Machine learning

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Improved Brainpower

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Superlearning Frequency

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

Machine Learning

Deep Learning - New Age for Study, Improve Concentration and Brain Power, Instrumental Study Music for Exam Study, to Focus on Learning

Deep Learning

Deep Learning – Instrumental Relaxing Music for Reading, Piano & Flute Sounds to Increase Brain Power, New Age Concentration Music

# 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

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning.

Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.

Managing Risk in Machine Learning Models

Deep Learning

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