What’s Next in AI is Fluid Intelligence

What’s Next in AI is Fluid Intelligence

Today's AI is narrow. Applying trained models to new challenges requires an immense amount of new data training, and time.

We need AI that combines different forms
of knowledge, unpacks causal relationships, and learns new things on its own.

In short, AI must have fluid intelligence—
and that's exactly what AI research teams are building.

Read our full vision and strategy

Today's AI is narrow. Applying trained models to new challenges requires an immense amount of new data training, and time.

We need AI that combines different forms
of knowledge, unpacks causal relationships, and learns new things on its own.

In short, AI must have fluid intelligence—
and that's exactly what AI research teams are building.

Read our full vision and strategy

Workstreams

Neurosymbolic AI

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

AI Hardware

Our digital and analog accelerators are driving massive improvements in computational power while remaining energy-efficient.

Secure, Trusted AI

Trust and security should be baked into
the core of any AI we put out into the world. We're building tools to help you ensure that it is.

AI Engineering

We're building tools to help AI creators reduce the time they spend training, maintaining,
 and updating their models.

See all teams

Publication collections

Aug 2020

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

Jul 2020

Association for Computational Linguistics (ACL)

Feb 2020

Association for the Advancement of Artificial Intelligence (AAAI)

Dec 2019

Conference on Neural Information Processing Systems (NeurIPS)

Aug 2019

International Joint Conference on Artificial Intelligence (IJCAI)

All AI publications

Featured

mit-ibm watson ai lab

Journey inside a new class of Analog AI hardware

At IBM Research we’re developing a new class of Analog AI hardware, purpose built to help innovators realize the promise of the next stages of AI.

Explore the hardware

Recent news

Blog

Using machine learning to solve a dense hydrogen conundrum

IBM Teams with Industry Partners to Bring Energy-Efficient AI Hardware to Hybrid Cloud Environments


21-Oct-2020

intern using quantum experience

Blog

RoboRXN: Automating Chemical Synthesis

RoboRXN: Automating Chemical Synthesis


26-Aug-2020

RoboRXN: Automating Chemical Synthesis

Experience a new era of improved public discourse with AI technology

Particapte in an active debate

18-Oct-2020

Blog

Federated Learning – machine learning where the data is

Federated Learning – machine learning where the data is


26-Aug-2020

Federated Learning – machine learning where the data is

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Experiments

The Open Source python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence.

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Compare VSRL with traditional reinforcement learning to see how they perform under different environmental conditions and with different amounts of training.

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Try CLAI, an open-source framework for AI-powered command line plugins. CLAI helps you navigate the command line more efficiently, removing roadblocks and finding missing dependencies.

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Publications

Date Content Title Journal / Venue
Jun 2020 Paper

Verifiably Safe Exploration for End-to-End Reinforcement Learning

arXiv
May 2020 Paper

Accurate deep neural network inference using computational phase-change memory

Nature Communications
Jan 2020 Paper

Towards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services Sector

IACR

AI research teams

AI Hardware
Algorithmic Acceleration
Auto AI (tools)
Computer Vision
Explainability
Fairness
Knowledge and Reasoning
Machine Learning
Natural Language
Process Automation
Robustness
Speech
Transparency and Accountability
Value Alignment