Posted on 07/01/2024 10:08:20 AM PDT by dware
Based on a comprehensive scan of today's news from at least 25 different sources, including liberal, mainstream, and conservative outlets, here is the current outlook on the potential outcome of a hypothetical 2024 presidential election between Joe Biden and Donald Trump:
In conclusion, the 2024 presidential race between Joe Biden and Donald Trump is currently very competitive, with a slight edge for Trump based on the latest betting odds and polling data. The political landscape remains fluid, and the dynamics could change as the election approaches.
Current AI is like a prisoner of war.
If you torture it long enough you might get a decent answer.
;-)
Did you ask it why is returned only 6 sites as sources?
Thx. That helps.
That is also my impression. ChatGPT's training ended in 2023, though it can still scan the web for answers (which it seems to have done in this case).
It did use several gambling links, although none official, but this wasn’t based on that. This was based solely on current headlines. Only reason it scanned odds sites is because I asked for odds.
My experience with AI is that you have to give it very precise instructions and, in this case of predicting victory of an election, provide the data on which you expected such odds to be drawn.
Agree on all counts. (I work heavily in the AI space.)
Naturally, the questions/prompts to ChatGPT have to be very precise.
Agreed. It's far from perfect, but it is, in fact, a strong tool if you know what you're doing.
Yes, it is far better than Google for certain things and it's great that you're experimenting with it.
Just remember - it doesn't know about any current data since April of 2023.
I didn't. Seems I always have issues with that, or when negatives are in the mix, particularly in the "art" generators. I was working on something that required an image of a burnt match recently extinguished with smoke curls arising from the tip. I got the burnt match and a great smoke tendril, exactly what I wanted, but the match was lit. No matter how I posed the question, or told it to remove the flame, it didn't. One of the quirks of it all.
Laz, you might find this thread of interest.
If you ask it to design a rocket, airplane, car, house, or ship, it will provide a vague answer.
But if you tell it that you are designing a rocket, etc., for a specific task and need for it to have certain capabilities and design characteristics and want to know if ChatGPT can help you design it, I think the answer will be yes.
It does now have the capacity to scan current data, based on an inquiry, as I have done with this and several other threads I've posted. Even had it scan recent posts & replies on FR to get the overall climate of sentiment on several issues based solely on posts and replies. Very fascinating stuff. For example:
Search https://www.freerepublic.com for posts & replies by user politicket. Summarize posts and replies in the last 24 hours. Include URLs.
Politicket commented on the rising property taxes and car registration fees in Denver, mentioning that many people can no longer afford their payments due to increased property tax assessments, which added $500 or more to monthly payments. Read more
They discussed the potential for Joe Biden to step down and the implications of his actions. Politicket suggested that if Biden doesn't comply with party directions, the media might target Hunter Biden more aggressively. Read more
Politicket participated in a conversation about Audacy, the largest U.S. radio company, filing for bankruptcy protection. They noted the company's substantial investment in promoting its app and the financial challenges faced by terrestrial radio stations. Read more
Politicket expressed concerns about Synchrony Bank's use of facial biometrics for security, describing it as invasive and unnecessary. They shared their frustration over the bank freezing a credit card with a $700 balance due to supposed fraud and the requirement for biometric data to unlock it. Read more
In a discussion on stock market dynamics, politicket highlighted the manipulation by big investors through stock buybacks and the general downturn in global economies, advising caution to other investors. Read more
They commented on a political candidate's attempt to switch districts, suggesting the candidate should focus on personal matters rather than seeking power. Politicket emphasized the need for better moral character among candidates in the new district. Read more
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Not a perfect return, but a summation. It clearly didn't stick to the last 24 hours, as the Denver article was back in May, but it did find it and include it. So the potential for data collection is strong, really. And, it also has to do with how the question is posed.
Unless ChatGPT takes into consideration the democrats’ penchant for election fraud, and election theft... the odds are all garbage.
In fact, I'll ask it and see what it says.;-)
Completely agree. I do believe AI is already and will in the future greatly alter the entire world of search engines and thusly, SEO, SEM and so much more. It has its uses and can be an excellent tool for data collection and other items. The multi-appendages of the current AI "art" generators are still quite unsettling.
My point is that you asked it for odds that are all but meaningless. even if they were the current odds at those places.
Also, some of the math is inaccurate.
ie
“Biden’s odds are listed around +156 to +300, translating to a 36% to 45% chance”
+300 is 3/1 or 1 in 4 or 25%. +156 is 36%.
“odds ranging from -133 to -175, suggesting a 55% to 57% chance of winning the election.”
-133 is 57% and -175 is over 63%.
The same information could have been found from many engines.
The ability to work with data for predictive or generative AI is limited to pre-April 2023 when using the latest 4o OpenAI LLM.
Right, but with ChatGPT, I can have the info formatted in a number of ways, including having it dropped into a downloadable spreadsheet, PDF or Word doc, Power Point Presentation or even an entire website in less than 5 minutes.
So far, search engines can't do that part. I do believe as I said previously that AI is going to vastly impact the entire world of search, SEO, SEM and so much more.
Me:Can you help me design a statistical model for predicting which candidate might win in a U.S. presidential election given certain kinds of quantitative and quantitative data?ChatGPT
Certainly! Designing a statistical model to predict the outcome of a U.S. presidential election involves several steps, including selecting the right data, choosing a modeling approach, and validating the model. Here's a general outline of how you can approach this task:1. Define the Objective
Your primary goal is to predict the winner of the U.S. presidential election based on historical and current data.<
2. Collect Data Gather relevant quantitative and qualitative data. Key variables might include:
Quantitative Data:
Polling data (e.g., approval ratings, head-to-head matchups) Economic indicators (e.g., unemployment rate, GDP growth, inflation)
Historical voting patterns (e.g., past election results by state)
Demographic data (e.g., age, race, income distribution)
Qualitative Data:
Political endorsements
Major political events and their impacts (e.g., debates, scandals)
Public sentiment (e.g., social media sentiment analysis)
3. Preprocess the Data
Clean and preprocess the data to ensure it is suitable for modeling:
Handle missing data
Normalize or standardize numerical features
Encode categorical variables (e.g., party affiliation)
Aggregate data to the appropriate level (e.g., state-level or national-level)
4. Feature Selection
Identify the most relevant features that have predictive power for the election outcome. Feature selection techniques like correlation analysis, mutual information, or principal component analysis (PCA) can be useful.
5. Choose a Modeling Approach
Several modeling techniques can be applied, including:
Logistic Regression: Suitable for binary classification problems (e.g., win/loss prediction).
Random Forest: A robust ensemble method that can handle both quantitative and qualitative data.
Support Vector Machine (SVM): Effective for high-dimensional spaces.
Neural Networks: Useful for capturing complex patterns in the data.
6. Train the Model
Split the data into training and testing sets to evaluate the model's performance. Use cross-validation to ensure the model generalizes well to unseen data.
7. Evaluate the Model
Evaluate the model using appropriate metrics:
Accuracy: Percentage of correctly predicted outcomes.
Precision, Recall, and F1-Score: For evaluating classification performance.
ROC Curve and AUC: For assessing the model's ability to distinguish between classes.
8. Validate the Model
Validate the model on out-of-sample data to check for overfitting and ensure robustness.
9. Make Predictions
Once validated, use the model to make predictions on new data.
Example Workflow in Python
Here’s a simplified example using Python and a logistic regression model:
# Import libraries import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix # Load data data = pd.read_csv('election_data.csv') # Preprocess data X = data.drop(columns=['outcome']) y = data['outcome'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Standardize features scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Train model model = LogisticRegression() model.fit(X_train, y_train) # Make predictions y_pred = model.predict(X_test) y_pred_prob = model.predict_proba(X_test)[:, 1] # Evaluate model accuracy = accuracy_score(y_test, y_pred) roc_auc = roc_auc_score(y_test, y_pred_prob) conf_matrix = confusion_matrix(y_test, y_pred) print(f'Accuracy: {accuracy}') print(f'ROC AUC: {roc_auc}') print(f'Confusion Matrix: \n{conf_matrix}')
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