2) Audio and image identification is indeed an area where great strides have been made, and if those are relevant to your business, then you’re in great luck, and you should absolutely leverage that. But this goes back toward my “it should be aimed at a specific use case” statement; image identification is much much more narrow than how AI is being hyped.
3) Personally I’m less than impressed with the progress that voice assistants have made; they present great, but they have utterly failed to make a dent in my day to day living. It seems like end users regularly oscillate between “this is amazing”, “this is stupid”, and “this is incredibly creepy”. In my opinion this area saw a great big leap forward about 5-6 years ago and progress has basically stopped.
If you read back about older AI bubbles, what you’ll find is that there are bits and pieces left over from those bubbles that are unquestionably improvements on what came before. But critically these improvements fell well short of the grandiose promises made by AI boosters at the time. It’s entirely probable that this AI boom will follow a similar pattern; a massive cycle of hype and collapse that leaves behind useful techniques and tools that nonetheless fall well short of what was promised during the cycle.
As far as what is and is not considered to be “AI”; that definition has changed readily over the years. Ground breaking techniques are regularly called “AI” until they stop being called that and get new names. We’re already seeing this process work now with ML.
I think there are the AI leaders, and then there is everyone else. What is the difference?
The leaders are mostly big tech, who have driven the step-change advances you describe. This, IMHO, was due to their pre-existing mastery of data. They already had a ton of well-organized data, because they were engineering cultures, and data was the lifeblood of their business (ads, search, shopping). Once ML/AI came into the picture, it was full steam ahead.
Most others are (blind) followers, and cannot tease apart the engineering bit from the (data) science hype. They get fixated on the latter (data science and ML/AI) and forget the engineering, or "scale" part.
The first question should not be about AI/ML, but on the other hand, do you have solid (data) engineering where your data is easily accessible to any data scientist? By now it should be apparent that "data is the new oil" and will be useful even if you don't plan to do deep learning.
If you don't have solid (data) engineering and "data at scale" for anyone, anywhere, then your ML/AI efforts are doomed.
Data first, only then ML/AI. See "Data Science Hierarchy of Needs": https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc00...
In the traditional CS sense, the things you mention could all be considered weak AI.
I predict as we continue to transition to stronger AIs with more advanced capabilities we will continue to see a shift in what people think of as “AI”.
[1]https://en.m.wikipedia.org/wiki/Artificial_general_intellige...
Obviously this isn't all "AI" in the historical CS sense of the word. It's a mix of progress in things like signal processing, computer vision, NLP, neural nets and transformers, and the raw computer engineering that has made all of that practical on modern hardware.
Are you and I sticks-in-the-mud for not just going along with calling this "AI"? Is there actual worthwhile nuance by calling things their proper names rather than just kowtowing to the word that normal people have latched onto?