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How does A.I. impact election prediction

We’re a few days out from the 2024 United States Presidential Election. The polls currently show Trump and Harris pretty much tied. But how is A.I. playing a role in this type of analytics?

AI plays a significant role in election prediction by analyzing vast amounts of data to identify patterns, trends, and correlations that help forecast election outcomes. Here’s how AI is applied in this field:

Above: AI helps predict elections, but could it influence them?

1. Polling Analysis and Sentiment Prediction

  • Polling Data: AI can process and analyze polling data, identifying patterns that may indicate how groups are likely to vote. AI models help correct for biases in polling by accounting for demographic shifts and sampling errors.
  • Sentiment Analysis: AI can analyze social media, news, and other public content to gauge voter sentiment toward candidates and issues. By processing text data through natural language processing (NLP), it can predict whether public opinion is shifting.

2. Voter Behavior Modeling

  • Voter Segmentation: Machine learning models can categorize voters based on factors like age, location, political ideology, and socioeconomic status. This segmentation allows AI models to make more precise predictions by assessing how different voter demographics might behave.
  • Turnout Prediction: By looking at past turnout data and current sentiment, AI can forecast who is likely to vote and which demographics may drive higher turnout. AI considers factors like weather, current events, and candidate popularity when predicting turnout.

3. Predictive Analytics

  • Forecasting Models: Advanced machine learning models, including regression analysis and neural networks, are used to forecast outcomes based on historical data, trends, and real-time information. This includes models like random forests, gradient boosting, and recurrent neural networks (RNNs).
  • Data Fusion: By combining data sources (such as polling, economic indicators, social media sentiment, and demographic data), AI models build a more comprehensive prediction framework. This approach helps mitigate the weaknesses of any single data source.

4. Real-Time Event Analysis

  • Event Impact Prediction: AI can analyze the effects of events like debates, scandals, or economic reports. By tracking real-time public reaction and integrating this data into prediction models, AI can adapt predictions as events unfold.
  • Social Media Dynamics: AI examines the reach and spread of social media posts and hashtags to determine how influential certain narratives or pieces of news are in swaying public opinion.

5. Predictive Uncertainty and Scenario Analysis

  • Uncertainty Analysis: AI can model various election scenarios, predicting potential outcomes and their likelihood. This approach helps analysts and campaigns understand the factors with the greatest impact on possible results.
  • Error Correction: AI models can also be tuned to account for uncertainties in data quality and model assumptions, refining predictions as new data becomes available.

In short, AI enhances the precision, adaptability, and depth of election predictions by continuously learning from diverse datasets and adapting to the rapidly changing dynamics of electoral cycles.

October 29, 2024 by Advertising

Who is Jalali Hartman?

Jalali Hartman is an entrepreneur, engineer, and global advocate for accessible robotics and artificial intelligence.

He founded ROBAUTO, a company originally founded in Boulder, Colorado, which focused early on during the A.I. revolution on creating robotics solutions to assist people, especially in healthcare contexts.

A notable achievement is the development of robots designed to help individuals with autism improve communication and social skills. These robots, such as the BiBli robot, are tailored based on feedback from autistic users, their families, and caregivers, and are designed to be both affordable and customizable to the needs of the individual.

TedX “Think Like a Robot” Jalali Hartman (Autism Research) (youtube.com)

Today, Robauto.ai is one the leading A.I. and robotics education and news platforms and Jalali is currently involved with leveraging A.I. for organizational growth for a variety of organizations. He is the recipient of the 2014 Global IP Champion Award and has been nominated for a medal of honor for his work with autism and robotics.

Hartman’s broader mission with ROBAUTO is to make AI and robotics more accessible to the public, hence the trademark slogan “Robots for the people”.

He has been involved in tech since the late 1990s, working on a range of projects, from eCommerce algorithms to social media technology. In addition to his work at ROBAUTO, he has co-founded other companies, including Yovia, one of the first social media influencer networks and Marketplace Manager, and early multi-channel ecommerce platform that won eBay’s Innovation Award and grew to 4,000+ customers.

Hartman is an accomplished outdoor adventurer and has claimed first-routes on multiple open water swim crossings in North and Central America as well as establishing new climbing and alpine skiing routes throughout the Rocky Mountains.

October 13, 2024 by Advertising Artificial General Intelligence Artificial Intelligence Basics of Robotics

Google’s First Party (1P) Data and A.I.

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Google’s 1P (First-Party) Data refers to data that is collected directly by Google from its own users through its various services and products, such as Search, YouTube, Gmail, Google Maps, and Android devices. This data is invaluable for improving Google’s products and services, personalizing user experiences, and developing new technologies, including advancements in artificial intelligence (A.I.) and machine learning. Here’s how Google utilizes its 1P Data:

Product and Service Improvement

  1. Search Optimization:
    • Personalization: Google uses search data to personalize search results, making them more relevant to individual users based on their search history, location, and preferences.
    • Query Understanding: By analyzing search queries and user interactions, Google enhances its understanding of natural language, enabling more accurate and context-aware search results.
  2. YouTube Recommendations:
    • Content Personalization: Data from users’ viewing history, likes, and subscriptions help in recommending videos that are likely to interest individual users.
    • Ad Targeting: Advertisers can target specific demographics and interests based on users’ video-watching behavior.
  3. Gmail and Google Workspace:
    • Spam Detection: Google analyzes email data to improve spam filters, ensuring that unwanted messages are effectively identified and blocked.
    • Smart Features: Features like Smart Compose and Smart Reply leverage user data to offer personalized suggestions and enhance productivity.

A.I. and Machine Learning Development

  1. Training Data for Models:
    • Machine Learning: Google’s 1P Data is used to train machine learning models, improving their accuracy and efficiency. For example, Google Photos uses image data to enhance object recognition and search capabilities.
    • Natural Language Processing (NLP): Data from Google Search, Assistant, and other text-based services are utilized to train NLP models, enabling better language understanding and generation.
  2. Improving Voice Recognition:
    • Google Assistant: Voice data from Google Assistant interactions helps in refining speech recognition algorithms, making the assistant more responsive and accurate in understanding user commands.
  3. Autonomous Systems:
    • Waymo (Self-Driving Cars): Data from Google Maps and location services are crucial for developing and refining autonomous driving technologies.

User Experience Enhancement

  1. Personalized Ads:
    • Targeted Advertising: Google uses browsing history, search queries, and other user interactions to deliver personalized ads that are more likely to be relevant to users, thereby improving the effectiveness of ad campaigns.
  2. Customizing Content:
    • News and Discover: Google uses user interests and past behavior to curate news articles and content that align with individual preferences in the Google News app and the Discover feed.
  3. Location-Based Services:
    • Google Maps: Location data helps in providing real-time traffic updates, personalized route suggestions, and recommendations for nearby places.

Privacy and Ethical Considerations

While leveraging 1P Data, Google emphasizes the importance of user privacy and data security. Some key measures include:

  1. Data Anonymization:
    • Google often anonymizes and aggregates data to ensure individual users cannot be identified, maintaining privacy while still extracting valuable insights.
  2. User Control:
    • Users have control over their data through Google’s privacy settings, allowing them to manage what data is collected, how it’s used, and what information is shared.
  3. Compliance with Regulations:
    • Google adheres to global privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe, ensuring that data usage practices comply with legal standards.

Conclusion

Google’s use of 1P Data is central to its operations, enabling it to enhance product functionality, develop advanced A.I. technologies, and provide personalized user experiences. By carefully balancing innovation with privacy considerations, Google aims to maintain user trust while leveraging its data assets to drive technological progress.

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How Google Uses First-Party Data: An In-Depth Look

Google’s first-party (1P) data refers to the information it collects directly from users through its various services and products, such as Search, YouTube, Gmail, Google Maps, and Android devices. This data plays a crucial role in improving Google’s products and services, personalizing user experiences, and driving advancements in artificial intelligence (A.I.) and machine learning. Here’s a closer look at how Google utilizes its 1P data.

Enhancing Products and Services

  1. Search Optimization:
    • Personalization: Google leverages search data to tailor search results to individual users. This personalization is based on users’ search histories, locations, and preferences, making the search experience more relevant and useful.
    • Query Understanding: By analyzing search queries and user interactions, Google continually enhances its understanding of natural language, enabling it to deliver more accurate and context-aware search results.
  2. YouTube Recommendations:
    • Content Personalization: Data from users’ viewing histories, likes, and subscriptions help YouTube recommend videos that match individual interests, keeping users engaged with content they enjoy.
    • Ad Targeting: Advertisers benefit from targeted advertising, which is informed by users’ video-watching behaviors and interests, making ads more relevant and effective.
  3. Gmail and Google Workspace:
    • Spam Detection: Google uses email data to improve its spam filters, ensuring that unwanted messages are effectively identified and kept out of users’ inboxes.
    • Smart Features: Features like Smart Compose and Smart Reply analyze users’ email habits to offer personalized suggestions, enhancing productivity and making email management more efficient.

Advancing A.I. and Machine Learning

  1. Training Data for Models:
    • Machine Learning: Google’s vast data collection is essential for training machine learning models, which improves their accuracy and efficiency. For example, Google Photos uses image data to enhance object recognition and search capabilities.
    • Natural Language Processing (NLP): Data from Google Search, Assistant, and other text-based services train NLP models, improving their ability to understand and generate human language.
  2. Improving Voice Recognition:
    • Google Assistant: Voice data from interactions with Google Assistant helps refine speech recognition algorithms, making the assistant more responsive and accurate in understanding user commands.
  3. Autonomous Systems:
    • Waymo (Self-Driving Cars): Data from Google Maps and location services are crucial for developing and refining autonomous driving technologies, ensuring safe and efficient navigation.

Enhancing User Experience

  1. Personalized Ads:
    • Targeted Advertising: Google uses browsing history, search queries, and other user interactions to deliver personalized ads. This makes the ads more relevant to users and improves the effectiveness of advertising campaigns.
  2. Customizing Content:
    • News and Discover: Google curates news articles and content based on user interests and past behavior, providing personalized recommendations in the Google News app and the Discover feed.
  3. Location-Based Services:
    • Google Maps: Location data helps Google Maps provide real-time traffic updates, personalized route suggestions, and recommendations for nearby places, enhancing the overall user experience.

Privacy and Ethical Considerations

While leveraging 1P data, Google places a strong emphasis on user privacy and data security. Here are some key measures they take:

  1. Data Anonymization:
    • Google often anonymizes and aggregates data to ensure that individual users cannot be identified, maintaining privacy while still extracting valuable insights.
  2. User Control:
    • Users have control over their data through Google’s privacy settings, allowing them to manage what data is collected, how it’s used, and what information is shared.
  3. Compliance with Regulations:
    • Google adheres to global privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe, ensuring that data usage practices comply with legal standards.

Conclusion

Google’s use of first-party data is central to its mission of improving its products and services, advancing A.I. research, and providing personalized user experiences. By carefully balancing innovation with privacy considerations, Google aims to maintain user trust while leveraging its data to drive technological progress.

May 30, 2024 by Advertising Artificial Intelligence

Search as the ultimate complex system

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If you’ve heard of the Santa Fe Institute – a think tank founded by scientists seeking a way to explore their research outside of the bounds of normal structures – you may have heard about Complexity.

Search is the ultimate complex system that is probably impacting you and or organization right now. Since the beginning of Internet Time, computers have been indexing and ranking websites. Systems like search engines use signals, combined with the content and meta content within your website, to determine if your website or content is the best search result.

The impacts can make or break a business as it moves in and out of a popular search category.

But what are the elements of this system? How can you impact your own search results?

1. Other websites referencing you. For example, if I wanted to get this website to rank well for restaurant hood cleaning, the anchor link used signals to search engines that this website (robauto.ai) found the content important enough to link back to it.

2. Site speed. Particularly Google wants to serve fast, mobile friendly websites.

3. Community support. This is where your social media comes in. Even if your posts don’t get a lot of interaction, people liking and commenting around a URL is just another signal you are sending.

Complexity Explorer

February 12, 2024 by Advertising Artificial Intelligence

How SPAM Filters Use Machine Learning

Free computer code screen image

Spam filters use machine learning techniques to distinguish between spam and legitimate emails based on patterns and characteristics in the data. SPAM detection and prevention is a prevalent use of machine learning. If you are having issues with deliverability, here are a few insights as to why. The process typically involves the following steps:

  1. Data Collection:
    • The spam filter gathers a large dataset of emails that are labeled as either spam or non-spam (ham). This dataset is used for training the machine learning model.
  2. Feature Extraction:
    • Relevant features are extracted from the emails. Features can include the sender’s email address, subject line, body content, presence of certain keywords, formatting, and more.
  3. Feature Representation:
    • The extracted features are converted into a format suitable for machine learning algorithms. This could involve creating numerical representations or vectors that capture the relevant information.
  4. Training the Model:
    • Machine learning algorithms, such as Naive Bayes, Support Vector Machines (SVM), or more advanced methods like neural networks, are trained using the labeled dataset. The algorithm learns to identify patterns associated with spam and non-spam emails.
  5. Model Evaluation:
    • The trained model is evaluated using a separate dataset not seen during training. This evaluation helps assess how well the model generalizes to new, unseen data.
  6. Adjustment and Tuning:
    • The spam filter may be fine-tuned based on the evaluation results. This could involve adjusting parameters, using different algorithms, or incorporating feedback from users.
  7. Deployment:
    • The trained and tuned model is deployed in the spam filter system to analyze incoming emails in real-time.
  8. Real-Time Scoring:
    • As new emails arrive, the spam filter scores each email based on the learned patterns. The score indicates the likelihood of the email being spam.
  9. Threshold Setting:
    • A threshold is set to determine when an email is classified as spam. Emails with scores above the threshold are marked as spam, while those below are considered legitimate.
  10. Feedback Loop:
    • Some spam filters incorporate a feedback loop where user interactions, such as marking an email as spam or moving it to the inbox, are used to continually improve the model over time.

Machine learning enables spam filters to adapt to evolving patterns and tactics used by spammers. It allows the system to learn from experience and improve its ability to accurately classify emails as spam or legitimate, making it a dynamic and effective solution for email filtering.

January 25, 2024 by Advertising

How to signal Google (Conversion Goals)

ferris wheel in city
  1. Sign in to Google Ads:
    • Go to the Google Ads website (ads.google.com).
    • Sign in with your Google account.
  2. Access Conversion Tracking:
    • In the Google Ads dashboard, navigate to the “Tools & Settings” menu.
    • Under “Measurement,” select “Conversions.”
  3. Create a Conversion Action:
    • Click the “+” button to create a new conversion action.
    • Choose the type of conversion action you want to track (e.g., website, app, phone calls).
    • Provide the necessary details for the conversion action, such as the name, value, and counting method.
  4. Get the Conversion Tracking Tag:
    • After creating a conversion action, you’ll be provided with a conversion tracking tag.
    • Copy the tag and add it to the relevant pages on your website. This is usually placed between the <head> tags.
  5. Upload Offline Conversion Data (if applicable):
    • If you have offline conversions (e.g., sales made over the phone or in-store), you can upload this data to Google Ads.
    • Prepare a file with the required information (e.g., GCLID, conversion name, conversion time, conversion value).
    • In the Google Ads dashboard, go to “Tools & Settings” > “Conversions” and select the conversion action.
    • Choose “Uploads” and follow the instructions to upload your offline conversion data.
  6. Verify Conversion Tracking:
    • After implementing the tracking tag, it’s essential to verify that conversions are being recorded accurately.
    • Use the “Tag Assistant” Chrome extension or Google Tag Assistant to check if the tag is firing correctly.
  7. Monitor Conversion Performance:
    • Once conversion tracking is set up, monitor the performance of your campaigns in the Google Ads dashboard.
    • Analyze the conversion data to make informed decisions about your advertising strategy.

Today’s advertisers need to understand A.I. One aspect of that that impacts most businesess – Google Search. Here is a quick guide for an advanced “signal” you can send to Google. Google tracks calls and web forms (conversion events) on your websites naturally. However what about qualified leads or ‘closed won’. The Google Robots need to learn about those (most importantly).

Please note that these steps are a general guideline, and the user interface and features within Google Ads may have changed.

January 25, 2024 by Advertising Artificial Intelligence

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