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Why Artificial Intelligence and Machine Learning?


With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify ​how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).



Are you interested in learning about artificial intelligence (AI) and machine learning (ML)? Have you wondered how these amazing fields can help you or your business?

Artificial intelligence and machine learning are helping people and businesses achieve key goals, obtain actionable insights, drive critical decisions, and create exciting, new, and innovative products and services.

Author Simon Sinek, in his highly influential book ​Start With Why,  does an excellent job of explaining that the why  should be the most important driving force behind virtually everything.

Golden circle

This definitely applies to Artificial Intelligence and Machine Learning as well, and so understanding and describing why  these fields should be used for a given need is critical, and then should be followed by ​how  they’re used (e.g., processes, algorithms, data scientists), and lastly by what  is produced as a result (e.g., product, service, recommendation engine, smart assistant).

These fields benefit businesses and customers alike, although each have different goals. These goals should be considered the why that drives any artificial intelligence or machine learning solution.

Business goals include things like increasing revenue and profits, cutting costs, improving operational efficiency, and so on. Businesses are also very interested in increasing customer acquisition, retention, and growth.

Customers, on the other hand, have goals like getting a specific job done (e.g.,​ JTBD framework), such as interacting with friends and family via social media, getting recommendations on movies to watch or items to buy, becoming better organized, and increasing productivity. People also want to use well-designed products that offer a great user experience, i.e., are enjoyable, easy to use, and easy to understand.

With these goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify ​how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).

These days there are many amazing, real-world applications of artificial intelligence and machine learning being deployed to benefit both customers and companies. Some application categories include:

  • Prediction and classification
  • Recommender systems
  • Recognition
  • Computer vision
  • Clustering and anomaly detection
  • Natural language (NLP, NLG, NLU)
  • Hybrid and miscellaneous (e.g., autonomous vehicles, robotics, IoT)

To learn more about artificial intelligence and machine learning, including driving goals, definitions, types, algorithms, processes involved, important tradeoffs and considerations, and examples of real-world applications for each category, check out my Goal-Driven Artificial Intelligence and Machine Learning class on Skillshare!

Get two free months of premium membership on Skillshare, with full access to my class, and also thousands of other classes covering many different topics by using this link: http://skl.sh/2sEOYGT

Cheers, and enjoy!

 
Bio: Alex Castrounis is a product and data science leader, technologist, mentor, educator, speaker, and writer. Alex spent ten years as a race strategist, data scientist, vehicle dynamicist, and software engineer for IndyCar and Indianapolis 500 racing teams. Alex also founded InnoArchiTech, and writes for the InnoArchiTech blog at www.innoarchitech.com. For updates or to learn more, follow @innoarchitech on Twitter, or sign up for the InnoArchiTech newsletter.

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