Designometry	–	Formalization	of	Artifacts	and	Methods Soenke	Ziesche Maldives	National	University Male',	Maldives soenke.ziesche@mnu.edu.mv Roman	V.	Yampolskiy University	of	Louisville Louisville,	KY	40292,	USA roman.yampolskiy@louisville.edu Abstract Two	interconnected	surveys	are	presented,	one	of	artifacts	and	one	of	designometry.	Artifacts are	objects,	which	have	an	originator	and	do	not	exist	in	nature.	Designometry	is	a	new	field	of study,	which	aims	to	identify	the	originators	of	artifacts.	The	space	of	artifacts	is	described	and also	domains,	which	pursue	designometry,	yet	currently	doing	so	without	collaboration	or common	methodologies.	On	this	basis,	synergies	as	well	as	a	generic	axiom	and	heuristics	for the	quest	of	the	creators	of	artifacts	are	introduced.	While	designometry	has	various	areas	of applications,	the	research	of	methods	to	detect	originators	of	artificial	minds,	which	constitute a	subgroup	of	artifacts,	can	be	seen	as	particularly	relevant	and,	in	the	case	of	malevolent artificial	minds,	as	contribution	to	AI	safety. Keywords:	Designometry,	artifacts,	intellectology,	minds,	AI	safety Introduction Yampolskiy	(2016)	introduced	the	field	of	designometry,	which	aims	to	detect	signatures	of originators	within	artifacts.	Owing	to	the	diversity	of	artifacts	this	type	of	research	is	currently pursued	independently	in	different	domains.	Therefore,	Yampolskiy	(2016)	proposes	an overarching	approach	through	synergies	and	particularly	through	consolidation	of	methods	of analysis	from	specific	domains.	As	he	highlights	there	could	be	a	particular	demand	in	the future	to	determine	the	originators	of	a	specific	type	of	artifacts,	which	are	artificial	or engineered	life	forms	or	minds. In	this	article	we	tackle	this	problem	by	presenting	a	survey	of	artifacts	and	a	survey	of designometry.	In	the	survey	of	artifacts	we	summarize	existing	definitions	and	ontologies, followed	by	an	innovative	approach	to	describe	the	space	of	artifacts	by	allocating	identification numbers	to	them.	In	the	designometry	survey	we	describe	fields,	which	pursue	designometry albeit	not	calling	it	that.	Thereafter,	we	analyze	the	tools	and	methods	of	these	fields	and	infer one	abstract	axiom	as	well	as	general	heuristics	for	anyone	trying	to	profile	creators	of	artifacts, with	a	special	focus	on	artificial	minds.	We	also	establish	a	link	to	the	field	of	intellectology, which	has	been	introduced	by	Yampolskiy (2015). 2 Artifacts Definition Artifacts	have	been	a	topic	in	philosophy	for	a	long	time.	Aristotle	distinguished	between	things "that	exist	by	nature"	and	those	existing	"from	other	causes".	For	the	latter	group	he	names	a bed	or	a	coat	as	examples	and	calls	them	"artificial	products"	since	it	requires	an	art	of	making things. In	the	meantime	various	other	definitions	for	artifacts	have	been	provided,	which	do	not	differ very	much.	For	our	purposes	the	one	by	Hilpinen	(1993,	156–157)	is	sufficient:	"An	object	is	an artifact	if	and	only	if	it	has	an	author."	In	the	literature	instead	of	author	also	creator,	originator or	agent	is	used. Ontology While	artifacts	have	be	described	already	for	a	long	time	as	the	definitions	above	show,	formal ontologies	have	been	developed	only	much	more	recently1.	Around	the	end	of	the	twentieth century	research	towards	formal	knowledge	representation	systems	intensified	since	this	was required	for	various	applications	in	the	IT	field.	Borgo	and	Vieu	(2009)	present	a	detailed approach	to	extend	formal	ontologies	for	knowledge	representation	to	include	artifacts.	Among various	existing	ontologies	they	propose	that	the	Descriptive	Ontology	for	Linguistic	and Cognitive	Engineering	(DOLCE)	is	best	suited	for	artifacts2. Borgo	and	Vieu	(2009)	added	to	existing	categories	in	DOLCE	the	category	of	Physical	Artefact, which	became	a	subcategory	of	Physical	Endurant,	thus	a	sibling	of	the	categories	Amount	of Matter,	Physical	Object	and	Feature.	Furthermore,	they	made	use	of	the	quality	feature	in DOLCE	by	assigning	all	physical	artifacts	a	single	individual	quality	named	capacity	that characterizes	the	capacities	of	the	artifacts.	This	new	quality	enabled	Borgo	and	Vieu	(2009)	to formalize	a	series	of	notions	based	on	philosophical	distinctions	as	well	as	commonsense intuitions. There	are	various	ways	to	categorize	artifacts	according	to	their	qualities.	As	this	is	a	wide	topic of	philosophical	research3,	here	only	the	most	important	categories	are	mentioned:	An	example for	a	singular,	concrete	object	would	be	the	Eiffel	Tower	in	Paris.	If	the	simulation	of	universes turns	out	to	be	feasible,	then	a	simulated	universe	would	be	an	extreme	example	for	a	singular, concrete	object.	As	opposed	to	singular	objects,	which	are	unique,	there	are	also	artifacts, which	have	more	than	one	or	many	instances.	The	instances	of	such	artifacts	have	the	same blueprint	and	the	same	characteristics.	An	example	would	be	paper	clips.	A	particular	instance of	this	type	of	artifact	would	be	in	this	example	a	specific	paper	clip.	As	opposed	to	concrete 1	See	for	an	overview:	Franssen	et	al.	(2013). 2	See	http://www.loa.istc.cnr.it/old/Papers/DOLCE2.1-FOL.pdf 3	See	for	an	overview:	Hilpinen	(2011). 3 objects,	artifacts	can	be	also	abstract.	An	example	would	be	a	text	or	any	object	in	a	digital environment.	The	above	categories	can	be	divided	further	in	subcategories.	For	example singular,	concrete	objects	may	be	independent	or	dependent.	An	example	for	an	independent object	would	be	a	chair.	As	opposed	to	this	singular,	concrete	dependent	objects	cannot	exist without	a	substrate.	An	example	would	be	a	tunnel	under	a	mountain.	This	object	cannot	be detached	from	that	mountain.	In	addition	to	numerous	other	criteria	such	as	the	method	of manufacture,	material	properties	or	the	intended	use	of	the	artifact	it	can	be	also	distinguished if	an	artifact	has	one	author	or	more	than	one	author	(collectively	produced	artifacts)	or whether	the	artifact	is	intended	by	the	author	or	unintended.	For	example,	when	a	tailor makes	a	coat	for	his	customer,	her	intention	is	to	make	a	coat	of	a	certain	size	and	style,	but she	also	produces	scraps	of	cloth	as	by-products	of	her	work.	Moreover,	there	are	also biological	artifacts,	which	are	based	on	DNA	sequences	that	are	not	found	in	nature,	i.e. artificial	recombinant	molecules. Space of artifacts As	shown	above,	there	have	been	various	approaches	to	classify	artifacts,	yet	there	appears	to be	no	attempt	to	describe	the	space	of	artifacts	by	allocating	artifacts	identification	numbers4. Every	ever-produced	artifact	can	be	assigned	a	unique	identification	number	based	on	two parameters:	the	location5	coordinates	and	time	where	and	when	the	creator	initiated	the production	of	the	artifact.	(This	also	includes	then	unfinished	or	"work-in-progress"	artifacts	as they	are	already	considered	artifacts	once	the	production	has	begun.)	Already	these	two parameters	allow	for	unique	identification	of	artifacts,	including	those,	which	have	many instances,	e.g.	paper	clips.	Even	in	factories	where	large	numbers	of	paper	clips	are	produced, the	location	and	time	of	the	beginning	of	their	production	differs	if	the	coordinates	are sufficiently	fine-tuned. For	dependent	artifacts	the	location	never	changes,	e.g.	the	Eiffel	Tower,	while	others	are moved	around,	yet	for	identification	the	location	coordinates	are	used	where	its	creation began.	In	the	same	way	unique	identification	numbers	can	be	assigned	to	abstract	artifacts. Obviously,	there	are	many	location-time	pairs,	which	do	not	represent	artifacts	(i.e.	every	pair, where	and	when	no	creation	of	an	artifact	began). Yampolskiy	(2015)	suggested	that	the	Kolmogorov	Complexity	measure	could	be	applied	to identification	numbers	representing	mind	designs,	for	example,	to	determine	whether	shorter representations	of	the	original	number	are	possible.	The	same	idea	could	be	transferred	to	the identification	numbers	of	artifacts. Based	on	this	approach	of	location-time	pairs	it	can	be	inferred	that	the	set	of	identification numbers	of	artifacts	is	finite. 4	Similarly,	Yampolskiy	(2015)	has	described	the	space	of	possible	mind	designs. 5	The	specification	of	the	location	is	not	topic	of	this	paper,	but	it	should	be	as	precise	as	possible.	This	means	for artifacts	produced	by	nanotechnology	the	location	would	be	measured	on	an	atomic	and	molecular	scale. 4 As	shown	these	two	parameters	(location	and	time)	allow	already	for	unique	identification	of artifacts,	yet	a	third	interesting	parameter	would	be	the	creator	of	the	artifact.	Creators	of artifacts	have	always	a	mind	(which	distinguishes	artifacts	from	other	objects	created	by nature),	and	Yampolskiy	(2015)	has	shown	that	the	space	of	potential	minds	is	infinite,	but countable,	which	enables	us	to	use	the	identification	number	of	a	mind	as	third	parameter	and define	the	space	of	artifacts	as	follows: Space	of	artifacts	=	{m,	t,	l	|	A	mind	m	initiated	the	production	of	an	artifact	at	the	time	t	and the	location	l} As	we	now	have	three	parameters,	also	the	possible	subsets	with	the	cardinal	number	two,	i.e. creator-location	and	creator-time,	allow	for	unique	identification	of	artifacts.	Similarly,	there are	many	creator-location	and	creator-time	pairs,	which	do	not	represent	artifacts. For	various	reasons	and	within	different	sciences	it	is	of	interest	to	uncover	the	time	of creation,	the	location	of	creation	and/or	the	creator	of	an	artifact.	The	particular	process	to identify	the	creator	of	an	artifact	has	been	called	"designometry"	by	Yampolskiy	(2016). Designometry survey Definition Yampolskiy	(2016)	introduced	designometry	as	a	subfield	of	intellectology	(see	Yampolskiy, 2015,	and	Ziesche	&	Yampolskiy,	2017)	and	describes	it	as	the	field	of	study,	which	aims	"[...]	to uncover	a	'signature'	of	the	originator	in	the	artifact	and	from	it	to	identify	the	agent responsible	or	to	at	least	learn	some	properties,	of	the	design	process,	which	produced	the artifact".	Yampolskiy	(2016)	highlights	that	designometry	could	become	particularly	relevant	for potentially	engineered	life	or	minds.	Looking	at	the	creator-location-time	triple	defined	above to	identify	artifacts,	designometry	is	exploring	ways	to	retrieve	the	first	parameter	of	the	triple, i.e.	the	creator. The	finite	set	of	all	artifacts	can	be	split	into	the	two	subsets	of	all	those	artifacts	whose creators	can	be	identified	and	those	artifacts	whose	creators	cannot	be	identified.	However, this	subdivision	depends	on	two	more	parameters,	which	are	the	types	of	minds	(see Yampolskiy,	2015)	who	are	pursuing	the	designometry	and	the	times	of	their	embodiment. Certain	types	of	minds	may	have	the	capacity/intelligence	to	successfully	identify	the originators	of	certain	artifacts	after	a	certain	time	of	embodiment,	involving	education, research	etc,	while	this	may	be	beyond	the	capacity/intelligence	of	other	minds. Essentially,	designometry	is	a	two-stage	process,	which	looks	successively	at	the	following	two questions: 5 1)	Is	an	object	an	artifact? 2)	If	yes,	is	it	possible	to	identify	the	mind	who	created	this	artifact? Within	various	sciences	the	quest	for	the	creator	of	artifacts	is	part	of	the	research;	a	fact	that motivated	us	to	introduce	classifications	of	artifacts	above.	The	type	of	artifact	differs depending	on	the	particular	science,	and	methodologies	are	usually	not	coordinated	among	the sciences.	It	is	an	aim	of	designometry	to	create	synergies	so	that	sciences	could	benefit	from each	other	in	their	studies.	Therefore,	in	what	follows	we	survey	all	these	fields	in	which relevant	work	is	done	and	try	to	abstract	away	details	about	the	field,	while	keeping	the methods	in	an	effort	to	extract	generic	methods	of	designometry.	As	we	will	see,	some	of	these fields	focus	only	on	the	second	question	above	since	by	definition	of	their	domain	they	deal with	artifacts	only,	e.g.	stylometry,	while	for	other	domains	both	questions	are	relevant,	e.g. archaeology. Archaeology / archaeometry Archaeology	is	the	classic	science	dealing	with	artifacts.	Archaeology	is	restricted	to	artifacts produced	by	humans	and	deals	mostly	with	concrete	artifacts	since	over	99	per	cent	of	the human	development	has	occurred	before	abstract	artifacts	such	as	written	texts	or	digital objects	existed.	Different	phases	can	be	distinguished:	A	significant	amount	of	time	and resources	of	an	archaeological	investigation	is	dedicated	to	survey	areas	of	interest	and	to uncover	artifacts,	often	through	excavation.	Only	in	the	subsequent	phase	the	artifacts	are analyzed,	for	which	methods	of	archaeometry	are	applied.	Sophisticated	techniques	have	been developed	to	determine	the	time	of	creation	as	well	as	the	function	of	unknown	artifacts,	while the	identification	of	a	specific	creator	of	an	ancient	artifact	is	in	many	cases	neither	possible	nor pursued.	An	example	for	uncovering	a	signature	within	archaeometric	research	is	provided	by Labati	at	al.	(2012)	for	the	specific	case	of	latent	fingerprints	on	clay	artifacts.	To	distinguish	this domain	from	the	other	ones	below	it	can	be	stated	that	archaeology	and	archaeometry	do neither	deal	with	biological	artifacts,	nor	concentrate	on	abstract	ones,	which	are	both	of interest	for	designometry. Artimetrics Yampolskiy	and	Gavrilova	(2012)	introduced	the	innovative	field	of	artimetrics	as	an	extension to	the	known	domain	of	biometrics.	In	addition	to	be	able	to	identify,	classify	and	authenticate biological	entities	through	sensors,	which	is	the	field	of	biometrics,	the	need	arose	to	be	able	to do	the	same	with	their	virtual	representatives,	i.e.	embodied	as	well	as	virtual	robots,	software, and	virtual	reality	agents.	The	field	of	artimetrics	aims	to	identify	such	artificial	entities	based on	their	outputs	or	behavior.	In	the	ontology	above	it	was	mentioned	that	artifacts	could	be abstract	or	virtual. 6 Astrobiology Astrobiology	is	a	science,	which,	among	other	things,	examines	whether	extant	or	extinct	life	in the	universe	exists	or	existed,	of	which	we	are	not	aware,	and	if	yes,	through	which	methods	it can	be	detected.	One	established	yet	up	to	now	unsuccessful	method	is	to	monitor electromagnetic	radiation	for	signs	of	transmissions	from	other	life	outside	earth.	To complement	these	efforts	Freitas	(1983)	proposed	the	Search	for	Extraterrestrial	Artifacts based	on	his	"Artifact	Hypothesis:	A	technologically	advanced	extraterrestrial	civilisation	has undertaken	a	long-term	programme	of	interstellar	exploration	via	transmission	of	material artifacts."	He	distinguishes	four	classes	of	potential	artifacts:	Astroengineering,	self-replicating artifacts,	passive	artifacts	and	active	probes.	In	this	regard	astrobiology	would	be	an	example where	both	issues	above	are	relevant:	First,	objects	have	to	be	identified	as	artifact,	which	is potentially	followed	by	the	quest	for	the	creator.	However,	Haqq-Misra	and	Kopparapu	(2012) show	that	despite	long	lasting	searches	non-terrestrial	artifacts	would	likely	remain	not	be detected	because	of	the	vastness	of	the	universe.	Wright	(2017)	suggests	considering	"a	prior indigenous	technological	species"	in	our	solar	system,	thus	earth	or	nearby	planets	would	have to	be	scrutinized	for	artifacts	of	such	species. Behavioral biometrics Due	to	the	proliferation	of	interaction	between	humans	and	electric	devices	the	need	for various	authentication	methods	increases.	In	addition	to	often	used	unique	physiological characteristics	of	a	user,	another	category	are	unique	behavioral	characteristics.	The	latter	have been	surveyed	by	Yampolskiy	and	Govindaraju	(2008)	and	can	be	divided	into	behaviors	that produce	artifacts,	e.g.	texts,	emails,	sketches	or	programming	codes,	and	those	which	do	not produce	outputs,	e.g.	car	driving	style,	game	strategies,	lip	movements	or	mouse	dynamics. These	artifacts	in	the	first	category	may	include	unique	signatures	of	the	user,	which	can	be further	analyzed	through	stylometric	methods,	which	are	introduced	below. Forensics science Forensic	science	concerns	the	collection	and	analysis	of	evidence	linked	to	a	criminal investigation.	A	subset	of	the	evidence	could	be	artifacts,	for	example	an	exploded	or unexploded	device,	in	which	case	the	identification	of	the	creator	may	advance	the	criminal investigation. The	subfield	digital	forensics	addresses	evidence	found	in	digital	devices,	which	becomes	more and	more	relevant.	All	such	evidence	are	abstract	artifacts.	Methods	to	identify	the	authors	of such	digital	content	are	described	below	under	"Code	stylometry". Stylometry Stylometry	is	a	domain,	which	initially	focused	on	the	particular	artifact	of	written	texts,	i.e.	the identification	of	an	author	of	an	anonymous	or	disputed	text.	There	it	is	sometimes	called 7 authorship	attribution.	This	field	has	progressed	recently	a	lot	due	to	the	availability	of	a	large digital	text	corpus,	which	can	be	utilized	for	statistical	analysis.	Moreover,	stylometry	has	been extended	to	other	areas	of	creative	artifacts.	Backer	and	van	Kranenburg	(2006)	deliver	an approach	for	the	subfield	of	musical	stylometry	based	on	statistical	pattern	recognition.	Various approaches	also	tackle	the	subfield	of	visual	stylometry,	for	example	Hughes	et	al.	(2012),	Qi	et al.	(2013)	or	Jacobsen	and	Nielsen	(2013).	The	prime	motive	is	often	to	find	methods	to authenticate	songs	and	paintings	respectively	and	at	the	same	time	to	uncover	forgery. Code stylometry Code	stylometry	is	the	subfield,	which	aims	to	finds	methods	to	de-anonymise	the	creator	of programming	codes,	which	is	also	motivated	to	improve	detection	of	plagiarism.	Code stylometry	is	particularly	interesting	since	automated	methods	through	machine	learning	have been	developed	which	can	be	applied	to	a	large	code	corpus.	Caliskan-Islam	et	al.	(2015a) examine	machine-learning	methods	to	identify	source	code	authors	of	C/C++	using	coding	style. A	distinction	has	to	be	made	whether	source	code	or	merely	compiled	binary	code	is	available for	analysis.	Binary	code	is	much	harder	to	de-anonymise,	which	was	tackled,	for	example,	by Rosenblum	et	al.	(2011)	and	further	expanded	by	Caliskan-Islam	et	al.	(2015b). Simulation detection Bostrom	(2003)	analyzed	the	likelihood	that	we	are	living	in	a	computer	simulation.	If	this	were true,	our	universe	itself	would	be	an	artifact	(and	the	set	of	naturally	occurring	things	in	our universe	would	be	empty)	and	the	identification	of	its	creator/simulator	would	be	of	high scientific	interest,	yet	extremely	difficult	if	not	impossible.	Due	to	these	challenges	there	are not	many	(scientific)	attempts	in	this	regard.	For	example,	Hsu	and	Zee	(2006)	argue	that cosmic	microwave	background	could	be	used	as	a	communication	channel	with	a	potential creator/simulator.	Beane	et	al.	(2012)	take	a	different	approach	and	aim	to	show	that	a simulator	could	be	in	principle	detected	because	of	the	finiteness	of	resources.	Both	these methods	of	designometry	would	differ	from	all	the	other	methods	listed	here. Synthetic biology Synthetic	biology	is	an	interdisciplinary	field,	which	is	still	in	early	stages	and	examines	the design	and	construction	of	new	biological	entities	or	the	redesign	of	existing	biological	entities (see	e.g.	Hutchison	et.	al.,	2016).	For	various	reasons	it	can	be	of	interest	based	on	the	resulting biological	artifact	to	identify	its	creator.	The	creator	may	have	placed,	for	example,	intentionally a	signature	in	the	DNA,	which	is	called	steganography6 and	is	the	practice	of	concealing information	within	unsuspicious	cover	carriers.	Beck	et	al.	(2013)	provide	an	approach	to	find such	messages,	thus	possibly	identify	the	author.	If	the	creator	has	left	no	such	message, potentially	methods	from	code	stylometry	could	be	applied,	i.e.	looking	for	specific	patterns	in the	synthetic	code	to	reveal	the	author's	identity. 6	See	for	an	overview:	Katzenbeisser	and	Petitcolas	(2000). 8 Designometry methodologies In	this	section,	we	aim	to	present	generic	methods	for	designometry,	either	by	deriving	them from	the	individual	methods	applied	by	the	above	fields,	which	use	designometry,	or	by introducing	original	techniques.	These	methods	constitute	of	one	axiom	and	a	set	of	heuristics. As	described,	designometry	is	a	two-stage	process,	each	stage	using	different	approaches, which	is	reflected	in	the	structure	of	this	section. a. Is an object an artifact? Some	of	the	fields	above	have	to	address	this	question	first,	e.g.	archaeology,	while	others	do not	have	to	since	by	definition	they	exclusively	deal	with	artifacts,	e.g.	stylometry.	As	described above,	even	for	biological	samples	this	question	may	be	relevant	and	they	may	turn	out	as artifacts.	While	synthetic	biology	deals	with	the	manufacture	of	new	biological	entities,	the reverse	question	how	to	separate	engineered	from	natural	biological	entities	has	not	received	a lot	of	attention	in	the	literature	of	the	field.	Yampolskiy	(2016)	stresses	that	precisely	this	issue could	become	very	important	in	the	future	because	of	expected	advances	in	synthetic	biology, which	may,	however,	lead	to	unforeseeable	and	undesirable	consequences. We	propose	the	following	axiom	to	determine	whether	an	object	is	an	artifact: Axiom 1 An	object	is	an	artifact	if	it	contains	writing. We	define	writing	as	a	means	of	communication	that	uses	signs.	Therefore,	if	written	signs	can be	found	in	an	object,	we	conclude	that	the	object	is	an	artifact.	Signs	are	studied	in	the	field	of semiotics.	We	use	here	the	dyadic7	approach	by	Saussure	(1983),	which	distinguishes	between a	signifier,	i.e.	the	form	a	sign	takes,	and	the	signified,	i.e.	the	concept	a	signifier	refers	to. Written	signs	can	be	iconic	or	symbolic8.	Iconic	signs	are	characterized	by	a	similarity	between signifier	and	signified.	An	example	is	a	drawing	of	a	chair.	Symbolic	signs	are	arbitrary	and	the relation	between	signifier	and	signified	is	conventional	within	a	particular	language.	An	example is	the	writing	of	the	word	"chair".	This	means	symbolic	signs	can	be	anything	as	long	as	there	is a	social	convention	about	its	meaning9. Symbolic	signs,	which	are	the	main	subject	of	Saussure's	research,	create	for	the	above	axiom the	challenge	to	identify	writing	in	an	artifact.	This	challenge	would	be	particularly	hard	for 7	An	alternative	in	semiotics	is	the	triadic	approach	by	Peirce	(1958). 8	Another	category	are	signs	that	are	indexical.	These	are	signs	where	the	signified	causes	the	signifier.	For example,	fire	causes	smoke;	in	other	words,	smoke	signifies	fire.	However,	this	category	does	not	apply	to	written signs. 9	In	semiotics	it	is	usually	argued	that	also	the	grasping	of	the	meaning	of	iconic	signs	involves	to	some	extent social	conventions. 9 artifacts,	which	were	created	by	a	type	of	mind	whose	written	language	humans	do	not	know. Using	Saussure's	terms,	the	two-fold	challenge	is	not	only	to	get	an	understanding	of	the signifier,	but	before	to	determine	whether	any	material	within	an	artifact	constitutes	at	all	a signifier. Ways	to	tackle	this	challenges	are	explored	in	the	subfield	exosemiotics.	Exosemiotics	studies signs	that	theoretically	could	be	understood	by	other	minds	(see	e.g.	Reed,	2000).	An	early attempt	is	the	constructed	language	Lincos,	which	Freudenthal	(1960)	created	to	be understandable	by	any	possible	intelligent	mind.	Yet	the	space	of	possible	minds	is	vast	(see e.g.	Yampolskiy,	2015)	and	so	could	be	potentially	their	respective	use	of	signs. Once	an	object	has	positively	been	declared	to	be	an	artifact	the	second	designometric	query has	to	be	tackled,	which	we	approach	by	the	following	heuristics: b. Is it possible to identify the mind who created this artifact? Heuristic b.1 One	option	to	identify	the	mind	who	created	the	artifact	is	to	look	for	an	intentional signature. This	heuristic	is	relevant	for	artifacts	where	the	creators	intentionally	included	writing,	which allows	backtracking	to	the	creator.	In	trivial	cases	these	could	be	literally	signatures	or	other physical	or	digital	watermarks,	which	serve	for	copyright	protection.	Yet	these	signatures	could be	also	forgeries,	which	is	another	field	of	research.	Intentional	signatures	on	artifacts	are relevant	in	forensic	science	as	they	may	serve	as	claim	of	responsibility	for	a	misdoing.	One example	would	be	illegal	graffiti	with	so-called	tagging	as	a	form	of	personal	expression. Likewise,	forensic	scientists	have	to	be	aware	of	forgeries.	A	special	case	are	intentional,	but hidden	signatures,	which	was	introduced	above	as	steganography	and	is	explored	for	example in	synthetic	biology. In	some	contexts	the	identification	of	a	species	as	creator	instead	of	an	individual	is	already sufficient.	This	is	the	case	quite	often	in	archaeometric	research,	but	also	in	astrobiology	it would	be	an	unprecedented	success	if	an	artifact	with	a	signature	of	a	species	not	originating from	earth	could	be	found.	An	example	for	such	an	artifact	would	be	the	so-called	Pioneer plaques,	which	travelled	to	space	on	board	the	Pioneer	10	and	Pioneer	11	space	probes	in	1972 and	1973	respectively.	These	plaques	contain	a	pictorial	message,	based	on	exosemiotic considerations,	for	a	potential	encounter	with	extraterrestrial	life,	for	whom	it	is	intended	to	be sufficient	to	identify	the	human	species	as	creator	of	this	artifact	rather	than	the	human individuals	involved	in	the	production	of	the	plaques. 10 Heuristic b.2 One	option	to	identify	the	mind	who	created	the	artifact	is	to	look	for	an	unintentional signature. While	intentional	signatures	often	signal	that	the	creator	wants	to	be	linked	to	this	artifact	and no	scientific	work	for	identification	is	required,	the	situation	is	different	when	the	creator obviously	does	not	want	to	be	identified	and	searching	for	unintentional	signatures	is	used	as	a method	to	do	so. This	heuristic	is	even	more	common	in	forensic	science	than	b.1	since	usually	culprits	do	not want	to	be	identified.	Therefore,	methods	of	forensic	science	focus	on	the	revelation	of unintentional	signatures	at	artifacts	linked	to	a	crime	such	as	fingerprints	or	more	recently DNA.	The	search	for	signatures	is	not	a	priority	within	archaeometric	research,	but	one	example by	Labati	at	al.	(2012)	about	latent	fingerprints	on	clay	artifacts	was	mentioned	above. Other	types	of	unintentional	signatures	would	be	certain	patterns,	which	could	be unambiguously	retraced	to	the	creator.	Two	categories	can	be	distinguished:	Whether	it	is attempted	to	detect	the	pattern	from	the	design	and/or	the	behavior	of	the	artifact	or	from	the underlying	code,	which	defines	the	artifact. Examples	for	the	first	category	would	be	artimetrics	and	behavioral	biometrics.	Here,	a	black box	approach	is	applied	and	the	search	for	patterns	focuses	on	the	observed	properties,	output or	behavior.	Another	example	are	the	nests	of	weaverbirds.	Bailey	et	al.	(2015)	describe	how nests	can	be	attributed	to	individual	weaverbirds	through	computer-aided	image	texture classification.	The	signatures	have	to	be	considered	unintentional	if	we	assume	that	the	minds of	the	creators	are	not	sufficiently	sophisticated.	Also	in	certain	contexts	the	identification	of	a species	through	unintentional	signatures	instead	of	an	individual	is	already	scientifically satisfying.	This	is	the	case	for	many	animal-built	structures10. An	example	for	the	exploration	of	patterns	in	the	code	would	be	code	stylometry.	In	the context	of	biological	entities	the	distinction	above	could	be	formulated	as	to	search	for	a signature	in	the	phenotype	or	in	the	genotype	of	the	organism.	Overall,	the	availability	of	a code	or	genotype	is	preferred	since	this	allows	performing	more	precise	analysis	of	a	specific dataset. If	the	desirable	code	is	not	at	hand	another	tool	related	to	designometry	becomes	relevant, which	is	reverse	engineering.	This	is	the	process	of	taking	an	artifact	and	trying	to	obtain knowledge	about	its	construction	plan	or	its	code.	Reverse	engineering	can	be	hard	and	costly. Villaverde	and	Banga	(2014)	provide	an	overview	of	available	reverse	engineering	methods	for biological	entities,	which	is	relevant	since	our	special	interest	lies	in	designometry	related	to engineered	life. 10	See	for	an	overview:	Hansell	(2005). 11 Therefore,	reverse	engineering	can	be	seen	as	a	function	from	the	space	of	artifacts, represented	by	the	set	of	identification	numbers	of	artifacts,	to	the	code	of	the	artifact, whereby	the	artifacts	are	represented	by	its	identification	number: re:	{artifacts}	->	{code} Artifacts	with	more	than	one	instance	have	individual	identification	numbers	as	defined	above, but	share	the	same	code,	thus	this	function	is	not	bijective. Heuristic b.3 One	option	to	limit	the	set	of	minds	who	created	the	artifact	is	to	focus	on	those	minds	who have	a	motive,	goal	or	drive	to	create	this	artifact	(and	to	potentially	sign	it). Artifacts	are	created	for	a	reason,	thus	the	potential	creators	of	a	particular	artifact	can	be narrowed	down	to	the	subset	of	minds	that	have	a	motive,	goal	or	drive	to	create	this	artifact. In	forensic	science	potential	motives	for	a	crime	play	an	important	role.	According	to	Bostrom's (2012)	orthogonality	thesis	the	goals	and	the	intelligence	levels	of	minds	are	independent	of each	other;	hence	the	heuristics	7	and	8	are	independent.	Regarding	artifacts,	which	are	crated by	artificial	minds,	Omohundro	(2007	and	2008)	defines	general	drives	for	such	activities: Efficiency,	self-preservation,	acquisition,	and	creativity.	This	heuristic	addresses	also	potential writing	on	the	artifact,	which	is	relevant	for	the	above	axiom	and	heuristics,	as	this	may	reveal	a sub-goal	by	the	creator	to	not	only	produce	the	artifact,	but	also	to	sign	it. Heuristic b.4 One	option	to	limit	the	set	of	minds	who	created	the	artifact	is	to	focus	on	those	minds	who have	the	necessary	skillset,	education	and	intelligence	to	create	this	artifact. Apart	from	artifacts,	which	minds	can	create	only	because	of	their	genetic	predisposition,	it requires	intelligence	as	well	as	education	to	create	certain	artifacts,	which	only	a	subset	of minds	acquires.	For	example,	for	a	long	time	only	Chinese	and	Japanese	had	the	knowledge	and skills	to	produce	porcelain,	while	the	production	of	European	porcelain	only	started	in	18th century.	Therefore,	the	set	of	creators	of	any	porcelain	produced	before	1700	can	be	limited	to Chinese	and	Japanese	minds. The	minds	with	a	certain	skillset	to	create	certain	artifacts	could	be	subsets	within	a	species, but	also	whole	species.	Above	we	introduced	animal-built	structures,	and	for	example,	if	we look	at	the	artifact	spider	webs,	it	has	many	instances,	but	the	set	of	potential	creators	is limited	to	the	set	of	spiders. It	has	to	be	noted	that	the	creator	does	not	have	to	be	the	inventor	of	a	particular	artifact,	but has	acquired	the	skills	to	produce	the	artifact.	The	inventor	of	an	artifact	with	more	than	one instance	could	be	identified	by	checking,	which	creator-location-time	triple	of	all	instances	of 12 this	artifact	has	the	lowest/earliest	time	number,	thus	to	determine	when	this	artifact	was produced	for	the	first	time11. The	degree	of	skills	required	to	produce	an	artifact	varies	highly.	It	can	be	inferred	that	the more	sophisticated	the	artifact	is,	the	smaller	is	the	subset	of	minds	who	is	able	to	create	this	of artifact.	As	designometry	aims	to	focus	on	sophisticated	artifacts	such	as	artificial	minds	in particular,	this	heuristic	could	be	useful. Heuristic b.5 One	option	to	limit	the	set	of	minds	who	created	the	artifact	is,	if	the	time	and	the	location coordinates	when	and	where	the	production	of	the	artifact	was	initiated	can	be	determined, to	look	only	at	minds	who	were	embodied	at	this	time	and	were	able	to	reach	this	location. As	established	above,	all	artifacts	can	be	defined	through	a	creator-location-time	triple,	and designometry	addresses	the	quest	for	the	creator.	However,	the	other	two	parameters	could be	supportive	as	this	heuristic	shows. This	heuristic	is	for	example	applied	in	archaeology	and	also	in	forensic	science.	While	in archaeometric	research	it	is	often	satisfying	to	limit	the	set	of	creators	to	a	certain	epoch,	in forensic	science	the	manhunt	is	for	specific	individuals.	Therefore,	the	whereabouts	of	suspects in	relation	to	artifacts,	which	are	linked	to	the	investigation,	constitutes	critical	information. Heuristic b.6 One	option	to	identify	the	mind	who	created	the	artifact	is	to	look	for	witnesses,	testimonies, coverage,	footage,	logfiles	or	sensor	data	of	the	creation	of	the	artifact. According	to	this	heuristic	information	about	the	creator	of	an	artifact	could	be	gathered	if another	mind	or	another	sufficiently	equipped	artifact	has	witnessed	the	creation	of	the artifact.	On	this	heuristic	we	have	to	rely	especially	if	the	artifact	does	not	exist	anymore.	One example	would	be	the	Seven	Wonders	of	the	Ancient	World,	of	which	nowadays	only	the	Great Pyramid	of	Giza	still	is	in	existence.	That	the	other	six	also	existed	and	that	for	example	the creator	of	the	Statue	of	Zeus	at	Olympia	was	a	Greek	sculptor	named	Phidias	we	have	to believe	because	of	testimonies. For	testimony	by	humans	the	criterion	of	credibility	is	relevant.	However,	more	and	more features	and	activities	in	the	physical	world	are	captured	electronically	e.g.	through	sensors. These	data	naturally	include	also	evidence	about	creation	of	artifacts.	This	could	be	relevant	for the	creation	of	artifacts,	which	involves	computers	such	as	artificial	minds,	as	logfiles	or	similar digital	traces	may	link	to	the	creator. 11	An	exceptional	case	are	artifacts	that	were	invented	more	than	once	independently	at	different	times	and	dates. An	example	is	the	calculus,	which	is	credited	to	both	Newton	and	Leibniz. 13 Although	the	introduced	axiom	and	heuristics	mark	an	early	phase	of	designometry	the envisaged	contributions	of	designometric	methods	can	be	summarized	as	follows: • To	provide	synergies	to	fields,	which	have	the	overlapping	sub-goal	to	identify	creators	of artifacts,	but	which	did	not	cooperate	much	yet,	and	enhance	as	well	as	consolidate	their efforts. • To	provide	time-critical	input	to	AI	safety	from	an	innovative	angle	as	the	heuristics	are applicable	to	the	subset	of	artificial	minds. Designometry as a sub-branch of intellectology As	Yampolskiy	(2016)	suggested	designometry	can	be	seen	as	a	subfield	of	intellectology,	which was	introduced	by	Yampolskiy	(2015)	in	order	to	examine	in	more	detail	features	of	the	space of	minds.	More	precisely,	designometry	can	be	seen	as	a	function	from	the	space	of	artifacts	to the	space	of	minds. d:	{artifacts}	->	{minds} Since	we	have	shown	that	artifacts	can	have	identification	numbers	assigned	to	and	Yampolskiy (2015)	has	done	the	same	for	the	space	of	minds	this	is	a	function	between	natural	numbers: d:	N ->	N This	means	the	designometry	function	maps	the	unique	identification	number	of	an	artifact	to the	unique	identification	number	of	the	mind	of	its	creator.	A	special	case	is	the	category "collectively	produced	artifacts",	which	was	introduced	above.	These	artifacts	have	more	than one	creator,	i.e.	the	identification	number	of	the	artifact	is	mapped	on	a	set	of	numbers,	which are	the	identification	numbers	of	all	its	creators. As	shown	above	an	intermediate	stage	is	often	(but	not	always)	to	map	the	artifact	to	its	code, in	which	then	hints	for	the	mind,	who	created	the	artifact,	may	be	found. The	introduction	of	the	creator-location-time	triple	enables	us	to	define	further	interesting	subcategories.	Creator-time	pairs	for	artifacts	are	only	possible	during	the	time	period	when	the creator	with	this	particular	mind	has	been	embodied;	a	fact	that	is	utilized	in	heuristic	b.5 above.	An	interesting	set	is,	for	example,	the	set	of	all	artifacts	produced	by	a	particular	mind during	its	embodiment. Of	particular	relevance	is	the	set	of	artifacts	that	comprises	all	artificial	minds,	which	can	be also	referred	to	as	the	set	of	not	naturally	occurring	minds	within	the	space	of	possible	minds, and	the	question	which	minds	are	capable	to	create	them	(see	heuristic	b.4	above)?	In	other words,	for	the	set	of	artificial	minds	what	is	the	set	of	potential	creators	within	the	creatorlocation-time	triple? 14 Artificial	minds	constitute	the	intersecting	set	of	the	set	of	minds	and	the	set	of	artifacts.	Since artificial	minds	may	also	produce	other	artifacts,	a	nested	constellation	emerges.	This	raises further	interesting	questions:	What	is	the	set	of	artifacts	that	can	only	be	produced	by	artificial minds,	but	not	by	naturally	occurring	minds?	Are	there	artifacts,	which	can	only	be	produced	by those	artificial	minds,	which	themselves	have	been	produced	by	artificial	minds?	This	question can	be	applied	to	deeper	levels	of	nesting	too.	In	this	regard	it	makes	probably	sense	to distinguish	between	offsprings	of	artificial	minds	resulting	from	reproduction,	which	are	by definition	also	artifacts,	and	those	artifacts,	which	are	produced	by	artificial	minds	outside	of reproduction.	One	trivial	conclusion	is	that	the	time	parameter	of	the	creating	mind	must	be lower	than	the	time	parameter	of	the	created	mind. Through	the	introduced	methodologies	of	designometry	various	sets	and	subsets	of	artifacts can	be	defined	as	partly	illustrated	in	Figure	1. Figure 1: Categories of artifacts including artificial minds12 Conclusion and future work We	have	presented	two	surveys,	a	survey	of	artifacts	and	a	survey	of	designometry.	We demonstrated	how	these	surveys	are	interconnected.	The	new	field	of	designometry	aims	to find	general	tools	and	methods	to	identify	the	creator	of	artifacts.	Currently	this	field	is	divided 12	The	sizes	of	the	areas	do	not	represent	proportions	of	their	cardinality. 15 in	specialized	subfields	for	particular	purposes,	e.g.	criminal	investigation,	or	particular	artifacts, e.g.	fine	art. We	aimed	to	provide	a	bigger	picture	by	specifying	the	space	of	artifacts	through	a	creatorlocation-time	triple	and	extracted	the	two-stage	process	that	first	it	has	to	be	ascertained	that an	object	is	an	artifact	and	then	the	quest	for	the	creator	has	to	be	tackled.	For	both	stages	we have	formulated	an	axiom	and	general	heuristics,	some	of	which	were	partly	and	rather implicitly	applied	before	and	some	of	which	are	innovative.	We	have	determined	that	writing on	an	object	is	a	clear	indicator	that	the	object	is	an	artifact.	For	the	identification	of	the creator	the	examination	of	the	code	for	signs	appears	to	be	more	promising	than	scrutinizing properties	or	behavior	of	an	artifact.	Therefore,	procedures	such	as	reverse	engineering	to obtain	the	code	are	a	relevant	instrument	for	designometry. Yampolskiy	(2016)	proposed	the	field	of	designometry	with	an	outlook	towards	a	particular subset	of	artifacts,	which	are	artificial	minds.	Given	the	ongoing	progress	in	the	concerned technologies,	such	research	is	very	timely.	Methods	to	find	out	the	creator	of	artificial	minds are	likely	to	be	relevant	for	several	reasons,	e.g.	for	proper	registration,	but	can	be	seen	in particular	as	a	contribution	to	the	field	of	AI	safety,	which	entails	the	identification	of originators	of	malicious	systems	as	a	critical	step	to	curtail	such	systems	if	possible.	Both	the specification	of	the	space	of	artifacts	as	well	as	the	proposed	axiom	and	the	initial	set	of heuristics	to	identify	creators	of	artifacts	can	be	seen	as	groundwork	for	future	AI	safety research. Regarding	future	work,	in	addition	to	the	proposals	and	open	questions,	which	were	mentioned above,	we	may	look	at	a	fourth	parameter	to	define	the	space	of	artifacts,	supplemental	to creator,	time	and	location,	which	would	be	the	goal	or	purpose	of	the	particular	artifact.	In heuristic	b.3	above	we	looked	at	the	goals	of	the	creators	of	artifacts,	which	could	be connected	to	the	goals	of	the	artifacts,	referred	to	by	Tegmark	(2017)	as	outsourcing	of	goals through	engineering.	The	feature	"intended	use	of	the	artifact"	has	been	introduced	before within	the	ontology	of	artifacts,	but	it	had	not	been	linked	to	AI	safety.	In	addition	to establishing	who	created	artifacts	and	when	and	where,	it	is	many	contexts	important	to	know whether	the	artifact	has	benevolent	or	malevolent	goals. Tegmark	(2017)	highlights	the	increasing	relevance	of	goals	of	artifacts	since	in	addition	to	living organisms	having	goals	a	"[...]	rapidly	growing	fraction	of	matter	was	rearranged	by	living organisms	to	help	accomplish	their	goals."	He	also	presents	data	that	show	that	"[...]	most matter	on	Earth	that	exhibits	goal-oriented	properties	may	soon	be	designed	rather	than evolved".	This	interesting	observation	motivates	us	to	expand	our	work	towards	goals	of artifacts. Finally,	it	has	to	be	reiterated	as	in	Yampolskiy	(2016)	that	when	we	discuss	engineered	life	and artificial	minds,	we	do	not	support	by	any	means	non-naturalistic	notions	be	it	god(s), creationist	myths	or	religion,	but	merely	the	engineering	of	biological	entities. 16 References Aristotle,	Physica,	in	The	Works	of	Aristotle	Translated	into	English	(Volume	II),	David	Ross	(ed.), Oxford:	Clarendon	Press,	1930. Backer,	E.,	&	van	Kranenburg,	P.	(2005).	On	musical	stylometry-a	pattern	recognition approach.	Pattern	Recognition	Letters,	26(3),	299-309. http://www.sciencedirect.com/science/article/pii/S0167865504003393 Bailey,	I.	E.,	Backes,	A.,	Walsh,	P.	T.,	Morgan,	K.	V.,	Meddle,	S.	L.,	&	Healy,	S.	D.	(2015).	Image analysis	of	weaverbird	nests	reveals	signature	weave	textures.	Royal	Society	open	science,	2(6), 150074. http://rsos.royalsocietypublishing.org/content/2/6/150074 Beane,	S.	R.,	Davoudi,	Z.,	&	Savage,	M.	J.	(2014).	Constraints	on	the	Universe	as	a	Numerical Simulation.	The	European	Physical	Journal	A,	50(9),	148. https://arxiv.org/pdf/1210.1847.pdf Beck,	M.	B.,	Rouchka,	E.	C.,	&	Yampolskiy,	R.	V.	(2012,	October).	Finding	data	in	DNA:	computer forensic	investigations	of	living	organisms.	In	International	Conference	on	Digital	Forensics	and Cyber	Crime	(pp.	204-219).	Springer,	Berlin,	Heidelberg. https://pdfs.semanticscholar.org/c8c3/fe9bb61cf44b29a1bdc1e40cdb5a6894c978.pdf Borgo,	S.,	&	Vieu,	L.	(2009).	Artefacts	in	formal	ontology.	Handbook	of	philosophy	of	technology and	engineering	sciences,	9,	273-307. ftp://ftp.irit.fr/IRIT/LILAC/BV-HBPT09.pdf Bostrom,	N.	(2003).	Are	You	Living	In	a	Computer	Simulation?	Philosophical	Quarterly.	53(211): p.	243-255. https://ora.ox.ac.uk/objects/uuid:44c386c4-5d9e-4ecf-a47c9631a2a59747/datastreams/ATTACHMENT01 Bostrom,	N.	(2012).	The	Superintelligent	Will:	Motivation	and	Instrumental	Rationality	in Advanced	Artificial	Agents.	In	V.C.	Müller	(Ed.),	Theory	and	Philosophy	of	AI.	Special	issue, Minds	and	Machines,	22	(2),	71-85. Caliskan-Islam,	A.,	Harang,	R.,	Liu,	A.,	Narayanan,	A.,	Voss,	C.,	Yamaguchi,	F.,	&	Greenstadt,	R. (2015,	August).	De-anonymizing	programmers	via	code	stylometry.	In	24th	USENIX	Security Symposium	(USENIX	Security),	Washington,	DC. 17 https://www.cs.drexel.edu/~ac993/papers/caliskan_deanonymizing.pdf Caliskan-Islam,	A.,	Yamaguchi,	F.,	Dauber,	E.,	Harang,	R.,	Rieck,	K.,	Greenstadt,	R.,	&	Narayanan, A.	(2015b).	When	coding	style	survives	compilation:	De-anonymizing	programmers	from executable	binaries.	arXiv	preprint	arXiv:1512.08546. https://arxiv.org/pdf/1512.08546.pdf Franssen,	M.,	Kroes,	P.,	Reydon,	T.,	&	Vermaas,	P.	E.	(Eds.).	(2013).	Artefact	kinds:	Ontology	and the	human-made	world	(Vol.	365).	Springer	Science	&	Business	Media. Freitas,	R.	A.	(1983).	The	search	for	extraterrestrial	artifacts(SETA).	British	Interplanetary Society,	Journal	(Interstellar	Studies),	36,	501-506. http://www.rfreitas.com/Astro/SETAJBISNov1983.htm Freudenthal,	H.	(1960).	Lincos,	Design	of	a	Language	for	Cosmic	Intercourse.	Amsterdam: North-Holland. Hansell,	M.H.	(2005).	Animal	Architecture.	Oxford	University	Press,	Oxford	pp	321. Haqq-Misra,	J.,	&	Kopparapu,	R.	K.	(2012).	On	the	likelihood	of	non-terrestrial	artifacts	in	the Solar	System.	Acta	Astronautica,	72,	15–20. https://arxiv.org/pdf/1111.1212.pdf Hilpinen,	R.	(1993).	Authors	and	artifacts.	In	Proceedings	of	the	Aristotelian	Society	(Vol.	93,	pp. 155-178).	Aristotelian	Society,	Wiley. Hilpinen,	R.	(2011).	Artifact.	In	E.	N.	Zalta	(ed.),	The	Stanford	Encyclopedia	of	Philosophy. https://plato.stanford.edu/entries/artifact/ Hsu,	S.,	&	Zee,	A.	(2006).	Message	in	the	Sky.	Modern	Physics	Letters	A,	21(19),	1495-1500. https://arxiv.org/pdf/physics/0510102.pdf Hughes,	J.	M.,	Mao,	D.,	Rockmore,	D.	N.,	Wang,	Y.,	&	Wu,	Q.	(2012).	Empirical	mode decomposition	analysis	for	visual	stylometry.	IEEE	transactions	on	pattern	analysis	and	machine intelligence,	34(11),	2147-2157. http://ieeexplore.ieee.org/abstract/document/6127875/ 18 Hutchison,	C.	A.,	Chuang,	R.	Y.,	Noskov,	V.	N.,	Assad-Garcia,	N.,	Deerinck,	T.	J.,	Ellisman,	M.	H., ...	&	Pelletier,	J.	F.	(2016).	Design	and	synthesis	of	a	minimal	bacterial	genome.	Science, 351(6280),	aad6253. https://pdfs.semanticscholar.org/9c1c/932fca27afa2ada8c1653ce9d22500e1abe6.pdf Jacobsen,	C.	R.,	&	Nielsen,	M.	(2013).	Stylometry	of	paintings	using	hidden	Markov	modelling	of contourlet	transforms.	Signal	Processing,	93(3),	579-591. http://people.math.aau.dk/~mnielsen/reprints/2013_stylometry.pdf Katzenbeisser,	S.,	&	Petitcolas,	F.	(2000).	Information	hiding	techniques	for	steganography	and digital	watermarking.	Artech	house. Labati,	R.	D.,	Genovese,	A.,	Piuri,	V.,	&	Scotti,	F.	(2012).	Two-view	contactless	fingerprint acquisition	systems:	a	case	study	for	clay	artworks.	In	Biometric	Measurements	and	Systems	for Security	and	Medical	Applications	(BIOMS),	2012	IEEE	Workshop	on	(pp.	1-8).	IEEE. http://piurilabs.di.unimi.it/Papers/bioms_2012.pdf Omohundro,	S.M.	(2007).	The	Nature	of	Self-Improving	Artificial	Intelligence.	Paper	presented at	Singularity	Summit	2007,	San	Francisco,	CA. Omohundro,	S.M.	(2008).	The	Basic	AI	Drives.	In	P.	Wang,	B.	Goertzel	&	S.	Franklin	(Eds.), Proceedings	of	the	First	AGI	Conference,	Frontiers	in	Artificial	Intelligence	and	Applications, Volume	171.	Amsterdam:	IOS	Press,	483-492. Peirce,	C.S.	(1958):	Collected	Writings	(8	Vols.).	(Ed.	Charles	Hartshorne,	Paul	Weiss	&	Arthur	W Burks).	Cambridge,	MA:	Harvard	University	Press. Qi,	H.,	Taeb,	A.,	&	Hughes,	S.	M.	(2013).	Visual	stylometry	using	background	selection	and wavelet-HMT-based	Fisher	information	distances	for	attribution	and	dating	of	impressionist paintings.	Signal	Processing,	93(3),	541-553. http://www.sciencedirect.com/science/article/pii/S0165168412003623 Reed,	M.	L.	(2000).	Exosemiotics:	an	inter-disciplinary	approach.	Acta	Astronautica,	46(10),	719723. Rosenblum,	N.,	Zhu,	X.,	&	Miller,	B.	(2011).	Who	wrote	this	code?	Identifying	the	authors	of program	binaries.	Computer	Security–ESORICS	2011,	172-189. http://ftp.cs.wisc.edu/paradyn/papers/Rosenblum11Authorship.pdf 19 de	Saussure,	Ferdinand	(1983):	Course	in	General	Linguistics	(trans.	Roy	Harris).	London: Duckworth Tegmark,	M.	(2017).	Life	3.0	Being	Human	in	the	Age	of	Artificial	Intelligence.	New	York:	Knopf. Villaverde,	A.	F.,	&	Banga,	J.	R.	(2014).	Reverse	engineering	and	identification	in	systems biology:	strategies,	perspectives	and	challenges.	Journal	of	the	Royal	Society	Interface,	11(91), 20130505. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3869153/ Wright,	J.	T.	(2017).	Prior	indigenous	technological	species.	International	Journal	of Astrobiology,	1-5. https://arxiv.org/pdf/1704.07263.pdf Yampolskiy,	R.	V.	(2015).	The	space	of	possible	mind	designs.	In	International	Conference	on Artificial	General	Intelligence	(pp.	218-227).	Springer	International	Publishing. https://www.researchgate.net/profile/Roman_Yampolskiy/publication/300646188_The_Space _of_Possible_Mind_Designs/links/5737f1b408ae9ace840bfa3a.pdf Yampolskiy, R. V. (2016). On the origin of synthetic life: attribution of output to a particular algorithm.	Physica	Scripta,	92(1),	013002. https://www.researchgate.net/profile/Roman_Yampolskiy/publication/310736484_On_the_ori gin_of_synthetic_life_Attribution_of_output_to_a_particular_algorithm/links/5836117708aec3 fe331c5203/On-the-origin-of-synthetic-life-Attribution-of-output-to-a-particular-algorithm.pdf Yampolskiy,	R.	V.,	&	Gavrilova,	M.	L.	(2012).	Artimetrics:	Biometrics	for	Artificial	Entities."	IEEE Robotics	&	Automation	Magazine	19,	no.	4,	48-58. Yampolskiy,	R.	V.,	&	Govindaraju,	V.	(2008).	Behavioural	biometrics:	a	survey	and	classification. International	Journal	of	Biometrics,	1(1),	81-113. https://pdfs.semanticscholar.org/1958/eebd997a2e90b88e1f8bb5345ec88408a1ce.pdf Ziesche,	S.,	&	Yampolskiy,	R.	V.	(2017).	High	Performance	Computing	of	Possible	Minds. International	Journal	of	Grid	and	High	Performance	Computing	(IJGHPC),	9(1),	37-47.