Unlikely AI Unveils Neuro-Symbolic Approach

Unlikely AI Unveils Neuro-Symbolic Approach

Léa Bergeron
2 min read

Unlikely AI Unveils "Neuro-Symbolic" Approach to AI

Unlikely AI, a UK-based startup, has introduced its groundbreaking "neuro-symbolic" method for artificial intelligence, with a focus on addressing common issues such as bias, hallucination, and accuracy. Founded in 2019 by William Tunstall-Pedoe, the mind behind the Evi voice assistant purchased by Amazon, resulting in the birth of Alexa, Unlikely AI has secured a hefty $20 million in seed funding. The company has recently reinforced its team with two vital appointments: Tom Mason, the former CTO of Stability AI, and Fred Becker, now the chief administrative officer.

Unlikely AI's platform integrates deep learning capabilities with traditional software methodologies to augment dependability and mitigate environmental impact. Tunstall-Pedoe underscores the significance of crafting a "reliable" AI that can seamlessly integrate various applications while upholding precision and transparency.

Key Takeaways

  • Unlikely AI pioneers a "neuro-symbolic" approach, amalgamating neural networks and symbolic AI.
  • The startup endeavors to tackle AI weak spots including bias, hallucination, and accuracy concerns.
  • Noteworthy appointments of former Stability AI CTO Tom Mason and Fred Becker as chief administrative officer at Unlikely AI.
  • The company seeks to curtail the environmental impact and expenses linked with extensive AI models.
  • Unlikely AI, anchored in London and Cambridge, eyes significant AI implementation and influence.


Unlikely AI's neuro-symbolic approach has the potential to redefine AI reliability, impacting industry giants and startups alike. The fusion of deep learning and traditional methodologies aims to alleviate bias and enhance accuracy, potentially reshaping market dynamics. Key recruitments from Stability AI and Skype underscore strategic expansion, impressing investor confidence and operational efficiency. In the near term, competitors in the industry may embrace analogous strategies, while in the long run, wider adoption could reshape AI benchmarks, influencing global tech policies and sustainability practices.

Did You Know?

  • Neuro-Symbolic AI:
    • Melds neural networks (deep learning) with symbolic AI (rule-based systems).
    • Aims to harness the strengths of both methodologies: employing neural networks for pattern recognition and data-learning, and symbolic AI for logical reasoning and interpretability.
    • Addresses prevalent AI issues such as bias and hallucination by merging structured reasoning with unstructured data processing.
  • Foundational Models:
    • Denotes large-scale AI models trained on extensive datasets, capable of executing a broad spectrum of tasks without specific retraining.
    • Examples encompass GPT-3 and BERT, pre-trained on extensive text corpora and adaptable for diverse applications.
    • Unlikely AI is contemplating whether to develop its proprietary foundational model or adopt a mixed strategy, balancing the advantages of a unified model with the flexibility of open-source alternatives.
  • Environmental Impact of AI:
    • Large AI models, particularly those based on deep learning, necessitate substantial computational resources and energy, contributing to carbon emissions.
    • Unlikely AI's emphasis on mitigating environmental impact involves optimizing their models for efficiency, possibly through more streamlined architectures or hybrid methods that require less power-intensive training and deployment.
    • This consideration echoes a growing consciousness in the tech sector about the sustainability of AI development and deployment.

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