion — a brain with loads of heart. You will be amazed with how much Miko can do — be it chatting away about the facts of the world or adapting and responding to your child’s needs. Miko has a wide pool of knowledge and an even wider pool of fun.

Meet Miko:

About the company: Miko is a consumer electronics company founded on the pillars of Robotics, Artificial Intelligence, and Internet of Things. Miko was founded in October, 2014 by three IIT Bombay post-graduates and is today, driven by a twenty-member team of roboticists, academicians, and neuropsychologists. The core team of roboticists has been together since 2009, making some of the world’s most widely acclaimed robots. Among these is India’s most capable autonomous underwater vehicle, which ranked among the best internationally and performed tasks for the Indian Navy.

Client: Miko
Agency : Xtrathin Design Pvt. Ltd.
Creative Director : Karthikeyan Ramachandran
Xtrathin Team : Ashwini Gaikar

Director : Anwar Sayed
Producer : Imran Khatri
DOP : Bijitesh De
Creative Producer : Azhar Sayed
Production Designer : Yatin Powle
Location: Mr. Ketan Gandhi

Line Producers : Hussain Syed
Assistant Directors : Neepa Mitra, Noopur Nautiyal
Assistant DOP : Rahul Madhukar
Wardrobe Stylist : Tarun Nathani
Make-Up & Hair : J.D Jagtap
Production Manager : Leena Verma
Cast : Hearty Singh, Keeya Khanna, Anuj Khurana

Editor : Tathagatha Basu
Colourist : Nicola Gasparri
Online Artiste : Chetan Ail
Post Studio : After
Composer : Aditya Narayan
Lyricist : Chhavi Sodhani
Singer : Nayantara Bhatkal
Sound Engineer : Rishabh Agarwal
Sound Studio : Splice
Post Producer : Niyaz Khan

Miko needs a smartphone to communicate with the user. Please refer to the website to understand the product.

14:00-14:20 Jes Frellsen, University of Cambridge Bayesian generalised ensemble Markov chain Monte Carlo
14:20-14:40 Adam Scibior, University of Cambridge Probabilistic programming with effect systems
14:40-15:00 Andy Gordon, Microsoft Research Fabular: Regression Formulas as Probabilistic Programming
15:00-15:20 John Hong, University of Cambridge Comparing Matrix Factorization algorithms on a level playing field

Stanford Professor Richard Socher gives a TEDX talk on the present and future of Deep Learning and AI. Implement Deep Learning:

For the full episode Where AI is today and where it’s going. | Richard Socher | TEDxSanFrancisco click here:

For more videos like this:

This Deep Learning tutorial is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We’ll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we’ll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.

Below topics are explained in this Deep Learning tutorial:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow

To learn more about Deep Learning, subscribe to our YouTube channel:

Watch more videos on Deep Learning:

#DeepLearningTutorial #DeepLearning #DeepLearningAndNeuralNetworks #WhatIsDeepLearning#DeepLearningCourse #Simplilearn

Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you’ll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial Neural Networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial Neural Networks.
Advancements in Deep Learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in Deep Learning models, learn to operate TensorFlow to manage Neural Networks and interpret the results. According to, the median salary for engineers with Deep Learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand Neural Networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional Neural Networks, recurrent Neural Networks, training deep networks and high-level interfaces
4. Build Deep Learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial Neural Networks
6. Troubleshoot and improve Deep Learning models
7. Build your own Deep Learning project
8. Differentiate between machine learning, Deep Learning and artificial intelligence

There is booming demand for skilled Deep Learning engineers across a wide range of industries, making this Deep Learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this Deep Learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in Deep Learning

Learn more at:

For more information about Simplilearn’s courses, visit:
– Facebook:
– Twitter:
– LinkedIn:
– Website:

Get the Android app:
Get the iOS app:

Explore India’s most trending technology program and learn how the inclusion of Machine Learning & Artificial Intelligence in your organization can accelerate your career or business growth.
Amity Online’s Post Graduate Diploma in Machine Learning & Artificial Intelligence (PGD-ML&AI)
11 Months | Online + Campus Learning | Live Projects & Case Studies | Career Assistance
Know more at

Data, analytics and machine learning are the foundation for AI (artificial intelligence). The challenge with data is the variety across locations (cloud, on-prem, private cloud), types (structured, unstructured), and platforms (operational database, data warehouse, hadoop, fast data platforms, etc). Once we deal with our data management, we are able to move on to analytics, which lets us extract insight from our data. Predictive analytics leads us to machine learning. Once we develop enough machine learning models, we are beginning to reach AI.

IBM’s solution to managing big data is the Hybrid Data Management Platform. Check it out:

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New advances in machine learning are helping organizations optimize operations, maximize output, and automate manual processes. However, the scalability of such solutions across hundreds or thousands of unique assets is extremely difficult and requires teams of data scientists and technicians to constantly build, test, and deploy machine learning models.
What if you could automatically create customized machine learning models? In this session, we will discuss how Darwin™, SparkCognition’s automated model building platform, solves these problems for Apergy by automating machine learning model development. We will also offer a window into how Darwin™ empowers upstream oil and gas, as well as other industries, to improve business operations with the assistance of Google’s powerful Cloud Platform.


Event schedule →

Watch more Machine Learning & AI sessions here →
Next ‘18 All Sessions playlist →

Subscribe to the Google Cloud channel! →

( ** AI & Deep Learning with Tensorflow Training: ** )
This Edureka video on “Deep Learning Frameworks” ( provides you an insight into the top 8 Deep Learning frameworks you should consider learning

00:38 Chainer
01:40 CNTK
03:12 Caffe
04:46 MXNet
05:55 Deeplearning4j
07:42 Keras
09:21 PyTorch
10:45 TensorFlow
12:10 Conclusion

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future:

Check out our Deep Learning blog series:
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#PyTorch #TensorFlow #DeepLearning #Python

Got a question on the topic?
Please share it in the comment section below and our experts will answer it for you.

About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

– – – – – – – – – – – – – –

Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

– – – – – – – – – – – – – –

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – –

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

For more information, please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Come and find out the latest development of Android on-device machine learning, including Android platform, ML Kit, and TensorFlow Lite.

Watch more #io19 here:
Android & Play at Google I/O 2019 Playlist →
Google I/O 2019 All Sessions Playlist →
Learn more on the I/O Website →

Subscribe to the Android Developers Channel →
Get started at →

Speaker(s): Matej Pfajfar‎, Hoi Lam, Dong Chen, Laurence Moroney


Organizers: Jason (Jinquan) Dai Location: Room 151 A-C & G Time: 0900-1200 (Half Day — Morning) Description: Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows. Schedule: 0900 Motivation 0910 Overview 0930 Analytics Zoo for Spark and BigDL 1000 Morning Break 1030 Distributed Training and Inference 1100 Advanced Applications 1130 Real-World Applications 1150 Q&A

AI and deep learning will be front and center at GTC 2017 across key industries like healthcare and financial services. Register now:

Rising AI startups will discuss key technologies and discoveries. Innovative researchers will speak to critical breakthroughs accelerated with GPU-based deep learning. The NVIDIA Deep Learning Institute will offer hands-on technical training on the latest open-source frameworks and GPU-accelerated deep learning platforms.


Human in the loop Machine learning and AI for the people

Paco Nathan is a unicorn. It’s a cliche, but gets the point across for someone who is equally versed in discussing AI with White House officials and Microsoft product managers, working on big data pipelines and organizing and part-taking in conferences such as Strata in his role as Director, Learning Group with O’Reilly Media.

Nathan has a mix of diverse background, hands-on involvement and broad vision that enables him to engage in all of those, having been active in AI, Data Science and Software Engineering for decades. The trigger for our discussion was his Human in the Loop (HITL) framework for machine learning (ML), presented in Strata EU.

Human in the loop

HITL is a mix and match approach that may help make ML both more efficient and approchable. Nathan calls HITL a design pattern, and it combines technical approaches as well as management aspects.

HITL combines two common ML variants, supervised and unsupervised learning. In supervised learning, curated (labeled) datasets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data. In unsupervised learning, the idea is that running lots of data through an algorithm will reveal some sort of structure.

The less common ML variant that HITL builds on is called semi-supervised, and an important special case of that is known as “active learning.” The idea is to take an ensemble of ML models, and let them “vote” on how to label each case of input data. When the models agree, their consensus gets used, typically as an automated approach.

When the models disagree or lack confidence, decision is delegated to human experts who handle the difficult edge cases. Choices made by experts are fed back to the system to iterate on training the ML models.

Nathan says active learning works well when you have have lots of inexpensive, unlabeled data — an abundance of data, where the cost of labeling itself is a major expense. This is a very common scenario for most organizations outside of the Big Tech circle, which is what makes it interesting.

But technology alone is not enough. What could be a realistic way to bring ML, AI, and automation to mid-market businesses?

AI for the people
In Nathan’s experience, most executives are struggling to grasp what the technology could do for them and identify suitable use cases. Especially for mid-market businesses, AI may seem like a far cry. But Nathan thinks they should start as soon as possible, and not look to outsource, for a number of reasons:

We are at a point where competition is heating up, and AI is key. Companies are happy to share code, but not data. The competition is going to be about data, who has the best data to use. If you’re still struggling to move data from one silo to another, it means you’re behind at least 2 or 3 years.

Better allocate resources now, because in 5 years there will already be the haves and have nots. The way most mid-market businesses get on board is by seeing, and sharing experiences with, early adopters in their industry. This gets them going, and they build confidence.

Getting your data management right is table stakes – you can’t talk about AI without this. Some people think they can just leapfrog to AI. I don’t think there will be a SaaS model for AI that does much beyond trivialize consumer use cases. “Alexa, book me a flight” is easy, but what about “Alexa, I want to learn about Kubernetes”? It will fall apart.

This on-demand webinar covers the various ways in which artificial intelligence (AI) and machine learning (ML) are coming to dominate the cyber security landscape.

This webinar provides you with an understanding of how the various types of machine learning techniques are being applied to cyber security and how those techniques are being tailored to solve particular problems in cyber security. It also covers why using multiple artificial intelligence or machine learning-based solutions enhances a defense-in-depth approach to security and how the fundamentals of cyber defense and offense are changing due to the greater adoption of these solutions.

Talk 1: Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
This talk will reflect on the challenges faced with scaling Uber’s Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing our hardware. We will provide a behind-the-scenes look at the current data technology landscape at Uber, including various open-source technologies (e.g. Hadoop, Spark, Hive, Presto, Kafka, Avro) as well as open-sourced in-house-built solutions such as Hudi, Marmaray, etc. We’ll dive into the technical aspects of how our ingestion platform was re-architected to bring in 10+ trillion events/day, with 100+ TB new data/day, at minute-level latency, how our storage platform was scaled to reliably store 100+ PB of data in the data lake, and our processing platform was designed to efficiently serve millions of queries and jobs/day while processing 1+ PB per day. You’ll leave the talk with greater insight into how data truly powers each and every Uber experience and will be inspired to re-envision your own data platform to be more extensible and scalable.

Speaker : Reza Shiftehfar (Uber)
Reza Shiftehfar currently leads Uber’s Hadoop Platform team. His team helps build and grow Uber’s reliable and scalable Big Data platform that serves petabytes of data utilizing technologies such as Apache Hadoop, Apache Hive, Apache Kafka, Apache Spark, and Presto. Reza is one of the founding engineers of Uber’s data team and helped scale Uber’s data platform from a few terabytes to over 100 petabytes while reducing data latency from 24+ hours to minutes. Reza holds a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign.

Talk2 : Michelangelo PyML – Uber’s Platform for Rapid Python ML Model Development

Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, hundreds of data scientists, engineers, product managers, and researchers work on ML solutions across the company. This talk will cover a brief history of Uber’s machine learning platform – Michelangelo. We will take a closer look into a model life-cycle of prototyping, validation, and productionization and the importance of frictionless experience at each stage of this process. And finally, we will focus on PyML – a new extension of Michelangelo that enables faster Python ML model development and seamless integration with Uber’s production infrastructure.

Speaker: Stepan Bedratiuk (Uber)
Stepan Bedratiuk is a lead engineer on Michelangelo’s PyML team. His work focused on scaling model deployment pipelines and model serving services. Prior to ML platform team, Stepan worked on Uber’s data platform team and helped to unify and scale the data access layer. Stepan holds B.S. and M.S. in Applied Mathematics from the Taras Shevchenko National University of Kyiv, Ukraine.

Presented at the Matroid Scaled Machine Learning Conference 2018 | #scaledmlconf

So we’ve talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.

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Neste episódio, recomendamos a palestra Better Medicine Through Machine Learning, em que Suchi Saria apresenta um caso prático de uso do machine learning na saúde!

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François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

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0:00 – Introduction
1:14 – Self-improving AGI
7:51 – What is intelligence?
15:23 – Science progress
26:57 – Fear of existential threats of AI
28:11 – Surprised by deep learning
30:38 – Keras and TensorFlow 2.0
42:28 – Software engineering on a large team
46:23 – Future of TensorFlow and Keras
47:53 – Current limits of deep learning
58:05 – Program synthesis
1:00:36 – Data and hand-crafting of architectures
1:08:37 – Concerns about short-term threats in AI
1:24:21 – Concerns about long-term existential threats from AI
1:29:11 – Feeling about creating AGI
1:33:49 – Does human-level intelligence need a body?
1:34:19 – Good test for intelligence
1:50:30 – AI winter

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“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.

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WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture.

Machine Learning: Living in the Age of AI | A WIRED Film

Artificial intelligence is being used to do many things from diagnosing cancer, stopping the deforestation of endangered rainforests, helping farmers in India with crop insurance, it help you find the Fyre Fest Documentary on Netflix (or Hulu), or it can even be used to help you save money on your energy bill.

But how could something so helpful be racist?

Become an Inevitable/Human:

PyData London 2018

Machine learning and data science applications can be unintentionally biased if care is not taken to evaluate their effect on different sub-populations. However, by using a “fair” approach, machine decision making can potentially be less biased than human decision makers.

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

The Vienna Deep Learning Meetup and the Centre for Informatics and Society of TU Wien jointly organized an evening of discussion on the topic of Ethics and Bias in AI. As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals.

Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance – all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms.

Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society’s divides, such as the digital divide or income inequality?

This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields:


– Prof. Moshe Vardi: “Deep Learning and the Crisis of Trust in Computing”
– Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI”

Panelists: Ethics and Bias in AI

– Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University
– Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien
– Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna
– Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna
– Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG

Moderator: Markus Mooslechner, Terra Mater Factual Studios

The evening will be complemented by networking & discussions over snacks and drinks.

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