Built my very first ML model for news classification using data I’ve manually collected and collated over the last year or so. Is 93% accuracy good enough? I should probably test it against existing datasets.
Overheard: A group of older ladies at brunch, discussing how ChatGPT is this magical thing which can take any prompt and write it like any poet or author they know. Some were amazed and some unmoved at the pace of technology.
Loved listening to how generative AI is a different thing for different people.
The boy looked on as raindrops kept falling on the windshield in an uneven fashion. The car was fast and the terrain was whooshing past without refrain. The raindrops were now forming teams and racing down to the bottom, to their ultimate destiny, the race deciding on their fates. The bright light from the sky glinted in the boy’s eyes and he leaned forward to track the race better. The windshield was now nearly full and the two drops that had started it all were gaining speed and weight. The boy was now rooting for one of the drops and hating the other with all his heart. The father, who was driving, noted loudly, “hmmm, can’t see anything!” and simply hit the wiper switch once, to clear the screen. Suddenly, the race was gone, the raindrops were gone and their destinies were gone. The boy stared on, not sure what to do now.
In a few minutes, two new raindrops were racing and the boy was rooting hard for one of them. Indeed, the other one was evil. The drops were now thin, because the rain had increased and as often as the drops picked up new passengers, they were hit by falling drops and split into smaller bits. The father again said, “Bah! Can’t see anything in this rain!” and turned the wiper on. The race was again gone and so was the glint in the boy’s eyes.
The wife and kids were out. He had the entire place to himself and for the longest time, he just sat on the sofa and relaxed for a much deserved break from work. Soon, he noticed the dust on the carpet. He brought out the vacuum and worked his way from the living room all the way to the bedrooms. Then he noticed that one of the bulbs was out and he went ahead and replaced it. After that, he realized that the trash needed to be thrown and the laundry done, so he did all that. Finally, feeling tired, he sat down on the couch and blinked. By the time he woke up, the family was pulling into the drive-way and he realized he’d done all those things only in his dreams.
The Airhostess was bored. This was her third back to back flight from one country to another and she had no idea how she would be getting home. Then she was told she’d be flying Economy from this country to her home country, where she’d have to change two flights to get home. She grew angry and threw the tray down. So much for getting home comfortably!
Of late, I’ve noticed that Gawker is too much of a tongue in cheek blog. Most of their headlines are scathing, almost as if they’re doing so to get more hits on the site. First they criticized App.net and called it snobbish and a waste of money and now they published a headline about Obama on Reddit that sounded like they got paid by the anti-Obama camp to do the headline. I don’t understand why they’re doing it and if it’s succeeding, but this gold-digging behavior on Gawker’s part does not bode well for the website. No one wants to keep listening to the lone rants of an angry man (both articles are by Adrian Chen) and we as netizens would much rather look at brighter sides of the stories than concentrate on the first bad thing that comes to mind.