Price to Income Ratio:    4.83    10.74 Mortgage as Percentange of Income:    35.86%

39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004

39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004 Share of World Internet Users: 1.1 %

39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004 Share of World Internet Users: 1.1 %

32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

Score 50 / 100

Score 50 / 100 i A country or territory’s score indicates the perceived level of public sector corruption on a scale of 0 (highly corrupt) to 100 (very clean). Learn more.

Score 50 / 100

Score 50 / 100

Score 82 / 100 i A country or territory’s score indicates the perceived level of public sector corruption on a scale of 0 (highly corrupt) to 100 (very clean). Learn more.

Score 82 / 100

Score 82 / 100

Score 82 / 100

13 / 180 i A country's rank indicates its position relative to the other countries in the index. Learn more. Score 77 / 100

Score 77 / 100

Score 77 / 100

Rank 13 / 180 i A country's rank indicates its position relative to the other countries in the index. Learn more. Score 77 / 100

Quality of Green and Parks

Water Pollution39.11

Quality of Green and Parks Low 29.17 High 73.08

Quality of Green and Parks Low 29.1

Quality of Green and Parks63.04 High

Garbage Disposal Satisfaction

Quality of Green and Parks Very High 82.88 High 71.96

Very High 82.88 High 71.96

Quality of Green and Parks Very High 83.14 Very High 86.32

Quality of Green and Parks Very High 83.1

Quality of Green and Parks Very High 83.26 High 76.22

Quality of Green and Parks Very High 83.26 High 76.22 Contributors:227153Last Update:August 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 77.05 }); $( "#second_air_quality" ).progressbar({ value: 74.33 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 86.58 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.21 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 75.82 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.35 }); $( "#first_clean_and_tidy" ).progressbar({ value: 68.35 }); $( "#second_clean_and_tidy" ).progressbar({ value: 69.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 57.91 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 56.47 }); $( "#first_water_quality" ).progressbar({ value: 69.25 }); $( "#second_water_quality" ).progressbar({ value: 66.13 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 80.32 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 78.60 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 83.26 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 76.22 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

Air quality80.80 Very High Drinking Water Quality and Accessibility85.04 Very High Garbage Disposal Satisfaction77.63 High Clean and Tidy74.44 High Quiet and No Problem

Garbage Disposal Satisfaction75.35 High

Garbage Disposal Satisfaction75.82 High Clean and Tidy68.35 High Quiet and No Problem with Night Lights57.91 Moderate Water Quality69.25 High Comfortable to Spend Time in the City80.32 Very High Quality of Green and Parks83.26 Very High

Air quality77.86 High Drinking Water Quality and Accessibility87.22 Very High Garbage Disposal Satisfaction82.85

Garbage Disposal Satisfaction76.29 High

Garbage Disposal Satisfaction76.36 High

Garbage Disposal Satisfaction71.45 High

Garbage Disposal Satisfaction51.92 Moderate

Garbage Disposal Satisfaction47.92 Moderate

Garbage Disposal Satisfaction High 77.72 High 75.35 Clean and Tidy High 74.74 High 69.35 Quiet and No Problem with Night Lights High 73.28 Moderate 56.47 Water Quality High 72.80 High 66.13 Comfortable to Spend Time in the City Very High 85.61 High 78.60 Quality of Green and Parks Very High 86.17 High 76.22 Contributors:102153Last Update:July 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 87.75 }); $( "#second_air_quality" ).progressbar({ value: 74.33 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 79.95 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.21 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 77.72 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.35 }); $( "#first_clean_and_tidy" ).progressbar({ value: 74.74 }); $( "#second_clean_and_tidy" ).progressbar({ value: 69.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 73.28 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 56.47 }); $( "#first_water_quality" ).progressbar({ value: 72.80 }); $( "#second_water_quality" ).progressbar({ value: 66.13 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 85.61 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 78.60 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 86.17 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 76.22 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

AdelaidePerth Health Care System Index:

Health Care System Index: 76.88

Health Care System Index:

Health Care System Index:

Health Care System Index:

Health Care System Index:

Health Care System Index: 73.57

Health Care System Index: 74.15

ealth Care System Index:    70.18    65.07

ealth Care System Index: 65.07

Health Care System Index: 74.92

Health Care System Index: 70.46

Safety Index: 55.34

Safety Index: 53.97

Safety Scale:    55.80

Safety Index: 62.24

Index   TorontoFlorence Crime Index:    35.18    37.76 Safety Scale:    64.82

Safety Index: 67.80

Safety Index: 63.75

Safety Scale:    69.18

Safety Index: 56.17

(adsbygoogle = window.adsbygoogle || []).push({}); Crime > Australia > Sydney Crime in Sydney, Australia Compare Sydney with: jQuery(document).ready(function($) { jQuery('#city_selector_city_id1').autocomplete({ source: 'https://www.numbeo.com/common/CitySearchJson', minLength: 1, delay: 3, autoFocus: true, select: function(event, ui) { event.preventDefault(); $('#city_selector_city_id1').val(ui.item.label); $('#city_id1').val(ui.item.value); jQuery('#dispatch_form').submit(); }, focus: function(event, ui) { event.preventDefault(); } }) .keydown(function(e){ if (e.keyCode === 13){ if ($('#city_id1').val() == '') { e.preventDefault(); } var val = $('#city_selector_city_id1').val(); $('#name_city_id1').val(val); } }); // end autocompleter definition }); // end document ready Do you live in Sydney? Add data for Sydney Index Crime Index: 37.89 Safety Index: 62.11

Safety Index: 55.89

afety Scale:    66.54    55.89

Water Pollution Low 27.59 Low 32.97

Water Pollution Low 27.59

Very High 82.10 Very High 87.07

Quality of Green and Parks Very High 82.10

CO2 Emission Index:    5,446.92

CO2 Emission Index:

Time Index (in minutes):    35.88

Time Index (in minutes):

CO2 Emission Index:    6,489.83

CO2 Emission Index:    6,489.83    5,764.28

Time Index (in minutes):    43.18

Time Index (in minutes):    43.18    42.14

CO2 Emission Index:    6,564.11  

Time Index (in minutes):    38.87

CO2 Emission Index:    6,564.11    4,587.53

Time Index (in minutes):    38.87    33.06

CO2 Emission Index:    5,419.09

CO2 Emission Index:    5,419.09    5,711.01

Time Index (in minutes):    43.98

Time Index (in minutes):    43.98    39.99

   3,956.71

CO2 Emission Index:    3,956.71    3,308.53

Time Index (in minutes):

Time Index (in minutes):    32.07    41.33

   122.73    142.91 Time Index (in minutes):    34.00

12  Canada 0.890

12  Canada 0.890 16.

12  Canada 0.890

12  Canada 0.890

35  Italy 0.814

35  Italy 0.814

35  Italy 0.814

1  Australia 0.939

1  Australia 0.939

1  Australia

1  Australia 0.939

35  Italy 0.814 16.3 10.9 26

Google also says location records stored in My Activity are used to target ads. Ad buyers can target ads to specific locations — say, a mile radius around a particular landmark — and typically have to pay more to reach this narrower audience. While disabling “Web & App Activity” will stop Google from storing location markers, it also prevents Google from storing information generated by searches and other activity. That can limit the effectiveness of the Google Assistant, the company’s digital concierge. Sean O’Brien, a Yale Privacy Lab researcher with whom the AP shared its findings, said it is “disingenuous” for Google to continuously record these locations even when users disable Location History. “To me, it’s something people should know,” he said.

Google says that will prevent the company from remembering where you’ve been. Google’s support page on the subject states: “You can turn off Location History at any time. With Location History off, the places you go are no longer stored.” That isn’t true. Even with Location History paused, some Google apps automatically store time-stamped location data without asking. (It’s possible, although laborious, to delete it .)

Data mining can be defined broadly as: “the application of specific algorithms for extracting patterns from data.” [17]

hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

There is also a need for mechanisms to support transformations and processesover time, both for scientific data and scientific ideas. These mechanisms should not only help the user visualize but also express time and change.

Contrary to paper notes, computer files do not display the traces or versions that led to their final state.

This is related to the fact that biology researchers are in a creative process and reflect on their decisions in order to explore new leads or justify their decisions. Paper laboratory notebooks show this temporality ofthoughts.

Temporal Data and Data Temporality: Time is change, not only ord

City Data Exchange is a public-private partnership that explores the possibilities of data exchange. The project investigates the purchase, sale and sharing of a wide variety of data types between different types of users in the city - citizens, public institutions and private companies. The project is a collaboration between the City of Copenhagen, the Capital Region, CLEAN and Hitachi. The idea behind the cooperation is to create a data hub that supports innovation, and which improves the quality of life in the Copenhagen area. The project aims at establishing a marketplace for data owned by both public authorities and private companies. In this way, the project aims to enable large, small and medium-sized enterprises, start-up companies, universities and the public sector to collaborate by consolidating several sources of information. So far, the project has conducted several experiments aimed at organizational and technical setup of a computer market. The technical part of this can be seen on the platform developed by Hitachi, citydataexchange.com .

Open Data DK is a union consisting of a number of Danish municipalities and regions aimed at making public data open and accessible for citizens and businesses. The goal is to increase transparency in public administration and support data-driven growth. The Copenhagen portal for city data contains information about infrastructure, traffic, cultural events and much more. You can find the portal here .

Although the spatio-temporal variation in rumor quantity and content has long been of interest to thefield, collecting data that accounts for temporalandspatial characteristics of rumoring has been extraordinarily difficult to dowith any degree of precision. Some have been able to capture rumoring data with some degree of temporal precision (Bordiaand Rosnow, 1998; Danzig, 1958; Greenberg, 1964) or with some spatial precision (Larsen, 1954), but bridging the two hasbeen difficult. Synthesizing temporal and spatial rumoring data across a wide variety of events had long been beyond thecapabilities of researchers. Simply gathering reliable data on rumoring was already fraught with challenges.

where applicable, any rating in the form of a data trust score that may be assignedto the data fiduciary under section 35;and

the period for which the personal data will beretained in terms of section 10 or where such period is not known, the criteria for determining such period;

Upon receipt of notification, the Authority shall determine whether such breach should be reported by the data fiduciaryto the data principal, taking into account the severity of the harm that may be caused to such data principal or whether some action is required on the part of the data principal to mitigate suchharm.

“Personal data breach”means any unauthorised or accidental disclosure, acquisition, sharing, use, alteration, destruction, loss of access to, of personal data that compromises the confidentiality, integrity or availability of personal data to a data principal;

We noticed that the people who use the data are usually not the same people who produce the data, and they often don’t know where to find the information about the data they try to use. Since the Schematizer already has the knowledge about all the schemas in the Data Pipeline, it becomes an excellent candidate to store information about the data. Meet our knowledge explorer, Watson. The Schematizer requires schema registrars to include documentation along with their schemas. The documentation then is extracted and stored in the Schematizer. To make the schema information and data documentation in the Schematizer accessible to all the teams at Yelp, we created Watson, a webapp that users across the company can use to explore this data. Watson is a visual frontend for the Schematizer and retrieves its information through a set of RESTful APIs exposed by the Schematizer.

Mayor de Blasio and his administration have made progress in meeting their goal of building 200,000 affordable units over the span of a decade, as 21,963 new units were added in 2016, the most in 27 years. However, there continues to be a shortage in East Harlem. Out of the nearly 20,000 affordable units, the city brought to all five boroughs, just 249 units have been built in East Harlem, according to a new report by the Department of Housing and Preservation Development (HPD). To better accommodate these residents, the city plans on expediting the construction of 2,400 units of affordable housing over the next few years, as DNA Info reported.

Furthermore, and differentiating digital time from clock time, he suggests that a lack of adherence to chronological time is compounded by the fact that digital technologies connect with a flow of information that is al-ways and instantly available. He argues that continual change, which is bound up with web services such as social network sites, blogs and the news, is central to the experi-enced need for constant connectivity.

It is not the data that are valuable, he says, but the services powered by them. Some firms are just better at developing new offerings than others.

Kahneman concluded his aforementioned presentation to academics by arguing that computers or robots are better than humans on three essential dimensions: they are better at statistical reasoning and less enamoured with stories; they have higher emotional intelligence; and they exhibit far more wisdom than humans.

But, while Kahneman calls for large-scale replications of priming studies, the argument here is not that we need more studies or data to verify that people indeed miss blatantly obvious gorillas. Instead, we need better interpretation and better theories.

webinar

With over 65% contribution to the total revenue, talent solutions are the most important services and tools included in the LinkedIn business model. Talent solutions include premium recruiting tools for the companies and recruiters to help them find the most suitable employees/partners for their business.

LinkedIn apart from being the best recruitment platform is also a sought-after social networking website by marketers to execute their marketing campaigns. This service contributes to over 18% of the total revenue of the company and offers features which let companies to not only create a company page but also enhance their marketing efforts by creating sponsored content, sponsored InMails and text advertisements.

The mobile version of the website was launched in February 2008 and the company was bought by Microsoft in February 2016 for $26.2 billion.

In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

About 600,000 people visit News Genius a month, Lehman said, a figure that had grown 10 times since before President Donald Trump was inaugurated. And the number of people who annotate a post on Genius each month is now at 10,000, up 30 percent from the start of the year. “More people are using News Genius now than ever,” Lehman said. Meanwhile, overall traffic to the website and apps has grown to 62 million a month.

Soon after, Genius made a definitive push to realize Andreessen’s vision. By 2015, Genius claimed 40 million visitors to its website a month, 1 million of whom had annotated a post.

But the biggest problem with the annotator from Genius’ perspective is that few individuals are using it. After more than two years of development, the Chrome extension has only 12,320 users. It was last updated in June 2016.

Right now, they estimate the global taxi market is worth $108 billion, which is triple the size of the $36-billion ride-hailing market. At the same time, they calculate an average of 15 million ride-hailing trips a day globally, which they expect to increase to 97 million by 2030.

Recent studies have indicated that Uber’s U.S. driver churn has sharply increased this year, to rates as high as 96%. Needless to say, it’s hard (and costly) to maintain double-digit growth rates, when only 4% of mission critical, de facto employees stay on the job for more than a year.

Figure 9. Constraint plots

Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.

Our posterior samples are available online (10.5281/zenodo.162965).

S7 Data Release 2.0

Acero F., Ackermann M., Ajello M. et al (Fermi-LAT) 2015 arXiv:1501.02003Preprint

The catalog of fakes used to generate the efficiency grids in Section 3 are available in a persistent directory: doi:10.5258/SOTON/D0030.

1- 13 A13 --- Planet Planet 15- 15 I1 --- robust Robust flag (1) 17- 23 F7.3 d Per Orbital period 25- 28 F4.1 Rgeo Rad Planet radius 30- 33 F4.2 Rgeo E_Rad 1{sigma} upper error bound on Rad 35- 38 F4.2 Rgeo e_Rad 1{sigma} lower error bound on Rad 40- 40 A1 --- r_Rad Source of planet-star radius ratio (2) 42- 44 F3.1 solMass Mstar Mass of host star 46- 49 F4.2 solMass E_Mstar 1{sigma} upper error bound on Mstar 51- 54 F4.2 solMass e_Mstar 1{sigma} lower error bound on Mstar 56- 56 I1 --- l_Md Md upper limit flag (3) 58- 63 F6.2 Mgeo Md Planet mass from default prior 65- 69 F5.2 Mgeo E_Md ?="" 1{sigma} upper error bound on Md 71- 74 F4.2 Mgeo e_Md ?="" 1{sigma} lower error bound on Md 76- 81 F6.2 g/cm3 rhod Planet density from default prior 83- 87 F5.2 g/cm3 E_rhod ?="" 1{sigma} upper error bound on rhod 89- 92 F4.2 g/cm3 e_rhod ?="" 1{sigma} lower error bound on rhod 94- 94 I1 --- l_Mh Mh upper limit flag (3) 96-100 F5.1 Mgeo Mh Planet mass from high mass prior 102-107 F6.2 Mgeo E_Mh ?="" 1{sigma} upper error bound on Md 109-112 F4.2 Mgeo e_Mh ?="" 1{sigma} lower error bound on Md 114-119 F6.2 g/cm3 rhoh Planet density from high mass prior 121-126 F6.2 g/cm3 E_rhoh ?="" 1{sigma} upper error bound on rhod 128-131 F4.2 g/cm3 e_rhoh ?="" 1{sigma} lower error bound on rhod 133-155 A23 --- Ref References (4)

WISE J085510.83−071442.5

“In short, they have no history of supporting the machine learning research community and instead they are viewed as part of the disreputable ecosystem of people hoping to hype machine learning to make money.”

Thus, the digital object ossifies out of two histories, one virtual and another visual.Within computation, the object arises out of a desire to create a model of the worldwithin the computer but at the same time out of an attempt to create a whole new visualworld native to the compute