Out of your list, translating language and solving protein folding are the only 2 that are likely to have any kind of major impact on the world in the somewhat near future, in my opinion. Image recognition may be a distant third, as it could prove very useful as a tool in many domains that have lots of visual data to sift through.
Voice recognition is neat, and it is extremely useful in certain niches, but it is generally far inferior to mouse+keyboard or touch or dedicated buttons in all cases where those are practical. It is almost unusable in public spaces. Text-to-speech is mostly in the same boat - extremely useful in some niches, not a game-changer in any way I expect.
Text generation is only "realistic" in shape, but for now remains a novelty. Picture generation is the same. They are at best at the level where it already costs pennies to obtain similar texts through human labor. Having conversations in a meaningful way is even farther.
Playing Go better than humans is a fun gimmick - an awesome achievement in some sense, but with 0 practical applications outside of Go.
Overall, there has been good progress in AI/ML, but I don't expect we'll see any major changes too soon from this. Especially since the idea of actually seeing commercially available self-driving cars has been pushed back a decade or three compared to the initial optimism.
I'd also add that most of the progress in ML/AI in the last few years has essentially been of a technological/engineering kind. There haven't really been that many deep scientific insights, we don't necessarily understand any of these problem spaces better. We've just been able to match certain neural net architectures with certain problem spaces, and massively advanced in realistically-available computing power to actually train these nets on enough data.
But we haven't gotten any new insights into stuff like language from GPT-3, for example. We haven't even gotten any new insights into Go from AlphaGo!