{"id":6483,"date":"2021-11-02T18:37:45","date_gmt":"2021-11-02T11:37:45","guid":{"rendered":"https:\/\/science-technology.vn\/?p=6483"},"modified":"2021-11-03T13:28:52","modified_gmt":"2021-11-03T06:28:52","slug":"tri-tue-nhan-tao-cac-khai-niem-hoc-may-mang-no-ron-hoc-sau-xu-li-ngon-ngu-tu-nhien-nhan-dang-tieng-noi-thi-giac-may-tinh","status":"publish","type":"post","link":"https:\/\/science-technology.vn\/?p=6483","title":{"rendered":"K\u0129 ngh\u1ec7 Tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o – C\u00e1c kh\u00e1i ni\u1ec7m: H\u1ecdc m\u00e1y, M\u1ea1ng n\u01a1 ron, H\u1ecdc s\u00e2u, X\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean, Nh\u1eadn d\u1ea1ng ti\u1ebfng n\u00f3i, Th\u1ecb gi\u00e1c m\u00e1y t\u00ednh"},"content":{"rendered":"\n

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Tr\u00edch ra t\u1eeb video c\u1ee7a Gs. John Vu (12:35-25:57) t\u1ea1i<\/p>\n\n\n\n

https:\/\/drive.google.com\/file\/d\/1AhVDvTKGMaYKLSQe6LMVdPYyqTz0uavB\/view?usp=sharing<\/a><\/p>\n\n\n\n

Thu\u1eadt ng\u1eef kh\u00e1c m\u00e0 nhi\u1ec1u ng\u01b0\u1eddi li\u00ean k\u1ebft v\u1edbi tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o c\u00f3 t\u00ean l\u00e0 h\u1ecdc m\u00e1y. H\u1ecdc m\u00e1y l\u00e0 m\u1ed9t trong nhi\u1ec1u thu\u1eadt gi\u1ea3i \u0111\u01b0\u1ee3c d\u00f9ng trong AI. Ngay c\u1ea3 ng\u00e0y nay h\u1ecdc m\u00e1y nh\u1eadn \u0111\u01b0\u1ee3c nhi\u1ec1u s\u1ef1 quan t\u00e2m. H\u1ecdc m\u00e1y th\u1ef1c s\u1ef1 l\u00e0 l\u0129nh v\u1ef1c li\u00ean quan t\u1edbi thi\u1ebft k\u1ebf ch\u01b0\u01a1ng tr\u00ecnh cho ph\u00e9p m\u00e1y h\u1ecdc v\u00e0 l\u00e0m d\u1ef1 \u0111o\u00e1n t\u1eeb m\u1ecdi d\u1eef li\u1ec7u m\u00e0 kh\u00f4ng c\u00f3 s\u1ef1 h\u1ed7 tr\u1ee3 n\u00e0o \u0111\u00f3 hay \u0111\u01b0\u1ee3c l\u1eadp tr\u00ecnh b\u1edfi ng\u01b0\u1eddi l\u1eadp tr\u00ecnh. Nh\u1eefng thu\u1eadt gi\u1ea3i n\u00e0y \u0111ang \u0111\u01b0\u1ee3c d\u00f9ng trong nhi\u1ec1u h\u1ec7 th\u1ed1ng AI nh\u01b0 nh\u1eadn d\u1ea1ng m\u1eb7t, g\u1ee3i \u00fd \u00e2m nh\u1ea1c, l\u1ecdc th\u01b0 r\u00e1c, v\u00e0 cho vi\u1ec7c ph\u00e1t hi\u1ec7n v\u1ea5n \u0111\u1ec1 trong c\u00e1c th\u1ec3 ch\u1ebf t\u00e0i ch\u00ednh v\u00e0 ng\u00e2n h\u00e0ng. Ch\u00fang ta s\u1ebd n\u00f3i th\u00eam v\u1ec1 h\u1ecdc m\u00e1y trong v\u00e0i b\u00e0i gi\u1ea3ng ti\u1ebfp.<\/p>\n\n\n\n

Thu\u1eadt ng\u1eef kh\u00e1c m\u00e0 nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n c\u00f3 l\u1ebd \u0111\u00e3 t\u1eebng nghe th\u1ea5y l\u00e0 m\u1ea1ng n\u01a1 ron. M\u1ea1ng n\u01a1 ron l\u00e0 ch\u00eca kho\u00e1 \u0111\u1ec3 x\u1eed l\u00ed cho h\u1ecdc m\u00e1y. V\u00e0 n\u00f3 th\u1ef1c s\u1ef1 bao g\u1ed3m nhi\u1ec1u t\u1ea7ng thu\u1eadt gi\u1ea3i. M\u00f4 h\u00ecnh h\u1ecdc m\u00e1y l\u00e0 c\u1ea5u tr\u00fac theo c\u00e1ch n\u00e0o \u0111\u00f3 gi\u1ed1ng nh\u01b0 n\u01a1 ron trong n\u00e3o ng\u01b0\u1eddi. V\u1ec1 c\u0103n b\u1ea3n m\u1ea1ng n\u01a1 ron bao g\u1ed3m v\u00e0i t\u1ea7ng n\u01a1 ron t\u01b0\u01a1ng t\u1ef1 nh\u01b0 n\u01a1 ron n\u00e3o ng\u01b0\u1eddi, m\u00e0 n\u1ea1p d\u1eef li\u1ec7u l\u1eabn cho nhau. Cho n\u00ean m\u1ee9c n\u00e0y ph\u00e2n t\u00edch d\u1eef li\u1ec7u, \u0111i t\u1edbi h\u00ecnh m\u1eabu d\u1ef1 \u0111o\u00e1n n\u00e0o \u0111\u00f3, g\u1eedi k\u1ebft qu\u1ea3 cho m\u1ee9c ti\u1ebfp \u0111\u1ec3 ti\u1ebfp t\u1ee5c l\u00e0m m\u1ecbn d\u1eef li\u1ec7u v\u00e0 h\u1ecdc t\u1eeb m\u1ee9c tr\u01b0\u1edbc. Cu\u1ed1i c\u00f9ng ch\u00fang \u0111i t\u1edbi k\u1ebft qu\u1ea3 n\u00e0o \u0111\u00f3 t\u1ed1t h\u01a1n nhi\u1ec1u. M\u1ea1ng n\u01a1 ron l\u00e0 t\u01b0\u01a1ng \u0111\u1ed1i m\u1edbi nh\u01b0ng vi\u1ec7c l\u00e0m m\u00f4 h\u00ecnh, thi\u1ebft k\u1ebf v\u00e0 x\u00e2y d\u1ef1ng m\u1ea1ng n\u01a1 ron \u0111\u00e3 t\u1ea1o ra kh\u00e1c bi\u1ec7t l\u1edbn l\u00e0m cho m\u00e1y t\u1ed1t h\u01a1n, AI t\u1ed1t h\u01a1n c\u00e1c c\u00e1ch l\u00e0m m\u00f4 h\u00ecnh kh\u00e1c.<\/p>\n\n\n\n

V\u1ec1 c\u0103n b\u1ea3n trong khi hu\u1ea5n luy\u1ec7n m\u1ea1ng n\u01a1 ron, c\u00e1ch th\u1ee9c n\u00e0o \u0111\u00f3, ph\u01b0\u01a1ng ti\u1ec7n n\u00e0o \u0111\u00f3, h\u1ec7 s\u1ed1 n\u00e0o \u0111\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c g\u1eafn v\u1edbi c\u00e1i v\u00e0o, \u0111i\u1ec1u \u0111\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c li\u00ean t\u1ee5c bi\u1ebfn thi\u00ean t\u1eeb c\u00e1i ra, t\u1eeb c\u00e1i n\u00e0y sang c\u00e1i kia. Cu\u1ed1i c\u00f9ng ch\u00fang s\u1ebd \u0111i t\u1edbi nh\u1eefng d\u1ef1 \u0111o\u00e1n r\u1ea5t t\u1ed1t. Ch\u00fang ta s\u1ebd n\u00f3i nhi\u1ec1u h\u01a1n v\u1ec1 m\u1ea1ng n\u01a1 ron rong v\u00e0i b\u00e0i gi\u1ea3ng ti\u1ebfp.<\/p>\n\n\n\n

C\u00f3 hai \u0111i\u1ec1u l\u00e0 r\u1ea5t quan tr\u1ecdng \u0111\u1ec3 b\u1ea1n bi\u1ebft trong h\u1ecdc m\u00e1y. M\u1ed9t \u0111i\u1ec1u \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t. Trong h\u1ecdc m\u00e1y b\u1ea1n c\u00f3 h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t v\u00e0 h\u1ecdc kh\u00f4ng gi\u00e1m s\u00e1t. H\u1ecdc c\u00f3 gi\u00e1m s\u00e1t l\u00e0 k\u0129 thu\u1eadt \u0111\u1ec3 d\u1ea1y c\u00e1c h\u1ec7 th\u1ed1ng AI b\u1eb1ng vi\u1ec7c d\u1ea1y ch\u00fang qua vi\u1ec7c d\u00f9ng m\u1ed9t s\u1ed1 l\u1edbn c\u00e1c v\u00ed d\u1ee5 \u0111\u01b0\u1ee3c \u0111\u00e1nh nh\u00e3n. Vi\u1ec7c \u0111\u00e1nh nh\u00e3n c\u00e1c v\u00ed d\u1ee5 \u0111\u01b0\u1ee3c ti\u1ebfn h\u00e0nh b\u1edfi c\u00f4ng nh\u00e2n ph\u1ea7n m\u1ec1m. Ng\u01b0\u1eddi l\u1eadp tr\u00ecnh nh\u1eadn di\u1ec7n d\u1eef li\u1ec7u n\u00e0o \u0111\u00f3 v\u00e0 v\u1ec1 c\u0103n b\u1ea3n \u0111\u00e1nh nh\u00e3n ch\u00fang \u0111\u1ec3 cho ph\u00e9p m\u00e1y h\u1ecdc. V\u1ec1 c\u0103n b\u1ea3n khi m\u00e1y \u0111ang h\u1ecdc b\u1ea1n gi\u00e1m s\u00e1t vi\u1ec7c h\u1ecdc b\u1eb1ng c\u00e1ch thay \u0111\u1ed5i d\u1eef li\u1ec7u, b\u1eb1ng vi\u1ec7c l\u00e0m r\u00f5 r\u00e0ng, b\u1eb1ng vi\u1ec7c ph\u00e2n lo\u1ea1i d\u1eef li\u1ec7u cho m\u00e1y h\u1ecdc. V\u1ec1 c\u0103n b\u1ea3n m\u1ed9t trong c\u00e1c k\u0129 thu\u1eadt m\u00e0 ch\u00fang ta s\u1ebd n\u00f3i nhi\u1ec1u th\u00eam trong l\u1edbp nh\u1eadp m\u00f4n l\u00e0 v\u1ec1 h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t.<\/p>\n\n\n\n

M\u1ed9t khi m\u00e1y \u0111\u00e3 \u0111\u01b0\u1ee3c h\u1ecdc, h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 \u00e1p d\u1ee5ng m\u1ed9t nh\u00e3n cho d\u1eef li\u1ec7u m\u1edbi v\u00e0 gi\u1ea3i quy\u1ebft b\u00e0i to\u00e1n. Ch\u1eb3ng h\u1ea1n, n\u1ebfu b\u1ea1n hu\u1ea5n luy\u1ec7n m\u00e1y \u0111\u1ec3 h\u1ecdc v\u1ec1 con ch\u00f3 trong b\u1ee9c \u1ea3nh th\u1ebf th\u00ec cu\u1ed1i c\u00f9ng m\u00e1y c\u00f3 th\u1ec3 h\u1ecdc \u0111\u01b0\u1ee3c c\u00e1ch nh\u1eadn di\u1ec7n v\u1eadt n\u00e0o \u0111\u00f3 b\u00ean trong b\u1ee9c \u1ea3nh.<\/p>\n\n\n\n

K\u0129 thu\u1eadt kh\u00e1c c\u00f3 t\u00ean l\u00e0 d\u1eef li\u1ec7u kh\u00f4ng gi\u00e1m s\u00e1t, t\u01b0\u01a1ng ph\u1ea3n v\u1edbi d\u1eef li\u1ec7u c\u00f3 gi\u00e1m s\u00e1t, d\u1eef li\u1ec7u kh\u00f4ng gi\u00e1m s\u00e1t d\u00f9ng c\u00e1ch ti\u1ebfp c\u1eadn kh\u00e1c ho\u00e0n to\u00e0n, v\u1edbi thu\u1eadt gi\u1ea3i nh\u1eadn di\u1ec7n nh\u1eefng h\u00ecnh m\u1eabu nh\u01b0 v\u1eady trong d\u1eef li\u1ec7u. Vi\u1ec7c t\u00ecm s\u1ef1 t\u01b0\u01a1ng t\u1ef1 nh\u01b0 v\u1eady c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c d\u00f9ng \u0111\u1ec3 ph\u00e2n lo\u1ea1i d\u1eef li\u1ec7u c\u1ee7a n\u00f3. Thu\u1eadt gi\u1ea3i n\u00e0y kh\u00f4ng \u0111\u01b0\u1ee3c thi\u1ebft l\u1eadp \u0111\u1ec3 l\u1ea5y ra b\u1ea3n th\u00e2n ki\u1ec3u d\u1eef li\u1ec7u x\u00e1c \u0111\u1ecbnh. Nh\u01b0ng n\u00f3 nh\u00ecn v\u00e0o d\u1eef li\u1ec7u n\u00e0o \u0111\u00f3 v\u00e0 ph\u00e2n lo\u1ea1i v\u00e0 ph\u00e2n l\u1edbp th\u00e0nh c\u00e1c nh\u00f3m theo s\u1ef1 t\u01b0\u01a1ng t\u1ef1.<\/p>\n\n\n\n

Ch\u1eb3ng h\u1ea1n, Google News g\u1ed9p nh\u00f3m m\u1ecdi b\u00e0i b\u00e1o c\u00f3 ch\u1ee7 \u0111\u1ec1 t\u01b0\u01a1ng t\u1ef1 trong ng\u00e0y v\u00e0 t\u1ea1o ra tin t\u1ee9c cho \u0111\u1ed9c gi\u1ea3 c\u1ee7a h\u1ecd. Ch\u00fang ta s\u1ebd n\u00f3i nhi\u1ec1u h\u01a1n v\u1ec1 h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t v\u00e0 h\u1ecdc kh\u00f4ng gi\u00e1m s\u00e1t trong b\u00e0i gi\u1ea3ng ti\u1ebfp s\u1eafp t\u1edbi.<\/p>\n\n\n\n

Thu\u1eadt ng\u1eef kh\u00e1c m\u00e0 nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n c\u00f3 l\u1ebd \u0111\u00e3 nghe t\u1edbi khi ch\u00fang ta nh\u1eafc t\u1edbi tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 h\u1ecdc s\u00e2u. H\u1ecdc s\u00e2u l\u00e0 m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y n\u01a1i m\u1ea1ng n\u01a1 ron \u0111\u01b0\u1ee3c m\u1edf r\u1ed9ng th\u00e0nh m\u1ea1ng kh\u1ed5ng l\u1ed3 v\u1edbi nhi\u1ec1u t\u1ea7ng. Ch\u00fang \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n b\u1eb1ng vi\u1ec7c d\u00f9ng s\u1ed1 l\u01b0\u1ee3ng d\u1eef li\u1ec7u qui m\u00f4 r\u1ea5t l\u1edbn. Trong m\u1ea1ng n\u00e0y, m\u1ea1ng n\u01a1 ron nh\u00e2n t\u1ea1o \u0111\u01b0\u1ee3c thu x\u1ebfp theo c\u00e1c t\u1ea7ng \u0111\u01b0\u1ee3c li\u00ean n\u1ed1i. T\u1ea7ng \u0111\u01b0a v\u00e0o nh\u1eadn d\u1eef li\u1ec7u t\u1eeb th\u1ebf gi\u1edbi b\u00ean ngo\u00e0i, t\u1ea7ng \u0111\u01b0a ra s\u1ebd ch\u1ec9 \u0111\u1ea1o c\u00e1ch h\u1ec7 th\u1ed1ng \u0111\u00e1p \u1ee9ng cho nh\u1eefng th\u00f4ng tin \u0111\u00f3. Gi\u1eefa nh\u1eefng t\u1ea7ng \u0111\u00f3, m\u1ed9t t\u1ea7ng n\u01a1 ron ph\u1ee5 th\u00eam x\u1eed l\u00ed d\u1eef li\u1ec7u b\u1eb1ng vi\u1ec7c g\u1eafn tr\u1ecdng s\u1ed1 n\u00e0o \u0111\u00f3, h\u1ec7 s\u1ed1 n\u00e0o \u0111\u00f3 l\u00ean th\u00f4ng tin ch\u00fang nh\u1eadn t\u1eeb c\u00e1c t\u1ea7ng \u0111i tr\u01b0\u1edbc. V\u00e0 b\u1eb1ng vi\u1ec7c \u0111i t\u1edbi \u0111a t\u1ea7ng, \u0111a ph\u00e2n t\u00edch, \u0111a d\u1ef1 \u0111o\u00e1n, ch\u00fang s\u1ebd \u0111i t\u1edbi c\u00e1i g\u00ec \u0111\u00f3 t\u1ed1t h\u01a1n nhi\u1ec1u, ch\u00ednh x\u00e1c h\u01a1n \u1edf ch\u1ed7 k\u1ebft th\u00fac.<\/p>\n\n\n\n

Trong m\u1ea1ng h\u1ecdc s\u00e2u ch\u00fang c\u00f3 th\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 ph\u1ee9c t\u1ea1p v\u00ec s\u1ed1 l\u01b0\u1ee3ng kh\u1ed5ng l\u1ed5 c\u00e1c n\u01a1 ron l\u00e0m vi\u1ec7c c\u00f9ng nhau. V\u1ec1 c\u0103n b\u1ea3n \u0111\u00e2y l\u00e0 s\u1ed1 l\u01b0\u1ee3ng c\u1ef1c l\u1edbn \u0111a n\u01a1 ron, \u0111a t\u1ea7ng, t\u1ea1i m\u1ed7i m\u1ed9t trong ch\u00fang \u0111\u1ec1u n\u1ea1p c\u00e1i ra cho n\u01a1 ron ti\u1ebfp n\u00f3. Cho n\u00ean b\u1ea1n \u0111i t\u1edbi vi\u1ec7c l\u00e0m m\u1ecbn t\u1ed1t h\u01a1n nhi\u1ec1u v\u00e0 ch\u00ednh x\u00e1c h\u01a1n r\u1ea5t nhi\u1ec1u v\u00e0 hi\u1ec7u n\u0103ng cao.<\/p>\n\n\n\n

N\u1ec1n t\u1ea3ng c\u1ee7a m\u1ecdi tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean. V\u00e0 \u0111\u00e2y c\u0169ng l\u00e0 m\u1ed9t trong nh\u1eefng khu v\u1ef1c \u0111\u00e3 v\u1eeba kh\u00f4ng \u0111\u01b0\u1ee3c \u0111\u1ed9ng ch\u1ea1m \u0111\u00fang v\u1eeba kh\u00f4ng thu \u0111\u01b0\u1ee3c s\u1ef1 ph\u1ed5 bi\u1ebfn. Nh\u01b0ng t\u00f4i ngh\u0129 n\u00f3 l\u00e0 n\u1ec1n t\u1ea3ng v\u00ec x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean l\u00e0 c\u00e1ch ch\u00fang ta l\u00e0m cho m\u00e1y t\u00ednh hi\u1ec3u vi\u1ec7c x\u1eed l\u00ed v\u00e0 thao t\u00e1c ng\u00f4n ng\u1eef con ng\u01b0\u1eddi. \u0110\u1ec3 l\u00e0m \u0111i\u1ec1u \u0111\u00f3, ch\u00fang ph\u1ea3i hi\u1ec3u l\u01b0\u1ee3ng kh\u1ed5ng l\u1ed3 th\u00f4ng tin t\u1eeb qui t\u1eafc v\u0103n ph\u1ea1m t\u1edbi c\u00fa ph\u00e1p t\u1edbi c\u00e1c c\u1ee5m t\u1eeb th\u00f4ng th\u01b0\u1eddng v\u00e0 gi\u1ecdng n\u00f3i. Do \u0111\u00f3 \u0111\u00e2y c\u0169ng l\u00e0 thu\u1eadt gi\u1ea3i r\u1ea5t ph\u1ee9c t\u1ea1p, ph\u1ea7n m\u1ec1m r\u1ea5t ph\u1ee9c t\u1ea1p cho ph\u00e9p m\u00e1y hi\u1ec3u ng\u1eef c\u1ea3nh c\u1ee7a ng\u00f4n ng\u1eef m\u00e0 ch\u00fang ta n\u00f3i.<\/p>\n\n\n\n

Ch\u1eb3ng h\u1ea1n, trong nh\u1eadn d\u1ea1ng ti\u1ebfng n\u00f3, gi\u1ecdng n\u00f3i con ng\u01b0\u1eddi tr\u1edf th\u00e0nh d\u1eef li\u1ec7u \u00e2m thanh, m\u00e0 \u0111\u01b0\u1ee3c chuy\u1ec3n th\u00e0nh d\u1eef li\u1ec7u v\u0103n b\u1ea3n, v\u00e0 th\u1ebf r\u1ed3i c\u00f3 kh\u1ea3 n\u0103ng t\u1ef1 x\u1eed l\u00ed n\u00f3. X\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean l\u00e0 r\u1ea5t quan tr\u1ecdng v\u00ec ng\u00e0y nay c\u00f3 nhi\u1ec1u nhi\u1ec1u h\u1ec7 th\u1ed1ng d\u00f9ng ti\u1ebfng n\u00f3i. B\u1ea1n c\u00f3 th\u1ec3 th\u1ea5y ch\u1eb3ng h\u1ea1n, Alexa, nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n \u0111\u00e3 t\u1eebng d\u00f9ng c\u00e1i \u0111\u00f3. C\u00e1i \u0111\u00f3 c\u0169ng l\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o d\u00f9ng x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean. Ch\u00fang ta s\u1ebd n\u00f3i nhi\u1ec1u h\u01a1n v\u1ec1 \u0111i\u1ec1u n\u00e0y trong v\u00e0i b\u00e0i gi\u1ea3ng ti\u1ebfp.<\/p>\n\n\n\n

Ch\u1eb3ng h\u1ea1n, b\u1ea1n c\u00f3 th\u1ea5y r\u1eb1ng trong tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o trong n\u0103m 2011, nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n ch\u1eafc \u0111\u00e3 th\u1ea5y m\u00e1y t\u00ednh Watson c\u1ee7a IBM \u0111\u01b0\u1ee3c l\u00ean c\u00e1c \u0111\u1ea7u \u0111\u1ec1 tr\u00ean c\u00e1c b\u00e1o khi n\u00f3 th\u1eafng cu\u1ed9c bi\u1ec3u di\u1ec5n h\u1ecfi \u0111\u00e1p Jeopardy, \u0111\u00e1nh b\u1ea1i ng\u01b0\u1eddi ch\u01a1i gi\u1ecfi nh\u1ea5t trong bu\u1ed5i bi\u1ec3u di\u1ec5n \u0111\u00e3 t\u1eebng \u0111\u01b0\u1ee3c t\u1ea1o ra. M\u00e1y t\u00ednh Watson c\u1ee7a IBM d\u00f9ng x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u l\u1edbn tr\u00ean c\u00e1c kho d\u1eef li\u1ec7u bao la m\u00e0 n\u00f3 x\u1eed l\u00ed \u0111\u1ec3 tr\u1ea3 l\u1eddi c\u00e1c c\u00e2u h\u1ecfi do con ng\u01b0\u1eddi \u0111\u1eb7t ra, th\u01b0\u1eddng trong m\u1ed9t ph\u1ea7n gi\u00e2y. Nhi\u1ec1u ph\u1ea7n m\u1ec1m trong Watson \u0111\u01b0\u1ee3c t\u1ea1o ra \u1edf Carnegie Mellony. V\u00e0 t\u00f4i \u0111\u00e3 t\u1eebng l\u00e0m vi\u1ec7c v\u1edbi Ts. Harris \u2026 v\u1ec1 h\u1ec7 th\u1ed1ng n\u00e0y. V\u00e0 \u0111\u00e2y l\u00e0 m\u1ed9t trong nh\u1eefng n\u1ed7 l\u1ef1c \u0111\u1ea7u ti\u00ean d\u00f9ng AI trong x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean.<\/p>\n\n\n\n

Cho n\u00ean ch\u00fang ta h\u00e3y nh\u00ecn v\u00e0o c\u00e1ch m\u1ecdi ng\u01b0\u1eddi d\u00f9ng AI trong cu\u1ed9c s\u1ed1ng th\u01b0\u1eddng ng\u00e0y. V\u1ec1 c\u0103n b\u1ea3n AI \u0111\u00e3 t\u1eebng hi\u1ec7n di\u1ec7n trong nhi\u1ec1u m\u1eb7t c\u1ee7a cu\u1ed9c s\u1ed1ng c\u1ee7a ch\u00fang ta. B\u1ea1n \u0111\u00e3 th\u1ea5y Ng\u01b0\u1eddi tr\u1ee3 l\u00ed th\u00f4ng minh nh\u01b0 Siri, Alexa v\u00e0 Cortana, t\u1ea5t c\u1ea3 ch\u00fang \u0111\u1ec1u d\u00f9ng x\u1eed l\u00ed ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean \u0111\u1ec3 hi\u1ec3u c\u00e1c m\u1ec7nh l\u1ec7nh ti\u1ebfng n\u00f3i v\u00e0 tr\u1ea3 l\u1eddi c\u00e2u h\u1ecfi c\u1ee7a b\u1ea1n, s\u1eafp \u0111\u1eb7t nh\u1eefng nh\u1eafc nh\u1edf, t\u00ecm nh\u1ea1c b\u1ea1n th\u00edch, \u0111i\u1ec1u ch\u1ec9nh b\u1ed9 \u1ed5n nhi\u1ec7t, ki\u1ec3m so\u00e1t h\u1ec7 th\u1ed1ng an ninh trong nh\u00e0 v.v.<\/p>\n\n\n\n

Khi b\u1ea1n d\u00f9ng c\u1eeda h\u00e0ng tr\u1ef1c tuy\u1ebfn n\u00e0o \u0111\u00f3, nh\u01b0 Amazon hay Alibaba, h\u1ecd c\u0169ng c\u00f3 ph\u1ea7n m\u1ec1m tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o theo d\u00f5i m\u1eb7t h\u00e0ng b\u1ea1n \u0111\u00e3 t\u1eebng nh\u00ecn v\u00e0o, ch\u00fang nh\u1eadn di\u1ec7n \u0111i\u1ec1u b\u1ea1n mua v\u00e0 d\u00f9ng nh\u1eefng th\u00f4ng tin n\u00e0y \u0111\u1ec3 th\u00edch \u1ee9ng s\u1ea3n ph\u1ea9m h\u1ee3p v\u1ecb v\u1edbi b\u1ea1n v\u00e0 g\u1ee3i \u00fd b\u1ea1n mua n\u00f3. L\u00e0 kh\u00e1ch h\u00e0ng, \u0111i\u1ec1u n\u00e0y ti\u1ebft ki\u1ec7m cho b\u1ea1n nhi\u1ec1u th\u1eddi gian t\u00ecm ki\u1ebfm \u0111i\u1ec1u b\u1ea1n c\u1ea7n. Nh\u01b0ng v\u1edbi c\u00f4ng ti, v\u1edbi ng\u01b0\u1eddi b\u00e1n h\u00e0ng, n\u00f3 c\u00f3 ngh\u0129a l\u00e0 c\u00f3 kh\u1ea3 n\u0103ng d\u1ef1 \u0111o\u00e1n nhu c\u1ea7u c\u1ee7a nh\u1eefng s\u1ea3n ph\u1ea9m nh\u01b0 v\u1eady. Cho n\u00ean h\u1ecd c\u00f3 kho v\u1eeba ph\u1ea3i v\u00e0o th\u1eddi gian \u0111\u00fang \u1edf ch\u1ed7 \u0111\u00fang. \u0110i\u1ec1u \u0111\u00f3 c\u0169ng c\u00f3 th\u1ec3 c\u1ea3i ti\u1ebfn th\u1eddi gian giao h\u00e0ng, l\u00e0m t\u1ed1i \u0111a c\u01a1 h\u1ed9i h\u1ecd c\u00f3 kh\u1ea3 n\u0103ng b\u00e1n cho b\u1ea1n c\u00e1i g\u00ec \u0111\u00f3 b\u1ea1n c\u1ea7n.<\/p>\n\n\n\n

M\u1ed9t trong nh\u1eefng \u0111i\u1ec1u r\u1ea5t ph\u1ed5 bi\u1ebfn n\u1ebfu b\u1ea1n mua m\u1ed9t cu\u1ed1n s\u00e1ch t\u1eeb Amazon, m\u1ed9t ki\u1ec3u s\u00e1ch b\u1ea1n \u0111\u1ecdc, b\u1ea1n s\u1ebd th\u1ea5y r\u1eb1ng trong v\u00e0i tu\u1ea7n ti\u1ebfp, Amazon li\u00ean t\u1ee5c g\u1ee3i \u00fd c\u00e1c s\u00e1ch t\u01b0\u01a1ng t\u1ef1, “nh\u1eefng ng\u01b0\u1eddi \u0111\u00e3 mua cu\u1ed1n s\u00e1ch n\u00e0y c\u0169ng mua s\u00e1ch n\u00e0y v\u00e0 mua s\u00e1ch kia.” V\u1ec1 c\u0103n b\u1ea3n h\u1ecd g\u1ee3i \u00fd c\u00e1i g\u00ec \u0111\u00f3 d\u1ef1a tr\u00ean ph\u1ea7n m\u1ec1m ri\u00eang c\u1ee7a h\u1ecd, ph\u1ea7n m\u1ec1m th\u00f4ng minh bi\u1ebft \u0111i\u1ec1u \u01b0a chu\u1ed9ng c\u1ee7a b\u1ea1n l\u00e0 g\u00ec.<\/p>\n\n\n\n

Nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n \u0111\u00e3 t\u1eebng d\u00f9ng Uber ch\u1ee9? Uber \u0111\u00e3 vi\u1ebft ph\u1ea7n m\u1ec1m, \u0111\u00e2y c\u0169ng l\u00e0 ph\u1ea7n m\u1ec1m tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o d\u00f9ng h\u1ecdc m\u00e1y \u0111\u1ec3 d\u1ef1 \u0111o\u00e1n khi n\u00e0o xe b\u1ea1n \u0111\u1eb7t s\u1ebd t\u1edbi v\u00e0 bao nhi\u00eau xe ph\u1ea3i c\u00f3 \u1edf \u0111\u1ecba \u0111i\u1ec3m n\u00e0o \u0111\u00f3 v\u00e0o th\u1eddi gian n\u00e0o \u0111\u00f3 trong ng\u00e0y. Cho n\u00ean h\u1ecd ch\u1eafc l\u00e0 h\u1ecd c\u00f3 \u0111\u1ee7 xe \u0111\u1ec3 cung c\u1ea5p d\u1ecbch v\u1ee5 cho kh\u00e1ch h\u00e0ng.<\/p>\n\n\n\n

V\u00e0 nhi\u1ec1u ng\u01b0\u1eddi trong c\u00e1c b\u1ea1n mua xe h\u01a1i m\u1edbi trong v\u00e0i n\u0103m qua, c\u00e1c b\u1ea1n c\u00f3 l\u1ebd th\u1ea5y r\u1eb1ng xe c\u00f3 m\u1ecdi lo\u1ea1i ph\u1ea7n m\u1ec1m. V\u00e0 v\u1ec1 c\u0103n b\u1ea3n c\u00f3 ch\u1ee9a th\u1ecb gi\u00e1c m\u00e1y t\u00ednh \u0111\u1ec3 cho ph\u00e9p b\u1ea1n l\u00e1i xe an to\u00e0n h\u01a1n nhi\u1ec1u so v\u1edbi xe c\u1ed5 h\u01a1n, v\u00ec ch\u00fang c\u00f3 th\u1ec3 nh\u1eadn di\u1ec7n v\u00e0 theo d\u00f5i l\u01b0u th\u00f4ng quanh xe c\u1ee7a b\u1ea1n khi b\u1ea1n \u0111ang \u0111\u1ecbnh \u0111\u1ed5i l\u00e0n hay t\u1ef1 \u0111\u1ed9ng nh\u1ea5n phanh n\u1ebfu ch\u00fang c\u00f3 kh\u1ea3 n\u0103ng nh\u1eadn di\u1ec7n nguy hi\u1ec3m ph\u00eda tr\u01b0\u1edbc.<\/p>\n\n\n\n

H\u1ec7 th\u1ed1ng kh\u00e1c \u0111ang \u0111\u01b0\u1ee3c d\u00f9ng r\u1ea5t ph\u1ed5 bi\u1ebfn trong v\u00e0i n\u0103m qua l\u00e0 h\u1ec7 th\u1ed1ng gi\u00e1m s\u00e1t th\u1ebb t\u00edn d\u1ee5ng. Ch\u00fang d\u00f9ng ph\u1ea7n m\u1ec1m tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o \u0111\u1ec3 ki\u1ec3m kh\u1ea3 n\u0103ng c\u00e1c ho\u1ea1t \u0111\u1ed9ng l\u1eeba \u0111\u1ea3o. Ch\u1eb3ng h\u1ea1n n\u1ebfu b\u1ea1n bao gi\u1edd c\u0169ng mua b\u00e1n trong th\u00e0nh ph\u1ed1 n\u01a1i b\u1ea1n s\u1ed1ng, v\u00e0 \u0111\u1ed9t nhi\u00ean m\u1ed9t giao d\u1ecbch x\u1ea3y ra t\u1eeb ch\u1ed7 kh\u00e1c, ho\u00e0n to\u00e0n b\u00ean ngo\u00e0i. B\u1ea1n s\u1ed1ng \u1edf b\u1edd t\u00e2y, v\u00e0 nhi\u1ec1u giao d\u1ecbch x\u1ea3y ra \u1edf b\u1edd \u0111\u00f4ng. \u0110i\u1ec1u \u0111\u00f3 d\u1ef1ng c\u1edd \u0111\u1ecf, b\u00e1o \u0111\u1ed9ng. C\u00f3 th\u1ec3 th\u1ebb t\u00edn d\u1ee5ng c\u1ee7a b\u1ea1n \u0111\u00e3 b\u1ecb t\u1ed5n h\u1ea1i, hay c\u00f3 th\u1ec3 th\u1ebb t\u00edn d\u1ee5ng c\u1ee7a b\u1ea1n c\u00f3 th\u1ec3 b\u1ecb m\u1ea5t v\u00e0 ai \u0111\u00f3 kh\u00e1c c\u00f3 th\u1ec3 d\u00f9ng n\u00f3. V\u00e0 h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 t\u1ea1o ra b\u00e1o \u0111\u1ed9ng cho b\u1ea1n. Nh\u1eadn di\u1ec7n h\u00ecnh m\u1eabu gi\u1eefa vi\u1ec7c d\u00f9ng th\u1ebb t\u00edn d\u1ee5ng n\u00e0o \u0111\u00f3, ng\u00e2n h\u00e0ng c\u00f3 th\u1ec3 gi\u00e1m s\u00e1t v\u00e0 ph\u00e1t hi\u1ec7n c\u00e1c ho\u1ea1t \u0111\u1ed9ng l\u1eeba \u0111\u1ea3o.<\/p>\n\n\n\n

Thu\u1eadt ng\u1eef kh\u00e1c c\u0169ng \u0111\u01b0\u1ee3c li\u00ean k\u1ebft v\u1edbi tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o l\u00e0 th\u1ecb gi\u00e1c m\u00e1y t\u00ednh. Th\u1ecb gi\u00e1c m\u00e1y t\u00ednh l\u00e0 nh\u1eb1m gi\u00fap m\u00e1y t\u00ednh nh\u1eadn di\u1ec7n v\u00e0 x\u1eed l\u00ed nh\u1eefng h\u00ecnh \u1ea3nh n\u00e0o \u0111\u00f3 theo c\u00f9ng c\u00e1ch nh\u01b0 con ng\u01b0\u1eddi l\u00e0m. Ch\u00fang ta h\u1ecdc ph\u00e2n bi\u1ec7t gi\u1eefa c\u00e1c b\u1ed9 m\u1eb7t c\u1ee7a nh\u1eefng ng\u01b0\u1eddi kh\u00e1c nhau. Th\u1ecb gi\u00e1c m\u00e1y t\u00ednh c\u0169ng d\u1ea1y cho m\u00e1y nh\u1eadn d\u1ea1ng c\u00e1c v\u1eadt th\u1ec3 kh\u00e1c nhau m\u00e0 ch\u00fang c\u00f3 th\u1ec3 th\u1ea5y qua camera. B\u1eb1ng \u0111a t\u1ea7ng h\u1ec7 th\u1ed1ng, ch\u00fang c\u00f3 th\u1ec3 nh\u00ecn v\u00e0o pixel n\u00e0o \u0111\u00f3, nh\u1eadn di\u1ec7n m\u1ea7u s\u1eafc, chuy\u1ec3n \u0111\u1ed5i ch\u00fang th\u00e0nh c\u00e1c gi\u00e1 tr\u1ecb s\u1ed1, v\u00e0 t\u00ecm c\u00e1c h\u00ecnh m\u1eabu n\u00e0o \u0111\u00f3 m\u00e0 c\u00f3 th\u1ec3 nh\u1eadn di\u1ec7n m\u1ed9t nh\u00f3m c\u00e1c pixel m\u1ea7u t\u01b0\u01a1ng t\u1ef1 v\u00e0 \u0111\u01b0\u1eddng vi\u1ec1n. N\u00f3 c\u0169ng c\u00f3 th\u1ec3 gi\u00fap nh\u1eadn di\u1ec7n c\u00e1c v\u1eadt th\u1ec3 kh\u00e1c nhau.<\/p>\n\n\n\n

Th\u1ecb gi\u00e1c m\u00e1y t\u00ednh \u0111\u00e3 t\u1eebng \u0111\u01b0\u1ee3c d\u00f9ng m\u1ed9t c\u00e1ch c\u00f3 \u00fd ngh\u0129a trong nh\u1eadn d\u1ea1ng m\u1eb7t ng\u01b0\u1eddi m\u00e0 b\u1ea1n th\u1ea5y ng\u00e0y nay. Ch\u1eb3ng h\u1ea1n, v\u00e0i tu\u1ea7n tr\u01b0\u1edbc AI \u0111\u00e3 \u0111\u01b0\u1ee3c d\u00f9ng \u0111\u1ec3 nh\u1eadn di\u1ec7n m\u1ed9t ng\u01b0\u1eddi t\u1eeb \u1ea3nh s\u1ed1 tr\u00edch ra t\u1eeb video ghi l\u1ea1i. H\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n \u0111\u1ec3 ch\u1ecdn nh\u1eefng t\u00ednh n\u0103ng m\u1eb7t n\u00e0o \u0111\u00f3 v\u00e0 \u1ea3nh cho s\u1eb5n d\u1ef1a v\u00e0o c\u01a1 s\u1edf d\u1eef li\u1ec7u. V\u1ec1 c\u0103n b\u1ea3n m\u1ed9t ng\u01b0\u1eddi l\u00e0 t\u1ed9i ph\u1ea1m \u0111\u00e3 l\u00e0 m\u1ed9t ph\u1ea7n c\u1ee7a m\u1ed9t nh\u00f3m v\u00e0 nh\u00e0 ch\u1ee9c tr\u00e1ch c\u00f3 th\u1ec3 c\u00f3 kh\u1ea3 n\u0103ng b\u1eaft h\u1ecd, trong m\u1ed9t nh\u00f3m c\u1ee1 \u0111\u1ed9 60 ngh\u00ecn ng\u01b0\u1eddi, d\u1ef1a tr\u00ean nh\u1eadn d\u1ea1ng m\u1eb7t. \u0110i\u1ec1u n\u00e0y l\u00e0 r\u1ea5t r\u1ea5t ph\u1ed5 bi\u1ebfn \u0111\u00e3 \u0111\u01b0\u1ee3c d\u00f9ng trong th\u1ebf gi\u1edbi ng\u00e0y nay.<\/p>\n\n\n\n

—English version—<\/p>\n\n\n\n

Artificial Intelligence – Concepts: Machine learning, Neuron Network, Supervisedd Learning, Unsupervisedd Learning, Deep Learning, Natural Language Processing, Speech Recognisation, Computer Vision<\/p>\n\n\n\n

Extraxt from Prof. John Vu’s video (12:35-25:57) at<\/p>\n\n\n\n

https:\/\/drive.google.com\/file\/d\/1AhVDvTKGMaYKLSQe6LMVdPYyqTz0uavB\/view?usp=sharing<\/a><\/p>\n\n\n\n

Another term that many people associated artificial intelligence is called machine learning. Machine learning is one of many algorithms used in AI. Even today machine learning receives a lot of attentions. Machine learning is really a field concerning with designing a program that allow the machine to learn and to make a prediction from all these data without a certain assistance or being programmed by programmers. These algorithms are being used in many AI systems such as face recognition, music recommendations, spam filtering, and for detection in finance and banking institutes. We will talk more about machine learning in a next couple lectures.<\/p>\n\n\n\n

Another term that many of you probably have been heard is neuron network. A neuron network is a key to process machine learning. And it is really consist of several layers of algorithms. A machine learning model is a structure in a way that resembles the neuron in a human brain. Basically a neuron network consists of several layers of neurons similar to the human brain neuron, that feeds data to each other. So one level analyses data, comes up with  some predict patterns, sends the result to the next level to continue to refine the data and learning from these from previous one. Eventually they come up with some much better results. The neuron network is relative new but the modelling, the designing and the construction of neuron network were made the big difference of a better machine, better AI than the others.<\/p>\n\n\n\n

Basically during the training of a neuron network that certain way, certain mean, certain factor have been attached to the input, that was continued to vary from the output, from the other one. Eventually they will come to some very good predictions. We will talk more about neuron network in the next couple lectures.<\/p>\n\n\n\n

There are two things that are very important for you to know in machine learning. One is called supervised learning. In machine learning you have supervised learning and unsupervised learning. Supervised learning is a technique for teaching AI systems by training them using a large number of labeled examples. The labelling examples is carried out by a software workers. The programmer identifies certain data and basically labels them to allow the machine to learn. Basically when a machine is learning you supervise the learning by changing the data, by clarify, by classify data for the machine to learn. Basically one of the techniques we will talk much more in the introduction class is on supervised learning.<\/p>\n\n\n\n

Once the machine has been learnt, the system can apply a label to the new data and solve the problem. For example, if you train the machine to learn a dog in a photo of faces then eventually the machine can learn how to identify certain thing within a picture.<\/p>\n\n\n\n

Another technique iss called unsupervised data, in contract to the supervised data, unsupervised data uses a complete different approach, where the algorithm identifies such patterns in the data. Looking for such similarity that can be used to categrize its data. The algorithm was not set up to pick up specific type of the data itself. But it looks at a certain data and categorize and classify into groups by the similarity.<\/p>\n\n\n\n

For example, Google News is grouping all stories of similar topics in a day and creates a news for their readers. We shall talk more about unsupervised and supervised data in the next coming lectures.<\/p>\n\n\n\n

Another term that many of you probably have heard when we mention Artificial Intelligence is called deep learning. Deep learning is machine learning model where the neuron network are expanded into a huge network with multiple layers. They are trained using massive number of data. In this network the actificial neuron are arranged in interconnected layers. The input to layer to receive data from the outside world, the output layer would dictate how the system would respond those information. Between those layers, an additional hit layer of neuron that processes data by putting certain weights, certain factors on the information they receive from the preceding layers. And by going to multiple layers, multiple analysis, multiple predictions, they would come up with something much better, more accurate at the end.<\/p>\n\n\n\n

In a deep learning network they can solve complex problems because the huge number of neurons working together. Basically this is a massive number of multiple neurons, multiple layers, at each of them feeding the output to the next other. So you come up with much much better refine and much much better accruate and high performance.<\/p>\n\n\n\n

The foundation of every artificial intelligence is called natural language processing. And this is also one of the areas that has not been touched correctly or it does not gain a lot of popularity. But I think it is a fundamental because natural language processing is how do we get the computer to understand to process and to manipulate human languages. In order to do that, they have to understand a huge number of information from a grammatical rules to syntax to different colloquiallism and accents. Therefore this is also a very complex algorithm, very complex software that allow the machine to understand the contex of the language that we talk.<\/p>\n\n\n\n

For example, in speech recognition, a human voice becomes the audio data, which gests converted in text data, and then be able to process itself. The natural language processing is very important because today there many many systems are using voices. You can see for example, Alexa, many of you have been using that. That is also an AI using natural language processing. We will talk more about this topic in the next couple lectures.<\/p>\n\n\n\n

For example, have you seen that in artificial intelligence in 2011, many of you probably have seen the IBM Watson computer made headlines when it won the quiz show Jeopardy, beating the best players at the show had ever produced. The IBM Watson used natural language processing and big data analytics on vast repositories of data that it proceesed to answer human-posed questions, often in a fraction of second. Many of the software in the Watson is created at Carnegie Mellony. And I have been workinh with Dr. Harris Nighburd on the system. And this is one of things first attempt using AI in natural language processing.<\/p>\n\n\n\n

So let’s look into the way people using AI in the daily life. Basically AI has been presented in many aspects of our lives. You have seen a Smart Assistant like Siri, Alexa and Cortana, they all use natural language processing to understand the voice commands and answer your questions, setting up reminders, finding the music that you like, adjust thermostat, control home security system, etc.<\/p>\n\n\n\n

As you are using some online store like Amazon or Alibaba they also have artificial intelligence software that tracks what you have been looking at, they identify what you buy and use these information to taillor the product they appertize to you and recommends you to buy that. As the customer, this saves you a lot of time searching for what you want. But for the company, for the retailers it means being able to predict the demand of such products. So they have the right stock at the right time at the right place. That can also improve the delivery time, maximazes the chances they are able to sell you something that you need.<\/p>\n\n\n\n

One of things that is very popular if you buy a book from Amazon, a certain type of books you read, you will see that in a next couple of weeks, Amazon continues to recommend similar books, “people who bought this book also buy this book and buy that book.” Basically they recommend something based on their own software, smart software that knows what your preferences are.<\/p>\n\n\n\n

Many of you have been using Uber? Uber was writing software, this is also an artificial intelligence software that uses machine learning to predict when the car that you’ve booked will arrive and how many cars must be in certain location at certain time of the day. So they make sure they have enough cars for providing services to the customer.<\/p>\n\n\n\n

And many of you who buy a new car in the last of coupe years, you probably see that cars have all kinds of software. And basically computer vision to allow you driving the car in a much more safer than the older cars, because they can identify and track traffics around your car when you are planning to change lane or automatically put on the brake if they be able to identify a danger ahead.<\/p>\n\n\n\n

Another system that being used very popular in the last few years is the credit card monitoring systems. They use an artificial intelligence software to check for possibility of fraudulent activties. For example if you always shot in the city where you live, and suddently a transaction came from another place, completely outside. You live in the west coast, and a lot of transactions happen in the east coast. That is raising a red flag, an alert. Maybe your credit card has been compromised, or maybe your credit card may be lost and sombeody may use that. And it can creat an alert to you. Identify a pattern between the usage of certain credit card, the bank can monitor and detect fraudulent activities.<\/p>\n\n\n\n

Another term that is also associated with artificial intelligence is computer vision. Computer vision is aim to help computer to identify and process certain images in the same way like humen do. We learn to distinguishes between faces of different people. The computer vision is also teaching machine to recognize different objects that they can see through the camera.  By multiple layers of systems, they can look at certain pixels, identify colours, converte them into numerical values, and looking for certain patterns that can identify a group of similar colour pixels and textures. It can also help identify different objects.<\/p>\n\n\n\n

Computer vision have been used significantly in facial recognition that you will see today. For example, a few weeks ago AI has been used to identify a person from digital image of a video captured. The system can be trained to select certain facial features and given image based on the database. Basically a person who are criminal has been a part of the group and the authority can be able to arrest them, in a group of 60 thousand people, based on facial recognition. This is a very very popular case have been used in the world today.<\/p>\n","protected":false},"excerpt":{"rendered":"

Tr\u00edch ra t\u1eeb video c\u1ee7a Gs. John Vu (12:35-25:57) t\u1ea1i https:\/\/drive.google.com\/file\/d\/1AhVDvTKGMaYKLSQe6LMVdPYyqTz0uavB\/view?usp=sharing Thu\u1eadt ng\u1eef kh\u00e1c m\u00e0 nhi\u1ec1u ng\u01b0\u1eddi li\u00ean k\u1ebft v\u1edbi tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o c\u00f3 … <\/p>\n","protected":false},"author":2,"featured_media":6484,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"class_list":["post-6483","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"_links":{"self":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6483","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6483"}],"version-history":[{"count":5,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6483\/revisions"}],"predecessor-version":[{"id":6502,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6483\/revisions\/6502"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/media\/6484"}],"wp:attachment":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6483"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6483"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6483"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}