<prompt> Tloo ka lethathamo la mathata a tloaelehileng lipatlisisong tsa AI.nan  <response>: Instruction in English: Come up with a list of common problems in AI research. . Response in English: Here are some common problems in the field of AI research:1. Data Bias: Since AI algorithms learn from data, it is important that the data is free of bias. However, this is not always the case, and as a result, AI systems can perpetuate and even amplify existing societal biases.2. Transparency and Explainability: Oftentimes, the decision-making processes of AI systems are opaque, meaning that it can be difficult to understand how or why certain decisions were made. This lack of transparency and explainability can lead to mistrust of AI systems.3. Generalizability: Many AI algorithms are highly specialized and perform well on specific tasks, but perform poorly on tasks outside of their training. This lack of generalizability is a common problem in AI research, as it limits the ability of AI systems to adapt to new situations.4. Data Privacy: With the rise of AI and machine learning, the amount of data being collected and analyzed has increased dramatically. This has brought with it concerns about data privacy and security, especially in fields such as healthcare, finance, and education.5. Energy Usage: Training AI systems, particularly deep learning models, can be incredibly energy-intensive. In fact, some estimates suggest that the amount of energy required to train a single AI model has doubled every 3.5 months since 2012. This poses significant challenges in terms of the environmental sustainability of AI research.6. Regulation and Ethics: As AI becomes increasingly embedded in society, there are growing concerns about the ethical implications of AI research and development. Issues such as algorithmic bias, surveillance, and autonomous weapons are all hot-button topics that are currently shaping the future of AI regulation.7. Over-reliance on AI: As AI systems become increasingly advanced, there is a risk that individuals and organizations may become over-reliant on these systems, losing the ability to think critically and make decisions without algorithmic guidance. Ensuring that AI is used to augment, rather than replace, human decision-making is an important challenge facing the field of AI research. . Response in Sesotho: Mona ke mathata a mang a tloaelehileng lefapheng la lipatlisiso tsa AI: 1. Tsebetso ea Boitsebiso: Kaha li-algorithms tsa AI li ithuta ho tsoa ho data, ke habohlokoa hore data e se ke ea e-ba leeme. Leha ho le joalo, ha ho joalo kamehla, me ka lebaka leo, litsamaiso tsa AI li ka tsoela pele esita le ho holisa leeme le teng la sechaba. 2. Ponaletso le Tlhaloso: Hangata, litsebetso tsa ho etsa liqeto tsa litsamaiso tsa AI ha li bonahale, ho bolelang hore ho ka ba thata ho utloisisa hore na liqeto tse itseng li entsoe joang kapa hobaneng. Ho hloka pepenene hona le ho hlalosa ho ka lebisa ho se tšepe litsebetso tsa AI. 3. Kakaretso: Li-algorithms tse ngata tsa AI li ikhethile haholo me li sebetsa hantle mesebetsing e itseng, empa ha li sebetse hantle mesebetsing e kantle ho koetliso ea tsona. Ho haella hona ha kakaretso ke bothata bo tloaelehileng lipatlisisong tsa AI, kaha ho fokotsa bokhoni ba litsamaiso tsa AI ho ikamahanya le maemo a macha. 4. Lekunutu la Boitsebiso: Ka ho phahama ha AI le thuto ea mochine, palo ea lintlha tse bokelloang le ho hlahlojoa e eketsehile haholo. Sena se tlisitse lipelaelo mabapi le lekunutu la data le tsireletso, haholo-holo mafapheng a kang tlhokomelo ea bophelo bo botle, lichelete le thuto. 5. Tšebeliso ea Matla: Ho koetlisa litsamaiso tsa AI, haholo-holo mekhoa e tebileng ea ho ithuta, e ka ba matla haholo. Ha e le hantle, likhakanyo tse ling li fana ka maikutlo a hore matla a matla a hlokahalang ho koetlisa mohlala o le mong oa AI o imenne habeli likhoeling tse ling le tse ling tsa 3.5 ho tloha 2012. Sena se baka mathata a bohlokoa mabapi le ho boloka tikoloho ea lipatlisiso tsa AI. 6. Taolo le Melao ea Boitšoaro: Ha AI e ntse e ikakhela ka setotsoana sechabeng, ho na le matšoenyeho a ntseng a eketseha mabapi le litlamorao tsa boitšoaro tsa lipatlisiso le ntsetsopele ea AI. Litaba tse kang algorithmic bias, leihlo, le libetsa tse ikemetseng kaofela ke lihlooho tse chesang tse ntseng li bopa bokamoso ba taolo ea AI. 7. Ho itšetleha ho feta tekano ho AI: Ha litsamaiso tsa AI li ntse li tsoela pele ka ho eketsehileng, ho na le kotsi ea hore batho ka bomong le mekhatlo ba ka itšetleha haholo ka mekhoa ena, ba lahleheloa ke bokhoni ba ho nahana ka ho teba le ho etsa liqeto ntle le tataiso ea algorithmic. Ho netefatsa hore AI e sebelisetsoa ho eketsa, ho fapana le ho e nkela sebaka, ho etsa liqeto tsa batho ke phephetso ea bohlokoa e tobaneng le lefapha la lipatlisiso tsa AI. 