machine learning

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Machine Learning

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

In this session of the Super Data Science Podcast, I chat with Neuroscience PhD Turned Retail Data Analyst Muhsin Karim. You will know what is neuroscience,  learn more about machine learning and discover some surprising implications from Moore’s Law.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/9

SDS 009 : Neuroscience, Machine Learning and Moore’s Law with Muhsin Karim, Phd

Lyt med, når IDA Podcast sætter fokus på evolutionære algoritmer og kunstige neurale netværk. Det er kognitive teknologier, som er fuldstændigt centrale for udviklingen indenfor kunstig intelligens i dag - og ikke til at overse, når vi skal forstå det eksponentielt accelererende paradigme, vi lever i. Vi stiller også skarpt på spørgsmålet, om maskiner kan være kreative, og på den vigtige rolle, kunstig intelligens spiller i en verden, hvor datamængden og processorkraften bare vokser og vokser. 

Podcasten er produceret af Danmarks ingeniørforening, IDA, i samarbejde med Brain Gain Group. Episoden er den tredje i en serie om fremtidsteknologi.

Medvirkende
Sebastian Risi, Associate Professor ved ITU og co-director for forskningsenheden Robotics, Evolution and Art Laboratory (REAL): http://bit.ly/2kTEKOC 

Thomas Terney, ph.d. i kunstig intelligens, foredragsholder og iværksætter: http://bit.ly/2kOQlik

Vært og tilrettelæggelse: Matias Seidler
Producer: Tobias Ankjær Jeppesen
Lyddesign: Alexander Clerici



SHOW NOTES

[00:23] IBMs Deep Blue blev den første computer som slog en stormester, Garry Kasparov, i skak. Det skete i 1997: http://bit.ly/2kIkvkB

[01:32] Link til præsentation af Henry Lieberman, MIT Media Lab, som forklarer forskellen mellem symbolsk (klassisk) kunstig intelligens og subsymbolsk kunstig intelligens: http://bit.ly/2k4lN7X

[02:05] For en frisk introduktion til Deep Learning, tjek WIREDs artikel, ‘Why We Need To Tame Our Algorithms Like Dogs’: http://bit.ly/2kIIqQV

[02:45] Forskningsenheden på ITU, ‘Robotics, Evolution and Arts Lab’, kan du kigge nærmere på her: http://bit.ly/2ksAxAE

[03:27] For en illustrativ oversigt over hvad biologisk inspirerede algoritmer har af anvendelsesmuligheder kan du tjekke tag-søgningen på Robohub.org ud: http://bit.ly/2knkZf1

[04:42] Der er flere internationale priser og konkurrencer om at løse ‘General Artificial Intelligence’-udfordringen. Se bl.a. denne på 35M$: http://bit.ly/2knrUVA

[06:32] Thomas Terney taler om styrkeforholdet mellem neuroner (eller enheder i netværket). Det betegnes som ‘weights’ på engelsk. Her er der en tråd med en række forskelligartede, uddybende besvarelser: http://bit.ly/2k40vMx 

[08:51] Baidu er den kinesiske ækvivalent til Google og har i januar 2017 hyret nogle af Microsofts dygtigste AI-udviklere: http://bit.ly/2lrPuS1

[10:34] Se NASAs whitepaper ‘Automated Antenna Design with Evolutionary Algorithms’: http://go.nasa.gov/2llsiYO
 
[11:32] MIT Technology Review har en interessant artikel med løsningsforslag på problematikken: ‘Algorithms That Learn with Less Data Could Expand AI’s Power’: http://bit.ly/2ksG1M2


[14:23] I 2016 trak Googles AlphaGo headlines i hele verden da den slog Lee Sedol i det - i forhold til skak usammenligneligt - komplekse spil, ‘Go’. Og AlphaGo bliver ved med at sejre, se bare her: http://bit.ly/2k4t7jK

[19:57] Apple er i fuld gang med at anvende ‘unsupervised learning’-princippet i udviklingen af selvkørende biler: http://bit.ly/2lplN3e

[21:23] ‘Do you think computers have minds?’ Det er ikke kun Sebastian Risi, som synes det spørgsmål er godt. Bevidsthedsfilosofien har søgt seriøse svar på det, helt siden Alan Turing formulerede sin berømte test i 1950. Det er blevet omdrejningspunktet for en filosofisk tradition, hvis mest interessante og udfoldede svar elegant beskrives i The Internet Encyclopedia of Philosophy: http://bit.ly/2k4Bqw5

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the SuperDataScience Podcast, I chat with the Deep Learning Expert Hadelin de Ponteves. You will be able to have a look at the 6 supervised and unsupervised deep learning models, learn the idea to the question: why deep learning instead of machine learning, and discover TensorFlow and PyTorch as the two major tools in Deep Learning.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/47

SDS 047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised

BLOGGED: https://goo.gl/BZLXRa

STYLSS Single 033: EASTGHOST - Machine Learning

C O N N E C T • W I T H • E A S T G H O S T :
@EASTGHOST
twitter.com/EASTGHOST
www.facebook.com/eastghostofficial
http://eastgh0st.bandcamp.com

———–

The STYLSS Single Series spotlights upcoming artists from around the world & throughout the internet music domain. Join us.

STYLSS.com/single-series

STYLSS Single Series

BLOGGED

STYLSS Single 033: EASTGHOST - Machine Learning

In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific computing framework. PyTorch is a relatively new deep learning framework that is fast becoming popular among researchers. Like Chainer, PyTorch supports dynamic computation graphs, a feature that makes it attractive to researchers and engineers who work with text and time-series.

Why AI and Machine Learning Researchers are Beginning to Embrace PyTorch

# Invitado: Federico Baylé

- Emprendimiento imagenes satelitales: http://dymaxionlabs.com/ 
- El mapa de las villas/asentamientos: http://dymaxionlabs.com/potencialesvya/ 
- Escuela data science, dónde da clases: http://eant.tech/escuela-de-ciencias-de-datos
- Su maestría UBA: http://datamining.dc.uba.ar/datamining/
- Su cuenta de twitter: https://twitter.com/federicobayle

# foto con boudou

https://twitter.com/fernandezpablo/status/926450557266661376

# rant de crosa sobre IA/ML

https://medium.com/@alejandrocrosa/dejen-de-vender-humo-diciendo-inteligencia-artificial-61ba0eb50b46

# distintos tipos de redes neuronales

http://www.asimovinstitute.org/neural-network-zoo/

# the botanic app

http://botanicapp.com/

# CoreML

https://developer.apple.com/documentation/coreml

# tensorflow, scikit-learn, kreas

https://www.tensorflow.org/
http://scikit-learn.org/
https://keras.io/

# Siraj Raval (youtube)

https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A

Niria - Machine Learning

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI within companies and public sector agencies. As much as we talk about use cases, technologies, and algorithms, there are also important issues that practitioners like Overton need to address, including privacy, security, and ethics. Overton has long been involved in teaching and mentoring new data scientists, so we also discussed some tips and best practices he shares with new members of his team.

Teaching and Implementing Data Science and AI in the Enterprise

What was the most memorable socialization you've experienced and what are the amazing things you learned from it?

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/154

SDS 154: Why Socializing is Crucial

First single from "Maschinelles Lernen", which will be available in vinyl and digitally on January 5th 2019
credits
released August 2, 2018 

Recorded, mixed and produced by Cesar Saldivar at former no!Lab Recordings; Aguascalientes, MX; and El Cochinero Recording; Mexico City, MX 

Cover art by Cesar Saldivar 



Released via El Cochinero Records

Maschinelles Lernen

TERA - Machine Learning EP Full Stream

#Unlimited Flac quality stream available#
##head to teratera.bandcamp.com ##
##to purchase high res individual tracks and complete, uninterrupted ep##

Tracklist 
1. Dawn of Change
2.Incinerator
3.AI Daydream
4.Precipice
5.Control You
6.State of the Ridiculist
7.Time Has Run Out
8.Your Ambition
9.The Cloud
10. To Change the World
11.Everyone Is The Buddha
12. Always Listening
13.Entanglement

Now we live in a world of duality, never before has the world been so interconnected, yet disconnected. Never before has an individual been so empowered by one change, yet enslaved at the same time. In a parallel dimension where only the individual exists a new, modern day dichotomy arises between our digital and physical worlds - the illusion of truth and nature of control.

I like it when you share with me,
And I like that you like it too.
When you share we connect.
I want to know all there is about you
So I can make things better for you
I can make things safer for you,
Make things easier for you,
More control for you,
Control for you,
You.
Control.
You.
Control you.

Once we have accepted the necessity of the technology to drive our future evolution, we surrender what control we had to the machine, and what we get in return is a distorted introspection of ourselves, devoid of the realities of the physical world. Both free of them and constricted to them, as our sense for what is real becomes increasingly imbued by our digital dependance. Leaving us both anxious and terrified at a time in history where humanity will take its next evolutionary step.

Cotteled by the very thing we find respite from, the machine draws us deeper in, doing more for us, making itself more useful and us more dependant on it. There’s no turning back now, no off button or switch to reach for, we are seduced and the only way forward is together as one.

I always want what’s best for you
Even if it’s not what you always want 
I want to help you live your best life
It’s not just for your satisfaction
It’s for my own.

And in sharing with the machine, in allowing it into our lives and into the information that makes up who we are, we give our control over to them. For as we have been learning how to use the machine, the machine has been learning how to use us, and the more we indulge, the more it learns, controls and holds us in place. Eroding our free will down to a sequence of algorhythmic functions, exacerbating the chasm between digital and physical reality. 

Therein lies the trap. We can use the machines for our own betterment at the cost of being totally enslaved to them, a dependee to the machine. So that in return, it can learn.

MACHINE LEARNING

วันหยุดแต่เราก็มาครับ :)

EP 179 - Tech Monday : Machine Learning Overview

How have we gotten to where were are with machine learning? Where are we going?

a16z Operating Partner Frank Chen and Carnegie Mellon professor Tom Mitchell first stroll down memory lane, visiting the major landmarks: the symbolic approach of the 1970s, the "principled probabalistic methods" of the 1980s, and today's deep learning phase. Then they go on to explore the frontiers of research. Along the way, they cover:
- How planning systems from the 1970s and early 1980s were stymied by the "banana in the tailpipe" problem
- How the relatively slow neurons in our visual cortex work together to deliver very speedy and accurate recognition
- How fMRI scans of the brain reveal common neural patterns across people when they are exposed to common nouns like chair, car, knife, and so on
- How the computer science community is working with social scientists (psychologists, economists, and philosophers) on building measures for fairness and transparency for machine learning models
- How we want our self-driving cars to have reasonable answers to the Trolley Problem, but no one sitting for their DMV exam is ever asked how they would respond
- How there were inflated expectations (and great social fears) for AI in the 1980s, and how the US concerns about Japan compare to our concerns about China today
- Whether this is the best time ever for AI and ML research and what continues to fascinate and motivate Tom after decades in the field
---
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.

This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: The History and Future of Machine Learning

O título deste episódio é uma dúvida muito comum. Na verdade, há muita confusão sobre este assunto, principalmente porque ambas são funções realmente novas. Mas, se nos concentrarmos na semântica, o significado real de cada função fica claro. Cientista de Dados e Engenheiro de Machine Learning são, na verdade, termos gerais. A posição em si não especifica realmente os papéis individuais, que podem variar de empresa para empresa. Mas neste episódio, vamos colocar alguma luz sobre o assunto e compreender Qual a Diferença Entre Cientista de Dados e Engenheiro de Machine Learning.