This week, we’re exploring whether LLMs can perform real-world software engineering tasks, how Turing’s latest funding round is accelerating AGI progress, and why new AI benchmarks are essential for measuring real-world AI performance.

What we're thinking

At Turing, we’re testing the limits of AI in software engineering—pushing models beyond coding challenges into real-world development workflows. Our focus this week:

Autonomous issue resolution: Can AI models fix GitHub issues with just a code repository and issue description? SWE-Bench Verified ensures issues are well-defined and test coverage is robust.
Freelance AI engineering: SWE-Lancer evaluates whether AI models can earn $1M by completing real-world software tasks, from debugging to management decisions.
Breaking AI limitations: Even the best model, Claude 3.5 Sonnet, only achieved 21.1% success on coding tasks and 47% on SWE management—showing AI’s current gap in production-ready software development.

Going forward, post-training refinements, multimodal AI inputs, and structured reasoning will be critical to advancing AI toward real-world software engineering autonomy.

What we're doing

Turing Raises $111M to Accelerate the Future of AGI

Turing has secured $111M in Series E funding, valuing the company at $2.2B, to scale AGI infrastructure and real-world AI applications.

What’s next? Expanded R&D, go-to-market strategy, and AI adoption across industries—bridging AI advancements with mission-critical enterprise applications.

Coming Soon: Real-World AI Benchmarks for AGI Progress

Turing is launching a new suite of AI benchmarks to evaluate practical AGI capabilities, covering:

Software Engineering: Coding, debugging, system design.
Data Science: Full data pipeline benchmarks.
Math & Reasoning: Complex problem-solving.
Multimodal AI: Integrating text, images, and audio.
Industry-Specific AI: Tailored benchmarks for finance, retail, and more.

Why now? These benchmarks move beyond academic tests to measure AGI’s real-world impact.

What we're reading

We’re diving into three cutting-edge AI research papers this week:

Introducing SWE-Bench Verified
SWE-Bench Verified refines AI code-fixing benchmarks by filtering out unsolvable or ambiguous GitHub issues. With human-validated tasks, it provides a more reliable measure of AI’s software engineering capabilities.
SWE-Lancer: Can AI Earn $1M in Freelance Software Engineering?
SWE-Lancer evaluates LLMs on 1,400+ real-world freelance coding tasks from Upwork, testing their ability to debug, implement features, and make engineering decisions. The best model, Claude 3.5 Sonnet, only succeeded in 26.2% of tasks, underscoring AI’s current limitations in professional software development.
MLE-Bench: Evaluating AI in Machine Learning Engineering
MLE-Bench tests AI agents on 75 Kaggle competitions, covering tasks like model training, debugging, and dataset preparation. Despite rapid AI progress, the best-performing model, o1-preview with AIDE, only matched Kaggle bronze-level performance in 16.9% of challenges.

Where we’ll be

Turing will be at two major AI conferences in the coming months—join us to discuss the future of AGI:

ICLR 2025 [Singapore | Apr 24 – 28]
A top-tier deep learning conference covering representation learning, AI optimization, and theoretical advancements.
MLSys 2025 [Santa Clara, CA | May 12 – 15]
A major event focused on the intersection of machine learning and systems, discussing efficient AI model training, distributed learning, and AI hardware innovations.

If you’re attending, reach out—we’d love to connect and exchange insights!

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