Uncertainty in big data analytics: survey, opportunities, and challenges

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0206-3

#BigData #statistics #NLP

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Comparing Google's AI Speech Recognition To Human Captioning For Television News
https://www.forbes.com/sites/kalevleetaru/2019/06/09/comparing-googles-ai-speech-recognition-to-human-captioning-for-television-news/amp/

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Why 2019 is the year of Knowledge Graphs?
✔️#Knowledgegraph became a centerpiece of #Accentur and #Microsoft ’s toolkits.

✔️Knowledge graph lessons from Google, #Facebook, #eBay, #IBM.

✔️Graph algorithms and analytics by #Neo4j, #Nvidia and #AWS.

More about the why?
https://lnkd.in/g87BTrH

💥Great resources to get some hands-on experience:

✅ Implementing Knowledge Graphs in #Enterprises:
https://lnkd.in/ghisXMw

✅ How #Google’s Knowledge Graph Updates Itself:
https://lnkd.in/gayCpPw

✅ Extracting knowledge from knowledge graphs using #Facebook #Pytorch BigGraph.
https://lnkd.in/gHgj6AH

✅ The Data Fabric for #MachineLearning : #DeepLearning on Graphs. By Favio Vazquez
https://lnkd.in/gsCnTTM

✅ Why Knowledge Graphs Are Foundational to #ArtificialIntelligence
https://lnkd.in/g5WVARe

Absolutely essential for data scientists to upskill themselves, Knowledge Graphs are coming...
#datascience #AI

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Announcing fellowships for open-source developers

https://medium.com/palantir/fellowships-for-open-source-developers-3892e6b75ee1

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The Deep Learning Toolset — An Overview

http://bit.ly/2R2eUGh

#artificialintelligence, #bigdata, #businessanalytics, #datascience, #deeplearning, #ds #datascience, #iot, #machinelearning, #neuralnetworks #AI

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Google at ICML 2019

http://ai.googleblog.com/2019/06/google-at-icml-2019.html

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Top Artificial Intelligence Influencers To Follow in 2019

1. Geoffrey Hinton:
Geoffrey Everest Hinton is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.
https://twitter.com/geoffreyhinton

2. Yann LeCun:
Yann LeCun is a French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics, and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Facebook.
https://twitter.com/ylecun

3. Andrew Ng:
Andrew Ng is VP & Chief Scientist of Baidu; Co-Chairman and Co-Founder of Coursera; and an Adjunct Professor at Stanford University.
https://twitter.com/AndrewYNg?ref_src=twsrc%5Etfw&ref_url=http%3A%2F%2Fwww.andrewng.org%2Fabout%2F

4. Yoshua Bengio:
Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning. He is a professor at the Department of Computer Science at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).
https://in.linkedin.com/in/yoshuabengio

5. Ian J. Goodfellow:
Ian J. Goodfellowis a researcher working in machine learning, currently employed at Apple Inc. as its director of machine learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain. He has made several contributions to the field of deep learning.
https://twitter.com/goodfellow_ian

6. Fabio Moioli :
Fabio Moioli is Director of Consulting & Services at Microsoft .
16+ years executive experience in several industries and countries. Previously Vice President and Head of BU Telecom & Media at Capgemini, Associate at McKinsey & Co, Account and Delivery Manager at Ericsson.
https://twitter.com/fabiomoioli?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

7. Spiros Margaris:
Spiros Margaris venture capitalist and senior advisor (wefox Group, SparkLabs Group, Mediastalker, The Yield Growth Corp. and at F10 Fintech Incubator), is the founder of Margaris Ventures.He was ranked the international no. 1 FinTech, Blockchain, and Artificial Intelligence (AI) influencer by Onalytica. He published an AI white paper, “Machine learning in financial services: Changing the rules of the game,” for the enterprise software vendor SAP.
https://twitter.com/SpirosMargaris

8. Fei-Fei Li:
Fei-Fei Li, is a Professor of Computer Science at Stanford University. She is the director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab.
https://mobile.twitter.com/drfeifei

9. Jürgen Schmidhuber:
Jürgen Schmidhuber is CoFounder at NNAISENSE. His lab’s Deep Learning Neural Networks (since 1991) such as LSTM have revolutionised machine learning, and are now available to billions of users e.g., for greatly improved speech recognition on over 2 billion Android phones, greatly improved machine translation through Google (since 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Apple’s Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon’s Alexa, and numerous other applications.
https://mobile.twitter.com/nnaisense

10. Martin Ford:
Martin Ford is a futurist and the author of two books: The New York Times Bestselling Rise of the Robots: Technology and the Threat of a Jobless Future and The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, as well as the founder of a Silicon Valley-based software development firm.
https://mobile.twitter.com/MFordFuture

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How does a neural net represent language? See the visualizations and geometry in this PAIR team paper
https://arxiv.org/abs/1906.02715 and
blog post https://pair-code.github.io/interpretability/bert-tree/

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Consider attending the ICME workshops at Stanford. They also give certificates and include discounts for students

https://icme.stanford.edu/events/icme-summer-workshops-2019

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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

https://arxiv.org/abs/1906.04585

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If you’re starting out in the field of Computer Vision, find below an exhaustive list of topics one must know.

📘 A. Beginner level

Mathematics :

🔸 Linear Algebra
🔸Singular Value Decomposition
🔸 Introductory level Pattern Recognition
🔸 Principal Component Analysis
🔸 Kalman filtering
🔸 Fourier Transform
🔸 Wavelets

Image Processing:

🔸 Online Course offered by Duke University on Coursera
🔸 Digital Image Processing by Gonzalez and Woods

B. Advanced level

🔸 Linear Discriminant AnalysisProbability, Bayes rule, Maximum Likelihood, MAP
🔸 Mixtures and Expectation-Maximization Algorithm
🔸 Introductory level Statistical Learning
🔸 Support Vector Machines
🔸 Genetic Algorithms
🔸 Hidden Markov Models
🔸 Bayesian Networks

To gain practical knowledge about how things work especially the algorithms, start learning about OpenCV from Computer Vision perspective:

📘 Learning OpenCV: Computer Vision with the OpenCV Library
🔸 Tombone’s Computer Vision Blog

#ComputerVision
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The Department of Computing is a leading department of Computer Science among UK Universities. It has consistently been awarded the highest research rating (5*) in Research Assessment Exercises (RAE) and is rated 10th in the world by the Times Higher Education International Outlook. This post will be based at the South Kensington Campus.

An exciting opportunity has arisen for a research assistant to work under the direction of Prof Michael Bronstein.

The main aim of the project is to develop next-generation machine learning methods for graph- and manifold structured data, and apply them to a set of challenging problems from the domains of computer vision, pattern recognition, graphics, and medicine.

Within the project, the Research Associate will be responsible for the development of effective and efficient machine learning algorithms for deep learning on graph, focusing on generative models such as VAE or GAN. The applicant is expected to publish his/her works in top conferences (CVPR, ICCV, ECCV, ICML, and alike) and journal papers (TPAMI, IJCV, TAC, TIP, and other high-impact journals).

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range 34,397 – 37,486 per annum.

For applications queries please contact Jamie Perrins at j.perrins@imperial.ac.uk

For instructions on how to apply, please refer to: http://bit.ly/2F4vG2q

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Functional Adversarial Attacks

Article: https://arxiv.org/abs/1906.00001

25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/

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