Econstudentlog

Successes and Challenges in Neural Models for Speech and Language

Some links related to the coverage:
Speech recognition.
Machine translation.
Supervised learning.
Parsing.
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.).

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

Words

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 (..it 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.]

Links:

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.
Cybernetics.
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).
ACT-R.
NELL (Never-Ending Language Learning).
SHRDLU.
ALPAC.
Google translate.
Data mining. Sentiment analysis. Siri. Watson (computer).
Paro (robot).
Uncanny valley.
CogAff architecture.
Connectionism.
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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

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

Words

Most of these words are words which I encountered while reading the Jim Butcher books White Night, Small Favour, Turn Coat, and Changes.

Propitiate. Misericord. Skirling. Idiom. Cadge. Hapless. Roil. Kibble. Viridian. Kine. Shill. Steeple. Décolletage. Kukri. Rondure. Wee. Contrail. Servitor. Pastern. Fetlock.

Coterie. Crochet. Fibrillate. Knead. Divot. Avail. Tamale. Abalone. Cupola. Tuyere. Simulacrum. Bristle. Guff. Shimmy. Prow. Warble. Cannery. Twirl. Winch. Wheelhouse.

Teriyaki. Widdershins. Kibble. Slobber. Surcease. Amble. Invocation. Gasket. Chorale. Rivulet. Choker. Grimoire. Caduceus. Fussbudget. Pate. Scrunchie. Shamble. Ficus. Deposition. Grue.

Aliquot. Nape. Emanation. Atavistic. Menhir. Scrimshaw. Burble. Pauldron. Ornate. Stolid. Wry. Stamen. Ductwork. Speleothem. Philtrum. Hassock. Incipit. Planish. Rheology. Sinter.

 

November 29, 2017 Posted by | Books, Language | Leave a comment

Words

Most of the words below are words which I encountered while reading the Jim Butcher novels: Fool Moon, Grave Peril, Summer Knight, Death Masks, Blood Rites, Dead Beat, and Proven Guilty.

Gobbet. Corrugate. Whuff. Wino. Shinny. Ruff. Rubberneck. Pastel. Sidhe. Appellation. Tine. Clomp. Susurration. Bier. Pucker. Haft. Topiary. Tendril. Pommel. Swath.

Chitter. Wispy. Flinders. Ewer. Incongruous. Athame. Bole. Chitin. Prancy. Doily. Garland. Heft. Hod. Klaxon. Ravening. Juke. Schlep. Pew. Gaggle. Passel.

Scourge. Coven. Wetwork. Gofer. Hinky. Pratfall. Parti-color(ed). Clawhammer. Mesquite. Scion. Traction. Kirtle. Avaunt. Imbibe. Betimes. Dinky. Rebar. Maw. Strident. Mangel.

GeodePanacheLuminance. WickSusurrus. ChuffWhammy. Cuss. Ripsaw. Scrunch. Fain. Hygroscopicity. Anasarca. Bitumen. Lingula. Diaphoretic. Ketch. Callipygian. Defalcation. Serried.

November 7, 2017 Posted by | Books, Language | Leave a comment

Child psychology

I was not impressed with this book, but as mentioned in the short review it was ‘not completely devoid of observations of interest’.

Before I start my proper coverage of the book, here are some related ‘observations’ from a different book I recently read, Bellwether:

““First we’re all going to play a game. Bethany, it’s Brittany’s birthday.” She attempted a game involving balloons with pink Barbies on them and then gave up and let Brittany open her presents. “Open Sandy’s first,” Gina said, handing her the book.
“No, Caitlin, these are Brittany’s presents.”
Brittany ripped the paper off Toads and Diamonds and looked at it blankly.
“That was my favorite fairy tale when I was little,” I said. “It’s about a girl who meets a good fairy, only she doesn’t, know it because the fairy’s in disguise—” but Brittany had already tossed it aside and was ripping open a Barbie doll in a glittery dress.
“Totally Hair Barbie!” she shrieked.
“Mine,” Peyton said, and made a grab that left Brittany holding nothing but Barbie’s arm.
“She broke Totally Hair Barbie!” Brittany wailed.
Peyton’s mother stood up and said calmly, “Peyton, I think you need a time-out.”
I thought Peyton needed a good swat, or at least to have Totally Hair Barbie taken away from her and given back to Brittany, but instead her mother led her to the door of Gina’s bedroom. “You can come out when you’re in control of your feelings,” she said to Peyton, who looked like she was in control to me.
“I can’t believe you’re still using time-outs,” Chelsea’s mother said. “Everybody’s using holding now.”
“Holding?” I asked.
“You hold the child immobile on your lap until the negative behavior stops. It produces a feeling of interceptive safety.”
“Really,” I said, looking toward the bedroom door. I would have hated trying to hold Peyton against her will.
“Holding’s been totally abandoned,” Lindsay’s mother said. “We use EE.”
“EE?” I said.
“Esteem Enhancement,” Lindsay’s mother said. “EE addresses the positive peripheral behavior no matter how negative the primary behavior is.”
“Positive peripheral behavior?” Gina said dubiously. “When Peyton took the Barbie away from Brittany just now,” Lindsay’s mother said, obviously delighted to explain, “you would have said, ‘My, Peyton, what an assertive grip you have.’”

[A little while later, during the same party:]

“My, Peyton,” Lindsay’s mother said, “what a creative thing to do with your frozen yogurt.””

Okay, on to the coverage of the book. I haven’t covered it in much detail, but I have included some observations of interest below.

“[O]ptimal development of grammar (knowledge about language structure) and phonology (knowledge about the sound elements in words) depends on the brain experiencing sufficient linguistic input. So quantity of language matters. The quality of the language used with young children is also important. The easiest way to extend the quality of language is with interactions around books. […] Natural conversations, focused on real events in the here and now, are those which are critical for optimal development. Despite this evidence, just talking to young children is still not valued strongly in many environments. Some studies find that over 60 per cent of utterances to young children are ‘empty language’ — phrases such as ‘stop that’, ‘don’t go there’, and ‘leave that alone’. […] studies of children who experience high levels of such ‘restricted language’ reveal a negative impact on later cognitive, social, and academic development.”

[Neural] plasticity is largely achieved by the brain growing connections between brain cells that are already there. Any environmental input will cause new connections to form. At the same time, connections that are not used much will be pruned. […] the consistency of what is experienced will be important in determining which connections are pruned and which are retained. […] Brains whose biology makes them less efficient in particular and measurable aspects of processing seem to be at risk in specific areas of development. For example, when auditory processing is less efficient, this can carry a risk of later language impairment.”

“Joint attention has […] been suggested to be the basis of ‘natural pedagogy’ — a social learning system for imparting cultural knowledge. Once attention is shared by adult and infant on an object, an interaction around that object can begin. That interaction usually passes knowledge from carer to child. This is an example of responsive contingency in action — the infant shows an interest in something, the carer responds, and there is an interaction which enables learning. Taking the child’s focus of attention as the starting point for the interaction is very important for effective learning. Of course, skilled carers can also engineer situations in which babies or children will become interested in certain objects. This is the basis of effective play-centred learning. Novel toys or objects are always interesting.”

“Some research suggests that the pitch and amplitude (loudness) of a baby’s cry has been developed by evolution to prompt immediate action by adults. Babies’ cries appear to be designed to be maximally stressful to hear.”

“[T]he important factors in becoming a ‘preferred attachment figure’ are proximity and consistency.”

“[A]dults modify their actions in important ways when they interact with infants. These modifications appear to facilitate learning. ‘Infant-directed action’ is characterized by greater enthusiasm, closer proximity to the infant, greater repetitiveness, and longer gaze to the face than interactions with another adult. Infant-directed action also uses simplified actions with more turn-taking. […] carers tend to use a special tone of voice to talk to babies. This is more sing-song and attention-grabbing than normal conversational speech, and is called ‘infant-directed speech’ [IDS] or ‘Parentese’. All adults and children naturally adopt this special tone when talking to a baby, and babies prefer to listen to Parentese. […] IDS […] heightens pitch, exaggerates the length of words, and uses extra stress, exaggerating the rhythmic or prosodic aspects of speech. […] the heightened prosody increases the salience of acoustic cues to where words begin and end. […] So as well as capturing attention, IDS is emphasizing key linguistic cues that help language acquisition. […] The infant brain seems to cope with the ‘learning problem’ of which sounds matter by initially being sensitive to all the sound elements used by the different world languages. Via acoustic learning during the first year of life, the brain then specializes in the sounds that matter for the particular languages that it is being exposed to.”

“While crawling makes it difficult to carry objects with you on your travels, learning to walk enables babies to carry things. Indeed, walking babies spend most of their time selecting objects and taking them to show their carer, spending on average 30–40 minutes per waking hour interacting with objects. […] Self-generated movement is seen as critical for child development. […] most falling is adaptive, as it helps infants to gain expertise. Indeed, studies show that newly walking infants fall on average 17 times per hour. From the perspective of child psychology, the importance of ‘motor milestones’ like crawling and walking is that they enable greater agency (self-initiated and self-chosen behaviour) on the part of the baby.”

“Statistical learning enables the brain to learn the statistical structure of any event or object. […] Statistical structure is learned in all sensory modalities simultaneously. For example, as the child learns about birds, the child will learn that light body weight, having feathers, having wings, having a beak, singing, and flying, all go together. Each bird that the child sees may be different, but each bird will share the features of flying, having feathers, having wings, and so on. […] The connections that form between the different brain cells that are activated by hearing, seeing, and feeling birds will be repeatedly strengthened for these shared features, thereby creating a multi-modal neural network for that particular concept. The development of this network will be dependent on everyday experiences, and the networks will be richer if the experiences are more varied. This principle of learning supports the use of multi-modal instruction and active experience in nursery and primary school. […] knowledge about concepts is distributed across the entire brain. It is not stored separately in a kind of conceptual ‘dictionary’ or distinct knowledge system. Multi-modal experiences strengthen learning across the whole brain. Accordingly, multisensory learning is the most effective kind of learning for young children.”

“Babies learn words most quickly when an adult both points to and names a new item.”

“…direct teaching of scientific reasoning skills helps children to reason logically independently of their pre-existing beliefs. This is more difficult than it sounds, as pre-existing beliefs exert strong effects. […] in many social situations we are advantaged if we reason on the basis of our pre-existing beliefs. This is one reason that stereotypes form”. [Do remember on a related note that stereotype accuracy is one of the largest and most replicable effects in all of social psychology – US].

“Some gestures have almost universal meaning, like waving goodbye. Babies begin using gestures like this quite early on. Between 10 and 18 months of age, gestures become frequent and are used extensively for communication. […] After around 18 months, the use of gesture starts declining, as vocalization becomes more and more dominant in communication. […] By [that time], most children are entering the two-word stage, when they become able to combine words. […] At this age, children often use a word that they know to refer to many different entities whose names are not yet known. They might use the word ‘bee’ for insects that are not bees, or the word ‘dog’ to refer to horses and cows. Experiments have shown that this is not a semantic confusion. Toddlers do not think that horses and cows are a type of dog. Rather, they have limited language capacities, and so they stretch their limited vocabularies to communicate as flexibly as possible. […] there is a lot of similarity across cultures at the two-word stage regarding which words are combined. Young children combine words to draw attention to objects (‘See doggie!’), to indicate ownership (‘My shoe’), to point out properties of objects (‘Big doggie’), to indicate plurality (‘Two cookie’), and to indicate recurrence (‘Other cookie’). […] It is only as children learn grammar that some divergence is found across languages. This is probably because different languages have different grammatical formats for combining words. […] grammatical learning emerges naturally from extensive language experience (of the utterances of others) and from language use (the novel utterances of the child, which are re-formulated by conversational partners if they are grammatically incorrect).”

“The social and communicative functions of language, and children’s understanding of them, are captured by pragmatics. […] pragmatic aspects of conversation include taking turns, and making sure that the other person has sufficient knowledge of the events being discussed to follow what you are saying. […] To learn about pragmatics, children need to go beyond the literal meaning of the words and make inferences about communicative intent. A conversation is successful when a child has recognized the type of social situation and applied the appropriate formula. […] Children with autism, who have difficulties with social cognition and in reading the mental states of others, find learning the pragmatics of conversation particularly difficult. […] Children with autism often show profound delays in social understanding and do not ‘get’ many social norms. These children may behave quite inappropriately in social settings […] Children with autism may also show very delayed understanding of emotions and of intentions. However, this does not make them anti-social, rather it makes them relatively ineffective at being pro-social.”

“When children have siblings, there are usually developmental advantages for social cognition and psychological understanding. […] Discussing the causes of disputes appears to be particularly important for developing social understanding. Young children need opportunities to ask questions, argue with explanations, and reflect on why other people behave in the way that they do. […] Families that do not talk about the intentions and emotions of others and that do not explicitly discuss social norms will create children with reduced social understanding.”

“[C]hildren, like adults, are more likely to act in pro-social ways to ingroup members. […] Social learning of cultural ‘ingroups’ appears to develop early in children as part of general socio-moral development. […] being loyal to one’s ‘ingroup’ is likely to make the child more popular with the other members of that group. Being in a group thus requires the development of knowledge about how to be loyal, about conforming to pressure and about showing ingroup bias. For example, children may need to make fine judgements about who is more popular within the group, so that they can favour friends who are more likely to be popular with the rest of the group. […] even children as young as 6 years will show more positive responding to the transgression of social rules by ingroup members compared to outgroup members, particularly if they have relatively well-developed understanding of emotions and intentions.”

“Good language skills improve memory, because children with better language skills are able to construct narratively coherent and extended, temporally organized representations of experienced events.”

“Once children begin reading, […] letter-sound knowledge and ‘phonemic awareness’ (the ability to divide words into the single sound elements represented by letters) become the most important predictors of reading development. […] phonemic awareness largely develops as a consequence of being taught to read and write. Research shows that illiterate adults do not have phonemic awareness. […] brain imaging shows that learning to read ‘re-maps’ phonology in the brain. We begin to hear words as sequences of ‘phonemes’ only after we learn to read.”

October 29, 2017 Posted by | Books, Language, Neurology, Psychology | Leave a comment

Words

Almost all of the words below are words which I encountered while reading the novels Flashman and the Tiger, Flashman in the Great Game, Bellwether, and Storm Front.

Cavil. Thimblerig(/ger). Garboil. Gamine. Teetotum. Burgess. Clart. Wangle. Arrack. Surpassingly. Understrapper. Quince. Fiacre. Hackney. Furbelow. Fritillary. Dormer. Haddock. Chamois. Tizzy.

Claver. Aporia. Tyke. Clype. Gowk. Billycock. Mottle. Welkin. Hayrick. Ablution. Flanker. Baize. Assegai. Inspan. Knobkerrie. Cutty. Leek. Guttering. Costermonger. Kohl.

Genteel. Plenipotentiary. Trice. Tinker. Chapati. Clack. Rowan. Bracken. Tapster. Bosh. Durbar. Krait. Dacoit. Kepi. Ghat. Waler. Hackery. Bun-fight. Flapper. Wale.

Bellwether. Bouffant. Rumple. Snit. Dashiki. Puce. Shearling. Pinafore. Marcel. Cerulean. Wreaths. Planchette. Moxie. Gawky. Ichor. Thrum. Hibachi. Wain. Ecumene. Fricative.

 

October 26, 2017 Posted by | Books, Language | Leave a comment