Search Results for author:
Found 12 papers, 1 papers with code
A Gentle Introduction to Deep Nets and Opportunities for the Future
The first half of this tutorial will make deep nets more accessible to a broader audience, following “Deep Nets for Poets” and “A Gentle Introduction to Fine-Tuning.” We will also introduce GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models).
Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc
Even long arguments produced this way can be both trustworthy and interpretable, since the full step-by-step line of reasoning is always available, and for each step the provenance of the knowledge used can be documented and audited.
A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Human Language?
Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction.
Testing AI performance on less frequent aspects of language reveals insensitivity to underlying meaning
With successes in bottom-up challenges partially overshadowing shortcomings, the 'human-like' performance of Large Language Models has raised the question of how linguistic performance is achieved by algorithms.
DALL-E 2 Fails to Reliably Capture Common Syntactic Processes
Machine intelligence is increasingly being linked to claims about sentience, language processing, and an ability to comprehend and transform natural language into a range of stimuli.
A very preliminary analysis of DALL-E 2
The DALL-E 2 system generates original synthetic images corresponding to an input text as caption.
The Defeat of the Winograd Schema Challenge
The Winograd Schema Challenge - a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge - was proposed by Hector Levesque in 2011.
A Review of Winograd Schema Challenge Datasets and Approaches
The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test.
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute.
Innateness, AlphaZero, and Artificial Intelligence
The concept of innateness is rarely discussed in the context of artificial intelligence.
Deep Learning: A Critical Appraisal
Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet.
The Scope and Limits of Simulation in Cognitive Models
It has been proposed that human physical reasoning consists largely of running "physics engines in the head" in which the future trajectory of the physical system under consideration is computed precisely using accurate scientific theories.
