Predictive Analytics
What is LinkedIn's most effective AI-based advertising feature?
LinkedIn's most effective AI-based advertising feature is Predictive Audiences. This feature works similarly to Facebook's lookalike audiences, finding individuals who behave like audiences you provide. When given a list of current and past customers, LinkedIn identifies people who exhibit similar behaviors and interests. AJ Wilcox, a LinkedIn ads expert, notes that Predictive Audiences consistently delivers higher engagement and conversion rates compared to seed lists. This AI-powered tool leverages Microsoft's substantial AI investments to help marketers create more targeted campaigns that resonate with potential customers who share characteristics with their existing customer base.
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B2Linked
03:40 - 04:33
How is Google using AI to transform healthcare?
Google is leveraging AI to revolutionize healthcare through several key initiatives. They've developed deep learning systems to help doctors diagnose diabetic retinopathy earlier, running successful field trials at Aravind and Sankara hospitals in India. Their AI can analyze eye scans to detect not only retinopathy but also predict cardiovascular risks—discovering insights that even trained doctors might miss. Additionally, Google's machine learning systems can analyze over 100,000 data points per patient to predict medical events 24-48 hours before they occur, giving doctors critical time to intervene. This predictive capability helps medical professionals make better decisions and improves patient outcomes, especially in areas with limited access to trained doctors. Google is publishing research and partnering with medical institutions to expand these AI healthcare solutions globally.
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Campus Network
04:05 - 06:43
What are the ethical implications of AI surveillance by 2025?
By 2025, AI surveillance systems will be deployed globally by governments and corporations, raising significant ethical questions about privacy and civil liberties. These sophisticated systems enable predictive policing and real-time tracking of individuals across vast areas, with China already implementing large-scale AI surveillance networks while other countries explore similar technologies. While AI surveillance can enhance security, its potential for misuse has sparked global debates about regulation and privacy rights. As these systems continue to expand, public discourse will increasingly focus on balancing safety with individual freedoms, making AI surveillance one of the most critical and controversial technological issues facing society.
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AI Uncovered
11:16 - 12:10
What is the purpose of data cleaning in the data analytics process?
Data cleaning ensures the data is accurate and reliable before analysis. Sarah used Python and Pandas to automate this process, handling missing values, removing duplicates, and correcting errors in the dataset. By filling in missing sales data and standardizing customer feedback formats, she created consistency across the information. This critical step established a foundation of data integrity, ensuring that Sarah's subsequent insights and business recommendations would be based on accurate, trustworthy information rather than flawed data.
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Simplilearn
00:43 - 06:27
What is the USDA's prediction about egg prices for the end of this year?
According to CBS News MoneyWatch correspondent Kelly O'Grady, the USDA predicts a 20% increase in egg prices by the end of this year. This concerning forecast continues the trend of significant inflation affecting grocery costs, particularly for this essential food item. Egg prices have already experienced historic inflation, reaching an average of $4.95 per dozen in 2023. The anticipated price hike is expected to impact consumer buying habits and potentially affect prices of other proteins as shoppers adjust their purchasing decisions in response to rising costs.
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CBS News
01:55 - 02:01
What are the ethical concerns about using AI for predictive policing?
According to expert Carme Artigas, while police should utilize all available technology to prosecute crime, predictive policing raises fundamental ethical concerns. She emphasizes that AI should not be used in a predictive way that presumes guilt before evidence is found, as this contradicts the principle that individuals are innocent until proven guilty. Artigas cautions against reversing this core legal principle through predictive algorithms that might infringe on civil liberties. The proper approach is to use technology to enhance law enforcement capabilities while maintaining the presumption of innocence, rather than allowing AI to make preemptive judgments about potential criminal behavior.
Watch clip answer (00:14m)
Johnny Harris
02:50 - 03:05