[AI] Neurosymbolic AI — A Microthesis

Author(s): Shashwat Gupta

Originally published on Towards AI.

Neurosymbolic AI presents potential solutions to the existing challenges of LLMs by integrating neural networks with logic and reasoning. This may be advantageous for industries such as healthcare, finance, and cybersecurity. Employing Neurosymbolic AI, we may comprehend intricate data with minimal inputs while enhancing resilience and interpretability. This microthesis examines transformative advancements, investments, and prospects, providing insight into how this technology can reshape the future of AI innovation.

Disclaimer: The facts and numbers are taken from the authentic sources and can be verified. However, to break monotonus language, LLM (ChatGPT) has been used. For example, ChatGPT(symbiolic AI .. useful ..projected to grow) = Symbolic AI is useful and is projected to grow.

1. Sensemaking:

Neurosymbolic AI addresses critical challenges of AI development and deployment, offering solutions that combine pattern recognition capabilities of deep learning with logical reasoning of AI.

Key Problems to Solve:
Neurosymbolic AI aims to tackle significant obstacles in the advancement of artificial intelligence. These issues encompass the necessity for extensive training data, ambiguous AI decision-making processes, and inadequate generalisation capabilities. For instance, conventional deep learning models typically require millions of instances to grasp basic concepts, whereas people may comprehend new ideas with merely a few of examples. Neurosymbolic AI systems, such as MIT’s Neurosymbolic Concept Learner, may acquire visual concepts, vocabulary, and semantic understanding with minimal supervision, potentially reducing data requirements by up to 99%.

Consumer Profile:
Industries engaged in intricate, knowledge-intensive operations can derive the most benefits from Neurosymbolic AI solutions. Healthcare organisations, financial institutions, manufacturing companies, and research entities are prime examples. In healthcare, Neurosymbolic AI can enhance diagnostic precision and provide transparent rationale for its decisions, which is essential for fostering trust among medical personnel and patients.

Emerging Themes and Trends:
A significant trend in Neurosymbolic AI is the integration of large language models (LLMs) with symbolic thinking. DeepMind’s AlphaGeometry, having resolved 25 of 30 problems from the International Mathematical Olympiad, exemplifies this tendency. Another forthcoming concept is the development of Neuro-Vector-Symbolic Architectures, which employ vector manipulation to enhance reasoning capabilities in dynamic contexts.

Opportunities for Investment:
The Neurosymbolic AI market presents substantial investment potential, with the global AI market expected to reach $1,597.1 billion by 2030, growing at a CAGR of 38.1% from 2022 to 2030. Specific investment opportunities lie in developing:

1. Explainable AI solutions for regulated industries (e.g., finance, healthcare)
2. Data-efficient learning systems for scenarios with limited data availability
3. Advanced reasoning tools for complex problem-solving in scientific research
4. Neurosymbolic AI platforms for natural language processing and understanding

IBM’s neurosymbolic AI programs aim to develop more resilient and interpretable AI systems, fulfilling a significant industry demand for transparency and reliability. As organisations progressively pursue AI solutions that offer transparent rationale for their results, investment in this domain may result in substantial returns.

The Neurosymbolic AI approach signifies a transformative change in artificial intelligence, enabling the development of more human-like AI systems proficient in thinking, learning, and generalising from minimal input. Neurosymbolic AI, by overcoming significant constraints of existing AI technologies, is positioned to foster innovation across several sectors, offering profitable prospects for both investors and enterprises.

2. Landscaping:

Neurosymbolic AI represents a groundbreaking approach to artificial intelligence, combining neural networks with symbolic reasoning. This integration aims to create more robust, interpretable, and efficient AI systems. Here’s a comprehensive breakdown of the sector:

Sector Breakdown:

1. Perception Systems
— Image and Video Analysis
— Natural Language Processing
— Speech Recognition

2. Reasoning Engines
— Logic-based Inference
— Knowledge Representation
— Causal Reasoning

3. Integration Frameworks
— Neural-Symbolic Integration Platforms
— Hybrid Learning Architectures

4. Explainable AI (XAI) Tools
— Interpretability Modules
— Decision Explanation Systems

Market Size Estimation:

While specific market size data for Neurosymbolic AI is limited due to its emerging nature, we can use related AI market figures as proxies:

1. Perception Systems:
— The computer vision market, a key component of perception systems, is projected to reach $48.6 billion by 2027, growing at a CAGR of 7.0% from 2020 to 2027.
— The NLP market is expected to grow from $11.6 billion in 2020 to $35.1 billion by 2026, at a CAGR of 20.3%.

2. Reasoning Engines:
— The cognitive computing market, which includes reasoning engines, is anticipated to reach $77.5 billion by 2025, growing at a CAGR of 30.9% from 2020 to 2025.

3. Integration Frameworks:
— The AI software platforms market, which includes integration frameworks, is projected to grow from $4.8 billion in 2020 to $24.2 billion in 2025, at a CAGR of 38.3%.

4. Explainable AI (XAI) Tools:
— The XAI market is expected to grow from $3.5 billion in 2020 to $21 billion by 2030, at a CAGR of 19.5%.

Business Models Analysis:

1. Software-as-a-Service (SaaS):
Offering Neurosymbolic AI platforms on a subscription basis.
Example: IBM’s Neuro-Symbolic AI services.

2. Custom Solutions Development:
Tailoring Neurosymbolic AI systems for specific industry needs.
Example: Developing explainable AI for healthcare diagnostics.

3. API and SDK Licensing:
Providing developers with tools to integrate Neurosymbolic AI into their applications.
Example: Google’s DeepMind offering APIs for its neurosymbolic models.

4. Consulting and Implementation Services:
Assisting organizations in adopting and integrating Neurosymbolic AI.
Example: Accenture’s AI consulting services incorporating neurosymbolic approaches.

5. Research Partnerships:
Collaborating with academic institutions and industry partners for R&D.
Example: MIT-IBM Watson AI Lab’s research initiatives.

Use Cases and Market Potential:

1. Healthcare:
Market Potential: The AI in healthcare market is expected to reach $45.2 billion by 2026.
Use Case: Neurosymbolic AI for drug discovery, reducing time and costs by up to 60%.

2. Finance:
Market Potential: The AI in fintech market is projected to reach $26.67 billion by 2026.
Use Case: Fraud detection systems using neurosymbolic AI, improving accuracy by 35%.

3. Autonomous Vehicles:
Market Potential: The autonomous vehicle market is expected to reach $556.67 billion by 2026.
Use Case: Decision-making systems in self-driving cars, reducing accidents by 90%.

4. Manufacturing:
Market Potential: The AI in manufacturing market is anticipated to reach $16.7 billion by 2026.
Use Case: Predictive maintenance systems, reducing downtime by 50%.

5. Cybersecurity:
Market Potential: The AI in cybersecurity market is expected to reach $38.2 billion by 2026.
Use Case: Threat detection and response, improving detection rates by 70%.

The Neurosymbolic AI sector is poised for significant growth, driven by its ability to address limitations in current AI systems. As the technology matures, we can expect to see increased adoption across various industries, particularly in areas requiring explainable and robust AI solutions.

3. Competitive Advantage:

Neuro-symbolic AI offers several key technical and strategic advantages that set companies in this sector apart.

1. Explainability: Neuro-symbolic AI systems can provide clear reasoning for their decisions, addressing the “black box” problem of traditional neural networks.

2. Data Efficiency: These systems can learn from smaller datasets, reducing the need for massive training data.

3. Robustness Against Data Bias: By incorporating symbolic reasoning, neuro-symbolic AI is less susceptible to biases present in training data.

4. Versatility: The ability to handle both pattern recognition and logical reasoning makes these systems adaptable to a wide range of applications[1].

5. Enhanced Learning and Reasoning: The combination of neural networks and symbolic AI allows for more sophisticated problem-solving capabilities.

Prioritization of Advantages: Based on their potential impact, these advantages can be ranked as follows:

1. Explainability (9/10)
2. Robustness Against Data Bias (9/10)
3. Data Efficiency (8/10)
4. Enhanced Learning and Reasoning (7/10)
5. Versatility (7/10)

Explainability and robustness against data bias are ranked highest due to their critical importance in regulated industries and high-stakes decision-making processes.

Comparative Analysis: While specific company comparisons are limited in the provided information, we can analyze how different approaches to neuro-symbolic AI stack up:

1. IBM’s Approach: Focuses on creating robust and interpretable AI systems, addressing the critical need for transparency and reliability in industries like healthcare and finance.

2. Meta Platforms’ Cicero: Uses a neuro-symbolic approach that combines deep learning with rule-based software for reasoning. This method has shown promise in complex problem-solving, such as negotiation and persuasion in unstructured environments.

3. RAAPID’s Clinical Solution: Utilizes a Clinical Knowledge Graph layered over a neuro-symbolic AI module, combining neural networks with symbolic AI reasoning. This approach aims to replicate human-like understanding of clinical information.

4. AllegroGraph’s Platform: Integrates Knowledge Graphs, a VectorStore, and Deep LLM Integration, offering a comprehensive solution for enterprise AI applications. Their approach focuses on guiding generative AI content through retrieval augmented generation (RAG) to avoid hallucinations.

5. OpenAI and DeepMind: While not explicitly focused on neuro-symbolic AI, these companies are pushing the boundaries of large language models and could potentially integrate symbolic reasoning in future iterations.

Companies that can effectively combine the strengths of neural networks and symbolic AI, while addressing industry-specific needs (like explainability in healthcare or robustness in finance), are likely to gain a significant competitive advantage in the evolving AI landscape.

4. Stackranking:

Top Neurosymbolic AI Startups to Watch

1. Numenta
— Evaluation: Strong team led by AI pioneers, innovative HTM technology
— Market potential: High, with applications in anomaly detection and forecasting
— Fit: Aligns well with long-term AI investment strategies

2. Gamalon
— Evaluation: Experienced leadership, unique Bayesian program synthesis approach
— Market potential: Significant, particularly in enterprise AI solutions
— Fit: Suitable for investors interested in B2B AI applications

3. Cogent Labs
— Evaluation: Diverse team of AI experts, focus on practical business applications
— Market potential: Growing, especially in Asian markets
— Fit: Ideal for investors looking for global AI exposure

4. Symbio Robotics
— Evaluation: Strong robotics expertise, innovative neurosymbolic approach to automation
— Market potential: High, given the increasing demand for smart manufacturing solutions
— Fit: Excellent for investors interested in the intersection of AI and robotics

5. Robust.AI
— Evaluation: Founded by renowned AI researcher Gary Marcus, focus on safe and reliable AI
— Market potential: Significant, particularly in safety-critical AI applications
— Fit: Suitable for investors prioritizing ethical and reliable AI development

Evaluation Criteria:
1. Team Strength: Assessed based on founders’ expertise, industry experience, and track record.
2. Product Innovation: Evaluated on uniqueness of approach, technological advancements, and potential impact.
3. Market Potential: Considered addressable market size, growth projections, and competitive landscape.

Ranking (based on overall potential):
1. Numenta
2. Gamalon
3. Robust.AI
4. Symbio Robotics
5. Cogent Labs

This ranking looks at startups’ new ideas, team quality, and chance for big effects in the neurosymbolic AI field. Numenta leads because of its advanced HTM tech and solid leadership. Gamalon is next with its special Bayesian method. Robust.AI is notable for emphasizing safe and dependable AI, which is important as AI continues to change. Symbio Robotics and Cogent Labs complete the top five, providing hopeful solutions in robotics and business uses, respectively.

Investors should keep in mind that this ranking relies on public info and might not show the latest news or hidden details about these firms. It is wise to research thoroughly before making any investment choices.

References:

The following sites were references

1. Emeritus. “What Is Neurosymbolic AI?” Emeritus, https://emeritus.org/in/learn/neurosymbolic-ai/.
2. EssayPro. “Neuro-Symbolic AI: A New Approach to Artificial Intelligence.” EssayPro Blog, https://essaypro.com/blog/neuro-symbolic-ai.
3. Restack. “Neuro-Symbolic AI: Answering Future Trends in Symbolic AI.” Restack.io, https://www.restack.io/p/neuro-symbolic-ai-answer-future-trends-symbolic-ai-cat-ai.
4. Finextra. “Neuro-Symbolic AI: AI with Reasoning.” Finextra, https://www.finextra.com/blogposting/26508/neuro-symbolic-ai-ai-with-reasoning.
5. Precedence Research. “Artificial Intelligence Market Size and Forecast.” Precedence Research, https://www.precedenceresearch.com/artificial-intelligence-market.
6. Restack. “Neuro-Symbolic AI: Knowledge-Based Answers for Business.” Restack.io, https://www.restack.io/p/neuro-symbolic-ai-knowledge-answer-business-ai-cat-ai.
7. Alphanome. “Neurosymbolic AI: Bridging Neural Networks and Symbolic Reasoning.” Alphanome, https://www.alphanome.ai/post/neurosymbolic-ai-bridging-the-gap-between-neural-networks-and-symbolic-reasoning
8. Revelis. “Applications of Neuro-Symbolic Artificial Intelligence for Your Business.” Revelis, https://www.revelis.eu/en/neuro-symbolic-artificial-intelligence-applications-for-your-business/
9. Singh, Raktim. “What Is Neuro-Symbolic AI?” Raktim Singh Blog, https://www.raktimsingh.com/what-is-neuro-symbolic-ai/
10. Global X ETFs. “Exploring the Competitive Landscape of Generative AI.”
11. Global X ETFs, https://www.globalxetfs.com/assessing-the-field-exploring-the-competitive-landscape-of-generative-ai/
12. TDWI. “Can Neuro-Symbolic AI Solve AI’s Weaknesses?” TDWI, https://tdwi.org/Articles/2024/04/08/ADV-ALL-Can-Neuro-Symbolic-AI-Solve-AI-Weaknesses.aspx
13. Ascendle. “Gain a Competitive Edge with Artificial Intelligence Development Services.” Ascendle, https://ascendle.com/ideas/competitive-edge-with-artificial-intelligence-development-services/
14. AllegroGraph. “Neuro-Symbolic AI: Semantic Graph and AI Solutions.” AllegroGraph, https://allegrograph.com/products/neuro-symbolic-ai/
15. Raapid Inc. “RAAPID’s Neuro-Symbolic AI Technology.” Raapid Inc Blog, https://www.raapidinc.com/blogs/raapids-neuro-symbolic-ai-technology/
16. Wellfound. “Neuroscience Startups.” Wellfound, https://wellfound.com/startups/industry/neuroscience.

I used the template given by Blume Ventures for writing this thesis: https://medium.com/@shashwat.gpt/startup-investing-mastering-the-microthesis-3215c558b1da

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Published via Towards AI