Se non sto attento in classe una macchina mi ruberà il lavoro: esempi inaspettati di machine learning
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Presentazione sul Machine Learning e lo studio. Esempi di Machine Learning non intuitivi, tenuta in occasione di Young! Orienta il tuo Futuro 2017. Google TensorFlow, IBM Watson, Autodesk ed altri esempi di ML.
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Se non sto attento in classe una macchina mi ruberà il lavoro: esempi inaspettati di machine learning
- 1. SE NON STO ATTENTO IN CLASSE UNA MACCHINA MI RUBERÀ IL LAVORO ANDREA VACCARELLA YOUNG 2017 19/10/2017 Esempi inaspettati di Machine Learning
- 2. Giochi e Intelligenze Artificiali Cos’è il Machine Learning Cosa “vedono“ le Deep Convolutional Networks Esempi (si spera inaspettati) di Machine Learning
- 9. MACHINE LEARNING Computer Algorithms that have the ability to learn without being explicitly programmed Email Filtering Program Real Emails Spam Emails
- 10. MACHINE LEARNING Computer Algorithms that have the ability to learn without being explicitly programmed MACHINE LEARNING ALGORITHM Real Emails Spam Emails Real Emails Spam Emails
- 11. MACHINE LEARNING “I know I should do something with it, I’m not sure what” Detect objects or products in user’s living room photo Recommend products based on purchase history and search requests Understand product/brand/company sentiment on social media platforms Capture customer sentiment during customer service calls Extract text data from receipt images with printed content Opzimize inventory levels across locations based off user demand, region, weather and past purchase history Determine what type of receipt it is (automated entry, manual entry) … Google Cloud NEXT / 10 March 2017
- 13. REAL ESTATE Training 2 new Employee
- 14. REAL ESTATE Training 2 new Employee
- 15. REAL ESTATE Training 2 new Employee 1
- 16. Gradient Descent
- 17. REAL ESTATE Training 2 new Employee
- 18. REAL ESTATE
- 19. REAL ESTATE
- 20. REAL ESTATE
- 21. REAL ESTATE
- 22. Gradient Descent
- 23. Types of ML Algorithms Supervised learning Pattern Recognition (Classification) Regression (Approximation) Unsupervised learning Reinforcement learning
- 34. Prediction
- 35. Prediction
- 36. http://ijmlc.org/papers/50-A243.pdf Machine Learning based Method for Plagiarism Detection
- 37. (1) "Nearly all new malware differs less than 2% from previous malware” Eli David, CTO Deep Instinct In 2014, Kaspersky Lab was detecting 325,000 new malicious files every day (1)
- 40. Natural Language Generation Ogni Trimestre 10x https://automatedinsights.com/wordsmith
- 41. CIFAR10 data set 6000 pictures of birds Caltech-UCSD Birds-200–2011 data set 12000 pictures of birds
- 43. MACHINE LEARNING Computer Algorithms that have the ability to learn without being explicitly programmed MACHINE LEARNING ALGORITHM Birds Not Birds Birds Not Birds
- 44. MACHINE LEARNING 1 pass: 75.0% accuracy 10 passes: 91.7% accuracy 50 passes: 95.5% accuracy MACHINE LEARNING ALGORITHM Birds Not Birds Birds Not Birds
- 52. Google Pictures Show me all pictures of [Andrew] doing [Lessons] Video Vision API Smart Reply in Inbox by Gmail 10% of all responses by mobile Google Translate Google Neural Machine Translation system (GNMT),
- 56. Energy Consumption Google Data Centers
- 81. Other ML Applications Health Public Transportation Prices Forecasting Trading Design
Editor's Notes
- https://www.linkedin.com/in/mauriceconti/
- 1952 Tic Tac Toe (Tris) (fun fact: Google tic tac toe giochi)
- 1997 (42 years later) Deep Blue (IBM) beats Kasparov
- 2011 IBM Watson beats in Jeopardy (two Champs)
- 2016 Alpha Go beats Lee Sedol (Go World Champion)
- 2016, Fox chiede a IBM di far creare a Watson un trailer per il film Morgan, un Horror che parla di Intelligenze Artificiali
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
- Supervised (we wish to infer the mapping implied by the data). We are given a set of example pairs and the aim is to find a function in the allowed class of functions that matches the examples. Unsupervised: some data is give and the cost function to be minimized, that can be any function of the data and the networks’ output. Estimation problems, Clustering, Statistical distrib, Compression & Filtering Reinf. Learning (Stochastic control): data x is not usually given, but generated by an agent’s interactions with the environment. At each point in time, the agent performs an action and the evnv. Generates an observation and an instantaneous cost. Accordin to some unknow dynamics. The aim is to discover a policy for selecting actions that minimizes some measure of a long-term cost.
- As a human, you instantly recognize the hierarchy in this picture: The ground is covered in grass and concrete There is a child The child is sitting on a bouncy horse The bouncy horse is on top of the grass
- MIT 3 algoritmi basati sul machine learning per verificare chi copia in università (chi copia codice sorgente nei progetti e compiti di informatica)
- 82 milioni di emendamenti (85) al DDL Boschi sostituzioni di termini e punteggiatura che, pur mantenendo la struttura base di un emendamento, lo rendono diverso dagli altri 85 milioni. Creano milioni di varianti, piccole o grandi, partendo da un numero contenuto di modelli base, di regole grammaticali, di sinonimi.
- 82 milioni di emendamenti (85) al DDL Boschi Sostituzioni di termini e punteggiatura che, pur mantenendo la struttura base di un emendamento, lo rendono diverso dagli altri 85 milioni. Creano milioni di varianti, piccole o grandi, partendo da un numero contenuto di modelli base, di regole grammaticali, di sinonimi.
- Associated Press “scrive” dieci volte di più gli articoli. Ogni trimestre le aziende americane pubblicano I report finanziari. AP scrive articoli automaticamente grazie a software come ai NLG = natural language generation 90% degli articoli online, entro 10 anni.
- HOG: Histogram of Oriented Gradients
- Smart Reply by INBOX (gmail) https://static.googleusercontent.com/media/research.google.com/it//pubs/archive/45189.pdf https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
- Deep Mind (Deep Dreams) https://deepmind.com/
- http://www.telegraph.co.uk/technology/google/11684183/Googles-artificial-intelligence-interprets-photos-as-animals-with-creepy-results.html https://deepdreamgenerator.com https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB
- Smart Reply by INBOX (gmail) https://static.googleusercontent.com/media/research.google.com/it//pubs/archive/45189.pdf https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
- Eastern Bluebird = Sialia Sialis (Simbolo dello Stato di NY) Uno dei pochi Turdidi del continente americano, simbolo diOttimismo e Felicità) https://aiexperiments.withgoogle.com/bird-sounds
- https://aiexperiments.withgoogle.com/bird-sounds
- https://aiexperiments.withgoogle.com/bird-sounds
- Their initial research looked at dance hits from 1985 through 2014, but I asked one of the study authors, Dorien Herremans, to run 2015 top Billboard dance singles through their prediction tool. The algorithm predicted a 65 percent or higher probability of a hit for all of the top 10, and over 70 percent probability for 6 out of 10 songs. Not bad.
- Offer real-time food personalization to your customers Flavor Print sceglie ricette in base ai gusti culinari (basati su altre ricette predette) http://vivanda.com/ IBM Chef Watson
- http://mx3d.com/
- Supervised (we wish to infer the mapping implied by the data). We are given a set of example pairs and the aim is to find a function in the allowed class of functions that matches the examples. Unsupervised: some data is give and the cost function to be minimized, that can be any function of the data and the networks’ output. Estimation problems, Clustering, Statistical distrib, Compression & Filtering Reinf. Learning (Stochastic control): data x is not usually given, but generated by an agent’s interactions with the environment. At each point in time, the agent performs an action and the evnv. Generates an observation and an instantaneous cost. Accordin to some unknow dynamics. The aim is to discover a policy for selecting actions that minimizes some measure of a long-term cost.
- https://deepdreamgenerator.com/ https://artofericwayne.com/2015/07/08/google-deep-dream-getting-too-good/