Mirror, mirror on the wall, which is the best programming language (for AI/ML) of them all?
Previously, I delved into the maturity of software engineering within the AI domain.?
Today, I'll shed light on my findings regarding the maturity of programming languages tailored for AI/ML.
1 ) Python: The Gold Standard for AI/ML
Python is unequivocally the frontrunner for AI/ML development.
Versatility: From data engineering with numpy and pandas to machine learning via scikit-learn and xgboost, its expansive libraries, such as pytorch and tensorflow, cover the gamut.
Performance: Earlier critiques pointed to Python's speed. However, Python 3.12 has made strides here. For those chasing more speed, there's CPython.
Adoption Barriers: While I, an old-school advocate, have reservations about loosely-typed languages, Python's ease and vast community support are compelling.
If AI/ML is new territory for you, or if coding isn't your forte, Python is your best bet.
2 ) C++: The Powerhouse
C++ remains unparalleled for high-performance systems.
Niche Strengths: Whether it's real-time machine vision or algorithmic trading simulations, C++ excels.
Cost-effective: C++ negates the need for pricy GPUs, essential for many Python applications.
Challenges: C++ demands a steep learning curve. Mastery over memory allocation and I/O is vital. Plus, errors can be catastrophic.
Library Limitations: Open-source libraries aiding swift AI/ML development are sparse.
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Opt for C++ when high-performance, real-time systems are the goal, and you have a proficient C++ team ready.
3 ) RUST: The Emerging Challenger
Rust is AI/ML's rising star. Its open-source APIs are gaining traction but remain niche.
Developers love Rust for its minimalism, memory efficiency, and user-friendly programming appeal.
I foresee Rust growing in prominence, potentially rivaling C++ in the coming years.?
If you love C++ for high-performance, you cannot go wrong with being proficient in RUST.
but.the.best.MayNotBeEnough();
Prolog, Lisp, Octave, R, SAS, Matlab. Each has its niche, and they cater to specific AI/ML use cases. They remain valuable assets, chosen based on specific requirements.
A Peek Inside @Upsquare:?
Our AI Lab (we call it URL, DM to know what it is)? is engrossed in projects involving ASR, AI agents, and RAG. While Python is our staple, we lean on C++ for quantizing LLMs that bypass GPUs for better scalability.
Takeaways for Techies:
→ Choosing a language is like selecting a tool – it's all about the right fit for the task at hand.
#aTechishView
? AWS Ambassador???? AI/ML/GenAI, Cloud, digital transformation and engineering leader ? Building resilient enterprises for turbulent times ? Serial learner/unlearner ? O- blood donor ? Views on social media are my own
11 个月Nice one Amish. While engineering teams usually have an upper hand in selecting the technology stack, for companies that really (want to) excel on their AL/ML journey, the CEOs come with solid technical background on top of a sharp business mind. Some direction on which language/s to use for AI/ML might come from the top down in these cases. I have also written an article for such CEOs on how to beat the AI-powered competition here: https://www.dhirubhai.net/pulse/7-steps-savvy-ceos-should-take-beat-ai-powered-banavalikar-?utm_source=share&utm_medium=member_ios&utm_campaign=share_via