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AI for business: Capire l'opportunità

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Gianluca Mauro (AI-Academy) - Momento storico per investire nel Machine Learning

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AI for business: Capire l'opportunità

  1. 1. MLMeetup AIforbusiness:Capirel'opportunità gianluca@ai-academy.com
  2. 2. Simone,GianlucaeNicolò Entrepreneur and Statistiscian, Engineer, Self-driving car Engineer
  3. 3. Aboutme: Engineering -> Entrepreneurship -> Data Science gianluca@ai-academy.com
  4. 4. "L'hypedaAI"
  5. 5. "L'hypedaAI"
  6. 6. "L'hypedaAI"
  7. 7. PerchéAI? Perchéora?
  8. 8. Unpo'distoria:Dartmouth,1956
  9. 9. Whatisintelligence? “The true sign of intelligence is not knowledge but imagination” Albert Einstein
  10. 10. AI:generalevsristretta
  11. 11. PrimaapplicazionediAIristretta: English-Russiantranslator
  12. 12. Risultati: English: "The spirit is strong, but the flesh is weak" After English - Russian > Russian - English: "The whiskey is strong, but the meat is rotten"
  13. 13. Primo"AIWinter"(1966~1980s): Nopotenzadicalcolo. Nodati. Nometodi.
  14. 14. Secondo"AIspring": Isistemiesperti
  15. 15. ProblemiExpertSystems: Costosidarealizzare Moltosettorializzati Scarsacapacitàdigeneralizzazione
  16. 16. Un"nuovo"approccio:ilMachineLearning
  17. 17. WhatisMachineLearning? «A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E»
  18. 18. Funziona? "If one could devise a successful chess machine, one would seem to have penetrated to the core of human intellectual endeavor" Allen Newell, 1958
  19. 19. DeepBluevsGarryKasparov, 1997
  20. 20. DeepLearning:unnuovo(ultimo?)"AIspring"
  21. 21. DeepLearning:unnuovo(ultimo?)"AIspring"
  22. 22. DeepLearning:unnuovo(ultimo?)"AIspring"
  23. 23. DeepLearning:unnuovo(ultimo?)"AIspring" "It may be a hundred years before a computer beats humans at ’Go’, maybe even longer" The New York Times, 1997
  24. 24. AlphagovsLeeSedol,2016 "Master of Go Board Game Is Walloped by Google Computer Program." The New York Times, 2016
  25. 25. DeepLearning&Reasoning Level=Superhuman
  26. 26. DeepLearning&Computervision Level=Superhuman
  27. 27. DeepLearning&VoiceRecognition Level=Sameashuman
  28. 28. DeepLearning&SpeechSynthesis Level=Closetohuman
  29. 29. Perchèora?
  30. 30. Algoritmi Rumelhart et Al., Learning representations by back-propagating errors. 1986 Corinna Cortes and Vladimir Vapnik. Support-vector networks. 1995 50+ nuovi paper al giorno su arxiv.
  31. 31. Strumentiopensource Scikit Tensorflow Caffe Keras Theano
  32. 32. Strumentiinpratica:dante-bot def build_graph(batch_size, seq_len, vocab_size, rnn_size): x = tf.placeholder(tf.int32,[batch_size, seq_len]) y = tf.placeholder(tf.int32,[batch_size, seq_len]) cell = rnn_cell.GRUCell(rnn_size) init = cell.zero_state(batch_size, tf.float32) embeddings = tf.get_variable('embedding_matrix',[vocab_size, rnn_size]) rnn_inputs = tf.nn.embedding_lookup(embeddings, x) rnn_outputs, final_state = tf.nn.dynamic_rnn(cell, rnn_inputs, initial_state = init) with tf.variable_scope('softmax') as scope: W = tf.get_variable('W',[rnn_size, vocab_size]) b = tf.get_variable('b',[vocab_size], initializer=tf.constant_initializer(0.0)) rnn_outputs = tf.reshape(rnn_outputs, [-1, rnn_size]) y_ = tf.reshape(y, [-1]) logits = tf.matmul(rnn_outputs, W) + b predictions = tf.nn.softmax(logits) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y_) loss = tf.reduce_mean(cross_entropy) train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
  33. 33. Datidisponibili
  34. 34. Prezzostoragedati
  35. 35. Potenzacomputazionaledisponibile
  36. 36. PrezzoCPU
  37. 37. "Many of the papers, data sets, and software tools related to deep learning have been open sourced. [...] Software tools like Theano and TensorFlow, combined with cloud data centers for training, and inexpensive GPUs for deployment, allow small teams of engineers to build state-of-the-art AI systems." Chris Dixon, A16Z partner
  38. 38. Comerispondonogli investitori?
  39. 39. Comerispondonolecorporate?
  40. 40. Social: Pinterest deep-learning-powered reccommender: +30% repins Ecommerce: The Clymb ha avuto +175% revenue/1000 promo email, -72% churn (HBR) Customer service: 85% interazioni senza interazione umana nel 2020 (Gartner).
  41. 41. Marketing & sales: 76% delle aziende che usano ML hanno aumentato le proprie revenue (Accenture) Fintech: Banche che usano ML per promuovere prodotti ottengono +10% sales e -20% churn (Accenture). Ingegneria: Sight ha ridotto downtime macchine 50% e aumentato performance del 25% ...
  42. 42. Conclusioni "AI is the new electricity. Just as 100 years ago electricity transformed industry after industry, AI will now do the same." Andrew Ng, Chief Scientist at Baidu
  43. 43. Èilmomentodipassareda"MLasaproduct"a "MLasafeature".
  44. 44. Q&A

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