AI is everywhere – and adds value in many areas. But what good will it do us if AI becomes an end in itself and we no longer think about “the best solution”, but want to solve everything with AI across the board?
If we let AI generate long texts from key points, which are then (for time reasons) summarized again by another AI into key points, then this is somehow pointless.
If we generate suitable images artificially instead of looking for them, this may bring advantages, but it dilutes the original “purpose” of photos – namely the depiction of reality, the capture of a “real” event.
When AIs upload products to Amazon or social media, other AIs generate reviews or likes and other AIs send us product recommendations…. But let’s leave that alone.
I have the impression that AI is currently being used a lot because it is an (if not THE) absolute hype topic – and in many cases for good reason. However, AI has a number of decisive disadvantages. Among other things:
– ๐๐ฒ๐ต๐น๐ฒ๐ป๐ฑ๐ฒ ๐๐ฟ๐ธ๐น๐ฎฬ๐ฟ๐ฏ๐ฎ๐ฟ๐ธ๐ฒ๐ถ๐: AI processes are still not end-to-end explainable and comprehensible for humans (and machines). You do not “know” why an AI produces corresponding results, i.e. you cannot prove, guarantee or certify the process.
– ๐จ๐ป๐ธ๐น๐ฎ๐ฟ๐ฒ ๐ฅ๐ฒ๐ฐ๐ต๐๐ฒ-/๐๐ฎ๐๐ฒ๐ป๐๐ถ๐๐๐ฎ๐๐ถ๐ผ๐ป: For all AI products, the question of rights must be asked in the business environment, i.e. what was the AI trained with?i.e. what was used to train the AI, where is “my” data processed, who “owns” the results? Although we have the EU AI Act, many AI manufacturers are very sparing with information here. This can become a business risk for you.
– ๐ฅ๐ฒ๐ฐ๐ต๐ฒ๐ป๐น๐ฒ๐ถ๐๐๐๐ป๐ด: AI systems need an incredible amount of computing power (especially for training). Manufacturers (eventually) pay for this. You can currently get a lot of AI for little money. But at some point, all these investments must also pay off for the providers. Your investments in AI are therefore currently difficult to calculate and represent a certain risk.
I therefore advocate not completely forgetting the “good old algorithm”, the classic approach to software engineering. Reproducibility, verifiability (even mathematically), reliability and certifiability have given us a great deal of security and robust IT landscapes in recent decades.
So please let’s not throw this overboard too soon!
P.S.: there are very good algorithms for quickly finding images and for summarizing texts (which can be statistically proven)….

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