We went through a pandemic, sold our club, sacked Tuchel, sacked Potter, sacked Lampard again, Bruno managed 1 game, spent £1 billion, sold everyone that won the UCL, sacked Pochettino , became a mid table club and still won 2 trophies before Arsenal’s project.
Actually I was reading the book "A Poison Like No Other: How Microplastics Corrupted Our Planet and Our Bodies" just last week.
I didn't realize the extent to which plastics have come to permeate and mess with our entire environment. It's not just about the polymer granules of the plastic, which is problematic by itself when during their breakdown they get small enough to make their way everywhere, including inside our organs, brains, etc.
It's about the ~thousands of exotic chemicals that get mixed into the plastics to tune them: plasticizers (to make them more flexible/durable), stabilizers (to help them resist heat, light), flame retardants, colorants, fillers, antioxidants, UV stabilizers, antistatic agents, lubricants, biocides, etc etc. These chemicals leach from the plastics over time (by default, but especially when you e.g. when you microwave your food). The vast majority of these chemicals have never been evaluated for safety.
There's many other fun facts in the book. We already knew "recycling" of plastic is basically fiction. It also turns out that e.g. when you see "biodegradable" on your plastic, that doesn't mean in normal natural conditions - they only degrade via specific processing plants that are equipped to degrade them.
Toxic, indestructible, synthetic molecules are mixing through the organic environments and the food chain and quite likely poisoning the environment and us.
It definitely feels like we've allowed the convenience of plastics to get way ahead of our understanding of their global effects and that there are some major unpriced externalities in the industry.
John McAfee:
- worked as a programmer on the Apollo missions
- killed his dad
- started McAfee software
- forbes top 100
- presidential candidate
- wanted in 3 countries
- survived 50+ assassination attempts
- married a prostitute who had been hired to kill him
- behind Edward Snowden
- bitcoin billionaire
- hacked Hillary Clintons office by sending her staff free computers
- killed a few people in Belize
- lived on a boat in the Caribbean with armed guards
- has never paid income tax because he blackmailed the US government
- escaped captivity in Guatemala by faking a heart attack
- suicided in a Spanish prison
- may still be alive?
You:
- needs 300mg caffeine to write scripts in python
When someone visits your profile and steals your content, everything - cover, bio, followings, and so forth - except your profile picture, what do you call them?
Authenticity Is All You Need 🚀
# On the "hallucination problem"
I always struggle a bit with I'm asked about the "hallucination problem" in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.
We direct their dreams with prompts. The prompts start the dream, and based on the LLM's hazy recollection of its training documents, most of the time the result goes someplace useful.
It's only when the dreams go into deemed factually incorrect territory that we label it a "hallucination". It looks like a bug, but it's just the LLM doing what it always does.
At the other end of the extreme consider a search engine. It takes the prompt and just returns one of the most similar "training documents" it has in its database, verbatim. You could say that this search engine has a "creativity problem" - it will never respond with something new. An LLM is 100% dreaming and has the hallucination problem. A search engine is 0% dreaming and has the creativity problem.
All that said, I realize that what people *actually* mean is they don't want an LLM Assistant (a product like ChatGPT etc.) to hallucinate. An LLM Assistant is a lot more complex system than just the LLM itself, even if one is at the heart of it. There are many ways to mitigate hallcuinations in these systems - using Retrieval Augmented Generation (RAG) to more strongly anchor the dreams in real data through in-context learning is maybe the most common one. Disagreements between multiple samples, reflection, verification chains. Decoding uncertainty from activations. Tool use. All an active and very interesting areas of research.
TLDR I know I'm being super pedantic but the LLM has no "hallucination problem". Hallucination is not a bug, it is LLM's greatest feature. The LLM Assistant has a hallucination problem, and we should fix it.
</rant> Okay I feel much better now :)
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