Unleashing the Power of Fluid Artificial Intelligence

Table of Contents

1. Introduction
2. Today's State of AI
3. The Need for Fluid Intelligence in AI
4. The Journey to Achieve Fluid Intelligence
5. Merging Neural and Symbolic Techniques
6. IBM Research's Investment in Neurosymbolic AI
7. Helping Businesses Rapidly Adopt AI at Scale
8. Scaling AI in the Enterprise
   • Data Skills Operations
   • Automation in Data Discovery and Cleansing
   • Automating Feature Engineering
   • Continuous Monitoring and Improvements of Models
9. Governance in AI
   • Trusted AI vs. Governance
   • IBM Research's Approach to Governance
10. The Challenge of Scaling Compute in AI
    • The Double Every Three and a Half Months Trend
    • Purpose-Built Hardware for AI Workloads
    • IBM AI Hardware Research Center
11. Conclusion
12. The Future of AI and Solving Humanity's Problems

The Journey to Fluid Artificial Intelligence (AI)

In the rapidly evolving field of artificial intelligence (AI), it is essential to look ahead and understand what lies on the horizon. Today's AI is capable of staggering feats of pattern recognition, leveraging vast amounts of data and compute power. However, it is also narrow and inflexible. The next frontier in AI is achieving fluid intelligence – intelligence that is adaptable, robust, and able to learn on the fly. This article explores the journey towards fluid AI, the merging of neural and symbolic techniques, and IBM Research's investment in neurosymbolic AI. Furthermore, it discusses how IBM Research aims to help businesses rapidly adopt AI at scale, the challenges in scaling AI in the enterprise, the importance of governance in AI, and the need for purpose-built hardware to address the growing compute demands in AI. By merging the best of neural and symbolic techniques and addressing the challenges in scaling AI, we can pave the way for more advanced and versatile AI systems that have the potential to address humanity's most pressing problems.

1. Introduction

In this era of AI advancement, it is crucial to look towards the future and understand the trajectory of AI research and development. Today's AI is undoubtedly impressive, with the ability to perform Superhuman feats of pattern recognition. However, it has limitations. The next step in the evolution of AI is achieving fluid intelligence – intelligence that is adaptable, robust, and able to learn on the fly. This article explores the journey towards fluid AI and the merging of neural and symbolic techniques to achieve this goal.

2. Today's State of AI

Before we delve into what's next in AI, it is important to understand the current state of AI. Today's AI systems are powered by massive amounts of data and compute power, enabling them to excel in tasks such as Image Recognition and natural language processing. However, these AI systems lack adaptability and the ability to learn in the absence of training data. To achieve fluid intelligence, AI systems need to be capable of adapting to new tasks and environments on the fly.

3. The Need for Fluid Intelligence in AI

Fluid intelligence is the key to unlocking the next level of AI capabilities. It encompasses three critical characteristics: adaptability, robustness, and the ability to learn on the fly. Adaptable intelligence can leverage past experiences and knowledge from one task and apply them to different domains. Robust intelligence can handle situations that differ from its training environment and adapt to new conditions. Lastly, the ability to learn on the fly enables AI systems to acquire new knowledge and adjust to new situations in real-time.

4. The Journey to Achieve Fluid Intelligence

The path to achieving fluid intelligence in AI requires merging neural and symbolic techniques. Neural techniques excel in pattern recognition and learning from data, while symbolic techniques enable the modeling of the world through symbols and reasoning. By combining the power of neural networks with the interpretability and reasoning abilities of symbolic techniques, we can create AI systems that are both adaptable and capable of abstract reasoning.

5. Merging Neural and Symbolic Techniques

At IBM Research, we recognize the importance of merging neural and symbolic techniques to achieve fluid AI. Our research agenda is centered around neurosymbolic AI – an approach that combines the best of both worlds. By leveraging neural networks for pattern recognition and learning, and symbolic techniques for reasoning and abstraction, we aim to develop AI systems that possess the adaptability and robustness of fluid intelligence.

6. IBM Research's Investment in Neurosymbolic AI

IBM Research is deeply committed to advancing neurosymbolic AI. Our researchers are working on groundbreaking techniques that combine neural networks and symbolic reasoning. These innovations are aimed at bridging the gap between traditional AI based on knowledge representation and emerging neural techniques. By merging these two traditions, we are paving the way to achieving fluid intelligence in AI.

7. Helping Businesses Rapidly Adopt AI at Scale

While the journey to fluid AI is an important research endeavor, we also recognize the need to help businesses and enterprises adopt AI at scale. At IBM Research, we have a strong focus on AI engineering – providing tools and innovations to remove frictions and accelerate the adoption of AI in various industries. Our agenda in AI engineering revolves around two main aspects: scaling AI in the enterprise and addressing the compute scaling challenge.

8. Scaling AI in the Enterprise

Scaling AI in the enterprise involves addressing data skills operations and automating various aspects of the AI lifecycle. Many enterprises face challenges in data preparation, discovery, and cleansing, which can slow down their AI initiatives. IBM Research is leveraging cutting-edge AI techniques, such as neural embeddings and graph networks, to automate data discovery and cleansing, leading to significant improvements in metadata discovery and linking.

Additionally, automating feature engineering is crucial for scaling AI in the enterprise. Data scientists often spend months figuring out the optimal subset of data and transformations for their modeling activities. By using AI techniques, we can automate feature engineering, reducing the time and lines of code required to build models. This automation empowers data scientists to drive improvements in their business key performance indicators (KPIs).

Furthermore, IBM Research is developing AI-assisted tools for continuous monitoring and improvements of models. By monitoring and predicting model performance, identifying potential scenarios for decreased performance, and automating remediation and improvements, we can ensure that AI models stay effective and Relevant over time. These advancements remove frictions and accelerate the adoption of AI in the enterprise.

9. Governance in AI

Governance is a critical aspect of AI adoption in the enterprise. Trusted AI and governance go HAND in hand to ensure ethical and responsible AI deployment. Trusted AI involves developing AI techniques that are fair, explainable, robust, and transparent. IBM Research has a comprehensive agenda in trusted AI, with hundreds of scientific publications and open-source toolkits dedicated to fairness, explainability, and robustness.

On the other hand, governance focuses on operationalizing trusted AI in an enterprise setting. At IBM Research, we are pioneering an approach to governance through the use of fact sheets. These fact sheets automate the collection of critical information about AI models, such as creation dates, test results, and biases, throughout the model's lifecycle. Stakeholders, including data scientists, application developers, risk and compliance officers, and business owners, can access these fact sheets to ensure compliance with enterprise needs for security, risk, and governance.

10. The Challenge of Scaling Compute in AI

As AI models continue to grow in complexity and size, the compute requirements for training and inference increase exponentially. Addressing the challenge of scaling compute in AI is crucial for driving AI adoption at scale. Today's hardware, such as CPUs and GPUs, is no longer sufficient to meet the growing compute demands of AI models.

To address this challenge, IBM Research has established the IBM AI Hardware Research Center. This center focuses on developing purpose-built hardware for AI workloads. The research center's roadmap includes innovations in digital AI cores and analog AI, exploring reduced precision computing and in-memory computing to achieve greater power and performance gains.

11. Conclusion

The journey to fluid AI represents the next frontier in AI research and development. By merging neural and symbolic techniques and investing in AI engineering, we can achieve AI systems that possess adaptability, robustness, and the ability to learn on the fly. IBM Research is at the forefront of advancing neurosymbolic AI and helping businesses rapidly adopt AI at scale. Additionally, addressing the challenge of scaling compute in AI through purpose-built hardware is crucial for driving AI adoption across industries. With these advancements, AI has the potential to solve complex problems and Shape the future of society.

12. The Future of AI and Solving Humanity's Problems

Looking ahead, AI has immense potential to address humanity's most significant challenges, such as future work, Healthcare, climate change, and pandemic management. Leveraging the power of AI, we can find innovative solutions to these problems. At IBM Research, we are dedicated to applying and advancing AI to tackle these complex issues, shaping the future of how humanity addresses and overcomes these challenges.

Highlights:

• Achieving fluid intelligence is the next step in AI evolution.
• Merging neural and symbolic techniques is crucial for achieving fluid AI.
• IBM Research is deeply invested in neurosymbolic AI.
• IBM Research's focus includes scaling AI in the enterprise and addressing compute scaling challenges.
• Ethical AI governance is essential for responsible AI adoption.
• IBM Research is developing purpose-built hardware to address the compute demands in AI.
• AI has the potential to address significant challenges facing humanity.

FAQ:

Q: What is fluid intelligence in AI? A: Fluid intelligence refers to intelligence that is adaptable, robust, and able to learn on the fly. It is the ability of AI systems to adapt to new tasks and environments, handle situations that differ from training conditions, and learn from real-time experiences.

Q: How is IBM Research helping businesses adopt AI at scale? A: IBM Research is focusing on AI engineering to help businesses adopt AI at scale. This involves automating various aspects of the AI lifecycle, such as data discovery, cleansing, and feature engineering. IBM Research also develops AI-assisted tools for continuous monitoring and improvements of models. Additionally, the research aims to address the challenges of scaling compute in AI through purpose-built hardware.

Q: Why is governance important in AI? A: Governance ensures the responsible and ethical deployment of AI in an enterprise setting. It involves managing compliance, risk, and security aspects of AI models. IBM Research is pioneering an approach to AI governance through the use of fact sheets, automating the collection and presentation of critical model information to different stakeholders.

Q: How is IBM Research addressing the challenge of scaling compute in AI? A: IBM Research has established the IBM AI Hardware Research Center to develop purpose-built hardware for AI workloads. The research center explores innovations in digital AI cores and analog AI, focusing on reduced precision computing and in-memory computing to achieve greater power and performance gains.

Resources:

• IBM Research
• IBM AI Hardware Research Center