Datasets:
File size: 104,097 Bytes
a3be5d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560 |
WEBVTT
00:00.000 --> 00:03.080
The following is a conversation with Rajat Manga.
00:03.080 --> 00:04.960
He's an engineering director at Google,
00:04.960 --> 00:06.960
leading the TensorFlow team.
00:06.960 --> 00:09.160
TensorFlow is an open source library
00:09.160 --> 00:11.520
at the center of much of the work going on in the world
00:11.520 --> 00:14.040
in deep learning, both the cutting edge research
00:14.040 --> 00:17.720
and the large scale application of learning based approaches.
00:17.720 --> 00:19.480
But it's quickly becoming much more
00:19.480 --> 00:20.960
than a software library.
00:20.960 --> 00:23.760
It's now an ecosystem of tools for the deployment
00:23.760 --> 00:25.720
of machine learning in the cloud, on the phone,
00:25.720 --> 00:29.840
in the browser, on both generic and specialized hardware.
00:29.840 --> 00:31.920
TPU, GPU, and so on.
00:31.920 --> 00:34.200
Plus, there's a big emphasis on growing
00:34.200 --> 00:36.600
a passionate community of developers.
00:36.600 --> 00:39.760
Rajat, Jeff Dean, and a large team of engineers at Google
00:39.760 --> 00:42.720
Brain are working to define the future of machine learning
00:42.720 --> 00:46.200
with TensorFlow 2.0, which is now in alpha.
00:46.200 --> 00:49.120
I think the decision to open source TensorFlow
00:49.120 --> 00:51.720
is a definitive moment in the tech industry.
00:51.720 --> 00:54.360
It showed that open innovation can be successful
00:54.360 --> 00:56.840
and inspire many companies to open source their code,
00:56.840 --> 00:59.640
to publish, and in general engage in the open exchange
00:59.640 --> 01:01.160
of ideas.
01:01.160 --> 01:03.880
This conversation is part of the artificial intelligence
01:03.880 --> 01:05.000
podcast.
01:05.000 --> 01:07.760
If you enjoy it, subscribe on YouTube, iTunes,
01:07.760 --> 01:09.600
or simply connect with me on Twitter
01:09.600 --> 01:12.640
at Lex Friedman, spelled FRID.
01:12.640 --> 01:17.880
And now, here's my conversation with Rajat Manga.
01:17.880 --> 01:22.440
You were involved with Google Brain since its start in 2011
01:22.440 --> 01:24.800
with Jeff Dean.
01:24.800 --> 01:29.160
It started with disbelief, the proprietary machine learning
01:29.160 --> 01:32.760
library, and turned into TensorFlow 2014,
01:32.760 --> 01:35.760
the open source library.
01:35.760 --> 01:39.040
So what were the early days of Google Brain like?
01:39.040 --> 01:41.760
What were the goals, the missions?
01:41.760 --> 01:45.080
How do you even proceed forward once there's
01:45.080 --> 01:47.680
so much possibilities before you?
01:47.680 --> 01:50.520
It was interesting back then when I started,
01:50.520 --> 01:55.320
or when you were even just talking about it.
01:55.320 --> 01:58.800
The idea of deep learning was interesting
01:58.800 --> 02:00.400
and intriguing in some ways.
02:00.400 --> 02:04.840
It hadn't yet taken off, but it held some promise.
02:04.840 --> 02:08.680
It had shown some very promising and early results.
02:08.680 --> 02:11.360
I think the idea where Andrew and Jeff had started
02:11.360 --> 02:16.160
was what if we can take this, what people are doing in research,
02:16.160 --> 02:21.560
and scale it to what Google has in terms of the compute power,
02:21.560 --> 02:25.240
and also put that kind of data together, what does it mean?
02:25.240 --> 02:28.240
And so far, the results had been if you scale the computer,
02:28.240 --> 02:31.480
scale the data, it does better, and would that work?
02:31.480 --> 02:33.360
And so that was the first year or two.
02:33.360 --> 02:35.080
Can we prove that outright?
02:35.080 --> 02:37.440
And with disbelief, when we started the first year,
02:37.440 --> 02:40.760
we got some early wins, which is always great.
02:40.760 --> 02:41.880
What were the wins like?
02:41.880 --> 02:45.240
What was the wins where there are some problems to this?
02:45.240 --> 02:46.560
This is going to be good.
02:46.560 --> 02:49.640
I think the two early wins were one was speech
02:49.640 --> 02:52.200
that we collaborated very closely with the speech research
02:52.200 --> 02:54.760
team, who was also getting interested in this.
02:54.760 --> 02:57.760
And the other one was on images where
02:57.760 --> 03:03.120
the cat paper, as we call it, that was covered by a lot of folks.
03:03.120 --> 03:07.440
And the birth of Google Brain was around neural networks.
03:07.440 --> 03:09.280
So it was deep learning from the very beginning.
03:09.280 --> 03:10.760
That was the whole mission.
03:10.760 --> 03:18.960
So in terms of scale, what was the dream
03:18.960 --> 03:21.040
of what this could become?
03:21.040 --> 03:24.280
Were there echoes of this open source TensorFlow community
03:24.280 --> 03:26.240
that might be brought in?
03:26.240 --> 03:28.640
Was there a sense of TPUs?
03:28.640 --> 03:31.120
Was there a sense of machine learning
03:31.120 --> 03:33.680
is now going to be at the core of the entire company?
03:33.680 --> 03:36.040
Is going to grow into that direction?
03:36.040 --> 03:38.320
Yeah, I think so that was interesting.
03:38.320 --> 03:41.320
And if I think back to 2012 or 2011,
03:41.320 --> 03:45.240
and first was can we scale it in the year or so,
03:45.240 --> 03:47.520
we had started scaling it to hundreds and thousands
03:47.520 --> 03:48.080
of machines.
03:48.080 --> 03:51.040
In fact, we had some runs even going to 10,000 machines.
03:51.040 --> 03:53.840
And all of those shows great promise.
03:53.840 --> 03:56.760
In terms of machine learning at Google,
03:56.760 --> 03:58.760
the good thing was Google's been doing machine learning
03:58.760 --> 04:00.200
for a long time.
04:00.200 --> 04:02.120
Deep learning was new.
04:02.120 --> 04:05.000
But as we scale this up, we showed that, yes, that was
04:05.000 --> 04:07.840
possible, and it was going to impact lots of things.
04:07.840 --> 04:11.160
Like, we started seeing real products wanting to use this.
04:11.160 --> 04:12.720
Again, speech was the first.
04:12.720 --> 04:15.120
There were image things that photos came out of
04:15.120 --> 04:17.360
in many other products as well.
04:17.360 --> 04:20.120
So that was exciting.
04:20.120 --> 04:23.120
As we went into with that a couple of years,
04:23.120 --> 04:25.760
externally also academia started to,
04:25.760 --> 04:27.760
there was lots of push on, OK, deep learning's
04:27.760 --> 04:30.520
interesting, we should be doing more, and so on.
04:30.520 --> 04:35.560
And so by 2014, we were looking at, OK, this is a big thing.
04:35.560 --> 04:36.680
It's going to grow.
04:36.680 --> 04:39.400
And not just internally, externally as well.
04:39.400 --> 04:42.240
Yes, maybe Google's ahead of where everybody is,
04:42.240 --> 04:43.600
but there's a lot to do.
04:43.600 --> 04:46.640
So a lot of this start to make sense and come together.
04:46.640 --> 04:51.080
So the decision to open source, I was just chatting with Chris
04:51.080 --> 04:53.360
Flattner about this, the decision to go open source
04:53.360 --> 04:57.040
with TensorFlow, I would say for me personally,
04:57.040 --> 05:00.000
seems to be one of the big seminal moments in all
05:00.000 --> 05:01.680
of software engineering ever.
05:01.680 --> 05:04.600
I think that when a large company like Google
05:04.600 --> 05:08.680
decides to take a large project that many lawyers might argue
05:08.680 --> 05:12.840
has a lot of IP, just decide to go open source with it.
05:12.840 --> 05:15.200
And in so doing, lead the entire world in saying,
05:15.200 --> 05:19.280
you know what, open innovation is a pretty powerful thing.
05:19.280 --> 05:22.320
And it's OK to do.
05:22.320 --> 05:26.400
That was, I mean, that's an incredible moment in time.
05:26.400 --> 05:29.280
So do you remember those discussions happening?
05:29.280 --> 05:31.320
Are there open source should be happening?
05:31.320 --> 05:32.600
What was that like?
05:32.600 --> 05:36.840
I would say, I think, so the initial idea came from Jeff,
05:36.840 --> 05:39.320
who was a big proponent of this.
05:39.320 --> 05:42.400
I think it came off of two big things.
05:42.400 --> 05:46.280
One was research wise, we were a research group.
05:46.280 --> 05:50.240
We were putting all our research out there if you wanted to.
05:50.240 --> 05:51.680
We were building on other's research,
05:51.680 --> 05:54.920
and we wanted to push the state of the art forward.
05:54.920 --> 05:56.800
And part of that was to share the research.
05:56.800 --> 05:58.920
That's how I think deep learning and machine learning
05:58.920 --> 06:01.360
has really grown so fast.
06:01.360 --> 06:04.280
So the next step was, OK, now word software
06:04.280 --> 06:05.280
help for that.
06:05.280 --> 06:09.720
And it seemed like they were existing a few libraries
06:09.720 --> 06:12.160
out there, Tiano being one, Torch being another,
06:12.160 --> 06:13.960
and a few others.
06:13.960 --> 06:15.400
But they were all done by academia,
06:15.400 --> 06:19.000
and so the level was significantly different.
06:19.000 --> 06:22.040
The other one was, from a software perspective,
06:22.040 --> 06:27.120
Google had done lots of software that we used internally.
06:27.120 --> 06:29.120
And we published papers.
06:29.120 --> 06:31.680
Often there was an open source project
06:31.680 --> 06:33.600
that came out of that, that somebody else
06:33.600 --> 06:35.440
picked up that paper and implemented,
06:35.440 --> 06:38.280
and they were very successful.
06:38.280 --> 06:40.920
Back then, it was like, OK, there's
06:40.920 --> 06:44.200
Hadoop, which has come off of tech that we've built.
06:44.200 --> 06:46.240
We know that tech we've built is way better
06:46.240 --> 06:47.880
for a number of different reasons.
06:47.880 --> 06:51.680
We've invested a lot of effort in that.
06:51.680 --> 06:54.320
And turns out, we have Google Cloud,
06:54.320 --> 06:57.520
and we are now not really providing our tech,
06:57.520 --> 07:00.520
but we are saying, OK, we have Bigtable, which
07:00.520 --> 07:02.080
is the original thing.
07:02.080 --> 07:05.280
We are going to now provide HBase APIs on top of that, which
07:05.280 --> 07:07.480
isn't as good, but that's what everybody's used to.
07:07.480 --> 07:10.960
So there's like, can we make something that is better
07:10.960 --> 07:12.320
and really just provide?
07:12.320 --> 07:14.320
Helps the community in lots of ways,
07:14.320 --> 07:18.320
but it also helps push the right, a good standard forward.
07:18.320 --> 07:19.960
So how does Cloud fit into that?
07:19.960 --> 07:22.680
There's a TensorFlow open source library.
07:22.680 --> 07:25.800
And how does the fact that you can
07:25.800 --> 07:28.240
use so many of the resources that Google provides
07:28.240 --> 07:31.480
and the Cloud fit into that strategy?
07:31.480 --> 07:34.920
So TensorFlow itself is open, and you can use it anywhere.
07:34.920 --> 07:38.360
And we want to make sure that continues to be the case.
07:38.360 --> 07:42.080
On Google Cloud, we do make sure that there's
07:42.080 --> 07:43.800
lots of integrations with everything else,
07:43.800 --> 07:47.280
and we want to make sure that it works really, really well there.
07:47.280 --> 07:50.080
You're leading the TensorFlow effort.
07:50.080 --> 07:52.360
Can you tell me the history and the timeline of TensorFlow
07:52.360 --> 07:55.880
project in terms of major design decisions,
07:55.880 --> 08:01.240
like the open source decision, but really, what to include
08:01.240 --> 08:01.600
and not?
08:01.600 --> 08:03.600
There's this incredible ecosystem that I'd
08:03.600 --> 08:05.680
like to talk about, there's all these parts.
08:05.680 --> 08:12.120
But if you just some sample moments that
08:12.120 --> 08:15.960
defined what TensorFlow eventually became through its,
08:15.960 --> 08:19.400
I don't know if you were allowed to say history when it's just,
08:19.400 --> 08:21.240
but in deep learning, everything moves so fast
08:21.240 --> 08:23.400
in just a few years, it's already history.
08:23.400 --> 08:24.880
Yes, yes.
08:24.880 --> 08:29.760
So looking back, we were building TensorFlow.
08:29.760 --> 08:34.240
I guess we open sourced it in 2015, November 2015.
08:34.240 --> 08:39.800
We started on it in summer of 2014, I guess.
08:39.800 --> 08:42.960
And somewhere like three to six late 2014,
08:42.960 --> 08:45.320
by then we had decided that, OK, there's
08:45.320 --> 08:47.080
a high likelihood we'll open source it.
08:47.080 --> 08:49.560
So we started thinking about that and making sure
08:49.560 --> 08:51.320
that we're heading down that path.
08:53.960 --> 08:56.280
At that point, by that point, we'd
08:56.280 --> 08:59.280
seen a few lots of different use cases at Google.
08:59.280 --> 09:01.200
So there were things like, OK, yes,
09:01.200 --> 09:04.160
you want to run in at large scale in the data center.
09:04.160 --> 09:07.480
Yes, we need to support different kind of hardware.
09:07.480 --> 09:09.400
We had GPUs at that point.
09:09.400 --> 09:11.880
We had our first GPU at that point
09:11.880 --> 09:15.760
or was about to come out roughly around that time.
09:15.760 --> 09:18.640
So the design included those.
09:18.640 --> 09:21.760
We had started to push on mobile.
09:21.760 --> 09:24.880
So we were running models on mobile.
09:24.880 --> 09:28.080
At that point, people were customizing code.
09:28.080 --> 09:30.280
So we wanted to make sure TensorFlow could support that
09:30.280 --> 09:34.120
as well so that that became part of that overall
09:34.120 --> 09:35.200
design.
09:35.200 --> 09:38.040
When you say mobile, you mean like pretty complicated
09:38.040 --> 09:39.960
algorithms of running on the phone?
09:39.960 --> 09:40.480
That's correct.
09:40.480 --> 09:42.680
So when you have a model that you
09:42.680 --> 09:45.200
deploy on the phone and run it there, right?
09:45.200 --> 09:47.800
So already at that time, there was ideas of running machine
09:47.800 --> 09:48.720
learning on the phone.
09:48.720 --> 09:49.240
That's correct.
09:49.240 --> 09:51.360
We already had a couple of products
09:51.360 --> 09:53.240
that were doing that by then.
09:53.240 --> 09:55.480
And in those cases, we had basically
09:55.480 --> 09:59.280
customized handcrafted code or some internal libraries
09:59.280 --> 10:00.080
that we're using.
10:00.080 --> 10:03.280
So I was actually at Google during this time in a parallel,
10:03.280 --> 10:04.440
I guess, universe.
10:04.440 --> 10:09.240
But we were using Theano and CAFE.
10:09.240 --> 10:11.560
Was there some degree to which you were bouncing,
10:11.560 --> 10:15.440
like trying to see what CAFE was offering people,
10:15.440 --> 10:17.920
trying to see what Theano was offering
10:17.920 --> 10:21.320
that you want to make sure you're delivering on whatever that
10:21.320 --> 10:23.680
is, perhaps the Python part of thing.
10:23.680 --> 10:27.440
Maybe did that influence any design decisions?
10:27.440 --> 10:27.880
Totally.
10:27.880 --> 10:30.840
So when we built this belief, and some of that
10:30.840 --> 10:32.920
was in parallel with some of these libraries
10:32.920 --> 10:36.600
coming up, I mean, Theano itself is older.
10:36.600 --> 10:41.080
But we were building this belief focused on our internal thing
10:41.080 --> 10:42.880
because our systems were very different.
10:42.880 --> 10:44.480
By the time we got to this, we looked
10:44.480 --> 10:47.040
at a number of libraries that were out there.
10:47.040 --> 10:49.240
Theano, there were folks in the group
10:49.240 --> 10:52.080
who had experience with Torch, with Lua.
10:52.080 --> 10:54.720
There were folks here who had seen CAFE.
10:54.720 --> 10:58.800
I mean, actually, Yang Cheng was here as well.
10:58.800 --> 11:02.960
There's what other libraries?
11:02.960 --> 11:04.880
I think we looked at a number of things.
11:04.880 --> 11:06.800
Might even have looked at Jane and her back then.
11:06.800 --> 11:09.320
I'm trying to remember if it was there.
11:09.320 --> 11:12.240
In fact, yeah, we did discuss ideas around, OK,
11:12.240 --> 11:15.280
should we have a graph or not?
11:15.280 --> 11:19.280
And they were supporting all these together
11:19.280 --> 11:21.880
was definitely, you know, there were key decisions
11:21.880 --> 11:22.560
that we wanted.
11:22.560 --> 11:28.680
We had seen limitations in our prior disbelief things.
11:28.680 --> 11:31.320
A few of them were just in terms of research
11:31.320 --> 11:32.280
was moving so fast.
11:32.280 --> 11:34.520
We wanted the flexibility.
11:34.520 --> 11:36.280
We want the hardware was changing fast.
11:36.280 --> 11:39.160
We expected to change that so that those probably were two
11:39.160 --> 11:41.400
things.
11:41.400 --> 11:43.320
And yeah, I think the flexibility in terms
11:43.320 --> 11:45.280
of being able to express all kinds of crazy things
11:45.280 --> 11:46.840
was definitely a big one then.
11:46.840 --> 11:48.920
So what the graph decisions, though,
11:48.920 --> 11:53.720
with moving towards TensorFlow 2.0, there's more,
11:53.720 --> 11:56.680
by default, there'll be eager execution.
11:56.680 --> 11:59.160
So sort of hiding the graph a little bit
11:59.160 --> 12:02.120
because it's less intuitive in terms of the way
12:02.120 --> 12:03.520
people develop and so on.
12:03.520 --> 12:06.720
What was that discussion like with in terms of using graphs?
12:06.720 --> 12:09.320
It seemed it's kind of the theano way.
12:09.320 --> 12:11.600
Did it seem the obvious choice?
12:11.600 --> 12:15.720
So I think where it came from was our disbelief,
12:15.720 --> 12:18.560
had a graph like thing as well.
12:18.560 --> 12:19.720
It wasn't a general graph.
12:19.720 --> 12:23.160
It was more like a straight line thing.
12:23.160 --> 12:25.000
More like what you might think of Cafe,
12:25.000 --> 12:28.840
I guess, in that sense.
12:28.840 --> 12:31.080
And we always cared about the production stuff.
12:31.080 --> 12:33.480
Even with disbelief, we were deploying a whole bunch of stuff
12:33.480 --> 12:34.440
in production.
12:34.440 --> 12:37.960
So graph did come from that when we thought of, OK,
12:37.960 --> 12:40.800
should we do that in Python and we experimented with some ideas
12:40.800 --> 12:44.680
where it looked a lot simpler to use,
12:44.680 --> 12:47.880
but not having a graph meant, OK, how do you deploy now?
12:47.880 --> 12:51.080
So that was probably what tilted the balance for us.
12:51.080 --> 12:52.880
And eventually, we ended up with the graph.
12:52.880 --> 12:55.320
And I guess the question there is, did you?
12:55.320 --> 12:59.800
I mean, production seems to be the really good thing to focus on.
12:59.800 --> 13:02.400
But did you even anticipate the other side of it
13:02.400 --> 13:04.560
where there could be, what is it?
13:04.560 --> 13:05.240
What are the numbers?
13:05.240 --> 13:08.920
Something crazy, 41 million downloads?
13:08.920 --> 13:09.400
Yep.
13:12.680 --> 13:16.240
I mean, was that even like a possibility in your mind
13:16.240 --> 13:19.120
that it would be as popular as it became?
13:19.120 --> 13:24.880
So I think we did see a need for this a lot
13:24.880 --> 13:30.000
from the research perspective and early days of deep learning
13:30.000 --> 13:32.280
in some ways.
13:32.280 --> 13:33.040
41 million?
13:33.040 --> 13:37.640
No, I don't think I imagine this number then.
13:37.640 --> 13:42.760
It seemed like there's a potential future where lots more people
13:42.760 --> 13:43.760
would be doing this.
13:43.760 --> 13:45.640
And how do we enable that?
13:45.640 --> 13:49.560
I would say this kind of growth, I probably
13:49.560 --> 13:53.680
started seeing somewhat after the open sourcing where it was
13:53.680 --> 13:56.240
like, OK, deep learning is actually
13:56.240 --> 13:59.200
growing way faster for a lot of different reasons.
13:59.200 --> 14:02.720
And we are in just the right place to push on that
14:02.720 --> 14:06.040
and leverage that and deliver on lots of things
14:06.040 --> 14:07.440
that people want.
14:07.440 --> 14:09.760
So what changed once the open source?
14:09.760 --> 14:13.320
Like how this incredible amount of attention
14:13.320 --> 14:16.120
from a global population of developers,
14:16.120 --> 14:18.200
how did the projects start changing?
14:18.200 --> 14:21.880
I don't even actually remember it during those times.
14:21.880 --> 14:24.560
I know looking now, there's really good documentation.
14:24.560 --> 14:26.560
There's an ecosystem of tools.
14:26.560 --> 14:31.080
There's a YouTube channel now.
14:31.080 --> 14:33.760
It's very community driven.
14:33.760 --> 14:38.800
Back then, I guess 0.1 version.
14:38.800 --> 14:39.760
Is that the version?
14:39.760 --> 14:42.680
I think we called it 0.6 or 5, something like that.
14:42.680 --> 14:43.720
Something like that.
14:43.720 --> 14:47.200
What changed leading into 1.0?
14:47.200 --> 14:48.480
It's interesting.
14:48.480 --> 14:51.640
I think we've gone through a few things there.
14:51.640 --> 14:53.680
When we started out, when we first came out,
14:53.680 --> 14:56.080
people loved the documentation we have.
14:56.080 --> 14:58.800
Because it was just a huge step up from everything else.
14:58.800 --> 15:01.920
Because all of those were academic projects, people
15:01.920 --> 15:04.560
don't think about documentation.
15:04.560 --> 15:08.040
I think what that changed was instead of deep learning
15:08.040 --> 15:12.560
being a research thing, some people who were just developers
15:12.560 --> 15:15.080
could now suddenly take this out and do
15:15.080 --> 15:16.920
some interesting things with it.
15:16.920 --> 15:20.720
Who had no clue what machine learning was before then.
15:20.720 --> 15:22.520
And that, I think, really changed
15:22.520 --> 15:27.880
how things started to scale up in some ways and pushed on it.
15:27.880 --> 15:30.400
Over the next few months, as we looked at,
15:30.400 --> 15:31.960
how do we stabilize things?
15:31.960 --> 15:33.840
As we look at not just researchers,
15:33.840 --> 15:34.880
now we want stability.
15:34.880 --> 15:36.480
People want to deploy things.
15:36.480 --> 15:38.960
That's how we started planning for 1.0.
15:38.960 --> 15:42.240
And there are certain needs for that perspective.
15:42.240 --> 15:45.320
And so, again, documentation comes up,
15:45.320 --> 15:49.480
designs, more kinds of things to put that together.
15:49.480 --> 15:53.120
And so that was exciting to get that to a stage where
15:53.120 --> 15:56.400
more and more enterprises wanted to buy in and really
15:56.400 --> 15:58.720
get behind that.
15:58.720 --> 16:02.640
And I think post 1.0 and with the next few releases,
16:02.640 --> 16:05.240
their enterprise adoption also started to take off.
16:05.240 --> 16:08.000
I would say between the initial release and 1.0,
16:08.000 --> 16:11.000
it was, OK, researchers, of course.
16:11.000 --> 16:13.720
Then a lot of hobbies and early interest,
16:13.720 --> 16:15.920
people excited about this who started to get on board.
16:15.920 --> 16:19.000
And then over the 1.x thing, lots of enterprises.
16:19.000 --> 16:23.760
I imagine anything that's below 1.0
16:23.760 --> 16:27.160
gets pressured to be enterprise problem or something
16:27.160 --> 16:28.000
that's stable.
16:28.000 --> 16:28.800
Exactly.
16:28.800 --> 16:33.360
And do you have a sense now that TensorFlow is stable?
16:33.360 --> 16:35.520
It feels like deep learning, in general,
16:35.520 --> 16:37.800
is extremely dynamic field.
16:37.800 --> 16:39.680
So much is changing.
16:39.680 --> 16:43.400
Do you have a, and TensorFlow has been growing incredibly.
16:43.400 --> 16:46.720
Do you have a sense of stability at the helm of this?
16:46.720 --> 16:48.360
I mean, I know you're in the midst of it.
16:48.360 --> 16:50.360
Yeah.
16:50.360 --> 16:54.000
I think in the midst of it, it's often easy to forget what
16:54.000 --> 16:58.160
an enterprise wants and what some of the people on that side
16:58.160 --> 16:58.760
want.
16:58.760 --> 17:00.360
There are still people running models
17:00.360 --> 17:02.640
that are three years old, four years old.
17:02.640 --> 17:06.000
So inception is still used by tons of people.
17:06.000 --> 17:08.880
Even less than 50 is what, a couple of years old now or more.
17:08.880 --> 17:12.200
But there are tons of people who use that, and they're fine.
17:12.200 --> 17:16.200
They don't need the last couple of bits of performance or quality.
17:16.200 --> 17:19.600
They want some stability in things that just work.
17:19.600 --> 17:22.720
And so there is value in providing that with that kind
17:22.720 --> 17:25.160
of stability and making it really simpler,
17:25.160 --> 17:27.800
because that allows a lot more people to access it.
17:27.800 --> 17:31.640
And then there's the research crowd, which wants, OK,
17:31.640 --> 17:33.680
they want to do these crazy things exactly like you're
17:33.680 --> 17:37.000
saying, not just deep learning in the straight up models
17:37.000 --> 17:38.400
that used to be there.
17:38.400 --> 17:41.920
They want RNNs, and even RNNs are maybe old.
17:41.920 --> 17:45.520
They are transformers now, and now it
17:45.520 --> 17:48.720
needs to combine with RL and GANs and so on.
17:48.720 --> 17:52.160
So there's definitely that area, the boundary that's
17:52.160 --> 17:55.120
shifting and pushing the state of the art.
17:55.120 --> 17:57.120
But I think there's more and more of the past
17:57.120 --> 17:59.680
that's much more stable.
17:59.680 --> 18:02.680
And even stuff that was two, three years old
18:02.680 --> 18:04.920
is very, very usable by lots of people.
18:04.920 --> 18:07.440
So that part makes it a lot easier.
18:07.440 --> 18:09.800
So I imagine maybe you can correct me if I'm wrong.
18:09.800 --> 18:12.440
One of the biggest use cases is essentially
18:12.440 --> 18:15.160
taking something like ResNet 50 and doing
18:15.160 --> 18:18.520
some kind of transfer learning on a very particular problem
18:18.520 --> 18:19.600
that you have.
18:19.600 --> 18:24.480
It's basically probably what majority of the world does.
18:24.480 --> 18:27.040
And you want to make that as easy as possible.
18:27.040 --> 18:30.400
So I would say, for the hobbyist perspective,
18:30.400 --> 18:32.800
that's the most common case.
18:32.800 --> 18:34.800
In fact, the apps on phones and stuff
18:34.800 --> 18:37.680
that you'll see, the early ones, that's the most common case.
18:37.680 --> 18:40.320
I would say there are a couple of reasons for that.
18:40.320 --> 18:44.400
One is that everybody talks about that.
18:44.400 --> 18:46.120
It looks great on slides.
18:46.120 --> 18:48.120
That's a great presentation.
18:48.120 --> 18:50.040
Exactly.
18:50.040 --> 18:53.120
What enterprises want is that is part of it,
18:53.120 --> 18:54.480
but that's not the big thing.
18:54.480 --> 18:56.760
Enterprises really have data that they
18:56.760 --> 18:58.040
want to make predictions on.
18:58.040 --> 19:01.160
This is often what they used to do with the people who
19:01.160 --> 19:03.600
were doing ML was just regression models,
19:03.600 --> 19:06.440
linear regression, logistic regression, linear models,
19:06.440 --> 19:09.800
or maybe gradient booster trees and so on.
19:09.800 --> 19:11.760
Some of them still benefit from deep learning,
19:11.760 --> 19:14.440
but they weren't that that's the bread and butter,
19:14.440 --> 19:16.280
like the structured data and so on.
19:16.280 --> 19:18.200
So depending on the audience you look at,
19:18.200 --> 19:19.520
they're a little bit different.
19:19.520 --> 19:23.320
And they just have, I mean, the best of enterprise
19:23.320 --> 19:26.480
probably just has a very large data set
19:26.480 --> 19:28.640
where deep learning can probably shine.
19:28.640 --> 19:29.360
That's correct.
19:29.360 --> 19:30.320
That's right.
19:30.320 --> 19:32.240
And then I think the other pieces
19:32.240 --> 19:34.560
that they wanted, again, to point out
19:34.560 --> 19:36.400
that the developer summit we put together
19:36.400 --> 19:38.200
is that the whole TensorFlow Extended
19:38.200 --> 19:40.600
piece, which is the entire pipeline,
19:40.600 --> 19:43.560
they care about stability across doing their entire thing.
19:43.560 --> 19:46.200
They want simplicity across the entire thing.
19:46.200 --> 19:47.680
I don't need to just train a model.
19:47.680 --> 19:51.280
I need to do that every day again, over and over again.
19:51.280 --> 19:54.720
I wonder to which degree you have a role in, I don't know.
19:54.720 --> 19:57.040
So I teach a course on deep learning.
19:57.040 --> 20:01.320
I have people like lawyers come up to me and say,
20:01.320 --> 20:04.200
when is machine learning going to enter legal,
20:04.200 --> 20:05.560
the legal realm?
20:05.560 --> 20:11.720
The same thing in all kinds of disciplines, immigration,
20:11.720 --> 20:13.800
insurance.
20:13.800 --> 20:17.400
Often when I see what it boils down to is these companies
20:17.400 --> 20:19.760
are often a little bit old school in the way
20:19.760 --> 20:20.840
they organize the data.
20:20.840 --> 20:22.800
So the data is just not ready yet.
20:22.800 --> 20:24.040
It's not digitized.
20:24.040 --> 20:28.160
Do you also find yourself being in the role of an evangelist
20:28.160 --> 20:33.040
for let's organize your data, folks,
20:33.040 --> 20:35.440
and then you'll get the big benefit of TensorFlow?
20:35.440 --> 20:38.000
Do you have those conversations?
20:38.000 --> 20:45.160
Yeah, I get all kinds of questions there from, OK,
20:45.160 --> 20:49.000
what do I need to make this work, right?
20:49.000 --> 20:50.800
Do we really need deep learning?
20:50.800 --> 20:52.240
I mean, there are all these things.
20:52.240 --> 20:54.000
I already used this linear model.
20:54.000 --> 20:55.160
Why would this help?
20:55.160 --> 20:57.160
I don't have enough data, let's say.
20:57.160 --> 20:59.960
Or I want to use machine learning,
20:59.960 --> 21:01.760
but I have no clue where to start.
21:01.760 --> 21:04.920
So it's a great start to all the way to the experts
21:04.920 --> 21:08.520
who wise were very specific things, so it's interesting.
21:08.520 --> 21:09.600
Is there a good answer?
21:09.600 --> 21:12.480
It boils down to oftentimes digitizing data.
21:12.480 --> 21:15.240
So whatever you want automated, whatever data
21:15.240 --> 21:17.480
you want to make prediction based on,
21:17.480 --> 21:21.240
you have to make sure that it's in an organized form.
21:21.240 --> 21:23.920
Like with an intensive flow ecosystem,
21:23.920 --> 21:26.080
there's now you're providing more and more data
21:26.080 --> 21:28.960
sets and more and more pretrained models.
21:28.960 --> 21:32.400
Are you finding yourself also the organizer of data sets?
21:32.400 --> 21:34.480
Yes, I think with TensorFlow data sets
21:34.480 --> 21:38.360
that we just released, that's definitely come up where people
21:38.360 --> 21:39.200
want these data sets.
21:39.200 --> 21:41.560
Can we organize them and can we make that easier?
21:41.560 --> 21:45.320
So that's definitely one important thing.
21:45.320 --> 21:47.680
The other related thing I would say is I often tell people,
21:47.680 --> 21:50.960
you know what, don't think of the most fanciest thing
21:50.960 --> 21:53.320
that the newest model that you see.
21:53.320 --> 21:55.480
Make something very basic work, and then
21:55.480 --> 21:56.360
you can improve it.
21:56.360 --> 21:58.840
There's just lots of things you can do with it.
21:58.840 --> 22:00.080
Yeah, start with the basics.
22:00.080 --> 22:00.580
Sure.
22:00.580 --> 22:03.760
One of the big things that makes TensorFlow even more
22:03.760 --> 22:06.440
accessible was the appearance, whenever
22:06.440 --> 22:12.400
that happened, of Keras, the Keras standard outside of TensorFlow.
22:12.400 --> 22:18.200
I think it was Keras on top of Tiano at first only,
22:18.200 --> 22:22.480
and then Keras became on top of TensorFlow.
22:22.480 --> 22:29.840
Do you know when Keras chose to also add TensorFlow as a back end,
22:29.840 --> 22:33.960
who was it just the community that drove that initially?
22:33.960 --> 22:37.000
Do you know if there was discussions, conversations?
22:37.000 --> 22:40.920
Yeah, so Franco started the Keras project
22:40.920 --> 22:44.560
before he was at Google, and the first thing was Tiano.
22:44.560 --> 22:47.120
I don't remember if that was after TensorFlow
22:47.120 --> 22:49.640
was created or way before.
22:49.640 --> 22:52.000
And then at some point, when TensorFlow
22:52.000 --> 22:54.160
started becoming popular, there were enough similarities
22:54.160 --> 22:56.320
that he decided to create this interface
22:56.320 --> 22:59.200
and put TensorFlow as a back end.
22:59.200 --> 23:03.320
I believe that might still have been before he joined Google.
23:03.320 --> 23:06.720
So we weren't really talking about that.
23:06.720 --> 23:09.720
He decided on his own and thought that was interesting
23:09.720 --> 23:12.760
and relevant to the community.
23:12.760 --> 23:17.080
In fact, I didn't find out about him being at Google
23:17.080 --> 23:19.680
until a few months after he was here.
23:19.680 --> 23:21.840
He was working on some research ideas.
23:21.840 --> 23:24.480
And doing Keras and his nights and weekends project and stuff.
23:24.480 --> 23:25.280
I wish this thing.
23:25.280 --> 23:28.480
So he wasn't part of the TensorFlow.
23:28.480 --> 23:29.680
He didn't join initially.
23:29.680 --> 23:32.240
He joined research, and he was doing some amazing research.
23:32.240 --> 23:35.440
He has some papers on that and research.
23:35.440 --> 23:38.400
He's a great researcher as well.
23:38.400 --> 23:42.400
And at some point, we realized, oh, he's doing this good stuff.
23:42.400 --> 23:45.480
People seem to like the API, and he's right here.
23:45.480 --> 23:48.280
So we talked to him, and he said, OK,
23:48.280 --> 23:50.600
why don't I come over to your team
23:50.600 --> 23:52.800
and work with you for a quarter?
23:52.800 --> 23:55.440
And let's make that integration happen.
23:55.440 --> 23:57.200
And we talked to his manager, and he said, sure,
23:57.200 --> 23:59.720
what, quarter's fine.
23:59.720 --> 24:03.320
And that quarter's been something like two years now.
24:03.320 --> 24:05.040
So he's fully on this.
24:05.040 --> 24:12.000
So Keras got integrated into TensorFlow in a deep way.
24:12.000 --> 24:15.920
And now with TensorFlow 2.0, Keras
24:15.920 --> 24:19.400
is kind of the recommended way for a beginner
24:19.400 --> 24:21.960
to interact with TensorFlow, which
24:21.960 --> 24:24.640
makes that initial sort of transfer learning
24:24.640 --> 24:28.040
or the basic use cases, even for an enterprise,
24:28.040 --> 24:29.320
super simple, right?
24:29.320 --> 24:29.920
That's correct.
24:29.920 --> 24:30.440
That's right.
24:30.440 --> 24:32.040
So what was that decision like?
24:32.040 --> 24:38.640
That seems like it's kind of a bold decision as well.
24:38.640 --> 24:41.200
We did spend a lot of time thinking about that one.
24:41.200 --> 24:46.000
We had a bunch of APIs some bit by us.
24:46.000 --> 24:48.760
There was a parallel layers API that we were building
24:48.760 --> 24:51.560
and when we decided to do Keras in parallel,
24:51.560 --> 24:54.400
so they were like, OK, two things that we are looking at.
24:54.400 --> 24:55.960
And the first thing we was trying to do
24:55.960 --> 25:00.080
is just have them look similar, be as integrated as possible,
25:00.080 --> 25:02.200
share all of that stuff.
25:02.200 --> 25:05.800
There were also three other APIs that others had built over time
25:05.800 --> 25:09.000
because we didn't have a standard one.
25:09.000 --> 25:12.080
But one of the messages that we kept hearing from the community,
25:12.080 --> 25:13.200
OK, which one do we use?
25:13.200 --> 25:15.560
And they kept seeing, OK, here's a model in this one,
25:15.560 --> 25:18.840
and here's a model in this one, which should I pick?
25:18.840 --> 25:22.680
So that's sort of like, OK, we had to address that
25:22.680 --> 25:24.000
straight on with 2.0.
25:24.000 --> 25:26.320
The whole idea was we need to simplify.
25:26.320 --> 25:28.600
We had to pick one.
25:28.600 --> 25:34.600
Based on where we were, we were like, OK, let's see what
25:34.600 --> 25:35.640
are the people like.
25:35.640 --> 25:39.280
And Keras was clearly one that lots of people loved.
25:39.280 --> 25:41.600
There were lots of great things about it.
25:41.600 --> 25:43.880
So we settled on that.
25:43.880 --> 25:44.680
Organically.
25:44.680 --> 25:46.560
That's kind of the best way to do it.
25:46.560 --> 25:47.160
It was great.
25:47.160 --> 25:48.720
But it was surprising, nevertheless,
25:48.720 --> 25:51.120
to sort of bring in and outside.
25:51.120 --> 25:54.440
I mean, there was a feeling like Keras might be almost
25:54.440 --> 25:58.000
like a competitor in a certain kind of a two tensor flow.
25:58.000 --> 26:01.320
And in a sense, it became an empowering element
26:01.320 --> 26:02.200
of tensor flow.
26:02.200 --> 26:03.280
That's right.
26:03.280 --> 26:07.200
Yeah, it's interesting how you can put two things together
26:07.200 --> 26:08.280
which can align right.
26:08.280 --> 26:11.760
And in this case, I think Francois, the team,
26:11.760 --> 26:15.480
and a bunch of us have chatted and I think we all
26:15.480 --> 26:17.320
want to see the same kind of things.
26:17.320 --> 26:20.360
We all care about making it easier for the huge set
26:20.360 --> 26:21.440
of developers out there.
26:21.440 --> 26:23.440
And that makes a difference.
26:23.440 --> 26:27.280
So Python has Guido van Rossum, who
26:27.280 --> 26:30.320
until recently held the position of benevolent
26:30.320 --> 26:31.960
dictator for life.
26:31.960 --> 26:36.040
Right, so there's a huge successful open source
26:36.040 --> 26:37.320
project like tensor flow.
26:37.320 --> 26:40.680
Need one person who makes a final decision.
26:40.680 --> 26:45.480
So you did a pretty successful tensor flow Dev Summit
26:45.480 --> 26:47.520
just now, last couple of days.
26:47.520 --> 26:51.080
There's clearly a lot of different new features
26:51.080 --> 26:55.480
being incorporated in amazing ecosystem, so on.
26:55.480 --> 26:57.320
How are those design decisions made?
26:57.320 --> 27:00.960
Is there a BDFL in tensor flow?
27:00.960 --> 27:05.800
And or is it more distributed and organic?
27:05.800 --> 27:09.880
I think it's somewhat different, I would say.
27:09.880 --> 27:16.160
I've always been involved in the key design directions.
27:16.160 --> 27:17.560
But there are lots of things that
27:17.560 --> 27:20.960
are distributed where their number of people, Martin
27:20.960 --> 27:24.760
Wick being one who has really driven a lot of our open source
27:24.760 --> 27:27.360
stuff, a lot of the APIs.
27:27.360 --> 27:29.200
And there are a number of other people
27:29.200 --> 27:32.720
who have been pushed and been responsible
27:32.720 --> 27:35.240
for different parts of it.
27:35.240 --> 27:37.840
We do have regular design reviews.
27:37.840 --> 27:40.680
Over the last year, we've really spent a lot of time opening up
27:40.680 --> 27:44.160
to the community and adding transparency.
27:44.160 --> 27:45.880
We're setting more processes in place,
27:45.880 --> 27:49.600
so RFCs, special interest groups, really
27:49.600 --> 27:53.560
grow that community and scale that.
27:53.560 --> 27:57.680
I think the kind of scale that ecosystem is in,
27:57.680 --> 28:00.240
I don't think we could scale with having me as the lone
28:00.240 --> 28:02.320
point of decision maker.
28:02.320 --> 28:03.440
I got it.
28:03.440 --> 28:05.880
So yeah, the growth of that ecosystem,
28:05.880 --> 28:08.040
maybe you can talk about it a little bit.
28:08.040 --> 28:10.720
First of all, when I started with Andre Karpathi
28:10.720 --> 28:13.640
when he first did ComNet.js, the fact
28:13.640 --> 28:15.360
that you can train in your own network
28:15.360 --> 28:18.480
and the browser in JavaScript was incredible.
28:18.480 --> 28:21.000
So now TensorFlow.js is really making
28:21.000 --> 28:26.920
that a serious, a legit thing, a way
28:26.920 --> 28:29.560
to operate, whether it's in the back end or the front end.
28:29.560 --> 28:32.720
Then there's the TensorFlow Extended, like you mentioned.
28:32.720 --> 28:35.360
There's TensorFlow Lite for mobile.
28:35.360 --> 28:37.480
And all of it, as far as I can tell,
28:37.480 --> 28:39.640
it's really converging towards being
28:39.640 --> 28:43.440
able to save models in the same kind of way.
28:43.440 --> 28:46.680
You can move around, you can train on the desktop,
28:46.680 --> 28:48.800
and then move it to mobile, and so on.
28:48.800 --> 28:49.280
That's right.
28:49.280 --> 28:52.320
So this is that cohesiveness.
28:52.320 --> 28:55.240
So can you maybe give me whatever
28:55.240 --> 28:58.840
I missed, a bigger overview of the mission of the ecosystem
28:58.840 --> 29:02.120
that's trying to be built, and where is it moving forward?
29:02.120 --> 29:02.800
Yeah.
29:02.800 --> 29:05.720
So in short, the way I like to think of this
29:05.720 --> 29:09.760
is our goals to enable machine learning.
29:09.760 --> 29:13.320
And in a couple of ways, one is we
29:13.320 --> 29:16.560
have lots of exciting things going on in ML today.
29:16.560 --> 29:18.160
We started with deep learning, but we now
29:18.160 --> 29:21.400
support a bunch of other algorithms too.
29:21.400 --> 29:23.760
So one is to, on the research side,
29:23.760 --> 29:25.360
keep pushing on the state of the art.
29:25.360 --> 29:27.240
Can we, how do we enable researchers
29:27.240 --> 29:28.960
to build the next amazing thing?
29:28.960 --> 29:31.800
So BERT came out recently.
29:31.800 --> 29:34.000
It's great that people are able to do new kinds of research.
29:34.000 --> 29:35.400
There are lots of amazing research
29:35.400 --> 29:37.600
that happens across the world.
29:37.600 --> 29:38.880
So that's one direction.
29:38.880 --> 29:41.400
The other is, how do you take that
29:41.400 --> 29:45.200
across all the people outside who want to take that research
29:45.200 --> 29:47.400
and do some great things with it and integrate it
29:47.400 --> 29:51.800
to build real products, to have a real impact on people?
29:51.800 --> 29:56.720
And so if that's the other axes in some ways.
29:56.720 --> 29:58.520
And a high level, one way I think about it
29:58.520 --> 30:02.480
is there are a crazy number of computer devices
30:02.480 --> 30:04.240
across the world.
30:04.240 --> 30:08.440
And we often used to think of ML and training and all of this
30:08.440 --> 30:10.800
as, OK, something you do either in the workstation
30:10.800 --> 30:13.600
or the data center or cloud.
30:13.600 --> 30:15.720
But we see things running on the phones.
30:15.720 --> 30:17.640
We see things running on really tiny chips.
30:17.640 --> 30:20.760
And we had some demos at the developer summit.
30:20.760 --> 30:25.160
And so the way I think about this ecosystem
30:25.160 --> 30:30.280
is, how do we help get machine learning on every device that
30:30.280 --> 30:32.520
has a compute capability?
30:32.520 --> 30:33.760
And that continues to grow.
30:33.760 --> 30:37.240
And so in some ways, this ecosystem
30:37.240 --> 30:40.280
has looked at various aspects of that
30:40.280 --> 30:42.440
and grown over time to cover more of those.
30:42.440 --> 30:44.640
And we continue to push the boundaries.
30:44.640 --> 30:48.640
In some areas, we've built more tooling and things
30:48.640 --> 30:50.040
around that to help you.
30:50.040 --> 30:52.800
I mean, the first tool we started was TensorBoard.
30:52.800 --> 30:56.920
You want to learn just the training piece, the effects
30:56.920 --> 30:59.840
for TensorFlow Extended to really do your entire ML
30:59.840 --> 31:04.760
pipelines if you care about all that production stuff,
31:04.760 --> 31:09.520
but then going to the edge, going to different kinds of things.
31:09.520 --> 31:11.800
And it's not just us now.
31:11.800 --> 31:15.120
We are a place where there are lots of libraries being built
31:15.120 --> 31:15.840
on top.
31:15.840 --> 31:18.440
So there are some for research, maybe things
31:18.440 --> 31:21.240
like TensorFlow Agents or TensorFlow Probability that
31:21.240 --> 31:23.480
started as research things or for researchers
31:23.480 --> 31:26.160
for focusing on certain kinds of algorithms,
31:26.160 --> 31:30.280
but they're also being deployed or reduced by production folks.
31:30.280 --> 31:34.000
And some have come from within Google, just teams
31:34.000 --> 31:37.040
across Google who wanted to do the build these things.
31:37.040 --> 31:39.680
Others have come from just the community
31:39.680 --> 31:41.840
because there are different pieces
31:41.840 --> 31:44.640
that different parts of the community care about.
31:44.640 --> 31:49.520
And I see our goal as enabling even that.
31:49.520 --> 31:53.240
It's not we cannot and won't build every single thing.
31:53.240 --> 31:54.840
That just doesn't make sense.
31:54.840 --> 31:57.320
But if we can enable others to build the things
31:57.320 --> 32:00.640
that they care about, and there's a broader community that
32:00.640 --> 32:02.880
cares about that, and we can help encourage that,
32:02.880 --> 32:05.240
and that's great.
32:05.240 --> 32:08.600
That really helps the entire ecosystem, not just those.
32:08.600 --> 32:11.280
One of the big things about 2.0 that we're pushing on
32:11.280 --> 32:14.680
is, OK, we have these so many different pieces, right?
32:14.680 --> 32:18.440
How do we help make all of them work well together?
32:18.440 --> 32:21.960
There are a few key pieces there that we're pushing on,
32:21.960 --> 32:23.840
one being the core format in there
32:23.840 --> 32:27.480
and how we share the models themselves through SAVE model
32:27.480 --> 32:30.440
and what TensorFlow Hub and so on.
32:30.440 --> 32:34.000
And a few of the pieces that we really put this together.
32:34.000 --> 32:37.240
I was very skeptical that that's, when TensorFlow.js came out,
32:37.240 --> 32:40.120
it didn't seem or deep learning.js.
32:40.120 --> 32:41.680
Yeah, that was the first.
32:41.680 --> 32:45.040
It seems like technically very difficult project.
32:45.040 --> 32:47.040
As a standalone, it's not as difficult.
32:47.040 --> 32:49.920
But as a thing that integrates into the ecosystem,
32:49.920 --> 32:51.240
it seems very difficult.
32:51.240 --> 32:53.200
So I mean, there's a lot of aspects of this
32:53.200 --> 32:54.200
you're making look easy.
32:54.200 --> 32:58.160
But on the technical side, how many challenges
32:58.160 --> 33:00.560
have to be overcome here?
33:00.560 --> 33:01.520
A lot.
33:01.520 --> 33:03.080
And still have to be overcome.
33:03.080 --> 33:04.840
That's the question here, too.
33:04.840 --> 33:06.160
There are lots of steps to it.
33:06.160 --> 33:08.160
I think we've iterated over the last few years,
33:08.160 --> 33:10.720
so there's a lot we've learned.
33:10.720 --> 33:14.200
I, yeah, and often when things come together well,
33:14.200 --> 33:15.080
things look easy.
33:15.080 --> 33:16.400
And that's exactly the point.
33:16.400 --> 33:18.280
It should be easy for the end user.
33:18.280 --> 33:21.320
But there are lots of things that go behind that.
33:21.320 --> 33:25.320
If I think about still challenges ahead,
33:25.320 --> 33:32.880
there are we have a lot more devices coming on board,
33:32.880 --> 33:35.280
for example, from the hardware perspective.
33:35.280 --> 33:37.640
How do we make it really easy for these vendors
33:37.640 --> 33:42.040
to integrate with something like TensorFlow?
33:42.040 --> 33:43.640
So there's a lot of compiler stuff
33:43.640 --> 33:45.320
that others are working on.
33:45.320 --> 33:48.320
There are things we can do in terms of our APIs
33:48.320 --> 33:50.520
and so on that we can do.
33:50.520 --> 33:55.840
As we, TensorFlow started as a very monolithic system.
33:55.840 --> 33:57.680
And to some extent, it still is.
33:57.680 --> 33:59.400
There are less lots of tools around it,
33:59.400 --> 34:02.960
but the core is still pretty large and monolithic.
34:02.960 --> 34:05.760
One of the key challenges for us to scale that out
34:05.760 --> 34:10.440
is how do we break that apart with clear interfaces?
34:10.440 --> 34:13.720
It's, in some ways, it's software engineering one
34:13.720 --> 34:18.520
one, but for a system that's now four years old, I guess,
34:18.520 --> 34:21.600
or more, and that's still rapidly evolving
34:21.600 --> 34:24.000
and that we're not slowing down with,
34:24.000 --> 34:28.240
it's hard to change and modify and really break apart.
34:28.240 --> 34:29.880
It's sort of like, as people say, right,
34:29.880 --> 34:32.560
it's like changing the engine with a car running
34:32.560 --> 34:33.560
or fixed benefits.
34:33.560 --> 34:35.200
That's exactly what we're trying to do.
34:35.200 --> 34:39.960
So there's a challenge here, because the downside
34:39.960 --> 34:43.840
of so many people being excited about TensorFlow
34:43.840 --> 34:48.600
and becoming to rely on it in many other applications
34:48.600 --> 34:52.200
is that you're kind of responsible.
34:52.200 --> 34:53.520
It's the technical debt.
34:53.520 --> 34:55.640
You're responsible for previous versions
34:55.640 --> 34:57.720
to some degree still working.
34:57.720 --> 34:59.920
So when you're trying to innovate,
34:59.920 --> 35:03.760
I mean, it's probably easier to just start from scratch
35:03.760 --> 35:05.800
every few months.
35:05.800 --> 35:07.160
Absolutely.
35:07.160 --> 35:10.880
So do you feel the pain of that?
35:10.880 --> 35:15.360
2.0 does break some back compatibility, but not too much.
35:15.360 --> 35:18.120
It seems like the conversion is pretty straightforward.
35:18.120 --> 35:20.240
Do you think that's still important,
35:20.240 --> 35:22.880
given how quickly deep learning is changing?
35:22.880 --> 35:26.360
Can you just, the things that you've learned,
35:26.360 --> 35:27.440
can you just start over?
35:27.440 --> 35:30.120
Or is there pressure to not?
35:30.120 --> 35:31.640
It's a tricky balance.
35:31.640 --> 35:36.840
So if it was just a researcher writing a paper who
35:36.840 --> 35:39.400
a year later will not look at that code again,
35:39.400 --> 35:41.560
sure, it doesn't matter.
35:41.560 --> 35:43.440
There are a lot of production systems
35:43.440 --> 35:45.480
that rely on TensorFlow, both at Google
35:45.480 --> 35:47.240
and across the world.
35:47.240 --> 35:49.760
And people worry about this.
35:49.760 --> 35:53.400
I mean, these systems run for a long time.
35:53.400 --> 35:57.240
So it is important to keep that compatibility and so on.
35:57.240 --> 36:00.960
And yes, it does come with a huge cost.
36:00.960 --> 36:02.920
We have to think about a lot of things
36:02.920 --> 36:06.960
as we do new things and make new changes.
36:06.960 --> 36:09.120
I think it's a trade off, right?
36:09.120 --> 36:12.960
You can, you might slow certain kinds of things down,
36:12.960 --> 36:15.440
but the overall value you're bringing because of that
36:15.440 --> 36:18.440
is much bigger because it's not just
36:18.440 --> 36:20.520
about breaking the person yesterday.
36:20.520 --> 36:24.840
It's also about telling the person tomorrow that, you know what?
36:24.840 --> 36:26.320
This is how we do things.
36:26.320 --> 36:28.520
We're not going to break you when you come on board
36:28.520 --> 36:30.320
because there are lots of new people who are also
36:30.320 --> 36:32.880
going to come on board.
36:32.880 --> 36:34.680
So one way I like to think about this,
36:34.680 --> 36:37.960
and I always push the team to think about it as well,
36:37.960 --> 36:39.640
when you want to do new things, you
36:39.640 --> 36:42.000
want to start with a clean slate,
36:42.000 --> 36:44.880
design with a clean slate in mind,
36:44.880 --> 36:48.160
and then we'll figure out how to make sure all the other things
36:48.160 --> 36:48.640
work.
36:48.640 --> 36:52.160
And yes, we do make compromises occasionally.
36:52.160 --> 36:55.200
But unless you design with the clean slate
36:55.200 --> 36:58.400
and not worry about that, you'll never get to a good place.
36:58.400 --> 36:59.120
That's brilliant.
36:59.120 --> 37:04.080
So even if you are responsible in the idea stage,
37:04.080 --> 37:07.680
when you're thinking of new, just put all that behind you.
37:07.680 --> 37:09.600
OK, that's really well put.
37:09.600 --> 37:12.480
So I have to ask this because a lot of students, developers,
37:12.480 --> 37:16.280
asked me how I feel about PyTorch versus TensorFlow.
37:16.280 --> 37:19.720
So I've recently completely switched my research group
37:19.720 --> 37:20.920
to TensorFlow.
37:20.920 --> 37:23.280
I wish everybody would just use the same thing.
37:23.280 --> 37:26.960
And TensorFlow is as close to that, I believe, as we have.
37:26.960 --> 37:32.000
But do you enjoy competition?
37:32.000 --> 37:35.800
So TensorFlow is leading in many ways, many dimensions
37:35.800 --> 37:39.000
in terms of the ecosystem, in terms of the number of users,
37:39.000 --> 37:41.200
momentum power, production level, so on.
37:41.200 --> 37:46.000
But a lot of researchers are now also using PyTorch.
37:46.000 --> 37:47.520
Do you enjoy that kind of competition,
37:47.520 --> 37:49.440
or do you just ignore it and focus
37:49.440 --> 37:52.320
on making TensorFlow the best that it can be?
37:52.320 --> 37:55.480
So just like research or anything people are doing,
37:55.480 --> 37:58.120
it's great to get different kinds of ideas.
37:58.120 --> 38:01.440
And when we started with TensorFlow,
38:01.440 --> 38:05.480
like I was saying earlier, it was very important for us
38:05.480 --> 38:07.440
to also have production in mind.
38:07.440 --> 38:08.960
We didn't want just research, right?
38:08.960 --> 38:11.280
And that's why we chose certain things.
38:11.280 --> 38:13.480
Now PyTorch came along and said, you know what?
38:13.480 --> 38:14.880
I only care about research.
38:14.880 --> 38:16.320
This is what I'm trying to do.
38:16.320 --> 38:18.400
What's the best thing I can do for this?
38:18.400 --> 38:21.120
And it started iterating and said, OK,
38:21.120 --> 38:22.520
I don't need to worry about graphs.
38:22.520 --> 38:25.200
Let me just run things.
38:25.200 --> 38:27.440
I don't care if it's not as fast as it can be,
38:27.440 --> 38:30.480
but let me just make this part easy.
38:30.480 --> 38:32.560
And there are things you can learn from that, right?
38:32.560 --> 38:36.720
They, again, had the benefit of seeing what had come before,
38:36.720 --> 38:40.520
but also exploring certain different kinds of spaces.
38:40.520 --> 38:43.560
And they had some good things there,
38:43.560 --> 38:46.680
building on, say, things like Jainer and so on before that.
38:46.680 --> 38:49.320
So competition is definitely interesting.
38:49.320 --> 38:51.040
It made us, you know, this is an area
38:51.040 --> 38:53.720
that we had thought about, like I said, very early on.
38:53.720 --> 38:56.600
Over time, we had revisited this a couple of times.
38:56.600 --> 38:59.000
Should we add this again?
38:59.000 --> 39:00.480
At some point, we said, you know what,
39:00.480 --> 39:02.920
here's it seems like this can be done well.
39:02.920 --> 39:04.280
So let's try it again.
39:04.280 --> 39:07.680
And that's how we started pushing on eager execution.
39:07.680 --> 39:09.880
How do we combine those two together,
39:09.880 --> 39:13.080
which has finally come very well together in 2.0,
39:13.080 --> 39:15.720
but it took us a while to get all the things together
39:15.720 --> 39:16.320
and so on.
39:16.320 --> 39:19.320
So let me, I mean, ask, put another way.
39:19.320 --> 39:21.800
I think eager execution is a really powerful thing,
39:21.800 --> 39:22.680
those added.
39:22.680 --> 39:24.320
Do you think he wouldn't have been,
39:25.840 --> 39:28.400
you know, Muhammad Ali versus Frazier, right?
39:28.400 --> 39:31.200
Do you think it wouldn't have been added as quickly
39:31.200 --> 39:33.760
if PyTorch wasn't there?
39:33.760 --> 39:35.440
It might have taken longer.
39:35.440 --> 39:36.280
No longer.
39:36.280 --> 39:38.960
It was, I mean, we had tried some variants of that before.
39:38.960 --> 39:40.920
So I'm sure it would have happened,
39:40.920 --> 39:42.240
but it might have taken longer.
39:42.240 --> 39:44.800
I'm grateful that TensorFlow is part of the way they did.
39:44.800 --> 39:47.760
That's doing some incredible work last couple of years.
39:47.760 --> 39:49.640
What other things that we didn't talk about?
39:49.640 --> 39:51.520
Are you looking forward in 2.0?
39:51.520 --> 39:54.040
That comes to mind.
39:54.040 --> 39:56.520
So we talked about some of the ecosystem stuff,
39:56.520 --> 40:01.440
making it easily accessible to Keras, eager execution.
40:01.440 --> 40:02.880
Is there other things that we missed?
40:02.880 --> 40:07.480
Yeah, so I would say one is just where 2.0 is,
40:07.480 --> 40:10.760
and, you know, with all the things that we've talked about,
40:10.760 --> 40:13.760
I think as we think beyond that,
40:13.760 --> 40:16.640
there are lots of other things that it enables us to do
40:16.640 --> 40:18.760
and that we're excited about.
40:18.760 --> 40:20.720
So what it's setting us up for,
40:20.720 --> 40:22.520
okay, here are these really clean APIs.
40:22.520 --> 40:25.640
We've cleaned up the surface for what the users want.
40:25.640 --> 40:28.320
What it also allows us to do a whole bunch of stuff
40:28.320 --> 40:31.600
behind the scenes once we are ready with 2.0.
40:31.600 --> 40:36.600
So for example, in TensorFlow with graphs
40:36.760 --> 40:37.720
and all the things you could do,
40:37.720 --> 40:40.600
you could always get a lot of good performance
40:40.600 --> 40:43.280
if you spent the time to tune it, right?
40:43.280 --> 40:47.720
And we've clearly shown that, lots of people do that.
40:47.720 --> 40:52.720
With 2.0, with these APIs where we are,
40:53.040 --> 40:55.120
we can give you a lot of performance
40:55.120 --> 40:57.040
just with whatever you do.
40:57.040 --> 41:01.400
You know, because we see these, it's much cleaner.
41:01.400 --> 41:03.720
We know most people are gonna do things this way.
41:03.720 --> 41:05.520
We can really optimize for that
41:05.520 --> 41:09.040
and get a lot of those things out of the box.
41:09.040 --> 41:10.400
And it really allows us, you know,
41:10.400 --> 41:13.880
both for single machine and distributed and so on,
41:13.880 --> 41:17.200
to really explore other spaces behind the scenes
41:17.200 --> 41:19.680
after 2.0 in the future versions as well.
41:19.680 --> 41:23.000
So right now, the team's really excited about that,
41:23.000 --> 41:25.800
that over time, I think we'll see that.
41:25.800 --> 41:27.720
The other piece that I was talking about
41:27.720 --> 41:31.600
in terms of just restructuring the monolithic thing
41:31.600 --> 41:34.320
into more pieces and making it more modular,
41:34.320 --> 41:36.800
I think that's gonna be really important
41:36.800 --> 41:41.800
for a lot of the other people in the ecosystem,
41:41.800 --> 41:44.760
other organizations and so on that wanted to build things.
41:44.760 --> 41:46.360
Can you elaborate a little bit what you mean
41:46.360 --> 41:50.680
by making TensorFlow more ecosystem or modular?
41:50.680 --> 41:55.000
So the way it's organized today is there's one,
41:55.000 --> 41:56.280
there are lots of repositories
41:56.280 --> 41:58.320
in the TensorFlow organization at GitHub,
41:58.320 --> 42:01.080
the core one where we have TensorFlow,
42:01.080 --> 42:04.080
it has the execution engine,
42:04.080 --> 42:08.280
it has, you know, the key backends for CPUs and GPUs,
42:08.280 --> 42:12.560
it has the work to do distributed stuff.
42:12.560 --> 42:14.360
And all of these just work together
42:14.360 --> 42:17.240
in a single library or binary,
42:17.240 --> 42:18.800
there's no way to split them apart easily.
42:18.800 --> 42:19.960
I mean, there are some interfaces,
42:19.960 --> 42:21.600
but they're not very clean.
42:21.600 --> 42:24.800
In a perfect world, you would have clean interfaces where,
42:24.800 --> 42:27.720
okay, I wanna run it on my fancy cluster
42:27.720 --> 42:29.360
with some custom networking,
42:29.360 --> 42:30.960
just implement this and do that.
42:30.960 --> 42:32.640
I mean, we kind of support that,
42:32.640 --> 42:34.560
but it's hard for people today.
42:35.480 --> 42:38.160
I think as we are starting to see more interesting things
42:38.160 --> 42:39.400
in some of these spaces,
42:39.400 --> 42:42.280
having that clean separation will really start to help.
42:42.280 --> 42:47.280
And again, going to the large size of the ecosystem
42:47.360 --> 42:50.120
and the different groups involved there,
42:50.120 --> 42:53.440
enabling people to evolve and push on things
42:53.440 --> 42:56.040
more independently just allows it to scale better.
42:56.040 --> 42:59.080
And by people, you mean individual developers and?
42:59.080 --> 42:59.920
And organizations.
42:59.920 --> 43:00.920
And organizations.
43:00.920 --> 43:01.760
That's right.
43:01.760 --> 43:04.200
So the hope is that everybody sort of major,
43:04.200 --> 43:06.880
I don't know, Pepsi or something uses,
43:06.880 --> 43:11.040
like major corporations go to TensorFlow to this kind of.
43:11.040 --> 43:13.640
Yeah, if you look at enterprise like Pepsi or these,
43:13.640 --> 43:15.520
I mean, a lot of them are already using TensorFlow.
43:15.520 --> 43:18.960
They are not the ones that do the development
43:18.960 --> 43:20.360
or changes in the core.
43:20.360 --> 43:21.920
Some of them do, but a lot of them don't.
43:21.920 --> 43:23.720
I mean, they touch small pieces.
43:23.720 --> 43:26.400
There are lots of these, some of them being,
43:26.400 --> 43:28.200
let's say hardware vendors who are building
43:28.200 --> 43:30.840
their custom hardware and they want their own pieces.
43:30.840 --> 43:34.160
Or some of them being bigger companies, say IBM.
43:34.160 --> 43:37.320
I mean, they're involved in some of our special interest
43:37.320 --> 43:39.960
groups and they see a lot of users
43:39.960 --> 43:42.640
who want certain things and they want to optimize for that.
43:42.640 --> 43:44.480
So folks like that often.
43:44.480 --> 43:46.400
Autonomous vehicle companies, perhaps.
43:46.400 --> 43:48.200
Exactly, yes.
43:48.200 --> 43:50.520
So yeah, like I mentioned, TensorFlow
43:50.520 --> 43:54.120
has been down on it 41 million times, 50,000 commits,
43:54.120 --> 43:58.360
almost 10,000 pull requests, 1,800 contributors.
43:58.360 --> 44:02.160
So I'm not sure if you can explain it,
44:02.160 --> 44:06.840
but what does it take to build a community like that?
44:06.840 --> 44:09.200
In retrospect, what do you think?
44:09.200 --> 44:12.080
What is the critical thing that allowed for this growth
44:12.080 --> 44:14.600
to happen and how does that growth continue?
44:14.600 --> 44:17.920
Yeah, that's an interesting question.
44:17.920 --> 44:20.240
I wish I had all the answers there, I guess,
44:20.240 --> 44:22.520
so you could replicate it.
44:22.520 --> 44:25.520
I think there are a number of things
44:25.520 --> 44:27.880
that need to come together, right?
44:27.880 --> 44:33.720
One, just like any new thing, there's
44:33.720 --> 44:37.960
a sweet spot of timing, what's needed,
44:37.960 --> 44:39.520
does it grow with what's needed.
44:39.520 --> 44:41.960
So in this case, for example, TensorFlow
44:41.960 --> 44:43.640
is not just grown because it has a good tool,
44:43.640 --> 44:46.640
it's also grown with the growth of deep learning itself.
44:46.640 --> 44:49.000
So those factors come into play.
44:49.000 --> 44:53.120
Other than that, though, I think just
44:53.120 --> 44:55.560
hearing, listening to the community, what they're
44:55.560 --> 44:58.400
doing, what they need, being open to,
44:58.400 --> 45:01.080
like in terms of external contributions,
45:01.080 --> 45:04.520
we've spent a lot of time in making sure
45:04.520 --> 45:06.840
we can accept those contributions well,
45:06.840 --> 45:09.400
we can help the contributors in adding those,
45:09.400 --> 45:11.240
putting the right process in place,
45:11.240 --> 45:13.320
getting the right kind of community,
45:13.320 --> 45:16.120
welcoming them, and so on.
45:16.120 --> 45:19.000
Like over the last year, we've really pushed on transparency.
45:19.000 --> 45:22.200
That's important for an open source project.
45:22.200 --> 45:23.760
People want to know where things are going,
45:23.760 --> 45:26.400
and we're like, OK, here's a process for you.
45:26.400 --> 45:29.320
You can do that, here are our seasons, and so on.
45:29.320 --> 45:32.880
So thinking through, there are lots of community aspects
45:32.880 --> 45:36.400
that come into that you can really work on.
45:36.400 --> 45:38.720
As a small project, it's maybe easy to do,
45:38.720 --> 45:42.240
because there's two developers, and you can do those.
45:42.240 --> 45:46.960
As you grow, putting more of these processes in place,
45:46.960 --> 45:49.080
thinking about the documentation,
45:49.080 --> 45:51.400
thinking about what two developers
45:51.400 --> 45:55.080
care about, what kind of tools would they want to use,
45:55.080 --> 45:56.840
all of these come into play, I think.
45:56.840 --> 45:58.400
So one of the big things, I think,
45:58.400 --> 46:02.560
that feeds the TensorFlow fire is people building something
46:02.560 --> 46:07.680
on TensorFlow, and implement a particular architecture
46:07.680 --> 46:09.480
that does something cool and useful,
46:09.480 --> 46:11.080
and they put that on GitHub.
46:11.080 --> 46:15.640
And so it just feeds this growth.
46:15.640 --> 46:19.560
Do you have a sense that with 2.0 and 1.0,
46:19.560 --> 46:21.880
that there may be a little bit of a partitioning like there
46:21.880 --> 46:26.040
is with Python 2 and 3, that there'll be a code base
46:26.040 --> 46:28.320
in the older versions of TensorFlow
46:28.320 --> 46:31.120
that will not be as compatible easily,
46:31.120 --> 46:35.600
or are you pretty confident that this kind of conversion
46:35.600 --> 46:37.960
is pretty natural and easy to do?
46:37.960 --> 46:41.480
So we're definitely working hard to make that very easy to do.
46:41.480 --> 46:44.040
There's lots of tooling that we talked about at the developer
46:44.040 --> 46:46.480
summit this week, and we'll continue
46:46.480 --> 46:48.280
to invest in that tooling.
46:48.280 --> 46:52.560
It's when you think of these significant version changes,
46:52.560 --> 46:55.720
that's always a risk, and we are really pushing hard
46:55.720 --> 46:59.160
to make that transition very, very smooth.
46:59.160 --> 47:03.000
I think, so at some level, people
47:03.000 --> 47:05.520
want to move when they see the value in the new thing.
47:05.520 --> 47:07.640
They don't want to move just because it's a new thing.
47:07.640 --> 47:11.400
And some people do, but most people want a really good thing.
47:11.400 --> 47:13.760
And I think over the next few months,
47:13.760 --> 47:15.400
as people start to see the value,
47:15.400 --> 47:17.640
we'll definitely see that shift happening.
47:17.640 --> 47:20.080
So I'm pretty excited and confident that we
47:20.080 --> 47:22.440
will see people moving.
47:22.440 --> 47:24.680
As you said earlier, this field is also moving rapidly,
47:24.680 --> 47:26.720
so that'll help because we can do more things.
47:26.720 --> 47:28.520
And all the new things will clearly
47:28.520 --> 47:32.280
happen in 2.x, so people will have lots of good reasons to move.
47:32.280 --> 47:36.160
So what do you think TensorFlow 3.0 looks like?
47:36.160 --> 47:40.320
Is there things happening so crazily
47:40.320 --> 47:42.520
that even at the end of this year,
47:42.520 --> 47:45.320
seems impossible to plan for?
47:45.320 --> 47:49.440
Or is it possible to plan for the next five years?
47:49.440 --> 47:50.800
I think it's tricky.
47:50.800 --> 47:55.760
There are some things that we can expect in terms of, OK,
47:55.760 --> 47:59.720
change, yes, change is going to happen.
47:59.720 --> 48:01.680
Are there some things going to stick around
48:01.680 --> 48:03.720
and some things not going to stick around?
48:03.720 --> 48:08.160
I would say the basics of deep learning,
48:08.160 --> 48:12.680
the convolutional models or the basic kind of things,
48:12.680 --> 48:16.280
they'll probably be around in some form still in five years.
48:16.280 --> 48:21.160
Will Aurel and Gans stay very likely based on where they are?
48:21.160 --> 48:22.840
Will we have new things?
48:22.840 --> 48:24.680
Probably, but those are hard to predict.
48:24.680 --> 48:29.080
And some directionally, some things that we can see
48:29.080 --> 48:32.800
is in things that we're starting to do
48:32.800 --> 48:36.560
with some of our projects right now is just
48:36.560 --> 48:39.120
to point out combining eager execution and graphs,
48:39.120 --> 48:42.240
where we're starting to make it more like just your natural
48:42.240 --> 48:43.160
programming language.
48:43.160 --> 48:45.640
You're not trying to program something else.
48:45.640 --> 48:47.240
Similarly, with Swift for TensorFlow,
48:47.240 --> 48:48.280
we're taking that approach.
48:48.280 --> 48:50.040
Can you do something round up?
48:50.040 --> 48:52.080
So some of those ideas seem like, OK,
48:52.080 --> 48:55.000
that's the right direction in five years
48:55.000 --> 48:58.360
we expect to see more in that area.
48:58.360 --> 49:01.760
Other things we don't know is, will hardware accelerators
49:01.760 --> 49:03.200
be the same?
49:03.200 --> 49:09.000
Will we be able to train with four bits instead of 32 bits?
49:09.000 --> 49:11.440
And I think the TPU side of things is exploring.
49:11.440 --> 49:13.960
I mean, TPU is already on version three.
49:13.960 --> 49:17.520
It seems that the evolution of TPU and TensorFlow
49:17.520 --> 49:24.080
are coevolving in terms of both their learning
49:24.080 --> 49:25.720
from each other and from the community
49:25.720 --> 49:29.720
and from the applications where the biggest benefit is achieved.
49:29.720 --> 49:30.560
That's right.
49:30.560 --> 49:33.320
You've been trying with eager with Keras
49:33.320 --> 49:36.480
to make TensorFlow as accessible and easy to use as possible.
49:36.480 --> 49:39.040
What do you think for beginners is the biggest thing
49:39.040 --> 49:40.000
they struggle with?
49:40.000 --> 49:42.080
Have you encountered that?
49:42.080 --> 49:44.280
Or is basically what Keras is solving
49:44.280 --> 49:48.680
is that eager, like we talked about TensorFlow?
49:48.680 --> 49:51.480
For some of them, like you said, the beginners
49:51.480 --> 49:54.840
want to just be able to take some image model.
49:54.840 --> 49:58.040
They don't care if it's inception or rest net or something else
49:58.040 --> 50:00.760
and do some training or transfer learning
50:00.760 --> 50:02.440
on their kind of model.
50:02.440 --> 50:04.400
Being able to make that easy is important.
50:04.400 --> 50:08.560
So in some ways, if you do that by providing them
50:08.560 --> 50:11.360
simple models with, say, in Hub or so on,
50:11.360 --> 50:13.680
they don't care about what's inside that box,
50:13.680 --> 50:15.120
but they want to be able to use it.
50:15.120 --> 50:17.600
So we're pushing on, I think, different levels.
50:17.600 --> 50:20.120
If you look at just a component that you get, which
50:20.120 --> 50:22.800
has the layers already smushed in,
50:22.800 --> 50:25.200
the beginners probably just want that.
50:25.200 --> 50:27.360
Then the next step is, OK, look at building
50:27.360 --> 50:29.000
layers with Keras.
50:29.000 --> 50:30.600
If you go out to research, then they
50:30.600 --> 50:33.120
are probably writing custom layers themselves
50:33.120 --> 50:34.360
or doing their own loops.
50:34.360 --> 50:36.320
So there's a whole spectrum there.
50:36.320 --> 50:38.600
And then providing the preentrain models
50:38.600 --> 50:44.760
seems to really decrease the time from you trying to start.
50:44.760 --> 50:46.800
So you could basically, in a Colab notebook,
50:46.800 --> 50:49.080
achieve what you need.
50:49.080 --> 50:51.280
So I'm basically answering my own question,
50:51.280 --> 50:54.240
because I think what TensorFlow delivered on recently
50:54.240 --> 50:57.000
is trivial for beginners.
50:57.000 --> 51:00.760
So I was just wondering if there was other pain points
51:00.760 --> 51:02.480
you're trying to ease, but I'm not sure there would.
51:02.480 --> 51:04.240
No, those are probably the big ones.
51:04.240 --> 51:07.080
I mean, I see high schoolers doing a whole bunch of things
51:07.080 --> 51:08.840
now, which is pretty amazing.
51:08.840 --> 51:11.360
It's both amazing and terrifying.
51:11.360 --> 51:12.640
Yes.
51:12.640 --> 51:16.920
In a sense that when they grow up,
51:16.920 --> 51:19.280
some incredible ideas will be coming from them.
51:19.280 --> 51:21.800
So there's certainly a technical aspect to your work,
51:21.800 --> 51:24.600
but you also have a management aspect
51:24.600 --> 51:28.000
to your role with TensorFlow, leading the project,
51:28.000 --> 51:31.080
a large number of developers and people.
51:31.080 --> 51:34.680
So what do you look for in a good team?
51:34.680 --> 51:37.400
What do you think Google has been at the forefront
51:37.400 --> 51:40.440
of exploring what it takes to build a good team?
51:40.440 --> 51:45.520
And TensorFlow is one of the most cutting edge technologies
51:45.520 --> 51:46.120
in the world.
51:46.120 --> 51:48.080
So in this context, what do you think
51:48.080 --> 51:50.480
makes for a good team?
51:50.480 --> 51:53.200
It's definitely something I think a fair bit about.
51:53.200 --> 51:59.560
I think in terms of the team being
51:59.560 --> 52:02.120
able to deliver something well, one of the things that's
52:02.120 --> 52:05.800
important is a cohesion across the team.
52:05.800 --> 52:10.400
So being able to execute together and doing things,
52:10.400 --> 52:11.440
it's not an end.
52:11.440 --> 52:14.120
Like at this scale, an individual engineer
52:14.120 --> 52:15.400
can only do so much.
52:15.400 --> 52:18.200
There's a lot more that they can do together,
52:18.200 --> 52:21.640
even though we have some amazing superstars across Google
52:21.640 --> 52:22.600
and in the team.
52:22.600 --> 52:26.200
But there's often the way I see it
52:26.200 --> 52:28.360
is the product of what the team generates
52:28.360 --> 52:34.440
is way larger than the whole individual put together.
52:34.440 --> 52:37.320
And so how do we have all of them work together,
52:37.320 --> 52:40.000
the culture of the team itself?
52:40.000 --> 52:43.000
Hiring good people is important.
52:43.000 --> 52:45.600
But part of that is it's not just that, OK,
52:45.600 --> 52:48.120
we hire a bunch of smart people and throw them together
52:48.120 --> 52:49.720
and let them do things.
52:49.720 --> 52:52.920
It's also people have to care about what they're building.
52:52.920 --> 52:57.320
People have to be motivated for the right kind of things.
52:57.320 --> 53:01.400
That's often an important factor.
53:01.400 --> 53:04.600
And finally, how do you put that together
53:04.600 --> 53:08.840
with a somewhat unified vision of where we want to go?
53:08.840 --> 53:11.200
So are we all looking in the same direction
53:11.200 --> 53:13.520
or just going all over?
53:13.520 --> 53:16.040
And sometimes it's a mix.
53:16.040 --> 53:21.400
Google's a very bottom up organization in some sense.
53:21.400 --> 53:24.680
Also research even more so.
53:24.680 --> 53:26.320
And that's how we started.
53:26.320 --> 53:30.840
But as we've become this larger product and ecosystem,
53:30.840 --> 53:35.040
I think it's also important to combine that well with a mix
53:35.040 --> 53:37.920
of, OK, here's the direction we want to go in.
53:37.920 --> 53:39.880
There is exploration we'll do around that.
53:39.880 --> 53:43.320
But let's keep staying in that direction, not just
53:43.320 --> 53:44.360
all over the place.
53:44.360 --> 53:46.880
And is there a way you monitor the health of the team?
53:46.880 --> 53:51.920
Sort of like, is there a way you know you did a good job?
53:51.920 --> 53:53.000
The team is good.
53:53.000 --> 53:56.960
I mean, you're saying nice things, but it's sometimes
53:56.960 --> 54:01.120
difficult to determine how aligned.
54:01.120 --> 54:04.480
Because it's not binary, it's not like there's tensions
54:04.480 --> 54:06.680
and complexities and so on.
54:06.680 --> 54:09.400
And the other element of this is the mesh of superstars.
54:09.400 --> 54:12.880
There's so much, even at Google, such a large percentage
54:12.880 --> 54:16.000
of work is done by individual superstars too.
54:16.000 --> 54:19.920
So there's a, and sometimes those superstars
54:19.920 --> 54:25.120
could be against the dynamic of a team and those tensions.
54:25.120 --> 54:27.320
I mean, I'm sure TensorFlow might be a little bit easier
54:27.320 --> 54:31.720
because the mission of the project is so beautiful.
54:31.720 --> 54:34.760
You're at the cutting edge, so it's exciting.
54:34.760 --> 54:36.640
But have you had struggle with that?
54:36.640 --> 54:38.360
Has there been challenges?
54:38.360 --> 54:39.800
There are always people challenges
54:39.800 --> 54:41.240
in different kinds of ways.
54:41.240 --> 54:44.520
That said, I think we've been what's
54:44.520 --> 54:49.320
good about getting people who care and have
54:49.320 --> 54:51.440
the same kind of culture, and that's Google in general
54:51.440 --> 54:53.480
to a large extent.
54:53.480 --> 54:56.760
But also, like you said, given that the project has had
54:56.760 --> 54:59.160
so many exciting things to do, there's
54:59.160 --> 55:02.080
been room for lots of people to do different kinds of things
55:02.080 --> 55:06.440
and grow, which does make the problem a bit easier, I guess.
55:06.440 --> 55:09.920
And it allows people, depending on what they're doing,
55:09.920 --> 55:13.120
if there's room around them, then that's fine.
55:13.120 --> 55:19.160
But yes, we do care about whether a superstar or not
55:19.160 --> 55:22.560
that they need to work well with the team across Google.
55:22.560 --> 55:23.760
That's interesting to hear.
55:23.760 --> 55:27.960
So it's like superstar or not, the productivity broadly
55:27.960 --> 55:30.520
is about the team.
55:30.520 --> 55:31.520
Yeah.
55:31.520 --> 55:32.960
I mean, they might add a lot of value,
55:32.960 --> 55:35.720
but if they're hurting the team, then that's a problem.
55:35.720 --> 55:38.720
So in hiring engineers, it's so interesting, right?
55:38.720 --> 55:41.840
The high rank process, what do you look for?
55:41.840 --> 55:44.240
How do you determine a good developer
55:44.240 --> 55:47.280
or a good member of a team from just a few minutes
55:47.280 --> 55:50.320
or hours together?
55:50.320 --> 55:51.920
Again, no magic answers, I'm sure.
55:51.920 --> 55:52.760
Yeah.
55:52.760 --> 55:56.240
And Google has a hiring process that we've refined
55:56.240 --> 56:00.880
over the last 20 years, I guess, and that you've probably
56:00.880 --> 56:02.200
heard and seen a lot about.
56:02.200 --> 56:05.280
So we do work with the same hiring process in that.
56:05.280 --> 56:08.280
That's really helped.
56:08.280 --> 56:10.880
For me in particular, I would say,
56:10.880 --> 56:14.200
in addition to the core technical skills,
56:14.200 --> 56:17.560
what does matter is their motivation
56:17.560 --> 56:19.560
in what they want to do.
56:19.560 --> 56:22.960
Because if that doesn't align well with where we want to go,
56:22.960 --> 56:25.320
that's not going to lead to long term success
56:25.320 --> 56:27.640
for either them or the team.
56:27.640 --> 56:30.640
And I think that becomes more important the more senior
56:30.640 --> 56:33.520
the person is, but it's important at every level.
56:33.520 --> 56:34.920
Like even the junior most engineer,
56:34.920 --> 56:37.680
if they're not motivated to do well at what they're trying to do,
56:37.680 --> 56:39.080
however smart they are, it's going
56:39.080 --> 56:40.320
to be hard for them to succeed.
56:40.320 --> 56:44.520
Does the Google hiring process touch on that passion?
56:44.520 --> 56:46.440
So like trying to determine.
56:46.440 --> 56:48.440
Because I think as far as I understand,
56:48.440 --> 56:52.000
maybe you can speak to it that the Google hiring process sort
56:52.000 --> 56:56.360
of helps the initial like determines the skill set there,
56:56.360 --> 56:59.840
is your puzzle solving ability, problem solving ability good.
56:59.840 --> 57:05.000
But I'm not sure, but it seems that the determining
57:05.000 --> 57:07.560
whether the person is like fire inside them
57:07.560 --> 57:09.840
that burns to do anything really doesn't really matter.
57:09.840 --> 57:11.520
It's just some cool stuff.
57:11.520 --> 57:15.320
I'm going to do it that I don't know.
57:15.320 --> 57:17.000
Is that something that ultimately ends up
57:17.000 --> 57:18.840
when they have a conversation with you
57:18.840 --> 57:22.600
or once it gets closer to the team?
57:22.600 --> 57:25.400
So one of the things we do have as part of the process
57:25.400 --> 57:28.600
is just a culture fit, like part of the interview process
57:28.600 --> 57:31.040
itself, in addition to just the technical skills.
57:31.040 --> 57:34.240
And each engineer or whoever the interviewer is,
57:34.240 --> 57:38.800
is supposed to rate the person on the culture and the culture
57:38.800 --> 57:39.960
fit with Google and so on.
57:39.960 --> 57:42.160
So that is definitely part of the process.
57:42.160 --> 57:45.800
Now, there are various kinds of projects
57:45.800 --> 57:46.960
and different kinds of things.
57:46.960 --> 57:50.040
So there might be variants in the kind of culture
57:50.040 --> 57:51.320
you want there and so on.
57:51.320 --> 57:52.720
And yes, that does vary.
57:52.720 --> 57:54.920
So for example, TensorFlow has always
57:54.920 --> 57:56.920
been a fast moving project.
57:56.920 --> 58:00.920
And we want people who are comfortable with that.
58:00.920 --> 58:02.640
But at the same time now, for example,
58:02.640 --> 58:05.200
we are at a place where we are also very full fledged product.
58:05.200 --> 58:08.440
And we want to make sure things that work really, really
58:08.440 --> 58:09.320
work right.
58:09.320 --> 58:11.680
You can't cut corners all the time.
58:11.680 --> 58:14.320
So balancing that out and finding the people
58:14.320 --> 58:17.560
who are the right fit for those is important.
58:17.560 --> 58:19.720
And I think those kind of things do vary a bit
58:19.720 --> 58:23.200
across projects and teams and product areas across Google.
58:23.200 --> 58:25.240
And so you'll see some differences there
58:25.240 --> 58:27.640
in the final checklist.
58:27.640 --> 58:29.600
But a lot of the core culture, it
58:29.600 --> 58:32.200
comes along with just the engineering, excellence,
58:32.200 --> 58:34.720
and so on.
58:34.720 --> 58:39.680
What is the hardest part of your job?
58:39.680 --> 58:41.920
I'll take your pick, I guess.
58:41.920 --> 58:44.440
It's fun, I would say.
58:44.440 --> 58:45.520
Hard, yes.
58:45.520 --> 58:47.240
I mean, lots of things at different times.
58:47.240 --> 58:49.160
I think that does vary.
58:49.160 --> 58:52.640
So let me clarify that difficult things are fun
58:52.640 --> 58:55.720
when you solve them, right?
58:55.720 --> 58:57.480
It's fun in that sense.
58:57.480 --> 59:02.600
I think the key to a successful thing across the board,
59:02.600 --> 59:05.320
and in this case, it's a large ecosystem now,
59:05.320 --> 59:09.800
but even a small product, is striking that fine balance
59:09.800 --> 59:12.000
across different aspects of it.
59:12.000 --> 59:17.000
Sometimes it's how fast you go versus how perfect it is.
59:17.000 --> 59:21.400
Sometimes it's how do you involve this huge community?
59:21.400 --> 59:22.360
Who do you involve?
59:22.360 --> 59:25.440
Or do you decide, OK, now is not a good time to involve them
59:25.440 --> 59:30.160
because it's not the right fit?
59:30.160 --> 59:33.640
Sometimes it's saying no to certain kinds of things.
59:33.640 --> 59:36.880
Those are often the hard decisions.
59:36.880 --> 59:41.000
Some of them you make quickly because you don't have the time.
59:41.000 --> 59:43.200
Some of them you get time to think about them,
59:43.200 --> 59:44.480
but they're always hard.
59:44.480 --> 59:49.200
So both choices are pretty good, those decisions.
59:49.200 --> 59:50.360
What about deadlines?
59:50.360 --> 59:58.200
Is this defined TensorFlow to be driven by deadlines
59:58.200 --> 1:00:00.360
to a degree that a product might?
1:00:00.360 --> 1:00:04.920
Or is there still a balance to where it's less deadline?
1:00:04.920 --> 1:00:08.920
You had the Dev Summit, they came together incredibly.
1:00:08.920 --> 1:00:11.440
Looked like there's a lot of moving pieces and so on.
1:00:11.440 --> 1:00:15.080
So did that deadline make people rise to the occasion,
1:00:15.080 --> 1:00:18.360
releasing TensorFlow 2.0 Alpha?
1:00:18.360 --> 1:00:20.360
I'm sure that was done last minute as well.
1:00:20.360 --> 1:00:25.600
I mean, up to the last point.
1:00:25.600 --> 1:00:28.600
Again, it's one of those things that you
1:00:28.600 --> 1:00:29.960
need to strike the good balance.
1:00:29.960 --> 1:00:32.040
There's some value that deadlines bring
1:00:32.040 --> 1:00:33.920
that does bring a sense of urgency
1:00:33.920 --> 1:00:35.720
to get the right things together.
1:00:35.720 --> 1:00:38.280
Instead of getting the perfect thing out,
1:00:38.280 --> 1:00:41.280
you need something that's good and works well.
1:00:41.280 --> 1:00:43.720
And the team definitely did a great job in putting that
1:00:43.720 --> 1:00:46.560
together, so it was very amazed and excited by everything,
1:00:46.560 --> 1:00:48.680
how that came together.
1:00:48.680 --> 1:00:50.640
That said, across the year, we try not
1:00:50.640 --> 1:00:52.520
to put out official deadlines.
1:00:52.520 --> 1:00:56.960
We focus on key things that are important,
1:00:56.960 --> 1:01:00.600
figure out how much of it's important,
1:01:00.600 --> 1:01:05.760
and we are developing in the open, internally and externally,
1:01:05.760 --> 1:01:07.920
everything's available to everybody.
1:01:07.920 --> 1:01:11.120
So you can pick and look at where things are.
1:01:11.120 --> 1:01:13.160
We do releases at a regular cadence,
1:01:13.160 --> 1:01:16.320
so fine if something doesn't necessarily end up with this
1:01:16.320 --> 1:01:19.600
month, it'll end up in the next release in a month or two.
1:01:19.600 --> 1:01:22.840
And that's OK, but we want to keep moving
1:01:22.840 --> 1:01:26.520
as fast as we can in these different areas.
1:01:26.520 --> 1:01:30.080
Because we can iterate and improve on things, sometimes
1:01:30.080 --> 1:01:32.920
it's OK to put things out that aren't fully ready.
1:01:32.920 --> 1:01:35.640
If you make sure it's clear that, OK, this is experimental,
1:01:35.640 --> 1:01:37.960
but it's out there if you want to try and give feedback.
1:01:37.960 --> 1:01:39.400
That's very, very useful.
1:01:39.400 --> 1:01:43.560
I think that quick cycle and quick iteration is important.
1:01:43.560 --> 1:01:47.200
That's what we often focus on rather than here's
1:01:47.200 --> 1:01:49.200
a deadline where you get everything else.
1:01:49.200 --> 1:01:52.880
It's 2.0, is there pressure to make that stable?
1:01:52.880 --> 1:01:57.760
Or like, for example, WordPress 5.0 just came out,
1:01:57.760 --> 1:02:01.760
and there was no pressure to, it was a lot of build updates
1:02:01.760 --> 1:02:04.960
that delivered way too late.
1:02:04.960 --> 1:02:06.440
And they said, OK, well, we're going
1:02:06.440 --> 1:02:09.680
to release a lot of updates really quickly to improve it.
1:02:09.680 --> 1:02:12.240
Do you see TensorFlow 2.0 in that same kind of way,
1:02:12.240 --> 1:02:15.240
or is there this pressure to once it hits 2.0,
1:02:15.240 --> 1:02:16.760
once you get to the release candidate,
1:02:16.760 --> 1:02:19.440
and then you get to the final, that's
1:02:19.440 --> 1:02:22.480
going to be the stable thing?
1:02:22.480 --> 1:02:26.680
So it's going to be stable in just like 1.0X
1:02:26.680 --> 1:02:32.080
was where every API that's there is going to remain in work.
1:02:32.080 --> 1:02:34.800
It doesn't mean we can't change things under the covers.
1:02:34.800 --> 1:02:36.720
It doesn't mean we can't add things.
1:02:36.720 --> 1:02:39.200
So there's still a lot more for us to do,
1:02:39.200 --> 1:02:41.080
and we continue to have more releases.
1:02:41.080 --> 1:02:42.920
So in that sense, there's still, I
1:02:42.920 --> 1:02:44.680
don't think we'd be done in like two months
1:02:44.680 --> 1:02:46.160
when we release this.
1:02:46.160 --> 1:02:49.880
I don't know if you can say, but is there, you know,
1:02:49.880 --> 1:02:53.680
there's not external deadlines for TensorFlow 2.0,
1:02:53.680 --> 1:02:58.520
but is there internal deadlines, artificial or otherwise,
1:02:58.520 --> 1:03:00.840
that you're trying to set for yourself,
1:03:00.840 --> 1:03:03.080
or is it whenever it's ready?
1:03:03.080 --> 1:03:05.680
So we want it to be a great product, right?
1:03:05.680 --> 1:03:09.880
And that's a big, important piece for us.
1:03:09.880 --> 1:03:11.160
TensorFlow is already out there.
1:03:11.160 --> 1:03:13.720
We have 41 million downloads for 1.x,
1:03:13.720 --> 1:03:15.880
so it's not like we have to have this.
1:03:15.880 --> 1:03:17.280
Yeah, exactly.
1:03:17.280 --> 1:03:19.320
So it's not like a lot of the features
1:03:19.320 --> 1:03:22.080
that we've really polishing and putting them together
1:03:22.080 --> 1:03:26.240
are there, we don't have to rush that just because.
1:03:26.240 --> 1:03:28.040
So in that sense, we want to get it right
1:03:28.040 --> 1:03:29.920
and really focus on that.
1:03:29.920 --> 1:03:31.520
That said, we have said that we are
1:03:31.520 --> 1:03:33.520
looking to get this out in the next few months,
1:03:33.520 --> 1:03:37.120
in the next quarter, and as far as possible,
1:03:37.120 --> 1:03:40.000
we'll definitely try to make that happen.
1:03:40.000 --> 1:03:44.360
Yeah, my favorite line was, spring is a relative concept.
1:03:44.360 --> 1:03:45.960
I love it.
1:03:45.960 --> 1:03:47.680
Spoken like a true developer.
1:03:47.680 --> 1:03:50.200
So something I'm really interested in,
1:03:50.200 --> 1:03:53.840
and your previous line of work is, before TensorFlow,
1:03:53.840 --> 1:03:57.720
you let a team and Google on search ads.
1:03:57.720 --> 1:04:02.840
I think this is a very interesting topic on every level,
1:04:02.840 --> 1:04:07.200
on a technical level, because if their best ads connect people
1:04:07.200 --> 1:04:10.080
to the things they want and need,
1:04:10.080 --> 1:04:12.280
and that they're worse, they're just these things
1:04:12.280 --> 1:04:15.840
that annoy the heck out of you to the point of ruining
1:04:15.840 --> 1:04:20.240
the entire user experience of whatever you're actually doing.
1:04:20.240 --> 1:04:23.600
So they have a bad rep, I guess.
1:04:23.600 --> 1:04:28.080
And on the other end, so that this connecting users
1:04:28.080 --> 1:04:32.120
to the thing they need to want is a beautiful opportunity
1:04:32.120 --> 1:04:35.360
for machine learning to shine, like huge amounts of data
1:04:35.360 --> 1:04:36.720
that's personalized, and you've got
1:04:36.720 --> 1:04:40.400
to map to the thing they actually won't get annoyed.
1:04:40.400 --> 1:04:43.760
So what have you learned from this Google that's
1:04:43.760 --> 1:04:45.160
leading the world in this aspect?
1:04:45.160 --> 1:04:47.560
What have you learned from that experience?
1:04:47.560 --> 1:04:51.520
And what do you think is the future of ads?
1:04:51.520 --> 1:04:54.040
Take you back to the end of that.
1:04:54.040 --> 1:04:59.720
Yes, it's been a while, but I totally agree with what you said.
1:04:59.720 --> 1:05:03.200
I think the search ads, the way it was always looked at,
1:05:03.200 --> 1:05:05.520
and I believe it still is, is it's
1:05:05.520 --> 1:05:08.240
an extension of what search is trying to do.
1:05:08.240 --> 1:05:10.560
The goal is to make the information
1:05:10.560 --> 1:05:14.680
and make the world's information accessible.
1:05:14.680 --> 1:05:17.120
With ads, it's not just information,
1:05:17.120 --> 1:05:19.120
but it may be products or other things
1:05:19.120 --> 1:05:20.800
that people care about.
1:05:20.800 --> 1:05:23.360
And so it's really important for them
1:05:23.360 --> 1:05:26.480
to align with what the users need.
1:05:26.480 --> 1:05:30.920
And in search ads, there's a minimum quality level
1:05:30.920 --> 1:05:32.320
before that ad would be shown.
1:05:32.320 --> 1:05:34.200
If we don't have an ad that hits that quality bar,
1:05:34.200 --> 1:05:35.960
it will not be shown, even if we have it.
1:05:35.960 --> 1:05:38.080
And OK, maybe we lose some money there.
1:05:38.080 --> 1:05:39.560
That's fine.
1:05:39.560 --> 1:05:41.200
That is really, really important,
1:05:41.200 --> 1:05:43.000
and I think that that is something I really
1:05:43.000 --> 1:05:45.040
liked about being there.
1:05:45.040 --> 1:05:48.120
Advertising is a key part.
1:05:48.120 --> 1:05:51.680
I mean, as a model, it's been around for ages, right?
1:05:51.680 --> 1:05:52.920
It's not a new model.
1:05:52.920 --> 1:05:57.440
It's been adapted to the web and became a core part of search
1:05:57.440 --> 1:06:02.120
and in many other search engines across the world.
1:06:02.120 --> 1:06:05.920
I do hope, like I said, there are aspects of ads
1:06:05.920 --> 1:06:06.680
that are annoying.
1:06:06.680 --> 1:06:09.600
And I go to a website, and if it just
1:06:09.600 --> 1:06:12.160
keeps popping an ad in my face, not to let me read,
1:06:12.160 --> 1:06:13.800
that's going to be annoying clearly.
1:06:13.800 --> 1:06:22.080
So I hope we can strike that balance between showing a good
1:06:22.080 --> 1:06:25.040
ad where it's valuable to the user
1:06:25.040 --> 1:06:30.960
and provides the monetization to the service.
1:06:30.960 --> 1:06:32.000
And this might be search.
1:06:32.000 --> 1:06:33.680
This might be a website.
1:06:33.680 --> 1:06:37.320
All of these, they do need the monetization for them
1:06:37.320 --> 1:06:39.640
to provide that service.
1:06:39.640 --> 1:06:45.720
But if it's done in a good balance between showing
1:06:45.720 --> 1:06:48.040
just some random stuff that's distracting
1:06:48.040 --> 1:06:50.920
versus showing something that's actually valuable.
1:06:50.920 --> 1:06:55.360
So do you see it moving forward as to continue
1:06:55.360 --> 1:07:00.960
being a model that funds businesses like Google?
1:07:00.960 --> 1:07:05.160
That's a significant revenue stream.
1:07:05.160 --> 1:07:08.080
Because that's one of the most exciting things,
1:07:08.080 --> 1:07:09.680
but also limiting things on the internet
1:07:09.680 --> 1:07:12.200
is nobody wants to pay for anything.
1:07:12.200 --> 1:07:15.360
And advertisements, again, coupled at their best
1:07:15.360 --> 1:07:17.360
are actually really useful and not annoying.
1:07:17.360 --> 1:07:22.320
Do you see that continuing and growing and improving?
1:07:22.320 --> 1:07:26.680
Or is there GC sort of more Netflix type models
1:07:26.680 --> 1:07:28.960
where you have to start to pay for content?
1:07:28.960 --> 1:07:31.000
I think it's a mix.
1:07:31.000 --> 1:07:32.840
I think it's going to take a long while for everything
1:07:32.840 --> 1:07:35.320
to be paid on the internet, if at all.
1:07:35.320 --> 1:07:36.160
Probably not.
1:07:36.160 --> 1:07:37.400
I mean, I think there's always going
1:07:37.400 --> 1:07:40.760
to be things that are sort of monetized with things like ads.
1:07:40.760 --> 1:07:42.800
But over the last few years, I would say
1:07:42.800 --> 1:07:44.760
we've definitely seen that transition
1:07:44.760 --> 1:07:48.560
towards more paid services across the web
1:07:48.560 --> 1:07:50.360
and people are willing to pay for them
1:07:50.360 --> 1:07:51.760
because they do see the value.
1:07:51.760 --> 1:07:53.600
I mean, Netflix is a great example.
1:07:53.600 --> 1:07:56.520
I mean, we have YouTube doing things.
1:07:56.520 --> 1:07:59.720
People pay for the apps they buy, more people
1:07:59.720 --> 1:08:03.120
they find are willing to pay for newspaper content,
1:08:03.120 --> 1:08:07.240
for the good news websites across the web.
1:08:07.240 --> 1:08:11.040
That wasn't the case even a few years ago, I would say.
1:08:11.040 --> 1:08:13.280
And I just see that change in myself as well
1:08:13.280 --> 1:08:14.840
and just lots of people around me.
1:08:14.840 --> 1:08:19.240
So definitely hopeful that we'll transition to that mix model
1:08:19.240 --> 1:08:23.400
where maybe you get to try something out for free,
1:08:23.400 --> 1:08:24.120
maybe with ads.
1:08:24.120 --> 1:08:27.080
But then there is a more clear revenue model
1:08:27.080 --> 1:08:30.600
that sort of helps go beyond that.
1:08:30.600 --> 1:08:34.760
So speaking of revenue, how is it
1:08:34.760 --> 1:08:39.400
that a person can use the TPU in a Google Colab for free?
1:08:39.400 --> 1:08:43.920
So what's the, I guess, the question is,
1:08:43.920 --> 1:08:48.880
what's the future of TensorFlow in terms of empowering,
1:08:48.880 --> 1:08:51.880
say, a class of 300 students?
1:08:51.880 --> 1:08:55.920
And I'm asked by MIT, what is going
1:08:55.920 --> 1:08:58.640
to be the future of them being able to do their homework
1:08:58.640 --> 1:09:00.200
in TensorFlow?
1:09:00.200 --> 1:09:02.800
Where are they going to train these networks, right?
1:09:02.800 --> 1:09:07.720
What's that future look like with TPUs, with cloud services,
1:09:07.720 --> 1:09:08.920
and so on?
1:09:08.920 --> 1:09:10.240
I think a number of things there.
1:09:10.240 --> 1:09:12.600
I mean, any TensorFlow open source,
1:09:12.600 --> 1:09:13.640
you can run it wherever.
1:09:13.640 --> 1:09:15.880
You can run it on your desktop, and your desktops
1:09:15.880 --> 1:09:19.480
always keep getting more powerful, so maybe you can do more.
1:09:19.480 --> 1:09:22.040
My phone is like, I don't know how many times more powerful
1:09:22.040 --> 1:09:23.520
than my first desktop.
1:09:23.520 --> 1:09:25.200
You'll probably train it on your phone, though.
1:09:25.200 --> 1:09:26.200
Yeah, that's true.
1:09:26.200 --> 1:09:28.080
Right, so in that sense, the power
1:09:28.080 --> 1:09:31.440
you have in your hand is a lot more.
1:09:31.440 --> 1:09:34.400
Clouds are actually very interesting from, say,
1:09:34.400 --> 1:09:37.840
students or courses perspective, because they
1:09:37.840 --> 1:09:40.040
make it very easy to get started.
1:09:40.040 --> 1:09:42.040
I mean, Colab, the great thing about it
1:09:42.040 --> 1:09:45.120
is go to a website, and it just works.
1:09:45.120 --> 1:09:47.560
No installation needed, nothing to, you know,
1:09:47.560 --> 1:09:49.960
you're just there, and things are working.
1:09:49.960 --> 1:09:52.280
That's really the power of cloud, as well.
1:09:52.280 --> 1:09:55.320
And so I do expect that to grow.
1:09:55.320 --> 1:09:57.920
Again, Colab is a free service.
1:09:57.920 --> 1:10:00.840
It's great to get started, to play with things,
1:10:00.840 --> 1:10:03.080
to explore things.
1:10:03.080 --> 1:10:08.200
That said, with free, you can only get so much, maybe.
1:10:08.200 --> 1:10:11.080
So just like we were talking about free versus paid,
1:10:11.080 --> 1:10:15.280
and there are services you can pay for and get a lot more.
1:10:15.280 --> 1:10:16.000
Great.
1:10:16.000 --> 1:10:18.480
So if I'm a complete beginner interested in machine
1:10:18.480 --> 1:10:21.560
learning and TensorFlow, what should I do?
1:10:21.560 --> 1:10:24.240
Probably start with going to a website and playing there.
1:10:24.240 --> 1:10:26.560
Just go to TensorFlow.org and start clicking on things.
1:10:26.560 --> 1:10:28.440
Yep, check out tutorials and guides.
1:10:28.440 --> 1:10:30.680
There's stuff you can just click there and go to Colab
1:10:30.680 --> 1:10:31.320
and do things.
1:10:31.320 --> 1:10:32.360
No installation needed.
1:10:32.360 --> 1:10:34.040
You can get started right there.
1:10:34.040 --> 1:10:34.840
OK, awesome.
1:10:34.840 --> 1:10:36.720
Roger, thank you so much for talking today.
1:10:36.720 --> 1:10:37.440
Thank you, Lex.
1:10:37.440 --> 1:10:46.680
Have fun this week.
|