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The Retirement Train - Straight Talk

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Welcome to "The Retirement Train - Straight Talk" where I discuss my retirement Journey, Retirement ideas, and other Retirement Tidbits I used and still use today. Get on board the Train and join our journey! Disclaimer: When discussing financial matters**I AM NOT A LICENSED FINANCIAL ADVISOR OR FINANCIAL PLANNER. THIS CHANNEL IS MEANT TO PROVIDE GENERAL INFORMATION ONLY AND DISCUSSION POINTS THAT MAY INTEREST YOU. PLEASE CHECK WITH A PROFESSIONAL BEFORE ACTING ON SPECIFIC FINANCIAL OPTIONS OR OPINIONS**

SATIRE

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Jeanne "Bean" Murdock, a very EDUCATED punk, is a satirist who writes and performs her own material. Her comedy is observational humor with a sassy, naive perspective, performed on roller skates where she can. "It\\\'s OK to laugh." Jeanne is also an author and screenwriter. Jeanne has hosted two health and fitness radio shows, in the past, and is looking to collaborate in airing a satirical show. Her idea is inspired by Bob (Elliott) and Ray (Goulding), old-time radio hosts who voiced many characters and skits. Actor\\\'s resume and Writer\\\'s resume available. Author of: "The Every Excuse in the Book Book: How to benefit from exercising by overcoming your excuses" "That\\\'s a Bunch of Quackery! How to avoid being pick-pocketed by misleading claims in the fitness industry" "Successful Dating at Last! A workbook for understanding each other" "It\\\'s Hard to Find Good Help these Days: A customer service manual for businesses"

Retired in the Philippines

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These are videos of my life in the Philippines. In 1979 I went to Guam to be a missionary. I travelled in the Philippines in 1980 and went to seminary in Baguio in 1981. After returning to the U.S. I missed the Philippines and wanted to return. It took 40 years for that dream to come true. I met my wife online and then in person when she came to the U.S. for a tour. We got married in the courthouse and over a year later I retired and we moved to the Philippines. We spent the following year building my wife's dream house near her parents' house in Tacloban City, where we now live. We designed it to have a music room/office/man cave. I bought a piano for it and display the few records I shipped here, all my music books and other books, and many of my dad's paintings. We are involved in our UCCP church and that is where I have found friendships and opportunities to serve, through choir and home group leadership. We have a couple of good pastors and wonderful ministries for women and men. For 2 years I longed to have another motorcycle, so I began a careful study of what I would like to buy. I couldn't bring myself to have a typical small scooter with CVT. I wanted to shift and have some power. But mostly I wanted a very low seat height. In May of 2023 I purchased a 2018 Honda CMX500 (Rebel 500), a 471cc manual shift motorcycle. So, now I spent my time at home enjoying computer games, practicing the piano, studying and composing music, and riding my motorcycle.

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We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Fifteen years ago, Quiroga et al.1 discovered that the human brain possesses multimodal neurons. These neurons respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the “Halle Berry” neuron, a neuron featured in both Scientific American⁠(opens in a new window) and The New York Times⁠(opens in a new window), that responds to photographs, sketches, and the text “Halle Berry” (but not other names). Two months ago, OpenAI announced CLIP⁠, a general-purpose vision system that matches the performance of a ResNet-50,2 but outperforms existing vision systems on some of the most challenging datasets. Each of these challenge datasets, ObjectNet, ImageNet Rendition, and ImageNet Sketch, stress tests the model’s robustness to not recognizing not just simple distortions or changes in lighting or pose, but also to complete abstraction and reconstruction—sketches, cartoons, and even statues of the objects. Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP. One such neuron, for example, is a “Spider-Man” neuron (bearing a remarkable resemblance to the “Halle Berry” neuron) that responds to an image of a spider, an image of the text “spider,” and the comic book character “Spider-Man” either in costume or illustrated. Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction. We discover that the highest layers of CLIP organize images as a loose semantic collection of ideas, providing a simple explanation for both the model’s versatility and the representation’s compactness.