On intelligence: its creation and understanding 💭
Surya Ganguli aptly summarizes the past progress, collaboration, and future directions of AI, neuroscience, and psychology. Challenges that AI + neuroscience may tackle hand-in-hand in the future include:
- Biologically plausible credit assignment;
- Incorporating synaptic complexity (going beyond using a single scalar for neurons);
- Taking cues from systems-level modular brain architecture (continuously evolving but always staying adaptive; many different modules);
- Unsupervised learning, transfer learning and curriculum design;
- Building world models for understanding, planning, and active causal learning.
How to Present a Scientific Poster at a Mega-Conference
🖼 It can be overwhelming to present a poster to a large crowd. In addition, as an attendee, you want to be engaged by the presenter. Charles Sutton gives three great practical tips on how to present a poster to a large audience:
- Be aware of what’s going on around you.
- Use your body language.
- Speak up.
Open-Ended Learning with POET
👾 This blog post accompanies the POET research paper by Uber AI
. The proposed framework generates new and more challenging environments together with agents that are trained to solve them, leading to agents with increasingly complex and novel capabilities. I hope we see more accompanying blog posts full of examples and beautifully written paragraphs, such as the following:
Open-endedness […] at its best […] continue(s) to generate new tasks in a radiating tree of challenges indefinitely.
Evolution is in effect an open-ended process that in a single run created all forms of life on Earth.
Tensor Considered Harmful
⚠️ Harvard’s Alexander Rush argues in this piece (with lots of examples!) that the Tensor class used in many DL libraries is broken as it leads to bad habits such as exposing private dimensions, keeping type information in documentation, etc. It proposes a proof-of-concept of an alternative approach, named tensors, with named dimensions.