Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6954883683765944320
#AI #Transformers #Language #Models: It’s Often Wrong: “They’re very good at writing text with the proficiency of human beings… Something they’re not very good at is being factual. It looks very coherent. It’s almost true. But it’s often wrong.” https://lnkd.in/gN8NbsEy :
#AI Doesn’t Have #CommonSense, It Can’t Make #Sense, It Doesn’t Know Anything About #Meaning: https://lnkd.in/eetCEAz :
How to Design #Agile #Reliable #Resilient #AI #Quantum:
#SocioTechnical: https://lnkd.in/et2wpKe
#Telecom #Networks-#Systems #Engineering R&D: https://lnkd.in/euQwpgw :
The tech industry’s latest #artificial #intelligence #constructs can be pretty convincing if you ask them what it feels like to be a #sentient #computer, or maybe just a dinosaur or squirrel. But they’re not so good — and sometimes #dangerously #bad — at handling other seemingly straightforward tasks.
Take, for instance, #GPT3, a system that can generate paragraphs of human-like #text based on what it’s learned from a vast #database of digital books and online writings. It’s considered one of the most advanced of a new generation of #AI #algorithms that can converse, generate readable text on demand and even produce novel images and video.
Among other things, GPT-3 can write up most any text you ask for — a cover letter for a zookeeping job, say, or a Shakespearean-style sonnet set on Mars. But when Pomona College professor Gary Smith asked it a simple but #nonsensical #question about walking upstairs [ i.e., something that the program’s data did not contain a #Prior #Pattern on in the #Training #Database ], GPT-3 muffed it.
“Yes, it is safe to walk upstairs on your hands if you wash them first,” the AI replied.
These powerful and power-chugging #AI #systems, technically known as “large language models” because they’ve been trained on a huge body of text and other media, are already getting baked into customer service chatbots, Google searches and “auto-complete” email features that finish your sentences for you. But most of the #tech #companies that built them have been #secretive about their #inner #workings, making it hard for outsiders to understand the #flaws that can make them a source of #misinformation, #racism and other #harms.
#WhyModelsFail: Princeton University: ModelRiskArbitrage.com
#Competitive #pressure to build the most eloquent or informative system — and #profit from its #applications — is one of the reasons that most tech companies keep a tight lid on them and don’t collaborate on community norms, said Percy Liang, an associate computer science professor at Stanford University.
“We’ve seen announcement after announcement after announcement of people doing this kind of work, but with #very #little #transparency, very little ability for people to really look under the hood and peek into how these #models work.”
AWS Partner-NYS Cloud VC-PE: Silicon Valley-Wall Street-Pentagon: Global Risk Management Network LLC
