Making too many enemies : Hutto and Myin's attack on computationalism

Peer reviewe

Tekoäly tieteenalojen dialogissa

Tekoälyn tai laajemmin älykkäiden teknologioiden tutkimus on aina perustunut tieteenalojen dialogille. Tekoälytutkimus syntyi kesällä 1956, kun joukko tutkijoita vetäytyi Darthmouthin yliopiston kesäseminaariin pohtimaan, kuinka ajattelua voitaisiin mallintaa laskennallisesti. Tutkijoiden joukossa oli mm. matemaatikkoja, informaatioteoreetikkoja, tietojenkäsittelytieteilijöitä sekä käyttäytymis- ja kielitieteilijöitä

On Explaining Cognitive Phenomena : The Limits of Mechanistic Explanation

One task of cognitive science is to explain the information processing capacities of cognitive systems, and to provide a scientific account of how cognitive systems produce the adaptive and systematic intelligent behavior that they do. However, there are several disputes and controversies among cognitive scientists about almost every aspect of the question of how to fulfill this task. Some of these disputes originate from the fundamental issue of how to explain cognitive phenomena. In recent years, a growing number of philosophers have proposed that explanations of cognitive phenomena could be seen as instances of mechanistic explanation. In this dissertation, my aim is to examine to what extent the mechanistic account of explanation can be applied to explanations of complex cognitive phenomena, such as conceptual change. The dissertation is composed of five related research articles, which explore different aspects of mechanistic explanations. The first two articles explore the question, whether explanations of cognitive phenomena are mechanistic in the standard sense. The third and fourth articles focus on two widely shared assumptions concerning the mechanistic account of explanatory models: that (i) explanatory models represent, describe, correspond to or are similar to mechanisms in the world and that (ii) in order to be explanatory a model must represent the relevant causal or constitutive organization of a mechanism in the world. Finally, in the fifth article a sketch of a mechanistic explanation of conceptual change is outlined. The main conclusions of this dissertation can be summarized as four distinct, but related claims: (i) I argue that the standard mechanistic account of explanation can be applied to such cognitive explanations which track dependencies at the performance level. Those explanations refer to mechanisms which sustain or perform cognitive activity. However, (ii) if mechanistic explanations are extended to cover so-called computational or competence level explanations as well, a more liberal interpretation of the term mechanism may be needed (Rusanen and Lappi 2007; Lappi abd Rusanen 2011). Moreover (iii) it is also argued that computational or competence level explanations are genuinely explanatory, and that they are more than mere descriptions of computational tasks. Rather than describing the causal basis of certain performances of the target system, or how that system can have certain capacities or competences, they explain why and how certain principles govern the possible behavior or processes of the target system. Finally, (iv) I propose that the information semantic account of representational character of scientific models can offer a naturalist account of how models depict can depict their targets, and offer also an objective account of how explanatory models can depict the relevant properties of their target systems (Rusanen and Lappi 2012; Rusanen under review).Väitöskirjassa tarkastellaan sitä, missä määrin mekanistinen selitysmalli soveltuu monimutkaisten kognitiivisten ilmiöiden, kuten esimerkiksi käsitteellisen muutoksen, selittämiseen. Väitöskirja koostuu viidestä artikkelista, joissa tarkastellaan mekanistista selittämisnäkemystä eri näkökulmista. Väitöskirjan kahdessa ensimmäisessä artikkelissa tarkastellaan kysymystä siitä, missä määrin kognitiivisten ilmiöiden selitykset ovat mekanistisia. Kolmannessa ja neljännessä artikkelissa painopiste on mekanistiseen selitysnäkemykseen usein liitetyssä lisäolettamuksessa. Sen mukaan selitykset annetaan kuvaamalla selitettävän ilmiön kannalta oleelliset tekijät ja rakenteet riittävällä tarkkuudella. Väitöskirjan viidennessä artikkelissa sovelletaan mekanistista selitysmallia käsitteellisen muutoksen selittämiseen. Väitöskirjan keskeiset johtopäätökset voidaan tiivistää kolmeen väitteeseen. Näistä ensimmäinen käsittelee mekanistisen selitysmallin soveltuvuutta kognitiivisten ilmiöiden selittämiseen. Toinen väite tarkastelee sitä, kuinka selittävien mekanismimallien kannalta keskeinen käsite, relevanssi, tulisi ymmärtää ja kolmas selittävien mallien ja maailman välistä suhdetta. Yksityiskohtaisemmin, väitöskirjassa esitetään, että: (1) ns. perinteinen mekanistinen selitysnäkemys soveltuu lähinnä sellaisiin kognitiivisiin selityksiin, jotka jäljittävät rakenteellisia tai kausaalisia riippuvuuksia ns. performanssitasolla. Jos mekanistista selitysnäkemystä sovelletaan myös ns. kompetenssitason selityksiin, edellyttää se mekanismin käsitteen muokkaamista. (2) Selittävien mekanismimallien tulisi kuvata selitettävien ilmiöiden kannalta oleelliset eli relevantit tekijät riittävällä tarkkuudella. Selitysten tapauksessa jonkin tekijän relevanssi eli oleellisuus määräytyy pääasiallisesti maailmassa vallitsevien riippuvuussuhteiden, ei mallin laatijoiden, rakentajien tai käyttäjien intentioiden perusteella. (3) Selittävä malli voi aidosti esittää eli representoida kohdejärjestelmäänsä, jos ja vain jos malli kantaa informaatiota kohdejärjestelmästä tai sen osista

Pikseleitä, kohinaa ja haurautta

Peer reviewe

Action control, forward models and expected rewards : representations in reinforcement learning

Publisher Copyright: © 2021, The Author(s).The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way to explicate the so-called action-oriented views of cognitive systems in representational terms.Peer reviewe

Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game : A Thought Experiment on AI Ethics

In this chapter, we reflect on the deployment of AI as a pedagogical and educational instrument. When AI enters into classrooms, it becomes as a project with diverse members who have differing stakes, and it produces various socio-cognitive-technological questions that must be discussed. Furthermore, AI is developing fast and renders obsolete old paradigms for, e.g. data access, privacy, and transparency. AI may bring many positive consequences in schools — not only for individuals, or teachers, but for the educational system as a whole. On the other hand, there are also serious risks. Thus, the analysis of the educational uses of AI in future schools pushes us to compare the possible benefits (for example, using AI-based tools for supporting different learners) with the possible risks (for example, the danger of algorithmic manipulation, or a danger of hidden algorithmic discrimination). Practical solutions are many, for example the Solid protocol by Tim Berners-Lee, but are often conceived as solutions to single problems, with limited application. We describe a thought experiment: "education as a massively multiplayer social online game". Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player: which is a role designed to maximise protection and benefit for human players. AI models that understand the game space provide an API for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with the game space. Our thought experiment clarifies the steep challenges, and also the opportunity, of AI in education.Peer reviewe

Representaatiot ja vahvistusoppimisalgoritmit

Peer reviewe

Dominant Distal Myopathy 3 (MPD3) Caused by a Deletion in the HNRNPA1 Gene

Background and Objectives To determine the genetic cause of the disease in the previously reported family with adult-onset autosomal dominant distal myopathy (myopathy, distal, 3; MPD3). Methods Continued clinical evaluation including muscle MRI and muscle pathology. A linkage analysis with single nucleotide polymorphism arrays and genome sequencing were used to identify the genetic defect, which was verified by Sanger sequencing. RNA sequencing was used to investigate the transcriptional effects of the identified genetic defect. Results Small hand muscles (intrinsic, thenar, and hypothenar) were first involved with spread to the lower legs and later proximal muscles. Dystrophic changes with rimmed vacuoles and cytoplasmic inclusions were observed in muscle biopsies at advanced stage. A single nucleotide polymorphism array confirmed the previous microsatellite-based linkage to 8p22-q11 and 12q13-q22. Genome sequencing of three affected family members combined with structural variant calling revealed a small heterozygous deletion of 160 base pairs spanning the second last exon 10 of the heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) gene, which is in the linked region on chromosome 12. Segregation of the mutation with the disease was confirmed by Sanger sequencing. RNA sequencing showed that the mutant allele produces a shorter mutant mRNA transcript compared with the wild-type allele Immunofluorescence studies on muscle biopsies revealed small p62 and larger TDP-43 inclusions. Discussion A small exon 10 deletion in the gene HNRNPA1 was identified as the cause of MPD3 in this family. The new HNRNPA1-related phenotype, upper limb presenting distal myopathy, was thus confirmed, and the family displays the complexities of gene identification.Peer reviewe

Forest genetic resources strategy for Europe

202