Guide-yakabatana mibvunzo · Pakati Nhanho

Mashiripiti AI Yakareba-Context Code Models Quiz

Ongorora Mashiripiti eLTM modhi, kudzoreredza-refu-mamiriro, HashHop ongororo, codebase kufunga, uye dambudziko remutengo kutevedzana kwavo algorithm zvinangwa.

Nzira dzekutungamira dzinoenderanaMashiripiti AI Yakareba Context Code Models
Mubvunzo 1 ye 8

What software-development direction has Magic said it is pursuing?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. What software-development direction has Magic said it is pursuing?
    • Developing AI models and agents for software-engineering tasks
    • Replacing all software repositories with image datasets
    • Building a spreadsheet-only OCR service
    • Generating code without considering project context
  2. What maximum context capacity did Magic announce for LTM-2-mini in August 2024?
    • Up to 100 thousand tokens
    • Kusvika miriyoni zana tokens
    • Up to 1 million tokens
    • Up to 10 billion tokens
  3. What does Magic use HashHop to evaluate?
    • How well a model predicts a familiar fact from pretraining
    • Whether generated source code passes every production test
    • Retrieval and chaining over random hash pairs placed in a supplied long context
    • How quickly a model compiles a new programming language
  4. Nei naive transformer kutarisisa iri dambudziko re100-miriyoni-token mamiriro?
    • Hazvidhuri chinhu
    • Inongoshanda pasina Indaneti
    • Inodzima inopinza
    • Inoyera quadratically nehurefu hwekutevedzana, ichiva inodhura zvakanyanya
  5. Ndeipi kambani yakabatana nayo naMashiripiti muna 2024 kuti ivake supercomputers?
    • Google Cloud
    • Apple
    • Spotify
    • Adobe
  6. What does a larger context window make possible for a coding task?
    • Kukurumidza kutaipa pane kiibhodhi
    • Include more cross-file code and documentation in one context, without guaranteeing project-wide correctness
    • Zvirinani screen resolution
    • Mabhiri emagetsi akachipa
  7. What comparison did Magic report for LTM-2-mini’s sequence-dimension algorithm?
    • A 1,000-fold improvement in total cost for every deployed model
    • A 1,000-fold increase in the accuracy of all code edits
    • About 1,000 times lower per-token algorithmic cost than Llama 3.1 405B attention at 100M context in the reported configuration
    • A fixed 1,000-token limit for every LTM model
  8. In a simple illustrative lookup with A → B, B → C, and C → D, what chain follows from A?
    • A → C → B → D
    • A → B → D → C
    • A → D → C → B
    • A → B → C → D