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

1. MLMeetup
AIforbusiness:Capirel'opportunità
gianluca@ai-academy.com

2. Simone,GianlucaeNicolò
Entrepreneur and Statistiscian, Engineer, Self-driving car Engineer

3. Aboutme:
Engineering -> Entrepreneurship -> Data Science
gianluca@ai-academy.com

4. "L'hypedaAI"

6. "L'hypedaAI"

7. "L'hypedaAI"

8. PerchéAI?
Perchéora?

9. Unpo'distoria:Dartmouth,1956

10. Whatisintelligence?
“The true sign of intelligence is not knowledge but
imagination”
Albert Einstein

11. AI:generalevsristretta

12. PrimaapplicazionediAIristretta:
English-Russiantranslator

14. 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"

15. Primo"AIWinter"(1966~1980s):
Nopotenzadicalcolo.
Nodati.
Nometodi.

16. Secondo"AIspring":
Isistemiesperti

17. ProblemiExpertSystems:
Costosidarealizzare
Moltosettorializzati
Scarsacapacitàdigeneralizzazione

18. Un"nuovo"approccio:ilMachineLearning

19. 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»

20. 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

21. DeepBluevsGarryKasparov,
1997

22. DeepLearning:unnuovo(ultimo?)"AIspring"

23. DeepLearning:unnuovo(ultimo?)"AIspring"

24. DeepLearning:unnuovo(ultimo?)"AIspring"

25. 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

26. AlphagovsLeeSedol,2016
"Master of Go Board Game Is Walloped by Google
Computer Program."
The New York Times, 2016

27. DeepLearning&Reasoning
Level=Superhuman

28. DeepLearning&Computervision
Level=Superhuman

30. DeepLearning&VoiceRecognition
Level=Sameashuman

32. DeepLearning&SpeechSynthesis
Level=Closetohuman

33. Perchèora?

34. 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.

35. Strumentiopensource
Scikit
Tensorflow
Caffe
Keras
Theano

36. 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)

37. Datidisponibili

39. Prezzostoragedati

40. Potenzacomputazionaledisponibile

41. PrezzoCPU

42. "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

43. Comerispondonogli
investitori?

45. Comerispondonolecorporate?

47. 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).

48. 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%
...

49. 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

50. Èilmomentodipassareda"MLasaproduct"a
"MLasafeature".

51. Q&A