Impact of (SARS-CoV-2) COVID 19 on the five main indigenous language-speaking areas in Veracruz Mexico: The case of the Popoluca from the Soteapan Area

Philosophy

The importance of the working document is that it allows the analysis of the information and the status of
          cases associated with (SARS-CoV-2) COVID-19 as open data at the municipal, state and national level, with a
          daily record of patients, according to a age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19
          according to the following characteristics: a) Positive, b) Negative, c) Suspicious. Likewise, it presents
          information related to the identification of an outpatient and / or hospitalized patient, attending to their
          medical development, identifying: a) Recovered, b) Deaths and c) Active, in Phase 3 and Phase 4, in the five
          main population areas speaker of indigenous language in the State of Veracruz - Mexico. The data analysis is
          carried out through the application of a data mining algorithm, which provides the information, fast and
          timely, required for the estimation of Medical Care Scenarios of (SARS-CoV-2) COVID-19, as well as for know
          the impact on the indigenous language speaking population in Veracruz. For this purpose, the following study zones are presented: a) Totonacapan Zone, b) Huasteco from the Tantoyuca Zone, c) Otomi from the
          Inxhuatlan de Madero Zone, d) Nahuatl from the Zongolica Zone, e) Nahuatl from the Chicontepec Zone, f)
          Nahualt from the Pajapan Zone and g) Popoluca from the Soteapan Zone. This data article presents the
          information as of August 1, 2020 corresponding to the Popoluca from the Soteapan Area.

. Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 Impact of (SARS-CoV-2) COVID 19 on the five main indigenous languagespeaking areas in Veracruz Mexico: The case of the Popoluca from the Soteapan Area Authors Carlos Medel-Ramírez1 , Hilario Medel-López2 Affiliations 1. Universidad Veracruzana / Instituto de Investigaciones y Estudios Superiores Económicos y Sociales 2. Universidad Veracruzana / Instituto de Antropología Corresponding author Carlos Medel-Ramírez ( firstname.lastname@example.com ) Abstract The importance of the working document is that it allows the analysis of the information and the status of cases associated with (SARS-CoV-2) COVID-19 as open data at the municipal, state and national level, with a daily record of patients, according to a age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negative, c) Suspicious. Likewise, it presents information related to the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Active, in Phase 3 and Phase 4, in the five main population areas speaker of indigenous language in the State of Veracruz Mexico. The data analysis is carried out through the application of a data mining algorithm, which provides the information, fast and timely, required for the estimation of Medical Care Scenarios of (SARS-CoV-2) COVID-19, as well as for know the impact on the indigenous language speaking population in Veracruz. For this purpose, the following study zones are presented: a) Totonacapan Zone, b) Huasteco from the Tantoyuca Zone, c) Otomi from the Inxhuatlan de Madero Zone, d) Nahuatl from the Zongolica Zone, e) Nahuatl from the Chicontepec Zone, f) Nahualt from the Pajapan Zone and g) Popoluca from the Soteapan Zone. This data article presents the information as of August 1, 2020 corresponding to the Popoluca from the Soteapan Area. Keywords (SARS-CoV-2) COVID-19, Algorithm (SARS-CoV-2) COVID-19, Mexico, identification of patients, Popoluca from the Soteapan Zone Specifications Table Subject Infectious Diseases Specific subject area Information from the Viral Respiratory Diseases Epidemiological Surveillance System for (SARS-CoV-2) COVID-19 in Mexico corresponding to the Popoluca from the Soteapan Area. Type of data Table Figure . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 How data were acquired Government of Mexico. Health Secretary. Databases Covid-19 México https://datos.gob.mx/busca/dataset/informacion-referente-a-casoscovid-19-en-mexico/resource/e8c7079c-dc2a-4b6e-8035-08042ed37165 Instruments: Software Orange Data Mining version 3.26.0 https://orange.biolab.si Make and model and of the instruments used: Algorithm for the identification of patients according to following characteristics: a) Positive, b) Negatives, c) Suspects. Likewise, it presents information regarding the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Assets Data format The information is presented in raw in CVS format, the Ministry of Health of Mexico since April 14, 2020 published the cases associated with (SARSCoV-2) COVID-19 as open data. The data processing corresponds to the records on the epidemic (SARS-CoV-2) COVID-19 at 1 August 2020. The treatment of the information is carried out through the application software for data mining Orange version 3.26.0, in which the algorithm for the analysis of information is filtered to present the current scenario of the Popoluca from the Soteapan Area in Mexico of the SARS-CoV-2 (COVID 19). Parameters for data collection The information is presented at the municipal, state and national levels, with a daily registry of patients, according to age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negatives, c) Suspects. Likewise, it presents information regarding the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Assets. Study area The municipalities that comprise the Popoluca from the Soteapan Area are: Acayucan, Catemaco, Coatzacoalcos, Hueyapan de Ocampo, Mecayapan, Minatitlan, Sayula de Aleman, Soteapan, Texistepec. Description of data collection This information is filtered to present the current scenario in the Popoluca from the Soteapan Area in Mexico of the SARS-CoV-2 (COVID 19) in a fast and timely manner, to support public decision-making in health matters. Data source location Institution: Universidad Veracruzana / Instituto de Investigaciones y Estudios Superiores Económicos y Sociales Country: México Data accessibility Raw data can be retrieved from the Github repository https://github.com/CMedelR/dataCovid19/edit/master/README.md Value of the Data . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 • The Algorithm for the identification of patients (SARS-CoV-2) COVID 19 in Mexico allows to analyze at the municipal, state and national level, the registry of patients, according to age, sex, comorbidities, for condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negative, c) Suspicious, as well as presenting information on the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b ) Deaths and c) Assets, in Phase 3 and Phase 4, in a fast and timely manner, to support public decision-making in health matters. • Taking into account their strategic roles in public health and researchers can use the data from this study to identify the action scenario for decision-making in the combat of (SARS-CoV-2) COVID 19 in Phase 3 and Phase 4 corresponding to the Popoluca from the Soteapan Area • The importance of data analysis is that it allows identifying cases (SARS-CoV-2). COVID-19 in Mexico is concentrated daily and knowing the impact on the population and allows preparing action scenarios to make public health policy decisions to combat SARS-CoV-2) COVID-19 in the main five areas that concentrate the speaking population of Nahuatl, Totonaco, Huasteco, Popoluca, Otomi in Veracruz Mexico. Data Description The source of information on the number of registered cases of (SARS-CoV-2) COVID-19 at 1 August 2020 for Mexico comes from the website https://datos.gob.mx/busca/dataset/informacion-referente-a-casos-covid19-en-mexico by the Ministry of Health, with the participation of the National Council for Science and Technology (CONACYT), the Center for Research in Geospatial Information Sciences (CENTROGEO), the National Laboratory for Geo-Intelligence (GEOINT), the Data Laboratory of the National Laboratory for Geointelligence (DataLab), where the registry of COVID-19 cases (SARS-CoV-2) COVID-19 is concentrated, and is the official means of communication and information on the epidemic in the Popoluca from the Soteapan Area in Mexico. The information of the cases (SARS-CoV-2) COVID-19 in Mexico is concentrated on a daily basis since April 19, 2020, communication and official information on the epidemic in Mexico, the data are presented at the municipal, state and national levels, with a daily registry of patients, according to age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negatives, c) Suspects. Likewise, it presents information regarding the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Assets. The data processing corresponds to the records on the epidemic (SARS-CoV-2) COVID-19 at 1 August 2020. The treatment of the information is carried out through the application software for data mining and visual programming Orange Data Mining version 3.26.0. Orange Data Mining is a machine learning and data mining suite for data analysis through Python scripting and visual programming. [1] According to (WHO, 2020) the (SARS-CoV-2) COVID-19 disease pattern presents 4 scenarios identified from the confirmation of Laboratory Diagnosis: a) Not Infected or b) Infected, in this finally, the following categories are observed, taking into account age and specific comorbidities in each case: a) Mild Infection, b) Moderate Infection, c) Severe Infection and d) Critical Infection. Depending on the category observed in Patients who have a Confirmation of Infected, as in the case of a) or b) it can assume the character of Outpatient, so the strategy is isolation or "quarantine" at home, where the result It is hoped that he will recover. Regarding the Patients who have a Confirmation of Infected, in categories c) and d) they assume the character of Hospitalized Patient, with a probability of requiring care in Intensive Care Units and requiring Intubation, and where it is hoped to save as many patients as possible. . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 The importance of the research is that it allows identifying the action scenario for making public health policy decisions to combat CO(SARS-CoV-2) COVID-19, since they consider the following states of process in medical treatment, in order to carry out the Estimate of Scenarios for Medical Care of the (SARS-CoV-2) COVID-19 under the following premises of hospital care: 1. A patient with a positive (SARS-CoV-2) COVID-19 laboratory diagnosis can be considered: a) Outpatient, or b) Hospitalized. 2. If the (SARS-CoV-2) COVID-19 Positive patient is Hospitalized, the following should be considered: a) Enter the Intensive Care Unit or b) Do not enter the Intensive Care Unit. 3. If the (SARS-CoV-2) COVID-19 Positive patient is Hospitalized and Entered into the Intensive Care Unit, the following should be considered: a) The patient requires intubation or b) The patient does NOT require intubation. Methods The information is presented in raw in CVS format, the Ministry of Health of Mexico. The data processing corresponds to the records on the epidemic (SARS-CoV-2) COVID-19 at 1 August 2020. The treatment of the information is carried out through the application software for data mining Orange version 3.26.0, in which the algorithm for the analysis of information are developed and it is filtered to present the current scenario in Mexico of the SARS-CoV-2 (COVID 19). In this way, the algorithm that is presented allows us to project the requirements for the use of installed infrastructure in the face of the growing requirement for patient care Positive (SARS-CoV-2) COVID-19, allowing the identification of scenarios at the national, state and municipal levels. The construction of the algorithm is based on the following definitions. Definition 1: Total Patients to consider in Model (SARS-CoV-2) COVID-19.It is the number of total patients according to the confirmatory laboratory result or not of (SARS-CoV-2) COVID-19). Be: TP SARS-CoV-2 i j = Total patients according to (SARS-CoV-2) COVID-19 confirmatory laboratory result Which consists of: TP SARS-CoV-2 i j = (P + SARS-CoV-2 i j) + (PSARS-CoV-2 i j) + (Px SARS-CoV-2 i j), where: i = State, j = Municipality Of which: P+ SARS-CoV-2 i j = Patient with a positive (SARS-CoV-2) COVID-19 result in the State, Municipality PSARS-CoV-2 i j = Patient with negative (SARS-CoV-2) COVID-19 result in the State, Municipality Px SARS-CoV-2 i j = Patient with pending confirmation (SARS-CoV-2) COVID-19 in the State, Municipality Definition 2: Identification of a suspected (SARS-CoV-2) COVID-19 case.This is the patient who undergoes an initial qualification according to the initial diagnostic characteristics indicated in the case definitions for surveillance by the World Health Organization for primary care of (SARS-CoV-2) COVID-19 cases. Be: CsCOVID 19 (SARS-CoV-2) = Patient with initial classification as a suspected case of (SARS-CoV-2) COVID-19 Where: Cs (SARS-CoV-2) COVID-19 = Cs (SARS-CoV-2) COVID-19 Type 1 + Cs (SARS-CoV-2) COVID-19 Type 2 + Cs (SARSCoV-2) COVID-19 Type 3 Of which: According to the World Health Organization, there are 3 categories (identified as Type 1, Type 2 and Type 3) to identify suspected cases of (SARS-CoV-2) COVID-19, defined below: 1. Cs (SARS-CoV-2) COVID-19 Type 1.Is a patient with acute respiratory disease (fever and at least one sign / symptom of respiratory disease, with no other aetiology that fully explains the clinical presentation and a history of travel or residence in a country / area or territory that reports local transmission of COVID-19 disease during the 14 days prior to the onset of symptoms. . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 2. Cs (SARS-CoV-2) COVID-19 Type 2.He is a patient with an acute respiratory disease, who has been in contact with a confirmed or probable COVID-19 case in the last 14 days before the onset of symptoms. 3. Cs (SARS-CoV-2) COVID-19 Type 3.Is a patient with severe acute respiratory infection (fever and at least one sign / symptom of respiratory illness (eg cough, shortness of breath) and requiring hospitalization and without another etiology that fully explains the clinical presentation. Definition 3: Total Patients to consider in the (SARS-CoV-2) COVID-19 Model .It is the number of total patients according to the confirmatory laboratory result or not of (SARS-CoV-2) COVID-19). Be: TP SARS-CoV-2 i j = Total patients according to confirmatory laboratory result or not of (SARS-CoV-2) COVID19 Which consists of: TP SARS-CoV-2 i j = (P + SARS-CoV-2 i j) + (PARS-CoV-2 i j) + (Px ARS-CoV-2 i j) , where: i = State, j = Municipality Of which: P + SARS-CoV-2 i j = Patient with a positive (SARS-CoV-2) COVID-19 result in the State, Municipality PARS-CoV-2 i j = Patient with negative (SARS-CoV-2) COVID-19 result in the State, Municipality Px ARS-CoV-2 i j = Patient with pending confirmation (SARS-CoV-2) COVID-19 in the State, Municipality Definition 4: Positive Patients for (SARS-CoV-2) COVID-19 i j.It is the number of patients with laboratory results with positive confirmation for (SARS-CoV-2) COVID-19 i j . It has: P + SARS-CoV-2 i j = Patient with a positive (SARS-CoV-2) COVID-19 result in the State, Municipality Definition 5.Medical Treatment Strategy for a patient with positive laboratory confirmation for (SARS-CoV2) COVID-19 i j .It is the Action Plan in Medical Treatment for a patient with positive laboratory confirmation for SARS-CoV-2 in attention to your degree of infection and comorbidities present that is channeled to determine the Physician. According to the Strategy of Medical Care required for Patients with a Positive SARS-CoV-2 Result, according to their degree of identified infection, they have the following. Be: ET P + SARS-CoV-2 i j = Medical Treatment Strategy P + SARS-CoV-2 i j The medical treatment for a patient with a positive laboratory result for (SARS-CoV-2) COVID-19, based on the Medical Treatment Strategy (ETM P + SARS-CoV-2 ij), based on his degree of infection and present comorbidities, poses two action scenarios : i) Outpatient (SARS-CoV-2) COVID-19 patient or ii) Hospitalized (SARS-CoV-2) COVID-19 patient. Be: i) Outpatient COVID19 patient. P + SARS-CoV-2 i j Outpatient = Positive (SARS-CoV-2) COVID-19 with Outpatient mode in the State, Municipality ii) COVID19 Patient Hospitalized. P + SARS-CoV-2 i j Hospitalized = Positive (SARS-CoV-2) COVID-19 with modality Hospitalized in the State, Municipality where: Depending on the degree of infection (I1, I2 or I3), the Hospitalized (SARS-CoV-2) COVID-19 Patient may require: i) Access to the Intensive Care Area without Intubation or ii) Access to the Intensive Care Area with Intubation. . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 Definition 6.Patients with a Positive (SARS-CoV-2) COVID-19 Result Hospitalized with Access to the Intensive Care area.It is the number of Patients with a Positive SARS-CoV-2 Result Hospitalized with Access to the Intensive Care area, according to its degree of infection. Be: P + SARS-CoV-2 i j Hospital Intensive Care = Positive (SARS-CoV-2) COVID-19 with modality Hospitalized in the State, Municipality Definition 7.Patients with a positive (SARS-CoV-2) COVID-19 result Hospitalized with access to the Intensive Care Area with Intubation.It is the number of Patients with a Positive (SARS-CoV-2) COVID-19 Result Hospitalized with Access to the Intensive Care area with Intubation. Be: P + SARS-CoV-2 i j Hospital Intensive Care with Intubation = Positive (SARS-CoV-2) COVID-19 with Hospitalized modality and intubation in the State, Municipality. Definition 8.P + SARS-CoV-2 i j Deaths.Deaths of Patients with a positive result for SARS-CoV-2. Deaths are all those positive to (SARS-CoV-2) COVID-19 where one is indicated in the data record (DATE_DEF other than the value "99-99-9999"). Definition 9.- (SARS-CoV-2) COVID-19 case fatality rate.It is the proportion of people who die from (SARSCoV-2) COVID-19 among the Patients with a positive (SARS-CoV-2) COVID-19 result in a given period and area. Be: TL SARS-CoV-2 i j = (SARS-CoV-2) COVID-19 case fatality rate Where: (SARS-CoV-2) COVID-19 case fatality rate = [(Deaths of Patients with a Positive (SARS-CoV-2) COVID-19 Result in the State or Municipality) / (Total of Patients with a Positive (SARS-CoV-2) COVID-19 result in the State or Municipality)] x 100 Of which: DP+ SARS-CoV-2 i j = Deaths of Patients with a positive (SARS-CoV-2) COVID-19 result in the State / Municipality And: P+ SARS-CoV-2 i j = Total Patients with a positive (SARS-CoV-2) COVID-19 result in the State / Municipality So: TL SARS-CoV-2 i j = [D P + SARS-CoV-2 i j / P + SARS-CoV-2 i j] x 100 The data processing corresponds to the records on the epidemic (SARS-CoV-2) COVID-19 at 1 August 2020. The treatment of the information is carried out through the application software for data mining Orange version 3.26.0, in which the algorithm for the information analysis are developed. (See Figure 1, below). According to information from the Ministry of Health, the following records are available at the national level: . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 Figure 1. Algorithm for the identification of patients (SARS-CoV-2) COVID 19 in the Popoluca from the Soteapan Area in Mexico Orange Data Mining version 3.26.0 Source: Self made. With information provided by the Ministry of Health as of 1 June 2020 and Orange Data Mining version 3.25.1 . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 According to information from the Ministry of Health in Mexico, the following records are available at the national level: 1. The total number of cases in Mexico as of august 1, 2020, is 999,697 cases of which, based on the confirmatory or non-laboratory result for (SARS-CoV-2) COVID-19, the following classification is obtained: a ) 434,193 patients with a positive result for (SARS-CoV-2) COVID-19, b) 477,733 patients with a non-positive result for (SARS-CoV-2) COVID-19 and c) 87,771 patients with a pending result to determine (SARS-CoV -2) COVID-19. (See table 1) 2. The number of patients with a positive (SARS-CoV-2) COVID-19 result is 434,193 of which: a) 316,860 are care outpatients and b) 117,333 are hospitalized patients. (See table 2) 3. The total number of patients (SARS-CoV-2) COVID-19 hospitalized (include positive and non positive results) is 192,214 of whom 78,088 are women and 114,126 are men. (See Table 3) 4. The total number of hospitalized patients with a positive result for (SARS-CoV-2) COVID-19 is 117,219, of whom: a) 9,400 patients enter the intensive care unit; while b) 107,819 patients do not enter the intensive care unit. (See Table 4) 5. Only 4,874 Hospitalized with a positive (SARS-CoV-2) COVID-19 patients admitted to the intensive care unit required intubation; while 4,526 patients did not require intubation. (See Table 5) 6. Likewise, to date 42,158 deaths from positive (SARS-CoV-2) COVID-19 patients have been registered nationwide, of which 3,486 deaths corresponded to Positive (SARS-CoV-2) COVID-19 patients who were in intensive care and intubation and 38,672 corresponded to (SARS-CoV-2) COVID-19 positive patients who were not in intensive care. (See Table 6). 7. The fatality rate of patients with a positive result for SARS-CoV-2) COVID-19 at nationwide as of august 1, 2020, is estimated at 10.93%. The five states that registered the highest fatality rates as of August 1, 2020 are the following: a) Morelos with a rate of 2039%, b) Baja California with 19.67%, c) Sinaloa with a rate of 17.46% , d) Colima with a rate of 17: 00% and e) State of Mexico with a rate of 15.37%. For its part, the State of Veracruz registered a rate of 13.20% while Mexico City registered a rate of 9.75% (See Table 7). 8. Figure 1 shows the main comorbidities identified in hospitalized patients with a positive result for (SARS-CoV-2) COVID-19, nationwide as of August 1, 2020. 9. In the State of Veracruz, the total number of cases related to (SARS-CoV-2) COVID-19 as of August 1, 2020 is 34,904 of which: a) 21,582 correspond to patients with positive confirmation for (SARS-CoV2 ) COVID-19, b) 10,518 are patients with a negative result for (SARS-CoV-2) COVID-19 and c) 2,804 are patients with results pending confirmation. (See Table 8). 10. In the State of Veracruz, 4.97% of all patients with a positive result for (SARS-CoV-2) COVID-19 are concentrated at the national level as of August 1, 2020. (See Table 1 and Table 8). 11. For its part, in the Popoluca from the Soteapan Area the total number of cases related to (SARS-CoV2) COVID-19 as of August 1, 2020 is 3,850 of which: a) 2,417 ccorrespond to patients with positive confirmation for (SARSCoV2) COVID-19, b) 1,247 are patients with a negative result for (SARS-CoV2) COVID-19 and c) 186 are patients with results pending confirmation. (See Table 9). 12. The percentage of patients with a positive result for (SARS-CoV-2) COVID-19 is 62.78% with respect to the total number of cases registered in the Popoluca from the Soteapan Area. (See Table 9). . Working paper: Data mining for the study of the COVID-19 Epidemic (SARS-CoV-2) and the impact on the indigenous population in Veracruz: Algorithm for the identification of COVID patients (SARS-CoV-2), August 19 to 1, 2020 According to information from the Ministry of Health, in the the Popoluca from the Soteapan Area the following records are available at the national level: 1. The number of patients with a positive (SARS-CoV-2) COVID-19 result is 2,417 of which: a) 997 are women and b) 1,420 are men. The total number of women with a positive (SARS-CoV-2) COVID19, only 3 are women who speak the indigenous language and 994 are women who do not speak the indigenous language. On the other hand, the total number of men with a positive (SARS-CoV2) COVID-19, 8 are men who speak the indigenous language and 1,412 are men who do not speak the indigenous language. (See Table 10). 2. Of the 2,417 positive cases for (SARS-CoV-2) COVID 19, only 11 correspond to patients who speak an indigenous language and 2,406 are patients who are not speakers of an indigenous language. (See Table 11) 3. From the 2,417 patients with (SARS-CoV-2) positive COVID-19, according to their primary hospital care, there are: a) 1,211 are patients who did not require hospitalization and b) 1,206 are patients who required hospitalization. From the 1,206 patients with (SARS-CoV-2) COVID-19 positive who were hospitalized: a) 10 correspond to speakers of the indigenous language and b) 1,196 are patients who do not speak the indigenous language. The 1,211 patients with (SARS-CoV-2) COVID19 positive who were not hospitalized, only 1 were patients speaking the indigenous language hospitalized and 1,210 were patients who did not speak the indigenous language hospitalized. (See Table 12). 4. From the 11 patients who speak the indigenous language, these come from: Coatzacoalcos 4 patient, Minatitlan 5 patient and Soteapan 2. (See Table 13) 5. From the 10 hospitalized (SARS-CoV-2) COVID-19 positive Indigenous language speaker patients : a) 0 patients enter the Intensive Care Unit; while b) 5 patients do not enter the Intensive Care Unit. Only 1 Hospitalized (SARS-CoV-2) COVID-19 Positive patients required intubation in Coatzacoalcos. (See Table 14). 6. As of August 1, 2020 there have been 1 deaths of hospitalized patients (SARS-CoV-2) COVID-19 whose results were positive and were indigenous language speakers in the the Popoluca from the Soteapan Area in Coatzacoalcos. (See Figure 2), at the end of the section, where the associated comorbidities of the indigenous language-speaking patients who died in the the Popoluca from the Soteapan Area, as August 1 2020. 10 Table 1. Total number of cases in Mexico 1 August 2020, According to Sex and Result at (SARS-CoV-2) COVID-19 Sex Result Women Men Total Positive (SARS-CoV-2) COVID-19 203,284 230,909 434,193 No positive (SARS-CoV-2) COVID-19 254,331 223,402 477,733 Pending result 43,466 44,305 87,771 Total 501,081 498,616 999,697 Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 Table 2. Total number of cases in Mexico as of August 1, 2020, according to type of patient and result in (SARS-CoV-2) COVID-19 Patient type Result Ambulatory Hospitalized Total Positive (SARS-CoV-2) COVID-19 316,860 117,333 434,193 No positive (SARS-CoV-2) COVID-19 418,514 59,219 477,733 Pending result 72,109 15,662 87,771 Total 807,483 192,214 999,697 Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 Table 3. Total number of cases in Mexico as of August 1, 2020, according to sex and type of patient Patient type Sex Ambulatory Hospitalized Total Women 422,993 78,088 501,081 Men 384,490 114,126 498,616 Total 807,483 192,214 999,697 Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 11 Table 4. Total number of cases in Mexico as of August 1, 2020, according type of patient and Intensive care unit Patient type Intensive care unit No Intensive care unit Total Hospitalized 9,400 107,819 117,219 Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 Table 5. Total number of cases in Mexico as of August 1, 2020, according to hospitalized patient in intensive care unit and intubation condition Patient type Intubated patient No Intubated patient Total Hospitalized intensive care unit 4,874 4,526 9,400 Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 Table 6. Number of deceased patients with positive COVID-19 (SARSCoV-2), nationwide and according to hospital care condition as of August 1, 2020 Patient type Women Men Total At the national level 14,878 27,280 42,158 Intensive care unit patients with intubation 1,085 2,401 3,486 % Deceased patients requiring care in intensive care units and intubation, by sex 7.3% 8.0% 8.3% Source: Government of Mexico. Health Secretary. Information from the Epidemiological Surveillance System for Viral Respiratory Diseases 1 August 2020 12 Table 7. (SARS-CoV-2) COVID-19 fatality rate by state in Mexico as of August 1, 2020 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Estado Total deceased Covid positives 19 Case fatality rate 1 Aguascalientes 257 4,120 6.24% 2 Baja California 2,674 13,594 19.67% 3 Baja California Sur 182 4,498 4.05% 4 Campeche 500 4,588 10.90% 5 Chiapas 645 13,116 4.92% 6 Chihuahua 194 1,875 10.35% 7 Ciudad de México 7,244 74,314 9.75% 8 Coahuila de Zaragoza 967 5,775 16.74% 9 Colima 904 5,317 17.00% 10 Durango 288 4,167 6.91% 11 Guanajuato 1,009 21,378 4.72% 12 Guerrero 1,415 11,032 12.83% 13 Hidalgo 1,033 6,901 14.97% 14 Jalisco 1,549 13,313 11.64% 15 México 8,225 53,513 15.37% 16 Michoacán de Ocampo 780 9,910 7.87% 17 Morelos 836 4,101 20.39% 18 Nayarit 391 3,518 11.11% 19 Nuevo León 1,118 18,032 6.20% 20 Oaxaca 968 10,673 9.07% 21 Puebla 2,446 20,355 12.02% 22 Querétaro 449 3,726 12.05% 23 Quintana Roo 1,015 7,840 12.95% 24 San Luis Potosí 522 9,921 5.26% 25 Sinaloa 2,230 12,774 17.46% 26 Sonora 1,949 17,890 10.89% 27 Tabasco 1,988 21,747 9.14% 28 Tamaulipas 1,008 17,130 5.88% 29 Tlaxcala 713 4,627 15.41% 30 Veracruz de Ignacio de la Llave 2,849 21,582 13.20% 31 Yucatán 875 10,098 8.67% 32 Zacatecas 249 2,768 9.00% Total 47,472 434,193 10.93% 13 Figure 1 Comorbidities identified in hospitalized patients with a positive result for (SARS-CoV-2) COVID-19, nationwide as of August 1, 2020 Sex Age Sex =1 Women, Sex = 2 Men Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 14 Table 8. Total number of cases in Veracruz State as of August 1, 2020, According to Sex and Result at (SARS-CoV-2) COVID-19 Sex Result Women Men Total Positive (SARS-CoV-2) COVID-19 9,250 12,332 21,582 No positive (SARS-CoV-2) COVID-19 5,663 4,865 10,518 Pending result 1,285 1,519 2,804 Total 16,198 18,706 34,904 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Table 9. Total number of cases in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, According to Sex and Result at (SARSCoV-2) COVID-1 Sex Result Women Men Total Positive (SARS-CoV-2) COVID-19 997 1,420 2,417 No positive (SARS-CoV-2) COVID-19 664 583 1,247 Pending result 87 99 186 Total 1,748 2,102 3,850 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 15 Table 10. Total number of cases with result positive at (SARS-CoV-2) COVID-19 in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, according to sex, indigenous language speaker status and Result Women Men Total Indigenous language speaker 3 8 11 Non-indigenous language speaker 984 1,390 2,374 Not specified 10 22 32 Total 997 1,420 2,417 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020, Table 11. Total number of cases according to the result for (SARS-CoV-2) COVID-19 in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, according to sex and condition of speaker of an indigenous language Result Positive (SARS- CoV-2) COVID 19 Non Positive (SARS-CoV2) COVID-19 Pending result Indigenous language speaker 11 8 3 Non-indigenous language speaker 2,374 2,446 555 Not specified 32 40 0 Total 2,417 2,494 558 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020, 16 Table 12. Total number of cases with result positive at (SARS-CoV-2) COVID-19 in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, according to primary hospital care condition and indigenous language speaker status Result Non Hospitalized Hospitalized Total Indigenous language speaker 1 10 11 Non-indigenous language speaker 1,200 1,174 2,374 Not specified 10 22 32 Total 1,211 1,206 2,417 Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Table 13. Total number of cases with result positive at (SARS-CoV-2) COVID-19 in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, according to primary indigenous language speaker status and municipality Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Municipality Speak indigenous language Non Speak indigenous language Not specified Total Acayucan 0 126 2 128 Catemaco 0 54 0 54 Coatzacoalcos 4 1,470 20 1,494 Hueyapan de Ocampo 0 17 0 17 Mecayapan 0 3 0 3 Minatitlan 5 677 10 692 Sayula de Aleman 0 15 0 15 Soteapan 2 3 0 5 Texistepec 0 7 0 7 Tatahuicapan de Juarez 0 2 0 2 Total 11 2,374 32 2,417 17 Table 14. Total number of cases with result positive at (SARS-CoV-2) COVID-19 in the Popoluca from the Soteapan Area in Mexico as of August 1, 2020, according to status according to primary hospital care condition in Intensive care unit Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Figure 2 Comorbidities identified in hospitalized patients with a positive result for (SARS-CoV-2) COVID-19, in the Popoluca from the Soteapan Area in Mexico of August 1, 2020 Sex Age Sex =1 Women, Sex = 2 Men Source: Own elaboration with Government of Information from the Mexico. Health Secretary. Epidemiological Surveillance System for Viral Respiratory Diseases as of August 1, 2020 Municipality Intensive care unit Non Intensive care unit Not specified Total Coatzacoalcos 0 3 0 3 Minatitlán 0 2 0 2 Total 0 5 0 5 18 Map 1 Popoluca from the Soteapan Area Source: Own elaboration. With Catálogo de metadatos geográficos. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. División política municipal, 1:250000. 2010. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. 19 References [1] Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: Data Mining Toolbox in Python, Journal of Machine Learning Research 14(Aug): 2349−2353. https://dl.acm.org/doi/pdf/10.5555/2567709.2567736 [2] Government of Mexico. Health Secretary. Databases Covid-19 México. https://datos.gob.mx/busca/dataset/informacion-referente-a-casos-covid-19-en-mexico [3] Software Orange Data Mining version 3.26.1 https://orange.biolab.si [4] World Health Organization. (2020). Laboratory testing for coronavirus disease (COVID-19) in suspected human cases. Interim guidance. 19 March 2020. Recuperado de: https://www.who.int/publicationsdetail/laboratory-testing-for-2019-novel-coronavirus-in-suspected-human-cases-20200117 [5] Medel-Ramírez, Carlos and Medel-Lopez, Hilario, Data Mining for the Study of the Epidemic (SARSCoV -2) COVID-19: Algorithm for the Identification of Patients (SARS-CoV-2) COVID 19 in Mexico (June 3, 2020). Available at SSRN: https://ssrn.com/abstract=3619549 or http://dx.doi.org/10.2139/ssrn.3619549 [6] INEGI, (2010). Catálogo de metadatos geográficos. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. División política municipal, 1:250000. 2010. Palabras clave: 2010, Área Geoestadística, División, Límite, Municipios. Fecha de publicación: 04-07-2011, del metadato 20-07-2017. Available at : http://www.conabio.gob.mx/informacion/gis/ Reference to a dataset: [7] Raw data can be retrieved from the Github repository https://github.com/CMedelR/dataCovid19/


Philosophy

Editorial AI and the Mechanistic Forces of Darkness Eric Dietrich Program in Philosophy, Computers, and Cognitive Science Binghamton University Under the Superstition Mountains in central Arizona toil those who would rob humankind o f its humanity. These gray, soulless monsters methodically tear away at our meaning, our subjectivity, our essence as transcendent beings. With each advance, they steal our freedom and dignity. Who are these denizens of darkness, these usurpers of all that is good and holy? None other than humanity's arch-foe: The Cognitive Scientists -AI researchers, fallen philosophers, psychologists, and other benighted lovers of computers. Unless they are stopped, humanity -you and I -will soon be nothing but numbers and algorithms locked away on magnetic tape. What are the prospects of stopping these . . . these cognitive scientists? Not good; their power is enormous. They have on their side the darkest of forces: modern, Western logocentrism. Using this source, they aim at nothing less than a complete objectifying of humankind. This objectification -this replacing of the human spirit with a computational model of mind -is not only the most pernicious assault we humans have ever experienced, it is arguably the most insidious. It doesn't matter whether or not the objectifying world view is correct (arguments against it, even devastating arguments, apparently have no effect on it). All that matters is that it is useful in some limited technological sense. Why? Because, given humankind's love o f technology and our ability to re-invent ourselves, cognitive science's technological success will virtually guarantee that we will re-invent ourselves as computers. I quote G. B. Madison: [AI]'s real significance or worth [lies] solely in what it may contribute to the advancement of technology, to our ability to manipulate reality (including human reality), [but] it is not for all that an innocuous intellectual endeavor and is not without posing a serious danger to a properly human mode of existence. Because the human being is a self-interpreting or self-defining being and because, in addition, human understanding has natural tendency t o misunderstand itself (by interpreting itself to itself in terms of the objectified by-products of its own idealizing imagination; e.g., in terms of computers or logic machines) -because of this there is a strong possibility that, fascinated with their own technological prowess, moderns may very well attempt to understand themselves on the model of a 2 computational machine and, in so doing , actually make themselves over into a kind o f machine and fabricate for themselves a machinelike society structured solely in accordance with the dictates of calculative, instrumental rationality (Madison, 1991). In other words, it doesn't matter whether humans really are computers or not; it doesn't matter whether we can explain human thought in terms of algorithms or not, all that matters is the love affair between humans and their computer technologies. Who can resist an enemy so appealing that you want to emulate it? The power of the mechanistic forces of darkness lies in its allure. We want to be machines. And so we will be . . . cold, lifeless, computers, forsaking our transcendent reality, our properly human mode of existence, and instead worshiping instrumental rationality. This attack on cognitive science saddens me, and not just because I am a cognitive scientist, and not just because I don't like being told my life's work is pointless or evil. Professor Madison belongs to a large and varied group of philosophers, stretching from Heidegger to Searle and Dreyfus, who have attacked scientific psychology (cognitive and otherwise) for one reason or another. We cognitive scientists (and indeed scientists of all stripes) have been accused o f everything from killing the human spirit to just plain stupidity. Artificial intelligence has perhaps received the brunt of the attack in recent years, but all of cognitive science has been lambasted at one t ime or other by those who fear the human sciences. Such attacks go with the territory. Anyone who cannot stand the heat, ought to stay out of the kitchen. No, it's not the heat. This attack on cognitive science saddens me because it is stark testimony to two depressing human traits: our tendency to oversimplify, and our steadfast refusal since the dawn of time to see ourselves as animals and therefore as part of the natural order. I am fully aware that, also since the dawn of time, there have been many who resisted oversimplification and who not only viewed themselves and all humans as part of nature, but delighted in such a perspective. If not for all of these men and women, we would have teetered on the brink of extinction centuries ago. Still, i t's hard to be optimistic. Our two traits are evidenced in abundance today, and they play off of each other in a sort of positive feedback loop which shows every sign of waxing. This essay is about the version of this feedback loop which exists between AI and cognitive science, on the one hand, and those who fear the mechanistic forces of darkness, on the other. I call this latter group the anti-mechanists . It is a mixed group comprising a large contingent of postmodernist philosophers as well as philosophers such as John Searle who 3 consider themselves part of the Western, rationalistic tradition. (To illustrate just how mixed this group is, the postmodernists use the phrase "Western, rationalistic tradition" derogatively.) Actually, I won't be discussing all anti-mechanists. Their numbers are legion, and they have attacked AI and cognitive science from many different directions. I am going to focus on those anti-mechanists who, like Madison, above, attack AI because of its seeming necessary attachment to logic. I need a name for this arm of the anti-mechanist army, so I will call them the logophobes , and their position logophobic . First, I will discuss AI's tendency to oversimplify. Then I will suggest that the logophobes' violent reaction to AI and cognitive science -their dread of the mechanistic forces of darkness -is really a reaction to this oversimplification, a reaction fueled by their refusal to view humankind as animals who are just as much a part of the natural order as cockroaches. (This refusal to see humankind as part of the natural order is what all antimechanists have in common.) Logocentrism, as I said, is the source of the mechanistic forces' power, and what the logophobes fear. Logocentrism, in its anti-mechanist usage (its post-modern usage), is the love of logic: making logic or instrumental rationality central to our world-view. In the beginning, though, was logos , and logocentrism should be the love of logos. "Logos," the word, is a Greek noun which, in the classical period, expressed a range of meanings covered today by such words as "word," "speech," "argument," "doctrine," "explanation," "principle," and "reason." Logos, the thing, is wisdom and reason and rationality, existing not as properties of human minds, but rather as cosmic principles in a disembodied, eternal state. The ancient Greeks (particularly the PreSocratics, Sophists, and Stoics) enshrined logos as the controlling principle of the universe. Even up through the Judeo-Christian tradition, logos is associated with divine wisdom. But wisdom is a far cry from logic. Somewhere along the line, "logocentrism" became pejorative. Somewhere along the line, wisdom was pushed aside, and logic took its place. And I think we all know where: Logical Positivism. Logical positivism was an immense and incredibly powerful philosophical movement of the early decades of the twentieth century. It had its roots in the nineteenth century philosophy called positivism developed by Auguste Comte. Comte held that humankind, each individual human, and indeed every branch of human knowledge grows by going through three stages. The first is the theological stage in which all natural phenomena are seen as the direct products o f supernatural agents who, more often than not, are focused on humans either for good or for ill. Second, comes the metaphysical stage in which the supernatural agents are replaced by abstract, but not necessarily physical, forces. Finally, comes the positive stage, in which the search for ultimate causes is abandoned, and observation and reasoning are brought to bear on the task o f 4 discovering the laws of nature. Knowledge is reconstructed in terms of experience, and the scientific world-view reigns. Science and the scientific method, according to Comte, are the highwater mark both of human culture and of each of us as individuals. Logical positivism took off from here. With the impressive developments in logic, mathematics, and physics in the early part of this century, the philosophers, mathematicians, economists, and physicists of the Vienna Circle together with the likes of Karl Popper and Ludwig Wittgenstein (in his Tractatus period, and against his wishes, apparently), set out to rid the world once and for all of everything that was unanswerable, imprecise, unverifiable, and metaphysical. (The Circle included such luminaries as Otto Neurath, Rudolf Carnap, Moritz Schlick, Herbert Feigl, and Felix Kaufmann.) In short, the logical positivists set out to rid the world of all the big philosophical questions that humans have wrestled with since the beginning. They rejected all philosophical questions as meaningless. Under the scythe of logic, fell such questions as "How ought we treat our fellow beings?" "What, if anything, can we know?" and "What is reality?" The logical positivists were intent on destroying philosophy. They proposed to replace it with science (the scientific outlook and empiricism), mathematics, and above all, logic. You have to take your hats off to these men and women: heady with the power of Frege's then recently developed formal axiomatic theory (1879), Russell and Whitehead's Principia Mathematica (1910-1913), and the then recent developments in science (notably biology and physics), they set out to put all of human knowledge on a basis as firm as granitic bedrock. With their new logical apparatus, they intended to define all of human knowledge in terms of verifiable, direct observation. It was a worthy endeavor. And during their heyday their influence was enormous. The successes of science and mathematics have always made everything associated with them seem utterly true and unquestionable. In our early twentieth century, this "halo effect" was especially strong in the new anti-philosophy based on science, mathematics, and logic. Unfortunately, it didn't work. For technical reasons, some of their foundational principles were too strong, and threatened to destroyed not only philosophy but science as well. Obviously the medicine was too strong. But more importantly, they never had any success in logically reconstructing human knowledge (or even scientific knowledge) in terms of experience. The connection between what we think and what we experience is far too complicated to be captured by something as simple as logic (and logic is not simple when compared to other artifacts humans have created; that it is too simple to serve as the language of thought and experience is testament to how complex thought and experience are). And, finally, try as they might, the 5 logical positivists couldn't make philosophy disappear. Humans rationally and legitimately wonder about "the big issues," issues such as "What is the world?" and "How can we be certain of what we know?" Ultimately, logical positivism was unmasked for what it was, a genuine, but naive attempt to simplify the world in a way in which it couldn't be simplified. Philosophy is nothing i f not difficult precisely because it tackles the big questions, and the big questions are very messy. The logical positivists failed to appreciate the vast gulf between what they wanted to revamp (human knowledge) and the tools they had for revamping it. In short, logical positivism attempted to oversimply the world and our knowledge of it. Logical positivism, as a philosophy, had completely faded sometime around the mid-1950s (or possibly as early as the 1940s or as late as the 1960s, historians disagree). But, as is wellknown to philosophers, especially philosophers of science, logical positivism didn't really die. Like a contagious disease, it moved out of the philosophy departments and down the halls to our less immune colleagues. In the transfer, it became attenuated; it lost its high-minded goal of making human knowledge solid and secure, while retaining the goal of couching everything in logic so that all could be rendered perspicuous and easily dealt with. Science has gone on to embrace the complexity of nature. Chaos theory and the rising sciences and mathematics of complexity are a bright, invigorating testament to this. And nowhere is embracing nature in all its complexity more important than in psychology and cognitive science, for the brain is arguably the most complicated device on the planet. And yet nowhere is the existence of logical positivism more obvious. Cognitive science, especially artificial intelligence, is riddled with it. The sad truth is that logical positivism, in its attenuated form, has deeply infected artificial intelligence. I offer as evidence for this claim the quantity of research on logic that goes on in philosophy, and the percentage of all AI research that focuses on logic. Fully one-half of the papers Jetai receives report some logical result or other. There are entire conferences, books, and journals devoted to logic in AI. Much of this research is fascinating, important, and useful for unraveling and furthering our understanding of the vast domain that is logic. AI researchers have invented whole new kinds of logics, logics with very strange axioms whose behavior is quite unexpected. But at the end of the day, even with all our nonmonotonic, quantified, modal logics, we have not succeeded in explaining one iota of human cognition; at best we've merely described it. We have said what intelligence is; we haven't said how it occurs. And I say this knowing full well that AI logic researchers believe that they have almost completely unraveled the mystery o f one aspect of human cognition: commonsense reasoning -the sort of reasoning we all use 6 everyday to get through the world. But, alas, they have not. For starters, the things that get along best in the world are not robotic implementations of the nonmonotonic logics describing common sense, but the "situated" robots such as those being built by Rodney Brooks at MIT. (I refer the reader to Lynn Stein's 1994 paper in Jetai, vol. 6.4.) Secondly, it is not even clear that commonsense reasoning is a natural cognitive kind and that it should be getting the attention that it does. Commonsense reasoning considered just as a phenomenon strikes me as a surface feature of much more complicated, lower-level computations. Every time I read an article on common sense reasoning, I am reminded of a young geology student I knew who rocketed out of camp in the Snowy Range early one morning, mapping the boulders he saw, completely forgetting that boulders "float" on the surface soil. They have tumbled from nearby mountains, and are pushed along by erosion. Boulders are not indicative of the bedrock underlying the earth. To get at that you must carefully map outcroppings (not floating boulders) and fold in seismic and borehole data. I think common sense researchers are mapping "float." Consider for example, the greatest power of human cognition: creativity. Common sense reasoning has nothing to say about this. Researchers of common sense seem to assume that creativity is a minor aspect of human cognition and that they can somehow unravel common reasoning without saying anything about creativity. It is much more likely that a theory of human and machine cognition worth its salt will give a central place t o creativity and explain it and "common sense" as the joint products of a deep, underlying architecture. The twin beliefs that commonsense reasoning is real and all but explained are a symptom of the hold logic has on AI. There are many others. I will cite one more. AI logic researchers know of an important case where logic failed to explain some very human behavior, yet they persist in still using logic. I am speaking of the Davidson project. In the nineteen sixties and seventies, most philosophers of language labored at what was called the Davidson project. The goal of this project was to supply English with a semantic theory. Just as Chomsky, his colleagues, and their students were to supply a recursive syntactic theory for English (and all other natural languages), so were the Davidsonians to supply a recursive theory which specified for any sentence in English, what that sentence meant. The theory, according to Davidson, was to take the form of a truth definition. That is, for any sentence S in English, the semantic theory would specify the conditions under which S was true. Where did Davidson get this idea -the idea that meanings were truth conditions? From logic. In logic, meanings are truth conditions. So the Davidson project was founded on the hypothesis that English really is a disguised logical language. Philosophers of language engaged in the Davidson project, therefore, spent their time trying t o 7 discover which logic English really was. The Davidson project was actually a great moment in philosophy of language, in particular, and in philosophy, in general. Philosophers began with a prima facie plausible hypothesis -natural languages were (or could be fully described as) logic. They then set about to prove this. It is now widely known -if not widely acknowledged -that the Davidson project gloriously and spectacularly failed. The obvious conclusion is that English is not a disguised logic, and neither are any of the other natural languages on planet Earth. And now a modest suggestion to my fellow AI researchers: Isn't the failure of the Davidson project compelling evidence that logic's role in human cognition minimal at best? Isn't it now high time we consider the proposition that logic is all but useless in our quest to develop a theory of human and machine cognition? What to replace logic with is a very contentious question and one, thankfully, we must discuss at another time. Now to the other half of the feedback loop. AI researchers oversimplify when they use logics of one form or another to describe cognition. The logophobes react to this, first and foremost. Logophobes fear turning humans into logic machines, as Madison's quote, above, makes clear. The force of darkness they see is logic -logocentrism. Their fear is justified, i t seems to them, for two reasons. First, as I have discussed, AI researchers do in fact spend a lot of energy devising logical descriptions of human cognition, and thus focus on those aspects o f human cognition, like commonsense reasoning, where this logic agenda might in some sense succeed, concomitantly ignoring those very important aspects, like creativity, where logic is obviously o f little use. Second, logophobes know that deep down in the guts of every computer, exists some Boolean logic governing its behavior. They think this fact is more important than i t actually is. I will turn to this second reason momentarily. The logophobes' first reason would make some sense if AI was completely based on logic, but it is not. AI is a lot more than just logic. If half of Jetai's submissions are logic-based, the other half are not. We get papers on genetic algorithms, connectionism, systems, distributed AI, heuristic search, situated cognition, artificial life, robots, and the ever-popular, scruffy, knowledgebased, cognitive modeling. Why would logophobes attack AI (and cognitive science) for its commitment to logic if not all of AI is committed to logic? I'm not sure. Maybe their hatred o f logic blinds them to the other parts of AI. On the other hand, maybe they are so opposed t o viewing humans as fancy cockroaches that they intentionally lump all of AI under the logocentric 8 umbrella because they know it is easier to attack that way. There is some evidence that logophobes are using this latter strategy. The non-logic AI literature is quite large. It is pretty hard to miss, accidentally. It is much more likely that the logophobes are purposely ignoring this literature. Now for the logophobes second reason for fearing logocentrism: the idea that computers are logic machines. To begin with, computers are not just logic machines, and they are not just number crunchers. This is an extremely deep point, and one that people outside of the professional computer culture routinely misunderstand. Though I cannot do the details justice here, I will say a few things. A computer, any computer, comprises a hierarchy o f virtual machines . Each machine is different from the one below and above it, and each supervenes on the one below it. And most importantly, each virtual machine has a methodology and mode of explanation unique to it (for example, the explanation and debugging of your word-processor is very different from the explanation and debugging of your operating system, or your disk drive). These methodologies and modes of explanation cannot be reduced those of the machines below. To say that everything reduces to Boolean logic in a computer is exactly like saying psychology and biology reduce to physics. The claim is true in a technical, abstract sense, but it is epistemologically wrong. To try to reduce the behavior of, say, a connectionist system to the Boolean algebra of the gates in the supporting chip would be to completely lose what was important about the connectionist system in the first place. To press the comparison, the science of life is biology, not physics. If you try to reduce biology purely to physics you are going to wind up with an incomprehensibly complex mess, and you will be methodologically stymied. Now, it is true that computers cannot do everything -in fact, there are important mathematical proofs that they cannot do everything. The reason AI is a deep scientific enterprise -what makes it not vacuous -is that everyone in AI (and cognitive science, for that matter) is committed to the claim that what computers can do subsumes the functions that explain and implement cognition . (I have elided an enormous amount of detail here. I suggest reading Chalmers' "On implementing a computation." And though it is a bit self-serving, I also suggest reading my book Thinking Computers and Virtual Persons . ) So, logophobes have ignored, perhaps willfully, the vast part of AI that is not based on logic, and they have completely misunderstood the nature of computers and computation. They have vented their ire at all of us in cognitive science, when they should be focusing only on some of us -the logic researchers. Finally, they are correct about one thing: logic is inadequate for explaining cognition. It is interesting to note, that according to my analysis here, the AI logic 9 researchers and the logophobes share something in common -they both misunderstand the role and importance of logic: they both think it is more important than it is. I don't think my pointing out their error will make the logophobes feel more warmly towards cognitive science. Ultimately, I believe they are deeply opposed to viewing humans as anything "less" than spiritual, transcendent beings. They are, in short, deeply opposed to a science of humankind, and a scientific explanation of our most compelling attribute: our minds. As we finish the twentieth century, and move into the twenty-first, the false dichotomy of "mere machine" or "divine creation" continues to haunt us, bedeviling our efforts to understand ourselves as we really are -glorious machines whose complexities make us precious.* * I thank Clay Morrison, Larry Roberts, and Alan Strudler for comments on previous drafts of this essay. References Chalmers, D. (1994). On implementing a computation, Minds and Machines , December, 1994. Dietrich, E., ed. (1994) Thinking Computers and Virtual Persons . Academic Press: San Diego. Madison, G. (1991) Merleau-Ponty's Deconstruction of Logocentrism, in M. Dillon ed. Merleau-Ponty Vivant . SUNY Press: Albany. [In the table of contents, this paper is referred to as "Merleau-Ponty's Destruction of Logocentrism."] Stein, L. (1994) Imagination and situated cognition, J. of Experimental and Theoretical AI , 6.4, 393-407.