Become an AI Company in 90 Days

“Researchers are continually publishing effective AI models. Hardware is a commodity. But build the highest-quality proprietary dataset and you’ll crush the competition every time.”

I had a day off and the electrician took down my power for the day so I had some much anticipated time on my hands. And just as any other geek would do…I used the time to tear through an unsolicited but gratis book I received in the mail over the holidays.

The book titled, “Become An AI Company in 90 Days,” was written by Kevin Dewalt and produced by Russ Rands.

The book is broken into 4 parts including:

  • AI Fundamentals
  • Discovering AI Opportunities
  • Building a Winning Strategy
  • Launching Your First AI Product

Here’s the scoop:

    Author is legitimate and knows his stuff (Stanford AI experience)
    He has clearly worked with some large corporations based on some of his anecdotes (policy and cross functional alignment will be some of the toughest challenges)
    The instructions are specific and practical (top of line system for under $5K and reasonable data set size…good rule of thumb is 50,000 labeled examples)
    Book length of 132 pages is easily consumable in an afternoon
    Content is well written and understandable without an advanced degree in mathematics
    Includes examples that help bring the material to life (automated claims processing for the AI Canvas tool)

My Review: It’s a solid “A” and I recommend you check it out.

Get a free copy:

Note: I do not have any personal or professional connection to the authors, and I do not have any financial interest in the company or the book

An executive’s guide to AI – McKinsey

  • One of the highest rated McKinsey articles of the year is titled, “An executive’s guide to AI.” It’s a quick overview including key elements and terminology.
  • Artificial Intelligence
    • AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving.
    • Timeline
  • Machine Learning
    • Machine- learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction.

    Types of analytics

    • Descriptive, predictive, prescriptive
  • Major Types
    • Supervised, unsupervised, reinforcement

    Deep Learning

    • Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches.
  • Major Models
    • Convolutional neural network CNN
    • Recurrent neural network

    Business Cases

      Predict Call Center Volume
    • Predict power usage in an electrical grid distribution
    • Detect fraudulent activity for credit cards
    • Simple low-cost image classification
    • Forecast product demand and inventory
    • Predict the price of cars
    • Predict probability of patient joining healthcare
    • Predict prices that will be paid for a product

    Amazon has an AI vision and Rajeev Rastogi is shaping it

    “Rastogi is the director of machine learning at Amazon India and has been ranked among one of the top 10 data scientists in India. The e-commerce giant is looking at putting artificial intelligence and ML at the heart of everything it does. According to Rastogi, the problems that AI and ML need to spar within India are very different from the Amazon universe in the rest of the world, especially because the data available is not of high quality.

    Rastogi had been dabbling with data even earlier – he holds a PhD in computer science from the University of Austin at Texas and more than 50 patents – and Bell Labs India was the springboard into a career of solving problems using data. Yahoo! Labs came after the Bell Labs gig before Amazon convinced to switch over.”

    Deep learning and local maximums for recommendation engines

    I wouldn’t consider this “light reading” but it’s super interesting to understand and consider.

    “The clearest example of how that works is the increasingly ubiquitous recommendation engine. Almost every ecommerce site now lists other products that a visitor might be interested in while looking at an individual product. Figuring that those recommendations is an optimization problem, as the site owners want to show the items most likely to be add-on purchases for the visitor.

    As it is approaching lunch time, I’ll use a food related example. Imagine a customer going to a grocery store web site and going to the page listing the different types of bagels that are available. A global optimization might notice that a very high percentage of people who buy bagels also buy smoked salmon. Therefore, the salmon is placed on the page as an added buy.

    The problem is that all customers aren’t the same. The grocery site notices…”