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Machines and the Magic of Fast Learning - Strata Keynote
DATA CORPUS
DATA CORPUS
ACTORS
Applications
DATA CORPUS
ACTORS
Applications
OPERATORS
Data Science
DATA CORPUS
ACTORS
Applications
OPERATORS
Data Science
ACTORS + OPERATORS
Real-Time
Machine Learning
ACTORS
Applications
OPERATORS
Data Science
ACTORS + OPERATORS
Real-Time
Machine Learning
FEEDBACK LOOP
PRECISION
TUNING
DISCOVERY
DIGITAL DEFENDERS
OF CHILDREN
63% 5,000% 42%
Of underage sex
trafficking victims
are advertised
online
Increase in child
sexual abuse
imagery since
2007
Of victims met
perpetrators
online
DIGITAL DEFENSE IS A BIG AND FAST DATA CHALLENGE
100k
escort ads posted
every day in this
country
THORN SIFTS THROUGH MASSIVE AMOUNTS OF DATA
Machines and the Magic of Fast Learning - Strata Keynote
949:0.026740,961:0.011758,962:0.01 ...
4,996 POINTS
 CLASSIFICATION
 DE-DUPLICATION
 MATCHING
949:0.026740,961:0.011758,962:0.01 ... 12:0.005868,16:0.004575,49:0.002
193,52:0.009880,67:0.034832,72:0.
030992,77:0.012170,108:0.012382,
120:0.012916,125:0.005741,137:0.
015322,143:0.020548,157:0.03040
7,220:0.061202,228:0.026140,232:
0.040047,236:0.023434,242:0.0266
05,252:0.007459,264:0.022012,269
:0.016690,270:0.057932,282:0.011
975,292:0.028855,298:0.006937,31
7:0.005120,333:0.028555,338:0.03
9100,348:0.017727,358:0.055682,3
76:0.006209,386:0.028764,413:0.0
17220,417:0.018298,422:0.004943,
433:0.031690,443:0.011401,451:0.
016825,452:0.000745,458:0.01076
9,460:0.044923,471:0.039836,479:
0.008343,482:0.009446,484:0.0194
43,497:0.061289,502:0.015072,508
:0.029485,530:0.013753,532:0.007
153,543:0.044873,551:0.010136,55
5:0.012994,560:0.008001,563:0.03
8678,579:0.015128,610:0.007795,6
27:0.019286,634:0.021111,641:0.0
07065,642:0.007089,659:0.058285,
672:0.018122,674:0.024745,703:0.
012181,704:0.010520,705:0.01980
5,726:0.004800,734:0.020477,751:
0.005154,753:0.023470,763:0.0026
51,783:0.033653,786:0.010800,824
:0.017787,846:0.017696,850:0.040
618,853:0.006627,880:0.020177,88
7:0.040712,901:0.004130,902:0.01
2970,926:0.011321,949:0.026740,9
61:0.
067235,1551:0.002643,1569:0.030
303,1592:0.000982,1595:0.021256
,1606:0.029090,1619:0.030494,16
28:0.007809,1630:0.012805,1632:
0.074610,1658:0.046989,1663:0.0
11392,1683:0.025755,1689:0.0005
51,1690:0.019549,1707:0.002039,
1718:0.000027,1753:0.003988,176
1:0.016639,1787:0.004682,1788:0.
036989,1793:0.010178,1799:0.032
016,1820:0.001699,1862:0.026061
,1865:0.033358,1888:0.015540,18
93:0.015230,1913:0.029057,1917:
0.017459,1930:0.012725,1932:0.0
20591,1939:0.036401,1940:0.0014
55,1941:0.029777,1948:0.028731,
1950:0.015147,1966:0.008172,197
6:0.004087,2009:0.005937,2011:0.
026532,2016:0.018998,2023:0.003
567,2024:0.033425,2043:0.024501
,2060:0.035672,2077:0.026460,20
92:0.006496,2099:0.042786,2110:
0.031982,2117:0.026819,2118:0.0
02956,2127:0.002132,2171:0.0066
93,2174:0.006085,2193:0.038693,
2207:0.080437,2210:0.036449,221
5:0.027432,2216:0.000524,2228:0.
022542,2232:0.023016,2245:0.035
095,2258:0.008138,2291:0.014170
,2297:0.024569,2301:0.019651,23
10:0.037032,2333:0.010741,2337:
0.010183,2353:0.056520,2382:0.0
05700,2406:0.012346,2409:0.0459
50,2411:0.005816,2415:0.001264,
2424:0.046932,2439:0.010018,.
033653,786:0.010800,824:0.0177
87,846:0.017696,850:0.040618,8
53:0.006627,880:0.020177,887:0.
040712,901:0.004130,902:0.0129
70,926:0.011321,949:0.026740,9
61:0.011758,962:0.01,.003080,96
6:0.025391,969:0.008317,980:0.0
24180,999:0.025001,1003:0.0099
95,1018:0.026575,1024:0.014152
,1030:0.014807,1032:0.001685,1
037:0.059401,1041:0.008451,108
3:0.004498,1086:0.042539,1100:
0.019762,1107:0.003233,1111:0.
010055,1118:0.004970,1120:0.01
3391,1137:0.033611,1143:0.0041
84,1151:0.011988,1156:0.018991
,1164:0.005059,1165:0.009926,1
171:0.041736,1181:0.009872,118
7:0.001813,1188:0.010391,1193:
0.020764,1194:0.002471,1222:0.
006705,1238:0.009757,1246:0.06
7453,1259:0.042624,1264:0.0175
58,1265:0.019401,1269:0.015384
,1299:0.013593,1310:0.002139,1
359:0.006642,1371:0.034178,137
4:0.016396,1384:0.022928,1404:
0.017169,1408:0.009406,1418:0.
073914,1420:0.011940,1421:0.00
5672,1430:0.003974,1433:0.0027
76,1463:0.031537,1481:0.000885
,1485:0.039955,1492:0.023929,1
494:0.048229,1497:0.053608,150
8:0.003894,1518:0.011840,1524:
0.011318,1528:0.
949:0.026740,961:0.011758,962:0.01 ... 12:0.005868,16:0.004575,49:0.002
193,52:0.009880,67:0.034832,72:0.
030992,77:0.012170,108:0.012382,
120:0.012916,125:0.005741,137:0.
015322,143:0.020548,157:0.03040
7,220:0.061202,228:0.026140,232:
0.040047,236:0.023434,242:0.0266
05,252:0.007459,264:0.022012,269
:0.016690,270:0.057932,282:0.011
975,292:0.028855,298:0.006937,31
7:0.005120,333:0.028555,338:0.03
9100,348:0.017727,358:0.055682,3
76:0.006209,386:0.028764,413:0.0
17220,417:0.018298,422:0.004943,
433:0.031690,443:0.011401,451:0.
016825,452:0.000745,458:0.01076
9,460:0.044923,471:0.039836,479:
0.008343,482:0.009446,484:0.0194
43,497:0.061289,502:0.015072,508
:0.029485,530:0.013753,532:0.007
153,543:0.044873,551:0.010136,55
5:0.012994,560:0.008001,563:0.03
8678,579:0.015128,610:0.007795,6
27:0.019286,634:0.021111,641:0.0
07065,642:0.007089,659:0.058285,
672:0.018122,674:0.024745,703:0.
012181,704:0.010520,705:0.01980
5,726:0.004800,734:0.020477,751:
0.005154,753:0.023470,763:0.0026
51,783:0.033653,786:0.010800,824
:0.017787,846:0.017696,850:0.040
618,853:0.006627,880:0.020177,88
7:0.040712,901:0.004130,902:0.01
2970,926:0.011321,949:0.026740,9
61:0.
067235,1551:0.002643,1569:0.030
303,1592:0.000982,1595:0.021256
,1606:0.029090,1619:0.030494,16
28:0.007809,1630:0.012805,1632:
0.074610,1658:0.046989,1663:0.0
11392,1683:0.025755,1689:0.0005
51,1690:0.019549,1707:0.002039,
1718:0.000027,1753:0.003988,176
1:0.016639,1787:0.004682,1788:0.
036989,1793:0.010178,1799:0.032
016,1820:0.001699,1862:0.026061
,1865:0.033358,1888:0.015540,18
93:0.015230,1913:0.029057,1917:
0.017459,1930:0.012725,1932:0.0
20591,1939:0.036401,1940:0.0014
55,1941:0.029777,1948:0.028731,
1950:0.015147,1966:0.008172,197
6:0.004087,2009:0.005937,2011:0.
026532,2016:0.018998,2023:0.003
567,2024:0.033425,2043:0.024501
,2060:0.035672,2077:0.026460,20
92:0.006496,2099:0.042786,2110:
0.031982,2117:0.026819,2118:0.0
02956,2127:0.002132,2171:0.0066
93,2174:0.006085,2193:0.038693,
2207:0.080437,2210:0.036449,221
5:0.027432,2216:0.000524,2228:0.
022542,2232:0.023016,2245:0.035
095,2258:0.008138,2291:0.014170
,2297:0.024569,2301:0.019651,23
10:0.037032,2333:0.010741,2337:
0.010183,2353:0.056520,2382:0.0
05700,2406:0.012346,2409:0.0459
50,2411:0.005816,2415:0.001264,
2424:0.046932,2439:0.010018,.
033653,786:0.010800,824:0.0177
87,846:0.017696,850:0.040618,8
53:0.006627,880:0.020177,887:0.
040712,901:0.004130,902:0.0129
70,926:0.011321,949:0.026740,9
61:0.011758,962:0.01,.003080,96
6:0.025391,969:0.008317,980:0.0
24180,999:0.025001,1003:0.0099
95,1018:0.026575,1024:0.014152
,1030:0.014807,1032:0.001685,1
037:0.059401,1041:0.008451,108
3:0.004498,1086:0.042539,1100:
0.019762,1107:0.003233,1111:0.
010055,1118:0.004970,1120:0.01
3391,1137:0.033611,1143:0.0041
84,1151:0.011988,1156:0.018991
,1164:0.005059,1165:0.009926,1
171:0.041736,1181:0.009872,118
7:0.001813,1188:0.010391,1193:
0.020764,1194:0.002471,1222:0.
006705,1238:0.009757,1246:0.06
7453,1259:0.042624,1264:0.0175
58,1265:0.019401,1269:0.015384
,1299:0.013593,1310:0.002139,1
359:0.006642,1371:0.034178,137
4:0.016396,1384:0.022928,1404:
0.017169,1408:0.009406,1418:0.
073914,1420:0.011940,1421:0.00
5672,1430:0.003974,1433:0.0027
76,1463:0.031537,1481:0.000885
,1485:0.039955,1492:0.023929,1
494:0.048229,1497:0.053608,150
8:0.003894,1518:0.011840,1524:
0.011318,1528:0.
100s of millions of
images to match
select id, description,
vector_dot(images.val,
UNHEX("XXXXXXXX")) as distance
from images
having distance > CONSTANT;
• SIMD Euclidean
Measurement
Between Points
• 1,000x match
improvement time
THE MISSING DATABASE SYNTAX
2000+children saved in 2016
REAL-TIME HAS REAL WORLD IMPACT
21
Putting Fast Learning Practices To Work
MAPPING SOCIAL IMAGERY HANDWRITING
Application
Model
Evaluation
and
Regression
TensorFlow
Reference
Image Store
New
Image
Stream
MACHINE LEARNING FOR IMAGE RECOGNITION
Real-Time
Application
New
Image
Stream
Real-Time
Logistic
Regression
Reference
Image Store
High Speed Vector Algebra
TensorFlow
BUILDING FAST LEARNING FOR IMAGE RECOGNITION
REAL-TIME ANALYTICS
AT UBER-SCALE
11:00am
LL20 B
BUILDING REAL-TIME
DASHBOARDS
1:50pm
210 D/H
LATER TODAY
THANK YOU

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Machines and the Magic of Fast Learning - Strata Keynote

Editor's Notes

  • #8: With Real-time ML, time becomes the smarter transaction for action
  • #10: With real-time ML, opportunities are presented as actions due to precision and accuracy of identification
  • #11: Accelerate Victim Identification Disrupt the Dark Web Deter Predators
  • #20: Did we index this? Eric