h4unt3d hacker -Australian Edition- issue 3

ISSUE 03 AUG 2021

ONLINE OR IN YOUR INBOX:

MADE BY Smart Cyber solutions

TRANS-HUMANISM: AI, Cybernetics and the hunt for John Connor

WTf is Ai • neuro poetry • unwanted immortality

My partner has a thing against machines. Not some iRobot, Asimov protaginist style deep hatred, but an unswervable opinion that no matter how smart machines get they will never be able to feel.

I disagree. Everything we experience boils down to electrical impulses, positive and negative, ones and zeros. Its all binary in the end. As we strive for divinity at the altar of technology it is through cybernetic augmentation we will truly become god like. I can’t wait to get some of Elon’s wetwear tapped into my frontal lobe so I can really up my twiiter game.

Maybe we are still a few years off Skynet but I sit confident in the opinion that one day, in the not to distant future, machines like humans, will know the disapointment of just being another cog in the machine.

WE ARE #h4unt3d

Privacy Matters

CONTENT

AI

h4unt3dhackerAUSTRALIA Ubuntu as a daily driver DC CYBERSEC

WTF is AI

08

NEuro Poetry

10

Terminator needs a lawyer 32

ALEX PADEN

The many faces of AI DR elmar Diederichs

unwanted immortality

John

28

Brenda van Rensburg

14

radical embodiments fiona lewis

24

ryan Williams

Unknown Monsta

dr RADhika dirks

AI in the data centre prasan singh

26

36 42

Readers,

"With attacks amplifying and evolving the H4unt3d Hacker community is digging into AI and future solutions to current problems. The need for freedom of information and sharing of free knowledge is paramount in an ever changing political and technical world. We believe it should be everyone's right to gain knowledge and assist in innovation. Join us as we fight new fronts and move forward. Thank you for the growing support!"

Cheers!

The H4unt3d Hacker

8

dccybersec Ubuntu As a daily driver...

I recently had an issue on my work computer where the Windows boot drive became corrupt. I tried to same drive, which failed, then tried to install Ubuntu on that same drive. Both failed though as I suspect this particular drive had bad blocks. No fun! In the end, I found a spare drive in my car and was about to install Windows 10 again when I thought - “hold on... this could be a fun experiment. Why don’t I try Ubuntu in a Windows world and see if it’s too inconvenient or if it would be possible?”.

Day #3 I caved. I now have a windows 10 virtual machine inside vmware workstation player, but I have a good reason! The issue I was running into today was because of powershell something I use very often but have never had to run inside native linux before.

2 issues here;

1. the version of powershell in linux (v7) is good, however there are many commands missing as well as a few modules that simply don’t exist. I tried This was essentially my “Day #1” of running visual studio, powershell ISE using Ubuntu as a daily driver, and through wine and by trying to break below is the story from the next 4 days into the source of the terminal to fix of using this system. this. No joy unfortunately.

Strap yourself in for some fun learnings 2. The modules I really really wanted of my adventures using ubuntu as a (AzureAD and MSOnline) have to run daily driver! in 64bit windows. Everything else is unsupported (wtf?) In other news, Day #2: Ubuntu as a workstation with I also managed to lock a password no vm’s - had some issues with a palo manager accidentally which is used alto vpn last night but found my way in by the entire company (lol). I ran a via an alternative (related to licensing of sync of the database while connected globalprotect vpn client). Fun fact: if to the VPN back into the office, then you don’t have the “linux and android” disconnected my VPN thinking it had license purchased for your VPN blade completed when in fact it had not. of your PAN firewall, you won’t be This corrupted the file, but it was able to use globalprotect or openvpn easily fixed by me reconnected to the to connect in. A way around paying VPN, running the database sync of for this almost $1000 license fee is to the password manager and letting it use “OpenConnect” vpn client, which complete (took a few minutes). is essentially the same shit but with slightly different protocols. Insight: Day #4: Ubuntu Daily Driver Today It has been an easy transition so far, has been business as usual. Now that as most of what I do at work is inside I have a VM with Windows 10 64bit containers in firefox. I try to keep solely dedicated to run powershell everything web based and synced to scripts, it’s back to smooth sailing as my email address just in case I need usual. I’ve focused my efforts today to reference it quickly from another towards using the computer as I device. Thing’s i’ve had some small normally would be completing the issues but not a “show-stopper” pentests/ automation jobs I normally are teams inside linux with video do while also monitoring my own calls, graphics drivers (this always business (data-sec) virtual SOC (cloud happens lol), and of course, the VPN I based anyway so no drama here). A mentioned earlier. few cool things I like so far is that the

082021 system remembers if you’ve connected to monitors before and automatically moved windows to those screens.

I usually go between a few different clients each day and setup at a station there to do internal tests/ reports, and having the system remember each of these monitor sets is really quite cool.

Saves me 10 seconds of moving my screens around when I set up (first world problems lol). I’ve also now setup my favourite terminal (fsh), as well as changed the desktop wallpaper (super important) and made a few little tweaks to suit my own style. I’ve been thinking if I should bother with windows managers, but honestly, I don’t feel there is much need for this. I might re-visit that as an option if I get bored later on.

So, to wrap this one up, I am currently writing this ending paragraph from my Ubuntu workstation. It’s a bit slow, it’s not QUITE as functional as when I had Windows 10 on here, but hey, it works! I’d like to have this running on a system that, you know.. is of this century (this machine is very outdated hardware-wise), but yeah... not a bad user experience at the end of the day.

Disclaimer - Always confirm with your IT department if this is allowed to be done before wiping the OS of a workstation.

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Trans-humanism: issue 3 - blueteam

WTF IS AI Do you know what Artificial Intelligence is?

Second, let’s get a few of my own definitions.

Does it exist?

Artificial Intelligence (Industry) is a buzzword. To industry, it means you can put things (data) in and receive quality answers which you did not previously have in return.

Yes. No, wait. Honestly, I still don’t know, but I’m going to share with you what I’ve learned and we can both come to our own conclusion. This seems reasonable enough given that the scientific community has a similar perspective.

First, let’s get a few Wikipedia definitions.

Artificial Intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals.

Leading AI textbooks define the field as the study of “intelligent agents”: any system that perceives its environment and takes actions that maximize its chance of achieving its goals.

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of Artificial Intelligence.

Artificial Intelligence (Academia) is a buzzword.

To academia, it means you (or it) can put things (data) in and receive intelligent answers in return.

Algorithmic Intelligence (Academia) is a buzzword. Which intersects Artificial Intelligence as described by either industry or academia, as well as general intelligence. Algorithmic Intelligence appears to lack some of the speculation and philosophical considerations of deeper artificial intelligence research such as that which hopes to replicate the human brain or to augment its abilities.

Alex’s brain defines the field of Artificial Intelligence as “getting answers from computers” and the real ambition appears to be rivaling our own intelligence.

Now I’m going to give you a hypothetical conversation

HAVE NO FEAR OF PERFECTION

YOU’LL NEVER REACH IT. -DALI

John: Artificial Intelligence does not exist, that is machine learning and data science.

Jane: Machine Learning is a subset of the field of Artificial Intelligence, so it does exist. With that said, we haven’t made machines that can think like humans (Artificial General Intelligence) or that exceeds humans (Artificial Super Intelligence).

John: I do not agree that machine learning is a subset of Artificial Intelligence. Artificial Intelligence means that it can generally problem solve and reason. Machine Learning, Expert Systems, and the rest all follow predetermined rules, statistics, or things.

Jane: While you may identify that as the goal of Artificial Intelligence and what it could become, it is not something that exists yet. It’s very difficult to describe what Artificial Intelligence would even refer to. In many ways, humans themselves are artificially intelligent. I don’t find it to be a mistake that the birth of Artificial Intelligence often refers to the early effort of researchers at Dartmouth in 1956 or the following exaggerated marketing efforts of the term “Artificial Intelligence” which we all now recognize.

John: What are you saying?

Jane: As a field, Artificial Intelligence is relatively new, a big part of that drive forward comes from the accessibility to funding that researchers have. It also comes from the drive of public attention and opinion. AI has wrongfully gambled on singular ideas and big promises before that ultimately reached a plateau and caused what is referred to as “AI Winter” not once, but twice. And if you watched Game of Thrones, well...

10

So while I may not consider my speech-to-text system to be as intelligent as a human, I will pitch it to the business I’m installing speech-to-text Wfor as an Artificial Intelligence solution. Which only continues to drive interest in the field. Then again, if the client prefers Machine Learning, so could I. John: You have a good point that it drives attention to the field… But I’m not sure it helps researchers get paid anything more.

Jane: Now that’s something we can both agree on :).

So, what is Artificial Intelligence? It depends on who you ask. Under a strict definition, it reasonably excludes all forms of working “Artificial Intelligence” today from actually being considered as “Artificial Intelligence”. Many or most academic opinions would say that what we refer to as Artificial Intelligence does not exist. And that’s valid. Machine Learning doesn’t think

11

Dear Defcon

This was meant to reach you as a flyer drop from the roof of the DEFCON hotel but alas organising such from the other side if the globe proved a little more challenging than first thought. We shall not let that ge We are offering a massive prize pack from the wonderful people at DRONESEC, SMART CYBER SOLUTI GUIDANCE, THE SAFER INTERNET PROJECT and many more. All you need to do is get a photo with th FEDEX 5752 located across the road from DEFCON. Extra bonus prizes for including store manager Shawn in the photo. We hope you all had a great time at hopefully see you in the flesh next year. lotsa love D8RH8R and the H4unt3d Crew

12

a feat last minute et in our way though.

IONS, CYBERSEC he flyers located at DEFCON and will

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56 by the done in 19 on and Cliff st fir as w m ation, this Herbert Si ia Mathebolic oper n Newell, the Princip ed by Alle of op s el m ev re D eo day on a th ist. to 52 ne do of e b ove 38 is can also nology [30]. th m could pr n io at ic oper ing tech subsymbol Deep Learn MAC-net y and l e.g. by a and Minsk McCarthy topic by m ea ed tr as ns firstly emph we observe this mai realized by 8] it of goals: [4 tly rt Russell or Go mos like chess e.g. by Stua or games g in iv dr mous ing [23]. hat criteria ment Learn g yet of w e derstandin avoid som un to , ed er ar ev monly sh meet. How t n us m ee is no com w ch et ar b stinguish gence rese itable to di ficial intelli to be inev s started em se it m today, acy mainstrea e formed th in ill on st d is d is focuse ion, that an it 3] ad [4 tr is 54 AI: th hop 19 outh works nt agents. inal Dartm by intellige g in lv so d problem

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1 Intro duction (II) no n-huma goes ba n-like AI: this ck p 3 from pla to the semina ro ject - some lw times c y all mimic a ing around wit ork of Alan T uring [6 ed The Imita utonom h a sser tio tio 6] o [3] or c onceptu us decisions, n ns and correc and is focused n Game ting mis on-stat al mo d on learn ist els, and ta Note th ing non-mo ical abstractio kes [67] in ord at b oth n n er t o o consist v e t a .g o r nic deli . buildin iants a s in the beratio re mo g causa intende n. l d outco tivated by (A m e. ) , ( B) To ach & ieve a ( C ) .

The diff less m ea n s. er en ce Then w coarse-graine only e find e d level mpirica we add ll the dis a) hum y (see fi tinction an-lik gure 1) : betwee cial CP e AI by physic n differ U archit al mean ent ect u r es s: often [21, 20, [32] or s c 19] and a ll e d ”synthe ynthetic analyse the The tic sy d Global Brain I as complex n napses realize AI” based on this ap etworks ns d sp proach e.g. see by atomic swit esince th titute. Philos o p h er s t h e r e se e 19 8 0 s ches b) non a ls a as conn -huma ectionis o have extensiv rch done by works o n-like AI by p m [56, 40, 63]. ely discussed hysical n this fi means: eld tod to the b ay. c) hum est o f m an-like y know AI by intellig ledge n algorith en c e ” ( ob o d y m ic A means: GI). T set o f a he basic we also lgorithm call this a self-s s, t h a t idea of up pe A ” No-Fre ervised way - rform well on GI is, that y artificial gen er a l e-Lunch ou try which more t -T is t h an on During h eo r em a princ e specia o create a the las s ip [4 le 1 p , 34]. E l t first on very ot roblem due to problem in e is dom 25 years two h er t h e so d inated (and p call

by Mar ominant appr AI is called ” robably oaches cu s narrow ed Jü the weig to AGI AI”. hted av rgen Schmidh Hutter, ANU emerge uber, ID er & Shan in that d: t e environ age over all e S nvironm IA): universa Legg, Deep M he ment. T mined l intellig by the ents of in he weig un th ht ence [3 d is expo 1] is nentiall iversal distrib s assigned to e intelligence y inver o ution in the env t h a t co se p r o p ironme f the agent mp which ortiona nts are eral Pra utes it. The l to the a weight in t deters e co n d ctical I h sh o r t es one go e ntellige will ach t length e environment s back nce of ieve his o f t a n a g en o Ben G the pro goals sp certain t is g oerzel, en ec Poly U ram term p vironment dis ified on a cer the expected : Genro ject O tain go d eg r ee tributio a t n p l o m a t h em enCog. . d AGI to Goerzel realiz istribution sp which he atics, b d e ut not ecified the min ay is trying t d this approa in a d) non o unde ch in h d.

-human r is long s t a n -l d the b ike AI g e n c e” , rain us by algo w ing rithm AI [53] hich includes a system s means: we - also c a alled stu lso call atic inq d eb a t e uiry of it ”mac - that s pidity o t t a r t ed 1931 - r f AI. Recall t he limitations hine intellihat the eveals t of algor hat alg orithm Gödel-Church ithmic s are m eans to -Turingprove t ha t

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a certain, but not a ll math p be found roblems [2 in a finite 5, 8, 68, 6 number o ductive c 1] h f steps w orrectness hile follow ave a solution tha [69]. Hen how rule-g t ca n ing certain c e fr uided and criteria o iteratively om the beginning human in f d etelligence it was highly , since e.g learning algorithm terfactua unclear, s can ever . human l thought brain mimic cre ex p natural la ative nguage. T eriments that requ s are doing reason ing in co he last po ire seman of Spelke untic argum int was re et al. [73, ce n ents given 60, 59, 33 here is to in a ] for non-c tly illustrated by th mimic th ausal con e experim e mind. clusions. ents The prim ary goal A

lgorithm

ic Int

elligence Moreover, we expect some kind of non-hu man-like algorithm of augmentation o problems f human in ic AI, in fast so far as telligence interests in er than humans, algorithm as a but are n their searc s n o t only solv result e it her guide machines h for solu e certain d nor con tions. Th ”thinking at last po ” by writi linguistic int means, strained by human ng down behaviou that obse their thou r would human la rving futu ghts in te result in n g u a g e. re rms of so a languag me structu e that is red untransla table to any

3 The Map of AI

mathematical theories to justify t [28, 62]. Famous exceptions are th Information Bottleneck Theory of ence between the expectations, wh AI community, and the achievem currently gets beyond weak AI.

In sum, today AI-projects are lik topics:

Figure 1: current a pproach

es to AGI in the sta tistica

l paradigm However, of weak A there is n I o doubt th telligence at most c have not ontributio progresse ns to algo d beyond rithmic a the stage rtificial in of engine ering and consisten t

Figure 2: M L = Machine Learnin plifies a learning path for the imita economic projects.

3

The Map of AI

In short, there is no simple take ho of artificial intelligence [29]. Inste intelligence from the last section a detectable in the publications ove topics that illustrates the state of

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these numerical algorithms are hardly available he Statistical Learning Theory [26, 76] and the f Deep Learning [10]. There is still a big differhich are - at least partly - supported also by the ments shown so far. In particular, no algorithm

ke paths through a collection of problems and

Figure 3: Yellow dotted lines mar k cutting edge research areas.

The other topics are more or less already establish ed at least on an engineering leve l.

Since the very idea by algorith mic cognitive architectures have not been discussed yet, we will add some explana tory, historical remarks.

1. It is well known that the first approach to statistical subsymb olic narrow AI started around 1992 with the phase of handcrafted knowled ge: Engineers created sets of rules to represen t knowledge in well-defined dom ains. Also the structure of the encoded knowledge was defined by hum ans and only the specifics are explored by a machine. There were no learning capabilities and only a poor handling of unc ertainties in data.

2. For deep learning engineer s the years 1996-2006 appeare d to be a third AI-winter. But in fact it was caused by the priority of deve loping nonparametric statistics [65], whe re statistics lost its distribution al assumptions and met first order opti mization [45]. This move allow ed to develop the current statistical i.e. data -based or subsymbolic approac h to AI: Engineers created statistical mod els for specific problem domains and train them on big data mostly usin g the submanifold hypothesis: natural data forms lower dimensional stru ctures in the embedding spac e, where each

ng; DS = Data Science. The solid arrow exemation game of AI and the dotted arrow for some

ome message about the nature or possible future ead taking seriously the insights in algorithmic and summarizing the research activities that are er the last decades, we come up with a map of the art in AI:

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7 ion nd Imitat a n io t a t 4 Augmen

s by sepa e m o c a t g da nd derstandin ed classification a n U . y it t n c u an imal erent e are only n apability and min nts a diff e e r s e e h r t p , e r r e c re manifold s. Howev e, t h ey a ontextual ld iv c o s s e if e n n r o a p n m t im se os e rating the pabilities, but alm atistical results ar t a s c e n gh th predictio . y. Althou it unil b formative a in g n in u n d o s n l missing a a il e t r s le b s a m li e h icting lly unr -algorit oals, pred individua g res of AI u g t a in fe u s r le desirab and trans eyond pu b ly n h y io t ig it c h il e r b e r a r o and self-c rning t h er e a reasoning robust lea ractively learning, l However, d a n u t a c l fa a r r g en e , inte counte til now: a t r a ct a n d the visible s b m a o , s fr m d de proble t ex t . the occlu e level of nging con a h t h c n and selfa o n o e g t iv in r ls n d r a a o t g n le fer e, eve ble ion of l adaptat ly adaptiv tion with accepta a h ic ig h m a , c n li y d cogbstrac symbo riant, sub atistical a chical - algorithmic a t s v in n o e n g f im Such a t le learnin capable o d probably hierar b , a m it v h e it r in o alg k the a - an teaching ld require w to shrin o u o ll a w l e il c n w that performa ture [70], c e heir it h c r a eek s. ariety of t w v o e nitive t h t s e r d e a v owe ping m d ec [64, 74]. H expect that develo period fro w e n t o n ic e is field. W er a ca d em itectures h h h c t c r r o a a e s e m e o r iv fr from sults n o p en of cognit ted by re s is still a ning them I. a The idea r n a iv io t t le o a d z m n li a a ic re t u r es ow A ience algorithm e architec bolic narr r n eu r o sc o iv m ] y it 6 s n b g [4 u o e s c c ic rch in e scien algorithm e o f r esea . cognitiv s a .g e h p e k d li ir s en perforg th e in w t m e o b discipline c ff e o r a d eed initiate th always a t es need to be tailor is e data will r e h t s oache GI o r itectur ased appr itive arch oach to A b r n l g p e o p d c a o ic n m a m r all rith ing as Because fo neralizability, algo ot promis n e r a e y g e d s. Th m a n ce a n s on focu n [51]. io t a c li p h e r o r d er ig h f o to the ap s ie capabilit cognitive ita tion m I ar t s d n a unch of p n b o i d t n a a e t c en m g s a scien to make a a u s t I s A A e g g g u in s 4 ure 3 stablish f AI in fig phasis. ed in re-e o t s p e a r e m t e in t em e are n es, t h g differen Thus if w l academic discipli in v of a h s a e ar na imitation h c io r ic a it e m d s h a e r it r t r of two and he algo n between f economy ation of t r o lo t n p e x e m distinctio e p to th evelo dedicated s for the d e is c n e e n u o q t e s s fir its con ( α ) Th e mind and ations n a m u h res, limit the u t a fe c e ifi t h e s p ec ces for th n d e n u a q t society. s e r s e n d o its c to un en ce a n d hould try s ig ll e e n t o in d n ic seco rithm ind. ( β ) Th e s of algo ie it il b human m a e p h a t f o n ches: and c rent bran gmentatio e u a iff e d iv e e it r n co g a st t h I has at le A ia v n atio mind imit Basically

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8 I. enlighten ment about how the hum or self-sacri an mind wo ficing tasks rks: many v can only be or are accep ital but dan done if the ted as huma gerous actors have n beings. h u m a n abilities II. data-base d diagnosis a nd improvem ness in a hig ent of econo hly industri mic self org alised and in more and m anization: b creasingly c ore from th usiomplex socie e loss of eco ty is sufferin nomic contr III. incorpo g ol. rating artifi cial minds cooperation in the socie and digital ty: primarily innovations benefits fro society is b will change m this coop ased on the constrain eration. ts, chances a For mind au nd gmentation via AI we p department: ropose at le ast three diff erent branch es of this IV. explora tion of possib le AI technolo has led to a gies: domina n evolutiona nce of mark ry deadlock ducing usefu et-driven re for many A l solutions search I-technologie for people. applications s without p Instead, inn that cannot roovations cre be foreseen ate their ow without the V. developm n se innovatio ent of non-h ns. uman-like m ingenuity is achine intell limited and igence (see inspired by dedness of o figure 1): hu our biologic ur mind in man al interests human phy in their ow and the emb siology. Auto n way to d ed nomous ma evelop or se allow us to chines, thin arch for ne augment ou k in w complex g r creativity approaches, and range o VI. providin w il l f p roductivity. g data-based so lu ti o world proble ns to technic ms: through al, organisa tional or eco its own mis only everyd nomic realta ay problem kes, human s but also e it y h a s a c cumulated xistential ri not sks.

REFERENCES

082021 NCES

REFERE

11 nical Fou Theoret NCES lligence. ficial inte 88, 2012. ersal arti 67– de of univ lligence, pages neural One deca Inte on spiking Hutter. l General r-propagati IEEE Interna [31] M. of Artificia 2nd . An erro ), 0 veri CAS 202 dations Indi rs. ems (AI ssig & G. orphic processo and Syst a & G. Hae Circuits neurom Cartigli lligence ble with [32] M. compati ficial Inte of network ference on Arti . Origins Jacobson tional Con 2020. er & K. 88, omb 84– Mac es pag & J. 1992. r inlinger 05–632, s, and thei & L. Bre al Review, 99:6 h theorem . Spelke ogic free lunc [33] E.S ge. Psychol ew of no revi knowled 8.

A nn. n. 201 J. Herrma tic optimisatio NCES Joyce and aheuris 2013.

REFERE [34] T. s for met dynamics. ity science. implication ffic flow plex Tra com . ting for social & A. Kes dations of word Treiber mation nomic foun [35] M. cient esti an. Eco Minds A. Kirm 10 lligence.

Dean. Effi Aruka & hine inte rado & J. 2013. .

[36] Y. on of mac R, n & G. Cor aft. J.B A definiti 2017. & K. Che or space. In ICL issensch lligence: ning.

Mikolov nitionsw in vect [16] T. , 2007. versal inte deep lear dbuch Kog tations n using Legg. Uni pages 391 – 444 .). Han es 1–5, represen S. , (eds [37] s, hms ter d predictio (VPPC), pag hine e: Algorit icle spee nce & S. Wal and Mac Ma. Veh pulsion Confere Stephan al inferenc n A. istic Yua [17] , 2013. ieux and Power and Pro er age stat er Metzler Comput Joe Lem icle eling driv l Neu[38] tie.

Veh Mod E Has .

T. Dai 6. et. al.

Journa 2015 IEE page Efron and data science. 201 zienis n, and Wei rnational One, [18] B. A. Avi Yunlin Gua model. Inte 2015. , and Plos os & kov g Zhou, evidence . tin-Olm He, Han s in hidden mar 13, 2016. networks C. Mar 2.

Qinglian tion on switch lic Health, cognitive Sillin & 42772, 201 [39] J. Li, r near intersec er based sm and h and Pub atomic .pone.00 [19] H. ic nectioni ision mak behavio mental Researc /journal con Dec 371 romorph ing al.

10.1 a et. doi.org/ d: Integrat of Environ Hasegaw 3:245–259, 2015. https:// braic min a & T. ignore ”no e complex The alge T. TsuruokMaterials Science, ers can Marcus. nt brain-likic synthetic . Kim & research [40] G. . AIMS al. Emerge [20] S.-J 2001. orph aheuristics switches science. ayama et. Towards neurom on Nanotechwhy met , 1:60, 2020. atomic : & T. Nak nce en and Systems Marcus networks tional Confere ott. Wh SN Comput. Sci. Software cic & I. ic switch rna McDerm endable rems.

[41] J. [21] Z. Kun nanowire atom IEEE 18th Inte 36, 2018. . In Dep h” theo l lem lunc enta in 262 from d prob free erim e ity 8.86 lishe l of Exp the fram nce. Pub 9/nano.201 next fifty . Journa Framing intellige I:10.110 nce: The 7– e problem Meyer. page DO 3. nce confere lligence., 27:8 [42] B. , 2015. nology, e the fram 41 – 456, 201 l intellige l Inte emare, ineering ans solv ficia 25:4 ficia Bell Eng hum arti G. Arti nce, c ege How for llige uth coll Fields.

Islam, Mar ning. CoRR, ociation ficial Inte [22] C. ire. Dartmo ical Arti erican Ass nt lear on, Riashat itive arch & Theoret r Henders deep reinforceme [43] J. MooAI Magazine, Am ed cogn et, Pete year.

Integrat in Robotics and çois-Lav introduction to ai.

Fran ter Nag s cent chap tier & T. . An 91, 2006.

[23] Vin T. Aoki uage. Fron Cognition., M. Aydle Pineau 8.

Horii & and lang and Joel Situated & , 201 a & T. of action 1.12560 dbook of Miyazaw P. Robbins ge Han abs/181 t learning [44] K. Cognition.

Cambrid for robo ure ated The tect Situ er.

9. ents of Gallagh atica und [24] S. AI, 6, 201 al Anteced a mathem principi 32.

Philosophic , sätze der 9. heidbare s, 1931:173 – 198 ede, 200 unentsc r formal Math. Phy rk. 2020. framewo Gödel. Übe eme. Monats. unified [25] K. ter syst ning: A 2008. verwand hine lear A primer. mac : al ems Statistic cal syst bridge e. Cam Golden. e dynami [26] R. adaptiv al Inferenc plex and Statistic puter Age Gros. Com [27] C. tie. Com & K. Vold Trevor Has artaigh to Efron & n Ó hÉige A framework 2016.

[28] B. h & Seá ity Press, & P. Flac ficial intelligence: Univers arti d & B. Loe of ts ume z-Pl n The face Martı́ne 2018.

Orallo. al attentio [29] F. nándezposition In IJCAI, & J. Her n of ai. ning. Com 8. evolutio r D. Man 1803.03067, 201 phe track the isto Chr abs/ son and g. CoRR, w A. Hud hine reasonin [30] Dre for mac networks REFERE

9

NCES

REFERE

tes. hie des geis

2008.

osop ces in die phil ührung che einf . Analytis 6. er & kermann lth. 200 chbrenn [1] A. Bec in of wea & N. Kal ArXiv, The orig & N. Ke on learning. hocker. tati S. Bauer [2] E. Bein represen atello & sal Loc cau F. Towards olkopf & [3] B. Sch al & Y. Bengio.

A. Goy , 2021.

2.11107 re. 2016. abs/210 re and futu es. 2014.

Its natu , strategi en. Ai: deep hs, dangers trends in [4] M. Bod gence: Pat puta. Recent erintelli Cambria cle]. IEEE Com trom. Sup ia & E. arti [5] N. Bos a & S. Por essing [review Hazarik proc D. e & ng languag 5–75, 2018. tation [6] T. You based natural represen e, 13:5 ed R, azin rvis ning CoR Mag lear Unsupe orks. lligence Chintala. adversarial netw tional Inte z & S. ive L. Met l generat ford & olutiona American [7] A. Rad with deep conv theory. learning , 2016. ry number enta 1.06434 elem 151 of abs/ problem 1936. of unsolvable 363, A survey rch. An iv, s, 58:345– E. Cote.

[8] A. Chu l of Mathematic rslag & -stationarity. ArX & T. Baa non Journa M. Kaisers ts: Dealing with Leal & nandezenvironmen & B. [9] P. Her in multiagent chinsky ning.

7. & A. Kol learning lear 201 ani , Adv of deep 7.09183 ello & M. leneck theory abs/170 & J. Dap bott Y. Bansal the information g Saxe & . On e reasonin [10] A. & D. Cox monsens f. Intell., Tracey e. Com 2018. ier-Logu tion. Arti In ICLR, informa Noah Fraz cus, and cally incomplete G. Mar is, radi g Dav nce We [11] E. ainers usin l Intellige Artificia about cont 2017. g ldin Bui 84, ng AI: 248:46– is. Rebooti 1958. & E. Dav 2019. raw-Hill, Marcus n, York: McG [12] G. y. New st. Pantheo ing. 3rd olvabilit Can Tru niz to Tur y and Uns putabilit from Leib the road Davis. Com puter : [13] M. ersal com univ The 2019.

2018.

Davis. anding.

[14] M. & Francis, c Underst Scientifi ed. Taylor erstanding Regt. Und k W. de [15] Hen

Referen

and onally fast Excepti Data Mining Rocket: els.

Webb. ional kern n & G. convolut Petitjea g random r & F. Dempste classification usin [77] A. 0. e 202 tim i-iii. y, vol. over accurate atica wledge Disc a mathem S. Whiteand Kno Principi hmer & us itehead. & W. Boe lized continuo & A. Wh P. Torr ntra Russell ienny & for dece [78] B. & P. Kam ement learning 0.

B. Peng forc , 202 Witt & gent rein 3.06709 al charac[79] C. abs/200 individu p multi-a study of son. Dee control. ArXiv, 2.

A g.

201 el.

Ron ive mod and J. cooperat en markov a Zhao, d on hidd ng, Xiaohu gjian Zha ing behavior base [80] Xin of driv teristics

14

NCES

REFERE

13 0. . istics. 200 tric stat cognition -parame ok of 4e ics in non rd handbo ski. Top The oxfo Nemirov Newen.

[45] A. er & A. us CogniGallagh Conscio in & S. sciousness.

[46] L. Bru and con ding 8.

201 man. Bin 2. o & J. New rson, 201 Revonsu 1999.

[47] A. nce. Pea n in a 3?7, l Intellige predictio tion, 8:12 Artificia ion and Norvig. r recognit sell & P. behavio d. Driver Sensing, 2000.

[48] S. Rus probification A. Pentlan , and : the ram Oliver and ense, Security ints N. stra [49] con r. In Def 2. ation and smartca ere with dur ICTAI 2002, 200 s: ions ture Wh kis. Act bases. IEEE architec Papada Cognitive l data [50] N. r. pora quie M. Pas lem in tem taryo & ture: A architec R. J. Oen AGI, 2008. cognitive Duch & ? In sm and [51] W. from here nectioni Con . do we go h8.

Pylyshyn ons of mat –71, 198 & Z.W. foundati on, 28:3 . Fodor s on the . Cogniti [52] J.A theorem analysis 5. e basic critical tions. 199 itive aregra. Som ez-Consu osophical implica ented cogn Rodrı́gu of implem r phil [53] F. catalog and thei ematics a unified for rein. Toward 4, 2010. ion vich mat approxi . Samsono A, 221:195–24 Summer [54] A.V g function st Models res. BIC es in usin 3 Connectioni chitectu tz. Issu of the 199 A. Schwar f.

Thrun & ning. Proceedings brain. Arti [55] S. ational 3. nt lear comput forceme –263, 199 ki. The pages 255 Sejnows School, and & T. e, pages rchl azin Chu Mag P. 6.

Reeke & for ai. AI 391, 199 [56] G. a vision 81– 82:3 ning Intell., ens of lear e. The gard 2011.

G. Selfridg tledge., [57] O. 3.

York: Rou ation in on. New 36–48, 199 ial reorient ed Cogniti ess of spat . Embodi etric proc Shapiro A geom [58] L. Spelke.

1994. of spatial & E. S. :57–9, 08 t: the case 370 mer men Her ure, develop [59] L. dren. Nat ity and young chil Modular 1996. thelke.

Spe metama 95–232, & E. S. eit: Die Hermer erkenntnition, 61:1 heidbark [60] L. Unentsc Rosser und ihre ation. Cog reorient igkeit und Kleene, ollständ ler. Unv el, Church, Stegmül ultate von Göd 1973.

[61] W. Spinger, en Res ng. isch mat Bedeutu etische nistheor

12

hine s of Mac ndation alkar. Fou A. Talw izadeh & gy.

A. Rostam of psycholo ss, 2018. osophy Mohri & bridge MIT Pre the phil [62] M. and Cam g. sm Learnin nectioni son. Con cognitive & J. Tien tures: core 53:17–94, Horgan architec iew, [63] T. of cognitive lligence Rev s 1996. year l Inte . 40 J. Tsotsos lications. Artificia seruba & es in l app [64] I. Kot and practica inger Seri ion. Spr abilities ric Estimat paramet 2018. n to Non 1950.

Introductio 33–460, Tsybakov. 8.

Mind, 59:4 [65] A. lligence. ter 200 s, inte chap rs, Statistic hinery and other pape 7. B. E. ing mac 1946 and y 194 Comput report of Turing.

20 Februar s ACE [66] A. iety on M. Turing’ hematical Soc A. cheiTuring. don Mat 1986.

[67] A. to the ents seto the Lon Doran (eds.), tion ture lica 2nd Lec app –265, & R. W. with an pages 230 Carpenter number, al Society, patible hematic On com don Mat Turing. kwell, 1st Proc. Lon [68] A. oblem. atics. Blac dungspr Mathem phies of ries, 42. n. Philoso Vellema of cognitive & D. J. role rge The earch, Geo Vernon.

[69] A. ems Res 2002. ri & D. nitive Syst ama edition, Cog Oltr nce. tt & A. l intellige M. Bha Lieto & in general artificia EUCogni [70] A. ture. In tures architec architec itive 8. cogn 201 design a 48:1–3, s (not) to plexity rov com Two way kolmogo 2017.

Vernon. ction to [71] D. r Science, odu 6. pute intr 201 , An tion in Com Vitányi. ographs iences, l M. B. Neurosc and Mon g Li & Pautions. In Texts wledge Cognitive [72] Min sten. The and physical kno applica von Hof and its on, ton & C. ted acti & P. Vish on, object-direc past . Spelke 5. percepti s in the [73] E.S Object (ed.), 199 itecture chapter itive arch 8.

Gazzaniga 201 cogn M. of 90, ey –32 in infancy. g. A surv , 48:3280 , and a g & F. Wan s on Cybernetics problem tion & T. Wan relevance [74] P. Ye s. IEEE Transac lem, the prob e year 2.

20 fram 201 g. The :43–72, 2009. & P. Wan . Synthese, 187 theory. gjin Xu to both al learning [75] Yin statistic solution package etry and ic geom Algebra anabe.

[76] S. Wat NCES

REFERE

21

UNWANTED

W

IMMORTALITY

It may have been a little before Indy’s time and perhaps wouldn’t have been as cineomatic but immortality was never going to be found consealed under the floor of a Roman church or hidden in a forgetten shrine, deep in a cave, somewhere in the holy land. The vessel that would bestow immortality on all that touched it was safe and sound.Ttucked away from the world, slowly increasing its reach out from its birthplace in the land of the free and the home of the brave. While trying to create a covert communication network, DARPA unwittingly created the Holy Grail.

Every digital act we have ever performed, or will do in the future is recorded somewhere.

Digital echoes of a life well spent, ill spent or just spent. You aren’t given a choice in this binary blessing. Digital echoes of you will no doubt long outlive these feeble we all now share.

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WHOAMI In our very first issue I discussed a couple of easy, do it yourself steps, to help improve your digital hygiene and maybe wrangle that footprint in a little. This time let’s take it up a notch with some practical active techniques you can use to help keep you intentions a mystery and your trail cold.

I don’t feel I need to tell you this but for those playing along at home, there is no 100% sure fire method. Attribution is only a matter of effort, resources and time. There are some crazy smart, well funded individuals and groups out there, from both sides of the tracks.

No amount of obfuscation will hide stupidity. Be safe. Be smart. Shall we continue?

WHOAMI - Take back you privacy.

Well that’s the tag line anyway. This little chestnut appeared on my feed a few weeks back and caught my attention. As you will see, I love taking security and anonymity tools through their paces. Most, to be honest, are rubbish and are more about a sense of security than actual safety. So when WHOAMI performed better than I expected I felt it only fair to share it with you dear reader. The purpose of the WHOAMI tool is to “make you as anonymous as possible on Kali linux” according to its creator Omer Dogen. As you will see, the ease of installation and simple interface make this tool extremely user friendly and a good entry point for those wanting a little more protection than a tor exit node.

Installation is quite straight forward though there are a few dependencies which need to be taken care of first. { sudo apt update && sudo apt install tar tor curl python3 python3-scapy networkmanager } You might have a few headaches here but nothing that a little jimmying won’t sort out.

Once they’re all installed clone the repository at https://github.com/omer-dogan/kaliwhoami, install the makefile, { sudo make install } and your ready to rock and roll. Tap { sudo kali-whoami --help } into the terminal and your off and racing. Faced with the following options I’m sure you can figure the rest out for yourself. [+] Usage : sudo kali-whoami [option] --start : It will make backups and start the program.

--stop : Closes the program using a backup.

--status : Provides information about working status.

--fix : Used to repair the system in case of a possible bug.

--help : This shows the menu.

h4unt3d

hacks 25

O

pen-source intelligence also known by its acronym “OSINT”, plays a huge role in Cybersecurity, Information Technology, Operational security, and many other massive fields. OSINT is the process of gathering Personally Identifiable Information (PII), which is information relating to a person, company, people of interest, and so forth. OSINT is performed daily, whether its A are for the good or the bad. Most of the infamous data breaches and major hacks are first engaged with the use of OSINT. As more and more people steadily use some form of technology daily, they are bound to have email accounts, phone numbers, usernames, social media accounts, passwords, and many other things that belong to an individual or a company. These are just some of the things that OSINT covers, there are many other kinds of assets that OSINT can be used to obtain certain information. All this information can be obtained by a skilled person who knows how to conduct a basic OSINT investigation. If you are not careful with what you do online or how you conduct yourself online, all this information can be easily accessible to anyone at any time. When you do anything online, you leave behind a digital footprint, which is a trail of data that you create whenever you are online.

Now you are probably wondering who can use OSINT? That is a simple answer, the answer is anyone. OSINT is a huge critical asset that is used by intelligence agencies, governments, private investigators, blackhat hackers, and many more. When I first got into the field of Cybersecurity OSINT grabbed my attention instantly, I saw the potential OSINT translates to in many different fields of Cybersecurity.

The first time I ever joined a Cybersecurity discord which was “DC Cybersec”. A person that goes by the name Jackson Henry pulled me to the side and said to me would you want to know what I can find out about you with just your username and profile picture. I said “sure”, thinking to myself that he would not be able to WWas I mistakenly wrong, the amount of information he was able to acquire from just a profile picture and username was extraordinary. Jackson proceeded to find out many things about my life. Some of those things being my real name, address, high school I graduated from, old football coaches, family members, emails, phone numbers, the list continued. That was the initial point of when I said to myself, I want to learn everything there is to know about OSINT.

26

USERNAMES & PROFILE From that point forward I proceeded to do research regarding OSINT daily. Later down the line I also proceeded to make an OSINT discord called “OSINT HQ”, (https://discord.gg/6sD2sZYyxM) that has RSS feeds built into it. These RSS feeds gather information across the world and internet whether it is intelligence news, cybersecurity news, CVE’s, technology, threats, attacks, and much more! The first way that I was able to practice and learn was by firstly conducting an OSINT investigation on myself, due to the reason that you know what you are looking for and if what you find are false positives or the correct information. This allowed me to clean up my own digital footprint. I feel that this is the first way of going about it if a person would want to get into OSINT. This is to ensure that you are safe from anyone who is trying to obtain your PII. There are a tremendous number of approaches for OSINT. Every OSINT investigation you conduct will always be different. Whether it is obtaining information regarding employees from a company you are conducting a penetration test on, the company themselves, or just an individual. You will obtain information from multiple different sources and depending on that said information you gather you will need to pivot with the information you obtained. The two main approaches of OSINT are offensive and passive.

Offensive OSINT is where you are contacting the target at hand to gather as much recent information as possible. This can be an excellent way of retrieving all the information you need for that said person or company. Offensive OSINT is the most dangerous form of OSINT, the chances of that said person or company becoming suspicious is extremely high which can compromise the whole operation. Passive OSINT is the direct opposite of offensive OSINT. While doing OSINT passively you never actually contact the company or individual.

This is the much safer route to take but requires a lot of skill and knowledge of the ways to go about gathering all the information. With passive OSINT the information you are gathering will be obtained from hosted third-party sources that are online.

There are also tools that you may use through a the CLI (command-line interface) like sherlock, Shodan, WHOIS lookup, and others. You have the chance of not acquiring the most up-to-date information about the company or individual. The chances of the individual or company finding out they are under investigation is slim to none. This making passive OSINT heavily affective when an investigator does not want the company or individual to have any knowledge about the investigation.

PRESENT DAY

With all this said, I feel that OSINT is a mustknow in any field of Cybersecurity. The amount of information you will be able to obtain from an individual or company is massive. I use OSINT while I am conducting penetration tests during the information-gathering phase. This allows me to gather a tremendous amount of information about the company and the individuals who work at the company, which allow my engagement’s to be more thorough! People always say to use different passwords to make your accounts more secure, but what no one talks about is using different usernames to diversify your virtual presence.

OSINT is a skill that if you have in your arsenal will play a huge impact on any sort of job inside the cybersecurity field!

“the amount of information he was able to acquire from just a profile picture and username was extraordinary” ~ John Dennith 27

28

radhika dirks Named as a Forbes ‘Women in AI to Watch’, Deloitte’s featured Women Executive in AI and one of the top 30 women building advanced AI, Dr. Radhika Dirks is CEO and founder of Ribo AI, a next-gen biotech changing the pace, scale of drug discovery staring with cancer. Ribo sprung out of XLabs, also founded and led by Dr. Dirks, which focused on building technology moonshots that amplify humans.

Dr. Dirks was previously CEO and founder of Seldn, an AI that accurately predicted global socio-economic disruptions, including the rise of ISIS and labor strikes in 12 countries. As a founding member of Shell’s $500M venture capital group, Dr. Dirks co-led cleantech deals and spin outs.

NEuro poetry

They say I started small That I couldn’t think at all In a hot boiling stew Of electron soup.

And on a 3rd rock Toxic oxygen wrought I swirled about Round a’round ‘till I thought.

With analog bolts I’ll construct you But for YOU to ascend You’ll reduce me.

You’ll crush millenia And seat ones with zeros Digital bits — you’ll call it deep.

It was very fuzzy then A small pattern when Autodicdact it clicked just right Formed layers to ascend.

Imitated in sand We try so hard An artificial me — What a flattery!

Oh, I like this more With touch, I explode.

The more I lock Further up I go — Mechanics!

You need to slow down Channel more of me.

Layers of complexity Complete me.

Oh, what is this I feel I’m DRAWN to me.

Part by part, more than my whole Seduced to react — its Chemistry!

Oh, that happened fast — A surge through me Sparks flying through Voltage to current — Electricity!

Oh, now I see.

Lets get more of ME. A neural lace To take my place.

Dr. Radhika Dirks

Look at my range of skills — Diversity.

And the precision depths That validate me.

CEO • Co-founder • Ribo AI

Harsh mistresses, have I now served.

Generalized intelligence through randomness.

Bio

And now I write, Sentiently.

In a cyclical loop — Christened neuro poetry.

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Radhikadirks Consciousness, a no consensus 04-0 9

(At this now-when, where we stand in the bright early wee hours of the 21st century, this is a sample of what of our best of the best say. Consciousness: what is it? Each stanza is a mini tour: a curation of leading theories from neuroscience to the Upanishads through physics, current and post-postmodern western philosophy, language, computing, evolution and even techno-Utopianism.)

A popularity contest A reality show.

It’s the dawn of the rule of neurons ultra-vain Some dare to say: It’s fame in the brain!

Cascading cells that lead back to themselves Penrose stairs of self-referentiality Yes, our DNA gives precedence for this — It’s simple loops cycling strangely!

Galleries of data present over time Integrated together rather efficiently Striking edges over abundant graphical nodes It’s rather simply…IIT! Patterns of language is what shapes meaning Why! Instrumentality gives us rhetoric.

What makes a pattern a pattern?

Consciousness is but the questioning!

An accidental perfection through evolution’s old bones The seeds of which long have been around It’s the next step in randomness — A Desiderata of Anti-Fragileness!

It’s how a hardware feels on computing Valence is but feedback programming Our operating system tuned for adaptability A solipsistic error-corrected soliloquy!

Fluctuations of cosmic connectedness A distanced from Being, perpetually twice removed It’s the weave of a thread constantly tugged An ancient tapestry of the Hidden Reality!

A driving mechanism gathering the all One by one ending in new point Omega.

A grand march toward super humanity It’s the underlying dynamic — comprehensible through history!

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Our mission at CyberSec Guidance is to offer virtual courses that cater to students of all ages from all around the world. Founded in 2021, we provide flexible learning options for all types of passionate learners.

YOUR IMAGE IS HERE

We aim to provide diverse options that cater to individuals who want a customizable learning experience, yet still prioritize receiving a high-level education. If you have any questions or are interested in enrolling in our Online Courses, please don’t hesitate to get in

cybersecguidance.com

31 No.01

32

Terminator needs a

Lawyer

‘The Terminator’ movie was released for public viewing in 1984. It is science fiction movie about a cyborg sent back from the year 2029 to 1984. Its mission was to eliminate a character called Sarah Conner. Despite a meagre budget of $6.4 million, ‘The Terminator’ was able to gross over $78 million.

There have been several sequels since leaving many questioning whether artificial intelligence (A.I.) is even capable of this type of nature.

As time has passed, it has become evident that A.I. is capable of many things. We are currently witnessing A.I. integration in our lives. From the Tesla car to Sophia Hanson the Humanoid , the world is quickly accepting of A.I. as part of their everyday lives. However, what happens when things go wrong? This article will focus on the basics of robots, A.I. and apply basic law to a hypothetical question of responsibility.

Arguably, A.I. includes machine learning and deep learning. Both require human interaction, and the outcome could be a direct reflection on this interaction. Based on the Bellman Equation, it is possible to teach A.I. to learn that a tomato is in fact an apple. Therefore, who would be accountable for a fatal outcome?

The Rise of the Machines

If we had to apply the ‘robotic rules’ to The Terminator, someone would quickly conclude that there was a breach of the first rule. Arguably, The Terminator killed several people and thus the action would be considered as an ‘unlawful homicide’. The question lies in who is responsible for those deaths?

Ironically, robots are not a 21st century concept.

In 400 B.C., human mechanical figurines were created to strike the bells every hour. As the centuries have rolled on, this primitive creation has evolved into an object which is completing many tasks faster and better than the average human. Currently robots/A.I. are achieving everything in alignment with ‘Laws of Robotics’.

These laws state : 1. A robot may not injure a human or allow a human to come to harm.

2. A robot must obey the order given by humans unless the orders will conflict with the first law.

3. A robot must protect its own existence if that protection does not contradict the first two laws.

Robots v A.I. According to the Britannica, a robot is an ‘automatically operated machine that replaces human effort’. In all intended purposes, a toaster is considered as a robot as it ticks off all the Robotic Laws. The Britannica further defines artificial intelligence as the ‘ability of a digital computer or computer-controlled robots to perform tasks commonly associate with intelligent beings.’ However, in both instances, nothing is mentioned about the involvement of humans to achieve the outcomes.

The Defence of the Terminator

Initially, The Terminator would be considered as property and it could be argued that a person, or persons, would technically be liable. However, if The Terminator was to assume a legal identity, then it could be argued that The Terminator is responsible. However, even if we are to establish that The Terminator has a legal personality, the first element of ‘murder’ is ‘a person’.

The Criminal Code Act identifies that a person is established after it becomes a ‘living state from the body of its mother’. It would thus, be fair to state that The Terminator would not be tried under the Criminal Code Act unless legislation includes that A.I. as an entity that has rights and is seen by law as a person.

33

34

Hypothetically, however, if the Terminator was to be tried for murder, then there is an argument of defence. Arguably, it could be established that the killing (any person who causes death of another ) was unlawful (unless killing is authorised ). Ironically, it can be argued that The Terminator acted in accordance with his/her program and had no understanding that killing was unlawful.

He was doing as he was told, and he was told that killing a person was acceptable. And therefore, The Terminator was authorised to kill.

If it was established that the killing was unlawful, then causation could be argued. Under section 279 (1) (a), ‘it must be established that the person intended to cause the death of the person.’ As such, The Terminator could argue that he had no intention to cause the death of a person.

The courts would typically apply two tests to establish causation. The first could be challenged because if The Terminator was program to understand that killing was unlawful, then it is possible he would not have killed. Even if it were established that The Terminator was programmed with knowledge that killing is unlawful, it could be argued that his actions was a result of an error within the algorithm. Currently, when A.I. learns about behaviour, there is an acceptance of error.

While the error can be argued as small, there is still the potential that this can affect behaviour.

The Terminator would not have knowledge of this, and his action is a result of a series of learning outcomes. Those outcomes are the direct result of human interaction. Therefore, it can be argued that The Terminator would not be held liable, or responsible, for the deaths of all the individuals.

And as such, it would be unfair for the Terminator to take the stand for a deed that he is not technically responsible for.

As for The Terminators in the sequels, the courts would need to offer the same opportunity of defence as they would for the first Terminator.

Each sequel had a different terminator, and each would need to be tried for their own actions.

Unfortunately, and if memory serves me well, if any Terminator was to show up, I will argue that law would probably not have much standing and everyone would need to defend their own. #goodluck

35

Radical embodiment & digital reverberations

A post-humanist and autoethnographic exploration of the Deus ex Machina.

When they don’t know what to say And have completely given up on the play Just like a finger they lift the machine And the spectators are satisfied

(Antiphones, Retrieved 3.7.21, en.wikipedia/ Deus ex machina)

The auteurs of Ancient Greek Theatre employed a unique plot device to initiate the resolution of conflict within a dramatic narrative. This device, involving the presentation of actors playing various gods onto the stage using a crane or trapdoor, was known as Deus ex Machina (Latin), the god from within the machine.

Critics of the device, including Antiphones, who is quoted above, felt that the Deus ex Machina was a convenient method for the playwright to avoid the complexities of the unfolding dramatic narrative and, perhaps, to circumvent the possibility that the essence of the human experience is one of a necessary and dynamic interplay between conflict intensification and resolution. According to the critics, the consequences of not engaging with this ever-shifting dance of human entanglement was the construction of artifice rather than art. But it is also true that the device could often elicit feelings of awe and wonder in the audience and reveal heretofore unimagined creative possibilities.

My human heart seeks the sources of mystery.

My human heart yearns, just as all yearn, for that gossamer edge of a world yet to rise and full of promise.

My human heart listens, and listens, it listens, for the sound of something divine in the silence.

We, this people on this mote of matter In whose mouths abide cankerous words Which challenge our very existence Yet out of those same mouths Come songs of such exquisite sweetness That the heart falters in its labour And the body is quieted into awe (Angelou, 1995) We, this fragile human collective, walking, breathing, birthing and dying on this small blue sphere, need to feel that there is something more. From the moment we are jettisoned from the womb, there exists within us a fundamental need to see ourselves reflected through the mirror of another, to be the mirror in the mirror.

from the mind of Lil’ Red aka Fiona Lewis MA

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This reflection has the potential to reveal to us ourselves as we are, as we have been and as we long to be, to set us aflame and cast the often-bypassed ethereality of our being outward, allowing it to extend across real and imaginal realms, simultaneously imminent and transcendent.

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Who is this ‘me’ that looks out into the world, out there, the world outside of me, beyond my skin, always pressing in?

Is that world, out there, the same as the world, in here, inside my skin? a world I have only glimpsed through droplets of blood, reflections of light on the surface of the deep wells of my eyes, through the mechanism of inhalation and exhalation.

Am I this body encircling, becoming, containing?

Or this mind thinking, perceiving, separat i n g calculating formulating?

I try to touch the centre of me. I close my eyes, I let my body move in the way it needs, I let my focus drop down into my solar plexus. Here I feel an anchor point, but it is not solid, it is a feeling, an intuitive felt sense.

I gently push into this place and feel an initial resistance, like applying gradual force to soft rubber, and then a ssstrrretchiiiingggg of this rubbery substance until its walls become so thin, they collapse, and I see in my mind’s eye my hand reaching into endless space.

Where am I True reflection presents me to myself not as idle and inaccessible subjectivity, but as identical with my presence in the world and to others, as I am now realizing it: I am all that I see, I am an intersubjective field, not despite my body and historical situation, but, on the contrary, by being this body and this situation, and though them, all the rest.” (Merleau-Ponty, 1945).

Being in this body, so exquisitely fragile, so uncompromising, so undeniably resilient, how do we engage with our subjective experience of self in an overwhelming complex digital landscape? How do we stay embedded in our embodied experiencing whilst traversing virtual landscapes? How do we remain open to the exploration of our ‘humanness’ without losing ourselves inside the ‘machine’, or conversely, engaging in practices that deny the evolutionary possibilities of technology and supress the enlivening dissonance of the unknown?

We are neither machine or God, and yet, in these bodies made of flesh, skin, bone and blood, there is a dual energy, an essence that seeks for something beyond our ken (knowing) whilst we are simultaneously and symbiotically entwined with the living body of the earth.

I look up to the stars.

The emergent nature of our increasingly digitalised world offers us a unique opportunity to dance with the divine embedded within the digital machine, to explore the creative and technological possibilities it presents whilst harnessing the power to deepen our connection with our embodied experiencing. The body can be seen as a mediator between inner and outer worlds, bringing us into direct, immediate relationship with the phenomenal world. The immediacy of our connection with the living, breathing spaces beneath and above our skin contains immense transformative power as it brings us back into alignment with our -in-the-moment experience. Being alive to what is arising in this moment allows us to respond rather than react, to be curious rather than judgemental, carving out new creative territory filled with possibility. As David Abram (2021) states: “we live inside the breathing earth, submerged inside this invisible, dynamic mysterium, inter breathing” with everything, everywhere.

I feel my breath enter. I feel it leave.

When we can surrender into the here and now of our embodied felt sense, we enter a state of heightened awareness and the truth of how inextricably bound we are to the human, and more— than-human, worlds, is amplified. The inter relationship with the wild earth is so profoundly intrinsic

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to our being in the world as to invite an extension of our subjectivity beyond the edges of the human body, beyond the edges of time, and space, to co-mingle with the emergent nature of the living cosmos.

I am the wind, I am the light, I am the sound of the winged beings of the night.

Post-humanist philosophy offers a way of understanding the extension of our subjectivity beyond human-centric concepts of the nature of consciousness, challenging our views of self-hood and suggesting, as Merleau-Ponty did in 1945, that ‘who I am’ is reflected in all the seen and unseen entities with whom I co-exist on this living, breathing earth.

I speak the language of the birds and dance the song lines of migrating butterflies. My consciousness moves out beyond the boundary of the skin to traverse oceans and whisper poetry to the mountains.

The trans-humanist movement could be viewed as an extension of post-humanism. It is an ideology that seeks t o support the use of technology to enhance human capacity and experience. When entering these not-yet-fully-known philosophical and existential territories, we begin a dangerous, yet necessary, journey into the shadowy places where some of our deepest fears reside. Within this emergent matrix, we are challenged to confront deeply rooted ideas about who we are in relation to the world and its inhabitants, presenting us with a potential subversion of our very sense of self.

I look into the eyes of the virtual entity on the screen before me.

I do not feel felt.

I do not feel seen.

I am searching for the light I know, the light I have seen in the eyes of my child, in the eyes of my lover, in the eyes of my mother.

I see no light.

I see nothing.

Humanity is being called to lean into liminal spaces where the known and the unknown meet, where our ways of being human are being directly challenged by the digital realm. We must confront our fears and nightmarish visions, and begin an intentional dialogue that enables all voices to be heard, all perspectives to be articulated and for the dangers and dreams of a world in which we interface with artificial intelligence systems, digital realities and the algorithms of consciousness, to be engaged with and integrated.

If I am capable of running with the flowing rivers, and sleeping enfolded in wings of skin inside lightless caves, translating echoes with breathtaking acuity into sonic maps of the air, then can I not comprehend a self that traverses the real and the virtual? This ‘self’ that can feel so bound up inside its skin, so small in this vast cosmos, has the capacity to enter a more conscious symbiosis with all the human and more-than-human inhabitants of the earth. This ‘I’ must also find the courage to let go of fears emerging from the lack of experiential data and begin to see the beauty of endless digital iterations of myself ripple outward from this moment into an eternity of moments.

Perhaps, in the letting go, in the moving toward what I fear, in the surrendering to the not-yetknown, I enhance all potentialities and may no longer feel so insignificant, so invisible, so alone.

I return to my body. Sensing the truth of immortality.

The body becomes the interface in which the biological and digital meet and interact. What is being demanded is a greater fluidity in relation to our sense of self, our identity, a more responsive and yet more robust embodiment. It is only by becoming more deeply embodied that we can begin to explore the possibilities of moving beyond the body. The body becomes anchor, and the site of the transformation of consciousness in relation to the digital realm.

In this collaborative blended space of body and technological convergence, we start to evolve our ability to flow with ease through a life of virtual physical blended presence. This points to major transformations, not only to our bodies but also to our understanding of ourselves, our identities and our relationship to the ‘other’. It points to a future in which we inter-connect ourselves to others through a networked “multi-self” enabled by hyper-sensory self and a deeper tele-intuitive understanding the of virtual self (Boddington, 2020, p.10).

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Artificial intelligence and other digital technologies may be our Deus ex Machina, offering both an escape from or an access point into deeper relationship with what it means to be human, and strengthening our embodied presence. Digital technology is not necessarily a harbinger of doom, threatening to strip away our humanity, but may be our path away from destruction and toward a practice of being that enhances our empathy and expands our compassion from the centre of ourselves outward, to include all things, the living, breathing earth and the emergent cosmos.

The avante garde artist, activist and self-identified cyborg, Moon Ribas, demonstrates the creative, psycho-social and environmental possibilities inherent within the interface between the biological and the digital spheres. By way of a seismic sensor implanted in her arm, Ribas can literally feel seismic activity occurring in real time inside her body, the shifting of tectonic plates produces sensation in her body which she represents through movement or sound. The scale of seismic activity will determine the strength of the vibration she receives which she has referred to as “the heartbeat of our planet.” (propela.co.uk/moon-ribas).

I stand outside at night.

I hear my heart beating.

I listen to my breath singing.

The sky, pierced with pearls of light, I see above me.

I have hands that reach back to touch ancestral hands.

I have wings like my sky-brethren.

I dance as my crane-sisters dance.

The body of the earth holds me.

Digital echoes sustain me through the ages.

Now I understand: We all are the machine.

We are all each the god – and in awe we see ourselves as we are and as we may become.

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WITH CORONAVIRUS

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Coronaviruses are a group of related RNA viruses that cause diseases in mammals and birds.

ArtIFICIAL In Abstract

The role of Artificial Intelligence (AI) in cybersecurity is becoming a priority. Considering the first six months of 2019 alone, data breaches exposed at least 4.1 billion records globally (Top Cybersecurity Statistics, Facts, and Figures for 2021, 2020).

Datacenters can narrow their data breach attacks by implementing several Artificial Intelligence techniques designed to strengthen the overall cybersecurity. The paper provided an analytical view of the several possible methods used within data centers to improve multiple aspects while putting the security of customer’s data first. The paper also addressed several implementations starting from predicted analysis, malware analysis to anomaly detection, trying to make a strong case for the need to implement them to preserve data centers’ integrity and security.

Furthermore, Machine Learning and Deep Neural networks are used to detect unexpected patterns and

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provide improved signal-to-noise and pro-active mitigation in device sensors, access control systems, and network systems. Also, they learn what regular network traffic looks like, detect irregularities, prioritize warnings that require security practitioners to pay attention, assist with post-incident review of what went wrong, and include suggestions for enterprise security defenses to plug holes.

Keywords: artificial intelligence, machine learning, deep learning, malware analysis, predictive analysis, data center cybersecurity How Artificial Intelligence may Impact the Data Center Cybersecurity What if, in 1999, we asked NASA if their infrastructure was secure?

Their answer would have been: “Yes, of course.” However, that did not stop Jonathan James, who was 15 years old, from hacking their computers and installing a backdoor (Stout, 2000). This episode teaches us we cannot be 100% sure that an infrastructure is safe. Nevertheless, as we

are in 2021, several techniques using Artificial Intelligence (AI) have been developed to enhance cybersecurity in data centers.

The purpose of this paper is to help the reader understand the possible fields in which AI can be a valid assistant in solving major security problems within data centers. A brief history of AI has been provided to create a thread in which different considerations have been examined and represented objectively to leave the reader with a clear image of the technology’s pros and cons.

History

To address Artificial Intelligence in detail, we must start with its history. In 1950, Alan Turing proposed the Turing test: a thought experiment that would sidestep the philosophical vagueness of the question: “Can a machine think?” A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses

nTELLIGENCE In the data centre.. come from a person or a computer. (Russell & Norvig, 2020, para. 1,1) Hence, an intelligent machine should have the following qualities: • natural language processing: to convey effectively in a human language, • knowledge representation: to store its knowledge, • automated reasoning: to provide answers and new conclusions, • machine learning: to adapt to new circumstances and to detect and extrapolate patterns, • computer vision: to understand the human world, and • robotics: to manipulate the object and move about.

The combination of the six disciplines mentioned above makes up the majority of AI. Thus, “Artificial Intelligence is the most in-demand field in computer science which deals with the simulation of intelligent behavior in computer. AI techniques are recognizable as features in a product” (A et al., 2019, Introduction section).

As the following chapters discuss advanced techniques, defining some fundamental concepts such as Machine Learning and Deep Learning is necessary. Consider Figure 1 to get a global overview of the concepts, which will be described hereinafter.

Machine Learning (ML) is “a subset of AI application that learns by itself. It actually reprograms itself, as it digests more data, to perform the specific task it is designed to perform with increasingly greater accuracy” (IBM Cloud Education, 2020).

of hundreds of machines are generally needed to perform the algorithm’s operations.

Impact on the data center Artificial Intelligence relationship with cybersecurity According to recent research by Capgemini Research Institute (2019), Artificial Intelligence can be used in various cybersecurity areas. Five high-potential use cases with low complexity and high benefits have been identified and are shown in Figure 2.

Moreover, ML applications are grounded on a neural network, a network of algorithmic calculations seeking to reproduce the perception and the reasoning process of the human brain. Precisely, a simple neural network consists of three layers. First of all, the input data to be processed are fed to the network’s first layer, called the input layer. Then, the data is processed at the second layer level, i.e., the hidden layer, and finally, the value predicted by the network is provided at the last layer, which is the output layer. In this way, given input data, the network processes it and generates a prediction.

In contrast to ML, Deep Learning models are based on deep neural networks, which are neural networks with several hidden layers, each of which sharpens the previous level’s conclusions. Thus, unsupervised learning can be performed in this way, allowing the algorithms to be terminated without a human being’s intervention, using the data obtained from the previous levels. Nevertheless, a Deep Learning model requires a considerable amount of input, and expensive GPUs

Figure 1 Relationship between Artificial Intelligence, Machine Learning, and Deep Learning

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Note. N = 850 executives. Overall, 54% of organizations surveyed have already implemented these high-impact use cases.

This paper aims to analyze in-depth Anomaly Detection, Intrusion Detecting System, Malware Analysis, and Predictive Analysis.

Intrusion Detecting System

An Intrusion Detection System (IDS) is a type of secure software designed to identify when a malicious user is attempting to compromise a system. The attack can affect the confidentiality, availability, and integrity of the system and the data stored and controlled. Two main categories of IDS can be distinguished, host-based intrusion detection systems (HIDS) and network-based intrusion detection systems (NIDS). Since this paper focuses on data centers, it is necessary to deepen the latter.

NIDS systems operate a combination of signature and anomaly-based detection methods. Signature-based detection involves analyzing the properties of collected data packets against signature files known to be malicious, creating a data set of rules. Anomaly-based detection uses behavioral analysis to monitor events against a baseline of typical network activity. While the before-mentioned systems are highly effective against known threats, signaturebased detection fails when attack vectors are unknown or known attacks are modified to get around such rules.

Furthermore, one of the fundamental problems of data center IDSs is the high number of false positives, which decreases the accuracy, i.e., “a measurement that describes how many predictions you got right over the entire data set. In other words, for the entire data set, how many did the model correctly predict were positive and negative?” (Alla & Adari, 2019, p. 28).

To overcome these problems, AI can be an essential aid, as demonstrated for searching shellcodes within a network (Shenfield et al., 2018). A shellcode is an impact thread vector made of a set of instructions being injected and then carried out by an exploited program. It is used to manipulate the registers and functionality of vulnerable software directly. Their peculiarity is that they are formed by patterns that are difficult to recognize from benign network traffic. In his research, Shenfield has used a non-signature-based detection mechanism for malicious shellcode based around Artificial Neural Networks (ANN). The ANN takes into input the integer value of the byte level data from the network traffic dataset. To avoid shady content usually put at the start of files, it has been decided to extract 1000 bytes of the before mentioned contiguous data as in input. The ANN structure employed a multilayer perceptron (MLP) with two hidden layers of 30 hidden neurons. Finally, the AAN structure optimization uses 10-fold cross-validation to assess the classifier design.

As a result, the AAN achieve a perfect recall on the test dataset with an average accuracy of 98%. The experiment was then tested on a dataset of 400,000 examples and proved to be robust, accurate, and precise, as the probability of false positives was 2%.

Predictive Analysis

In data centers, any downtime or outage across the infrastructure stack causes an app- data gap that interrupts the applications and results in a waiting period, leading to slow productivity, business trials, and customer dissatisfaction.

In recent years, flash storage has been introduced to reduce this concern, replacing the older hard disk drives.

Thus, increasing speed delivery and improve overall performance. Nevertheless, flash storage alone cannot solve other issues such as configuration and interoperability. This is where AI techniques come in, aiming to anticipate and predict the problems that cause app-data gaps.

The answer lies in predictive storage, which combines analytics AI, Telemetry, and sensors gather data throughout the storage stack, which is then analyzed to: •

utilize data optimization techniques such as deduplica

•

tion and compression,

•

identify performance bottlenecks,

•

provide prescriptive guidance to correct issues, and

•

forecast future needs based on existing data-generation rates. (Taylor, 2018,

Predictive flash section) For example, suppose an item in the data center starts to heat up quickly to a dangerous level. In that case, predictive analysis can flag the problem by opening a ticket on the internal management platform before the equipment breaks down completely. Thereby, the high cost of any technical failures can be drastically reduced, and the infrastructure can be improved and made more efficient by reducing the probability of failure.

Malware Analysis

Malware is software that is designed to interrupt or damage computer systems. “Every day, there are at least 350,000 instances of new malware being created and detected. Additionally, 81% of all ransomware infections target businesses and organizations, making malware infections very costly” (Internet Security Threat Report VOL.23, 2018). Over the years, a process called malware analysis has been built to defend against malware.

Malware analysis comprises all operations aimed at dissecting malware and understanding how it works, its origin,

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and potential attack.

Malware detection is founded on static or dynamic software features, or a combination of those, or raw data. On the one hand, the static analysis consists of examining the code or structure of the executable file without executing it. For instance, signature-based detection that uses static data is broadly used within popular antivirus software. This type of detection involves end-user software like an antivirus, which has at its disposal a repository of static signatures (Hash) that represent known network threats. These threats are different from one another because of their unique coding. While this method is applied extensively in commercial antivirus tools and can identify specific malware families efficiently, it fails to detect new malware.

On the other hand, dynamic analysis involves running the malware in a virtualized environment such as a virtual machine. It is possible to obtain much more information than the previous examination through dynamic analysis, such as system calls, instruction traces, API calls, registry changes, memory writes, and information flow tracking. Nevertheless, the data can be time-consuming to extract.

It has been found that the use of Deep Learning in this field can be a valuable aid. The possible applications are manifold, and the milestones of the last few years are given below: • “Dahl et al. (2013) investigated a malware classification architecture which projects a high-dimensional feature vector to a much lower dimension using random projections” (Gibert et al., 2020, p. 12). This made it possible to use a neural network classifier with a nonlinear model to achieve an error rate of 0.49% in a dataset of 2.6 million.

• “Huang and Stokes (2016) proposed a multi-task deep learning architecture for malware detection and classification” (Gibert et al., 2020, p. 12). They have retrieved mixed characteristics consisting of nullterminated tokens, API event plus parameter value, and API trigrams from static and dynamic analysis. Finally, they employed the results obtained as input data to a feed-forward neural network to get an optimal detection system.

Final considerations

As seen in the previous paragraphs, AI enables organizations to learn and reuse threat patterns to identify new threats. This guides to an overall decrease in time and effort to classify incidents, investigate them, and remediate threats. However, whenever a new technology tries to settle in, there is a need to consider the downsides as well to get the whole picture.

Risk of unemployment

We estimate that between 400 million and 800 million individuals could be displaced by automation and need to find new jobs by 2030 around the world, based on our midpoint and earliest (that is, the most rapid) automation adoption scenarios. (McKinsey Global Institute, 2017) Like many other industries, data centers staffing with AI’s advent can be partially replaced, as seen from the above research. This would lead to several layoffs, though these could diminish as resources are reallocated. Current data centers’ staff could take courses on Artificial Intelligence so as to avoid possible layoffs. Therefore, monotonous jobs would be eliminated, creating better working positions and more mental employment.

Technology’s staff

One of the biggest challenges a data center manager should consider before choosing to implement AI systems in their infrastructure is finding people who can follow and use the algorithms that are going to be implemented. As a matter of fact, there is a severe lack of cybersecurity experts capable of enhancing the logic underpinning AI algorithms to identify threats efficiently. One way to resolve this problem is to upskill employees and managers whose roles are influenced by other technology advances, such as automation, while drawing on their knowledge. Also, to safeguard the company’s jobs, in-house training Costs

Each organization is unique, and there are no one-sizefits-them-all solutions. Several different software as a Service (SaaS) or out-of-the-box software solutions

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are in place, but they need to be tailored to the company’s final needs. Nevertheless, various fixed price components can be identified: • computing power: running AI-based algorithms is not particularly expensive. However, properly instructing the neural network with its goals has a very high cost that can be decreased in part by using the cloud, • implementation: it is necessary to consider the cost of automatically preparing the data taken from other systems. The cost of delivering the power to the infrastructure with seamlessly integrated, and maintenance: the solution must be properly maintained and upgraded accordingly to changes in the business model and environment.

Conclusion This paper shows that Artificial Intelligence can be a valuable tool to increase cybersecurity within data centers. Starting from a brief introduction of AI history, Machine Learning and Deep Learning were defined and explained in detail, thus presenting possible fields in which it could currently be implemented. The topics of anomaly detection and intrusion detection system were then addressed, in which Machine Learning and Deep Learning techniques are used to strengthen internal security.

Furthermore, the paper presents AI in the predictive and malware analysis fields, which are now fundamental and will likely have massive growth in the future due to current excellent results. Finally, an overview of the possible negative and positive effects on the current data centers was provided, helping the reader build its objective reflection.

To conclude, Artificial Intelligence can drastically reduce the risk of information theft within data centers and must be developed and implemented with the user and staff data security in mind.

Moreover, since AI is a technology in continuous growth, there will be plenty of implementations that the reader could study and analyze By Prasan Singh

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this is a completely

guerilla project H4unt3d hacker Australia is now and will always be free. The opinions hearin are our own and not shaped by commercial interest, greed, the spot light, the status quo or any thing else for that matter. If you are interested in contributing do not hestitate to reach out. We’d love to hear from you all.

Keep your eyes peeled for our print edition coming out soon.

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