King John signing the Magna Carta at Runnymede.
Getty ImagesIn law school, grades are often viewed as predictors of future success: A students become law professors, B students become judges and C students become millionaires. But the adage may need updating. With neuro-symbolic AI, the coders and tech savants who master algorithms are poised to rule.
While lawyers will continue to engage in complex negotiations and master the art of persuasion, the advent of neuro-symbolic AI—a type of artificial intelligence that combines symbolic reasoning with deep learning—will redefine legal research and analysis, offering unparalleled precision and depth.
Google DeepMind recently unveiled a new system called AlphaGeometry, an AI system that successfully tackles complex geometry problems using neuro-symbolic AI. The achievement, first reported in January’s Nature, is akin to clinching a gold medal in the mathematical Olympics. AlphaGeometry marks a leap toward machines with human-like reasoning capabilities.
Beating the best? AlphaGeometry achieves human-level performance in the grueling International … [+] Mathematical Olympiad.
Source: Google DeepmindBut what is neuro-symbolic AI and what do we have now?
Neuro-symbolic AI combines two main approaches to artificial intelligence: symbolic AI and neural network-based AI.
GOFAI And Neural Network AI
Good Old-Fashioned AI – GOFAI, also known as symbolic AI — excels in environments with defined rules and objectives. It relies on predetermined rules to process information and make decisions, a method exemplified by IBM’s Deep Blue 1997 chess victory over Garry Kasparov. But GOFAI falters in ambiguous scenarios or those needing contextual insight, common in legal tasks.
On the other hand, we have AI based on neural networks, like OpenAI’s ChatGPT or Google’s Gemini. These models don’t rely on hardcoded rules. Instead, they learn from vast amounts of data, allowing them to handle various tasks involving natural language. They are adaptable and can deal with ambiguity and complex scenarios better than GOFAI.
Neuro-symbolic AI aims to merge the best of both worlds, combining the rule-based reasoning of GOFAI with the adaptability and learning capabilities of neural network-based AI.
Combining symbolic AI and neural networks creates neuro-symbolic AI.
AuthorAlphaGeometry Creates Benchmark For Neuro-Symbolic Reasoning
Conventional text-based AI models mainly focus on processing written words. But AlphaGeometry is different. It understands language and also applies logical reasoning. The system is good at recognizing patterns to make predictions. It also follows strict rules, similar to how the human brain operates.
This is like a seasoned chef who follows a recipe but knows when to improvise based on past cooking experiences. Thanks to this innovative approach, AlphaGeometry can logically reason. This ability has been hard to achieve in many other AI models.
Drawing inspiration from Daniel Kahneman’s Nobel Prize-recognized concept of “thinking, fast and slow,” DeepMind researchers Trieu Trinh and Thang Luong highlight the existence of dual-cognitive systems. “Akin to the idea of thinking, fast and slow, one system provides fast, ‘intuitive’ ideas, and the other, more deliberate, rational decision-making,” said Trinh and Luong.
Mathematical reasoning and learning meet intricate demands, setting crucial benchmarks in the quest to develop artificial general intelligence (AGI) capable of matching or surpassing human intellect.
AlphaGeometry dramatically amplifies the ability to uncover inspiration and employ deduction in its problem-solving, setting a precedent for revolutionizing AI-driven analysis that is closer to AGI. But what does this mean for the legal profession?
How Advances In Geometric Reasoning Will Shape Legal Analysis
The pioneering developments in neuro-symbolic AI, exemplified by AlphaGeometry, serve as a promising blueprint for reshaping legal analysis. Unlike traditional legal AI systems constrained by keyword searches and static-rule applications, neuro-symbolic AI adopts a more nuanced and sophisticated approach. It integrates the robust data processing powers of deep learning with the precise logical structures of symbolic AI, laying the groundwork for devising legal strategies that are both insightful and systematically sound.
Take contract analysis. Neuro-symbolic AI, drawing on techniques honed in geometric reasoning, could dissect complex contract structures, identify potential legal conflicts and suggest optimizations by understanding the underlying logic and interdependencies much like it navigates geometric spaces. For instance, it could suggest optimal contract structures that align with both legal requirements and business objectives, ensuring that every drafted contract is both compliant and strategically sound.
Similarly, legal precedent analysis stands to gain significantly. Unlike current neural network-based AI, which relies heavily on keyword matching, neuro-symbolic AI can delve deeper, grasping the underlying legal principles within case law. This enables the AI to employ a deductive approach, mirroring human legal reasoning, to understand the context and subtleties of legal arguments.
By harnessing this capability, it actively interprets nuances and predicts outcomes from a thorough analysis of precedents. These advancements will raise the standard of legal analysis by providing more sophisticated, context-aware and logically coherent evaluations than previously possible.
LONDON, ENGLAND – AI-DA Robot Makes Speech To The House Of Lords.
Getty ImagesThe Future Of Law Will Be Neuro-Symbolic
The future of law is undeniably intertwined with neuro-symbolic AI, blending human insight with machine precision. As this technology automates the mundane, lawyers must hone uniquely human skills—persuasive speaking and strategic negotiation—that no AI can yet mimic.
Navigating this transformed legal landscape will be imperative for lawyers aiming to complement AI’s capabilities while safeguarding their indispensable human touch. Mastering AI tools may soon become as crucial as interpreting the law itself.
As neuro-symbolic AI reshapes legal analysis, A students might find themselves engineering the future of legal tech, B students adeptly arguing its applications and C students?
Well, they own the AI companies.
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