The rapid proliferation of artificial intelligence, spearheaded by versatile large language models like ChatGPT, has sparked interest in AI capabilities tailored to specific use cases and data. This has led to exponential growth in specialized AI models that outperform generalist AI in targeted domains and applications. In this comprehensive guide, we‘ll do a deep dive into the what, why and how of specialized AI in 2024 and beyond.
Defining Specialized AI
Specialized AI refers to systems designed for narrowly defined tasks, leveraging custom training data and architectures. They contrast with artificial general intelligence aimed at human-level versatility. There are three primary types of specialized AI:
Vertical AI: Systems specialized for a particular industry or field like healthcare, finance, law. For example, an AI assistant optimized for medical language and trained on anonymized patient data.
Horizontal AI: Systems focused on specific business functions like customer service, HR, accounting. For instance, an AI that automates invoice processing based on historical transaction data.
Common AI: Systems that provide reusable capabilities like search, analytics, automation across domains. Such as AI copilots that generate workflows from natural language prompts.
Specialized AI allows granular customization to individual company data and use cases versus one-size-fits-all general AI.
Real-World Applications of Specialized AI
Specialized AI is driving transformation across diverse verticals and horizontals:
Healthcare
Startup HippocraticAI developed an AI assistant called Claude aimed at clinical documentation. It‘s optimized for medical language and outperforms general LMs in benchmarks designed with doctors. Claude can summarize patient visits into clinical notes and track key medical parameters. [1]
Finance
Bloomberg recently launched BloombergGPT, a 50 billion parameter model trained specifically on financial data including earnings reports, press releases, filings. Early benchmarks show it surpasses general LMs significantly in finance tasks like analyzing equity reports and market news generation. [4]
Manufacturing
AI-powered digital twins like Seebo allow manufacturing engineers to train behavioral models on their production line data. This enables defect prediction, yield optimization and automated root cause analysis tailored to each factory. [10]
Customer Support
AI support agents like Moveworks and Kore.ai analyze historical customer conversations to provide personalized resolutions. This reduces repetitive tasks for human agents. [11]
Automation
Boomi introduced a generative AI copilot that lets users automate workflows by describing requirements in natural language. This amplifies productivity on top of traditional low-code tools. [2]
The 4 Key Benefits of Specialized AI
Specialized AI delivers significant advantages over general artificial intelligence:
1. Higher Performance
Specialized models can accurately capture the nuances of vertical domains by training on relevant data. For example, Claude excels in medical language which is highly specific. Real-world tests show specialized AIs exceeding generalist models in domain-specific tasks. [1]
2. Improved User Experience
AI assistants like Claude can provide conversations and interfaces optimized for a particular industry versus generic experiences. This enhances user engagement.
3. Lower Compute Requirements
Specialized models require less data, smaller model sizes and hence reduced computing power for inference versus general AI. This allows enterprises to run models on internal infrastructure.
4. Enhanced Data Security and Control
Specialized AI gives enterprises greater control over sensitive training data and IP. Industry-specific models like BloombergGPT allow financial firms to avoid reliance on general AI models trained by external vendors on unknown data.
Performance Benchmarks: General vs Specialized AI
Studies have benchmarked specialized AI models against general language models:
- HippocraticAI‘s Claude outperformed GPT-3 by 13% in medical summarization tasks. [1]
- Anthropic‘s ConstitutionalAI exceeded GPT-3 by 22% in bar exam style legal questions. [7]
- BloombergGPT was 3x more accurate in equity analyst tasks. [4]
These benchmarks validate that specialized AI tuned to vertical data can surpass general AI‘s versatility.
Adoption Trends and Growth Projections
61% of organizations already use some form of AI, per a McKinsey survey. [8] Adoption is expected to surge over the next few years across verticals:
- Healthcare AI market estimated to grow from $8.1B in 2024 to $195B by 2030. [9]
- 81% of financial institutions plan to adopt AI solutions like BloombergGPT by 2024. [3]
- 67% of customers prefer chatbots with industry-specific knowledge. [11]
Customized vertical and horizontal AI solutions will become mainstream rather than nice-to-have innovations.
How Should Businesses Identify Relevant Specialized AI?
Enterprises should evaluate specialized AI solutions similar to vertical SaaS offerings:
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Leverage analyst landscapes: For instance, AIMultiple‘s AP AI landscape covers AI automation specific to accounts payable workflows. [5]
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Evaluate industry-focused vendors: Search for startups targeting your vertical as they build customized solutions with vertical data.
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Prioritize custom solutions: Choose vendors open to training AI models on your data even if starting with pre-built models.
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Conduct trials: Test specialized AI in pilot projects before scaling to ensure fit.
Key Trends Shaping the Future of Specialized AI
Based on my decade of experience in data analytics, here are some key trends that will shape specialized AI over the next 5 years:
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Consolidation within verticals: As offerings mature, convergence will happen, for instance healthcare AI vendors combining clinical documentation, predictive analytics, R&D applications etc. This will reduce tool sprawl.
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Persistence of fragmentation: Specialized AI innovation will remain fragmented across vendors given the nascency of techniques. New startups with emerging approaches will continue disrupting verticals.
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Rising adoption of few-shot learning: Reduced reliance on big training data will allow customized AI with limited data. For instance, AI assistants learning workflows from just a few examples. [6]
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Growing focus on transparency and ethics: As business usage grows, specialized AI will require audits for bias, explainability, and confidentiality especially in regulated verticals like finance and medicine.
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Shift from general pre-training: There will be less dependence on general models like GPT-3 for transfer learning. Vertical AI vendors will prefer training models from scratch with in-domain data.
The Way Forward for Businesses
Specialized AI innovation is still in early stages. But rapid advancements are bringing these technologies into the mainstream. Businesses that embrace specialized AI matching their vertical data and workflows will have a competitive edge. But they must evaluate solutions diligently, invest in customization and plan for transparency. With the right adoption strategy, enterprises can tap into the true potential of AI.
References
[1] Hippocratic AI Benchmarks[2] Boomi Generative AI Announcement
[3] Accenture Banking AI Adoption Survey
[4] BloombergGPT Press Release
[5] AIMultiple Accounts Payable Automation Landscape
[6] Few-Shot Learning Paper – Antreas Antoniou, Amos Storkey
[7] ConstitutionalAI Benchmarks
[8] McKinsey Global AI Survey
[9] GrandView Research Healthcare AI Market Report
[10] Seebo Product Capabilities
[11] Moveworks Customers and Kore.ai Industry Solutions