AI Data for Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio. Advantages for Employes: Calisthenics | AI Explorer

Machine Generated Data

Color Analysis

Face analysis

Amazon

Microsoft

Google

AWS Rekognition

Age	39-47
Gender	Male, 98.7%
Calm	99.9%
Surprised	6.3%
Fear	5.9%
Sad	2.2%
Angry	0%
Disgusted	0%
Confused	0%
Happy	0%

AWS Rekognition

Age	25-35
Gender	Male, 100%
Calm	96.8%
Surprised	6.3%
Fear	5.9%
Confused	2.8%
Sad	2.2%
Happy	0%
Angry	0%
Disgusted	0%

AWS Rekognition

Age	30-40
Gender	Male, 89.1%
Calm	99.3%
Surprised	6.3%
Fear	5.9%
Sad	2.2%
Angry	0.1%
Confused	0.1%
Disgusted	0%
Happy	0%

AWS Rekognition

Age	23-31
Gender	Female, 99.9%
Calm	99.9%
Surprised	6.3%
Fear	5.9%
Sad	2.2%
Confused	0.1%
Angry	0%
Happy	0%
Disgusted	0%

AWS Rekognition

Age	28-38
Gender	Male, 97%
Calm	63%
Surprised	23.8%
Disgusted	11%
Fear	6.6%
Angry	3.5%
Sad	2.4%
Confused	1.3%
Happy	0.4%

AWS Rekognition

Age	22-30
Gender	Male, 93.2%
Sad	99.1%
Calm	31.8%
Surprised	7%
Fear	6.3%
Happy	2.4%
Angry	1.8%
Confused	1%
Disgusted	0.7%

AWS Rekognition

Age	22-30
Gender	Female, 95.6%
Calm	84.6%
Fear	8.8%
Surprised	8.2%
Sad	3.5%
Disgusted	0.7%
Angry	0.5%
Happy	0.3%
Confused	0.3%

AWS Rekognition

Age	47-53
Gender	Male, 99.9%
Calm	97.8%
Surprised	6.4%
Fear	5.9%
Sad	2.5%
Happy	0.3%
Confused	0.3%
Angry	0.2%
Disgusted	0.1%

AWS Rekognition

Age	19-27
Gender	Male, 98.8%
Calm	97.2%
Fear	6.5%
Surprised	6.3%
Sad	2.2%
Confused	0.4%
Happy	0.3%
Disgusted	0.1%
Angry	0.1%

AWS Rekognition

Age	35-43
Gender	Female, 98.4%
Fear	95.7%
Calm	7.5%
Surprised	7%
Sad	2.7%
Confused	0.8%
Disgusted	0.4%
Angry	0.3%
Happy	0.3%

AWS Rekognition

Age	20-28
Gender	Female, 56.5%
Calm	32.4%
Confused	29%
Fear	21.4%
Surprised	8%
Sad	6.1%
Disgusted	2.7%
Happy	2%
Angry	1.3%

AWS Rekognition

Age	39-47
Gender	Male, 99.9%
Calm	97.7%
Surprised	6.8%
Fear	6%
Sad	2.2%
Confused	0.4%
Happy	0.4%
Angry	0.1%
Disgusted	0.1%

AWS Rekognition

Age	39-47
Gender	Male, 95.6%
Calm	69.9%
Sad	37.2%
Surprised	6.8%
Fear	6.5%
Confused	2.7%
Angry	0.9%
Happy	0.6%
Disgusted	0.3%

AWS Rekognition

Age	14-22
Gender	Female, 90.5%
Sad	94.2%
Fear	27.7%
Confused	14.3%
Surprised	12.4%
Calm	2%
Disgusted	0.8%
Angry	0.5%
Happy	0.4%

AWS Rekognition

Age	22-30
Gender	Male, 98.5%
Calm	81.9%
Surprised	7.3%
Confused	6.2%
Fear	6%
Angry	3.5%
Sad	3%
Disgusted	2.1%
Happy	1.6%

AWS Rekognition

Age	22-30
Gender	Female, 67.4%
Calm	49%
Happy	26.1%
Sad	12.6%
Surprised	7.6%
Fear	6.5%
Confused	4.3%
Angry	1.9%
Disgusted	1.4%

AWS Rekognition

Age	23-31
Gender	Female, 99%
Sad	99.9%
Confused	8%
Fear	7.1%
Surprised	6.7%
Calm	5.1%
Angry	3.4%
Disgusted	2.4%
Happy	0.9%

AWS Rekognition

Age	6-16
Gender	Male, 97.4%
Sad	98.6%
Confused	14.9%
Fear	12.2%
Surprised	9.8%
Calm	5.9%
Disgusted	2%
Happy	1.9%
Angry	1.1%

AWS Rekognition

Age	30-40
Gender	Male, 99.3%
Sad	80.8%
Calm	44.6%
Confused	14.2%
Surprised	6.5%
Fear	6.3%
Happy	1.4%
Disgusted	1.1%
Angry	0.4%

AWS Rekognition

Age	20-28
Gender	Male, 86.2%
Calm	94.7%
Surprised	6.5%
Fear	6%
Sad	2.5%
Happy	2.1%
Disgusted	0.5%
Confused	0.5%
Angry	0.4%

AWS Rekognition

Age	22-30
Gender	Male, 98.9%
Calm	60.2%
Fear	26.4%
Surprised	6.7%
Sad	6.6%
Confused	2.7%
Angry	1.5%
Disgusted	0.5%
Happy	0.3%

AWS Rekognition

Age	13-21
Gender	Male, 99.4%
Fear	82.2%
Surprised	9.5%
Confused	8.6%
Calm	8%
Sad	6.3%
Angry	3.1%
Disgusted	1.5%
Happy	1.5%

AWS Rekognition

Age	23-31
Gender	Male, 89.6%
Calm	86.4%
Happy	10%
Surprised	6.5%
Fear	6.3%
Sad	2.2%
Angry	1%
Disgusted	0.5%
Confused	0.3%

AWS Rekognition

Age	20-28
Gender	Male, 96.1%
Calm	75.9%
Sad	18.7%
Surprised	6.7%
Fear	6.3%
Confused	4.3%
Happy	0.7%
Angry	0.4%
Disgusted	0.4%

AWS Rekognition

Age	16-22
Gender	Male, 96.7%
Calm	88.1%
Surprised	7.1%
Fear	6.4%
Happy	4.9%
Sad	2.9%
Confused	1.1%
Disgusted	0.6%
Angry	0.3%

Microsoft Cognitive Services

Age	32
Gender	Female

Microsoft Cognitive Services

Age	39
Gender	Male

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Likely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Likely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Possible

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Likely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Unlikely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Very likely

Google Vision

Surprise	Very unlikely
Anger	Very unlikely
Sorrow	Very unlikely
Joy	Very unlikely
Headwear	Very unlikely
Blurred	Likely

Feature analysis

Amazon

Person	98.2%

Suit	87.5%

Captions

Microsoft
created on 2022-06-25

a group of people standing in front of a crowd posing for the camera	92.5%

a group of people posing for a photo	92.4%

a group of people standing in front of a building	92.3%

Text analysis

Amazon

GOING

INCOMING

Google

MARES GOING dibus INCOMING

MARES

GOING

dibus

INCOMING

Title Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio. Advantages for Employes: Calisthenics	Date c. 1904
People Artist: Unidentified Artist,	Classification Photographs
Credit Line Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.326.A.2

man	24.8

work	22.1

restaurant	21.8

business	20

people	19.5

male	19.1

interior	18.5

chair	17.6

room	17.4

hall	17

industry	16.2

building	15.2

person	14.8

table	14.7

industrial	13.6

modern	13.3

equipment	13.2

office	13.1

men	12.9

factory	12.5

adult	12.2

engineer	12.1

architecture	11.7

city	11.6

job	11.5

indoors	11.4

glass	11

dinner	10.9

working	10.6

urban	10.5

metal	10.4

cafeteria	10.3

smiling	10.1

clothing	10

happy	10

drink	10

machine	9.7

group	9.7

worker	9.6

uniform	9.5

file	9.4

furniture	9.3

center	9.3

power	9.2

inside	9.2

computer	9.1

black	9

manufacturing	8.8

laptop	8.7

party	8.6

sitting	8.6

dining	8.5

wine	8.5

place	8.4

device	8

food	7.9

counter	7.8

couple	7.8

office furniture	7.7

old	7.7

hotel	7.6

storage	7.6

eat	7.5

stock	7.5

military uniform	7.5

technology	7.4

occupation	7.3

meal	7.3

new	7.3

smile	7.1

night	7.1

steel	7.1

lunch	7.1

Black	89.7

Standing	86.4

Style	84

Black-and-white	82.6

Crew	79.1

Monochrome	76.6

Team	76.2

Event	73.9

Monochrome photography	73.7

Factory	72.9

Room	66.5

Metal	65.1

Machine	64.5

Crowd	63.2

Chair	61.5

Recreation	60.3

Music	58.9

Uniform	56

Vintage clothing	54.2

Classic	53.3

person	99.9

building	99.6

clothing	97.4

man	92.1

standing	84.4

black	76.5

posing	72.6

people	69.3

text	67.7

white	63.7

group	58.3

crowd	0.9

Assembly Line	100

Factory	100

Building	100

Person	98.2

Human	98.2

Person	98

Person	96

Person	95.9

Person	95.5

Person	89.9

Person	87.8

Suit	87.5

Coat	87.5

Overcoat	87.5

Clothing	87.5

Apparel	87.5

Person	86.5

Person	66.1

Manufacturing	63.6

Crowd	56.4

people portraits	85.8%

paintings art	9.3%

events parties	2.6%

streetview architecture	1.4%

Human Generated Data

Title

Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio. Advantages for Employes: Calisthenics

Date

c. 1904

People

Artist: Unidentified Artist,

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.326.A.2

Human Generated Data

Title

Industrial Problems, Welfare Work: United States. Ohio. Dayton. National Cash Register Company: Welfare Institutions of the National Cash Register Company, Dayton, Ohio. Advantages for Employes: Calisthenics

People

Artist: Unidentified Artist,

Date

c. 1904

Classification

Photographs

Credit Line

Harvard Art Museums/Fogg Museum, Transfer from the Carpenter Center for the Visual Arts, Social Museum Collection, 3.2002.326.A.2

Machine Generated Data

Tags

Amazoncreated on 2022-06-25

Imaggacreated on 2022-06-25

Googlecreated on 2022-06-25

Microsoftcreated on 2022-06-25

Color Analysis

Face analysis

Amazon

Microsoft

Google

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

AWS Rekognition

Microsoft Cognitive Services

Microsoft Cognitive Services

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Google Vision

Amazon
created on 2022-06-25

Imagga
created on 2022-06-25

Google
created on 2022-06-25

Microsoft
created on 2022-06-25

Microsoft
created on 2022-06-25