✎ Issue #4 - Out-of-the-box Ai Ready | The Ai Verticalization

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"If you can't explain it simply, you don't understand it well enough."                   - Albert E
 

Everyman's Ai ✎ Digest

September 25 · Issue #4 · View online
➥ Your "Low-Tech" Artificial Intelligence briefing on practical and fast-track ideas to grow your business today!

“If you can’t explain it simply, you don’t understand it well enough.”                   - Albert Einstein

1) Out-of-the-Box Ai Ready - The Ai Verticalization
In the future, will it be better if you know how to train a Machine Learning (ML) algorithm or, become an expert that understands the out-of-the-box Ai vendor landscape for your industry, buy vs. build tradeoff and Ai solution vendor vetting process?  Here are three good articles and the related section #2 below.
The Best Resumes will soon have “Skilled in Machine Learning” Instead of “Proficient in Excel”
Vertical AI Startups: Solving Industry-specific Problems by Combining AI and Subject Matter Expertise
The Current State of Machine Intelligence 3.0
2) "What's an Algorithm" Continued
In Issue #3 Click Here, we explored basic algorithm concepts & what makes Machine Learning algorithms different from non-ML algorithms.
Let’s continue to explore by further differentiating ML algorithms into three groups:
1) Supervised (samples of result dataset initially trains algorithm)
2) Unsupervised (algorithm isn’t pre-trained, learns as it “reads” data)
3) Reinforcement Learning (algorithm plays game for rewards to improve)
Below, see a very high-level ML landscape diagram. As you can see, there are a mind-boggling number of ML algorithms, does not include Reinforcement Learning and more being developed at an increasing rate. To program those ML algorithms, you have to configure them and train them with data to build an intelligent system.  
Machine Learning Mindmap from MachineLearningMastery.com
My point is that you do not need to learn how to configure and train ML models to make them work for you in your business any more than you have to learn the underlying software code that makes, for example, Microsoft Word or Excel work for you.
Even if you want to work directly with ML algorithms and build intelligent systems from “scratch,” that building process is itself being automated. Yes, by other ML algorithms (Reinforcement Learning) that optimize the building process automatically.  See cutting-edge DataRobot Machine Learning Automation software company website below.
DataRobot: Machine Learning Automation
Google’s New AI Is Better at Creating AI Than the Company’s Engineers: AI Can Create Itself
The decision becomes which approach can help you make the best business case either for hiring a machine learning expert or undertaking evaluating out-of-the-box Ai solution vendors. Somewhere in between the build vs. buy continuum is deciding to us pre-trained Machine Learning APIs from, for example, Google, AWS, IBM, Microsoft for sentiment analysis, image recognition, job search, e.g., Google Cloud AI
3) Show Me The Data! Have a Strategy
The Abyss of Analytics - Obsessing Over Analytics Data, Without any Strategy
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About Author | everymans.ai
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