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Theres a difference between a shiny new thing and a thing that works.
Recent advances in machine learning have surely created a lot of excitement and fear around artificial intelligence.
Game-playing bots that outmatch human champions.
A text-generating AI that writes articles in mere seconds.
Medical imaging algorithms that detect cancer years in advance.
How much of these technological advances are actually making it to the mainstream?
How much of it is unwarranted hype?
How will AI affect jobs?
How is machine learning changing the business model of companies?
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The book is a must-read for business leaders and executives.
Here are some of my key takeaways.
[Read:Are EVs too expensive?
Prediction Machines: The Simple Economics of Artificial Intelligence
What is prediction?
Again, the authors of Prediction Machine simplify: Prediction is the process of filling in missing information.
What will be the value of a stock in the future?
What is the probability that a loan applicant will default?
What is the likely answer to a certain email?
But why has the predictive power of machine learning become such a big deal today?
The authors ofPrediction Machinestake these two premises a step further.
When the price of something falls, we use more of it.
Thats simple economics and is happening right now with AI, they write.
Computers lowered the price of arithmetic.
The internet reduced the cost of distribution, communication, and search.
But prediction has become very cheap, which itself is a big deal.
Heres an example: Amazon currently uses machine learning algorithms to make sales recommendations.
Amazon uses machine learning to make recommendations.
Hopefully (for Amazon), the recommendations will convince me to purchase not one but two books.
And to be clear, Amazons recommendations are very decent.
In fact, I often search old books on Amazon to discover new related titles.
Right now, Amazon uses a shop-then-ship model.
The authors ofPrediction Machineshave done a great job of demystifying the economics of handling data for machine learning algorithms.
Prediction machines rely on data.
More and better data leads to better predictions.
In economic terms, data is a key complement to prediction.
It becomes more valuable as prediction becomes cheaper, they write.
Statisticians and machine learning practitioners know that data has decreasing returns to scale.
As you train your machine learning algorithms on more data, accuracy improvements come at slower rates.
Data is split into three categories: training, input, and feedback.
You need all three to develop and maintain an efficient machine learning model for your business.
This demands a business strategy in addition to technical ingenuity.
Data and prediction machines are complements.
If that data resides with others, you need a strategy to get it.
A prediction is not a decision.
Making a decision requires applying judgment to a prediction and then acting, the authors write.
And this, I think, is a crucial takeaway.
As machine prediction increasingly replaces the predictions that humans make, the value of human prediction will decline.
They are complements to prediction, meaning they increase in value as prediction becomes cheap.
In many cases, humans must judge and decide across multiple objectives that span across the short- and long-term.
They must assess dynamic situations and evaluate tradeoffs.
Those are areas where branches of AI such asreinforcement learningmight be able to fully automate tasks.
Many companies and business leaders come with a background in classic software development and business.
Those who adapt to the business of artificial intelligence are bound to reap the rewards.
Those who dont will be in for awful surprises.
you might read the original article here.
