Machine Learning
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 key features and benefits of Python in Excel?
Python in Excel integrates directly into the Excel environment through the new PY function, requiring no installation. Users can leverage popular Python libraries like Pandas and matplotlib within their Excel workbooks to create visualizations, train machine learning models, and perform advanced analytics. The system runs securely in Microsoft Cloud as a compliant Microsoft 365 service, protecting data privacy. This integration works seamlessly with Excel's native features including formulas, charts, pivot tables, and conditional formatting, combining Excel's flexibility with Python's analytical power to enhance data-driven decision making and storytelling.
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Microsoft 365
00:02 - 01:27
What is artificial intelligence?
Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that typically require human intelligence and discernment. It essentially refers to systems designed to act like humans in their ability to make decisions and solve problems. AI serves as a broad umbrella term encompassing numerous subfields with specialized applications. This technology enables machines to mimic human cognitive functions, making it possible for computers to handle complex tasks that previously required human judgment and reasoning abilities.
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Karl Kapp
01:06 - 01:34
What is computer vision and how is it applied to potato farming?
Computer vision is a technology that enables machines to understand and analyze images or videos through extensive data training. Unlike humans who can quickly interpret visual information, machines require significant relevant data to comprehend what they're seeing. In potato farming, computer vision uses instance segmentation to precisely identify individual potatoes, distinguishing them from rocks, equipment, or hands. This technology allows farmers to extract valuable information about potato size and weight, rather than just detecting their presence. By accurately tracing potato pixels, the system provides meaningful measurements that help growers maximize yields and make informed decisions.
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Future of Technology Series
15:52 - 17:38
How is AI and machine learning being utilized in the Proteus Maritime Domain Awareness Program?
AI and machine learning are helping Proteus process large volumes of imagery data at real-time speeds, which is crucial since vessels are continuously moving across vast oceans. The technology enables vessel identification by classifying ship types based on metrics like length and width. Additionally, AI algorithms have been tailored to detect specific fishing behaviors like long line, purse seine, and drift net fishing, helping identify illegal, unregulated, and unreported fishing activities. This capability allows authorities to monitor vessel tracks and recognize suspicious patterns that vary from day to day, significantly enhancing maritime surveillance effectiveness.
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GovCIO Media & Research
15:38 - 18:26
How can machine learning models improve credit scoring compared to traditional methods?
Machine learning models improve credit scoring by collating data across multiple sources including credit bureaus, bank accounts, money laundering statuses, and alternative payment histories. Unlike traditional credit scoring techniques, these ML models consider additional factors like monthly rental commitments that are typically overlooked. The model can explain which features are most important for credit decisions in layman's language, providing transparency. This approach creates a more sophisticated risk profiling system that delivers personalized credit recommendations, making the process more inclusive for applicants who might be underserved by conventional scoring methods.
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MongoDB
31:58 - 33:37