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.
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.
Featured
MIT-IBM
Watson AI Lab
We're partnering with the sharpest minds at MIT to advance AI research in areas like healthcare, security, and finance.
Recent news
Blog
AI Hardware Composer launches on two-year anniversary of IBM AI Hardware Center
14-April-2021
Blog
12 new Project Debater AI technologies available as cloud APIs
17-March-2021
Blog
IBM researchers check AI bias with counterfactual text
5-Feb-2021
Experiments
A no-code sandbox to experiment with neural network design, and analog devices, and algorithmic optimizers to build high accuracy deep learning models.
The APIs include natural language understanding capabilities that deal with wikification, semantic relatedness between Wikipedia concepts, short text clustering, and common theme extraction for texts.
The Open Source python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence.
Compare VSRL with traditional reinforcement learning to see how they perform under different environmental conditions and with different amounts of training.
Featured publications
| Date | Content | Title | Journal / Venue |
|---|---|---|---|
| March 2021 | Paper |
An autonomous debating system |
Nature |
| Jan 2020 | Paper |
Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks |
Nature Machine Intelligence (2021) |
| Oct 2020 | Paper |
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines |
AAAI (2021) |
| May 2020 | Paper |
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text |
AAAI (2021) |
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 |
