Considering Composite AI:

Could not agree more, but I am seeing practitioners wanting to stay with pure 'AI/Machine Learning'. It stays with the 'magic' of AI.  When hybrid methods should be used.  Why?  Because the other methods are seen as dated?  And require quite different training.  Are just classic analytics?  In typical analytics these are called 'Ensemble Methods', often mentioned here, we used them often, see the tags below.

Composite AI: What Is It, and Why You Need It    Alex Woodie in DataNami

You might have noticed a new term, “composite AI,” floating around the cybersphere. Don’t worry–it’s not a complex new technology that you must master. In fact, while the term may be new, the core idea behind it is not. Nevertheless, it’s likely a technique that you should be thinking about incorporating in your enterprise AI processes.

Gartner helped put composite AI on the map last summer, when it published its 2020 Hype Cycle for Emerging Technologies. Simply put, Composite AI refers to the “combination of different AI techniques to achieve the best result,” according to Gartner. That’s it. Simple enough, right?

So, what other AI techniques could that mean? It’s important here to keep in mind that AI is a very broad term. While some might believe that AI refers to the latest, greatest deep learning and neural network algorithms, AI actually covers much more under its sizable umbrella.

Machine learning and deep learning are types of AI. But there are many other types of AI that should be in your wheelhouse that fall outside of the machine learning/deep learning bubble. That includes traditional rules-based systems, natural language processing (NLP), optimization techniques, and graph techniques, according to Gartner.

A composite AI system is to be built atop a “composite architecture,” which Gartner identified as its number one Hype Cycle trend for 2020. A composite architecture (you might have guessed) incorporates packaged business capabilities that run atop a flexible data fabric, thereby enabling users to take be flexible and adaptable amidst rapidly changing systems and requirements.   ... "

Wolfram Alpha: Notebook Edition

Been a while since I used this in the enterprise, but here is a new release of interest.  I would heartily recommend this as a teaching environment for math for any engaged student who is using a computer.

The Ease of Wolfram|Alpha, the Power of Mathematica: Introducing Wolfram|Alpha Notebook Edition    ....   Wolfram|Alpha Notebook Edition

The Next Big Step for Wolfram|Alpha

Wolfram|Alpha has been a huge hit with students. Whether in college or high school, Wolfram|Alpha has become a ubiquitous way for students to get answers. But it’s a one-shot process: a student enters the question they want to ask (say in math) and Wolfram|Alpha gives them the (usually richly contextualized) answer. It’s incredibly useful—especially when coupled with its step-by-step solution capabilities.

But what if one doesn’t want just a one-shot answer? What if one wants to build up (or work through) a whole computation? Well, that’s what we created Mathematica and its whole notebook interface to do. And for more than 30 years that’s how countless inventions and discoveries have been made around the world. It’s also how generations of higher-level students have been taught.

But what about students who aren’t ready to use Mathematica yet? What if we could take the power of Mathematica (and what’s now the Wolfram Language), but combine it with the ease of Wolfram|Alpha?

Well, that’s what we’ve done in Wolfram|Alpha Notebook Edition.

It’s built on a huge tower of technology, but what it does is to let any student—without learning any syntax or reading any documentation—immediately build up or work through computations. Just type input the way you would in Wolfram|Alpha. But now you’re not just getting a one-shot answer. Instead, everything is in a Wolfram Notebook, where you can save and use previous results, and build up or work through a whole computation:   .... "