What Exactly The Heck Are Prescriptive Analytics?

On Feb. 20 Mike Gualtierimedicinebottlesmall from Forrester wrote: “Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique.” You may want to get the full report “Prescriptive Analytics: The Black Belt Of Digital Decisions

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Turn business problems into machine learning algorithms

machine_learning“To solve any business problem, you must first start with a business question. Some vendors speak too much about “magic.” In their eyes, you combine data, machine learning and cognitive artificial intelligence, and you get magic. That may be true someday in the future, but today, projects should start with a well-defined question, such as:

  • Can I recommend products to my visitors?
  • Can I predict who is going to leave me for competition among my current customer base?
  • Can I predict next month?

When developers are brought into the fold to deploy models and improve applications that answer real business problems, enterprises see results from machine learning” Read more

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How does Magic Square select the correct image?

magicsquareThe spring is around the corner and it’s time for fun. Play with this Magic Square and try to figure out the “business logic” behind its decisions.

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Ron Ross: IT Departments Should be Evacuated

evacuateitRon Ross just published a provocative post “IT Departments Should be Evacuated – Agree/Disagree?” In particular, he writes: “The days of traditional departmental IT staff having unfettered access to the financial assets of the corporate budget will end sooner than you might think. The current way of building business systems is unsustainable. If you think the cloud was something, just wait! I look at agile software development as the death throes of traditional IT. Beyond it there’s nowhere left to go to accelerate except to elevate the level of human interfaces with machines. Economics will demand it.” Agree/Disagree?

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Getting Real About AI in Insurance

aiforinsuranceSapiens gathered five of its leading subject matter experts for a quick Q&A to get real about artificial intelligence (AI) in the insurance industry. A quote: “Question. How insurers can prepare for a more “intelligent” future? Answer: The best way for insurers to prepare is to organize their data and make it accessible. Today’s insurers are coping with mountains of data. They will need to quickly transform this data into business decisions by using advanced analytics tools, and this organized data will be the foundation that machine learning will build upon.”  Read more

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A Short History of Machine Learning

aispringForbes published “A Short History of Machine Learning“. Here is a quote: “We don’t have autonomous androids struggling with existential crises — yet — but we are getting ever closer to what people tend to call ‘Artificial Intelligence’”

“Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don’t have to be explicitly programmed but can change and improve their algorithms by themselves.Continue reading

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They are not doing “cognitive computing” no matter how many times they say they are

aiwinterDr. Roger Schank published “The Fraudulent Claims Made by IBM About Watson and AI“.  Here are a few quotes: “I invented a field called Case Based Reasoning in the 80’s which was meant to enable computers to compare new situations to old ones and then modify what the computer knew as a result. We were able to build some useful systems. And we learned a lot about human learning. Did I think we had created computers that were now going to outthink people or soon become conscious? Of course not. I thought we had begun to create computers that would be more useful to people.”

“Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.”

“It would be nice if IBM would tone down the hype and let people know what Watson can actually do and stop making up nonsense about love fading and out thinking cancer. IBM is simply lying now and they need to stop. AI winter is coming soon.”

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