A tried and tested learning technique amongst humans is positive feedback. This is a characteristic such as a smile, emotion, or feeling that rewards people. Studies have shown we have increased satisfaction towards learning goals when positive affectivity is used. With that in mind, Microsoft Research wants to extend the idea to artificial intelligence (AI).
Microsoft Research wants to integrate the concept of positive feedback into machine learning models. Such a training technique would help AI build confidence and work more effectively to fulfilling a goal.
According to the research team running the project, learning through reinforcement is often used to achieve a goal through policy rewards. However, these rewards are often too broad so intrinsic rewards are more effective. These rewards depend on the task at hand and can more accurately highlight success in achieving a goal.
“We present a novel approach leveraging a task-independent intrinsic reward function trained on spontaneous smile behavior that captures positive affect.”
Looking for such an intrinsic approach in AI, Microsoft Research built a framework that is motivated by human feedback. For example, it is motivated by affects like happiness and a smile. This is possible through a computer vision platform.
Through the reward system, the framework predicts responses like a human smile and learns it as a positive feedback towards a general policy.
“Here we were not attempting to mimic affective processes, but rather to show that functions trained on affect like signals can lead to improved performance,” wrote the coauthors of the paper.
“In summary, we argue that such an intrinsically motivated learning framework inspired by affective mechanisms can be effective in increasing the coverage during exploration, decreasing the number catastrophic failures, and that the garnered experiences can help us learn general representations for solving tasks including depth estimation, scene segmentation, and sketch-to-image translation.”
Mbinu ya kujifunza iliyojaribiwa na kupimwa miongoni mwa wanadamu ni maoni mazuri. Hii ni sifa kama vile tabasamu, hisia, au hisia ambayo huwapa watu thawabu. Uchunguzi umeonyesha kwamba tunaongeza kuridhika kuelekea malengo ya kujifunza wakati hisia chanya zinatumiwa. Kwa kuzingatia hilo, Microsoft Research inataka kupanua wazo hilo kwa akili bandia (AI). Utafiti wa Microsoft unataka kuunganisha dhana ya maoni mazuri katika mifano ya kujifunza mashine. Mbinu kama hiyo ya mafunzo itasaidia AI kujenga ujasiri na kufanya kazi kwa ufanisi zaidi ili kutimiza lengo. Kulingana na timu ya utafiti inayoendesha mradi huo, kujifunza kupitia kuimarisha mara nyingi hutumiwa kufikia lengo kupitia thawabu za sera. Hata hivyo, zawadi hizi ni mara nyingi sana pana hivyo zawadi ya ndani ni ufanisi zaidi. Tuzo hizi hutegemea kazi iliyopo na zinaweza kuonyesha kwa usahihi zaidi mafanikio katika kufikia lengo. "Tunatoa mbinu mpya ya kutumia kazi ya thawabu ya ndani isiyo na kazi iliyofundishwa juu ya tabia ya tabasamu ya moja kwa moja ambayo inashika athari nzuri. ""Kutafuta mbinu kama hiyo ya ndani katika AI, Utafiti wa Microsoft ulijenga mfumo ambao unahamasishwa na maoni ya binadamu." Kwa mfano, huchochewa na hisia kama vile furaha na tabasamu. Hii inawezekana kupitia jukwaa la maono ya kompyuta. Kupitia mfumo wa thawabu, mfumo huo unatabiri majibu kama tabasamu ya binadamu na hujifunza kama maoni mazuri kuelekea sera ya jumla. "Hapa hatukuwa tukijaribu kuiga michakato ya kihisia, bali badala yake kuonyesha kwamba kazi zilizozoezwa juu ya athari kama ishara zinaweza kusababisha utendaji ulio bora", waliandika waandishi wa karatasi hiyo. Kwa muhtasari, tunasema kwamba mfumo kama huo wa kujifunza wenye motisha ya ndani ulioongozwa na taratibu za hisia unaweza kuwa na ufanisi katika kuongeza chanjo wakati wa utafutaji, kupunguza idadi ya kushindwa kwa msiba, na kwamba uzoefu uliokusanywa unaweza kutusaidia kujifunza uwakilishi wa jumla kwa kutatua kazi ikiwa ni pamoja na makadirio ya kina, mgawanyiko wa eneo, na tafsiri ya mchoro-kwa-picha.