Liquid AI, an MIT spin-off, has launched its first series of generative AI models called Liquid Foundation Models (LFMs). These models are built on a fundamentally new architecture, based on liquid neural networks (LNNs), that differs from the transformer architecture currently underpinning most generative AI applications.

Instead of transformers, LFMs use "computational units deeply rooted in the theory of dynamical systems, signal processing, and numerical linear algebra". This allows them to be more adaptable and efficient, processing up to 1 million tokens while keeping memory usage to a minimum.

LFMs come in three sizes:

LFM 1.3B: Ideal for highly resource-constrained environments.

LFM 3B: Optimised for edge deployment.

LFM 40B: A Mixture-of-Experts (MoE) model designed for tackling more complex tasks.

These models have already shown superior performance compared to other transformer-based models of comparable size, such as Meta's Llama 3.1-8B and Microsoft's Phi-3.5 3.8B. LFM-1.3B, for example, outperforms Meta's Llama 3.2-1.2B and Microsoft’s Phi-1.5 on several benchmarks, including the Massive Multitask Language Understanding (MMLU) benchmark.

One of the key advantages of LFMs is their memory efficiency. They have a smaller memory footprint compared to transformer architectures, especially for long inputs. LFM-3B requires only 16 GB of memory compared to the 48 GB required by Meta's Llama-3.2-3B.

LFMs are also highly effective in utilizing their context length. They can process longer sequences on the same hardware due to their efficient input compression.

While not open source, users can access LFMs through Liquid's inference playground, Lambda Chat, or Perplexity AI. Liquid AI is also optimising its models for deployment on various hardware, including those from NVIDIA, AMD, Apple, Qualcomm, and Cerebras.

Artificial intelligence (AI) is rapidly changing the video game industry, offering potential benefits and raising serious concerns. While some celebrate its potential to revolutionize game development, others, including many game developers, fear its impact on their livelihoods.

AI’s potential benefits for game developers include:

Reducing development costs and time: AI can automate time-consuming tasks, such as creating 3D environments, populating game worlds with assets, and testing gameplay.

Enhancing game quality: AI can help developers analyze data to improve game performance, identify and fix bugs, and create more realistic graphics and animations.

Personalizing the gaming experience: AI can tailor storylines, adjust difficulty levels, and create dynamic environments based on player preferences.

However, concerns exist about AI's potential negative impact on game developers:

Job displacement: As AI becomes more sophisticated, it could replace human artists, writers, and level designers, particularly those performing routine tasks.

Deskilling and job degradation: Some fear that artists, rather than creating original work, will be relegated to fixing AI-generated content.

Ethical concerns: The use of AI raises questions about copyright, ownership, and potential biases in algorithms.

The future of work in the gaming industry likely involves a hybrid model, with AI tools augmenting human creativity and skill.

The key is to ensure that AI is used to enhance the gaming experience rather than replace human ingenuity. This will require the industry to address ethical concerns, upskill its workforce, and foster collaboration between humans and AI.

The rise of AI in language translation has led to questions about the future of human translators. While some fear AI will render human translators obsolete, the sources suggest a more nuanced picture. AI translation tools offer undeniable advantages in speed, efficiency, and cost-effectiveness, particularly for large-scale and straightforward translations. However, AI still falls short when it comes to cultural nuance, handling ambiguity, and translating specialized or creative content.

The consensus among experts is that AI will augment, rather than replace, human translators. AI tools can assist translators by generating initial drafts, suggesting terminology, and maintaining consistency. This collaboration frees human translators to focus on the more complex aspects of language, ensuring accuracy, cultural appropriateness, and stylistic finesse.

The sources highlight several areas where human expertise remains crucial:

Conveying cultural and emotional nuances: Humans excel at understanding idioms, humor, and culturally specific references, which AI often misinterprets.

Accuracy in specialized fields: Technical, legal, and medical translations demand precise language and domain expertise, areas where AI is still developing.

Creative language use: Translating literature, marketing materials, and poetry requires creativity and stylistic sensitivity, something AI currently lacks.

Ultimately, the future of translation likely involves a symbiotic relationship between AI and human expertise. AI handles the routine tasks, while humans provide the nuanced understanding and creative touch, resulting in more efficient and effective communication across languages.