Tutorial Wat Virus(1)

Ok, ok.. Skang saya dah berada dalam mood yang baik, so.. Rasanya saya akan kongsi, 1 lagi tipz menarik dalam bab IT ni.. Virus.. Hee~ rasanya korang dah tau, kan.. Maksudnya.. Apa kata.. Lau saya ajar.. Dan guide korang.. Untuk buat virus ? :) serius, saya ikhlas nak ajar.. Tapi virus biasa je laa.. Yang kuat - kuat cam trojan horse, gweep, treeko dan lelain tu.. Urm.. Perlu ke ? :p nanti korang mendajal, pastu kena tangkap.. Salahkan saya lak.. T-T tak pepasal kena denda sal melanggar peraturan multimedia dan teknologi, akta mencipta program.. Ceh.. Ok, tadi saya janji nak buat virus, kan.. Mm.. Let'z start..

Doa Menyembuhkan

Doa Menyembuhkan Letakkanlah kedua ibu jari ke tanah atau lantai, lalu angkatlah dan ludahi satu kali pada debu atau tanah yang menempel pada ibu jari kita dan membaca; ??? ???? ???? ????? ????? ????? ???? ????? ???? ????.(???? ??????? ????? ?? ????? ??) Bismillaahi turbatu ardlinaa bi riiqoti ba�fdlinaa yusfa saqimunaa bi idzni robbinaa. Artinya; �gDengan menyebut asma Allah, ini tanah tempat kami dengan ludah (ruqiyah) salah seorang dari kami, sembuhlah orang sakit dengan izin Tuhan kami.�h(HR. Bukhori dan Muslim dari Ummul Mu�fminin �eAisyah r.a)

Pokemon Emerald GBA - All Pokemons Tipz

Pokémon Emerald Pokédex Treecko Where/How to catch: Given by Professor Birch on Route 101 Evolutions: Level 16 – Grovile level 36 – Sceptile Strong Against: Water, Electric, Grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Grovile Where/How to catch: Evolve from Treecko Evolutions: Level 36 – Sceptile Strong Against: Water, Electric, Grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Sceptile Where/How to catch: Evolve from Grovile Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Torchic Where/How to catch: Given by Professor Birch on Route 101 Evolutions: Level 16 – Combusken Level 36 – Blaziken Strong Against: Fire, Grass, Ice, Bug, Steel Weak Against: Water, Ground, Rock ~~~~~~~~~~~~~~ Combusken Where/How to catch: Evolve from Torchic Evolutions: Level 36 – Blaziken Strong Against: Fire, Grass, Ice, Bug, Dark, Steel Weak Against: Water, Ground, Flying, Psychic ~~~~~~~~~~~~~ Blaziken Where/How to catch: Evolve from Combusken Evolutions: (Fully evolved) Strong Against: Fire, Grass, Ice, Bug, Dark, Steel Weak Against: Water, Ground, Flying, Psychic ~~~~~~~~~~~~~ Mudkip Where/How to catch: Given by Professor Birch on Route 101 Evolutions: Level 16 – Marshtomp Level 36 – Swampert Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Marshtomp Where/How to catch: Evolve from Mudkip Evolutions: Level 36 – Swampert Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Swampert Where/How to catch: Evolve from Marshtomp Evolutions: (Fully Evolved) Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Poochyena Where/How to catch: Routes 101, 102, 103, 104, 110, 116, 117, 120, 121, 123; Petalburg Woods Evolutions: Level 18 – Mightyena Strong Against: Psychic, Ghost, Dark Weak Against: Fighting, Bug ~~~~~~~~~~~~~ Mightyena Where/How to catch: Evolve from Poochyena; Routes 120, 121, and 123 Evolutions: (Fully evolved) Strong Against: Psychic, Ghost, Dark Weak Against: Fighting, Bug ~~~~~~~~~~~~~ Zigzagoon Where/How to catch: Routes 101, 102, 103, 118, and 119 Evolutions: Level 20 – Linoone Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Linoone Where/How to catch: Evolve from Zigzagoon; Routes 118 and 119 Evolutions: (Fully evolved) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Wurmple Where/How to catch: Routes 101, 102 and 104; Petalburg Woods Evolutions: Level 7 – Silcoon or Cascoon; (Evolves randomly to either Pokémon) Strong Against: Grass, Fighting, Ground Weak Against: Fire, Flying, Rock ~~~~~~~~~~~~~ Silcoon Where/How to catch: Evolve from Wurmple; Petalburg Woods Evolutions: Level 10 – Beautifly Strong Against: Grass. Fighting, Ground Weak Against: Fire, Flying, Rock ~~~~~~~~~~~~~ Beautifly Where/How to catch: Evolve from Silcoon Evolutions: (Fully evolved) Strong Against: Bug, Flying, Ground, Grass Weak Against: Fire, Electric, Flying, Rock, Ice ~~~~~~~~~~~~~ Cascoon Where/How to catch: Evolve from Wurmple; Petalburg Woods Evolutions: Level 10 – Dustox Strong Against: Grass, Fighting, Ground Weak Against: Fire, Flying, Rock ~~~~~~~~~~~~~ Dustox Where/How to catch: Evolve from Cascoon Evolutions: (Fully evolved) Strong Against: Grass, Fighting, Psychic, Bug Weak Against: Fire, Flying, Psychic, Rock ~~~~~~~~~~~~~ Lotad Where/How to catch: Routes 102 and 114 Evolutions: Level 14 – Lombre Use Water Stone – Ludicolo Strong Against: Water, Ground, Steel Weak Against: Poison, Flying, Bug ~~~~~~~~~~~~~ Lombre Where/How to catch: Evolve from Lotad; 114 Evolutions: Use Water Stone – Ludicolo Strong Against: Water, Ground, Steel Weak Against: Poison, Flying, Bug ~~~~~~~~~~~~~ Ludicolo Where/How to catch: Evolve from Lombre with Water Stone Evolutions: (Fully evolved) Strong Against: Water, Ground, Steel Weak Against: Poison, Flying, Bug ~~~~~~~~~~~~~ Seedot Where/How to catch: Routes 102, 117, and 120 Evolutions: Level 14 – Nuzleaf Use Leaf Stone – Shiftry Strong Against: Water, Electric, grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Nuzleaf Where/How to catch: Evolve from Seedot; Route 114 Evolutions: Use Leaf Stone – Shiftry Strong Against: Water, Electric, Grass, Ground, Psychic, Ghost, Dark Weak Against: Fire, Ice, Fighting, Poison, Flying, Bug ~~~~~~~~~~~~~ Shiftry Where/How to catch: Evolve from Nuzleaf with Leaf Stone Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Ground, Psychic, Ghost, Dark Weak Against: Fire, Ice, Fighting, Poison, Flying, Bug ~~~~~~~~~~~~~ Taillow Where/How to catch: Routes 104, 115, and 116; Petalburg Woods Evolutions: Level 22 – Swellow Strong Against: Grass, Ground, Bug, Ghost Weak Against: Electric, Ice, Rock ~~~~~~~~~~~~~ Swellow Where/How to catch: Evolve from Taillow; Route 115 Evolutions: (Fully evolved) Strong Against: Grass, Ground, Bug, Ghost Weak Against: Electric, Ice, Rock ~~~~~~~~~~~~~ Wingull Where/How to catch: Routes 103, 104, 105, 106, 109, 110, 115, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, and 133; Ever Grande City, Mossdeep City, Lilycove City, Slateport City, and Mt. Pyre Evolutions: Level 25 – Pelipper Strong Against: Fire, Water, Fighting, Ground, Bug, Steel Weak Against: Electric, Rock ~~~~~~~~~~~~~ Pelipper Where/How to catch: Evolve from Wingull; Routes 103, 104, 105, 106, 109, 110, 115, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, and 133; Ever Grande City, Mossdeep City, Lilycove City, Slateport City, and Mt. Pyre Evolutions: (Fully evolved) Strong Against: Fire, Water, Fighting, Ground, Bug, Steel Weak Against: Electric, Rock ~~~~~~~~~~~~~ Ralts Where/How to catch: Route 102 Evolutions: Level 20 – Kirlia Level 30 – Gardevoir Strong Against: Fighting, Psychic Weak Against: Bug Ghost, Dark ~~~~~~~~~~~~~ Kirlia Where/How to catch: Evolve from Ralts Evolutions: Level 20 – Gardevoir Strong Against: Fighting, Psychic Weak Against: Bug, Ghost, Dark ~~~~~~~~~~~~~ Gardevoir Where/How to catch: Evolve from Kirlia Evolutions: (Fully evolved) Strong Against: Fighting, Psychic Weak Against: Bug, Ghost, Dark ~~~~~~~~~~~~~ Surskit Where/How to catch: Must trade from Pokémon Ruby and Pokémon Sapphire Evolutions: Level 22 – Masquerain Strong Against: Water, Ice, Fighting, Ground, Steel Weak Against: Electric, Flying, Rock ~~~~~~~~~~~~~ Masquerain Where/How to catch: Must trade from Pokémon Ruby or trade for Surskit, then evolve Evolutions: (Fully evolved) Strong Against: Bug, Fighting, Ground, Grass Weak Against: Fire, Electric, Flying, Rock, Ice ~~~~~~~~~~~~~ Shroomish Where/How to catch: Petalburg Woods Evolutions: Level 23 - Breloom Strong Against: Water, Electric, Grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Breloom Where/How to catch: Evolve from Shroomish Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Ground, Dark, Rock Weak Against: Fire, Ice, Poison, Flying, Psychic ~~~~~~~~~~~~~ Slakoth Where/How to catch: Petalburg Woods Evolutions: Level 18 – Vigoroth Level 36 – Slaking Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Vigoroth Where/How to catch: Evolve from Slakoth Evolutions: Level 36 – Slaking Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Slaking Where/How to catch: Evolve from Vigoroth Evolutions: (Fully evolved) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Abra Where/How to catch: Route 116, Granite Cave Evolutions: Level 16 – Kadabra Trade over the Game Boy Wireless Adapter – Alakazam Strong Against: Fighting, Psychic Weak Against: Ghost, Bug, Dark ~~~~~~~~~~~~~ Kadabra Where/How to catch: Evolve from Abra Evolutions: Trade over the Game Boy Wireless Adapter – Alakazam Strong Against: Fighting, Psychic Weak Against: Ghost, Bug, Dark ~~~~~~~~~~~~~ Alakazam Where/How to catch: Trade over Game Boy Wireless Adapter Evolutions: (Fully evolved) Strong Against: Fighting, Psychic Weak Against: Ghostm Bug, Dark ~~~~~~~~~~~~~ Nincada Where/How to catch: Route 116 Evolutions: Level 20 – Ninjask; – Have a empty Poké Ball and space in belt – Shedinja Strong Against: Electric, Fighting, Poison, Ground Weak Against: Fire, Water, Ice, Flying ~~~~~~~~~~~~~ Ninjask Where/How to catch: Evolve from Nincada Evolutions: (Fully evolved) Strong Against: Bug, Fighting, Ground, Grass Weak Against: Fire, Electric, Flying, Rock, Ice ~~~~~~~~~~~~~ Shedinja Where/How to catch: Evolve from Nincada — must have empty Poké ball and open slot on team Evolutions: (Fully evolved) Strong Against: Normal, Grass, Fighting, Poison, Ground, Bug Weak Against: Fire, Flying, Rock, Ghost, Dark ~~~~~~~~~~~~~ Whismur Where/How to catch: Route 116, Desert Tunnel, Rusturf Tunnel, and Victory Road Evolutions: Level 20 – Loudred Level 40 – Exploud Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Loudred Where/How to catch: Evolve from Whismur; Desert Tunnel, Victory Road Evolutions: Level 40 – Exploud Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Exploud Where/How to catch: Evolve from Loudred Evolutions: (Fully evolved) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Makuhita Where/How to catch: Granite Cave, Victory Road Evolutions: Level 24 – Hariyama Strong Against: Bug, Rock, Dark Weak Against: Flying, Psychic ~~~~~~~~~~~~~ Hariyama Where/How to catch: Evolve from Makuhita; Victory Road Evolutions: (Fully evolved) Strong Against: Bug, Rock, Dark Weak Against: Flying, Psychic ~~~~~~~~~~~~~ Goldeen Where/How to catch: Routes 102, 111, 114, 117, and 120; Meteor Falls, Petalburg City, Safari Zone, and Victory Road. Evolutions: Level 33 – Seaking Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Seaking Where/How to catch: Evolve from Goldeen; Safari Zone Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Magikarp Where/How to catch: All water routes with Old Rod Evolutions: Level 20 – Gyarados Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Gyarados Where/How to catch: Evolve from Magicarp; Sootopolis City Evolutions: (Fully evolved) Strong Against: Fire, Water, Fighting, Ground, Bug, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Azurill Where/How to catch: Breed Female Marill with Sea Incense Held item Evolutions: Develop a high level of friendship – Marill Level 18 – Azumarill Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Marill Where/How to catch: Evolve from Azurill with taming; Routes 102, 111, 112, 114, 117, and 120; Petalburg Woods and Safari Zone Evolutions: Level 18 – Azumarill Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Azumarill Where/How to catch: Evolve from Marill Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Geodude Where/How to catch: Granite Cave, Magma Hideout, Victory Road; Use Rock Smash in Routes 111, and 114, Safari Zone, and Victory Road Evolutions: Level 25 – Graveler Trade Graveler over Game Boy Wireless Adapter – Golem Strong Against: Normal, Fire, Electric, Poison, Flying, Rock Weak Against: Water, grass, Ice, Fighting, Ground, Steel ~~~~~~~~~~~~~ Graveler Where/How to catch: Evolve from Geodude; Magma Hideout and Victory Road Evolutions: Trade over Game Boy Wireless Adapter – Golem Strong Against: Normal, Fire, Electric, Poison, Flying, Rock Weak Against: Water, Grass, Ice, Fighting, Ground, Steel ~~~~~~~~~~~~~ Golem Where/How to catch: Trade Graveler over Game Boy Wireless Adapter Evolutions: (Fully evolved) Strong Against: Normal, Fire, Electric, Poison, Flying, Rock Weak Against: Water, Grass, Ice, Fighting, Ground, Steel ~~~~~~~~~~~~~ Nosepass Where/How to catch: Granite Cave (use Rock Smash) Evolutions: (Nosepass does not evolve) Strong Against: Normal, Fire, Poison, Flying Weak Against: Water, Grass, Fighting, Ground, Steel ~~~~~~~~~~~~~ Skitty Where/How to catch: Route 116 Evolutions: Use Moon Stone – Delcatty Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Delcatty Where/How to catch: Evolve from Skitty with Moon Stone Evolutions: (Fully evolved) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Zubat Where/How to catch: Granite Cave, Meteor Falls, Seafloor Cavern, Shoal Cave, and Victory Road Evolutions: Level 22 – Golbat develop a high level of friendship – Crobat Strong Against: Grass, Fighting, Poison, Ground, Bug Weak Against: Electric, Ice, Psychic, Rock ~~~~~~~~~~~~~ Golbat Where/How to catch: Evolve from Zubat; Granite Cave, Meteor Falls, Seafloor Cavern, Shoal Cave, Sky Piller, and Victory Road Evolutions: Develop a high level of friendship – Crobat Strong Against: Grass, Fighting, Poison, Ground, Bug Weak Against: Electric, Ice, Psychic, Rock ~~~~~~~~~~~~~ Crobat Where/How to catch: Evolve from Golbat with taming Evolutions: (Fully evolved) Strong Against: Grass, Fighting, Poison, Ground, Bug Weak Against: Electric, Ice, Psychic, Rock ~~~~~~~~~~~~~ Tentacool Where/How to catch: All water routes and bodies in Hoenn Evolutions: Level 30 – Tentacruel Strong Against: Fire, Water, Ice, Fighting, Poison, Bug, Steel Weak Against: Electric, Ground, Psychic ~~~~~~~~~~~~~ Tentacruel Where/How to catch: Evolve from Tentacool; Abandoned Ship (Super Rod) Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Fighting, Poison, Bug, Steel Weak Against: Electric, Ground. Psychic ~~~~~~~~~~~~~ Sableye Where/How to catch: Granite Cave, Sky Piller and Victory Road Evolutions: (Sableye does not evolve) Strong Against: Normal, Fighting, Poison, Psychic Weak Against: None ~~~~~~~~~~~~~ Mawile Where/How to catch: Victory Road Evolutions: (Mawile does not evolve) Strong Against: Normal, Grass, Ice, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark, Steel Weak Against: Fire, Fighting, Ground ~~~~~~~~~~~~~ Aron Where/How to catch: Granite Cave, Victory Road Evolutions: Level 32 – Lairon level 42 – Aggron Strong Against: Normal, Ice, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark Weak Against: Water, Fighting, Ground ~~~~~~~~~~~~~ Lairon Where/How to catch: Evolve from Aron; Victory Road Evolutions: Level 42 – Aggron Strong Against: Normal, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark Weak Against: Water, Fighting, Ground ~~~~~~~~~~~~~ Aggron Where/How to catch: Evolve from Lairon Evolutions: (Fully evolved) Strong Against: Normal, Ice, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark Weak Against: Water, Fighting, Ground ~~~~~~~~~~~~~ Machop Where/How to catch: Fiery Path Evolutions: Level 28 - Machoke trade over the Game Boy Wireless Apater – Machamp Strong Against: Bug, Rock, Dark Weak Against: Flying, Psychic ~~~~~~~~~~~~~ Machoke Where/How to catch: Evolve from Machop Evolutions: Trade over the Game Boy Wireless Adapter – Machamp Strong Against: Bug, Rock, Dark Weak Against: Flying, Psychic ~~~~~~~~~~~~~ Machamp Where/How to catch: Evolve from Machoke via the Game Boy Wireless Adpater Evolutions: (Fully evolved) Strong Against: Bug, Rock, Dark Weak Against: Flying, Psychic ~~~~~~~~~~~~~ Meditite Where/How to catch: Must trade from Pokémon Ruby or Pokémon Colosseum Evolutions: Level 37 – Medicham Strong Against: Fighting, Rock Weak Against: Flying, Ghost ~~~~~~~~~~~~~ Medicham Where/How to catch: Evolve from Meditite; must trade from Pokémon Ruby or Pokémon Colosseum Evolutions: (Fully evolved) Strong Against: Fighting, Rock Weak Against: Flying, Ghost ~~~~~~~~~~~~~ Electrike Where/How to catch: Routes 110 and 118 Evolutions: Level 26 – Manectric Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Manectric Where/How to catch: Evolve from Electrike; Route 118 Evolutions: (Fully evolved) Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Plusle Where/How to catch: 110 Evolutions: (Plusle does not evolve) Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Minun Where/How to catch: Route 110 Evolutions: (Minun does not evolve) Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Magnemite Where/How to catch: New Mauville Evolutions: Level 20 – Magneton Strong Against: Normal, Electirc, Grass, Ice, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark, Steel Weak Against: Fire, Fighting, Ground ~~~~~~~~~~~~~ Maneton Where/How to catch: Evolve from Magnemite; New Mauville Evolutions: (Fully evolved) Strong Against: Normal, Electric, Grass, Ice, Poison, Flying, Psychic, Bug, Rock, Ghost, Dragon, Dark, Steel Weak Against: Fire, Fighting, Ground ~~~~~~~~~~~~~ Voltorb Where/How to catch: New Mauville Evolutions: Level 30 – Electrode Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Electrode Where/How to catch: Evolve from Voltorb; New Mauville Evolutions: (Fully evolve) Strong Against: Electric, Flying, Steel Weak Against: Ground ~~~~~~~~~~~~~ Volbeat Where/How to catch: Route 117 Evolutions: (Volbeat does not evolve) Strong Against: Grass, Fighting, Ground Weak Against: Fire, Flying, Rock ~~~~~~~~~~~~~ Illumise Where/How to catch: Route 117 Evolutions: (Illumise does not evolve) Strong Against: Grass, Fighting, Ground Weak Against: Fire, Flying, Rock ~~~~~~~~~~~~~ Oddish Where/How to catch: Routes 110, 117, 119, 120, 121, and 123; Safari Zone Evolutions: Level 21 – Gloom use Leaf Stone -Vileplume or use Sun Stone – Bellossom Strong Against: Water, Electric, Grass, Fighting Weak Against: Fire, Ice, Flying, Psychic ~~~~~~~~~~~~~ Gloom Where/How to catch: Evolve from Oddish; Routes 121 and 123, Safari Zone Evolutions: Use Leaf Stone - Vileplume or use Sun Stone -Bellossom Strong Against: Water, Electric, Grass, Fighting Weak Against: Fire, Ice, Flying, Psychic ~~~~~~~~~~~~~ Vileplume Where/How to catch: Evolve from Gloom with Leaf Stone Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Fighting Weak Against: Fire, Ice, Flying, Psychic ~~~~~~~~~~~~~ Bellossom Where/How to catch: Evolve from Gloom with Sun Stone Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Doduo Where/How to catch: Safari Zone Evolutions: Level 31 – Dodrio Strong Against: Grass, Ground, Bug, Ghost Weak Against: Electric, Ice, Rock ~~~~~~~~~~~~~ Dodrio Where/How to catch: Evolve from Doduo; Safari Zone Evolutions: (Fully evolved) Strong Against: Grass, Ground, Bug, Ghost Weak Against: Electric, Ice, Rock ~~~~~~~~~~~~~ Roselia Where/How to catch: Must trade from Pokémon Ruby Evolutions: (Roselia does not evolve) Strong Against: Water, Electric, Grass, Fighting Weak Against: Fire, Ice, Flying, Psychic ~~~~~~~~~~~~~ Gulpin Where/How to catch: Route 110 Evolutions: Level 26 – Swalot Strong Against: Grass, Fighting, Poison, Bug Weak Against: Ground, Psychic ~~~~~~~~~~~~~ Swalot Where/How to catch: Evolve from Gulpin Evolutions: (Fully evolved) Strong Against: Grass, Fighting, Poison, Bug Weak Against: Ground, Psychic ~~~~~~~~~~~~~ Carvanha Where/How to catch: Routes 118 and 119 Evolutions: Level 30 – Sharpedo Strong Against: Fire, Water, Ice, Psychic, Ghost, Dark, Steel Weak Against: Electric, Grass, Fighting, Bug ~~~~~~~~~~~~~ Sharpedo Where/How to catch: Evolve from Carvanha; Routes 103, 118, 122, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, and 134 Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Psychic, Ghost, Dark, Steel Weak Against: Electric, Grass, Fighting, Bug ~~~~~~~~~~~~~ Wailmer Where/How to catch: Routes 103, 105, 106, 107, 108, 109, 110, 115, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, and 135; Ever Grande City, Mossdeep City, Lilycove City, Seafloor Cavern, Shoal Cave, Slateport City, and Sootopolis City Evolutions: Level 40 – Wailord Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Wailord Where/How to catch: Evolve from Wailmer; Route 129 Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Numel Where/How to catch: Route 122 and Fiery Path Evolutions: Level 33 – Camerupt Strong Against: Fire, Electric, Poison, Bug, Steel Weak Against: Water, Ground ~~~~~~~~~~~~~ Camerupt Where/How to catch: Evolve from Numel Evolutions: (Fully evolved) Strong Against: Fire, Electric, Poison, Bug, Steel Weak Against: Water, Ground ~~~~~~~~~~~~~ Slugma Where/How to catch: Route 113 and Fiery Path Evolutions: Level 38 - Magcargo Strong Against: Fire, Grass, Ice, Bug, Steel ~~~~~~~~~~~~~ Magcargo Where/How to catch: Evolve from Slugma Evolutions: (Fully evolved) Strong Against: Normal, Fire, Ice, Poison, Flying, Bug Weak Against: Rock, Ground, Fighting, Water ~~~~~~~~~~~~~ Torkoal Where/How to catch: Fiery Path and Magma Hideout Evolutions: (Torkoal does not evolve) Strong Against: Fire, Grass, Ice, Bug, Steel Weak Against: Water, Ground, Rock ~~~~~~~~~~~~~ Grimer Where/How to catch: Fiery Path Evolutions: Level 38 – Muk Strong Against: Grass, Fighting, Poison, Bug Weak Against: Ground, Psychic ~~~~~~~~~~~~~ Muk Where/How to catch: Evolve from Grimer Evolutions: (Fully evolved) Strong Against: Grass, Fighting, Poison, Bug Weak Against: Ground, Psychic ~~~~~~~~~~~~~ Koffing Where/How to catch: Fiery Path Evolutions: Level 35 – Weezing Strong Against: Grass, Fighting, Poison, Bug, Ground Weak Against: Psychic ~~~~~~~~~~~~~ Weezing Where/How to catch: Evolve from Koffing Evolutions: (Fully evolved) Strong Against: Grass, Fighting, Poison, Bug, Ground Weak Against: Psychic ~~~~~~~~~~~~~ Spoink Where/How to catch: Jagged Pass Evolutions: Level 32 – Grumpig Strong Against: Fighting, Psychic Weak Against: Bug, Ghost, Dark ~~~~~~~~~~~~~ Grumpig Where/How to catch: Evolve from Spoink Evolutions: (Fully evolved) Strong Against: Fighting, Psychic Weak Against: Bug, Ghost, Dark ~~~~~~~~~~~~~ Sandshrew Where/How to catch: Routes 111 and 113, Mirage Tower Evolutions: Level 22 – Sandslash Strong Against: Electric, Poison, Rock Weak Against: Water, Grass, Ice ~~~~~~~~~~~~~ Sandslash Where/How to catch: Evolve from Sandshrew Evolutions: (Fully evolved) Strong Against: Electric, Poison, Rock Weak Against: Water, Grass, Ice ~~~~~~~~~~~~~ Spinda Where/How to catch: Route 113 Evolutions: (Spinda does not evolve) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Skarmory Where/How to catch: Route 113 Evolutions: (Skarmory does not evolve) Strong Against: Normal, Grass, Poison, Ground, lying, Psychic, Bug, Ghost, Dragon, Dark, Steel Weak Against: Fire, Electric ~~~~~~~~~~~~~ Trapinch Where/How to catch: Route 111 and Mirage Tower Evolutions: Level 35 – Vibrava Level 45 – Flygon Strong Against: Electric, Poison, Rock Weak Against: Water, Grass, Ice ~~~~~~~~~~~~~ Vibrava Where/How to catch: Evolve from Trapinch Evolutions: Level 45 – Flygon Strong Against: Fire, Electric, Poison, Rock, Ground Weak Against: Ice, Dragon ~~~~~~~~~~~~~ Flygon Where/How to catch: Evolve from Vibrava Evolutions: (Fully evolved) Strong Against: Fire, Electric, Poison, Rock, Ground Weak Against: Ice, Dragon ~~~~~~~~~~~~~ Cacnea Where/How to catch: Route 111 Evolutions: Level 32 – Cacturne Strong Against: Grass, Electric, Water, Ground Weak Against: Fire, Ice, Poison, Flying, Bug ~~~~~~~~~~~~~ Cacturne Where/How to catch: Evolve from Cacnea Evolutions: (Fully evolved) Strong Against: Water, Electric, Grass, Ground, Psychic, Ghost, Dark Weak Against: Fire, Ice, Fighting, Poison, Flying, Bug ~~~~~~~~~~~~~ Swablu Where/How to catch: Routes 114 and 115 Evolutions: Level 35 – Altaria Strong Against: Grass, Ground, Bug, Ghost Weak Against: Electirc, Ice, Rock ~~~~~~~~~~~~~ Altaria Where/How to catch: Evolve from Swablu; Sky Piller Evolutions: (Fully evolved) Strong Against: Fire, Water, Grass, Fighting, Ground, Bug Weak Against: Ice, Rock, Dragon ~~~~~~~~~~~~~ Zangoose Where/How to catch: Must trade from Pokémon Ruby Evolutions: (Zangoose does not evolve) Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Seviper Where/How to catch: Route 114 Evolutions: (Seviper does not evolve) Strong Against: Grass, Fighting, Poison, Bug Weak Against: Ground, Psychic ~~~~~~~~~~~~~ Lunatone Where/How to catch: Must trade from Pokémon Ruby Evolutions: (Lunatone does not evolve) Strong Against: Normal, Fire, Poison, Flying, Psychic, Ground Weak Against: Waterm Grass, Bug, Ghostm Dark, Steel ~~~~~~~~~~~~~ Solrock Where/How to catch: Meteor Falls Evolutions: (Solrock does not evolve) Strong Against: Normal, FIre, Poison, Flying, Psychic, Ground Weak Against: Water, Grass, Bug, Ghost, Dark, Steel ~~~~~~~~~~~~~ Barboach Where/How to catch: Routes 111, 114, and 120; Meteor Falls and Victory Road Evolutions: Level 30 – Whiscash Strong Against: Fire, Electric, Poison, Rock, Steel Weak Against: Grass ~~~~~~~~~~~~~ Whiscash Where/How to catch: Evolve from Barboach Evolutions: (Fully evolved) Strong Against: Fire, Electric, Poison, Rock, Steel Weak Against: Grass ~~~~~~~~~~~~~ Corphish Where/How to catch: Routes 102 and 117, Petalburg City Evolutions: Level 30 – Crawdaunt Strong Against: Fire, Water, Ice, Steel Weak Against: Electric, Grass ~~~~~~~~~~~~~ Crawdaunt Where/How to catch: Evolve from Corphish Evolutions: (Fully evolved) Strong Against: Fire, Water, Ice, Psychic, Ghost, Dark, Steel Weak Against: Electric, Grass, Fighting, Bug ~~~~~~~~~~~~~ Baltoy Where/How to catch: Route 111 Evolutions: Level 36 – Claydol Strong Against: Electric, Fighting, Poison, Psychic, Rock, Ground Weak Against: Water, Grass, Ice, Bug, Ghost ~~~~~~~~~~~~~ Claydol Where/How to catch: Evolve from Baltoy; Sky Pillar Evolutions: (Fully evolved) Strong Against: Electric, Fighting, Poison, Psychic, Rock, Ground Weak Against: Water, Grass, Ice, Bug, Ghost, Dark ~~~~~~~~~~~~~ Lileep Where/How to catch: Resurrect Root Fossil Evolutions: Level 40 – Cradily Strong Against: Normal, Electric Weak Against: Ice, Fighting, Bug, Steel ~~~~~~~~~~~~~ Cradily Where/How to catch: Evolve from Lileep Evolutions: (Fully evolved) Strong Against: Normal, Electric Weak Against: Ice, Fighting, Bug, Steel ~~~~~~~~~~~~~ Anorith Where/How to catch: Resurrect Claw Fossil Evolutions: Level 40 – Armaldo Strong Against: Normal, Poison Weak Against: Water, Rock, Steel ~~~~~~~~~~~~~ Armaldo Where/How to catch: Evolve from Anorith Evolutions: (Fully evolved) Strong Against: Normal, Poison Weak Against: Water, Rock, Steel ~~~~~~~~~~~~~ Igglybuff Where/How to catch: Breed from Jigglypuff Evolutions: Develope a high level of friendship – Jigglypuff use Moon Stone – Wigglytuff Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Jigglypuff Where/How to catch: Evolve from Igglybuff with Friendship; Route 115 Evolutions: Use Moon Stone – Wigglytuff Strong Against: Ghost Weak Against: Fighting ~~~~~~~~~~~~~ Wigglytuff Where/How to catch: Evolve from Jigglypuff with Moon Stone Evolutions: (Fully evolved) Strong Against: Ghost Weak Against: Fighting

Chee

Rehat buat sementara waktu ! :D

Franz Kafka The Germanian

Franz Kafka (1883–1924) was a German-language writer of novels and short stories, and is regarded as one of the most influential authors of the 20th century . His works, such as " Die Verwandlung " ("The Metamorphosis"), Der Process ( The Trial ), and Das Schloss ( The Castle ), are filled with themes and archetypes of alienation, brutality, parent–child conflict, and mystical transformations. Kafka was born into a middle-class Jewish family in Prague , then part of the Austro-Hungarian Empire . He trained as a lawyer and worked for an insurance company, writing in his spare time – he complained all his life about his lack of time to write. Kafka wrote hundreds of letters to family and close female friends, including his fiancée Felice Bauer . Only a few of Kafka's stories appeared during his lifetime in story collections and literary magazines. His novels and other unfinished works were published posthumously, mostly by his friend Max Brod , who ignored his wish to have the manuscripts destroyed. Albert Camus and Jean-Paul Sartre are among the writers influenced by Kafka's work; the term Kafkaesque has entered the English language to describe surreal situations like those in his writing. ( Full article... ) Recently featured: Alec Douglas-Home  – Frank Pick  – Golden-crowned sifaka Archive – By email – More featured articles... In the news Nineteen firefighters are killed battling a wildfire (pictured) in the U.S. state of Arizona . Large protests against President Mohamed Morsi are held across Egypt . Croatia becomes the 28th member of the European Union . At least five planets , including three planets suspected of being habitable , are found orbiting the star Gliese 667 C . Kevin Rudd wins leadership of the Australian Labor Party , ousting incumbent Julia Gillard and becoming Prime Minister of Australia . DNA from a Middle Pleistocene horse is successfully sequenced , becoming the oldest genome ever sequenced. Tsakhiagiin Elbegdorj is reelected as President of Mongolia . The Cambodian Tailorbird , found in Phnom Penh , is identified as a new bird species.

Susah Hati.. (Baru)

Lalala.. Fikiran bercelaru.. Tak tau.. Apa tujuan wat semua ni..

Uc Browser(1)

200px Developer(s) UCWeb Initial release August 2004 Stable release 9.0 / January 24, 2013; 5 months ago (2013-01-24) Operating system Cross-platform ( S60 , Java , Windows Phone , Android , iOS , Windows CE , bada , MTK , BREW ) Engine U3 (based on Webkit ) Available in Chinese , English , Russian , Vietnamese , Indonesian , Portuguese , Spanish Type Mobile browser License Proprietary Freeware Website www.ucweb.com UC Browser (also known as UCWEB ) is a web browser for mobile devices , created by UC Mobile, a Chinese software company headquartered in Guangzhou, and with offices in Wuhan , Beijing , Chengdu , People's Republic of China , and Gurgaon , India . The web browser increases speeds by not loading web pages on the phone, but instead compressing and rendering them on a server, [1] similar to the operation of a thin client . The browser is available for a number of mobile platforms, [2] from low memory phones to high-end phones. [3] UCWeb Inc. (UCWeb) is a leading provider of mobile internet software technology and application services. Since its inception in 2004, UCWeb has stated its mission has been to provide better mobile internet experience to billions of users around the world. UC Browser, which is said to be the flagship product of UCWeb, is available on more than 3,000 different models of cell phones from over 200 cell phone manufacturers. In addition, UC Browser is said to be compatible with all mainstream operating systems such as Symbian variants, Android OS, iOS, Windows Mobile, Win CE, Java, MTK, and Blackberry. In June 2011, UCWeb released U3 kernel, the company’s proprietary product. Browsers based on U3 would give users a faster, more convenient and more secure web surfing experience. Today, 80% of its more than 1,000 employees are reported to be involved in product research and development. UCWeb has obtained or is in the process of applying for more than 200 patents in the field of mobile browsing. UCWeb has built a network of independent servers in Asia, North America, and Europe, serving over 400 million users in more than 150 countries and regions around the world. UC Browser is now available in 7 languages including English, Russian, Indonesian, and Vietnamese. As of September 2011, UC Browser has been downloaded over 1.5 billion times around the world, and user’s monthly page views has exceeded 160 billion. It recently gave way to the opening of its first overseas office in India in September 2011.

.. Courage ..

Tapi takpe, tq for my Shimeji.. Saya akan terus berusaha.. So, semuanya akan menjadi lebih baik.. :)

.. Susah.. Hati.. 2..

Minggu depan dah bentang.. Assignment still berhabuk kat bilik..

.. Susah.. Hati..

Hurm.. Tak tau.. Nak cakap apa gi..

Artificial intelligence

From Wikipedia, the free encyclopedia

"AI" redirects here. For other uses, see Ai.

For other uses, see Artificial intelligence (disambiguation).

Artificial intelligence (AI) is technology and a branch of computer science that studies and develops intelligent machines and software. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",^[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.^[2] John McCarthy, who coined the term in 1955,^[3] defines it as "the science and engineering of making intelligent machines".^[4]

AI research is highly technical and specialised, deeply divided into subfields that often fail to communicate with each other.^[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. There are subfields which are focused on the solution of specific problems, on one of several possible approaches, on the use of widely differing tools and towards the accomplishment of particular applications.

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.^[6] General intelligence (or "strong AI") is still among the field's long term goals.^[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.

The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.^[8] This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth, fiction and philosophy since antiquity.^[9] Artificial intelligence has been the subject of tremendous optimism^[10] but has also suffered stunning setbacks.^[11] Today it has become an essential part of the technology industry and many of the most difficult problems in computer science.^[12]

Contents   [hide]  1 History 2 Goals 2.1 Deduction, reasoning, problem solving 2.2 Knowledge representation 2.3 Planning 2.4 Learning 2.5 Natural language processing 2.6 Motion and manipulation 2.7 Perception 2.8 Social intelligence 2.9 Creativity 2.10 General intelligence 3 Approaches 3.1 Cybernetics and brain simulation 3.2 Symbolic 3.3 Sub-symbolic 3.4 Statistical 3.5 Integrating the approaches 4 Tools 4.1 Search and optimization 4.2 Logic 4.3 Probabilistic methods for uncertain reasoning 4.4 Classifiers and statistical learning methods 4.5 Neural networks 4.6 Control theory 4.7 Languages 5 Evaluating progress 6 Applications 6.1 Competitions and prizes 6.2 Platforms 7 Philosophy 8 Predictions and ethics 9 See also 10 References 10.1 Notes 11 References 11.1 AI textbooks 11.2 History of AI 11.3 Other sources 12 Further reading 13 External links

History[edit]

Main articles: History of artificial intelligence and Timeline of artificial intelligence

Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the bronze robot of Hephaestus, and Pygmalion's Galatea.^[13] Human likenesses believed to have intelligence were built in every major civilization: animated cult images were worshiped in Egypt and Greece^[14] and humanoid automatons were built by Yan Shi, Hero of Alexandria and Al-Jazari.^[15] It was also widely believed that artificial beings had been created by Jābir ibn Hayyān, Judah Loew and Paracelsus.^[16] By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots).^[17] Pamela McCorduck argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".^[9] Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.

Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.^[18][19] This, along with concurrent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.^[20]

The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.^[21] The attendees, including John McCarthy, Marvin Minsky, Allen Newell andHerbert Simon, became the leaders of AI research for many decades.^[22] They and their students wrote programs that were, to most people, simply astonishing:^[23] Computers were solving word problems in algebra, proving logical theorems and speaking English.^[24] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense^[25] and laboratories had been established around the world.^[26] AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do" and Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".^[27]

They had failed to recognize the difficulty of some of the problems they faced.^[28] In 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off all undirected exploratory research in AI. The next few years would later be called an "AI winter",^[29] a period when funding for AI projects was hard to find.

In the early 1980s, AI research was revived by the commercial success of expert systems,^[30] a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field.^[31] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.^[32]

In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry.^[12] The success was due to several factors: the increasing computational power of computers (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and a new commitment by researchers to solid mathematical methods and rigorous scientific standards.^[33]

On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.^[34] In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.^[35] Two years later, a team from CMU won the DARPA Urban Challenge when their vehicle autonomously navigated 55 miles in an Urban environment while adhering to traffic hazards and all traffic laws.^[36] In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy champions, Brad Rutter and Ken Jennings, by a significant margin.^[37] The Kinect, which provides a 3D body–motion interface for the Xbox 360, uses algorithms that emerged from lengthy AI research^[38] as does the iPhones's Siri.

Goals[edit]

The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.^[6]

Deduction, reasoning, problem solving[edit]

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions.^[39] By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.^[40]

For difficult problems, most of these algorithms can require enormous computational resources – most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for AI research.^[41]

Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step deduction that early AI research was able to model.^[42] AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches to AI mimic the probabilistic nature of the human ability to guess.

Knowledge representation[edit]

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

Main articles: Knowledge representation and Commonsense knowledge

Knowledge representation^[43] and knowledge engineering^[44] are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;^[45] situations, events, states and time;^[46] causes and effects;^[47] knowledge about knowledge (what we know about what other people know);^[48] and many other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations, concepts and so on that the machine knows about. The most general are called upper ontologies, which attempt to provide a foundation for all other knowledge.^[49]

Among the most difficult problems in knowledge representation are:

Default reasoning and the qualification problem

Many of the things people know take the form of "working assumptions." For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969^[50] as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.^[51]

The breadth of commonsense knowledge

The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base ofcommonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering — they must be built, by hand, one complicated concept at a time.^[52] A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology.^{[citation needed]}

The subsymbolic form of some commonsense knowledge

Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"^[53] or an art critic can take one look at a statue and instantly realize that it is a fake.^[54] These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.^[55] Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped thatsituated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.^[55]

Planning[edit]

A hierarchical control system is a form ofcontrol system in which a set of devices and governing software is arranged in a hierarchy.

Main article: Automated planning and scheduling

Intelligent agents must be able to set goals and achieve them.^[56] They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices.^[57]

In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.^[58] However, if the agent is not the only actor, it must periodically ascertain whether the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.^[59]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used byevolutionary algorithms and swarm intelligence.^[60]

Learning[edit]

Main article: Machine learning

Machine learning is the study of computer algorithms that improve automatically through experience^[61][62] and has been central to AI research since the field's inception.^[63]

Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning^[64] the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.^[65]

Within Developmental robotics, developmental learning approaches were elaborated for lifelong cumulative acquisition of repertoires of novel skills by a robot, through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. ^{[66] [67] [68][69]}

Natural language processing[edit]

A parse tree represents the syntacticstructure of a sentence according to someformal grammar.

Main article: Natural language processing

Natural language processing^[70] gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as Internet texts. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.^[71]

A common method of processing and extracting meaning from natural language is through semantic indexing. Increases in processing speeds and the drop in the cost of data storage makes indexing large volumes of abstractions of the users input much more efficient.

Motion and manipulation[edit]

Main article: Robotics

The field of robotics^[72] is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation^[73] andnavigation, with sub-problems of localization (knowing where you are, or finding out where other things are), mapping (learning what is around you, building a map of the environment), and motion planning (figuring out how to get there) or path planning (going from one point in space to another point, which may involve compliant motion - where the robot moves while maintaining physical contact with an object).^[74][75]

Perception[edit]

Main articles: Machine perception, Computer vision, and Speech recognition

Machine perception^[76] is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision^[77] is the ability to analyze visual input. A few selected subproblems are speech recognition,^[78] facial recognition and object recognition.^[79]

Social intelligence[edit]

Main article: Affective computing

Kismet, a robot with rudimentary social skills^[80]

Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects.^[81][82] It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science.^[83] While the origins of the field may be traced as far back as to early philosophical inquiries into emotion,^[84] the more modern branch of computer science originated with Rosalind Picard's 1995 paper^[85] on affective computing.^[86][87] A motivation for the research is the ability to simulate empathy. The machine should interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate response for those emotions.

Emotion and social skills^[88] play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to facilitate human-computer interaction, an intelligent machine might want to be able to display emotions—even if it does not actually experience them itself—in order to appear sensitive to the emotional dynamics of human interaction.

Creativity[edit]

Main article: Computational creativity

A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative, or systems that identify and assess creativity). Related areas of computational research are Artificial intuition and Artificial imagination.

General intelligence[edit]

Main articles: Strong AI and AI-complete

Most researchers think that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them.^[7] A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.^[89][90]

Many of the problems above may require general intelligence to be considered solved. For example, even a straightforward, specific task like machine translation requires that the machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence). A problem like machine translation is considered "AI-complete". In order to solve this particular problem, you must solve all the problems.^[91]

Approaches[edit]

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.^[92] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurology? Or is human biology as irrelevant to AI research as bird biology is toaeronautical engineering?^[93] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?^[94] Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?^[95] John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as synthetic intelligence,^[96] a term which has since been adopted by some non-GOFAI researchers.^[97][98]

Cybernetics and brain simulation[edit]

Main articles: Cybernetics and Computational neuroscience

In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.^[20] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

Symbolic[edit]

Main article: GOFAI

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI "good old fashioned AI" or "GOFAI".^[99] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background.^[100] Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Cognitive simulation

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.^[101][102]

Logic-based

Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.^[93] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.^[103] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.^[104]

"Anti-logic" or "scruffy"

Researchers at MIT (such as Marvin Minsky and Seymour Papert)^[105] found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford).^[94] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.^[106]

Knowledge-based

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.^[107] This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.^[30] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic[edit]

By the 1980s progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception,robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.^[95]

Bottom-up, embodied, situated, behavior-based or nouvelle AI

Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.^[108]Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of theembodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Computational Intelligence

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s.^[109] These and other sub-symbolic approaches, such as fuzzy systems andevolutionary computation, are now studied collectively by the emerging discipline of computational intelligence.^[110]

Statistical[edit]

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (likemathematics, economics or operations research). Stuart Russell and Peter Norvig describe this movement as nothing less than a "revolution" and "the victory of the neats."^[33] Critics argue that these techniques are too focused on particular problems and have failed to address the long term goal of general intelligence.^[111] There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between Peter Norvig and Noam Chomsky.^[112][113]

Integrating the approaches[edit]

Intelligent agent paradigm

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.^[2]

Agent architectures and cognitive architectures

Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.^[114] A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.^[115] Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system.^[116]

Tools[edit]

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Search and optimization[edit]

Main articles: Search algorithm, Mathematical optimization, and Evolutionary computation

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:^[117] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.^[118] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.^[119] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.^[73] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches^[120] are rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called "pruningthe search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies.^[121] Heuristics limit the search for solutions into a smaller sample size.^[74]

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.^[122]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selectingonly the fittest to survive each generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony or particle swarm optimization)^[123]and evolutionary algorithms (such as genetic algorithms, gene expression programming, and genetic programming).^[124]

Logic[edit]

Main articles: Logic programming and Automated reasoning

Logic^[125] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning^[126] and inductive logic programming is a method for learning.^[127]

Several different forms of logic are used in AI research. Propositional or sentential logic^[128] is the logic of statements which can be true or false. First-order logic^[129] also allows the use ofquantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy logic,^[130] is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems. Subjective logic^[131] models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a Beta distribution. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.

Default logics, non-monotonic logics and circumscription^[51] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;^[45] situation calculus, event calculus and fluent calculus (for representing events and time);^[46] causal calculus;^[47]belief calculus; and modal logics.^[48]

Probabilistic methods for uncertain reasoning[edit]

Main articles: Bayesian network, Hidden Markov model, Kalman filter, Decision theory, and Utility theory

Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.^[132]

Bayesian networks^[133] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),^[134] learning (using the expectation-maximization algorithm),^[135] planning (using decision networks)^[136] and perception (using dynamic Bayesian networks).^[137] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).^[137]

A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,^[138] information value theory.^[57] These tools include models such as Markov decision processes,^[139] dynamicdecision networks,^[137] game theory and mechanism design.^[140]

Classifiers and statistical learning methods[edit]

Main articles: Classifier (mathematics), Statistical classification, and Machine learning

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.^[141]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The most widely used classifiers are the neural network,^[142] kernel methods such as thesupport vector machine,^[143] k-nearest neighbor algorithm,^[144] Gaussian mixture model,^[145] naive Bayes classifier,^[146] and decision tree.^[147] The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.^[148]

Neural networks[edit]

Main articles: Neural network and Connectionism

A neural network is an interconnected group of nodes, akin to the vast network ofneurons in the human brain.

The study of artificial neural networks^[142] began in the decade before the field AI research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed thebackpropagation algorithm.^[149]

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.^[150] Among recurrent networks, the most famous is the Hopfield net, a form of attractor network, which was first described by John Hopfieldin 1982.^[151] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learningand competitive learning.^[152]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.^[153]

Control theory[edit]

Main article: Intelligent control

Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.^[154]

Languages[edit]

Main article: List of programming languages for artificial intelligence

AI researchers have developed several specialized languages for AI research, including Lisp^[155] and Prolog.^[156]

Evaluating progress[edit]

Main article: Progress in artificial intelligence

In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.^[157]

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.^[158]

One classification for outcomes of an AI test is:^[159]

1. Optimal: it is not possible to perform better.
2. Strong super-human: performs better than all humans.
3. Super-human: performs better than most humans.
4. Sub-human: performs worse than most humans.

For example, performance at draughts is optimal,^[160] performance at chess is super-human and nearing strong super-human (see computer chess: computers versus human) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.

A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from Kolmogorov complexity and data compression.^[161] Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.

An area that artificial intelligence had contributed greatly to is Intrusion detection.^[162]

Applications[edit]

An automated online assistant providing customer service on a web page – one of many very primitive applications of artificial intelligence.

This section requires expansion.(January 2011)

Main article: Applications of artificial intelligence

Artificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.^[163]

Competitions and prizes[edit]

Main article: Competitions and prizes in artificial intelligence

There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining, robotic cars, robot soccer and games.

Platforms[edit]

A platform (or "computing platform") is defined as "some sort of hardware architecture or software framework (including application frameworks), that allows software to run." As Rodney Brooks^[164] pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.

A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit PC-based but still an entire real-world system, to various robot platforms such as the widely available Roomba with open interface.^[165]

Philosophy[edit]

Main article: Philosophy of artificial intelligence

Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.^[166]

Turing's "polite convention"

We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test.^[157]

The Dartmouth proposal

"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for theDartmouth Conference of 1956, and represents the position of most working AI researchers.^[167]

Newell and Simon's physical symbol system hypothesis

"A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligences consist of formal operations on symbols.^[168]Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)^[169][170]

Gödel's incompleteness theorem

A formal system (such as a computer program) cannot prove all true statements.^[171] Roger Penrose is among those who claim that Gödel's theorem limits what machines can do. (See The Emperor's New Mind.)^[172]

Searle's strong AI hypothesis

"The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."^[173] John Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.^[174]

The artificial brain argument

The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original.^[90]

Predictions and ethics[edit]

Main articles: Artificial intelligence in fiction, Ethics of artificial intelligence, Transhumanism, and Technological singularity

Artificial Intelligence is a common topic in both science fiction and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.

In fiction, Artificial Intelligence has appeared fulfilling many roles.
These include:

• a servant (R2D2 in Star Wars)
• a law enforcer (K.I.T.T. "Knight Rider")
• a comrade (Lt. Commander Data in Star Trek: The Next Generation)
• a conqueror/overlord (The Matrix, Omnius)
• a dictator (With Folded Hands),
• a benevolent provider/de facto ruler (The Culture)
• a supercomputer (The Red Queen in Resident Evil "Gilium" in Outlaw Star)
• an assassin (Terminator)
• a sentient race (Battlestar Galactica/Transformers/Mass Effect)
• an extension to human abilities (Ghost in the Shell)
• the savior of the human race (R. Daneel Olivaw in Isaac Asimov's Robot series)

Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, including the films I Robot, Blade Runner and A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel human emotions. This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature.^[175] The subject is profoundly discussed in the 2010 documentary film Plug & Pray.^[176]

Martin Ford, author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future,^[177] and others argue that specialized artificial intelligence applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers to perform most routine and repetitive jobs. Ford predicts that many knowledge-based occupations—and in particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning^[178] and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible to outsource knowledge work.^[179]

Joseph Weizenbaum wrote that AI applications can not, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service orpsychotherapy^[180] was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life.^[181]

Many futurists believe that artificial intelligence will ultimately transcend the limits of progress. Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029. He also predicts that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "singularity".^[182]

Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either.^[183] This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with life-like muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Almost 20 years later, the first AI robotic pet (AIBO) came available as a companion to people. AIBO grew out of Sony's Computer Science Laboratory (CSL). Famed engineer Dr. Toshitada Doi is credited as AIBO's original progenitor: in 1994 he had started work on robots with artificial intelligence expert Masahiro Fujita within CSL of Sony. Doi's, friend, the artist Hajime Sorayama, was enlisted to create the initial designs for the AIBO's body. Those designs are now part of the permanent collections of Museum of Modern Art and the Smithsonian Institution, with later versions of AIBO being used in studies in Carnegie Mellon University. In 2006, AIBO was added into Carnegie Mellon University's "Robot Hall of Fame".

Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be friendly.^[184] He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably, because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).

Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" (1863), and expanded upon by George Dyson in his book of the same name in 1998.^[185]

See also[edit]

	AI portal
	Mind and Brain portal
	Chess portal
	Strategy games portal

Main article: Outline of artificial intelligence

• AI-complete
• Artificial intelligence in fiction
• Artificial Intelligence (journal)
• Artificial intelligence (video games)
• Synthetic intelligence
• Cognitive sciences
• Developmental robotics
• Computer Go
• Human Cognome Project
• Friendly artificial intelligence
• List of basic artificial intelligence topics
• List of AI researchers
• List of important AI publications
• List of AI projects
• List of machine learning algorithms
• List of emerging technologies
• List of scientific journals
• Philosophy of mind
• Technological singularity
• Never-Ending Language Learning

References[edit]

Notes[edit]

1. ^ Definition of AI as the study of intelligent agents:
   • Poole, Mackworth & Goebel 1998, p. 1, which provides the version that is used in this article. Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
   • Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" (Russell & Norvig 2003, p. 55).
   • Nilsson 1998
2. ^ ^a b The intelligent agent paradigm:
   • Russell & Norvig 2003, pp. 27, 32–58, 968–972
   • Poole, Mackworth & Goebel 1998, pp. 7–21
   • Luger & Stubblefield 2004, pp. 235–240
   The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria.
3. ^ Although there is some controversy on this point (seeCrevier (1993, p. 50)), McCarthy states unequivocally "I came up with the term" in a c|net interview. (Skillings 2006) McCarthy first used the term in the proposal for theDartmouth conference, which appeared in 1955. (McCarthy et al. 1955)
4. ^ McCarthy's definition of AI:
   • McCarthy 2007
5. ^ Pamela McCorduck (2004, pp. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other."
6. ^ ^a b This list of intelligent traits is based on the topics covered by the major AI textbooks, including:
   • Russell & Norvig 2003
   • Luger & Stubblefield 2004
   • Poole, Mackworth & Goebel 1998
   • Nilsson 1998
7. ^ ^a b General intelligence (strong AI) is discussed in popular introductions to AI:
   • Kurzweil 1999 and Kurzweil 2005
8. ^ See the Dartmouth proposal, under Philosophy, below.
9. ^ ^a b This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." (McCorduck 2004, p. 34) "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." (McCorduck 2004, p. xviii) "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." (McCorduck 2004, p. 3) She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods." (McCorduck 2004, pp. 340–400)
10. ^ The optimism referred to includes the predictions of early AI researchers (see optimism in the history of AI) as well as the ideas of modern transhumanists such as Ray Kurzweil.
11. ^ The "setbacks" referred to include the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine marketin 1987.
12. ^ ^a b AI applications widely used behind the scenes:
    • Russell & Norvig 2003, p. 28
    • Kurzweil 2005, p. 265
    • NRC 1999, pp. 216–222
13. ^ AI in myth:
    • McCorduck 2004, pp. 4–5
    • Russell & Norvig 2003, p. 939
14. ^ Cult images as artificial intelligence:
    • Crevier (1993, p. 1) (statue of Amun)
    • McCorduck (2004, pp. 6–9)
    These were the first machines to be believed to have true intelligence and consciousness. Hermes Trismegistusexpressed the common belief that with these statues, craftsman had reproduced "the true nature of the gods", their sensus and spiritus. McCorduck makes the connection between sacred automatons and Mosaic law (developed around the same time), which expressly forbids the worship of robots (McCorduck 2004, pp. 6–9)
15. ^ Humanoid automata:
    Yan Shi:
    • Needham 1986, p. 53
    Hero of Alexandria:
    • McCorduck 2004, p. 6
    Al-Jazari:
    • "A Thirteenth Century Programmable Robot". Shef.ac.uk. Retrieved 25 April 2009.
    Wolfgang von Kempelen:
    • McCorduck 2004, p. 17
16. ^ Artificial beings:
    Jābir ibn Hayyān's Takwin:
    • O'Connor, Kathleen Malone (1994). The alchemical creation of life (takwin) and other concepts of Genesis in medieval Islam. University of Pennsylvania. Retrieved 10 January 2007.
    Judah Loew's Golem:
    • McCorduck 2004, pp. 15–16
    • Buchanan 2005, p. 50
    Paracelsus' Homunculus:
    • McCorduck 2004, pp. 13–14
17. ^ AI in early science fiction.
    • McCorduck 2004, pp. 17–25
18. ^ This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis.
19. ^ Formal reasoning:
    • Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 0-15-601391-6.OCLC 46890682.
20. ^ ^a b AI's immediate precursors:
    • McCorduck 2004, pp. 51–107
    • Crevier 1993, pp. 27–32
    • Russell & Norvig 2003, pp. 15, 940
    • Moravec 1988, p. 3
    See also Cybernetics and early neural networks (in History of artificial intelligence). Among the researchers who laid the foundations of AI were Alan Turing, John Von Neumann,Norbert Wiener, Claude Shannon, Warren McCullough,Walter Pitts and Donald Hebb.
21. ^ Dartmouth conference:
    • McCorduck 2004, pp. 111–136
    • Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science."
    • Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence."
    • NRC 1999, pp. 200–201
22. ^ Hegemony of the Dartmouth conference attendees:
    • Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students."
    • McCorduck 2004, pp. 129–130
23. ^ Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish." Russell & Norvig 2003, p. 18
24. ^ "Golden years" of AI (successful symbolic reasoning programs 1956–1973):
    • McCorduck 2004, pp. 243–252
    • Crevier 1993, pp. 52–107
    • Moravec 1988, p. 9
    • Russell & Norvig 2003, pp. 18–21
    The programs described are Daniel Bobrow's STUDENT,Newell and Simon's Logic Theorist and Terry Winograd'sSHRDLU.
25. ^ DARPA pours money into undirected pure research into AI during the 1960s:
    • McCorduck 2004, pp. 131
    • Crevier 1993, pp. 51, 64–65
    • NRC 1999, pp. 204–205
26. ^ AI in England:
    • Howe 1994
27. ^ Optimism of early AI:
    • Herbert Simon quote: Simon 1965, p. 96 quoted inCrevier 1993, p. 109.
    • Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109.
28. ^ See The problems (in History of artificial intelligence)
29. ^ First AI Winter, Mansfield Amendment, Lighthill report
    • Crevier 1993, pp. 115–117
    • Russell & Norvig 2003, p. 22
    • NRC 1999, pp. 212–213
    • Howe 1994
30. ^ ^a b Expert systems:
    • ACM 1998, I.2.1,
    • Russell & Norvig 2003, pp. 22–24
    • Luger & Stubblefield 2004, pp. 227–331,
    • Nilsson 1998, chpt. 17.4
    • McCorduck 2004, pp. 327–335, 434–435
    • Crevier 1993, pp. 145–62, 197–203
31. ^ Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI:
    • McCorduck 2004, pp. 426–441
    • Crevier 1993, pp. 161–162,197–203, 211, 240
    • Russell & Norvig 2003, p. 24
    • NRC 1999, pp. 210–211
32. ^ Second AI winter:
    • McCorduck 2004, pp. 430–435
    • Crevier 1993, pp. 209–210
    • NRC 1999, pp. 214–216
33. ^ ^a b Formal methods are now preferred ("Victory of theneats"):
    • Russell & Norvig 2003, pp. 25–26
    • McCorduck 2004, pp. 486–487
34. ^ McCorduck 2004, pp. 480–483
35. ^ DARPA Grand Challenge – home page
36. ^ "Welcome". Archive.darpa.mil. Retrieved 31 October 2011.
37. ^ Markoff, John (16 February 2011). "On 'Jeopardy!' Watson Win Is All but Trivial". The New York Times.
38. ^ Kinect's AI breakthrough explained
39. ^ Problem solving, puzzle solving, game playing and deduction:
    • Russell & Norvig 2003, chpt. 3–9,
    • Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9,
    • Luger & Stubblefield 2004, chpt. 3,4,6,8,
    • Nilsson 1998, chpt. 7–12
40. ^ Uncertain reasoning:
    • Russell & Norvig 2003, pp. 452–644,
    • Poole, Mackworth & Goebel 1998, pp. 345–395,
    • Luger & Stubblefield 2004, pp. 333–381,
    • Nilsson 1998, chpt. 19
41. ^ Intractability and efficiency and the combinatorial explosion:
    • Russell & Norvig 2003, pp. 9, 21–22
42. ^ Psychological evidence of sub-symbolic reasoning:
    • Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task)
    • Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples).
    • Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From)
43. ^ Knowledge representation:
    • ACM 1998, I.2.4,
    • Russell & Norvig 2003, pp. 320–363,
    • Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345,
    • Luger & Stubblefield 2004, pp. 227–243,
    • Nilsson 1998, chpt. 18
44. ^ Knowledge engineering:
    • Russell & Norvig 2003, pp. 260–266,
    • Poole, Mackworth & Goebel 1998, pp. 199–233,
    • Nilsson 1998, chpt. ~17.1–17.4
45. ^ ^a b Representing categories and relations: Semantic networks, description logics, inheritance (including framesand scripts):
    • Russell & Norvig 2003, pp. 349–354,
    • Poole, Mackworth & Goebel 1998, pp. 174–177,
    • Luger & Stubblefield 2004, pp. 248–258,
    • Nilsson 1998, chpt. 18.3
46. ^ ^a b Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem):
    • Russell & Norvig 2003, pp. 328–341,
    • Poole, Mackworth & Goebel 1998, pp. 281–298,
    • Nilsson 1998, chpt. 18.2
47. ^ ^a b Causal calculus:
    • Poole, Mackworth & Goebel 1998, pp. 335–337
48. ^ ^a b Representing knowledge about knowledge: Belief calculus, modal logics:
    • Russell & Norvig 2003, pp. 341–344,
    • Poole, Mackworth & Goebel 1998, pp. 275–277
49. ^ Ontology:
    • Russell & Norvig 2003, pp. 320–328
50. ^ Qualification problem:
    • McCarthy & Hayes 1969
    • Russell & Norvig 2003^{[page needed]}
    While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge.
51. ^ ^a b Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption,abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"):
    • Russell & Norvig 2003, pp. 354–360,
    • Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335,
    • Luger & Stubblefield 2004, pp. 335–363,
    • Nilsson 1998, ~18.3.3
52. ^ Breadth of commonsense knowledge:
    • Russell & Norvig 2003, p. 21,
    • Crevier 1993, pp. 113–114,
    • Moravec 1988, p. 13,
    • Lenat & Guha 1989 (Introduction)
53. ^ Dreyfus & Dreyfus 1986
54. ^ Gladwell 2005
55. ^ ^a b Expert knowledge as embodied intuition:
    • Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI)
    • Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.)
    • Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)
    Note, however, that recent work in cognitive science challenges the view that there is anything like sub-symbolic human information processing, i.e., human cognition is essentially symbolic regardless of the level and of the consciousness status of the processing:
    • Augusto, Luis M. (2013). "Unconscious representations 1: Belying the traditional model of human cognition".Axiomathes. doi:10.1007/s10516-012-9206-z.
    • Augusto, Luis M. (2013). "Unconscious representations 2: Towards an integrated cognitive architecture".Axiomathes. doi:10.1007/s10516-012-9207-y.
56. ^ Planning:
    • ACM 1998, ~I.2.8,
    • Russell & Norvig 2003, pp. 375–459,
    • Poole, Mackworth & Goebel 1998, pp. 281–316,
    • Luger & Stubblefield 2004, pp. 314–329,
    • Nilsson 1998, chpt. 10.1–2, 22
57. ^ ^a b Information value theory:
    • Russell & Norvig 2003, pp. 600–604
58. ^ Classical planning:
    • Russell & Norvig 2003, pp. 375–430,
    • Poole, Mackworth & Goebel 1998, pp. 281–315,
    • Luger & Stubblefield 2004, pp. 314–329,
    • Nilsson 1998, chpt. 10.1–2, 22
59. ^ Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning:
    • Russell & Norvig 2003, pp. 430–449
60. ^ Multi-agent planning and emergent behavior:
    • Russell & Norvig 2003, pp. 449–455
61. ^ This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E."
62. ^ Learning:
    • ACM 1998, I.2.6,
    • Russell & Norvig 2003, pp. 649–788,
    • Poole, Mackworth & Goebel 1998, pp. 397–438,
    • Luger & Stubblefield 2004, pp. 385–542,
    • Nilsson 1998, chpt. 3.3 , 10.3, 17.5, 20
63. ^ Alan Turing discussed the centrality of learning as early as 1950, in his classic paper Computing Machinery and Intelligence.(Turing 1950) In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".(pdf scanned copy of the original)(version published in 1957, An Inductive Inference Machine," IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62)
64. ^ Reinforcement learning:
    • Russell & Norvig 2003, pp. 763–788
    • Luger & Stubblefield 2004, pp. 442–449
65. ^ Computational learning theory:
    • CITATION IN PROGRESS.^{[citation needed]}
66. ^ Weng, J., McClelland, Pentland, A.,Sporns, O., Stockman, I., Sur, M., and E. Thelen (2001) Autonomous mental development by robots and animals, Science, vol. 291, pp. 599–600.
67. ^ Lungarella, M., Metta, G., Pfeifer, R. and G. Sandini (2003).Developmental robotics: a survey. Connection Science, 15:151–190.
68. ^ Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M. and C. Yoshida (2009) Cognitive developmental robotics: a survey. IEEE Transactions on Autonomous Mental Development, Vol.1, No.1, pp.12--34.
69. ^ Oudeyer, P-Y. (2010) On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development, IEEE Transactions on Autonomous Mental Development, 2(1), pp. 2--16.
70. ^ Natural language processing:
    • ACM 1998, I.2.7
    • Russell & Norvig 2003, pp. 790–831
    • Poole, Mackworth & Goebel 1998, pp. 91–104
    • Luger & Stubblefield 2004, pp. 591–632
71. ^ Applications of natural language processing, includinginformation retrieval (i.e. text mining) and machine translation:
    • Russell & Norvig 2003, pp. 840–857,
    • Luger & Stubblefield 2004, pp. 623–630
72. ^ Robotics:
    • ACM 1998, I.2.9,
    • Russell & Norvig 2003, pp. 901–942,
    • Poole, Mackworth & Goebel 1998, pp. 443–460
73. ^ ^a b Moving and configuration space:
    • Russell & Norvig 2003, pp. 916–932
74. ^ ^a b Tecuci, G. (2012), Artificial intelligence. WIREs Comp Stat, 4: 168–180. doi: 10.1002/wics.200
75. ^ Robotic mapping (localization, etc):
    • Russell & Norvig 2003, pp. 908–915
76. ^ Machine perception:
    • Russell & Norvig 2003, pp. 537–581, 863–898
    • Nilsson 1998, ~chpt. 6
77. ^ Computer vision:
    • ACM 1998, I.2.10
    • Russell & Norvig 2003, pp. 863–898
    • Nilsson 1998, chpt. 6
78. ^ Speech recognition:
    • ACM 1998, ~I.2.7
    • Russell & Norvig 2003, pp. 568–578
79. ^ Object recognition:
    • Russell & Norvig 2003, pp. 885–892
80. ^ "Kismet". MIT Artificial Intelligence Laboratory, Humanoid Robotics Group.
81. ^ Thro, Ellen (1993). Robotics. New York.
82. ^ Edelson, Edward (1991). The Nervous System. New York: Remmel Nunn.
83. ^ Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review". Affective Computing and Intelligent Interaction.LNCS 3784. Springer. pp. 981–995.doi:10.1007/11573548.
84. ^ James, William (1884). "What is Emotion". Mind 9: 188–205. doi:10.1093/mind/os-IX.34.188. Cited by Tao and Tan.
85. ^ "Affective Computing" MIT Technical Report #321 (Abstract), 1995
86. ^ Kleine-Cosack, Christian (October 2006). "Recognition and Simulation of Emotions" (PDF). Archived from the original on 28 May 2008. Retrieved 13 May 2008. "The introduction of emotion to computer science was done by Pickard (sic) who created the field of affective computing."
87. ^ Diamond, David (December 2003). "The Love Machine; Building computers that care". Wired. Archived from the original on 18 May 2008. Retrieved 13 May 2008. "Rosalind Picard, a genial MIT professor, is the field's godmother; her 1997 book, Affective Computing, triggered an explosion of interest in the emotional side of computers and their users."
88. ^ Emotion and affective computing:
    • Minsky 2006
89. ^ Gerald Edelman, Igor Aleksander and others have both argued that artificial consciousness is required for strong AI. (Aleksander 1995; Edelman 2007)
90. ^ ^a b Artificial brain arguments: AI requires a simulation of the operation of the human brain
    • Russell & Norvig 2003, p. 957
    • Crevier 1993, pp. 271 and 279
    A few of the people who make some form of the argument:
    • Moravec 1988
    • Kurzweil 2005, p. 262
    • Hawkins & Blakeslee 2005
    The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn andJohn Searle in 1980.
91. ^ AI complete: Shapiro 1992, p. 9
92. ^ Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about" (Nilsson 1983, p. 10).
93. ^ ^a b Biological intelligence vs. intelligence in general:
    • Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering.
    • McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplioshed, and the other aimed at modeling intelligent processes found in nature, particularly human ones."
    • Kolata 1982, a paper in Science, which describesMcCathy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real"[1]. McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" (Maker 2006).
94. ^ ^a b Neats vs. scruffies:
    • McCorduck 2004, pp. 421–424, 486–489
    • Crevier 1993, pp. 168
    • Nilsson 1983, pp. 10–11
95. ^ ^a b Symbolic vs. sub-symbolic AI:
    • Nilsson (1998, p. 7), who uses the term "sub-symbolic".
96. ^ Haugeland 1985, p. 255.
97. ^ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.8384&rep=rep1&type=pdf
98. ^ Pei Wang (2008). Artificial general intelligence, 2008: proceedings of the First AGI Conference. IOS Press. p. 63. ISBN 978-1-58603-833-5. Retrieved 31 October 2011.
99. ^ Haugeland 1985, pp. 112–117
100. ^ The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique ofperceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt.
101. ^ Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech):
     • McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM)
     • Crevier 1993, pp. 145–149
102. ^ Soar (history):
     • McCorduck 2004, pp. 450–451
     • Crevier 1993, pp. 258–263
103. ^ McCarthy and AI research at SAIL and SRI International:
     • McCorduck 2004, pp. 251–259
     • Crevier 1993
104. ^ AI research at Edinburgh and in France, birth of Prolog:
     • Crevier 1993, pp. 193–196
     • Howe 1994
105. ^ AI at MIT under Marvin Minsky in the 1960s :
     • McCorduck 2004, pp. 259–305
     • Crevier 1993, pp. 83–102, 163–176
     • Russell & Norvig 2003, p. 19
106. ^ Cyc:
     • McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise"
     • Crevier 1993, pp. 239–243
     • Russell & Norvig 2003, p. 363−365
     • Lenat & Guha 1989
107. ^ Knowledge revolution:
     • McCorduck 2004, pp. 266–276, 298–300, 314, 421
     • Russell & Norvig 2003, pp. 22–23
108. ^ Embodied approaches to AI:
     • McCorduck 2004, pp. 454–462
     • Brooks 1990
     • Moravec 1988
109. ^ Revival of connectionism:
     • Crevier 1993, pp. 214–215
     • Russell & Norvig 2003, p. 25
110. ^ Computational intelligence
     • IEEE Computational Intelligence Society
111. ^ Pat Langley, "The changing science of machine learning", Machine Learning, Volume 82, Number 3, 275–279, doi:10.1007/s10994-011-5242-y
112. ^ Yarden Katz, "Noam Chomsky on Where Artificial Intelligence Went Wrong", The Atlantic, November 1, 2012
113. ^ Peter Norvig, "On Chomsky and the Two Cultures of Statistical Learning"
114. ^ Agent architectures, hybrid intelligent systems:
     • Russell & Norvig (2003, pp. 27, 932, 970–972)
     • Nilsson (1998, chpt. 25)
115. ^ Hierarchical control system:
     • Albus, J. S. 4-D/RCS reference model architecture for unmanned ground vehicles. In G Gerhart, R Gunderson, and C Shoemaker, editors, Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology, volume 3693, pages 11—20
116. ^ Subsumption architecture:
     • CITATION IN PROGRESS.^{[citation needed]}
117. ^ Search algorithms:
     • Russell & Norvig 2003, pp. 59–189
     • Poole, Mackworth & Goebel 1998, pp. 113–163
     • Luger & Stubblefield 2004, pp. 79–164, 193–219
     • Nilsson 1998, chpt. 7–12
118. ^ Forward chaining, backward chaining, Horn clauses, and logical deduction as search:
     • Russell & Norvig 2003, pp. 217–225, 280–294
     • Poole, Mackworth & Goebel 1998, pp. ~46–52
     • Luger & Stubblefield 2004, pp. 62–73
     • Nilsson 1998, chpt. 4.2, 7.2
119. ^ State space search and planning:
     • Russell & Norvig 2003, pp. 382–387
     • Poole, Mackworth & Goebel 1998, pp. 298–305
     • Nilsson 1998, chpt. 10.1–2
120. ^ Uninformed searches (breadth first search, depth first search and general state space search):
     • Russell & Norvig 2003, pp. 59–93
     • Poole, Mackworth & Goebel 1998, pp. 113–132
     • Luger & Stubblefield 2004, pp. 79–121
     • Nilsson 1998, chpt. 8
121. ^ Heuristic or informed searches (e.g., greedy best first andA*):
     • Russell & Norvig 2003, pp. 94–109,
     • Poole, Mackworth & Goebel 1998, pp. pp. 132–147,
     • Luger & Stubblefield 2004, pp. 133–150,
     • Nilsson 1998, chpt. 9
122. ^ Optimization searches:
     • Russell & Norvig 2003, pp. 110–116,120–129
     • Poole, Mackworth & Goebel 1998, pp. 56–163
     • Luger & Stubblefield 2004, pp. 127–133
123. ^ Artificial life and society based learning:
     • Luger & Stubblefield 2004, pp. 530–541
124. ^ Genetic programming and genetic algorithms:
     • Luger & Stubblefield 2004, pp. 509–530,
     • Nilsson 1998, chpt. 4.2.
     • Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 0-262-58111-6.
     • Koza, John R. (1992). Genetic Programming. MIT Press.ISBN 0-262-11170-5. Unknown parameter |subtitle=ignored (help)
     • Poli, R., Langdon, W. B., McPhee, N. F. (2008). A Field Guide to Genetic Programming. Lulu.com, freely available from http://www.gp-field-guide.org.uk/.ISBN 978-1-4092-0073-4.
125. ^ Logic:
     • ACM 1998, ~I.2.3,
     • Russell & Norvig 2003, pp. 194–310,
     • Luger & Stubblefield 2004, pp. 35–77,
     • Nilsson 1998, chpt. 13–16
126. ^ Satplan:
     • Russell & Norvig 2003, pp. 402–407,
     • Poole, Mackworth & Goebel 1998, pp. 300–301,
     • Nilsson 1998, chpt. 21
127. ^ Explanation based learning, relevance based learning,inductive logic programming, case based reasoning:
     • Russell & Norvig 2003, pp. 678–710,
     • Poole, Mackworth & Goebel 1998, pp. 414–416,
     • Luger & Stubblefield 2004, pp. ~422–442,
     • Nilsson 1998, chpt. 10.3, 17.5
128. ^ Propositional logic:
     • Russell & Norvig 2003, pp. 204–233,
     • Luger & Stubblefield 2004, pp. 45–50
     • Nilsson 1998, chpt. 13
129. ^ First-order logic and features such as equality:
     • ACM 1998, ~I.2.4,
     • Russell & Norvig 2003, pp. 240–310,
     • Poole, Mackworth & Goebel 1998, pp. 268–275,
     • Luger & Stubblefield 2004, pp. 50–62,
     • Nilsson 1998, chpt. 15
130. ^ Fuzzy logic:
     • Russell & Norvig 2003, pp. 526–527
131. ^ Subjective logic:
     • CITATION IN PROGRESS.^{[citation needed]}
132. ^ Stochastic methods for uncertain reasoning:
     • ACM 1998, ~I.2.3,
     • Russell & Norvig 2003, pp. 462–644,
     • Poole, Mackworth & Goebel 1998, pp. 345–395,
     • Luger & Stubblefield 2004, pp. 165–191, 333–381,
     • Nilsson 1998, chpt. 19
133. ^ Bayesian networks:
     • Russell & Norvig 2003, pp. 492–523,
     • Poole, Mackworth & Goebel 1998, pp. 361–381,
     • Luger & Stubblefield 2004, pp. ~182–190, ~363–379,
     • Nilsson 1998, chpt. 19.3–4
134. ^ Bayesian inference algorithm:
     • Russell & Norvig 2003, pp. 504–519,
     • Poole, Mackworth & Goebel 1998, pp. 361–381,
     • Luger & Stubblefield 2004, pp. ~363–379,
     • Nilsson 1998, chpt. 19.4 & 7
135. ^ Bayesian learning and the expectation-maximization algorithm:
     • Russell & Norvig 2003, pp. 712–724,
     • Poole, Mackworth & Goebel 1998, pp. 424–433,
     • Nilsson 1998, chpt. 20
136. ^ Bayesian decision theory and Bayesian decision networks:
     • Russell & Norvig 2003, pp. 597–600
137. ^ ^a b c Stochastic temporal models:
     • Russell & Norvig 2003, pp. 537–581
     Dynamic Bayesian networks:
     • Russell & Norvig 2003, pp. 551–557
     Hidden Markov model:
     • (Russell & Norvig 2003, pp. 549–551)
     Kalman filters:
     • Russell & Norvig 2003, pp. 551–557
138. ^ decision theory and decision analysis:
     • Russell & Norvig 2003, pp. 584–597,
     • Poole, Mackworth & Goebel 1998, pp. 381–394
139. ^ Markov decision processes and dynamic decision networks:
     • Russell & Norvig 2003, pp. 613–631
140. ^ Game theory and mechanism design:
     • Russell & Norvig 2003, pp. 631–643
141. ^ Statistical learning methods and classifiers:
     • Russell & Norvig 2003, pp. 712–754,
     • Luger & Stubblefield 2004, pp. 453–541
142. ^ ^a b Neural networks and connectionism:
     • Russell & Norvig 2003, pp. 736–748,
     • Poole, Mackworth & Goebel 1998, pp. 408–414,
     • Luger & Stubblefield 2004, pp. 453–505,
     • Nilsson 1998, chpt. 3
143. ^ kernel methods such as the support vector machine,Kernel methods:
     • Russell & Norvig 2003, pp. 749–752
144. ^ K-nearest neighbor algorithm:
     • Russell & Norvig 2003, pp. 733–736
145. ^ Gaussian mixture model:
     • Russell & Norvig 2003, pp. 725–727
146. ^ Naive Bayes classifier:
     • Russell & Norvig 2003, pp. 718
147. ^ Decision tree:
     • Russell & Norvig 2003, pp. 653–664,
     • Poole, Mackworth & Goebel 1998, pp. 403–408,
     • Luger & Stubblefield 2004, pp. 408–417
148. ^ Classifier performance:
     • van der Walt & Bernard 2006
149. ^ Backpropagation:
     • Russell & Norvig 2003, pp. 744–748,
     • Luger & Stubblefield 2004, pp. 467–474,
     • Nilsson 1998, chpt. 3.3
150. ^ Feedforward neural networks, perceptrons and radial basis networks:
     • Russell & Norvig 2003, pp. 739–748, 758
     • Luger & Stubblefield 2004, pp. 458–467
151. ^ Recurrent neural networks, Hopfield nets:
     • Russell & Norvig 2003, p. 758
     • Luger & Stubblefield 2004, pp. 474–505
152. ^ Competitive learning, Hebbian coincidence learning,Hopfield networks and attractor networks:
     • Luger & Stubblefield 2004, pp. 474–505
153. ^ Hierarchical temporal memory:
     • Hawkins & Blakeslee 2005
154. ^ Control theory:
     • ACM 1998, ~I.2.8,
     • Russell & Norvig 2003, pp. 926–932
155. ^ Lisp:
     • Luger & Stubblefield 2004, pp. 723–821
     • Crevier 1993, pp. 59–62,
     • Russell & Norvig 2003, p. 18
156. ^ Prolog:
     • Poole, Mackworth & Goebel 1998, pp. 477–491,
     • Luger & Stubblefield 2004, pp. 641–676, 575–581
157. ^ ^a b The Turing test:
     Turing's original publication:
     • Turing 1950
     Historical influence and philosophical implications:
     • Haugeland 1985, pp. 6–9
     • Crevier 1993, p. 24
     • McCorduck 2004, pp. 70–71
     • Russell & Norvig 2003, pp. 2–3 and 948
158. ^ Subject matter expert Turing test:
     • CITATION IN PROGRESS.^{[citation needed]}
159. ^ Rajani, Sandeep (2011). "Artificial Intelligence - Man or Machine". International Journal of Information Technology and Knowlede Management 4 (1): 173–176. Retrieved 24 September 2012.
160. ^ Game AI:
     • CITATION IN PROGRESS.^{[citation needed]}
161. ^ Mathematical definitions of intelligence:
     • Jose Hernandez-Orallo (2000). "Beyond the Turing Test".Journal of Logic, Language and Information 9 (4): 447–466. doi:10.1023/A:1008367325700. CiteSeerX:10.1.1.44.8943.
     • D L Dowe and A R Hajek (1997). "A computational extension to the Turing Test". Proceedings of the 4th Conference of the Australasian Cognitive Science jSociety. Retrieved 21 July 2009.
     • J Hernandez-Orallo and D L Dowe (2010). "Measuring Universal Intelligence: Towards an Anytime Intelligence Test". Artificial Intelligence Journal 174 (18): 1508–1539.doi:10.1016/j.artint.2010.09.006.
162. ^
     • Kumar 2012
163. ^ "AI set to exceed human brain power" (web article). CNN. 26 July 2006. Archived from the original on 19 February 2008. Retrieved 26 February 2008.
164. ^ Brooks, R.A., "How to build complete creatures rather than isolated cognitive simulators," in K. VanLehn (ed.), Architectures for Intelligence, pp. 225–239, Lawrence Erlbaum Associates, Hillsdale, NJ, 1991.
165. ^ Hacking Roomba » Search Results » atmel
166. ^ Philosophy of AI. All of these positions in this section are mentioned in standard discussions of the subject, such as:
     • Russell & Norvig 2003, pp. 947–960
     • Fearn 2007, pp. 38–55
167. ^ Dartmouth proposal:
     • McCarthy et al. 1955 (the original proposal)
     • Crevier 1993, p. 49 (historical significance)
168. ^ The physical symbol systems hypothesis:
     • Newell & Simon 1976, p. 116
     • McCorduck 2004, p. 153
     • Russell & Norvig 2003, p. 18
169. ^ Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules". (Dreyfus 1992, p. 156)
170. ^ Dreyfus' critique of artificial intelligence:
     • Dreyfus 1972, Dreyfus & Dreyfus 1986
     • Crevier 1993, pp. 120–132
     • McCorduck 2004, pp. 211–239
     • Russell & Norvig 2003, pp. 950–952,
171. ^ This is a paraphrase of the relevant implication of Gödel's theorems.
172. ^ The Mathematical Objection:
     • Russell & Norvig 2003, p. 949
     • McCorduck 2004, pp. 448–449
     Making the Mathematical Objection:
     • Lucas 1961
     • Penrose 1989
     Refuting Mathematical Objection:
     • Turing 1950 under "(2) The Mathematical Objection"
     • Hofstadter 1979
     Background:
     • Gödel 1931, Church 1936, Kleene 1935, Turing 1937
173. ^ This version is from Searle (1999), and is also quoted inDennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." (Searle 1980, p. 1). Strong AI is defined similarly byRussell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis."
174. ^ Searle's Chinese room argument:
     • Searle 1980. Searle's original presentation of the thought experiment.
     • Searle 1999.
     Discussion:
     • Russell & Norvig 2003, pp. 958–960
     • McCorduck 2004, pp. 443–445
     • Crevier 1993, pp. 269–271
175. ^ Robot rights:
     • Russell & Norvig 2003, p. 964
     • "Robots could demand legal rights". BBC News. 21 December 2006. Retrieved 3 February 2011.
     Prematurity of:
     • Henderson, Mark (24 April 2007). "Human rights for robots? We're getting carried away". The Times Online(London).^{[dead link]}
     In fiction:
     • McCorduck (2004, p. 190-25) discusses Frankensteinand identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e. robot rights.
176. ^ Independent documentary Plug & Pray, featuring Joseph Weizenbaum and Raymond Kurzweil
177. ^ Ford, Martin R. (2009), The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, Acculant Publishing, ISBN 978-1448659814.(e-book available free online.)
178. ^ "Machine Learning: A Job Killer?"
179. ^ AI could decrease the demand for human labor:
     • Russell & Norvig 2003, pp. 960–961
     • Ford, Martin (2009). The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future. Acculant Publishing. ISBN 978-1-4486-5981-4.
180. ^ In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool. (Crevier 1993, pp. 132–144)
181. ^ Joseph Weizenbaum's critique of AI:
     • Weizenbaum 1976
     • Crevier 1993, pp. 132–144
     • McCorduck 2004, pp. 356–373
     • Russell & Norvig 2003, p. 961
     Weizenbaum (the AI researcher who developed the firstchatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life.
182. ^ Technological singularity:
     • Vinge 1993
     • Kurzweil 2005
     • Russell & Norvig 2003, p. 963
183. ^ Transhumanism:
     • Moravec 1988
     • Kurzweil 2005
     • Russell & Norvig 2003, p. 963
184. ^ Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis 1: 88–100.
185. ^ AI as evolution:
     • Edward Fredkin is quoted in McCorduck (2004, p. 401).
     • Butler, Samuel (13 June 1863). "[[Darwin among the Machines]]". the Press (Christchurch, New Zealand)Wikilink embedded in URL title (help), Letter to the Editor.
     • Dyson, George (1998). Darwin among the Machiens. Allan Lane Science. ISBN 0-7382-0030-1.

References[edit]

AI textbooks[edit]

• Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). The Benjamin/Cummings Publishing Company, Inc. ISBN 0-8053-4780-1.
• Neapolitan, Richard; Jiang, Xia (2012). Contemporary Artificial Intelligence. Chapman & Hall/CRC. ISBN 978-1-4398-4469-4.
• Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers. ISBN 978-1-55860-467-4.
• Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
• Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 0-19-510270-3.
• Winston, Patrick Henry (1984). Artificial Intelligence. Reading, Massachusetts: Addison-Wesley. ISBN 0-201-08259-4.

History of AI[edit]

• Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3
• McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1
• Nilsson, Nils (2010), The Quest for Artificial Intelligence: A History of Ideas and Achievements, New York, ISBN 978-0-521-12293-1 Unknown parameter |publishier= ignored (help)

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