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\r\n\tThey are hypersensitive to chemical pollution, habitat degradation, a variation of river and groundwater quality, climate change and even the sun's ultraviolet radiation, amphibians are among the vertebrate groups most endangered by human activity, and their abundance in wetlands is always one of the best indicators of good environmental conservation.
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\r\n\tIn this book, we have considered all aspects of amphibians biology, diversity, conservation and potential use of amphibians as environmental indicators.
Cetacea (whales and dolphins) is a natural group that has for centuries generated a great deal of misunderstanding and controversy regarding its proper place in natural classification. As late as 1945 Simpson wrote that “Because of their perfected adaptation to a completely aquatic life, with all its attendant conditions of respiration, circulation, dentition, locomotion, etc., the cetaceans are on the whole the most peculiar and aberrant of mammals.”
Although both molecular and paleontological data have provided a much better understanding of the placement of this group among mammals, there is no question that despite being studied and dissected by dozens of naturalists since Aristotle, these animals were always misclassified. This group provides an interesting case study for intellectual inertia in the history of science. In other words, why did so many scientists misplace this group in the natural classification despite the fact that they themselves were gathering critical information that showed the close relationship these animals had to what we know today as mammals?
The aim of this chapter is to explore this question. To that end I will (1) survey the naturalists who studied cetaceans providing clues of their true nature, (2) describe the intellectual environment in which their conclusions were made, and (3) discuss the factors behind this intellectual inertia.
For the purpose of this chapter I have only taken into consideration works that had some scientific basis and/or that in some ways influenced the process of placing cetaceans as mammals. Authors are enumerated based on the date of the major publication they produced on cetaceans. For synonyms in names of marine mammals through time see Artedi (1738) and Linnaeus (1758).
Aristotle[1] - was the son of Nicomachus, the personal physician of King Amyntas of Macedon and Phaestis, a wealthy woman[1] -. Nicomachus may have been involved in dissections (Ellwood 1938, p. 36), a key tool in Aristotle’s biological studies, particularly on marine mammals. Aristotle lost both his parents when he was about 10 and from then on he was raised of his uncle and/or guardian Proxenus, also a physician (Moseley 2010, p. 6). Early Greek physicians known as asclepiads usually taught their children reading, writing, and anatomy (Moseley 2010, p. 10).
In 367 BCE Aristotle moved to Athens to study at Plato’s Academy, and later travelled throughout Asia Minor and studied living organisms while at the island of Lesbos (344-342 BCE) where he collected a lot of information about marine mammals. He later created his own philosophical school, the Lyceum, in Athens where most of his written work was produced between 335 and 323 BCE.
Aristotle is the first natural historian from whom we have any extensive work. One of his surviving opuses is Historia Animalium (inquiry about animals)[1] -. There he classified animals as follows (beginning from the top): “blooded” animals (referring to those with red blood, vertebrates) with humans at the top, viviparous quadrupeds (what we would call terrestrial mammals), oviparous quadrupeds (legged reptiles and amphibians), birds, cetaceans, fishes, and then “bloodless” animals (invertebrates). He named each one of these groups a “genus.”
Humans
Viviparous quadrupeds (terrestrial mammals)
Oviparous quadrupeds (reptiles and amphibians)
Birds
Cetaceans
FishMalacia (squids and octopuses)
Malacostraca (crustaceans)
Ostracoderma (bivalve mollusks)
Entoma (insects, spiders, etc.)
Zoophyta (jellyfishes, sponges, etc.)
Higher plants
Lower plants
Based on the “kinds” of animals and the varieties he described we can distinguish somewhere between 550 and 600 species. Most of them he had observed directly and even dissected but others were based on tales and he warned about the accuracy of those descriptions. For example, although he mentioned information in numerous occasions provided to him by fishers, many times (but not always) he debunks some of the fallacies he heard based on his own observations, particularly when it came to reproduction.
Of what we would consider today as mammals (including cetaceans) he described about 80 and about 130 species of fishes, which, again, underlines the extensive work, he did on marine creatures, mostly while living at Lesbos. Under the genus “Cetacea” he included at least three species: (1) “dolphins” probably a combination of striped dolphin (Stenella coeruleoalba, the most frequent species in the Mediterranean), the common dolphin (Delphinus delphis), and the bottlenose dolphin (Tursiops truncatus); (2) the harbor porpoise (Phocoena phocoena) which he described as “similar to dolphins but smaller and found in the Black Sea” (“Euxine”) (HA 566b9)[1] -; and, (3) the fin whales (Balaenoptera physalus) another common species in the Mediterranean at that time.
The motives behind Aristotle classification system, particularly animals, were not biological in nature but rather philosophical. For him these creatures were evidence for rational order in the universe. This approach meant that species were rigid elements of the world and, thus, he never contemplated mutability or anything close to evolution, despite the fact that earlier Greek philosophers such as Anaximander envisioned the mutability of species. Furthermore, Aristotle’s motive for conducting this categorization was done in such a way that we can then identify the causes that explain why animals are organized the way they are. His investigation into those causes is carried out in other surviving biological works (e.g., Parts of Animals). When describing species he adhered to his teleological doctrine of purposiveness in nature.
Aristotle was able to distinguish between homology and analogy, recognizing cetaceans as a natural group with many similarities with other mammals (“viviparous quadrupeds”). He considered cetaceans as “blooded” animals, adding, “viviparous such as man, and the horse, and all those animals that have hair; and of the aquatic animals, the whale kind as the dolphin and cartilaginous fishes” (HA 489a34-489b3). He also wrote: “all creatures that have a blow-hole respire and inspire, for they are provided with lungs. The dolphin has been seen asleep with its nose above water as he snores (HA 566b14). All animals have breasts that are internally or externally viviparous, as for instance all animals that have hair, as man and the horse; and the cetaceans, as the dolphin, porpoise and the whale -for these animals have breasts and are supplied with milk” (HA 521b21-25). Among the species he described were dolphins, orcas, and baleen whales, noting that “the [whale] has no teeth but does have hair that resemble hog bristle” (HA 519b9-15). Thus, he was the first to separate whales and dolphins from fish.
However, Aristotle placed whales and dolphins below reptiles and amphibians, because their lack of legs, despite his physiological and behavioral observations that they were related more closely to “viviparous quadrupeds” than to fish.
Aristotle followed his teacher Plato in classifying animals by progressively dividing them based on shared characters. This is an embryonic form of today’s classification more fully developed by Linnaeus. The reason he ordered the different “genera” the way he did was because he considered “vital heat” (characterized by method of reproduction, respiration, state at birth, etc.) as an index of superiority placing humans at the very top. Men were superior to women because they had more “vital heat.” On this he followed Hippocrates’s ideas, since the Greek physician thought there was an association between temperature and soul.
Yet he was not fully satisfied by this approach given that a number of “genera” had characters that were shared across groups, particularly when compared with their habitats. For example, both fishes and cetaceans had fins, but they differ markedly on other characters such as reproduction (oviparous vs. viviparous) or organs (gills vs. lungs, respectively).
Many of Aristotle’s observations about cetaceans remain accurate. In terms of internal anatomy he mentioned that they have internal reproductive organs (HA 500a33-500b6), that dolphins, porpoises, and whales copulate and are viviparous, giving birth to between one and two offsprings having two breasts located near the genital openings that produce milk (HA 504b21), that dolphins reach full size at the age of 10 and their period of gestation is 10 months, show parental care, some may live up to 30 years and this is known because fishers can individually identify them by marks on their bodies (HA 566b24), and that dolphins have bones (HA 516b11).
Regarding behavior and sensory organs he said that dolphins have a sense of smell but he could not find the organ (HA 533b1), that dolphins can hear despite the lack of ears (HA 533b10-14), produce sounds when outside the water (HA 536a1), that dolphins and whales sleep with their blowhole above the surface of the water (HA 537a34), are carnivorous (HA 591b9-15), and swim fast (HA 591b29).
He held that cetaceans are not fishes because they have hair, lungs (HA 489a34), lack gills, suckle their young by means of mammae, they are viviparous (HA 489b4), and that their bones are analogous to the mammals, not fishes. Still they he calls them “fishes” (HA 566b2-5).
These basic Aristotelian biological descriptions persisted for good and for bad until Charles Darwin’s evolutionary work. On one hand his descriptions were so accurate that Darwin admired Aristotle, to the point that he said privately that the intellectual heroes of his own time “were mere schoolboys compared to old Aristotle.”[1] - Yet the fact that Aristotle saw the natural world as fixed in time with no room for evolution and that he kept calling cetaceans “fishes,” would delay intellectual progress for many centuries when it came to the classification of these animals.
Aristotle’s influence on naturalists’ classification of life would extend until Darwin’s times when evolutionary views replaced the fixity of species as elements in nature.
Pliny the Elder[1] - was the son of an equestrian (the lower of the two aristocratic classes in Rome) and was educated in Rome. After serving in the military he became a lawyer and then a government bureaucrat. In these positions he travelled not only throughout what is Italy today but also what it would later became Germany, France and Spain as well as North Africa (Reynolds 1986).
He wrote a 37-volume Naturalis\n\t\t\t\t\tHistoria[1] - http://www.perseus.tufts.edu/hopper/text?doc=Plin.+Nat.+toc&redirect=true (ca. 77-79) in which according to himself he had compiled “20,000 important facts, extracted from about 2000 volumes by 100 authors” and was written for “the common people, the mass of peasants and artisans, and only then for those who devote themselves to their studies at leisure” (Preface 6). This is the earliest known encyclopedia of any kind, which has been interpreted as a Roman invention in order to compile information about the empire (Naas 2002, Murphy 2004). It was a rather disorganized book, whose prose has been criticized by many (Locher 1984). Pliny seemed to be more interested in what appeared to be curiousities than what were facts. This is a big collection of facts and fictions, based mostly said on things said by others.
He devoted 9 of the 37 volumes to animals and ordered them according to where they live. Volume IX (Historia Aquatilium) of Naturalis Historia is devoted to aquatic creatures, whether living in oceans, rivers or lakes, whether vertebrate or invertebrate, real or mythical. Based on their size he categorized as “monster” anything big, whether it is a whale, a sawfish or a tuna (IX 2,3).
He grouped together all known species of cetaceans (cete) but constantly mixed their descriptions with those of other marine mammals such as seals as well as with cartilaginous fishes, such as some sharks (pristis). Pliny mentioned the three species cited by Aristotle: dolphins (delphinus, probably a combination of striped dolphin [Stenella coeruleoalba] and the common dolphin [Delphinus delphis], IX 12-34), porpoises (porcus marinus, the harbor porpoise [Phocoena phocoena], IX 45) and whales (ballaena, possibly a combination of large toothless whales [mysticetes] IX 12-13). Then he added a few more: the thursio or tirsio (probably the bottlenose dolphin, Tursiops truncatus IX 34), the physeter (probably the sperm whale [Physeter macrocephalus] IX 8) found in the “Gallic Ocean” (probably the Bay of Biscay, IX 3, 4), the orca (probably the killer whale [Orcinus orca] IX 12-14), and the river dolphin from India (possibly Platanista gangetica, IX 46). He also mentioned some mythical creatures such as Homo marinus (Sea-Man, IX 10) and the Scolopendra marina (IX, 145) a mythical organism whose legend may be based on polychaetes, marine annelids characterized by the presence of many legs (Leitner 1972, p. 218).
Pliny recognized that neither whales nor dolphins have gills, that they suckle from the teats of their mothers, and that they are viviparous. In addition to these true facts copied from Aristotle, he mentioned exaggerations such as whales of four jugera (ca. 288 m) in length that because of their large size “are quite unable to move” (IX 2,3). In addition to some of the biological facts mentioned by Aristotle, Pliny adorns his narrative with all kind of casual tales about interactions between cetaceans and humans.
By lumping together all kinds of aquatic organisms it is hard to distinguish what he called “fish” and what he did not (see for example IX 44-45). His classification took a step back from Aristotle because he did not try for a comprehensive classification of animals. He failed to compare organisms based on shared or divergent characters. Many times he ordered creatures based on size, from the largest to the smallest. Yet, his work had great influence for 1700 years, which was unfortunate because he was an uncritical compiler of other people’s writings (even if they were contradictory). Pliny also created a number of unfounded impressions about the reality of nature. His only positive contribution was that he established the norm of always citing the sources of his information (in actuality 437 authors, whose works, in some cases, are no longer available).
During the middle ages, little progress was made in the sciences. Students were urged to believe what they read and not to question conventional wisdom. Logic determined truth, not observation. Free thought was non-existent and minds were filled with mythological explanations for the unknown. Marine mammals were depicted as monsters and little new information was generated.
The Renaissance was a time of awakening and the religious ideology began to be questioned. The translations of the works of Aristotle and Pliny into Latin and the introduction of the printing press helped to spread the little knowledge accumulated until that time about natural history in the western world. For example, by 1500 about 12 editions of Aristotle’s Historia Animalium and 39 of Pliny’s Historia Naturalis had seen the light, which is evidence of the popularity of these works. During this age of discovery the finding of species that were never mentioned neither by Aristotle nor the Bible, opened up scientific curiosity about new creatures around the world. Thus, people once again began to seek new knowledge. However, in these times, naturalists were more compilers of information than investigators despite the fact that they were performing more dissections that in turn uncovered new taxonomic possibilities. Still, scientists relied on environmental aspects to classify animals. Collecting was a primary activity during this era (Alves 2010, p. 54).
Belon[1] - was the first author studying marine mammals in this historical period. Little is known about his family and early years. He traveled extensively throughout Europe and the Middle East, including the Arabian Peninsula and Egypt. Among the places he visited were Rome where he met two other ichthyologists, Rondelet and Salviani (see below). He studied medicine at the University of Paris and botany at the University of Wittenberg, Germany. He served as a doctor and apothecary for French kings, as well as a diplomat, traveler, and as a secret agent (he was murdered under strange circumstances) (Wong 1970).
His L\'Histoire Naturelle des Estranges Poissons Marins (1551) was the first printed scholarly work about marine animals. This book was expanded and published in French in 1555 as La Nature et diversité des poissons including 110 species with illustrations for 103 of them.
Belon not only reproduced information from Aristotle and Pliny but also added his own observations including comparative anatomy and embryology. For him “fish” was anything living in the water. He divided “fishes” in two large groups: the first was “fish with blood” (as Aristotle had done) that included not only actual fishes but also cetaceans, pinnipeds, marine monsters and mythical creatures such as the “monk fish,” as well as other aquatic vertebrates such as crocodiles, turtles, and the hippopotamus. He called a second group “fishes without blood” and consisted of aquatic invertebrates (see also Delaunay 1926).
He ordered what we know as cetaceans today in a vaguely descending order based on size: Le balene (mysticete whales, although in the illustration he depicted a cetacean with teeth), Le chauderon (sperm whale? although he mentions the sawfish), Le daulphin (common dolphins on which he devoted 38 pages of this 55-page book), Le marsouin (porpoise), and L’Oudre (bottlenose dolphin) (for a rationale on the identification of these species see Glardon 2011, p. 393-398). He dissected common dolphins (D. delphis) and porpoises (P. phocaena) acquired at the fish market in Paris brought in by Normandy fishers, and probably a bottlenose dolphin (T. truncatus) as well.
He described these marine mammals as having a placenta, mammae, and hair on the upper lip of their fetus. Belon wrote that apart from the presence of hind limbs, they conform to the human body plan with features such as the liver, the sternum, milk glands, lungs, heart, the skeleton in general, the brain, genitalia. He also dealt with issues of breathing and reproduction (although from the description it is clear that he never saw one of these animals giving birth, since he depicted the newborn surrounded with a membrane). He drew the embryo of a porpoise and the skull of a dolphin (Fig. 1). Despite all this he did not make the connection between cetaceans and “viviparous quadrupeds” and based his entire classification on environmental foundations, as he made clear in the introduction of his work.
Wotton[1] - was the son of a theologian who did general studies at Oxford and studied medicine and Greek at Padua (1524-6). He was a practicing physician who published De
Illustrations of marine mammals by Belon (1551): (a) and (b) are representations of the common dolphin (Delphinus delphis); (c) a porpoise (Phocaena phocaena); (d) a bottlenose dolphin (Tursiops truncatus, although he uses the name of “Orca”) presumably giving birth; (e) the skull of a dolphin; (f) a porpoise fetus in a placenta, showing that he had actually dissected these animals.
Differentiis Animalium Libri Decem (1552), probably the first published book on natural history of the Renaissance. This was a 10-part (“books”) treatise that followed the classification structure by Aristotle while adding some comments from Pliny. In Book 8 (pp. 171-173) he placed Cete together with fishes because of the medium they inhabit. Except for entomology he did not conduct any original observations on animals nor include any illustrations. His contemporaries noted his lack of originality (Nutton 1985).
The list of cetacean species included Delphino (dolphins), Phocaena (porpoises), Balaena (mysticete whales), Orca (either the bottlenose dolphin or the killer whale) and Physeter (the sperm whale).
Rondelet[1] - was the son of a drug and spice merchant. He studied medicine at the University of Montpellier, one of the best medical schools in Europe at that time. While in Paris he studied anatomy under Johannes Guinther, who also taught Vesalius. Rondelet would later become Professor of medicine and Chancellor at Montpellier (Keller 1975). He probably acquired his interest in ichthyology at a young age while living in Montpellier (about 12 km from the coast) because his family owned a farm that was a stopping place for carts of fish from the Mediterranean (Oppenheimer 1936). During his trips as personal physician to Francois Cardinal Tournon (who was also the patron of Belon) to the Atlantic coasts of France, he became acquainted with the whaling industry. Rondelet met several contemporary ichthyologists while in Rome (1549-1550) such as Belon, Hippolyto Salviani, and Ulyssis Aldronvandi (Gudger 1934). Guillaume Pellicer, Bishop of Montpellier, who was also interested in fishes but never published on ichthyology, may have influenced Rondelet (Oppenheimer 1936, Dulieu, 1966).
He enjoyed dissecting and did so frequently for both teaching and research purposes. He published Libri de Piscibus Marinis in quibus verae Piscium effigies expressae sunt (1554) with a second part titled Universae Aquatilium Historiae pars altera (1555) about both marine and freshwater animals. Both were later translated into French as L’histoire entière des poissons (1558, 599), a monograph for teaching purposes.
After writing about food, habitat, morphology, and physiology, he described 145 freshwater and 190 marine species that included at least seven species of cetaceans: delphino (common dolphin), phocaena (porpoise), tursione (bottlenose dolphin, although the illustration more resembles a porpoise), balaena vulgo and balaena vera (two different species of mysticetes whose true identities are difficult to ascertain), orca (killer whale), and physetere (sperm whale) (Fig. 2). He also included among cetaceans the priste (sawfish) and mythical animals such as Pliny’s scolopendra cetacea, the monstruo leonino (a lion covered with scales and with a human face), the pisce monachi habitu (a fish that looks like a monk), and the pisce Episcopi habitu (a fish that looks like a bishop) of which he was skeptic. All together his book contained more species than previous published works. Each species description included the animal’s name in different languages, their morphology (external and internal), feeding habits, and use as food for humans. Species were differentiated similarly to Aristotle as blooded and non-blooded. Although Aristotle inspired the entire book, including teleological considerations in his discussions, Rondelet added some original ideas, especially concerning anatomy and descriptions of the small cetaceans he dissected. Rondelet made correlations between form, function, and environment.
Illustrations of marine mammals by Rondelet (1554): (a) a dolphin showing a fetus surrounded by a placenta indicating it was a viviparous animal; (b) a porpoise; (c) an unidentified species of mysticete, probably a right whale because may have been observed by Rondelet during a whaling operation in the Atlantic; (d) an unidentified species of mysticete that he never saw as evidenced by the depiction of barbels above the mouth; (e) orca (Orcinus orca); (f) a sperm whale (Physeter macrocephalus).
Despite noting differences, he grouped marine mammals with fish based on habitat. For example, he noted that fishes with scales lack lungs and have a three-chamber heart while what we know today as marine mammals have hearts with four chambers. He compared the anatomy of a dolphin to that of the pig and humans. Based on this and his descriptions of other internal organs, he considered marine mammals to be a type of aquatic quadruped. Yet, he did not propose a system of classification. He did not advance the notion of valid classification, but because of the quality of his descriptions his work remained as the main reference for about 100 years.
Gessner[1] - probably developed an interest in zoology after seeing the carcasses of furred animals at his father’s workshop where several furriers worked. He also lived with a great-uncle, an herbalist, who furthered his interest in natural history (Bay 1916, Gmelig-Nijboer 1977, p. 17, Wellisch 1984, p. 1). He was an avid traveler who studied theology and medicine in Bourges, Paris, Montpellier, and Basel (Fischer 1966) and had great facility for classical languages. During his travels Gessner met with Belon and Rondelet. He is considered as the “father of bibliography” because of his work on compiling information about books (Bay 1916). Gessner himself had a very large private library of more than 400 volumes (which was a very large private collection for his time) of which 19% of the volumes were on natural history and 13 of them were on zoology (Leu et al. 2008, pp. viii, 1, 13, 21). He published Historiae Animalium (1551-1558), an encyclopedic (4 volumes, 4,500 pages treatise) but uncritical compilation of information and bibliography in which he intended to itemize all of God’s creations. In addition to classic authors such as Aristotle and Pliny, Gessner obtained information from whomever he could correspond. He classified cetaceans among ‘aquatic animals,’ i.e., including fishes. The fourth volume (Piscium & Aquatilium) of 1297 pages was published in 1558 and was about the aquatic animals. A fifth volume on reptiles and arthropods was not published until 1587, posthumously. Historiae was added to the list of prohibited books because Gessner was Protestant. Yet, the 14 editions in different languages of this book reveal its popularity.
Gessner followed Aristotle’s classification of animals when it came to their grouping by volume (Vol. 1: viviparous quadrupeds; Vol. 2: oviparous quadrupeds; Vol. 3: birds; Vol. 4: aquatic animals; Vol. 5: serpents). He ordered them alphabetically, like a “Dictionarium,” in each volume, which did not provide a rational classification based on relationships of any kind; on the other hand this alphabetical order facilitated its use as an encyclopedic source. Gessner’s intention was to collect any piece of information ever written about each animal by any author in history, he cited nearly 250 authors including Rondelet (Libri de Piscibus Marinis, 1554), Belon (De Aquatilibus,1553), and Salviani (Aquatilium Animalium, 1554). The latter only mentioned marine mammals in passim.
Some of the “Cetis” described by Gessner (1558): (a) and (b) two examples of marine monsters; (c) a whale attacking a ship and another being flensed during whaling operations. Both show mysteces with teeth, which indicates that Gessner never saw these animals. This exemplifies that Gessner was an uncritical compiler of information.
Information included names of the animals in various languages (some times more than a dozen) comprising epithets and etymology (even inventing common names in other languages when those names were not available), physical features, geographic distribution, the animal’s way of living including diseases and their cures, behavior, utility towards man (e.g., for food or medical purposes), and tales. His work was full of illustrations: some were very accurate showing that he had first-hand knowledge of the animal in questions while other were bizarre or just invented, especially when dealing with mythical creatures.
Gessner included a 16-page-folio discussion about the dolphin very much along the lines of Aristotle and Pliny. As an uncritical compiler he included contradictory or totally false information such as mythical species and even “monsters.” In volume 4 he relied heavily on Belon and Rondelet. For example, Monachus marinus (sea monk, IV, p. 519) description was copied from Rondelet who, in turn, had received the description from Marguerite, Queen of Navarre, who heard it from Emperor Charles V’s ambassador, who had claimed to see the monster himself (Kusukawa 2010). He did not add much to what was already known. Among marine mammals he mentioned are the Balaena (mystecete whales, IV, p. 128) depicted more as sea monster than as an actual whale, Cetis diversis (IV, p. 207), an amalgam of marine monsters based on Olaus Magnus’s descriptions of sea monsters from seas from northern Europe, Hominis marinis (IV, p. 438), a collection of humanoid sea monsters such as the sea-monk and the sea-bishop. To certain extent he was skeptical of accuracy of some of these descriptions by other authors.
Many of the figures were made by others and copied directly from other books including those of “cetaceous” animals as was the case of a whale which was copied from Olaus Magnus’ map of the Northern Lands (IV, p. 176) (Fig. 3).
The last author who published anything of significance about marine mammals during the Renaissance was Aldrovandi[1] -. He was born to a noble and wealthy family, which allowed him to initially dedicate his life to his own pursuits. He was educated in Bologna, Padua, and Rome, receiving degrees in law and medicine although he never practiced those professions. He was appointed as the first professor of natural history in the University of Bologna. Although he was a pious Catholic, because of what he read he was charged with heresy. After producing himself in Rome, he was acquitted. While in Rome he met Rondelet and accompanied him to the fish markets where he became interested in ichthyology (which included the study of marine mammals) collecting specimens for his own museum. He traveled extensively throughout Italy and made a collection of about 11,000 animal specimens for pedagogical purposes; most of them can be found today at the Bologna Museum to which he bequeathed not only his specimens but also his library and unpublished manuscripts as well (Alves 2010, pp. 56-82). He also conducted dissections (Impey and McGregor 1985). He was a true encyclopedist following the tradition of the University of Bologna at that time (Tugnoli Pattaro 1994). He wrote extensively but the quality of his animal descriptions and illustrations were poor from the scientific viewpoint (Fig. 4). Aldrovandi was an uncritical compiler who included legends of mythical animals in his writings similar to the medieval bestiaries and in the tradition of Pliny.
Depiction of some marine animals by Aldrovandi (1613): Some show that he actually saw some of those skeletal pieces such as (a) a tooth possible from a sperm whale, (b) a baleen and the prominent tooth of a narwhal (Monodon monoceros), (c) a rib and a vertebra, possibly of a large whale, and (d) a scapula. In other cases he illustrated whales with human-like emotions (e); whales with feet (f); sawfishes with cetacean characteristics (g); and Pliny’s “Scolopendra cetacea” (h), which perpetuated the notion that such animal existed. Overall he was a very uncritical compiler when it came to marine mammals.
He published De piscibus libri V, et De cetis lib. vnus (1613) where he defined “Pisces” as animals covered with scales and “aquatilis” as “anything else that lives in the water” while recognizing that cetaceans are air-breathing creatures. The species that he mentioned were the ones cited by his predecessors: Balaena, Physeter, Orca, Delphino, Phocaena, and Tursione, while including the Manate Indorum, Phoca, Pristi (the sawfish), and the mythical Scolopendra Cetacea. From the illustrations (Fig. 4) it is clear he never saw any of these animals with the exception of some of their skeletal parts. As an uncritical compiler of information he did not add anything new to the knowledge of these creatures and, yet, was cited by later authors.
In this period, observation and experimentation moved to the forefront of science. Classification was based on similarities and differences in characters. During this time English physicians travelled to Padua, Bologna and Paris to be trained in human dissection since the status of medicine in England was still poor. People involved in these kind of activities had a background in either medicine (or “physic” as it was called then) and/or theology (Kruger 2004). During this time the center of gravity of science moved from the Mediterranean world to northern Europe, mostly England.
The first researcher of the biology of marine mammals in this period was Johann Jonston[1] -. Although born in Poland, Jonston’s father was Scottish and his mother German. He was educated in St Andrews, Frankfurt, Cambridge, and Leiden, receiving a medical degree from the last two institutions. He traveled extensively throughout Europe teaching, and despite offers for academic positions, he decided to make a living as an independent scholar (Miller 2008). He published Historiae naturalis de Piscibum Partem in 1657. Jonston was another encyclopedist who when it came to natural history was more a compiler than anything else, relying heavily on Gessner and Aldrovandi while adding some new information from New World creatures from George Marcgrave. Thus, he did not offer any significant critical view to his sources although his descriptions were briefer than those of his predecessors. He gave no hint of biological classification for marine mammals and also added further mistakes and legends (even ‘monsters’). He slightly modified Aldrovandi’s classification of fishes by adding ‘pelagic’ fishes. Yet his books were widely read and translated.
He dealt with cetaceans on pages 213-224 of his Historiae and included the same species as Aldrovandi: Balaena, Physetere, Orca, Delphino, Phocaena and the mythical scolopendra cetacea, the sawfish, pinnipeds, and the manatee among the cetaceans.
Charleton[1] - was the son of a church rector of modest means. He was educated at Oxford as a physician at that time when medical education in England emphasized scholastic approaches to knowledge and British colleges had inadequate anatomical staff and teaching facilities. The practical elements of practicing medicine were not acquired until after assisting a more experienced practicing medical doctor.
Charleton was a follower of epicurean atomism (materialism) (Kargon 1964) and an eclectic (Lewis 2001), whose interest in natural history was more or less theological because, as he said, men were obligated into “naming & looking into the nature of all Creatures” (Boot 2005, p. 119). In other words, just as Ray and Willoughby did later, natural science was the search a divine pattern in nature, part of the research agenda of the Royal Society – to which Charleton belonged (Rolleston 1940, Sharpe 1973). His publications showed him more as a compiler than as an innovator. His major contribution to science was the discovery that tadpoles turn into frogs (Booth 2005, p. 1).
He published two books dealing with animal classification: Onomasticon zoicon (1668) and Exercitationes de Differentiis & Nominibus Animalium (1677) works that listed the names of all known animals (including some fossils) in the western world in several languages with a somewhat taxonomy discussion, including remarks about these animals habits and habitats that contained anatomical descriptions of two animals that he had dissected. As Belon did over a century before, he divided “fishes” as either “with blood” (vertebrates) and “without blood” (invertebrates). He grouped under “Cetaceos” not only actual cetaceans but also the sawfish, seals, walruses, manatees, hippopotamus and the mythical “scolopendra cetacea.” The actual cetaceans described were Balaena vulgaris (probably the right whale), Physeter, & Physalus (probably the fin whale but also other species), Cetus dentatus (the sperm whale), Pustes (indeterminate species, maybe the beluga), Orca (the killer whale), Monoceros (the narwhal), Delphinus (probably a composite of delphinidae), and Phocaena (the porpoise).
Tyson[1] - was born into an affluent merchant family. He performed numerous dissections as a college student, obtained his medical degree at Oxford University and was a lecturer of Anatomy at the Barber-Surgeons Hall in London. Tyson was the first of the comparative anatomists in the modern sense. He did extensive dissections and was the first to use a microscope as part of his anatomical studies. His description of the highly convoluted cetacean brain as well as his recognition of the many homologies with "viviparous quadrupeds", rather than the fishes that they externally resembled, constituted a major landmark contribution to the history of biology (Kruger 2003).
In Phocaena, or, The anatomy of a porpess dissected at Gresham Colledge, with a preliminary discourse concerning anatomy and natural history of animals (1680), he noted that “What we have here is a signal Example of the same between Land-Quadrupeds and Fishes; for if we view a Porpess on the outside, there is nothing more than a fish; for if we view a Porpess on the inside, there is nothing less. (...) It is viviparous, does give suck, and hath all its Organs so contrieved according to the standard of them in Land-Quadrupeds; that one would almost think of it to be such, but it lives in the Sea, and hath but two fore-fins.” Adding later “The structure of the viscera and inward parts have so great an Analogy [sic] and resemblance to those of Quadrupeds, that we find them here almost the same. The greatest difference from them seems to be in the external shape, and wanting feet. But here too we observed that when the skin and flesh was taken off, the forefins did very well represent an Arm, there being the Scapula, an of Humeri, the Ulna, and Radius, and bone of the Carpus, the Metacarp, and 5 digiti curiously joynted. The Tayle too does very well supply the defect of feet both in swimming as also leaping in the water, as if both hinder feet were colligated into one, though it consisted not of articulated bones but rather Tendons and Cartilages.”
Tyson’s description of the internal anatomy of the porpoise is remarkable, particularly when it comes to its nervous system (Kruger 2003). In many ways he thought that the “porpess” was the transitional link between terrestrial mammals and fish.
In his monograph Tyson surveyed contributions from previous authors. He corresponded with John Ray (see below). Ray had also dissected a porpoise (an exercise on which he reported in a published form in 1671), nine years before Tyson but was far more superficial and added very little to what other authors such as Rondelet had done. Tyson met Ray around 1683 and the latter invited Tyson to contribute to Willughby’s De Historia Piscium (Montagu 1943, p. 103).
Tyson was critical of encyclopedic approaches and relying on classical authors when it came to natural history. He set new standards in terms of direct observation and comparative anatomy. He also established an understanding of homology not seen since Aristotle. He proved to be a very competent observer of internal anatomy and he saw comparative anatomy as a means to explain the Great Chain of Being (or scala naturae or ladder of nature) as proposed by Plato and Aristotle.
A contemporary of Tyson was Samuel Collins[1] -. The son of the rector of Rotherfield, Sussex, who got his education at Cambridge, Collins travelled to several universities in France, Italy and the Netherlands finally getting his medical degree at the University of Padua, later becoming physician of Charles II. He taught anatomy at the Royal College of Physicians[1] -. Collins published A Systeme of Anatomy (London 1685), which was the earliest attempts to illustrate the brains of a broad variety of mammals, birds, teleosts, and elasmobranchs in a remarkable two-volume folio edition of 1,263 pages. It included 73 full-page illustrations of very high quality. There he described a female porpoise. However, it seems that he had used Tyson’s previous descriptions and unfortunately says nothing about the brain of this cetacean. Had he had examined the brain of the porpoise he would have noted the great similarities of this organ with those among the “viviparous quadrupeds.” Collins did not discuss the similarities between the other internal organs of the porpoise and those called mammals today either. He acknowledged Tyson’ previous contributions in this matter.
In addition to Tyson, Collins\'s anatomy draws largely upon the works of Thomas Willis. In the opening Epistle-Dedicatory to James II he claimed that various chapters "are illustrated by the Dissection of other Animals (which I have performed with Care and Diligence, speaking the wonderous Works of the Glorious Maker) rendering the Parts of Man\'s Body more clear and more intelligible." In volume two of his huge work he described numerous folio copper plates containing the most extensive comparative anatomy of the brain then existing, an expansive account of the functional significance of his findings, as well as practical clinical commentary.
Ray[1] - was the first naturalist who truly represented this new era of careful observation. His father was a blacksmith and his mother was an herbal healer. He studied at the University of Cambridge, pursuing comparative anatomy although initially his main interest was botany. He taught Greek, mathematics and humanities at Cambridge but abandoned his teaching position after refusing to comply with the Act of Uniformity of 1662. He was a very religious person who undertook the study of nature to understand God’s creation (Raven 1950). Fairly early he developed a plan with his student and patron, Francis Willughby[1] - to produce a joint general natural history. To that end Ray and Willughby went on an extended tour of England and Europe (1662-1666), including the medical school at Montpellier. Although they did not always travel together both collected specimens, got involved in dissections and acquired books and illustrations (Kusukawa 2000), an endeavor bankrolled by Willughby. When Willughby died, Ray took over his parts of the general natural history. Willughby left him an annuity of £60 and Ray stayed on as tutor to Willughby’s children until 1675, when Willughby\'s mother, also his patron, died, and the widow immediately terminated the relationship. Ray inherited a small farm that also contributed to the family\'s maintenance while he earned money from his productive publishing. Therefore Ray had the financial freedom to pursue his intellectual interests.
Ray’s first published work on cetaceans was Dissection of a Porpess (1671). He does a much better job in describing the internal anatomy of this animal when compared with Rondelet but does not get into the detail that Tyson achieved later. During the narrative of his findings he keeps noticing that a porpoise has a lot in common with the “quadrupeds”. Yet he persisted calling them “fishes.”
Ray published Historia piscium (1686), under Willughby\'s name 14 years after his patron death, though Ray himself contributed the vast majority of the content. He carried out the first serious attempt to achieve a systematic arrangement, the success of which can be attributed by the fact that it served as a basis for the systematics work of the following century. His approach was based on direct observation, collaboration with other researchers, and critical reading of previous authors.
Historia Piscium is divided into two parts that were printed separatedly: the first is the narrative and the second, titled Ichthyographia, were the illustrations. Many libraries today have both bound together. As sources Ray used authors mentioned earlier in this chapter: Rondelet, Salviani, Gessner, Aldrovandi and Belon, among others. Yet, far from merely compiling information from them, Ray insisted in very comprehensive descriptions of species and discarded all monsters and mythical creatures mentioned by his predecessors. Ray not only removed narratives of marine invertebrates but also other aquatic animals such as the crocodile and the hippopotamus. He divided his subject matter into three groups: cetaceans, cartilaginous fishes, and bony fishes. He recognized that when it comes to reproduction and internal anatomy cetaceans are identical to the “viviparous quadrupeds.” Still, he kept cetaceans within the “piscium” despite the fact that he was well aware that they were biologically distinct from fishes.
In his narrative of species Ray moved away from in the practical aspects related to these animals. Aspects such as usage for medical purposes were very common among previous authors because of their medical background. Yet, Ray was very keen at compiling names on the belief that a universal language could be construct based on the knowledge of nature. As Kusukawa (2000) has argued convincingly, Ray believed that there was a need for “a construction of a universal language based on a table that properly expressed the natural order and relations between things.” Hence a precise description and classification was the route to achieve that goal. The final product counted not only on the intellectual support of the Royal Society’s members who provided constructive criticism and moral support but also their financial support. The cost of publishing Historia Piscium was not only very high, mostly because of the expense of the illustrations (187 plates), but also the 500 copies printed sold poorly. As a consequence the Society could not print Isaac Newton’s Principia.
Ray’s third publication related to marine mammals was Synopsis Methodica Animalium Quadrupedum et Serpentini Generis (1693). By then he was totally convinced that cetaceans were not fishes: “For except as to the place on which they live, the external form of the body, the hairless skin and progressive swimming motion, they have almost nothing in common with fishes, but remaining characters agree with the viviparous quadrupeds.” He placed today\'s terrestrial mammals (including the manatee) among the ‘hairy animals’ very close to the Cetaceum genus (cetaceans).
In Synopsis Ray included a section called Pisces Cetacei seu Belluae marinae where he expressed that these animals breath and give birth like the “oviparous quadrupeds.” He grouped them into two categories according to the presence of teeth much as we do today separating odontocetes from mysticetes. Ras was the first in doing so. The species he cited were Balaena vulgaris (Rondelet), Balaena (Fin-Fish), Physeter or Balaena physeteris, Orca (Rondelet & Belon), Cete (Sperm whale), Pot Walfish, Albus piscis cetaceus (white fish), Monoceros cetaceo (Narhual islandis), Delphino antiquorum (dolphin, from Rondelet), Phocaeno (Rondelet & Belon), dissecting a specimen of the latter in 1669.
Illustrations from Tyson’s (1680) description of the internal anatomy of a porpoise. Notice the remarkable accuracy of the depictions.
Ray developed a division of animals characterized by having blood, breathing by lungs, two ventricles in the heart, and being viviparous. Ray subdivided this group into aquatic (cetaceans) and terrestrial or quadruped including sirenians (manatees and dugongs). He rejected tales of fabulous animals while perfecting Aristotle’s classification by diving vertebrates into those having hearts with two ventricles (mammals and birds) from those with a single ventricle (reptiles, amphibians and fish). He also advanced the understanding of other groupings. He established the significance of the generic principle, defined species, and was a leading contributor to the gigantic task of classification.
Ray came close to recognizing mammals as a separate group based on “warm-blood,” vivipary, and hair. He conceded the relationship of cetaceans with viviparous quadrupeds; described genera and species; established ordinal classification of mammals; systematic phrases and names; used of descriptive phrases as well as monomial names (a taxonomic name consisting of a single word); a dichotomous (“A is B or not B”) classification of mammals. Yet, he lacked the vision or intellectual courage to reunite marine mammals with their terrestrial relatives and still placed the former with the fish “in accordance with common usage.” Still he was possibly the best naturalist of the seventeenth century.
Artedi[1] - was the son of a parish priest who developed an interest in fishes from an early age. He studied medicine at the University of Uppsala, devoting most of his time at studying natural history. At 29 years of age he went to London for a year to study natural history collections and described the sighting of a whale in November 1734, probably downstream of the London Bridge. He then moved to Leiden, The Netherlands, to complete his medical studies and there he met Linnaeus, whom he knew from their native Sweden, forging a lifelong personal and professional relationship. Linnaeus introduced him to an Amsterdam chemist, Albert Seba, and Artedi started working on Seba’s fish collection. Artedi died at the age of 30 by drowning in an Amsterdam canal. After his death, Linnaeus recovered his manuscripts and published Ichthyologia (1738) without amending Artedi’s original work. Despite the fact that this was an unfinished work, it was a fundamental publication that marked the origin of ichthyology as we know it today. After a long (96 pages) introduction describing previous authorities on ichthyology the second part deals with the taxonomic terminology he used, particularly regarding the concept of genus and distinguishing between species and varieties. His system set the basis for the modern systematic classification of living organisms later established by Linnaeus. In part three he went into the classification of species including detailed description of them, some of which he had dissected himself. For this Artedi is considered the father of ichthyology (Wheeler 1962, 1987, Broberg 1987).
Artedi separated actual fishes from cetaceans (which he called “plagiuri”) based on the plane of the caudal fin. He described 7 genera and 14 species including the manatee and the “siren” as follows:
Order:Plagiuri
Physeter
Balaena major (Ray, p. 15)
Balaena macrocephala (Ray, p. 16)
Delphinus
Delphinus (Phocaena) (Art. Syn. 104)
Delphinus (Delphin) (Art. Syn. 105)
Delphinus (Orca) (Art. Syn. 106)
Balaena
Balaena vulgaris (Ray p. 6, 16)
Balaena edentula Fin-Fish (Ray p. 6, 10)
Balaena tripinnis (Ray 16)
Balenae (Balaena tripinnis) (Ray 17)
Monodon
Monoceros pisces (Will. 42, Ray 11, Charleton 168)
Catodon
Balaena minor (Ray p. 15)
Balaena major (Ray p. 17, Will. P. 41)
Trichechus
Manatus (Rondelet p. 490, Gessner p. 213, Charleton 169, Aldrovandi 7
28, Jonston 223)
Siren
Homo marinus
Artedi established the basic classification of fishes that lasted for about 200 years and separated cetaceans into a totally different order than fishes; he apparently knew that they were different, but still tradition was difficult to break and thus he included them into his ichthyological treatise. He also established the basic branching of animal groups into Class, Maniples (Families), Genera, and Species, a system that was to be closely followed by Linnaeus (Wheeler 1987, Broberg 1987). His work set the foundations for what Linnaeus would culminate as the definitely recognition of cetaceans as distinct group within mammals.
Linnaeus (or Linné)[1] - had as a father a country person who loved plants. Linnaeus followed a medical career but was actually more interested in botany than in anything else. Linnaeus met Artedi in 1729 and their interests were complementary: Artedi, a zoologist interested mostly on fishes, and Linnaeus, interested in botany. He would later edit Artedi’s book in ichthyology that was published in 1738. What Linnaeus learned from Artedi set the basis for a better classification not only of plants but also animals in general.
Even some of Linnaeus students were developing a better understanding of cetaceans as being really close to “viviparous quadrupeds.” That was the case of Pehr Löfling[1] -, one of Linnaeus’ students who came very close to making major contributions to the true nature of dolphins and manatees based on his observations of these animals in South America. In his description of Amazon freshwater dolphin or boto, Löfling was clear about when writing that whales and dolphins were different from fishes: ”Pisces per pulmonibus spirantibus.” However, his early death and the fact that his manuscripts were never published prevented him from gaining recognition in the scientific community (Romero et al. 1997).
With all of this background, the botanist Linnaeus was ready to revolutionize biological classification and in the 10th Edition (1758) of his fundamental work Systema Naturae, he introduced the term Mammalia, and included Cete among them. For Linnaeus, mammals were united by having hair, being viviparous, and producing milk. He coined the term cetacea and separated them from fishes and grouped them with the rest of the viviparous quadrupeds based on the following characteristics: two-chamber heart, breathing by lungs, hollow ears, internal fertilization, and production of milk.
Thus, Linnaeus revolutionized the science of systematics by developing a fully natural system of classification, using consistently the binomial nomenclature, and designing species with Latinized names (genus and species). He developed a hierarchy (class, order, genus, species) as proposed by Artedi, with species defined as similar individuals bound together by reproduction, which also set the basis of the biological species concept. The use of telegraphic speech-like (very short sentences) diagnosis for species descriptions and the standardization of synonymies (same species with different names) in order to reach a taxonomic consensus made his classification even more useful since from now on one could find clarity on what a particular species was tracing its description to other authors. He also doubled the number of species described by Ray. Thus, despite the fact that he was not a zoologist per se nor was involved in dissection of animals, he was far from a compiler in that he applied critical thinking to the way he ordered nature.
This progress is even more remarkable when considering that Linnaeus was far from an evolutionist. For him species were fixed except for small variations due to climatic/local conditions. Yet, Linnaeus was, without question, the founder of systematics and the one who laid the foundations for the naturalists to become specialists and, therefore, opened the door for the first group of marine mammal specialists, now that these creatures were not longer considered “fishes.” It was not until Linnaeus that the science of taxonomy made the strides that have lead us to where we are today in our understanding of the natural world. Linnaeus understood biological principles and placed animals in groups based on homologies rather than using environment to drive classification, and this was what allowed him to recognized cetaceans as a distinct group within mammals.
Persuing at the information provided above there are a number of discernable patterns. One is the preponderance of pre-Linnean researchers interested in marine mammals who had a medical background of some sort. That is not surprising because medicine was the closest thing to science as a career existed until the eighteenth century. Also, being interested in medicine created more opportunities to dissect animals and, therefore, understanding of their internal anatomy that was particularly crucial in establishing the homology between cetaceans and the “viviparous quadrupeds.” Yet, this positive influence was marred by the proliferation of encyclopedists who, for the most part, were uncritical compilers of other authors’ information. However, the major impediment to any attempts to develop a natural classification for cetaceans was the insistence on classifying them by virtue of the environment in which they live, something that even diverted the thoughts of keen observers such as Ray and Artedi, despite of abundant evidence to the contrary having been collected since Aristotle.
Finally, we should not overlook the role played by intellectual inertia in the development of science. As Horder (1998) clearly demonstrated, scientists need to know the history of their field to avoid errors of the past, something that has also been argued for specific fields of biology (see Romero 2009, Chapter 1).
Explainable artificial intelligence (xAI) is one of the research topics that has been intriguing in recent years. Today, even if we are at the beginning of understanding this type of models, the studies that show interesting results about this issue are getting more and more intensive. In the near future, it is predicted that there will be years when the interpretability of artificial intelligence and deep meta-learning models is frequently explored [1]. It is thought to be a solution to overcome constraints in classical deep learning methods.
In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. Currently, in classical deep learning methods, input data and target (class) information can be trained with high performance and tested with new data input [2]. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms [3]. Many layers in a deep network allow it to recognize things at different levels of abstraction. For example, in a structure designed to recognize dogs, the lower layers recognize simple things such as outlines or color; the upper layers recognize more complex things like fur or eyes, and the upper layers define them all as a dog. Presumably speaking, the same approach can be applied to other inputs that lead a machine to teach itself. For example, it can be easily applied to the sounds that make up the words in the speech, the letters and words that form the sentences in the text, or the steering movements required to drive.
However, there are important shortcomings that current deep learning models are currently inadequate [4]. For deep learning, huge data sets are needed to train on, and these data sets must be inclusive/unbiased, and of good quality [5]. In addition, traditional deep learning requires a lot of time to train models for satisfying their purpose with an admissible amount of accuracy and relevancy [6]. Although deep learning is autonomous, it is highly susceptible to errors. Assume that an algorithm is trained with data sets small enough to not be inclusive [4]. The models trained by this way cause to irrelevant responses (biased predictions coming from a biased training set) being displayed to users [7]. One of the most important problems in artificial learning models is transparency and interpretability [8]. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable [9]. In other words, when you receive an output data against the input data, the deep learning model cannot provide the information for which reason the output is generated. The user cannot fully grasp the internal functions of these models and cannot find answers to question why and how the answers the models produce [10]. This situation creates difficulties in the application areas of these models in many aspects. For example, you stopped a taxi and got on it. The driver is such a driver that when he takes you to your destination, he turns right, turns left, and tries to get you on a strange route than you expect, but when you ask why he did so, he cannot give you a satisfactory answer. Would you be nervous? If there is no problem for you, you can ride an autonomous vehicle without a driver. As another example, when you go to the doctor, the doctor you send your complaint asks for tests and when you have those tests and send it to the doctor, the doctor tells you what your illness is. Even though he says his treatment, he does not give explanatory information about the cause of your illness. In this case, questions remain about what caused the disease and you would not be satisfied with the doctor. This is an important open point in artificial neural networks and deep learning models.
The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. One of these areas is the end user explanation section that includes social sciences. This area provides artificial intelligence to gain cognitive abilities. Another area is the human machine interface, where it can demonstrate the ability to explain; because explainable artificial intelligence needs a very high-level interaction with the user. And finally, deep learning models are an important part of an explicable artificial intelligence approach (Figure 1).
Explainable artificial intelligence (xAI) [8].
In this new approach, it is aimed to provide the user with the ability to explain the output data produced as well as being trained at high performance with the input data and target (class) information and tested with the new data input as in the classical machine learning models. This will create a new generation artificial intelligence approach that can establish a cause and effect relationship between input and output. It will also be the mechanism of monitoring the reliability of artificial intelligence from the user point of view. While a classic deep learning model can answer “what” or “who” questions, learning models in explainable artificial intelligence approaches can also answer “why,” “how,” “where,” and “when” questions [10] (Figure 2).
How can explainable artificial intelligence (xAI) be reliable [11]?
Explainability and accuracy are two separate domains. In general, models that are advantageous in terms of accuracy and performance are not very successful in terms of explainability. Likewise, methods with high explainability are also disadvantageous in terms of accuracy. When methods such as classical deep learning models, artificial neural networks support vector machines are utilized, they do not give reasons why, and how their outputs created in terms of explainability. On the other hand, they are very successful in accuracy and performance. Rule-based structures, decision trees, regression algorithms, and graphical methods are good explainability but not advantageous in terms of performance and accuracy. At this point, explanatory artificial intelligence (xAI), which is targeted to be at the highest level of both explainability and accuracy and performance, reveals its importance at this point (Figure 3).
Machine learning models with respect to accuracy-explainability domain [12].
There is a transformation of machine learning that has been going on since the 1950s, sometimes faster and sometimes slower. The most studied and remarkable area in the recent past is artificial learning, which aims to model the live decision system, behavior, and responses. Successful results in the field of artificial learning led to the rapid increase of AI applications. Further studies promise to be autonomous systems capable of self-perception, learning, decision-making, and action [13].
Especially after the 1990s, although deep learning concept and foundations go back to the past, the accompanying recurrent neural networks, convolutional neural networks, deep reinforcement learning, and adversarial generative networks have achieved remarkable successes. Although successful results are obtained, these systems are insufficient in terms of explaining the decisions and actions to human users and there are limits.
The U.S. Department of Defense (DoD) explains that it is facing the challenges posed by autonomous and symbiotic systems, which are becoming smarter with each passing day. Explaining artificial intelligence or especially explanatory machine learning is important in terms of being a preview that users will encounter machines with human-like artificial intelligence in the future [14, 15]. Explained artificial intelligence is one of the Defense Advanced Research Projects Agency (DARPA) programs aimed at the development of a new generation of artificial intelligence systems, where they understand the context and environment in which machines operate and build descriptive models that enable them to characterize the real world phenomenon over time. For this purpose, DARPA recently issued a call letter for the Explainable Artificial Intelligence (XAI)—Explanatory Artificial Intelligence project [15]. Within the scope of the project, it is aimed to develop a system of machine learning techniques that focus on machine learning and human-machine interaction, and produce explanatory models that will enable end users to understand, trust, and manage emerging artificial intelligence systems. According to the researchers from DARPA, the striking successes in machine learning have led to a huge explosion in new AI capabilities that enable the production of autonomous systems that perceive, learn, decide, and act on their own. Although these systems provide tremendous benefits, their effectiveness is limited due to the inability to explain machine decisions and actions to human users.
The Explanatory Artificial Intelligence project aims to develop the machine learning and computer-human interaction tools to ensure that the end user, who depends on decisions, recommendations, or actions produced by the artificial intelligence system, understands the reason behind the system’s decisions [1]. For example, an intelligence analyst who gets recommendations from big data analytics algorithms may need to understand why the algorithm advises to examine a particular activity further. Similarly, the operator, who tests a newly developed autonomous system, has to understand how he makes his own decisions to determine how the system will use it in future tasks.
The xAI tools will provide end users with explanations of individual decisions, which will enable them to understand the strengths and weaknesses of the system in general, give an idea of how the system will behave in the future, and perhaps teach how to correct the system\'s mistakes. The XAI project addresses three research and development challenges: how to build more models, how to design an explanation interface, and how to understand psychological requirements for effective explanations [2].
For the first problem, the xAI project aims to develop machine learning techniques to be able to manufacture explanatory models. To solve the second challenge, the program envisions integrating state-of-the-art human-machine interaction techniques with new principles, strategies, and techniques to produce effective explanations. To solve the third problem, the xAI project plans to summarize, disseminate, and apply existing psychological theory explanations. There are two technical areas in the program: the first is to develop an explanatory learning system with an explanatory model and an explanation interface; and the second technical area covers psychological theories of explanation [8].
In 2016, a self-driving car was launched on quiet roads in Monmouth County, New Jersey. This experimental tool developed by researchers at chip maker Nvidia did not look different from other autonomous cars; however, Google was different from what Tesla or General Motors introduced and showed the rising power of artificial intelligence. The car had not even followed a single instruction provided by an engineer or a programmer. Instead, it relied entirely on an algorithm that allowed him to learn to drive by watching a person driving [3]. It was an impressive success to have a car self-driving in this way. But it was also somewhat upsetting as it was not entirely clear how the car made its own decisions. The information from the vehicle’s sensors went directly to a huge artificial neural network that processes the data and then delivers the commands needed to operate the steering wheel, brakes, and other structures. The results seem to match the reactions you can expect from a human driver. But what if one day something unexpected happens; hits a tree or stops at the green light? According to the current situation, it may be difficult to find the cause. The system is so complex that even the engineers who designed it can find it difficult to pinpoint the cause of any action. Moreover, you cannot ask this; there is no obvious way to design such a system that can always explain why it does what it does. The mysterious mind of this vehicle points to a vague-looking issue of artificial intelligence. Artificial intelligence technology, which is located at the base of the car and known as deep learning, has proven to be very strong in problem-solving in recent years, and this technology has been widely applied in works such as image content estimation, voice recognition, and language translation. Now the same methods can be used to diagnose lethal diseases, make million-dollar business decisions, etc. to change all industries.
Currently, the mathematical models are used to help determine who will be on parole, who will be approved to borrow money, and who will be hired. If you can access these mathematical models, it is possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine learning approaches. These approaches can make automated decision-making completely incomprehensible. The most common of these approaches represents deep learning, a fundamentally different way of programming computers. Whether it is an investment decision or a medical decision, or a military decision, you do not want to rely solely on a “black box” method [1]. There is already a debate that it is a fundamental legal right to question a system of artificial intelligence about how it arrived at its conclusions. Starting in the summer of 2018, the European Union may require companies to provide users with an explanation of the decisions made by automated systems. This may be impossible even for systems that look comparatively simple on the surface, such as applications and Websites that use deep learning to offer advertising or song suggestions. Computers performing these services have programmed themselves and have done so in ways we cannot understand. Even the engineers who build these applications cannot fully explain their behavior.
As technology advances, we can go beyond some thresholds where using artificial intelligence in recent times requires a leap of faith. The mankind, of course, are not always able to fully explain our thought processes; but we find a variety of methods to intuitively trust people and measure them. Will this be possible for machines that think and make decisions differently than a person does? We have never built machines that operate in ways that their manufacturers do not understand. How long can we hope to communicate and deal with intelligent machines that can be unpredictable or incomprehensible? These questions take a journey toward new technology research on artificial intelligence algorithms, from Google to Apple and many other places between them, including a conversation with one of the greatest thinkers of our time.
You cannot see how the deep neural network works just by looking inside. The reasoning of a network is embedded in the behavior of thousands of nerves, which are stacked and tied to tens or even hundreds of layers, mixed together. Each of the nerves in the first layer receives an input, such as the voltage of a pixel in an image, and then performs a calculation before sending a new signal as an output. This output is sent to the next layer in a complex network, and this process continues until a general output is produced. There is also a process known as back propagation that modifies the calculations of individual nerves so that a network learns to produce a desired output. Because deep learning is inherently a dark black box by nature, artificial learning models designed with millions of artificial nerve cells with hundreds of layers like traditional deep learning models are not infallible [1]. Their reliability is questioned when simple pixel changes can be seriously misleaded by causing significant deviations in the weight values in all layers of the neural network, especially in an example such as a one-pixel attack [16]. So, it becomes inevitable to ask the question of how it can succeed or fail. With the success of this type of advanced applications, its complexity also increases and its understanding/clarity becomes difficult.
It is aimed to have the ability to explain the reasons of new artificial learning systems, identify their strengths and weaknesses, and understand how they will behave in the future. For an ideal artificial intelligence system, the best accuracy and best performance, as well as the best explainability and the best interpretability are required within the cause-effect relationship. The strategy developed to achieve this goal is to develop new or modified artificial learning techniques that will produce more explicable models. These models are aimed to be combined with state-of-the-art human-computer interactive interface techniques that can be translated into understandable and useful explanation dialogs for the end user (Figure 4).
Explainable artificial intelligence (xAI) project proposed by DARPA [14, 15].
In this structure, unlike the classical deep learning approaches, two different elements draw attention as well as a new machine learning process. One of these is the explanatory model and the other is the explanation interface. The process of deep neural network-based machine learning is explained at the core of the artificial intelligence approach. Among the known deep learning models, autoencoder, convolutional, recurrent (LSTM), deep belief network, or deep reinforcement learning can be preferred. However, it is also possible to use a hybrid structure where several deep learning approaches are used together. Autoencoder-type model of deep neural networks are multilayered perceptron structure. In convolution neural network-type models, layers consist of convolutional layer, ReLU activation function, and max pool layer. A conventional component of the LSTM is composed of a memory cell including input, output, and forget gates. For training, the backpropagation through time algorithm can be preferred. Although the most common form of deep reinforcement learning models is deep Q network (DQN), many different variations of this model can be addressed. Many different algorithms are used as optimization algorithm. Gradient-based algorithms are the most common form of these algorithms (Figure 5).
Deep learning models: (a) autoencoder [17], (b) convolutional neural network [18], and (c) recurrent (LSTM) neural network [19].
Explainable model is an adaptive rule-based reasoning system. It is a structure that reveals the cause-effect relations between input data and the results obtained from the machine learning process. This causal structure learns the rules with its own internal deep learning method. In this way, the explanatory artificial intelligence model allows it to explore the causes and develop new strategies against different situations [20].
The explanation interface is a part of the user interaction. It is similar to the question-answer interface in voice digital assistants. This interface consists of a decoder that evaluates the demands of the user and an encoder unit that enables the responses from the explanatory model, which constitutes the causal mechanism of the explainable artificial intelligence, to the user (Figure 6).
Semantic knowledge matching for explainable artificial intelligence model [21].
In fact, the large networks of semantic technologies (entities) and relationships associated with Knowledge Graphs (KGs) provide a useful solution for the issue of understandability, several reasoning mechanisms, ranging from consistency checking to causal inference [21]. The ontologies realizing these reasoning procedures provide a formal representation of semantic entities and relationships relevant to a particular sphere of knowledge [21]. The input data, hidden layers, encoded features, and predicted output of deep learning models are passed into knowledge graphs (KGs) or concepts and relationships of ontologies (knowledge matching) [21]. Generally, the internal functioning of algorithms to be more transparent and comprehensible can be realized by knowledge matching of deep learning components, including input features, hidden unit and layers, and output predictions with KGs and ontology components [21]. Besides that, the conditions for advanced explanations, cross-disciplinary and interactive explanations are enabled by query and reasoning mechanisms of KGs and ontologies [21].
Although explanatory artificial intelligence forms are of very different structures, all modules such as this explanation interface, explanatory model, and deep learning work in coordination with each other. For example, while a deep learning process estimates classes, such as the explanatory artificial intelligence model (xAI tool) developed by IBM, the concept features data obtained from this process, and another deep learning process using the same input data set produces an explanatory output for the predicted class label output [22] (Figure 7).
Explainable artificial intelligence (xAI) tool developed by IBM [22].
At this point, the explainable artificial intelligence (xAI) tool developed by IBM is referred as a self-explaining neural network (SENN) which can be trained end-to-end with back-propagation in case of that g depends on its arguments in a continuous way [18]. The input is transformed into a small set of interpretable basis features by a concept encoder [22]. The relevance scores are produced by an input-dependent parametrizer. A prediction to be generated is merged by an aggregation function. The full model to behave locally as a linear function on h(x) with parameters
As research and technology on machine learning progresses, artificial intelligence agents consistently display impressive learning performances that meet and exceed the cognitive skills of people in different fields. However, most AI programs are based on computing technology and even reinforcement learning (RL) models that try to regularly improve their knowledge to match human performance. By contrast, people can quickly learn new skills of new skills, simply by having a new skill [23]. The learning of the human brain so efficiently has surprised neuroscientists for years.
In traditional deep learning approaches, the system develops a data-specific model that is transmitted to it by learning from the data. The learning system will perform a certain task only for a certain environment. In the case of another environment, when a very different data is transmitted to it, this deep learning model will be insufficient to perform the task [24]. This issue reveals hard constraints in utilizing machine learning or data mining methods, since the relationship between the learning problem and the effectiveness of different learning algorithms is not yet understood. Under ideal conditions, a system should be designed in which the quality of the data given to the system differs and it can easily adapt to changes in different environments [25]. The deep learning methods used in the current situation are not successful in these situations. At this point, meta-learning, which learns to learn, is an integrated and hierarchical learning model over several different environmental models [26, 27]. As a subfield of machine learning, meta-learning learning algorithms are applied on metadata about machine learning experiments. Instead of classical machine learning approaches that only learn a specific task with single massive dataset, meta-learning is a high-level machine learning approach that learns other tasks together. Therefore, this approach requires a hierarchical structure that learns to learn a new task with distributed hierarchically structured metadata. It is generally applied for hyper parameter adjustment; recent applications have started to focus on a small number of learning. For example, if the system has already learned a few different models or tasks, meta-learning can generalize them and learn how to learn more efficiently. In this way, it can learn new tasks efficiently and create a structure that can easily adapt to changes in multiple tasks in different environments.
People are good at figuring out the meaning of a word after seeing it used only in a few sentences. Similarly, we want our ML algorithms to be generalized to new tasks, without the need for a large data set each time, and to change behavior after a few samples. In typical learning (on a single dataset), each sample targets pair functions as a training point. However, in a small number of learning situations, each “new” sample area is actually another task in itself. In other words, understanding the way that you use unique words in a particular social environment becomes a new task for your language-understanding model, and when you enter a different social environment, it means that the system can adapt to a different language-understanding model than before since it requires to dominate the words that are specific to that social environment. To make sure an ML framework can behave similarly, we have to train it on multiple tasks on its own, so we make each data set a new example of training [28] (Figure 8).
Meta-learning approach [29].
An alternative is to handle the task consecutively as a sequential input array and create a repetitive model that can create a representation of this array for a new task. Typically, in this case, we have a single training process with a memory or attention repetitive network [30]. This approach also gives good results, especially when the installations are properly designed for the task. The calculation performed by the optimizer during the meta-forward transition is very similar to the calculation of a repetitive network [31]. It repeatedly applies the same parameters over a series of inputs (consecutive weights and gradients of the model during learning). In practice, this means that we meet a common problem with repetitive networks. Since the models are not trained to get rid of training errors, they have trouble returning to a safe path when they make mistakes, and the models have difficulty generalizing longer sequences than those used in the order in which they were used. In order to overcome these problems, if the model learns an action policy related to the current educational situation, reinforcement learning approaches can be preferred [32] (Figure 9).
(a) Meta-reinforcement learning (stack of sub-policies representation) [33] and (b) meta-reinforcement learning (inner-outer loop representation) [34].
Formal reinforcement learning algorithm learns a policy for only single task.
In meta-reinforcement learning, there are two distinct processes. One of them is adaptation (inner-loop) behaving ordinary RL policy learning to produce sub-policy where
Another process is meta-training (outer-loop), which is described as meta-policy learning from all sub-policies in the adaptation process (inner-loop).
One of the main differentiers between the human brain and artificial intelligence structures such as deep neural networks, is the brain that utilizes different chemicals known as neurotransmitters to perform different cognitive functions. A new study by DeepMind believes that one of these neurotransmitters plays an important role in the brain\'s ability to quickly learn new topics. Dopamine acts as a reward system that strengthens connections between neurons in the brain.
The DeepMind team has used different meta-reinforcement learning techniques that simulate the role of dopamine in the learning process. Meta-learning trained a repetitive neural network (representing the prefrontal cortex) using standard deep reinforcement learning techniques (representing the role of dopamine) and then compared the activity dynamics of the repetitive network with actual data from previous findings in neuroscience experiments [27]. Recurrent networks are a good example of meta-learning because they can internalize past actions and observations and then use these experiences while training on various tasks.
The meta-learning model recreated the Harlow experiment by saying a virtual computer screen and randomly selected images, and the experiment showed that the “meta-RL agent” was learned in a similar way to the animals found in the Harlow Experiment, even when presented with the Harlow Experiment. All new images were never seen before. The meta-learning agent quickly adapted to different tasks with different rules and structures.
In this section, we will discuss the development of deep reinforcement learning models with an explicable approach to artificial intelligence. Deep reinforcement learning models are machine learning models that learn what action to take according to status and reward information by maximizing reward [27]. Generally, it is widely preferred in robotic, autonomous driverless vehicles, unmanned aerial vehicles, and games. Explanatory artificial intelligence, on the other hand, provides the knowledge of why action should be taken against the situation and reward for deep reinforcement learning models. In this way, it will be possible to gain the causal decision-making ability of the model by revealing the relational links between the input and output of the developed agent (Figure 10).
(a) Reinforcement learning and (b) inverse reinforcement learning [35].
In addition, it is possible to learn the reward derivation mechanism by using the inverse reinforcement learning model [36, 37]. In this case, unlike the previous approach, a meta-cognitive artificial intelligence model that can adapt to other environments instead of just one environment is developed [38, 39]. Taken together with the explainable artificial intelligence approach, it will be possible for the developed agent to develop his own strategy by establishing a cause-effect relationship. For example, the explainable meta-reinforcement learning agent to be developed means that in terms of meta-learning, it can learn to play Go, chess, checkers, and even learn and adapt when it is encountering a new game, and in terms of explainable artificial intelligence, it means that being aware of why it is doing any specific action against a move made by the opponent, it can explain this.
Next generation artificial intelligence structures are expected to have a hierarchical meta-learning ability that can adapt to many different environments, besides being a causal and explanatory power by establishing a cause-effect relationship. For this, serious effort is still needed to create flexible and interpretable models that can hold opinions from many different disciplines together and work in harmony.
We cannot ignore the advantages this will give us. For example, if we start with a medical application, after the patient data is examined, both the physician must understand and explain to the patient why he/she suggested that the explanatory decision support system suggested to the related patient that there was a “risk of heart attack.” At the same time, as a meta-learning agent of this system, it has the same ability against all other diseases and it will be possible to develop appropriate treatment strategies.
While coming to this stage, what data is evaluated first is another important criterion. It is also necessary to explain what data is needed and why, and what is needed for proper evaluation. In the future, next generation deep learning and artificial intelligence forms are expected to reach the level of intelligence (singularity), which has higher performance and ability than human level. Artificial intelligence and deep learning structures mentioned in this section are thought to shed light on reaching these levels. In particular, it can be said that meta-learning approaches are capable of supporting the formation of structures that learn and adapt to multiple tasks and are also called general artificial intelligence (AGI). In the same way, it can be stated that artificial intelligence structures will help the formation of self-awareness and artificial consciousness structures based on content and causality.
The authors declare no conflict of interest.
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