<prompt> Bolela liphephetso tse hlano tse amanang le ho kenya mefuta ea GPT litsebetsong tsa lefatse la nnete.nan  <response>: Instruction in English: Name five challenges associated with deploying GPT models in real-world applications. . Response in English: 1. Computational resources: GPT models are large neural networks that require significant computational power for both training and deployment. This can be a major challenge for organizations that lack the required infrastructure and resources.2. Data privacy and security: GPT models need large datasets to train on, which can pose a risk to data privacy and security. The machine learning process requires access to sensitive information that must be handled with utmost care.3. Bias and fairness: GPT models are only as good as the data they are trained on. If the data used to train the model is biased, the model will be biased, leading to issues with fairness in the predictions made. Ensuring that the data is diverse and representative is crucial to avoiding these issues.4. Model interpretability: GPT models are often seen as black boxes because it is difficult to understand how they make predictions. This can be a challenge when it comes to explaining how the model is working and making decisions, leading to potential trust issues among users.5. Model maintenance: Machine learning models, such as GPTs, need constant maintenance and updates to ensure they continue to perform well. This can be time-consuming and resource-intensive, leading to challenges in keeping the model accurate and up to date. . Response in Sesotho: 1. Mesebelisoa ea computational: Mehlala ea GPT ke marang-rang a maholo a methapo a hlokang matla a mangata a khomphutha bakeng sa koetliso le ho tsamaisoa. Sena e ka ba phephetso e kholo bakeng sa mekhatlo e hlokang lisebelisoa le lisebelisoa tse hlokahalang. 2. Lekunutu le tshireletso ya data: Meetso ya GPT e hloka disete tsa data tse kgolo ho ikwetlisa, e leng se ka behang kotsi ho khupamarama ya data le tshireletso. Mokhoa oa ho ithuta ka mochini o hloka phihlello ea tlhaiso-leseling e bohlokoa e tlamehang ho sebetsoa ka hloko e kholo. 3. Leeme le toka: Meetso ea GPT e ntle feela joalo ka data eo e koetliselitsoeng ho eona. Haeba data e sebelisitsoeng ho koetlisa mohlala e leeme, mohlala o tla ba leeme, e leng se lebisang litabeng tse nang le toka ho tse boletsoeng esale pele. Ho etsa bonnete ba hore data e fapane ebile e emela ho bohlokoa ho qoba litaba tsena. 4. Tlhaloso ea mohlala: Mehlala ea GPT hangata e bonoa e le mabokose a matšo hobane ho thata ho utloisisa hore na ba etsa likhakanyo joang. Sena e ka ba phephetso ha ho tluoa tabeng ea ho hlalosa hore na mohlala o sebetsa joang le ho etsa liqeto, e leng se lebisang litabeng tse ka bang teng tsa ho tšepana hara basebelisi. 5. Tlhokomelo ea mohlala: Mehlala ea ho ithuta ka mochini, joalo ka li-GPT, e hloka tlhokomelo e sa feleng le lintlafatso ho netefatsa hore li tsoela pele ho sebetsa hantle. Sena se ka nka nako le lisebelisoa tse ngata, se lebisang mathateng a ho boloka mohlala o nepahetse ebile o le nakong. 