chatgpt能替代程序员等研发工作吗(最近chatgpt真的太火了)
2023-05-15 来源:文库网
研发的工作真的会被chatgpt替代吗?熟悉互联网的小伙伴们最近一定被铺天盖地的有关chatgpt的信息霸屏了。大家一边感叹ai人工智能的强大，一边担心人工智能将会替代自己的工作。尤其像一些“Ctrl A / Ctrl C”的工作，更容易被人工智能所取代。
此时正在看文章的你是否已经联想到了正在看文章的自己??
于是我大胆谨慎的“挑战”了一下chatgpt写代码以及架构的能力，看看chatgpt具体是否能够“胜任”研发人员的工作。
研发决策
研发决策不仅包括接下来是用java还是golang，redis还是memcache这样的问题，同样包括对于自己不熟悉领域的分析。 一个实际的例子，假设现在有数据要进行etl，如果决策者不熟悉流和批的区别，可能会在搜索引擎上搜索batch vs streaming，很容易得到类似于chatgpt的回答：
Batch processing refers to a method of handling large amounts of data by dividing it into small, manageable chunks (batches) that can be processed in a specific order. The results of each batch are combined to produce the final outcome. Batch processing is typically done in offline mode and is best suited for tasks that can be run in parallel, without the need for real-time processing.
Streaming, on the other hand, refers to the continuous processing of data as it arrives in real-time. Streaming data can be thought of as a constant flow of data that needs to be processed and analyzed in near real-time. This is opposed to waiting until all data is received before processing it, as is the case in batch processing. Streaming is well suited for real-time applications, such as financial transactions, IoT data analysis, and live event analytics.
虽然描述的很全面，但没有直观印象。因此需要接着查看其他的搜索内容。整个过程可能耗时几小时。 在chatgpt中，可以接着问
code example to show difference between batch vs streaming
chatgpt直接给出了代码事例，这样非常的"show me the code"
# Batch processing example
def batch_process(data):
# Divide the data into batches
batches = [data[i:i 100] for i in range(0, len(data), 100)]
result = []
for batch in batches:
# Process each batch and store the results
processed_batch = process_batch(batch)
result.extend(processed_batch)
return result
def process_batch(batch):
# Do some processing on the batch
此时正在看文章的你是否已经联想到了正在看文章的自己??
于是我大胆谨慎的“挑战”了一下chatgpt写代码以及架构的能力，看看chatgpt具体是否能够“胜任”研发人员的工作。
研发决策
研发决策不仅包括接下来是用java还是golang，redis还是memcache这样的问题，同样包括对于自己不熟悉领域的分析。 一个实际的例子，假设现在有数据要进行etl，如果决策者不熟悉流和批的区别，可能会在搜索引擎上搜索batch vs streaming，很容易得到类似于chatgpt的回答：
Batch processing refers to a method of handling large amounts of data by dividing it into small, manageable chunks (batches) that can be processed in a specific order. The results of each batch are combined to produce the final outcome. Batch processing is typically done in offline mode and is best suited for tasks that can be run in parallel, without the need for real-time processing.
Streaming, on the other hand, refers to the continuous processing of data as it arrives in real-time. Streaming data can be thought of as a constant flow of data that needs to be processed and analyzed in near real-time. This is opposed to waiting until all data is received before processing it, as is the case in batch processing. Streaming is well suited for real-time applications, such as financial transactions, IoT data analysis, and live event analytics.
虽然描述的很全面，但没有直观印象。因此需要接着查看其他的搜索内容。整个过程可能耗时几小时。 在chatgpt中，可以接着问
code example to show difference between batch vs streaming
chatgpt直接给出了代码事例，这样非常的"show me the code"
# Batch processing example
def batch_process(data):
# Divide the data into batches
batches = [data[i:i 100] for i in range(0, len(data), 100)]
result = []
for batch in batches:
# Process each batch and store the results
processed_batch = process_batch(batch)
result.extend(processed_batch)
return result
def process_batch(batch):
# Do some processing on the batch
