𝐓𝐫𝐚𝐜𝐢𝐧𝐠 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬, 𝐍𝐨𝐭 𝐓𝐞𝐱𝐭: 𝐖𝐡𝐚𝐭 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝

𝑈𝑛𝑑𝑒𝑟𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 ℎ𝑜𝑤 𝑡ℎ𝑒 “𝑏𝑙𝑎𝑐𝑘 𝑏𝑜𝑥” 𝑤𝑜𝑟𝑘𝑠, 𝑎𝑛𝑑 𝑤ℎ𝑦 𝑖𝑡 𝑚𝑎𝑡𝑡𝑒𝑟𝑠

We often hear people call AI a “black box,”
a way to say 𝑤𝑒 𝑑𝑜𝑛’𝑡 𝑟𝑒𝑎𝑙𝑙𝑦 𝑘𝑛𝑜𝑤 ℎ𝑜𝑤 𝑖𝑡 𝑤𝑜𝑟𝑘𝑠.
Yet that phrase glosses over something deeper, and more human.

𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬)
don’t understand words the way we do.
They break your input into 𝐭𝐨𝐤𝐞𝐧𝐬, turn those tokens into 𝐧𝐮𝐦𝐛𝐞𝐫𝐬, and map them in a vast multi-dimensional space. In that space, your sentence becomes a 𝐬𝐡𝐚𝐩𝐞. The model doesn’t memorize or decode, it recognizes patterns.
It speaks 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲, not English.

𝐒𝐨 𝐰𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐁𝐥𝐚𝐜𝐤 𝐁𝐨𝐱?
It’s the invisible process between 𝐲𝐨𝐮𝐫 𝐢𝐧𝐩𝐮𝐭 and 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥’𝐬 𝐨𝐮𝐭𝐩𝐮𝐭. Not hidden by intent, just hidden by abstraction.

In Anthropic’s latest paper 📝 and demo, Claude writes a poem.

But before it finishes the first line, it’s already decided to rhyme 🐇 rabbit with 🥕 carrot. That’s not magic. That’s 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠, a shape being completed.

The model doesn’t think in steps. It sees the whole and works backward.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬
Different models give different outputs, even to the same prompt.
Because what they build internally varies, based on how they were trained.

🔹 Some think in code, some in conversation
🔹 Some rhyme, some reason
🔹 Some see images, others just words

So when you say,
“𝐼 𝑑𝑜𝑛’𝑡 𝑡𝑟𝑢𝑠𝑡 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙, 𝑖𝑡’𝑠 𝑎 𝑏𝑙𝑎𝑐𝑘 𝑏𝑜𝑥,”
what you’re really saying is, “𝐼 ℎ𝑎𝑣𝑒𝑛’𝑡 𝑦𝑒𝑡 𝑢𝑛𝑑𝑒𝑟𝑠𝑡𝑜𝑜𝑑 𝑡ℎ𝑒 𝑠ℎ𝑎𝑝𝑒 𝑜𝑓 𝑖𝑡𝑠 𝑡ℎ𝑖𝑛𝑘𝑖𝑛𝑔.”

#AI #genai #mindsetchange #blackbox

Forbes Technology Council Gartner Peer Experiences InsightJam.com PEX Network Theia Institute VOCAL Council IgniteGTM

𝗡𝗼𝘁𝗶𝗰𝗲: The views within any of my posts, or newsletters are not those of my employer or the employers of any contributing experts. 𝗟𝗶𝗸𝗲 👍 this? feel free to reshare, repost, and join the conversation!