Heads up, thought leaders! In Episode 52, we are celebrating. Many Digital Acceleration news in the last two weeks. Four thought-provoking stories about AI and ML
Let’s go!
Top 10 Artificial Intelligence APIs for Developers in 2023
Analytics Insight posted a fascinating article about APIs.
Why is this relevant?
I am a big supporter of using APIs to connect composable applications. Still, I am also a big supporter of giving a second (and a third) eye to ensuring (1) these composable applications are part of enterprise architecture and (2) the APIs are deployed and secured correctly.
Food for thought!
Artificial Intelligence Application in Investing
Forbes Magazine published an interesting article about AI utilization for securities analysis.
Why is this relevant? Two comments:
- AI is evaluated in many areas to enrich existing solutions (AI business models, Ep 50)
- You are the final decision maker, even if you follow the advice of news, influencers, subject matter experts, or AI-based solutions.
Food for thought!
Is Artificial Intelligence Good For Society?
Another post from Forbes Magazine
Thought-provocative.
Why is this relevant?
I like to challenge the assumptions behind a recommendation, so my first thought was: Define Society.
If society is defined as the status quo, AI will change our society in many ways: some will be incremental (evolutionary), while others will be disruptive (revolutionary).
That is why there is an essential trend in responsible AI.
I will love to get your opinion on responsible AI, either in the or by DM me.
Machine learning-based obesity classification considering 3D body scanner measurements.
Nature Magazine reported a science article about Machine Learning and Healthcare.
Though provocative.
Why is this relevant?
Even though less than 10% of the audience will understand the report on the link (me included), there is real value in commenting on the thought process of building the ML dataset and the methodology used.
This is inspired by feedback given by the audience in Ep 51
A couple of observations:
- Five experts on the topic wrote the report. -> Teamwork, AI/ML is an area that should be business-led and not an IT-siloed initiative.
- They started by documenting an average outcome -> This is where most ML datasets become biased. I strongly support building an ML model collecting relevant information (with a purpose) instead of collecting a large dataset to train the model. Once you know your model is stable, it makes sense to load the system with non-expected data and enrich it with non-expected outcomes. By providing unbiased information in this stage, the team is building a solid foundation for the following steps (garbage in, garbage out).
But this is just an opinion, what is yours? Let us know in the comments, or DM me.
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