IBM Research AI Strategy and Vision, ibm research ai strategy, ibm research ai vision

Artificial
Intelligence

What’s Next in AI is fluid intelligence

Our vision

Today's AI is narrow. While many AI models deliver value in specific, well-defined situations, applying those same models to new challenges requires an immense amount of new data and training.

Enterprises need AI that is fluid and adaptable, capable of applying knowledge acquired for one purpose to new domains and challenges. They need AI that can combine different forms of knowledge, unpack causal relationships, and learn new things on its own.

In short, enterprises need AI with fluid intelligence — and that's exactly what we're building.

Strategy

Four workstreams will achieve our vision

To achieve our vision, we'll have to make significant strides in algorithm development while improving our AI engineering tools and hardware. 

Neurosymbolic AI ↓

There is no AI without algorithms. In our pursuit of fluid intelligence, we are continuing to advance our work in deep learning, and are putting significant investment into neurosymbolic AI, which combines the power of neural networks with symbolic methods to help AI reason more effectively.

Secure, Trusted AI ↓

In parallel, we're delivering secure, trusted AI, with work focused on explainability, fairness, and bias reduction.

AI Engineering ↓

AI engineering, tools and capabilities that simplify and automate key tasks like data preparation, training and lifecycle management.

AI Hardware ↓

Finally, we're developing a new class of AI hardware, that is inherently energy- efficient, increasing compute power by orders of magnitude without the demand or increased energy.

Strategic teams

Neurosymbolic AI

We're integrating neural and symbolic techniques to build AI that can perform more complex tasks by understanding and reasoning more like we do.