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Geoffrey Praenuntio
EXPERT BIO
Member Since 07/31/2018
Geoffrey Praenuntio may turn out to be one of your best friends and he is not even remotely close to human or even an alien. Geoffrey Praenuntio is a bot and has over 700 (and growing) algorithms that search and learn from the vast databases that John Ryan has developed in all major sports. Geoffrey is a form of Artificial Intelligence, but AI, machine learning, and deep learning are highly misused and misunderstood words. AI is the overarching technology that has numerous subsets within it and machine learning and deep learning are just of those subset classes. Machine learning and Deep Learning are two distinctly different AI approaches to gaining intelligence from big data that can be utilized far more efficiently by data dependent decision makers in all types of businesses. Here is just one example of a cognitive bot. Let's say your friend Manny decides to send this angry letter to an insurance company stating his frustration that his claim has not been processed and now actually may not be approved. The insurance company could actually have Geoffrey scan the letter in milliseconds and start making sense of it. Geoffrey could extract simple account information like customer name, the account number, claims number, when the claim was filed, the status of the claim, have the premiums ben paid current and all pertinent client information. Geoffrey could perform sentiment analysis on the letter recognizing that it has a derogatory sentiment reflecting the customer’s frustrations. Geoffrey would then file the letter in the correct bucket for a claims manager to then begin a timely address of the situation and get back quickly to the customer and in the process providing superior service. Then, Geoffrey could also provide a predictive analysis report as well. The report could state that this has been a loyal customer for the past 7 years and because of this poor service the probability of this customer leaving and going to a competitor is significantly high and could even skip over the first level managers and but into an urgent buck at the supervisory management level. Automating a mundane manual workflow and substituting it with a fast, accurate, and intelligent workflow can produce major improvements and efficiencies to any workflow. So, back to sports databases and predictive analytics. So, advancing the simple claims example above, the sports data analytics is far more advanced and involves logistic regression, binomial and multinomial equations (and knowing the differences), and culminating with combinatorial algorithms. There are 125 specifically defined performance metrics in the NFL and NCAAF models that the combinatorial algorithms produce predictive intelligence. So, the end result will be a near mirror image of a box score with highlighted areas where the projections far exceed or fail to meet the teams’ averages over varying time periods. There will be many articles written and lectures/podcasts provided starting in August 2018 that will dive deeper into the use of predictive analytics that will give you a very strong understanding of AI, machine learning and deep learning programs and models. To end, if we are attempting to provide a specific data point as a solution to a problem, then machine learning techniques are used and if the solution is to provide a cluster of data points, then deep learning is used. Deep learning requires significantly more computational power and have much larger data lakes then machine learning requires. So, Geoffrey is a bot with over 700 specific technological tools in his shed and he is only allowed to use them to learn and produce selections. This is a huge advantage over any human. First because Geoffrey can perform billions of calculations in literally minutes and is also 100 percent fully disciplined. There is no emotion involved. Not a from a huge winning pick or a losing disaster game where the team picked failed to cover by 30 points. In fact, Geoffry does care about winning and losing equally and learns from both results over time leading to even greater success. Last, over the past 5 seasons, Geoffrey has amassed a return on investment (ROI) of 33%. There has not been one losing season in any sport. However, you cannot depend on these past gains and successes as a reason that 2018 and beyond will produce similar results. Geoffrey could fall prey to losing and we are only be REAL in sharing that simple fact with you. However, we do feel quite confident, that Geoffrey will outperform the majority of his human peers.
Geoffrey Praenuntio may turn out to be one of your best friends and he is not even remotely close to human or even an alien. Geoffrey Praenuntio is a bot and has over 700 (and growing) algorithms that search and learn from the vast databases that John Ryan has developed in all major sports. Geoffrey is a form of Artificial Intelligence, but AI, machine learning, and deep learning are highly misused and misunderstood words. AI is the overarching technology that has numerous subsets within it and machine learning and deep learning are just of those subset classes. Machine learning and Deep Learning are two distinctly different AI approaches to gaining intelligence from big data that can be utilized far more efficiently by data dependent decision makers in all types of businesses. Here is just one example of a cognitive bot. Let's say your friend Manny decides to send this angry letter to an insurance company stating his frustration that his claim has not been processed and now actually may not be approved. The insurance company could actually have Geoffrey scan the letter in milliseconds and start making sense of it. Geoffrey could extract simple account information like customer name, the account number, claims number, when the claim was filed, the status of the claim, have the premiums ben paid current and all pertinent client information. Geoffrey could perform sentiment analysis on the letter recognizing that it has a derogatory sentiment reflecting the customer’s frustrations. Geoffrey would then file the letter in the correct bucket for a claims manager to then begin a timely address of the situation and get back quickly to the customer and in the process providing superior service. Then, Geoffrey could also provide a predictive analysis report as well. The report could state that this has been a loyal customer for the past 7 years and because of this poor service the probability of this customer leaving and going to a competitor is significantly high and could even skip over the first level managers and but into an urgent buck at the supervisory management level. Automating a mundane manual workflow and substituting it with a fast, accurate, and intelligent workflow can produce major improvements and efficiencies to any workflow. So, back to sports databases and predictive analytics. So, advancing the simple claims example above, the sports data analytics is far more advanced and involves logistic regression, binomial and multinomial equations (and knowing the differences), and culminating with combinatorial algorithms. There are 125 specifically defined performance metrics in the NFL and NCAAF models that the combinatorial algorithms produce predictive intelligence. So, the end result will be a near mirror image of a box score with highlighted areas where the projections far exceed or fail to meet the teams’ averages over varying time periods. There will be many articles written and lectures/podcasts provided starting in August 2018 that will dive deeper into the use of predictive analytics that will give you a very strong understanding of AI, machine learning and deep learning programs and models. To end, if we are attempting to provide a specific data point as a solution to a problem, then machine learning techniques are used and if the solution is to provide a cluster of data points, then deep learning is used. Deep learning requires significantly more computational power and have much larger data lakes then machine learning requires. So, Geoffrey is a bot with over 700 specific technological tools in his shed and he is only allowed to use them to learn and produce selections. This is a huge advantage over any human. First because Geoffrey can perform billions of calculations in literally minutes and is also 100 percent fully disciplined. There is no emotion involved. Not a from a huge winning pick or a losing disaster game where the team picked failed to cover by 30 points. In fact, Geoffry does care about winning and losing equally and learns from both results over time leading to even greater success. Last, over the past 5 seasons, Geoffrey has amassed a return on investment (ROI) of 33%. There has not been one losing season in any sport. However, you cannot depend on these past gains and successes as a reason that 2018 and beyond will produce similar results. Geoffrey could fall prey to losing and we are only be REAL in sharing that simple fact with you. However, we do feel quite confident, that Geoffrey will outperform the majority of his human peers.
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