Amaurosis. Metanoia. Adit. Scansion. Gavage. Psephology. Sphaleron. Axonotmesis. Galena. Pingo. Girdling. Snag (ecology). Apophenia. Cenote. Neurotmesis. Acerose. Perseverant. Elapid. Aorist. Kana.

Intaglio. Hiragana. Palinal. Cathemerality. Calque. Numinous. Geas. Afforestation. Crumhorn. Senicide. Catenane. Extispicy/haruspex. Cataplerosis. Ophiolite. Diglossia. Hagiographer. Stylometry. Ossifrage. Pleuston/Neuston. Praline.

Saponification. Culet. Myiasis. Epithalamium. Thigmonasty. Stultiloquy. Thigmotropism. Aerospike. Calabash. Pandanus. Dumbwaiter. Doula. Hypocaust. Cynophobia. Flashover. Backdraft. Pyrolysis. Slat. Phugoid. Toxophily.

Irredentism. Crutching. Threnody. Petroglyph. Protologism. Aileron. Bunding. Phylactery. Guyot. Coupure. Barbette. Apophasis. Fissiparous. Marl. Syrinx. Bocage. Camouflet. Mulesing. Trypophobia. Berm.

January 17, 2021 Posted by | Books, Language | Leave a comment

Dyslexia (I)

A few years back I started out on another publication edited by the same author, the Wiley-Blackwell publication The Science of Reading: A Handbook. That book is dense and in the end I decided it wasn’t worth it to finish it – but I also learned from reading it that Snowling, the author of this book, probably knows her stuff. This book only covers a limited range of the literature on reading, but an interesting one.

I have added some quotes and links from the first chapters of the book below.

“Literacy difficulties, when they are not caused by lack of education, are known as dyslexia. Dyslexia can be defined as a problem with learning which primarily affects the development of reading accuracy and fluency and spelling skills. Dyslexia frequently occurs together with other difficulties, such as problems in attention, organization, and motor skills (movement) but these are not in and of themselves indicators of dyslexia. […] at the core of the problem is a difficulty in decoding words for reading and encoding them for spelling. Fluency in these processes is never achieved. […] children with specific reading difficulties show a poor response to reading instruction […] ‘response to intervention’ has been proposed as a better way of identifying likely dyslexic difficulties than measured reading skills. […] To this day, there is tension between the medical model of ‘dyslexia’ and the understanding of ‘specific learning difficulties’ in educational circles. The nub of the problem for the concept of dyslexia is that, unlike measles or chicken pox, it is not a disorder with a clear diagnostic profile. Rather, reading skills are distributed normally in the population […] dyslexia is like high blood pressure, there is no precise cut-off between high blood pressure and ‘normal’ blood pressure, but if high blood pressure remains untreated, the risk of complications is high. Hence, a diagnosis of ‘hypertension’ is warranted […] this book will show that there is remarkable agreement among researchers regarding the risk factors for poor reading and a growing number of evidence-based interventions: dyslexia definitely exists and we can do a great deal to ameliorate its effects”.

“An obvious though not often acknowledged fact is that literacy builds on a foundation of spoken language—indeed, an assumption of all education systems is that, when a child starts school, their spoken language is sufficient to support reading development. […] many children start school with considerable knowledge about books: they know that print runs from left to right (at least if you are reading English) and that you read from the front to the back of the book; and they are familiar with at least some letter names or sounds. At a basic level, reading involves translating printed symbols into pronunciations—a task referred to as decoding, which requires mapping across modalities from vision (written forms) to audition (spoken sounds). Beyond knowing letters, the beginning reader has to discover how printed words relate to spoken words and a major aim of reading instruction is to help the learner to ‘crack’ this code. To decode in English (and other alphabetic languages) requires learning about ‘grapheme–phoneme’ correspondences—literally the way in which letters or letter combinations relate to the speech sounds of spoken words: this is not a trivial task. When children use language naturally, they have only implicit knowledge of the words they use and they do not pay attention to their sounds; but this is precisely what they need to do in order to learn to decode. Indeed, they have to become ‘aware’ that words can be broken down into constituent parts like the syllable […] and that, in turn, syllables can be segmented into phonemes […]. Phonemes are the smallest sounds which differentiate words; for example, ‘pit’ and ‘bit’ differ by a single phoneme [b]-[p] (in fact, both are referred to as ‘stop consonants’ and they differ only by a single phonemic feature, namely the timing of the voicing onset of the consonant). In the English writing system, phonemes are the units which are coded in the grapheme-correspondences that make up the orthographic code.”

“The term ‘phoneme awareness‘ refers to the ability to reflect on and manipulate the speech sounds in words. It is a metalinguistic skill (a skill requiring conscious control of language) which develops after the ability to segment words into syllables and into rhyming parts […]. There has been controversy over whether phoneme awareness is a cause or a consequence of learning to read. […] In general, letters are easier to learn (being concrete) than phoneme awareness is to acquire (being an abstract skill). […] The acquisition of ‘phoneme awareness’ is a critical step in the development of decoding skills. A typical reader who possesses both letter knowledge and phoneme awareness can readily ‘sound out’ letters and blend the sounds together to read words or even meaningless but pronounceable letter strings (nonwords); conversely, they can split up words (segment them) into sounds for spelling. When these building blocks are in place, a child has developed ‘alphabetic competence’ and the task of becoming a reader can begin properly. […[ Another factor which is important in promoting reading fluency is the size of a child’s vocabulary. […] children with poor oral language skills, specifically limited semantic knowledge of words, [have e.g. been shown to have] particular difficulty in reading irregular words. […] Essentially, reading is a ‘big data’ problem—the task of learning involves extracting the statistical relationships between spelling (orthography) and sound (phonology) and using these to develop an algorithm for reading which is continually refined as further words are encountered.”

“It is commonly believed that spelling is simply the reverse of reading. It is not. As a consequence, learning to read does not always bring with it spelling proficiency. One reason is that the correspondences between letters and sounds used for reading (grapheme–phoneme correspondences) are not just the same as the sound-to-letter rules used for writing (phoneme–grapheme correspondences). Indeed, in English, the correspondences used in reading are generally more consistent than those used in spelling […] many of the early spelling errors children make replicate errors observed in speech development […] Children with dyslexia often struggle to spell words phonetically […] The relationship between phoneme awareness and letter knowledge at age 4 and phonological accuracy of spelling attempts at age 5 has been studied longitudinally with the aim of understanding individual differences in children’s spelling skills. As expected, these two components of alphabetic knowledge predicted the phonological accuracy of children’s early spelling. In turn, children’s phonological spelling accuracy along with their reading skill at this early stage predicted their spelling proficiency after three years in school. The findings suggest that the ability to transcode phonologically provides a foundation for the development of orthographic representations for spelling but this alone is not enough—information acquired from reading experience is required to ensure spellings are conventionally correct. […] for spelling as for reading, practice is important.”

“Irrespective of the language, reading involves mapping between the visual symbols of words and their phonological forms. What differs between languages is the nature of the symbols and the phonological units. Indeed, the mappings which need to be created are at different levels of ‘grain size’ in different languages (fine-grained in alphabets which connect letters and sounds like German or Italian, and more coarse-grained in logographic systems like Chinese that map between characters and syllabic units). Languages also differ in the complexity of their morphology and how this maps to the orthography. Among the alphabetic languages, English is the least regular, particularly for spelling; the most regular is Finnish with a completely transparent system of mappings between letters and phonemes […]. The term ‘orthographic depth’ is used to describe the level of regularity which is observed between languages — English is opaque (or deep), followed by Danish and French which also contain many irregularities, while Spanish and Italian rank among the more regular, transparent (or shallow) orthographies. Over the years, there has been much discussion as to whether children learning to read English have a particularly tough task and there is frequent speculation that dyslexia is more prevalent in English than in other languages. There is no evidence that this is the case. But what is clear is that it takes longer to become a fluent reader of English than of a more transparent language […] There are reasons other than orthographic consistency which make languages easier or harder to learn. One of these is the number of symbols in the writing system: the European languages have fewer than 35 while others have as many as 2,500. For readers of languages with extensive symbolic systems like Chinese, which has more than 2,000 characters, learning can be expected to continue through the middle and high school years. The visual-spatial complexity of the symbols may add further to the burden of learning. […] when there are more symbols in a writing system, the learning demands increase. […] Languages also differ importantly in the ways they represent phonology and meaning.”

“Given the many differences between languages and writing systems, there is remarkable similarity between the predictors of individual differences in reading across languages. The ELDEL study showed that for children reading alphabetic languages there are three significant predictors of growth in reading in the early years of schooling. These are letter knowledge, phoneme awareness, and rapid naming (a test in which the names of colours or objects have to be produced as quickly as possible in response to a random array of such items). Researchers have shown that a similar set of skills predict reading in Chinese […] However, there are also additional predictors that are language-specific. […] visual memory and visuo-spatial skills are stronger predictors of learning to read in a visually complex writing system, such as Chinese or Kannada, than they are for English. Moreover, there is emerging evidence of reciprocal relations – that learning to read in a complex orthography hones visuo-spatial abilities just as phoneme awareness improves as English children learn to read.”

“Children differ in the rate at which they learn to read and spell and children with dyslexia are typically the slowest to do so, assuming standard instruction for all. Indeed, it is clear from the outset that they have more difficulty in learning letters (by name or by sound) than their peers. As we have seen, letter knowledge is a crucial component of alphabetic competence and also offers a way into spelling. So for the dyslexic child with poor letter knowledge, learning to read and spell is compromised from the outset. In addition, there is a great deal of evidence that children with dyslexia have problems with phonological aspects of language from an early age and specifically, acquiring phonological awareness. […] The result is usually a significant problem in decoding—in fact, poor decoding is the hallmark of dyslexia, the signature of which is a nonword reading deficit. In the absence of remediation, this decoding difficulty persists and for many reading becomes something to be avoided. […] the most common pattern of reading deficit in dyslexia is an inability to read ‘new’ or unfamiliar words in the face of better developed word-reading skills — sometimes referred to as ‘phonological dyslexia’. […] Spelling poses a significant challenge to children with dyslexia. This seems inevitable, given their problems with phoneme awareness and decoding. The early spelling attempts of children with dyslexia are typically not phonetic in the way that their peers’ attempts are; rather, they are often difficult to decipher and best described as bizarre. […] errors continue to reflect profound difficulties in representing the sounds of words […] most people with dyslexia continue to show poor spelling through development and there is a very high correlation between (poor) spelling in the teenage years and (poor) spelling in middle age. […] While poor decoding can be a barrier to reading comprehension, many children and adults with dyslexia can read with adequate understanding when this is required but it takes them considerable time to do so, and they tend to avoid writing when it is possible to do so.”


History of dyslexia research. Samuel Orton. Rudolf Berlin. Anna Gillingham. Orton-Gillingham(-Stillman) approach. Thomas Richard Miles.
Seidenberg & McClelland’s triangle model.
“The Simple View of Reading”.
The lexical quality hypothesis (Perfetti & Hart). Matthew effect.
ELDEL project.
Diacritical mark.
Phonetic radicals.

September 15, 2019 Posted by | Books, Language, Psychology | Leave a comment


Many of the words below I encountered while reading the books One of our Thursdays is missing, The secret of our success, Bowling alone, Thief of Time, and The Major Works of Samuel Johnson.

Damson. Greengage. Ingle. Marchioness. Tuberose. Flue. Titushky. Cowling. Soteriology. Piazza. Rake-off. Rusk. Babbittry. Aeolipile. SpallationLeister. Weir. Puffin. Omnipercipient. Shiv.

Vociferation. Ebriety. Playbill. Surtout. Outvie. Copiousness. Animadvert. Vendible. Silvicolous. Leveret. Novitiate. Commodious. Appellative. Preterite. Apostasize. Commixture. Sepulture. Desiccative. Siccity. Philology.

Incivism. Prorogation. Metonym. Apologue. Altricial(ity). Palilalia. Macaroon. Compositionality. Alloparental. Pizzle. Cholo. Epizeuxis. Cursorial. Misprision. Terrestriality. Pranny. Epistrophe. Analepsis. Corvid. Zorbing.

Polyptoton. Antanaclasis. Kern. Scrumtrulescent. Cotillion. Confute. Pinner. Declension. Piscatory. Jointure. Vulnerary. Subtilize. Sublunary. Ebullition. Affright. Exorbitance. Impost. Judicature. Fulminate. Cogency.

September 7, 2019 Posted by | Books, Language | Leave a comment


Many of the words below are words I encountered while reading the books Lost in a good book, The Eyre Affair, In Gods We Trust: The Evolutionary Landscape of Religion, and The Complete Saki: 144 Collected Novels and Short Stories.

Ergotropic. Trophotropic. Abreaction. Nomological. Triskaidekaphobia. Casuistry. Nonsequitous. Amontillado. Contrail. Nacelle. Potluck. Sizar. Herpetology. Phenology. Fustigate. Tintinnabula. Phoropter. Vexillology. Quondam. Onomastic.

Glossolalia. Scrupulosity. Proclaim. Pablum. Ochlocracy. Probate. Anacyclosis. Anastylosis. Diphyodonty. Pakicetus. Gymnure. Sojourner. Rescission. Illocution. Sylvatic. Diabolist. Lariat. Carcinization. Champerty. Barratry.

Pannus. Vitiate. Svengali. Brevet. Scud. Vermicelli. Couplet. Offertory. Rognon. Mangold. Dissentient. Heller. Desultory. Crinkle. Whitsuntide. Syce. Variegation. Novelette. Wassail. Kith.

Astrakhan. Satrap. Halva. Precipitancy. Hie. Lambkin. Toque. Wapiti. Spiraea. Pleasaunce. Berberis. Goodly. Estaminet. Lyddite. Acclamation. Burgh. Wharfage. Tamarin. Chaffer. Catafalque.

July 22, 2019 Posted by | Books, Language | Leave a comment

Successes and Challenges in Neural Models for Speech and Language

Some links related to the coverage:
Speech recognition.
Machine translation.
Supervised learning.
Context-free grammar.
Kernel Approximation Methods for Speech Recognition.
Convolutional neural network.
Dependency parsing | NLP-progress.
Natural Language Processing (Almost) from Scratch (Collobert et al.).
A Fast and Accurate Dependency Parser using Neural Networks (Chen and Manning, 2014).
Question answering.
Natural Questions: a Benchmark for Question Answering Research (Kwiatkowski et al.)
Attention Is All You Need (Vaswani et al. 2017).
Softmax function.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al.).

May 5, 2019 Posted by | Computer science, Language, Lectures | Leave a comment


The words below were mainly words I encountered while reading the books Artificial intelligence, a very short introduction,
Cognitive Neuroscience, -ll-, and The Complete Saki: 144 Collected Novels and Short Stories (…the post only contains words from the first half – this book is very long (…and highly recommended)).

Clapotis. Aedile. Proventriculus. Sortition. Fug. Ecumenical. Credal. Obstreperous. Officiant. Oneirology. Unadulterated. Risible. Onomasti komodein. Recusancy. Saltire. Anent. Propaedeutic. Patristic. Plectrum. Voxel.

Cark. Deimatic. Phasmid. Peptonize. Tomtit. Maffick. Hartebeest. Preceptress. Pavonicide. Halma. Quatrain. Epigrammatic. Missal. Chaffinch. Psalmody. Bittern. Vergeress. Snaffle. Quagga. Heretofore.

Jacquerie. Plaguy. Cajolery. Madder. Picquet. Potage. Votive. Dissention. Begird. Medlar. Whirligig. Recessional. Lory. Ditty. Alarum. Skewbald. Burg. Convolvulus. Stotting. Entr’acte.

Counterfoil. Bandicoot. Tercentenary. Schipperke. Jangle. Serry. Snuggery. Benignant. Jonquil. Wyandot. Francolin. Lanner. Aspic. Paddock. Sloe. Malmaison. Umber. Drake. Pullet. Borzoi.

March 23, 2019 Posted by | Books, Language | Leave a comment

Artificial intelligence (I?)

This book was okay, but nothing all that special. In my opinion there’s too much philosophy and similar stuff in there (‘what does intelligence really mean anyway?’), and the coverage isn’t nearly as focused on technological aspects as e.g. Winfield’s (…in my opinion better…) book from the same series on robotics (which I covered here) was; I am certain I’d have liked this book better if it’d provided a similar type of coverage as did Winfield, but it didn’t. However it’s far from terrible and I liked the authors skeptical approach to e.g. singularitarianism. Below I have added some quotes and links, as usual.

“Artificial intelligence (AI) seeks to make computers do the sorts of things that minds can do. Some of these (e.g. reasoning) are normally described as ‘intelligent’. Others (e.g. vision) aren’t. But all involve psychological skills — such as perception, association, prediction, planning, motor control — that enable humans and animals to attain their goals. Intelligence isn’t a single dimension, but a richly structured space of diverse information-processing capacities. Accordingly, AI uses many different techniques, addressing many different tasks. […] although AI needs physical machines (i.e. computers), it’s best thought of as using what computer scientists call virtual machines. A virtual machine isn’t a machine depicted in virtual reality, nor something like a simulated car engine used to train mechanics. Rather, it’s the information-processing system that the programmer has in mind when writing a program, and that people have in mind when using it. […] Virtual machines in general are comprised of patterns of activity (information processing) that exist at various levels. […] the human mind can be understood as the virtual machine – or rather, the set of mutually interacting virtual machines, running in parallel […] – that is implemented in the brain. Progress in AI requires progress in defining interesting/useful virtual machines. […] How the information is processed depends on the virtual machine involved. [There are many different approaches.] […] In brief, all the main types of AI were being thought about, and even implemented, by the late 1960s – and in some cases, much earlier than that. […] Neural networks are helpful for modelling aspects of the brain, and for doing pattern recognition and learning. Classical AI (especially when combined with statistics) can model learning too, and also planning and reasoning. Evolutionary programming throws light on biological evolution and brain development. Cellular automata and dynamical systems can be used to model development in living organisms. Some methodologies are closer to biology than to psychology, and some are closer to non-reflective behaviour than to deliberative thought. To understand the full range of mentality, all of them will be needed […]. Many AI researchers [however] don’t care about how minds work: they seek technological efficiency, not scientific understanding. […] In the 21st century, […] it has become clear that different questions require different types of answers”.

“State-of-the-art AI is a many-splendoured thing. It offers a profusion of virtual machines, doing many different kinds of information processing. There’s no key secret here, no core technique unifying the field: AI practitioners work in highly diverse areas, sharing little in terms of goals and methods. […] A host of AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers weren’t aiming only for specialist systems. They were also hoping for systems with general intelligence. Each human-like capacity they modelled — vision, reasoning, language, learning, and so on — would cover its entire range of challenges. Moreover, these capacities would be integrated when appropriate. Judged by those criteria, progress has been far less impressive. […] General intelligence is still a major challenge, still highly elusive. […] problems can’t always be solved merely by increasing computer power. New problem-solving methods are often needed. Moreover, even if a particular method must succeed in principle, it may need too much time and/or memory to succeed in practice. […] Efficiency is important, too: the fewer the number of computations, the better. In short, problems must be made tractable. There are several basic strategies for doing that. All were pioneered by classical symbolic AI, or GOFAI, and all are still essential today. One is to direct attention to only a part of the search space (the computer’s representation of the problem, within which the solution is assumed to be located). Another is to construct a smaller search space by making simplifying assumptions. A third is to order the search efficiently. Yet another is to construct a different search space, by representing the problem in a new way. These approaches involve heuristics, planning, mathematical simplification, and knowledge representation, respectively. […] Often, the hardest part of AI problem solving is presenting the problem to the system in the first place. […] the information (‘knowledge’) concerned must be presented to the system in a fashion that the machine can understand – in other words, that it can deal with. […] AI’s way of doing this are highly diverse.”

“The rule-baed form of knowledge representation enables programs to be built gradually, as the programmer – or perhaps an AGI system itself – learns more about the domain. A new rule can be added at any time. There’s no need to rewrite the program from scratch. However, there’s a catch. If the new rule isn’t logically consistent with the existing ones, the system won’t always do what it’s supposed to do. It may not even approximate what it’s supposed to do. When dealing with a small set of rules, such logical conflicts are easily avoided, but larger systems are less transparent. […] An alternative form of knowledge representation for concepts is semantic networks […] A semantic network links concepts by semantic relations […] semantic networks aren’t the same thing as neural networks. […] distributed neural networks represent knowledge in a very different way. There, individual concepts are represented not by a single node in a carefully defined associative net, but by the changing patterns of activity across an entire network. Such systems can tolerate conflicting evidence, so aren’t bedevilled by the problems of maintaining logical consistency […] Even a single mind involves distributed cognition, for it integrates many cognitive, motivational, and emotional subsystems […] Clearly, human-level AGI would involve distributed cognition.”

“In short, most human visual achievements surpass today’s AI. Often, AI researchers aren’t clear about what questions to ask. For instance, think about folding a slippery satin dress neatly. No robot can do this (although some can be instructed, step by step, how to fold an oblong terry towel). Or consider putting on a T-shirt: the head must go in first, and not via a sleeve — but why? Such topological problems hardly feature in AI. None of this implies that human-level computer vision is impossible. But achieving it is much more difficult than most people believe. So this is a special case of the fact noted in Chapter 1: that AI has taught us that human minds are hugely richer, and more subtle, than psychologists previously imagined. Indeed, that is the main lesson to be learned from AI. […] Difficult though it is to build a high-performing AI specialist, building an AI generalist is orders of magnitude harder. (Deep learning isn’t the answer: its aficionados admit that ‘new paradigms are needed’ to combine it with complex reasoning — scholarly code for ‘we haven’t got a clue’.) That’s why most AI researchers abandoned that early hope, turning instead to multifarious narrowly defined tasks—often with spectacular success.”

“Some machine learning uses neural networks. But much relies on symbolic AI, supplemented by powerful statistical algorithms. In fact, the statistics really do the work, the GOFAI merely guiding the worker to the workplace. Accordingly, some professionals regard machine learning as computer science and/or statistics —not AI. However, there’s no clear boundary here. Machine learning has three broad types: supervised, unsupervised, and reinforcement learning. […] In supervised learning, the programmer ‘trains’ the system by defining a set of desired outcomes for a range of inputs […], and providing continual feedback about whether it has achieved them. The learning system generates hypotheses about the relevant features. Whenever it classifies incorrectly, it amends its hypothesis accordingly. […] In unsupervised learning, the user provides no desired outcomes or error messages. Learning is driven by the principle that co-occurring features engender expectations that they will co-occur in future. Unsupervised learning can be used to discover knowledge. The programmers needn’t know what patterns/clusters exist in the data: the system finds them for itself […but even though Boden does not mention this fact, caution is most definitely warranted when applying such systems/methods to data ( remains true that “Truth and true models are not statistically identifiable from data” – as usual, the go-to reference here is Burnham & Anderson)]. Finally, reinforcement learning is driven by analogues of reward and punishment: feedback messages telling the system that what it just did was good or bad. Often, reinforcement isn’t simply binary […] Given various theories of probability, there are many different algorithms suitable for distinct types of learning and different data sets.”

“Countless AI applications use natural language processing (NLP). Most focus on the computer’s ‘understanding’ of language that is presented to it, not on its own linguistic production. That’s because NLP generation is even more difficult than NLP acceptance [I had a suspicion this might be the case before reading the book, but I didn’t know – US]. […] It’s now clear that handling fancy syntax isn’t necessary for summarizing, questioning, or translating a natural-language text. Today’s NLP relies more on brawn (computational power) than on brain (grammatical analysis). Mathematics — specifically, statistics — has overtaken logic, and machine learning (including, but not restricted to, deep learning) has displaced syntactic analysis. […] In modern-day NLP, powerful computers do statistical searches of huge collections (‘corpora’) of texts […] to find word patterns both commonplace and unexpected. […] In general […], the focus is on words and phrases, not syntax. […] Machine-matching of languages from different language groups is usually difficult. […] Human judgements of relevance are often […] much too subtle for today’s NLP. Indeed, relevance is a linguistic/conceptual version of the unforgiving ‘frame problem‘ in robotics […]. Many people would argue that it will never be wholly mastered by a non-human system.”

“[M]any AI research groups are now addressing emotion. Most (not quite all) of this research is theoretically shallow. And most is potentially lucrative, being aimed at developing ‘computer companions’. These are AI systems — some screen-based, some ambulatory robots — designed to interact with people in ways that (besides being practically helpful) are affectively comfortable, even satisfying, for the user. Most are aimed at the elderly and/or disabled, including people with incipient dementia. Some are targeted on babies or infants. Others are interactive ‘adult toys’. […] AI systems can already recognize human emotions in various ways. Some are physiological: monitoring the person’s breathing rate and galvanic skin response. Some are verbal: noting the speaker’s speed and intonation, as well as their vocabulary. And some are visual: analysing their facial expressions. At present, all these methods are relatively crude. The user’s emotions are both easily missed and easily misinterpreted. […] [An] point [point], here [in the development and evaluation of AI], is that emotions aren’t merely feelings. They involve functional, as well as phenomenal, consciousness […]. Specifically, they are computational mechanisms that enable us to schedule competing motives – and without which we couldn’t function. […] If we are ever to achieve AGI, emotions such as anxiety will have to be included – and used.”

[The point made in the book is better made in Aureli et al.‘s book, especially the last chapters to which the coverage in the linked post refer. The point is that emotions enable us to make better decisions, or perhaps even to make a decision in the first place; the emotions we feel in specific contexts will tend not to be even remotely random, rather they will tend to a significant extent to be Nature’s (…and Mr. Darwin’s) attempt to tell us how to handle a specific conflict of interest in the ‘best’ manner. You don’t need to do the math, your forebears did it for you, which is why you’re now …angry, worried, anxious, etc. If you had to do the math every time before you made a decision, you’d be in trouble, and emotions provide a great shortcut in many contexts. The potential for such short-cuts seems really important if you want an agent to act intelligently, regardless of whether said agent is ‘artificial’ or not. The book very briefly mentions a few of Minsky’s thoughts on these topics, and people who are curious could probably do worse than read some of his stuff. This book seems like a place to start.]


GOFAI (“Good Old-Fashioned Artificial Intelligence”).
Ada Lovelace. Charles Babbage. Alan Turing. Turing machine. Turing test. Norbert WienerJohn von Neumann. W. Ross Ashby. William Grey Walter. Oliver SelfridgeKenneth Craik. Gregory Bateson. Frank Rosenblatt. Marvin Minsky. Seymour Papert.
A logical calculus of the ideas immanent in nervous activity (McCulloch & Pitts, 1943).
Propositional logic. Logic gate.
Arthur Samuel’s checkers player. Logic Theorist. General Problem Solver. The Homeostat. Pandemonium architecture. Perceptron. Cyc.
Fault-tolerant computer system.
Programmed Data Processor (PDP).
Artificial life.
Forward chaining. Backward chaining.
Rule-based programming. MYCIN. Dendral.
Semantic network.
Non-monotonic logic. Fuzzy logic.
Facial recognition system. Computer vision.
Bayesian statistics.
Helmholtz machine.
DQN algorithm.
AlphaGo. AlphaZero.
Human Problem Solving (Newell & Simon, 1970).
NELL (Never-Ending Language Learning).
Google translate.
Data mining. Sentiment analysis. Siri. Watson (computer).
Paro (robot).
Uncanny valley.
CogAff architecture.
Constraint satisfaction.
Content-addressable memory.
Graceful degradation.
Physical symbol system hypothesis.

January 10, 2019 Posted by | Biology, Books, Computer science, Engineering, Language, Mathematics, Papers, Psychology, Statistics | Leave a comment


Many of the words below are words which I encountered while reading the books Reaper Man, Enter a Murderer, The Case of the Velvet Claws, The Case of the Sulky Girl, The Case of the Curious Bride, and The Thirteen Gun Salute.

Sodality. Triturate. Aboral. Cloture. Abbess. Cortege. Ideograph. Tarn. Tranche. Dexter and sinister. Prolegomenon. Animalier. Scumble. Alembic. Toxophily/toxophilite. Knurl. Sparge. Stook. Susurrous. Calcination.

Pizzicato. Valance. Ineffable. Bunnia. Hitch, Contrabandist. Recalcitrant. Admonish. Codling. Countenance. Fid. Kittiwake. Marline. Colcannon. Soffit. Spirket. Gradus. Bate. Supersession. Furlong.

Palmary. Banian. Boustrophedon. Gridiron. Sinew. Garstrake. Gumma. Hygrometer. Premonitory. Binturong. Proa. Turmeric. Gamelan. Feudatory. Clepsydra. Colophony/rosin. ShipwrightBenight. Gaur. Banteng.

Subjacent. Superjacent. Scull. Isopod. Tierer. Castrametation. Dictograph. Administratrix. Commingle. Negligee. Shyster. Cuspidor. Sanitarium. Repudiate. Res gestae. Corpus delicti. Pothook. Carouse. Withal. Probative.

November 17, 2018 Posted by | Books, Language | Leave a comment


The words included in the post below were mostly words which I encountered while reading the books Personal Relationships, Circadian Rhythms, Quick Service, Principles of memory, Feet of Clay, The Reverse of the Medal, and The Letter of Marque.

Camouflet. Dissimulation. Nomological. Circumlocutory. EclosionPuissant. Esurient. Hisperic. Ambigram. Scotophilic. Millenarianism. Sonder. Pomology. Oogonium. Vole. Tippler. Autonoetic. Engraphy/engram. Armigerous. Gazunder/guzunder.

Frizzle. Matorral. SclerophyllXerophyte. Teratoma. Shallop. Quartan. Ablative. Prolative. Dispart. Ptarmigan. Starbolins. Idolatrous. Spoom. Cablet. Hostler. Chelonian. Omnium. Toper. Rectitude.

Marthambles. Combe. Holt. Stile. Plover. Andiron. Delf. Boreen. Thief-taker. Patten. Subvention. Hummum. Bustard. Lugger. Vainglory. Penetralia. Limicoline. Astragal. Fillebeg/filibeg. Voluptuous.

Civet. Moil. Impostume. Frowsty. Bob. Snuggery. Legation. Brindle. Epergne. Chough. Shoneen. Pilaff. Phaeton. Gentian. Poldavy. Grebe. Orotund. Panoply. Chiliad. Quiddity.

September 27, 2018 Posted by | Books, Language | Leave a comment


The words below are mostly words which I encountered while reading the books Pocket oncology, Djinn Rummy, Open Sesame, and The Far Side of the World.

Hematochezia. Neuromyotonia. Anoproctitis. Travelator. Brassica. Physiatry. Clivus. Curettage. Colposcopy. Trachelectomy. Photopheresis. Myelophthisis. Apheresis. Vexilloid. Gonfalon. Eutectic. Clerisy. Frippery. Scrip. Bludge.

Illude. Empyrean. Bonzer. Vol-au-vent. Curule. Entrechat. Winceyette. Attar. Woodbine. Corolla. Rennet. Gusset. Jacquard. Antipodean. Chaplet. Thrush. Coloratura. Biryani. Caff. Scrummy.

Beatific. Forecourt. Hurtle. Freemartin. Coleoptera. Hemipode. Bespeak. Dickey. Bilbo. Hale. Grampus. Calenture. Reeve. Cribbing. Fleam. Totipalmate. Bonito. Blackstrake/Black strake. Shank. Caiman.

Chancery. Acullico. Thole. Aorist. Westing. Scorbutic. Voyol. Fribble. Terraqueous. Oviparous. Specktioneer. Aprication. Phalarope. Lough. Hoy. Reel. Trachyte. Woulding. Anthropophagy. Risorgimento.


August 2, 2018 Posted by | Books, Language | Leave a comment


The words included in this post are words which I encountered while reading the books: 100 cases in orthopaedics and rheumatology, Managing Gastrointestinal Complications of Diabetes, American Naval History: A very short introduction, Big Data: A very short introduction, Faust among Equals, Pocket Oncology, My Hero, and Odds and Gods.

Angulation. Soleus. Mucoid. Plantarflex. Pronation. Arthrosis. Syndesmosis. Ecchymosis. Diastasis. Epicondyle. Pucker. Enthesopathy. Paresis. Polyostotic. Riff. Livedo. Aphtha/aphthous. Pathergy. Annular. Synovium/synovial.

Scallop. Tastant. Incantatory. Radeau. Gundalow. Scrivener. Pebbledash. Chrominance. Tittle. Capitonym. Scot. Grayling. Terylene. Pied-à-terre. Solenoid. Fen. Anaglypta. Loud-hailer. Fauteuil. Dimpsy.

Seborrhea. Anasarca. Emetogenic. Trachelectomy. Brachytherapy. Nomogram. Trusty. Biff. Pantechnicon. Porpentine. Budgerigar. Nerk. Glade. Slinky. Gelignite. Boater. Seamless. Jabberwocky. Fardel. Kapok.

Aspidistra. Cowpat. Countershaft. Tinny. Ponce. Warp. Weft. Recension. Bandstand. Strimmer. Chasuble. Champer. Bourn. Khazi. Zimmer. Ossuary. Suppliant. Nock. Taramosalata. Quoit.

July 6, 2018 Posted by | Books, Language | Leave a comment


Most of the words included below are words which I encountered while reading the Tom Holt novels Ye Gods!Here Comes The SunGrailblazers, and Flying Dutch, as well as Lewis Wolpert’s Developmental Biology and Parminder & Swales’s text 100 Cases in Orthopaedics and Rheumatology.

Epigraphy. Plangent. Simony. Simpulum. Testoon. Sybarite/sybaritic. Culverin. Niff. Gavotte. Welch. Curtilage. Basilar. Dusack. Galliard. Foolscap. Spinet. Netsuke. Pinny. Shufti. Foumart.

Compere. Triune. Sistrum. Tenon. Buckshee. Jink. Chiropody. Slingback. NarthexNidus. Subluxation. Aponeurosis. Psoas. Articular. Varus. Valgus. Talus. Orthosis/orthotics. Acetabulum. Labrum.

Peculation. Purler. Macédoine. Denticle. Inflorescence. Invagination. Intercalate. Antalgic. Chondral. Banjax. Bodge/peck. Remora. Chicory. Gantry. Aerate. Erk. Recumbent. Pootle. Stylus. Vamplate.

Tappet. Frumenty. Woad. Breviary. Witter. Errantry. Pommy. Lychee. Priory. Bourse. Phylloxera. Dozy. Whitlow. Crampon. Brill. Fiddly. Acrostic. Scrotty. Ricasso. Tetchy.

June 10, 2018 Posted by | Books, Language | Leave a comment


Most of the words below are words which I encountered while reading the books 100 cases in emergency medicine and critical care, Frozen Assets, Money in the Bank, Ice in the bedroom, Treason’s Harbour, Earth, Air, Fire and Custard, and May Contain Traces of Magic.

Talus/talar. Mortise. Empyema. Tragus. Otorrhoea. Lordosis. Chemosis. Eversion. Coryza. Atopy. Ectropion. Fly-tipping. Favism. Quillet. Hyperthymesia. Barratry. Simoom. Corium. Inexpugnable. Sly.

Portentous. Distaff. Dipsomaniac. Peart. Nippy. Frenetic. Azeotrope. Tumbril. Ratty. Exordium. Zareba. Bezel. Gregale. Gaberlunzie. Chelengk. Deboshed. Coriaceous. Battel. Rufous. Skink.

Lascar. Milksop. Polenta. Compline. Zither. Stroppy. Calomel. Spangly. Postern. Unregenerate. Vertiginous. Judder. Perspex. Swizzle. Lambently. Sprog. Flollop. Dodgem. Prurient. Gazump.

Cathexis. Scrounge. Quaerens. Tine. Tape measure. Strimmer. Bardiche. Martel. Demiurge. Copra. Grubby. Stonking. Campanology. Taramasalata. Muliebrity. Slumgullion. Flocculate. Mollycoddle. Bloviate. Kitsch.


May 20, 2018 Posted by | Books, Language | Leave a comment


Most of the words below are words which I encountered while reading the books 100 cases in Surgery, The portable door, Expecting Someone Taller, and The Ionian Mission.

Hypernym/hyponym. Comminution. Scute. Introgression. Polysemous/polysemy. Flashover. Homophily. Opprobrious. Venturous. Remissive. Scuzzy. Funicular. Atelectasis. Valvulae conniventes. Haustrum/haustra. Anticlastic. Manubrium. Serpiginous. Trismus. Villagisation.

Bradawl. Barberry. Coppice. Squelch. Scry. Wodge. Graunch. Vergence. Encashment. Epitome. Crosspatch. Houndstooth. Bumf. Philter/philtre. Commemorative. Rapacious. Bisque. Mordant. Cochineal. Convocation.

Grobian. Cappabar/capperbar. Looby. Levanter. Vane. Circumambient. Shearwater. Scrove. Purcit. Opisthotonus. Slop. Dimity. Pinchbeck. Dactyl. Tramontane. Afflatus. Tamarisk. Pernicious. Coaming. Beylik.

Chrestomathy. Irade. Mastic. Levin. Mangonel. Uncovenanted. Theogony. Cruet. Emboss. Trafficator. Gymkhana. Martingale. Buddleia. Surcingle. Droopy. Nobble. Emery. Stemma. Wadi. Prosopography.


April 22, 2018 Posted by | Books, Language | Leave a comment


Most of the words below are words which I encountered while reading the books The Fortune of War, The Surgeon’s Mate, In Your Dreams, and Who’s Afraid of Beowulf.

Pervenche. Intromit. Subfusc. Inspissated. Supple. Ukase. Commensal. Croft. Scantling. Compendious. Nympholept. Forfantery (an unsual – but very useful – link, for an unusual word). Trunnion. Hominy. Slubberdegullion. Lickerish. Brail. Grapnel. Swingle. Altumal.

Éclaircissement. Costiveness. Vang. Heady. Mort. Cingulum. Swingeing. Avifauna. Carminative. Accoucheur. Peccavi. Grommet. Woolding. Scow. Gibbous. Tierce. Burgoo. Tye. Inclement. Lobscouse.

Irrefragable. Gurnard. Bilaterian. Malmsey. Corbel. Jakes. Bonnet. Doddle. Rock dash. Purlin. Pillock. Graunch. Chirrup. Skive. Pelmet. Feckless. Pedalo. Howe. Tannin. Garnet.

Delate. Derisory. Saveloy. Flan. Quillon. Corvid. Hierophant. Thane. Laconic. Chthonic. Cowrie. Repique. Broch. Cheep. Carborundum. Shieling. Bothy. Meronomy. Petard. Mereology.


April 5, 2018 Posted by | Books, Language | Leave a comment


Almost all the words included in this post are words which I encountered while reading the books The Mauritius Command, Desolation Island and You Don’t Have to Be Evil to Work Here, But it Helps.

Aleatory. Tenesmus. Celerity. Pelisse. Collop. Clem. Aviso. Crapulous. Farinaceous. Parturient. Tormina. Scend. Fascine. Distich. Appetency/appetence. Calipash. Tergiversation. Polypody. Prodigious. Teredo.

Rapacity. Cappabar. Chronometer. Figgy-dowdy. Chamade. Hauteur. Futtock. Obnubilate. Offing. Cleat. Trephine. Promulgate. Hieratic. Cockle. Froward. Aponeurosis. lixiviate. Cupellation. Plaice. Sharper.

Morosity. MephiticGlaucous. Libidinous. Grist. Tilbury. Surplice. Megrim. Cumbrous. Pule. Pintle. Fifer. Roadstead. Quadrumane. Peacoat. Burgher. Cuneate. Tundish. Bung. Fother.

Dégagé. Esculent. Genuflect. Lictor. Drogue. Oakum. Spume. Gudgeon. Firk. Mezzanine. Faff. Manky. Titchy. Sprocket. Conveyancing. Apportionment. Plonker. Flammulated. Cataract. Demersal.

March 15, 2018 Posted by | Books, Language | Leave a comment


The words included in this post are words which I encountered while reading Patrick O’Brian’s books Post Captain and HMS Surprise. As was also the case the last time I posted one of these posts, I had to include ~100 words, instead of the ~80 I have come to consider ‘the standard’ for these posts, in order to include all the words of interest which I encountered in the books.

MésallianceMansuetude. Wen. Raffish. Stave. Gorse. Lurcher. Improvidence/improvident. Sough. Bowse. Mump. Jib. Tipstaff. Squalid. Strum. Hussif. Dowdy. Cognoscent. Footpad. Quire.

Vacillation. Wantonness. Escritoire/scrutoire. Mantua. Shindy. Vinous. Top-hamper. Holystone. Keelson. Bollard/bitts. Wicket. Paling. Brace (sailing). Coxcomb. Foin. Stern chaser. Galliot. Postillion. Coot. Fanfaronade.

Malversation. Arenaceous. Tope. Shebeen. Lithotomy. Quoin/coign. Mange. Curricle. Cockade. Spout. Bistoury. Embrasure. Acushla. Circumambulation. Glabrous. Impressment. Transpierce. Dilatoriness. Conglobate. Murrain.

Anfractuous/anfractuosity. Conversible. Tunny. Weevil. Posset. Sponging-house. Salmagundi. Hugger-mugger. Euphroe. Jobbery. Dun. Privity. Intension. Shaddock. Catharpin. Peccary. Tarpaulin. Frap. Bombinate. Spirketing.

Glacis. Gymnosophist. Fibula. Dreary. Barouche. Syce. Carmine. Lustration. Rood. Timoneer. Crosstrees. Luff. Mangosteeen. Methitic. Superfetation. Pledget. Innominate. Jibboom. Pilau. Ataraxy.

February 27, 2018 Posted by | Books, Language | Leave a comment


The words below are mostly words I encountered while reading Wolfe’s The Claw of the Conciliator and O’Brian’s Master and Commander. I wanted to finish off my ‘coverage’ of those books here, so I decided to include a few more words than usual (the post includes ~100 words, instead of the usual ~80).

Threnody. Noctilucent. Dell. Cariole. Rick. Campanile. Obeisance. Cerbotana. Caloyer. Mitre. Orpiment. Tribade/tribadism (NSFW words?). Thiasus. Argosy. Partridge. Cenotaph. Seneschal. Ossifrage. Faille. Calotte.

Meretrice. Bijou. Espalier. Gramary. Jennet. Algophilia/algophilist. Clerestory. Liquescent. Pawl. Lenitive. Bream. Bannister. Jacinth. Inimical. Grizzled. Trabacalo. Xebec. Suet. Stanchion. Beadle.

Philomath. Gaby. Purser. Tartan. Eparterial. Otiose. Cryptogam. Puncheon. Neume. Cully. Carronade. Becket. Belay. Capstan. Nacreous. Fug. Cosset. Roborative. Comminatory. Strake.

Douceur. Bowsprit. Orlop. Turbot. Luffing. Sempiternal. Tompion. Loblolly (boy). Felucca. Genet. Steeve. Gremial. Epicene. Quaere. Mumchance. Hance. Divertimento. Halliard. Gleet. Rapparee.

Prepotent. Tramontana. Hecatomb. Inveteracy. Davit. Vaticination/vaticinatory. Trundle. Antinomian. Scunner. Shay. Demulcent. Wherry. Cullion. Hemidemisemiquaver. Cathead. Cordage. Kedge. Clew. Semaphore. Tumblehome.

February 21, 2018 Posted by | Books, Language | Leave a comment


The great majority of the words included below are words which I encountered while reading Gene Wolfe’s The Shadow of the torturer. The rest of the words are words which I encountered while reading The Oxford Handbook of Endocrinology and Diabetes as well as various ‘A Short Introduction to…‘-books.

Coloboma. Paresis. Exstrophy. Transhumance. Platybasia. Introitus. Ichthyology. Atresia. Nival. Dormer. Tussock. Mullion. Tholus. Delectation. Carnelian. Camisa. Soubrette. Cacogenic. Anacrisis. Sedge.

Barbican. Gallipot. Stele. Badelaire. Chalcedony. Helve. Armiger. Caracara. Saros. Blazon. Presentment. Refectory. Citrine. Eidolon. Obverse. Glaive. Inutile. Hypostase. Leman. Pursuivant.

Cabochon. Palfrenier. Limpid. Burse. Thurible. Anacreontic. Pardine. Nigrescent. Chrism. Pageantry. Capybara. Tinsel. Rebec. Shewbread. Excruciation. Cataphract. Sateen. Dhow. Rheostat. Caique.

Baldric. Paterissa. Bartizan. Peltast. Dray. Lochage. Miter. Discommode. Lambrequin. Dross. Proscenium. Jelab. Cymar/simar. Vicuna. Monomachy. Champian. Dulcimer. Lamia. Nidorous. Mensal.

January 19, 2018 Posted by | Books, Language | Leave a comment


It’s been a while since I posted one of these posts.

I know for certain that quite a few of the words included below are words which I encountered while reading the Jim Butcher books Ghost Story, Cold Days, and Skin Game, and I also know that some of the ones I added to the post more recently were words I encountered while reading the Oxford Handbook of Endocrinology and Diabetes. Almost half of the words were however words which had just been added at some point in the past to a list I keep of words I’d like to eventually include in posts like these; that list had grown rather long and unwieldy so I decided to include a lot of words from that list in this post – I have almost no idea where I encountered most of those words (I try to add to that list whenever I encounter a word I particularly like or a word with which I’m not familiar, regardless of the source, and I usually do not add a source).

Chemosis. Asthenia. Arcuate. Onycholysis. Nubble. Colliery. Fomite. Riparian. Guglet/goglet. Limbus. Stupe. Osier. Synostosis. Amscray. Slosh. Dowel. Swill. Tocometer. Raster. Squab.

Antiquer. Ritzy. Boutonniere. Exfiltrate. Lurch. Placard. Futz. Bleary. Scapula. Bobble. Frigorific. Skerry. Trotter. Raffinate. Truss. Despoliation. Primogeniture. Whelp. Ethmoid. Rollick.

Fireplug. Taupe. Obviate. Koi. Doughboy. Guck. Flophouse. Vane. Gast. Chastisement. Rink. Wakizashi. Culvert. Lickety-split. Whipsaw. Spall. Tine. Nadir. Periwinkle. Pitter-patter.

Sidle. Iridescent. Feint. Flamberge. Batten. Gangplank. Meander. Flunky. Futz. Thwack. Prissy. Vambrace. Tasse. Smarmy. Abut. Jounce. Wright. Ebon. Skin game. Shimmer.

December 27, 2017 Posted by | Books, Language | Leave a comment