Sports Prediction - Revision history

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:43:57Z

BPeat at 16:06, 3 September 2023

2023-09-03T16:06:20Z

BPeat at 19:45, 2 September 2023

2023-09-02T19:45:42Z

BPeat at 11:39, 17 August 2023

2023-08-17T11:39:11Z

BPeat at 17:20, 10 July 2023

2023-07-10T17:20:34Z

BPeat at 15:34, 9 July 2023

2023-07-09T15:34:38Z

BPeat at 14:12, 9 July 2023

2023-07-09T14:12:39Z

BPeat at 14:09, 9 July 2023

2023-07-09T14:09:24Z

BPeat at 19:22, 4 July 2023

2023-07-04T19:22:12Z

BPeat at 18:09, 27 June 2023

2023-06-27T18:09:55Z

@@ Line 20: / Line 20: @@
 * [[Sports]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [http://www.engadget.com/ai-predicts-a-dodgers-world-series-win-after-a-covi-dshortened-season-130014351.html AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget]

@@ Line 17: / Line 17: @@
 [http://www.google.com/search?q=sports+gambling+V+prediction+deep+machine+learning+ML ...Google search]
-* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Sports]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

@@ Line 19: / Line 19: @@
 * [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
 * [[Sports]]
-* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [http://www.engadget.com/ai-predicts-a-dodgers-world-series-win-after-a-covi-dshortened-season-130014351.html AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget]

@@ Line 354: / Line 354: @@
 = Excel - Sports Prediction =
-* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database]] ... [[Graph]] ... [[LlamaIndex]]
+* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 <youtube>2Bp8bytUN24</youtube>

@@ Line 17: / Line 17: @@
 [http://www.google.com/search?q=sports+gambling+V+prediction+deep+machine+learning+ML ...Google search]
-* [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
 * [[Sports]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

@@ Line 94: / Line 94: @@
 <youtube>k7hSD_-gWMw</youtube>
 <b>Predicting sports winners using data analytics with Pandas and scikit-learn by Robert Layton
-</b><br>The Pandas and scikit-learn packages combine together to produce a powerful toolkit for data analytics. In this talk, we will be using them together to analyse the outcome of NBA games, trying to predict the winner of a match. There is plenty of data out there to allow us to create good predictions – the key is getting it in the right format and building the right model.  In this talk we will go through importing data from the net, cleaning it up, creating new features, and building a predictive model. We then evaluate how well we did, using recent NBA data. The model we use will be a decision tree ensemble called a random forest. PyCon Australia is the national conference for users of the [[Python]] Programming Language. In 2015, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World.  July 31-August 4, Brisbane, Queensland, Australia
+</b><br>The Pandas and scikit-learn packages combine together to produce a powerful toolkit for data analytics. In this talk, we will be using them together to analyse the outcome of NBA games, trying to predict the winner of a match. There is plenty of data out there to allow us to create good predictions – the key is getting it in the right format and building the right model.  In this talk we will go through importing data from the net, cleaning it up, creating new features, and building a [[Predictive Analytics|predictive model]]. We then evaluate how well we did, using recent NBA data. The model we use will be a decision tree ensemble called a random forest. PyCon Australia is the national conference for users of the [[Python]] Programming Language. In 2015, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World.  July 31-August 4, Brisbane, Queensland, Australia
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 <youtube>6tQhoUuQrOw</youtube>
 <b>Predicting the Winning Team with Machine Learning
-</b><br>[[Creatives#Siraj Raval|Siraj Raval]] Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the [[Python#scikit-learn|scikit-learn]] machine learning library as our predictive tool. [http://github.com/llSourcell/Predicting_Winning_Teams Code for this video]
+</b><br>[[Creatives#Siraj Raval|Siraj Raval]] Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the [[Python#scikit-learn|scikit-learn]] machine learning library as our [[Predictive Analytics|predictive tool]]. [http://github.com/llSourcell/Predicting_Winning_Teams Code for this video]
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 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Meta, Facebook, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=sports+gambling+predict+_prediction+artificial+intelligence+deep+learning Youtube search...]
 [http://www.google.com/search?q=sports+gambling+V+prediction+deep+machine+learning+ML ...Google search]
-* [[Case Studies]]
+* [[Sports]]
-** [[Sports]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [http://www.engadget.com/ai-predicts-a-dodgers-world-series-win-after-a-covi-dshortened-season-130014351.html AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget]
@@ Line 30: / Line 38: @@
 <youtube>yeh0ar2kB_Q</youtube>
 <b>A Modern Love Story: Machine Learning & The Global Sports Betting Industry | ICED(AI)
-</b><br>Developments in Artificial Intelligence, particularly those within the subfield of Machine Learning, are revolutionizing virtually every industry on the planet. The global sports betting industry, especially with the United States' repeal of PASPA in 2018, is ripe for disruption. Exponential increases in the ability to collect, distribute, and analyze sports data have led to an influx of top engineers entering the space. This presentation will focus on some of the principal ways in which Machine Learning is revolutionizing the industry, ranging from oddsmaking and risk management to fraud detection and responsible gaming implementations. It will also offer a guide to the economics of the business side of the industry and discuss relevant current topics in the tech space, such as adversarial machine learning.  ABOUT THE SPEAKER Lloyd Danzig is the Chairman & Founder of the International Consortium for the Ethical Development of Artificial Intelligence, a non-profit dedicated to ensuring that rapid developments in A.I. are made with a keen eye toward the long-term interests of humanity. He is also the Founder & CEO of Sharp Alpha Advisors, a sports betting business and investment consultancy with a focus on companies deploying cutting-edge tech.  He has previously managed institutional portfolios for BlackRock, data science initiatives for Samsung, and Machine Learning engines for sportsbook operators, along with a lifelong passion for entrepreneurship and innovation. He has been privileged to be featured as a guest speaker on the evolving role of Machine Learning in gaming at numerous prestigious universities including Stanford University, Columbia University, and The Wharton School of Business, in addition to private sector conferences including QConAI, Betting on Sports, The AI Summit, MathSport International, The All American Sports Betting Summit, Sport & Society, and IAGR 2019.
+</b><br>[[Development]]s in Artificial Intelligence, particularly those within the subfield of Machine Learning, are revolutionizing virtually every industry on the planet. The global sports betting industry, especially with the United States' repeal of PASPA in 2018, is ripe for disruption. Exponential increases in the ability to collect, distribute, and analyze sports data have led to an influx of top engineers entering the space. This presentation will focus on some of the principal ways in which Machine Learning is revolutionizing the industry, ranging from oddsmaking and risk management to fraud detection and responsible gaming implementations. It will also offer a guide to the economics of the business side of the industry and discuss relevant current topics in the tech space, such as adversarial machine learning.  ABOUT THE SPEAKER Lloyd Danzig is the Chairman & Founder of the International Consortium for the Ethical [[Development]] of Artificial Intelligence, a non-profit dedicated to ensuring that rapid [[development.\]]s in A.I. are made with a keen eye toward the long-term interests of humanity. He is also the Founder & CEO of Sharp Alpha Advisors, a sports betting business and investment consultancy with a focus on companies deploying cutting-edge tech.  He has previously managed institutional portfolios for BlackRock, data science initiatives for Samsung, and Machine Learning engines for sportsbook operators, along with a lifelong passion for entrepreneurship and innovation. He has been privileged to be featured as a guest speaker on the evolving role of Machine Learning in gaming at numerous prestigious universities including Stanford University, Columbia University, and The Wharton School of Business, in addition to private sector conferences including QConAI, Betting on Sports, The AI Summit, MathSport International, The All American Sports Betting Summit, Sport & Society, and IAGR 2019.
 |}
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 <youtube>McV4a6umbAY</youtube>
 <b>AI for Imperfect-Information Games: Beating Top Humans in No-Limit Poker
-</b><br>Despite AI successes in perfect-information games, the hidden information and large size of no-limit poker have made the game difficult for AI to tackle. Libratus is an AI that, in a 120,000-hand competition, defeated four top pros in heads-up no-limit Texas hold’em poker, the leading benchmark in imperfect-information game solving. This talk explains why imperfect-information games are fundamentally more difficult than perfect-information games, and the advances in Libratus that overcame those challenges. In particular, this talk describes new methods for real-time planning in imperfect-information games that have theoretical guarantees. Additional research has extended these methods to deeper game trees, enabling the development of the master-level poker AI Modicum which was constructed using only a 4-core CPU and 16 GB of RAM. These algorithms are domain-independent and can be applied to a variety of strategic interactions involving hidden information.
+</b><br>Despite AI successes in perfect-information games, the hidden information and large size of no-limit poker have made the game difficult for AI to tackle. Libratus is an AI that, in a 120,000-hand competition, defeated four top pros in heads-up no-limit Texas hold’em poker, the leading benchmark in imperfect-information game solving. This talk explains why imperfect-information games are fundamentally more difficult than perfect-information games, and the advances in Libratus that overcame those challenges. In particular, this talk describes new methods for real-time planning in imperfect-information games that have theoretical guarantees. Additional research has extended these methods to deeper game trees, enabling the [[development]] of the master-level poker AI Modicum which was constructed using only a 4-core CPU and 16 GB of RAM. These algorithms are domain-independent and can be applied to a variety of strategic interactions involving hidden information.
 |}
 |}<!-- B -->