@gql

The University of Cambridge is hiring an Assistant/Associate Professor in AI and Machine Learning to lead fundamental research that rethinks current approaches and develops next-generation models, from post-transformer architectures to probabilistic, causal and robust learning.

https://www.cam.ac.uk/jobs/university-assistantassociate-professor-in-artificial-intelligence-and-machine-learning-nr49860

An Error Taxonomy for Property Graph Data by Valerie Restat, Dominique Hausler, Désirée Bruns, and Uta Störl

dbt Charts introduces an open-source, declarative approach that treats dashboards as code—living alongside dbt models so they can be version-controlled, reviewed in pull requests, and validated just like data transformations. By combining #SQL and YAML, humans and AI agents can collaboratively build, modify, and ship trustworthy visualizations that stay in sync when underlying models change. This gives AI the governed context it needs to produce reliable answers while extending analytics engineering practices all the way through the BI layer.

Sample chart definition using #SQL and YAML:

queries:
  revenue_by_month:
    #sql: |
      SELECT date_trunc('month', ordered_at) AS month,
             sum(product_price) AS revenue
      FROM MY_DATABASE.MY_SCHEMA.order_items
      GROUP BY 1
      ORDER BY 1

charts:
  revenue_trend:
    type: line
    query: revenue_by_month
    x: month
    y: revenue
    title: "Revenue by month"

LLM-generated TypeQL queries reach higher final accuracy than #SQL or Cypher once retries are permitted, because TypeDB’s strong type system converts most mistakes into clear, visible errors the model can fix, whereas #SQL failures frequently return silent wrong answers that go undetected. On the Reactome pathway database, TypeQL accuracy climbed from 75 % to 92 % with a modest retry budget while MySQL moved only from 82 % to 83 %, leaving TypeQL the most reliable choice for agentic systems that rely on error feedback.

https://typedb.com/blog/why-llm-written-typeql-can-end-up-more-accurate-than-sql-once-you-allow-retries

Despite fears that AI would automate entry-level coding and trigger mass joblessness, data for summer 2026 shows recent college graduates overall weren't significantly worse off finding work than in recent summers, even when including those who want a job but aren't actively searching.

At the same time, hiring has slowed in highly AI-exposed occupations like programming, where computer science graduates are taking longer to land first roles and more often starting in lower-paying industries, reflecting not just AI but also trade policy, rates, and broader uncertainty, and pushing engineering work toward directing, evaluating, and improving AI output with stronger emphasis on domain knowledge, creativity, communication and leadership i.e. Ontology Economy

https://funginstitute.berkeley.edu/news/computer-science-after-ai/