Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Porous Hollow TiO2 Microparticles for Photocatalysis: Exploiting Novel ABC Triblock Terpolymer Templates Synthesised in Supercritical CO2

Reversible addition–fragmentation chain transfer (RAFT) mediated dispersion polymerisation in supercritical carbon dioxide (scCO2) is an efficient and green method for synthesising block copolymer microparticles with internal nanostructures. Here we report for the first time the synthesis of phase separated poly(methyl methacrylate-block-styrene-block-4-vinylpyridine) (PMMA-b-PS-b-P4VP) triblock terpolymer microparticles using a simple two-pot sequential synthesis procedure in scCO2, with high monomer conversions and no purification steps. The microparticles, produced directly and without further processing, show a complex internal nanostructure, appearing as a “lamellar with spheres” [L + S(II)] type morphology. The P4VP block is then exploited as a structure-directing agent for the fabrication of TiO2 microparticles. Through a simple and scalable sol–gel and calcination process we produce hollow TiO2 microparticles with a mesoporous outer shell. When directly compared to porous TiO2 particles fabricated using an equivalent PMMA-b-P4VP diblock copolymer, increased surface area and enhanced photocatalytic efficiencies are observed

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42828/1/10726_2005_Article_BF01384251.pd

ART1FICIAL INTELLIGENCE 85 Inferring DNA Structures from Segmentation Data

The analysis of DNA structure from restriction enzyme segmentation data has been viewed by molecular geneticists as one of their simpler analysis problems. This paper treats segmentation problems as a case study in the selection between data-driven and model-driven hypothesisformation. The main purpose of this paper is to set forth some useful considerations for selecting a problem solving approach according to the characteristics of a domain. The case study illustrates why an exhaustive model-driven approach, which operates primarily by ruling out all the wrong answers, is a good approach for this domain. A program called GAl solves segmentation problems using technique