Lex Fridman (DeepTweets: Generating Fake Tweets with Neural Networks Trained on Individual Twitter Accounts)

I fine-tuned GPT-2 neural net on people's tweets to create #AI versions of them. Surprisingly realistic and at times profound. Here's a real tweet about tunnels from Elon Musk rewritten by AI versions of Just Bieber, Kanye West, and Katy Perry.
Details: https://lnkd.in/eaWkeqg

✴️ @AI_Python_EN

We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


✴️ @AI_Python_EN

Simulation of fan emotions during a basketball game:
https://flowingdata.com/2019/06/07/simulation-of-fan-emotions-during-a-basketball-game/

✴️ @AI_Python_EN

Free website design workshop in Boston
https://www.facebook.com/events/2424424517786811/?ti=icl

✴️ @AI_Python_EN

If you've read job descriptions in data lately you are probably confused. Are you a data scientist, machine learning engineer, or research scientist? Instead of title matching, try asking yourself these questions:

1. Can you use statistics to answer questions about a situation that is new to you? Meaning, is your comfort with stats solid enough that you can bring it to bear appropriately depending on scenario?

2. Can you explain why a particular model performs well in a scenario, rather than just noting it does well? Meaning, do you understand the inner workings of models to tune and make sense of why they do what they do?

3. If someone mentions time and space complexity to you, does it make sense? In a big data world, thinking carefully about load of a particular algorithm is extremely important. This matters particularly for MLE and science positions.

4. Can you build something new? Maybe there isn't a perfect algorithm for what you want. Maybe the package in R doesn't exist. Can you make it happen if you need to?

5. Do you know what it means to put something into production? Do you have examples of how you've succeeded or failed with this?

These questions are not all encompassing, but they point to some of the key skillsets you'll need.

#datascience #analytics #data

✴️ @AI_Python_EN

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

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

#BigData #statistics #NLP

✴️ @AI_Python_EN

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/

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

Announcing fellowships for open-source developers

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

✴️ @AI_Python_EN

The Deep Learning Toolset — An Overview

http://bit.ly/2R2eUGh

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

✴️ @AI_Python_EN

Google at ICML 2019

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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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/

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN