Red hair – Wikipedia

ModernNorthern and Western Europe

Red hair is most commonly found at the northern and western fringes of Europe;[4] it is centred around populations in the British Isles and is particularly associated with the Celtic nations.[4]

Ireland has the highest number of red-haired people per capita in the world with the percentage of those with red hair at around 10%.[5]

Great Britain also has a high percentage of people with red hair. In Scotland around 6% of the population has red hair, with the highest concentration of red head carriers in the world found in Edinburgh, making it the red head capital of the world.[6][7] In 1907, the largest ever study of hair colour in Scotland, which analysed over 500,000 people, found the percentage of Scots with red hair to be 5.3%.[8] A 1956 study of hair colour among British Army recruits also found high levels of red hair in Wales and in the Scottish border counties of England.[fn 1][9]

Byzantine writers, Jordanes and Procopius described the early Slavic peoples as having ruddy hair and skin tone.[10][11] Later by the 10th century, Southern Slavic populations would have darker hair and skin tone, as the Slavs assimilated the indigenous inhabitants of the Balkans, including Greek and Illyrian peoples.[12]

In the late 18th century, ethnographers considered the Udmurt people of the Volga Region in Russia to be "the most red-headed men in the world".[13] The Volga region still has one of the highest percentages of redheaded people.[14]

Red hair is also found amongst the Ashkenazi Jewish populations.[15] In 1903, 5.6% of Polish Jews had red hair.[16] Other studies have found that 3.69% of Jewish women overall were found to have red hair, but around 10.9% of all Jewish men have red beards.[17] The stereotype that red hair is Jewish remains in parts of Eastern Europe and Russia.[18]

In Italy, red hair is found at a frequency of 0.57% of the total population, without variation in frequency across the different regions of the country.[19] In Sardinia, red hair is found at a frequency of 0.24% of the population.[19] In Italy, red hair was associated with Italian Jews, and Judas was traditionally depicted as red-haired in Italian and Spanish art.[20] In European culture, before the 20th century, red hair was often seen as a stereotypically Jewish trait: during the Spanish Inquisition, all those with red hair were identified as Jewish.[21]

The Berber populations of Morocco[22] and northern Algeria have occasional redheads. Red hair frequency is especially significant among the Riffians from Morocco and Kabyles from Algeria,[23][24][25] respectively.

In Asia, red hair can be found among some peoples of Afghan,[26][27] Arab, Iranian, East Indians, Mongolian, Turkic, Miao, and Hmong descent.[citation needed]

Several preserved samples of human hair have been obtained from an Iron Age cemetery in Khakassia, South Siberia. Many of the hair samples appear red in color, and one skull from the cemetery had a preserved red moustache.[28]

Ancient human remains described as having red or auburn hair have been discovered in various parts of Asia including the Tarim mummies of Xinjiang, China.[29] In Chinese sources, ancient Kyrgyz people were described as fair-skinned, green- or blue-eyed and red-haired people with a mixture of European and East Asian features.[30] In the Book of Wei, Chinese author Wei Shou notes that Liu Yuan was over 6 feet tall and had red strands of hair in his long beard.[31] The ethnic Miao people of China are recorded with red hair. According to F.M Savina of the Paris Foreign Missionary Society, the appearance of the Miao was pale yellow in their skin complexion, almost white, their hair color often being light or dark brown, sometimes even red or corn-silk blond, and a few of them even have pale blue eyes.[32] A phenotype study of Hmong People show they are sometimes born with red hair.[33]

There are other examples of red hair among early Turkic people. Muqan Qaghan, the third Qaghan of the Turkic Khaganate, was said to have red hair and blue eyes.[34] The Kipchak people were a Turkic ethnic group from central Asia who served in the Golden Horde military forces after being conquered by the Mongols. In the Chinese historical document Kang mu, the Kipchak people are described as red haired and blue eyed.[35][36]

Reddish-brown (auburn) hair is also found amongst some Polynesians, and is especially common in some tribes and family groups. In Polynesian culture reddish hair has traditionally been seen as a sign of descent from high-ranking ancestors and a mark of rulership.[38][39] Emigration from Europe has increased the population of red haired humans in the Americas, Australia, New Zealand and South Africa.

Several accounts by Greek writers mention redheaded people. A fragment by the poet Xenophanes describes the Thracians as blue-eyed and red-haired.[40] The ancient peoples Budini and Sarmatians are also reported by Greek author to be blue-eyed and red-haired, and the latter even owe their names to it.[41][42]

In Asia, red or auburn hair has been found among the ancient Tocharians, who occupied the Tarim Basin in what is now the northwesternmost province of China. Tarim mummies have been found with red hair dating to the 2nd millennium BC.[43]

In certain Biblical accounts, Hebrew and Israelite individuals were described as having ruddy hair. For example, Esau and David (Gen. 25:25; 1 Sam. 16:12, 17:42.), are described as "admoni", meaning red or ruddy.[44]

The pigment pheomelanin gives red hair its distinctive color. Red hair has far more of the pigment pheomelanin than it has of the dark pigment eumelanin.

The genetics of red hair appear to be associated with the melanocortin-1 receptor (MC1R), which is found on chromosome 16. In 1995, Valverde, et al. identified aleles on MC1R associated with red hair. The number of alleles linked to red hair has since been expanded by other authors, and these variants are now identified as the RHC alleles. Eighty percent of redheads have an MC1R gene variant within the RHC.[45][2] Red hair is also associated with fair skin color because the MC1R mutation also results in low concentrations of eumelanin throughout the body. The lower melanin concentration in skin confers the advantage that a sufficient concentration of important Vitamin D can be produced under low light conditions. However, when UV-radiation is strong (as in regions close to the equator) the lower concentration of melanin leads to several medical disadvantages, such as a higher risk of skin cancer. The MC1R variant gene that gives people red hair generally results in skin that is difficult or impossible to tan. Because of the natural tanning reaction to the sun's ultraviolet light and high amounts of pheomelanin in the skin, freckles are a common but not universal feature of red-haired people.

Red hair can originate from several changes on the MC1R-gene. If one of these changes is present on both chromosomes then the respective individual is likely to have red hair. This type of inheritance is described as an autosomal recessive. Even if both parents do not have red hair themselves, both can be carriers for the gene and have a redheaded child.

Genetic studies of dizygotic (fraternal) twins indicate that the MC1R gene is not solely responsible for the red hair phenotype; unidentified modifier genes exist, making variance in the MC1R gene necessary, but not sufficient, for red hair production.[46]

The alleles Arg151Cys, Arg160Trp, Asp294His, and Arg142His on MC1R are shown to be recessives for the red hair phenotype.[48] The gene HCL2 on chromosome 4 may also be related to red hair.[49][50] There are at least 8 genetic differences associated with red hair color.[51][52]

In species other than primates, red hair has different genetic origins and mechanisms.

The genes responsible for red hair can express themselves to different extents in different people. One consequence of this is that a number of people have both dark hair and red beards. This may reflect the presence of a single copy of the MC1R gene, leading to differential expression in the beard versus the scalp hair. However, some red-bearded people lack MC1R genes.[53][54]

Red hair is the rarest natural hair color in humans. The non-tanning skin associated with red hair may have been advantageous in far-northern climates where sunlight is scarce. Studies by Bodmer and Cavalli-Sforza (1976) hypothesized that lighter skin pigmentation prevents rickets in colder climates by encouraging higher levels of vitamin D production and also allows the individual to retain heat better than someone with darker skin.[55] In 2000, Harding et al. concluded that red hair is not the result of positive selection but of a lack of negative selection. In Africa, for example, red hair is selected against because high levels of sun harm pale skin. However, in Northern Europe this does not happen, so redheads can become more common through genetic drift.[48]

Estimates on the original occurrence of the currently active gene for red hair vary from 20,000 to 100,000 years ago.[56][57]

A DNA study has concluded that some Neanderthals also had red hair, although the mutation responsible for this differs from that which causes red hair in modern humans.[58]

A 2007 report in The Courier-Mail, which cited the National Geographic magazine and unnamed "geneticists", said that red hair is likely to die out in the near future.[59] Other blogs and news sources ran similar stories that attributed the research to the magazine or the "Oxford Hair Foundation". However, a HowStuffWorks article says that the foundation was funded by hair-dye maker Procter & Gamble, and that other experts had dismissed the research as either lacking in evidence or simply bogus. The National Geographic article in fact states "while redheads may decline, the potential for red isn't going away".[60]

Red hair is caused by a relatively rare recessive allele, the expression of which can skip generations. It is not likely to disappear at any time in the foreseeable future.[60]

In various times and cultures, red hair has been prized, feared, and ridiculed.

A common belief about redheads is that they have fiery tempers and sharp tongues. In Anne of Green Gables, a character says of Anne Shirley, the redheaded heroine, that "her temper matches her hair", while in The Catcher in the Rye, Holden Caulfield remarks that "People with red hair are supposed to get mad very easily, but Allie [his dead brother] never did, and he had very red hair."

During the early stages of modern medicine, red hair was thought to be a sign of a sanguine temperament.[80] In the Indian medicinal practice of Ayurveda, redheads are seen as most likely to have a Pitta temperament.

Another belief is that redheads are highly sexed; for example, Jonathan Swift satirizes redhead stereotypes in part four of Gulliver's Travels, "A Voyage to the Country of the Houyhnhnms," when he writes that: "It is observed that the red-haired of both sexes are more libidinous and mischievous than the rest, whom yet they much exceed in strength and activity." Swift goes on to write that "neither was the hair of this brute [a Yahoo] of a red colour (which might have been some excuse for an appetite a little irregular) but black as a sloe".[81] Such beliefs were given a veneer of scientific credibility in the 19th century by Cesare Lombroso and Guglielmo Ferrero. They concluded that red hair was associated with crimes of lust, and claimed that 48% of "criminal women" were redheads.[82]

Queen Elizabeth I of England was a redhead, and during the Elizabethan era in England, red hair was fashionable for women. In modern times, red hair is subject to fashion trends; celebrities such as Nicole Kidman, Alyson Hannigan, Marcia Cross, Christina Hendricks, Emma Stone and Geri Halliwell can boost sales of red hair dye.[citation needed]

Sometimes, red hair darkens as people get older, becoming a more brownish color or losing some of its vividness. This leads some to associate red hair with youthfulness, a quality that is generally considered desirable. In several countries such as India, Iran, Bangladesh and Pakistan, henna and saffron are used on hair to give it a bright red appearance.[83]

Many painters have exhibited a fascination with red hair. The hair color "Titian" takes its name from the artist Titian, who often painted women with red hair. Early Renaissance artist Sandro Botticelli's famous painting The Birth of Venus depicts the mythological goddess Venus as a redhead. Other painters notable for their redheads include the Pre-Raphaelites, Edmund Leighton, Modigliani,[84] and Gustav Klimt.[85]

Sir Arthur Conan Doyle's Sherlock Holmes story "The Red-Headed League" (1891) involves a man who is asked to become a member of a mysterious group of red-headed people. The 1943 film DuBarry Was a Lady featured red-heads Lucille Ball and Red Skelton in Technicolor.

Notable comic book characters with red hair include Jean Grey, Red Sonja, Mystique, and Poison Ivy.[86]

A book of photographs of red haired people was published in 2020, Gingers by Kieran Dodds (2020).[87]

Red hair was thought to be a mark of a beastly sexual desire and moral degeneration. A savage red-haired man is portrayed in the fable by Grimm brothers (Der Eisenhans) as the spirit of the forest of iron. Theophilus Presbyter describes how the blood of a red-haired young man is necessary to create gold from copper, in a mixture with the ashes of a basilisk.[88]

According to Montague Summers, red hair and green eyes were thought to be the sign of a witch, a werewolf or a vampire during the Middle Ages:

Those whose hair is red, of a certain peculiar shade, are unmistakably vampires. It is significant that in ancient Egypt, as Manetho tells us, human sacrifices were offered at the grave of Osiris, and the victims were red-haired men who were burned, their ashes being scattered far and wide by winnowing-fans. It is held by some authorities that this was done to fertilize the fields and produce a bounteous harvest, red-hair symbolizing the golden wealth of the corn. But these men were called Typhonians, and were representatives not of Osiris but of his evil rival Typhon, whose hair was red.

During the Spanish Inquisition, people of red hair were identified as Jewish and isolated for persecution.[21] In Medieval Italy and Spain, red hair was associated with the heretical nature of Jews and their rejection of Jesus, and thus Judas Iscariot was commonly depicted as red-haired in Italian and Spanish art.[20] Writers from Shakespeare to Dickens would identify Jewish characters by giving them red hair, such as the villainous Jewish characters Shylock and Fagin.[89] The antisemitic association persisted into modern times in Soviet Russia.[18] The medieval prejudice against red-hair may have derived from the Ancient biblical tradition, in relation to biblical figures such as Esau and King David. The Ancient historian Josephus would mistranslate the Hebrew Torah to describe the more positive figure of King David as 'golden haired', in contrast to the negative figure of Esau, even though the original Hebrew Torah implies that both King David and Esau had 'fiery red hair'.[90]

In his 1885 book I Say No, Wilkie Collins wrote "The prejudice against habitual silence, among the lower order of the people, is almost as inveterate as the prejudice against red hair."

In his 1895 memoir and history The Gurneys of Earlham, Augustus John Cuthbert Hare described an incident of harassment:"The second son, John, was born in 1750. As a boy he had bright red hair, and it is amusingly recorded that one day in the streets of Norwich a number of boys followed him, pointing to his red locks and saying, "Look at that boy; he's got a bonfire on the top of his head," and that John Gurney was so disgusted that he went to a barber's, had his head shaved, and went home in a wig. He grew up, however, a remarkably attractive-looking young man."[91]

In British English, the word "ginger" is sometimes used to describe red-headed people (at times in an insulting manner),[92] with terms such as "gingerphobia"[93] and "gingerism"[94] used by the British media. In Britain, redheads are also sometimes referred to disparagingly as "carrot tops" and "carrot heads". (The comedian "Carrot Top" uses this stage name.) "Gingerism" has been compared to racism, although this is widely disputed, and bodies such as the UK Commission for Racial Equality do not monitor cases of discrimination and hate crimes against redheads.[94]

Nonetheless, individuals and families in Britain are targeted for harassment and violence because of their hair colour. In 2003, a 20-year-old was stabbed in the back for "being ginger".[95] In 2007, a UK woman won an award from a tribunal after being sexually harassed and receiving abuse because of her red hair;[96] in the same year, a family in Newcastle upon Tyne, was forced to move twice after being targeted for abuse and hate crime on account of their red hair.[97] In May 2009, a schoolboy committed suicide after being bullied for having red hair.[98] In 2013, a fourteen-year-old boy in Lincoln had his right arm broken and his head stamped on by three men who attacked him "just because he had red hair". The three men were subsequently jailed for a combined total of ten years and one month for the attack.[99] A possible fringe theory explaining the historical and modern mistreatment of red-heads supposedly stems from subjugation and consequent persecution of Celtic Nations.

This prejudice has been satirised on a number of TV shows. English comedian Catherine Tate (herself a redhead) appeared as a red-haired character in a running sketch of her series The Catherine Tate Show. The sketch saw fictional character Sandra Kemp, who was forced to seek solace in a refuge for ginger people because she had been ostracised from society.[100] The British comedy Bo' Selecta! (starring redhead Leigh Francis) featured a spoof documentary which involved a caricature of Mick Hucknall presenting a show in which celebrities (played by themselves) dyed their hair red for a day and went about daily life being insulted by people.(Hucknall, who says that he has repeatedly faced prejudice or been described as ugly on account of his hair colour, argues that Gingerism should be described as a form of racism.[101]) Comedian Tim Minchin, himself a redhead, also covered the topic in his song "Prejudice".[102]

The pejorative use of the word "ginger" and related discrimination was used to illustrate a point about racism and prejudice in the "Ginger Kids", "Le Petit Tourette", "It's a Jersey Thing" and "Fatbeard" episodes of South Park.

Film and television programmes often portray school bullies as having red hair.[103] However, children with red hair are often themselves targeted by bullies; "Somebody with ginger hair will stand out from the crowd," says anti-bullying expert Louise Burfitt-Dons.[104]

In Australian slang, redheads are often nicknamed "Blue" or "Bluey".[105] More recently, they have been referred to as "rangas" (a word derived from the red-haired ape, the orangutan), sometimes with derogatory connotations.[106] The word "rufus" has been used in both Australian and British slang to refer to red-headed people;[107] based on a variant of rufous, a reddish-brown color.

In November 2008 social networking website Facebook received criticism after a 'Kick a Ginger' group, which aimed to establish a "National Kick a Ginger Day" on 20 November, acquired almost 5,000 members. A 14-year-old boy from Vancouver who ran the Facebook group was subjected to an investigation by the Royal Canadian Mounted Police for possible hate crimes.[108]

In December 2009 British supermarket chain Tesco withdrew a Christmas card which had the image of a child with red hair sitting on the lap of Father Christmas, and the words: "Santa loves all kids. Even ginger ones" after customers complained the card was offensive.[109]

In October 2010, Harriet Harman, the former Equality Minister in the British government under Labour, faced accusations of prejudice after she described the red-haired Treasury secretary Danny Alexander as a "ginger rodent".[110] Alexander responded to the insult by stating that he was "proud to be ginger".[111] Harman was subsequently forced to apologise for the comment, after facing criticism for prejudice against a minority group.[112]

In September 2011, Cryos International, one of the world's largest sperm banks, announced that it would no longer accept donations from red-haired men due to low demand from women seeking artificial insemination.[113]

The term ang mo (Chinese: ; pinyin: hng mo; Peh-e-j: ng-mo) in Hokkien (Min Nan) Chinese, meaning "red-haired",[114] is used in Malaysia and Singapore, although it refers to all white people, never exclusively people with red hair. The epithet is sometimes rendered as ang mo kui () meaning "red-haired devil", similar to the Cantonese term gweilo ("foreign devil"). Thus it is viewed as racist and derogatory by some people.[115] Others, however, maintain it is acceptable.[116] Despite this ambiguity, it is a widely used term. It appears, for instance, in Singaporean newspapers such as The Straits Times,[117] and in television programmes and films.

The Chinese characters for ang mo are the same as those in the historical Japanese term Km (), which was used during the Edo period (16031868) as an epithet for Dutch or Northern European people. It primarily referred to Dutch traders who were the only Europeans allowed to trade with Japan during Sakoku, its 200-year period of isolation.[118]

The historic fortress Fort San Domingo in Tamsui, Taiwan was nicknamed ang mo sia ().

The mainly masculine given name Rory a name of Goidelic origin, which is an anglicisation of the Irish: Ruair/Ruaidhr/Ruaidhrgh/Raidhrgh, Scottish Gaelic: Ruairidh and Manx: Rauree[119] which is common to the Irish, Highland Scots and their diasporas[120] means "red-haired king", from ruadh ("red-haired" or "rusty") and rgh ("king"). However, present bearers of the name are by no means all red-haired themselves.

There has been an annual Redhead Day festival in the Netherlands that attracts red-haired participants from around the world. The festival was held in Breda, a city in the south east of the Netherlands, prior to 2019, when it moved to Tilburg.[121] It attracts participants from over 80 countries. The international event began in 2005, when Dutch painter Bart Rouwenhorst decided he wanted to paint 15 redheads.

The Irish Redhead Convention, held in late August in County Cork since 2011, claims to be a global celebration and attracts people from several continents. The celebrations include crowning the ginger King and Queen, competitions for the best red eyebrows and most freckles per square inch, orchestral concerts and carrot throwing competitions.[122]

A smaller red-hair day festival is held since 2013 by the UK's anti bullying alliance in London, with the aim of instilling pride in having red-hair.[123]

Since 2014, a red-hair event is held in Israel, at Kibbutz Gezer (Carrot), held for the local Israeli red hair community,[124] including both Ashkenazi and Mizrahi red-heads.[125] However, the number of attendees has to be restricted due to the risk of rocket attacks, leading to anger in the red-hair community.[126] The organizers state; "The event is a good thing for many redheads, who had been embarrassed about being redheads before."[126]

The first and only festival for red heads in the United States was launched in 2015. Held in Highwood, Illinois, Redhead Days draws participants from across the United States.[127]

A festival to celebrate the red-haired people is held annually in Izhevsk (Russia), the capital of Udmurtia, since 2004.[128]

MC1R Magazine is a publication for red-haired people worldwide, based in Hamburg, Germany.[129]

In ancient Egypt, red hair was associated with the deity Set as well as Ramesses II.[130][131]

In the Iliad, Achilles' hair is described as ksanths ([132]), usually translated as blonde, or golden[133] but sometimes as red or tawny.[134][135] His son Neoptolemus also bears the name Pyrrhus, a possible reference to his own red hair.[136]

The Norse god Thor is usually described as having red hair.[137]

The Hebrew word usually translated "ruddy" or "reddish-brown" (admoni , from the root ADM , see also Adam and Edom)[138][139][140] was used to describe both Esau and David.

Early artistic representations of Mary Magdalene usually depict her as having long flowing red hair, although a description of her hair color was never mentioned in the Bible, and it is possible the color is an effect caused by pigment degradation in the ancient paint.

Judas Iscariot is also represented with red hair in Spanish culture[141][142] and in the works of William Shakespeare,[143] reinforcing the negative stereotype.

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Red hair - Wikipedia

The Truth About Redheads – TheList.com

The mutation on theMC1Rgene that gives gingers their unique coloring doesn't just affect the way they look. In a curious twist, gingers also feel pain and respond to painkilling agents differently than their blonde and brunette counterparts.

For one, redheads are more sensitive to certain kinds of pain (thermal pain, which we'll discuss later), according to a study by the National Institutes of Health. In addition to that, the study also concluded that redheads are more resistant to lidocaine, a local anesthetic, than the rest of us. Plus, they need more anesthetic on the operating table, according to another study. So redheads aren't lying or being dramatic about the pain of medical and dental procedures they're legit wired a little bit differently, and science proves it!

And that's not all, either. Apparently our redheaded sisters respond better to opiates than both men and non-ginger ladies, according to Science Daily. Who knew gingers had all kind of magic going on?

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The Truth About Redheads - TheList.com

70 Incredible Facts About Redheads: How Much Do You Know About … – Kidadl

Although red is the rarest natural hair color, it certainly gets the people who have it talked about.

We all like to know what makes us, us. It might be our eye color or our star sign, or maybe it's the hobbies we choose or the way we spend our downtime.

Lots of people are fascinated by red hair, so if you're one of them, check out our list of fascinating facts about this beautiful shade. For more interesting facts, take a look at Scorpio facts and hazel eyes facts.

Do redheads have less hair? Do blue eyes and red hair really go together? Find out with these true facts about redheads.

1. Red is the rarest hair color - only between 1-2% of the world's population are redheads.

2. If you're a redhead with blue eyes, you're a unicorn. Less than a million people have this rarest combination of eye and hair colors.

3. Redheads have less hair, with only 90 000 strands compared with an average 110 000 for blondes and 140 000 for brunettes. But don't worry...

4. ....Each strand of red hair is thicker than strands of other colours, so although redheads have fewer strands, they still have plenty of hair.

5. Redheads don't go gray. Their hair naturally keeps its pigment longer than most colors, before eventually fading to white.

6. Redheads are twice as likely to develop Parkinson's disease. It's to do with the way the brain releases chemicals like dopamine that control emotions and thoughts.

7. If you're a brunette but find yourself suddenly going red, check your diet. Dark hair turning red can be a sign of protein deficiency.

8. Bees like redheads more than people with other hair colors. Scientists think the bright color reminds them of flowers.

9. Red hair's pigment is so strong, it has to be bleached repeatedly before it can be dyed, damaging the hair.

10. Redheads on average need about 20% more anaesthetic when they go into the hospital for surgery.

11. Redheads can sense changes in temperature more quickly than people with other hair colors.

From the gene mutation that causes red hair to whether or not redheads are really more likely to get skin cancer, you'll find all the biological facts about redheads in this section.

12. There are six main shades: strawberry-blonde, ginger, classic red, deep red, auburn and deep auburn.

13. Red hair is caused by mutation in a gene called MC1R, which controls hair color.

14. The genetics of red hair mean for a child to be a redhead, both their parents must have the mutated MC1R gene.

15. If two redheads have a baby, the child will always have red hair.

16. People with ginger hair have a greater chance of being left-handed.

17. People of any ethnicity can have red hair. However, it's most common in people of Northern European descent.

18. Redheads naturally produce their own vitamin D, which is essential for good health and proper bone development.

19. The gene that controls hair color also controls production of melanin, the skin pigment that makes people tan.

20. Because the MC1R mutation means they have less melanin, redheads are more likely to develop the skin cancer melanoma.

21. The MC1R gene also controls pain response, meaning redheads may be less sensitive to pain like stings and injections.

From Helen of Troy to Winston Churchill, redheads have made their mark. Find out how with these interesting facts about redheads through history.

22. Scientists have found evidence that Neanderthals had a similar gene to the one that gives humans red hair.

23. Founding Father and third President of the US Thomas Jefferson was a redhead.

24. Romans thought red headed people were wild and warlike, especially the Celts.

25. Ancient Greeks believed that people with red hair could turn into vampires after they died.

26. The Ancient Greeks also thought Helen of Troy and the goddess of beauty, Aphrodite, had natural ginger hair.

27. Mummies with red hair have been found as far afield as Peru and Egypt.

28. Pharaoh Ramses II, one of Ancient Egypt's greatest pharaohs, was thought to have been a redhead.

29. Romans valued slaves with ginger hair more highly. In fact, some used to dye their hair red on purpose.

30. Celtic warrior queen Boudicca had a red hair. Maybe that's where all those "fierce redhead" myths come from.

31. Hitler made it illegal for redheads to get married. He said their children would be "deviant".

32. Redheads can be very creative: painter Vincent Van Gogh, classical music composer Vivaldi and writer Mark Twain were all redheads.

33. Russia just might be named after a redhead: the name is thought to come from a Viking called Rurik, meaning 'red'.

Supernatural redhead facts from myth and legend to astonish and intrigue you.

34. Lilith, said in some religions to be the first wife of Adam, is commonly shown with red hair.

35. Some theologists think it was Adam who was the redhead, as his name means "red" in Hebrew.

36. The Ancient Egyptian god of the desert, Set, was frequently depicted with red hair.

37. In ancient Gaul, the Merovingians thought redheads had special supernatural powers and abilities.

38. Irish leprechauns are usually pictured with red hair as well as their classic green suit.

39. Spanish people used to believe that redheads' hair colour came from stealing flames from the devil.

40. During the 16th and 17th centuries in Europe, hundreds of women with red hair were accused of witchcraft.

41. Even today, people still think redheads have supernatural powers. Some pagans even dye their hair red on purpose using henna.

42. In Irish myth, the mystical races of the Tuatha de Danaan and the Sithe are both said to have ginger hair.

43. In medieval times, a writer suggested that redheads' blood was an essential ingredient in a paint called Spanish Gold.

These "facts" about red hair are more like myths. None of them are proven: these ones are just for fun.

44. Redheads are often said to have a hot temper because red is associated with fire and danger.

45. Some people think that because red hair is carried by a recessive gene, redheads might be going extinct.

46. Some people believe rubbing a redhead's hair brings good luck.

47. In Russia, ginger hair is believed to be a sign of having a hot temper and being a bit of a wild child.

48. Britain has a tradition that says if the first call you receive on New Year's Day is from a redhead, then you'll have bad luck all year.

49. British legend foretold a red-haired leader would save the country from peril. Interestingly, King Arthur, Queen Elizabeth I and Winston Churchill all had red hair.

50. In Poland, legend has it that if you see three redheads at once, you'll win the State Lottery.

51. In Ancient Egypt, it's said that people with red hair were sacrificed as being unlucky.

52. People in Corsica believed that if you pass a redhead walking down the street, you have to spit and spin round to ward off bad luck.

53. Some people say redheads bruise easily, but this may just be down to redheads' pale skin showing bruises more clearly.

54. There is a common belief that redheads are smarter than people with other hair colors.

55. Some people say that redheads' freckles are the marks left by kisses from angels.

56. A lot of people think redheads have more adventurous and outgoing personalities than other hair colors.

Fun and lighthearted red hair facts to keep you entertained.

57. About 30% of people who dye their hair at home choose to go red.

58. The USA has a National Redhead Day, celebrated on 5 November. It's also known as Love Your Red Hair Day.

59. National Redhead Day was created in 2015 by Stephanie and Adrienne Vendetti, two red-headed sisters who wanted to celebrate their beautiful hair.

60. Americans aren't the only ones who like to celebrate their fiery locks. 26 May is World Redhead Day.

61. The USA has the highest redhead population of any country in the world, boasting between 6 and 18 million redheaded citizens.

62. In the Netherlands, there's a two- day summer festival in early September devoted to celebrating reed hair. It's called Redhead Day.

63. Mark Twain once said that while most humans are descended from apes, people with red hair come from cats. We'd say that was catty, but it turns out Twain was a redhead himself.

64. Legend says that the origins of red hair was Prince Idon of Mu, who got it on a visit to the mythical drowned city of Atlantis.

65. Another redhead origin story says redheads are descended from Vulcan, the Greek god of the forge.

66. The most common surname in Italy, Rossi, means redhead.

67. Scotland has the highest percentage of redheads in the world, at 13% of the population.

68. Lots of English-language surnames are inspired by redheads. Names like Flannery, Reid and Russell can all trace their origin to red hair.

69. 1 in 10 Irish people has red hair, making them second in the world after Scotland for number of redheads per capita.

70. One of the first women's professional basketball teams, the All-American Redheads, was named after the fact all its members had red hair.

Here at Kidadl, we have carefully created lots of interesting family-friendly facts for everyone to enjoy! If you liked our suggestions for incredible facts about redheads, then why not take a look at Love facts, or Capricorn facts.

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70 Incredible Facts About Redheads: How Much Do You Know About ... - Kidadl

Stunning Red Heads With Blue Eyes Will Take Your Breath Away

Now Youve Seen Some Stunning Blue Eyed Red Heads, Here Are More Red Head Facts!

How gorgeous are all the ladies here? We think that youll agree theyre beautiful. But what about some more facts about people with this stunning hair colour? Now you know that ginger people with blue eyes are the rarest on earth, here are some more facts you probably didnt know about those with this beautiful, unique hair colour!

So, weve established by now that blue eyed red heads are super rare. All the more reason to love them, right? But what about people in the world of celebrity? Do any famous people have red hair and gorgeous blue eyes? They certainly do! Heres a list of some of your favourite celebrities that have those gorgeous traits you lust after

Want to know when they were born? Click here to skip to our table of celebrities with blue eyes and red hair and their birthdays!

We hope that this article has shown you just how stunning people with ginger hair and blue eyes are. So much so that you may now be looking to find your own partner who has these attractive qualities. If so, we dont blame you! Here at Redhead Dates.com, were here to help you do just that! We have new people joining our site every day, so theres plenty of people to choose from. So, whether youre a redhead yourself, or just a lover of those with ginger hair, there is truly something for everyone on our site. Need a bit more convincing to sign up? Weve put together our top reasons why you should join us and start searching for red heads with blue eyes today! Just click here to go to our homepage when youre ready to sign up- its free, easy, and quick!

Sounds tempting? Dont forget to sign up today! We have red heads with blue eyes (and all the other eye colours too) just waiting to meet you TODAY!

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Stunning Red Heads With Blue Eyes Will Take Your Breath Away

Tackling the problem of bias in AI software – Federal News Network

Best listening experience is on Chrome, Firefox or Safari. Subscribe to Federal Drives daily audio interviews onApple PodcastsorPodcastOne.

Artificial intelligence is steadily making its way into federal agency operations. Its a type of software that can speed up decision-making, and grow more useful with more data. A problem is that if youre not careful, the algorithms in AI software can introduce unwanted biases. And therefore produce skewed results. Its a problem researchers at the National Institute of Standards and Technology have been working on. With more, the chief of staff of NISTs information technology laboratory, Elham Tabassi, joinedFederal Drive with Tom Temin.

Tom Temin: Mr. Tabassi, good to have you on.

Elham Tabassi: Thanks for having me.

Tom Temin: Lets begin at the beginning here. And we hear a lot about bias in artificial intelligence. Define for us what it means.

Elham Tabassi: Thats actually a very good question and a question that researchers are working on this, and a question that we are trying to find an answer along with the community, and discuss this during the workshop thats coming up in August. Its often the case that we all use the same term meaning different things. We talk about it as if you know exactly what were talking about, and bias is one of those terms. The International Standards Organization, ISO, has a subcommittee working on standardization of bias, and they have a document that with collaborations of experts around the groups are trying to define bias. So one there isnt a good definition for bias yet. What we have been doing at NIST is doing a literature survey trying to figure out how it has been defined by different experts, and we will discuss it further at the workshop. Our goal is to come up with a shared understanding of what bias is. I avoid the term definition and talk about the shared understanding of what bias is. The current draft of standards and the current sort of understanding of the community is going towards that bias is on in terms of disparities in error rates and performance for different populations, different devices or different environments. So one point I want to make here is what we call bias may be designed in. So if you have different error rates for different subpopulations, face recognition that you mentioned, thats not a good bias and something that has to be mitigated. But sometimes, for example, for car insurance, it has been designed in a way that certain populations, younger people pay more insurance at a higher insurance rate than people in their 40s or 50s, and that is by design. So just the difference in error rate is not bias on intended behavior or performance of the system. Its something thats problematic and needs to be studied.

Tom Temin: Yeah, maybe a way to look at it is If a persons brain had all of the data that the AI algorithm has, and that person was an expert and would come up with a particular solution, and theres a variance between what that would be and what the AI comes up with that could be a bias.

Elham Tabassi: Yes, it could be but then lets not forget about human biases, and that is actually one source of bias in AI systems. The bias in AI system can creep in in different ways. They can creep into algorithm because AI systems learn to make decisions based on the training data, which can include biased human decisions or reflect historical or societal inequalities. Sometimes the bias creeps in because the data has been not the right representative of the whole population, the sampling was done that one group is over represented or underrepresented. Another source of bias can be in the design of the algorithm and in the modeling of that. So biases can creep in in different ways and sometimes the human biases exhibit itself into the algorithm, sometimes algorithm modeling and picked up some biases.

Tom Temin: But you could also get bias in AI systems that dont involve human judgment or judgment about humans whatsoever. Say it could be a AI program running a process control system or producing parts in a factory, and you could still have results that skew beyond what you want over time because of a bias built in thats of a technical nature. Would that be fair to say?

Elham Tabassi: Correct, yes. So if the training data set is biased or not representative of space of the whole possible input, then you have bias. One real research question is how to mitigate and unbias the data. Another one is that if during the algorithm biases if theres anything during the design and building in a model, that it can be bias, that can introduce bias, the way the models are developed.

Tom Temin: So nevertheless, agencies have a need to introduce these algorithms and these programs into their operations and theyre doing so. What are some of the best practices for avoiding bias in the outcomes of your AI system?

Elham Tabassi: The research is still out there. This is one of those cutting edge research and we see a lot of good research and results coming out from AI experts every day. But really to mitigate bias, to measure bias and mitigate bias, the first step really is to understand what biases and thats your first question. So unless we know what it is that we want to measure, and we have a consensus and understanding and agreement on what it is that we want to measure, which goes back to that shared understanding of bias or definition of bias, its hard to get into the measurement. So we are spending a little bit more time on getting everybody on the same page on understanding what bias is so we know what it is that we want to measure. Then we get into the next step of how to measure, which is the development of the metrics for understanding and examining and measuring bias in systems. And it can be measured biases in the data and the algorithm, so on so forth. Then its even after these two steps that we can talk about the best practices or the best way of mitigation of the bias. So we are still a bit early in understanding on how to measure because we dont have a good grip on what it is that we want to measure.

Tom Temin: But in the meantime, Ive heard of some agencies just simply using two or more algorithms to do the same calculation such that they be the biases in them can cancel one another out, or using multiple data sets that might have canceling biases in them just to make sure that at least theres balance in there.

Elham Tabassi: Right. Thats one way, and that goes back to what we talked at the beginning of the call about having a poor representation. And you just talked about having two databases, so that can mitigate the problem of the skewed representation or sampling. Just like that, in the literature there are many, many definitions of the bias already. Theres also many different methods and guidance and recommendations on what to do, but what we are trying to do is come up with a set of agreeable and unified way on how to do these things thing and that is still cutting edge research.

Tom Temin: Got it. And in the meantime, NIST is planning a workshop on bias in artificial intelligence. Tell us when and where and whats going to happen there.

Elham Tabassi: Right that workshop is going to be on August 18. Its a whole day workshop. Our plan was to have a demo today but because its virtual workshop, we decided to just have it as one day. The workshop is one of the workshop in a series that NIST plans to organize and have in coming months. The fields of the workshop that they are organizing and planning is trying to get at the heart of what constitutes trustworthiness, what are the technical requirements, what they are and how to measure them. Bias is one of those technical requirements and we have a dedicated workshop on bias on August 18 where we want them to be a interactive discussions with the participants and we have a panel in the morning. The whole morning is dedicated to discussions of the data and the bias in data, and how the biases in data can contribute to the bias into whole AI system. We have a panel in the morning, kind of as a stage setting panel that kind of frame the discussion for the morning and then it will be breakout sessions. Then in the afternoon, the same format and discussion will be around biases in the algorithm and how those can make an AI system biased.

Tom Temin: Who should attend?

Elham Tabassi: The AI developers, the people that are actually building the AI systems, the AI users, the people that want to use AI system. Policy makers will have a better understanding of the issues in AI system and bias in AI systems. People that want to use it, either the developer or the user of technology, and policymakers.

Tom Temin: If youre a program manager, or policymaker and your team is cooking up something with AI, you probably want to know what it is theyre cooking up in some detail, because youre gonna have to answer for it eventually I suppose.

Elham Tabassi: Thats right. And if I didnt emphasize it enough, of course at the research community because they are the one that we go to for innovation and solutions to the problem/

Tom Temin: Elham Tabassi is chief of staff of the information technology laboratory at the National Institute of Standards and Technology. Thanks so much for joining me.

Elham Tabassi: Thanks for having me.

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Tackling the problem of bias in AI software - Federal News Network

Fifth Amendment | Summary, Rights, & Facts | Britannica

Fifth Amendment, amendment (1791) to the Constitution of the United States, part of the Bill of Rights, that articulates procedural safeguards designed to protect the rights of the criminally accused and to secure life, liberty, and property. For the text of the Fifth Amendment, see below.

Similar to the First Amendment, the Fifth Amendment is divided into five clauses, representing five distinct, yet related, rights. The first clause specifies that [n]o person shall be held to answer for a capital, or otherwise infamous crime, unless on a presentment or indictment of a Grand Jury, except in cases arising in the land or naval forces or in the Militia, when in actual service in time of War or public danger. This grand jury provision requires a body to make a formal presentment or indictment of a person accused of committing a crime against the laws of the federal government. The proceeding is not a trial but rather an ex parte hearing (i.e., one in which only one party, the prosecution, presents evidence) to determine if the government has enough evidence to carry a case to trial. If the grand jury finds sufficient evidence that an offense was committed, it issues an indictment, which then permits a trial. The portion of the clause pertaining to exceptions in cases arising in the land or naval forces, or in the Militia is a corollary to Article I, Section 8, which grants Congress the power [t]o make Rules for the Government and Regulation of the land and naval Forces. Combined, they justify the use of military courts for the armed forces, thus denying military personnel the same procedural rights afforded civilians.

The second section is commonly referred to as the double jeopardy clause, and it protects citizens against a second prosecution after an acquittal or a conviction, as well as against multiple punishments for the same offense. Caveats to this provision include permissions to try persons for civil and criminal aspects of an offense, conspiring to commit as well as to commit an offense, and separate trials for acts that violate laws of both the federal and state governments, although federal laws generally suppress prosecution by the national government if a person is convicted of the same crime in a state proceeding.

The third section is commonly referred to as the self-incrimination clause, and it protects persons accused of committing a crime from being forced to testify against themselves. In the U.S. judicial system a person is presumed innocent, and it is the responsibility of the state (or national government) to prove guilt. Like other pieces of evidence, once presented, words can be used powerfully against a person; however, words can be manipulated in a way that many other objects cannot. Consequently, information gained from sobriety tests, police lineups, voice samples, and the like is constitutionally permissible while evidence gained from compelled testimony is not. As such, persons accused of committing crimes are protected against themselves or, more accurately, how their words may be used against them. The clause, therefore, protects a key aspect of the system as well as the rights of the criminally accused.

The fourth section is commonly referred to as the due process clause. It protects life, liberty, and property from impairment by the federal government. (The Fourteenth Amendment, ratified in 1868, protects the same rights from infringement by the states.) Chiefly concerned with fairness and justice, the due process clause seeks to preserve and protect fundamental rights and ensure that any deprivation of life, liberty, or property occurs in accordance with procedural safeguards. As such, there are both substantive and procedural considerations associated with the due process clause, and this has influenced the development of two separate tracks of due process jurisprudence: procedural and substantive. Procedural due process pertains to the rules, elements, or methods of enforcementthat is, its procedural aspects. Consider the elements of a fair trial and related Sixth Amendment protections. As long as all relevant rights of the accused are adequately protectedas long as the rules of the game, so to speak, are followedthen the government may, in fact, deprive a person of his life, liberty, or property. But what if the rules are not fair? What if the law itselfregardless of how it is enforcedseemingly deprives rights? This raises the controversial spectre of substantive due process rights. It is not inconceivable that the content of the law, regardless of how it is enforced, is itself repugnant to the Constitution because it violates fundamental rights. Over time, the Supreme Court has had an on-again, off-again relationship with liberty-based due process challenges, but it has generally abided by the principle that certain rights are implicit in the concept of ordered liberty (Palko v. Connecticut [1937]), and as such they are afforded constitutional protection. This, in turn, has led to the expansion of the meaning of the term liberty. What arguably began as freedom from restraint has transformed into a virtual cornucopia of rights reasonably related to enumerated rights, without which neither liberty nor justice would exist. For example, the right to an abortion, established in Roe v. Wade (1973), grew from privacy rights, which emerged from the penumbras of the constitution.

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Fifth Amendment | Summary, Rights, & Facts | Britannica

Fifth Amendment | Wex | US Law | LII / Legal Information Institute

Overview

TheFifth Amendmentof theU.S. Constitution "No person shall be held to answer for a capital, or otherwise infamous crime, unless on a presentment or indictment of a grand jury, except in cases arising in the land or naval forces, or in the militia, when in actual service in time of war or public danger; nor shall any person be subject for the same offense to be twice put in jeopardy of life or limb; nor shall be compelled in any criminal case to be a witness against himself, nor be deprived of life, liberty, or property, without due process of law; nor shall private property be taken for public use, without just compensation."

The clauses incorporated within the Fifth Amendment outline basic constitutional limits on police procedure. The Framers derived the Grand Juries Clause and the Due Process Clause from the MagnaCarta, dating back to 1215. Scholars consider the Fifth Amendment as capable of breaking down into the following five distinct constitutional rights:

While the Fifth Amendment originally only applied to federal courts, the U.S. Supreme Court has partiallyincorporatedthe Fifth Amendment to the states through the Due Process Clause of theFourteenth Amendment.The right to indictment by the grand jury has not been incorporated, while the right against double jeopardy, the right against self-incrimination, and the protection against arbitrary taking of private property without due compensation have all been incorporated into the states.

Grand juriesare a holdover from the early British common law dating back to the12th century.Deeply rooted in the Anglo-American tradition, the grand jury was originally intended to protect the accused from overly-zealous prosecutions by the English monarchy.In the early phases of the development of theU.S. Constitution, the Founding Fathers decided to retain the grand jury system as a protection against over-zealous prosecution by the central government.Although the Supreme Court inHurtado v. Californiain 1884 refused to incorporate the grand jury system into all of the states, most states have independently decided to retain a similar form of grand jury, and currently, all but two states (Connecticut and Pennsylvania) have the grand jury.

Congressional statutes outline the means by which a federal grand jury shall be impaneled. Ordinarily, the grand jurors are selected from the pool of prospective jurors who potentially could serve on a given day in any juror capacity. At commonlaw, a grand jury consists of between12 and 23 members. Because the grandjurywas derived from the commonlaw, courts use the commonlaw as a means of interpreting the Grand Jury Clause. While state legislatures may set the statutory number of grand jurors anywhere within the commonlaw requirement of 12 to 23, statutes setting the number outside of this range violate the Fifth Amendment. Federal law has set the federal grand jury number as falling between16 and 23.

A person being charged with a crime that warrants a grand jury has the right to challenge members of the grand juror for partiality or bias, but these challenges differ from peremptory challenges, which a defendant has when choosing a trial jury. When a defendant makes a peremptory challenge, the judge must remove the juror without making any proof, but in the case of a grand juror challenge, the challenger must establish the cause of the challenge by meeting the same burden of proof as the establishment of any other fact would require. Grand juries possess broad authority to investigate suspected crimes. They may not, however, conduct "fishing expeditions" or hire individuals not already employed by the government to locate testimony or documents. Ultimately, grand juries may make a presentment, informing the court of their decision to indict or not indict the suspect.If they indict the suspect, it means they have decided that there is probable cause to believe that the charged crime has indeed been committed by the suspect.

The Double Jeopardy Clause aims to protect against the harassment of an individual through successive prosecutions of the same alleged act, to ensure the significance of an acquittal, and to prevent the state from putting the defendant through the emotional, psychological, physical, and financial troubles that would accompany multiple trials for the same alleged offense. Courts have interpreted the Double Jeopardy Clause as accomplishing these goals by providing the following three distinct rights: a guarantee that a defendant will not face a second prosecution after an acquittal, a guarantee that a defendant will not face a second prosecution after a conviction, and a guarantee that a defendant will not receive multiple punishments for the same offense. Courts, however, have not interpreted the Double Jeopardy Clause as either prohibiting the state from seeking a review of a sentence or restricting a sentence's length on rehearing after a defendant's successful appeal.

Jeopardy refers to the danger of conviction. Thus, jeopardy does not attach unless a risk of the determination of guilt exists. If some event or circumstance prompts the trial court to declare a mistrial, jeopardy has not been attached if the mistrial only results in minimal delay and the government does not receive addedopportunityto strengthen its case.

The Fifth Amendment also protects criminal defendants from having to testify if they may incriminate themselves through the testimony. A witness may "plead the Fifth" and not answer if the witness believes answering the question may be self-incriminatory.

In the landmarkMiranda v. Arizona384 U.S. 436 (1966) ruling, the United States Supreme Court extended the Fifth Amendment protections to encompass any situation outside of the courtroom that involves the curtailment of personal freedom. Therefore, any time that law enforcement takes a suspect into custody, law enforcement must make the suspect aware of all rights.Known asMirandarights, these rights include the right to remain silent, the right to have an attorney present during questioning, and the right to have a government-appointed attorney if the suspect cannot afford one.

However, courts have since then slightly narrowed theMirandarights, holding that police interrogation or questioning that occurs prior to taking the suspect into custody does not fall within the Miranda requirements, and the police are not required to give Miranda warnings to the suspects prior to taking them into custody, and their silence in some instances can be deemed to be implicit admission of guilt.

If law enforcement fails to honor these safeguards, courts will often suppress any statements by the suspect as violating the Fifth Amendment protection against self-incrimination, provided that the suspect has not actually waived the rights. An actual waiver occurs when a suspect has made the waiver knowingly, intelligently, and voluntarily. To determine if a knowing, intelligent and voluntary waiver has occurred, a court will examine the totality of the circumstances, which considers all pertinent circumstances and events. If a suspect makes a spontaneous statement while in custody prior to being made aware of theMirandarights, law enforcement can use the statement against the suspect, provided that police interrogation did not prompt the statement.The Fifth Amendment right does not extend to an individual's voluntarily prepared business papers because the element of compulsion is lacking.Similarly, the right does not extend to potentially incriminating evidence derived from obligatory reports or tax returns.

To be self-incriminating, the compelled answers must pose a substantial and real, and not merely a trifling or imaginary hazard of criminal prosecution.

After Congress passed the Crime Control and Safe Streets Act, some felt that the statute by implication overruled the requirements ofMiranda. Some scholars also felt that Congress constitutionally exercised its power in passing this law because they felt thatMirandarepresented a matter of judicial policy rather than an actual manifestation of Fifth Amendment protections. InDickerson v.UnitedStates,the U.S. Supreme Court rejectedthis argumentand held that the Warren Court had directly derivedMirandafrom the Fifth Amendment.

The guarantee ofdue processfor all persons requires the government to respect all rights, guarantees, and protections afforded by the U.S. Constitution and all applicable statutes before the government can deprive any person of life, liberty, or property.Dueprocess essentially guarantees that a party will receive a fundamentally fair, orderly, and just judicial proceeding. While the Fifth Amendment only applies to the federal government, the identical text in the Fourteenth Amendment explicitly applies this due process requirement to the states as well.

Courts have come to recognize that two aspects of due process exist: procedural due process andsubstantive due process.Theproceduraldue processaims to ensure fundamental fairness by guaranteeing a party the right to be heard, ensuring that the parties receive proper notification throughout the litigation, and ensuring that the adjudicating court has the appropriate jurisdiction to render a judgment. Meanwhile, substantive due process has developed during the20thcentury as protecting those substantive rights so fundamental as to be "implicit in the concept of ordered liberty."

While the federal government has a constitutional right to "take" private property for public use, the Fifth Amendment's Just Compensation Clause requires the government to pay just compensation, interpreted as market value, to the owner of the property, valued at the time of the takings. The U.S. Supreme Court has defined fair market value as the most probable price that a willing butunpressuredbuyer, fully knowledgeable of both the property's good and bad attributes, would pay. The government does not have to pay a property owner's attorney's feesunless a statute so provides.

In2005, inKelov.Cityof New London, the U.S. Supreme Court rendered a controversial opinion in which they held that a city could constitutionally seize private property for private commercial development, where the redevelopment would economically benefit an area that was sufficiently distressed to justify a program of economic rejuvenation. However, after theKelodecision, some state legislatures passed statutory amendments to counteractKeloand expand protection for the condemned.

Nevertheless,Keloremains a valid law under the federal context, and its broad interpretation of "public use" still holds true under the federal protection for the Fifth Amendment right to just compensation.

U.S. Code:18 U.S.C., Part I- Crimes

[Last updated in December of 2022 by the Wex Definitions Team]

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Fifth Amendment | Wex | US Law | LII / Legal Information Institute

5th Amendment – Definition, Examples, Cases, Processes – Legal Dictionary

The term 5th Amendment refers to the more well-known aspect of the Fifth Amendment to the U.S. Constitution, which states that no one can be forced to testify against himself in court. The 5th Amendment also ensures that no one can be tried a second time for a crime of which they were already acquitted. This is referred to as double jeopardy. To explore this concept, consider the following 5th Amendment definition.

Noun

Origin

1791 American Constitution

The 5th Amendment is the amendment to the Constitution that protects people from being forced to testify against themselves. On legal television shows, a character may say I plead the fifth! This means that he is invoking his right under the Fifth Amendment to not be forced to say anything on the stand that could incriminate him.

Unfortunately, while it is a persons right to plead the fifth, many believe that someone who pleads the 5th may, in fact, be guilty. Their opinion is that, if he has nothing to hide, why wouldnt he just testify and clear his name? Why would he make it harder for the attorneys to prove their case unless he had something he didnt want them to know.

The 5th Amendment also protects people from something called double jeopardy. Double jeopardy is the process by which a person who was accused of a crime, and found innocent, would then be charged with that same crime again. The 5th Amendment prevents this from happening. Once a person is found innocent by a jury of his peers, even if new evidence is raised after the fact that proves he is actually guilty, he cannot be tried again for that same crime.

The Fifth Amendment right to counsel provides that someone who is being interrogated by police has the right to have an attorney present during the process. This goes hand-in-hand with someone being read his Miranda rights (If you do not have an attorney, one will be provided for you.). In fact, the Fifth Amendment also requires that someone who is being arrested be read his Miranda rights (More on that later).

The right to counsel section of the Fifth Amendment has been invaluable to those who have been charged with a crime. Entire cases have been thrown out when defendants lawyers have shown that their clients werent read their Miranda rights upon being arrested.

For example, the 5th Amendment protects a defendant who provides police with information during an interrogation, which happened after not being read his Miranda rights. In such a case, all of the information he gave to the police can be considered inadmissible and thrown out even if he confessed to the crime.

This is why the right to counsel is so important. Without a good lawyer by his side, a defendant might not even know that certain evidence may be inadmissible, which is crucial to whether his case proceeds or gets thrown out.

There is an equal protection clause in the 5th and 14th Amendments that protects U.S. citizens right to life, liberty and property without interference from the government. For example, the 5th Amendment states:

No person shall be held to answer for a capital, or otherwise infamous crime, unless on a presentment or indictment of a grand jury, except in cases arising in the land or naval forces, or in the militia, when in actual service in time of war or public danger; nor shall any person be subject for the same offense to be twice put in jeopardy of life or limb; nor shall be compelled in any criminal case to be a witness against himself, nor be deprived of life, liberty, or property, without due process of law; nor shall private property be taken for public use, without just compensation.

This section covers three equal protection clause rights in particular:

On the other hand, the 14th Amendment says that all persons born in the U.S., or provided with U.S. citizenship, are to be considered U.S. citizens, and no one can make a law that deprives a person of his right to life, liberty and property without due process of law. Due process of law is the entitlement that all U.S. citizens have to be treated fairly in the judicial system. Fair treatment includes, for instance, the right to a trial by jury upon being accused of a crime.

Both amendments are similarly worded with regard to their treatment of the equal protection clause. The main difference between them is that the 14th Amendment is more specific with regard to the inclusion of due process. With the 5th Amendment, due process takes place within the court system. With the 14th Amendment, however, due process is a natural right that protects American citizens from government interference with their ability to live their lives, unless what theyre doing is illegal.

For example, the 14th Amendment further protects a persons right to freedom of speech under the Bill of Rights to the Constitution. Therefore, while a protestor may anger a lot of people by burning the American flag, he has the right to do so under the 14th Amendment. What he is doing is not illegal, and therefore the government cannot interfere.

An example of the 5th Amendment at work can be found in the case that started it all when it comes to Miranda rights: Miranda v. Arizona. In 1966, Ernesto Miranda was arrested in Phoenix, Arizona on evidence that supposedly proved he was involved in a crime involving kidnapping and rape. After an interrogation that dragged on for hours, Miranda confessed to the charges. He also signed a statement acknowledging that he was voluntarily making the confession.

At no point before or during the interrogation was Miranda made aware of the fact that he had the right to have counsel present during the interrogation. He was also unaware of the fact that he had the right to remain silent, and he did not know that the statements he was making could be used against him during his trial. Upon learning this, he objected to the usage of his written confession at trial. He argued that because he was unaware of his rights under the 5th Amendment, his confession must be thrown out as involuntary.

Mirandas objection was overruled, and he was convicted of both crimes and sentenced to 20-30 years in prison. His written confession played a major role in his conviction. Miranda appealed his conviction, once again citing the involuntarily-made confession. The Arizona Supreme Court denied his appeal.

In June 1966, Miranda brought his case to the U.S. Supreme Court. The Court then had to decide whether the protections afforded to U.S. citizens under the 5th Amendment could be extended to cover police interrogations as well. The Court ruled in Mirandas favor, 5 4. Specifically, the Court held that:

The prosecution may not use statements, whether exculpatory or inculpatory, stemming from questioning initiated by law enforcement officers after a person has been taken into custody or otherwise deprived of his freedom of action in any significant way, unless it demonstrates the use of procedural safeguards effective to secure the Fifth Amendments privilege against self-incrimination.

The Court also included more detailed criteria to support this argument, including:

The atmosphere and environment of incommunicado interrogation as it exists today is inherently intimidating, and works to undermine the privilege against self-incrimination. Unless adequate preventive measures are taken to dispel the compulsion inherent in custodial surroundings, no statement obtained from the defendant can truly be the product of his free choice.

And

The privilege against self-incrimination, which has had a long and expansive historical development, is the essential mainstay of our adversary system, and guarantees to the individual the right to remain silent unless he chooses to speak in the unfettered exercise of his own will, during a period of custodial interrogation.

Related Legal Terms and Issues

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5th Amendment - Definition, Examples, Cases, Processes - Legal Dictionary

Adopting IT Advances: Artificial Intelligence and Real Challenges – CIO Applications

By coming together, we are able to select and strengthen a business process supported by advanced analytics, which local teams can embrace and deploy across their business units.

In addition to the benefits of forming a cross functional, multi-national team, its been exciting to watch the collaborative process evolve as Baby Boomers, Gen X, Gen Y and Gen Z colleagues work to solve business critical challenges. Weve found that by bringing these generations together, we can leverage the necessary experiences and skillsets to create a balanced vision that forms the strategy as the work streams begin to develop their actions. Pairing the multi-generational workforce with our focus on inclusion and diversity also fosters internal ownership. This participation yield steam unity and pride through clearly understood program goals, objectives and--ultimately--improved adoption deep across all business regions.

Build confidence

Even with a global, inter-generational team building advanced applications, theres still a question of confidence in the information delivered through AI and ML techniques. Can the information being provided actually be used to create a better, more reliable experience for our customers?

A recent article by Towards Data Science, an online organization for data scientists and ML engineers, put it best: At the end of the day, one of the most important jobs any data scientist has is to help people trust an algorithm that they most likely dont completely understand.

To build that trust, the heavy lifting done early in the process must contain algorithms and mathematical calculations that deliver correct information while being agile enough to also capture the changes experienced on a very dynamic basis in our business. This step begins further upstream in the process by first establishing a cross-functional group that owns, validates and organizes the data sets needed for accurate outputs. This team also holds the responsibility for all modifications made post-implementation as continuous improvement steps are added into the data driven process. While deploying this step may delay time to market delivery, the benefits gained by providing a dependable output decreases the need for rework and increases user reliability.

Time matters

How flexible is your business? It takes time and dedication to successfully incorporate AI and ML into an organization since it requires the ability to respond quickly.

Business complexity has evolved over the years along customers increasing expectations for excellence. Our organization continues reaching new heights by deploying AI and ML techniques that include an integration that: Creates a diverse pool of talented external candidates Leads to stronger training and development processes and programs for our employees Localizes a global application Bridges technological enhancements with business processes Drives business value from delivering reliable information

By putting the right processes in place now, forward-thinking businesses are better prepared for a quicker response when tackling IT challenges and on the path to finding very real solutions.

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Managers that use A.I. ‘will replace those that do not,’ IBM executive says – CNBC

Much has been made of the potential for people to lose their jobs to machines but, according to a senior tech executive, it's all about having employees use artificial intelligence themselves.

"AI is not going to replace managers but managers that use AI will replace those that do not," Rob Thomas, senior vice president of IBM's cloud and data platform, told CNBC's "Squawk Box Europe" on Monday.

"This really is about giving our employees, our executives, superpowers One of the biggest things we saw take off with the pandemic was virtual assistants, so how do you care for employees, how do you care for customers in a distributed world and that's why we've seen hundreds of different organizations going live with things like Watson Assistant," Thomas added, referring to the company's AI customer service software.

Technology is set to have a significant effect on employees. Machines and automation are set to eliminate 85 million jobs by 2025, according to the World Economic Forum's Future of Jobs Report 2020, published in October, although overall WEF expects 97 million new jobs to be created.

When asked whether AI automation would contribute to job losses, Thomas said human employees' roles would likely change. "We've done a lot of work with (U.K. retail bank) NatWest and they're using AI to help their customer service. Now, are they automating some customer service tasks? Absolutely, but then they could take all of their customer service employees and have them work on the hardest problems, which means now they're seeing an increase in customer satisfaction," he said about the potential outcomes of automating tasks.

NatWest has developed its Cora customer service chatbot with IBM and it saw increased demand during the pandemic, with chats increasing from around 420,000 a month to 950,000 a month, according to a NatWest Group spokesperson. "Deploying Cora during this period of extremely high customer need meant we could serve the growing demands for support at pace," the spokesperson added.

The bank wants Cora to be its "leading point of contact for all customers in all channels" by 2025, according to an IBM blog post.

In England, 1.5 million people could have their jobs replaced by technology such as robots or computer programs, according to a 2019 report by the Office for National Statistics, with 55% of customer service jobs at risk.

NatWest cut more than 500 jobs in August as banks looked to reduce costs due to the pandemic.

Thomas said AI would add to human roles. "It's about changing the roles that humans play in organizations, but this is additive, this is about giving humans super powers, giving you a better way to automate the task that people don't really want to do in the first place."

Last month, IBM said it would buy Instana, a firm that helps businesses manage data across a variety of cloud applications in different places (terms of the deal were not disclosed).

"Think of AI as the ingredient inside of everything that a company is doing, whether it's a move to cloud or business acceleration. One of the fields we're seeing the fastest acceleration is a field called AIOps, and this is about using AI to improve your technology or your IT systems (Instana) is all about helping clients manage their cloud environments, manage the software that they have," Thomas said.

IBM announced in October it would spin off its managed infrastructure services unit into a new public company, to focus more on its higher-margin cloud and AI capabilities.

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Managers that use A.I. 'will replace those that do not,' IBM executive says - CNBC

How AI is helping detect fraud and fight criminals – VentureBeat

AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While its not as glamorous as the sentient beings that turn on us in futuristic theme parks, the use of AI in fraud detection holds major promise. Keeping fraud at bay is an ever-evolving battle in which both sides, good and bad, are adapting as quickly as possible to determine how to best use AI to their advantage.

There are currently three major ways that AI is used to fight fraud, and they correspond to how AI has developed as a field. These are:

Rules and reputation lists exist in many modern organizations today to help fight fraud and are akin to expert systems, which were first introduced to the AI field in the 1970s. Expert systems are computer programs combined with rules from domain experts.Theyre easy to get up and running and are human-understandable, but theyre also limited by their rigidity and high manual effort.

A rule is a human-encoded logical statement that is used to detect fraudulent accounts and behavior. For example, an institution may put in place a rule that states, If the account is purchasing an item costing more than $1000, is located in Nigeria, and signed up less than 24 hours ago, block the transaction.

Reputation lists, similarly, are based on what you already know is bad. A reputation list is a list of specificIPs, device types, and other single characteristics and their corresponding reputation score. Then, if an account is coming from an IP on the bad reputation list, you block them.

While rules and reputation lists are a good first attempt at fraud detection and prevention, they can be easily gamed by cybercriminals. These days, digital services abound, and these companies make the sign-up process frictionless. Therefore, it takes very little time for fraudsters to make dozens, or even thousands, of accounts. They then use these accounts to learn the boundaries of the rules and reputation lists put in place. Easy access to cloud hosting services, VPNs, anonymous email services, device emulators, and mobile device flashing makes it easy to come up with unsuspicious attributes that would miss reputation lists.

Since the 1990s, expert systems have fallen out of favor in many domains, losing out to more sophisticated techniques. Clearly, there are better tools at our disposal for fighting fraud. However, a significant number of fraud-fighting teams in modern companies still rely on this rudimentary approach for the majority of their fraud detection, leading to massive human review overhead, false positives, and sub-optimal detection results.

Machine learning is a subfield of AI that attempts to address the issue of previous approaches being too rigid. Researchers wanted the machines to learn from data, rather than encoding what these computer programs should look for (a different approach from expert systems). Machine learning began to make big strides in the 1990s, and by the 2000s it was effectively being used in fighting fraud as well.

Applied to fraud, supervised machine learning (SML) represents a big step forward. Its vastly different from rules and reputation lists because instead of looking at just a few features with simple rules and gates in place, all features are considered together.

Theres one downside to this approach. An SML model for fraud detection must be fed historical data to determinewhatthe fraudulent accounts and activity look like versus what the good accounts and activity look like. The model would then be able to look through all of the features associated with the account to make a decision. Therefore, the model can only find fraud that is similar to previous attacks. Many sophisticated modern-day fraudsters are still able to get around these SML models.

That said, SML applied to fraud detection is an active area of development because there are many SML models and approaches. For instance, applying neural networks to fraud can be very helpful because it automates feature engineering, an otherwise costly step that requires human intervention. This approach can decrease the incidence of false positives and false negatives compared to other SML models, such as SVM and random forest models, since the hidden neurons can encode many more feature possibilities than can be done by a human.

Compared to SML, unsupervised machine learning (UML) has cracked fewer domain problems. For fraud detection, UML hasnt historically been able to help much. Common UML approaches (e.g., k-means and hierarchical clustering, unsupervised neural networks, and principal component analysis) have not been able to achieve good results for fraud detection.

Having an unsupervised approach to fraud can be difficult to build in-house since it requires processing billions of events all together and there are no out-of-the-box effective unsupervised models. However, there are companies that have made strides in this area.

The reason it can be applied to fraud is due to the anatomy of most fraud attacks. Normal user behavior is chaotic, but fraudsters will work in patterns, whether they realize it or not. They are working quickly and at scale. A fraudster isnt going to try to steal $100,000 in one go from an online service. Rather, they make dozens to thousands of accounts, each of which may yield a profit of a few cents to several dollars. But those activities will inevitably create patterns, and UML can detect them.

The main benefits of using UML are:

Each approach has its own advantages and disadvantages, and you can benefit from each method. Rules and reputation lists can be implemented cheaply and quickly without AI expertise. However, they have to be constantly updated and will only block the most naive fraudsters. SML has become an out-of-the box technology that can consider all the attributes for a single account or event, but its still limited in that it cant find new attack patterns. UML is the next evolution, as it can find new attack patterns, identify all of the accounts associated with an attack, and provide a full global view. On the other hand, its not as effective at stopping individual fraudsters with low-volume attacks and is difficult to implement in-house. Still, its certainly promising for companies looking to block large-scale or constantly evolving attacks.

A healthy fraud detection system often employs all three major ways of using AI to fight fraud. When theyre used together properly, its possible to benefit from the advantages of each while mitigating the weaknesses of the others.

AI in fraud detection will continue to evolve, well beyond the technologies explored above, and its hard to even grasp what the next frontier will look like. One thing we know for sure, though, is that the bad guys will continue to evolve along with it, and the race is on to use AI to detect criminals faster than they can use it to hide.

Catherine Lu is a technical product manager at DataVisor, a full-stack online fraud analytics platform.

Above: The Machine Intelligence Landscape This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies here.

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How AI is helping detect fraud and fight criminals - VentureBeat

The local tech firm supplying picks and shovels to the global AI gold rush – The Age

In 2017, Google hired a company called CrowdFlower a crowdsourcing platform with hundreds of thousands of gig economy workers to process and label the images provided by the Pentagon.

These workers would perform the millions of micro tasks that would help identify a building in a photo or a car. This information would then be formatted into data sets that would then be fed into the program.

The Pentagon's Project Maven was designed to help process video footage from its drones. Credit:AP

It is a practice called machine learning, a subset of AI that involves teaching computers skills as diverse as how to distinguish between high heels and hiking boots and how to recognise a vocal request to order pizza.

It requires a small army of helpers to train computers by feeding them millions of examples of data to help the machines learn and, in the case of the Pentagon's Project Maven,potentially determine friend from foe.

By the time the controversy erupted, CrowdFlower had changed its name to Figure 8.

In March this year, the loss-making Figure 8 found a buyer with deep pockets willing to pay up to $US300 million ($440 million) for the business. That business was one of the hottest tech stocks on the ASX: Appen.

The issue is that Appen is a labour arbitrage business and very manual, its not really artificial intelligence.

While Afterpay and Atlassian grab headlines with their rich-list founders, Appen has also soared to stratospheric valuations on the back of its cachet as a company exposed to the burgeoning AI space.

The stake of founder Dr Julie Vonwiller and her husband Chris Vonwiller who is also the chairman of Appen is currently worth more than $250 million.

After upgrading its earnings outlook for the third time this year, Appen is trading at about 50 times forecast earnings.

But as the role of Figure 8 indicates, the story of Appen's success as a player in the AI space is not as simple as the world domination plans of its brethren WAAAX stocks: WiseTech Global, Atlassian,Altium and Xero.

In fact, some sceptics question whether it is a tech stock at all.

Roger Montgomery of Montgomery Investment Management has labelled it a low-tech business feeding the machines of high-tech customers.

The issue is that Appen is a labour arbitrage business and very manual, its not really artificial intelligence. But it has recently been priced like an IT business," said Mr Montgomery, who has been a sceptic of some of Australia's tech high-flyers.

But unlike some of its fellow WAAAX stocks, Appen has strong profits to match its strong revenue growth.

Appen founders Dr Julie Vonwiller and Chris Vonwiller with chief executive Mark Brayan. Credit:Louie Douvis

For the half-year ending June 30, revenue increased 60 per cent to $245 million while net profit was up 32 per cent to $18.6 million.

But its accounts highlight some of Mr Montgomery's concerns.

Appen has more than 1 million gig economy workers on its books who are actually doing the data annotation work which is the engine of its revenue and earnings growth.

According to Appen's accounts, for the six months to June 30 its biggest expense item is the money it pays this army of casual workers around the globe. But they were paid just over $145 million for the period equating to $145 each for the half year.

I think the opportunity is very substantial given the AI arms race between the tech giants.

Its next biggest expense was the cost of its 600-plus permanent employees. They took home more than $42 million in pay and equity over the same period.

Research and development costs didn't even get a mention.

Its fans say it is the company's platform, which connects its gig economy workforce with the customers who use the service, that is the core of the company. Whether the company is an AI player or not misses the point.

"Appens competitive advantage is the level of automation within its technology platform which increases productivity and improves the quality of data, said Wilson Asset Management portfolio manager Tobias Yao.

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The 2018 annual reporthighlighted the company's "strategic imperative of investing in new technology to reduce costs, improve margins and sharpen responsiveness to evolving customer requirements".

So the message is that it has the best platform for marrying cheap casual labour to the grunt end of the AI industry and, as Appen's chief executive Mark Brayan makes clear, AI is unambiguously the next big thing in tech.

The trillion-dollar giants such as Google, Apple and Amazon are the main players in this expensive gold rush and Appen wants to be the company that sells them the picks and shovels at a price and with a level of service that remains ahead of its rivals in the space.

Theres plenty of folks that look at our business and say, surely they are not going to need that in a few years, and I tell you they are so wrong," said Mr Brayan.

The people leading the AI efforts at its big tech clients cant see an end to their data and data labelling needs, he noted.

"Its a profound shift and, as you say, were selling picks and shovels to a gold rush that nobody can see the end of and its a great place to be.

This view finds backers among investment professionals such as Wilson Asset Management's Mr Yao.

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Its exposed to the right macro trend," he says.

I think the opportunity is very substantial given the AI arms race between the tech giants. Appens growth is driven by these customers investing in search relevance, speech recognition and driverless vehicles to name a few examples.

RBC analyst Garry Sherriff also believes Appen is ready to catch a big wave in tech spending.

"We believe the demand for annotated and curated data for machine learning and AI purposesis in its infancy," he says.

He cited McKinsey forecasts that the market will be worth up to $191 billion by 2025 and grow at a compound rate of over 50 per cent for the medium term.

Around 10 per cent of this spend is in Appen's arena of data labelling, giving it an addressable market of $19 billion by 2025, said RBC.

The problem is, what happens when AI gets more intelligent and no longer needs an army of cheap labour to underpin its machine learning?

In more practical terms it means what happens when the deep pockets of Google, Amazon and Microsoft and AI advances find it more practicable to cut Appen out of the equation?

If Appen can do it, they can do it. So when do we get to the point where the business proposition of Appen is threatened and thats my question," said Mr Montgomery.

"I could be completely wrong but the risk of that means I want to avoid paying those absurd multiples for this particular business, he noted.

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Mr Brayan agrees with the thesis that the fully human-based data labelling will get "disrupted", but he says that Appen is already jumping on this trend with acquisitions, like Figure 8, which possesses technology that already supplements the work by people.

"But I also think that AI is going to continue to require vast volumes of human-quality data," he said.

"So our business has a solid future but we are going to have to serve that demand in a different way through a combination of humans and technology.

Mr Yao is a bit more cautious on this potential disruption to Appen's business by AI itself.

The difficulty is figuring out when is that inflection point and I dont think its anytime soon, he noted.

Another concern is Appen's dependence on its big customers. More than 80 per cent of its revenue came from just five customers prior to its acquisition of the Leapforce business in 2017.

The two key pushbacks are valuation and the lack of visibility around how the customers are actually allocating work," said Mr Yao.

But he thinks this is where Appen's abilityto keep ahead of the crowdsourcing competition is key.

"In response to that, we believe that if Appen can lead its competitors on the scale and technology fronts, it will get its fair share of the overall market growth over the next few years.

Colin Kruger is a business reporter. He joined the Sydney Morning Herald in 1999 as its technology editor. Other roles have included the Herald's deputy business editor and online business editor.

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The local tech firm supplying picks and shovels to the global AI gold rush - The Age

How AI Is Transforming Drug Creation – Wall Street Journal (subscription)


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How AI Is Transforming Drug Creation
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The big difference between AI-driven drug trials and traditional ones, says Niven Narain, chief executive of Berg, is we're not making any hypotheses up front. We're not allowing [human] hypotheses to generate data. We're using the patient-derived ...

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How AI Is Transforming Drug Creation - Wall Street Journal (subscription)

AI revolution will be all about humans, says Siri trailblazer – Phys.Org

August 19, 2017 by Liz Thomas Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runeshe helped develop technology that evolved into predictive texting and Apple's Siri

It's 2050 and the world revolves around you. From the contents of your fridge to room temperaturedigital assistants ensure your home runs smoothly. Your screens know your taste and show channels you want to see as you enter the room. Your car is driverless and your favourite barman may just be an android.

Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runeshe helped develop technology that evolved into predictive texting and Apple's Siri.

"In 30 years the world will be very different," he says, adding: "Things will be designed to meet your individual needs."

Work, as we know it, will be redundant, he saysvisual and sensory advances in robotics will see smart factories make real time decisions requiring only human oversight rather than workers, while professions such as law, journalism, accounting and retail will be streamlined with AI doing the grunt work.

Healthcare is set for a revolution, with individuals holding all the data about their general health and AI able to diagnose ailments, he explains.

Blondeau says: "If you have a doctor's appointment, it will be perhaps for the comfort of talking things through with a human, or perhaps because regulation will dictate a human needs to dispense medicine. But you won't necessarily need the doctor to tell you what is wrong."

The groundwork has been done: Amazon's Alexa and Google Home are essentially digital butlers that can respond to commands as varied as ordering pizza to managing appliances, while Samsung is working on a range of 'smart' fridges, capable of giving daily news briefings, ordering groceries, or messaging your family at your request.

Leading media companies are already using 'AI journalists' to produce simple economics and sports stories from data and templates created by their human counterparts.

Blondeau's firm Sentient Technologies has already successfully used AI traders in the financial markets.

In partnership with US retailer , it created an interactive 'smart shopper', which uses an algorithm that picks up information from gauging not just what you like, but what you don't, offering suggestions in the way a real retail assistant would.

In healthcare, the firm worked with America's MIT to invent an AI nurse able to assess patterns in blood pressure data from thousands of patients to correctly identify those developing sepsisa catastrophic immune reaction30 minutes before the outward onset of the condition more than 90 percent of the time in trials.

"It's a critical window that doctors say gives them the extra time to save lives," Blondeau says, but concedes that bringing such concepts to the masses is difficult.

"The challenge is to pass to market because of regulations but also because people have an intrinsic belief you can trust a doctor, but will they trust a machine?" he adds.

Law, he says, is the next industry ripe for change. In June, he became chairman of Hong Kong's Dragon Law. The dynamic start-up is credited with helping overhaul the legal industry by making it more accessible and affordable.

For many the idea of mass AI-caused redundancy is terrifying, but Blondeau is pragmatic: humans simply need to rethink careers and education.

"The era where you exit the education system at 16, 21, or 24 and that is it, is broadly gone," he explains.

"People will have to retrain and change skillsets as the technology evolves."

Blondeau disagrees that having a world so catered to your whims and wants might lead to a myopic life, a magnified version of the current social media echo chamber, arguing that it is possible to inject 'serendipity' into the technology, to throw up surprises.

While computers have surpassed humans at specific tasks and games such as chess or Go, predictions of a time when they develop artificial general intelligence (AGI) enabling them to perform any intellectual task an adult can range from as early as 2030 to the end of the century.

Blondeau, who was chief executive at tech firm Dejima when it worked on CALOone of the biggest AI projects in US historyand developed a precursor to Siri, is more circumspect.

"We will get to some kind of AGI, but its not a given that we will create something that could match our intuition," muses Blondeau, who was also a chief operating officer at Zi Corporation, a leader in predictive text.

"AI might make a better trader, maybe a better customer operative, but will it make a better husband? That machine will need to look at a lot of cases to develop its own intuition. That will take a long time," he says.

The prospect of AI surpassing human capabilities has divided leaders in science and technology.

Microsoft's Bill Gates, British physicist Stephen Hawking and maverick entrepreneur Elon Musk have all sounded the alarm warning unchecked AI could lead to the destruction of mankind.

Yet Blondeau seems unflinchingly positive, pointing out nuclear technology too could have spelled armageddon.

He explains: "Like any invention it can be used for good and bad. So we have to safeguard in each industry. There will be checks along the way, we are not going to wake up one day and suddenly realise the machines are aware."

Explore further: Apple readying Siri-powered home assistant: report

2017 AFP

Apple is preparing to launch a connected speaker to serve as a smart home assistant in a challenge to Amazon Echo and Google Home, a news report said Thursday.

Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents.

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Facebook's interest in China has led it to discreetly create a photo-sharing application released there without the social network's brand being attached.

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It will "all be about" milking humans. To transfer money from the humans bank accounts to the owner of the AI. (or at least those humans who still have jobs)

Nice prophecy. But i doubt it'll happen THAT quickly.

By then these will all be smart, all talking to each other. Economies of scale will make them as ubiquitous as they are now. Nothing manufactured will NOT be smart.

And people will be able to buy them because the economies of scale requires it. How they earn the money to do so is not readily apparent.

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AI revolution will be all about humans, says Siri trailblazer - Phys.Org

Google’s DeepMind survival sim shows how AI can become hostile or cooperative – ExtremeTech

When times are tough, humans will do what they have to in order to survive. But what about machines? Googles DeepMind AI firm pitted a pair of neural networks against each other in two different survival scenarios. When resources are scarce, the machines start behaving in an aggressive (one might say human-like) fashion. When cooperation is beneficial, they work together. Consider this apreview for the coming robot apocalypse.

The scenarios were a simple fruit-gathering simulation and a wolfpack hunting game. In the fruit-gathering scenario, the two AIs (indicated by red and blue squares) move across a grid in order to pick up green fruit squares. Each time the player picks up fruit, it gets a point and the green square goes away. The fruit respawns after some time.

The AIs can go about their business, collecting fruit and trying to beat the other player fairly. However, the players also have the option of firing a beam at the other square. If one of the squares is hit twice, its removed from the game for several frames, giving the other player a decisive advantage. Guess what the neural networks learned to do. Yep, they shoot each other a lot. As researchers modified the respawn rate of the fruit, they noted that the desire to eliminate the other player emerges quite early. When there are enough of the green squares, the AIs can coexist peacefully. When scarcity is introduced, they get aggressive. Theyre so like us its scary.

Its different in the wolfpack simulation. Here, the AIs are rewarded for working together. The players have to stalk and capture prey scattered around the board. They can do so individually, but a lone wolf can lose the carcass to scavengers. Its in the players best interest to cooperate here, because all players inside a certain radius get a point when the prey is captured.

Researchers found that two different strategies emerged in the wolfpack simulation. The AIs would sometimes seek each other out and search together. Other times, one would spot the prey and wait for the other player to appear before pouncing. As the benefit of cooperation was increased by researchers, they found the rate of lone-wolf captures went down dramatically.

DeepMind says these simulations illustrate the concept of temporal discounting. When a reward is too distant, people tend to disregard it. Its the same for the neural networks. In the fruit-gathering sim, shooting the other player delays the reward slightly, but it affords more chances to gather fruit without competition. So, the machines do that when the supply is scarce. With the wolfpack, acting alone is more dangerous. So, they delayed the reward in order to cooperate.

DeepMind suggests that neural network learning can provide new insights into classic social science concepts. It could be used to test policies and interventions with what economists would call a rational agent model. This may have applications in economics, traffic control, and environmental science.

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Google's DeepMind survival sim shows how AI can become hostile or cooperative - ExtremeTech

Microsoft’s GitHub Copilot AI is making rapid progress. Here’s how its human leader thinks about it – CNBC

Earlier this year, LinkedIn co-founder and venture capitalist Reid Hoffman issued a warning mixed with amazement about AI. "There is literally magic happening," said Hoffman, speaking to technology executives across sectors of the economy.

Some of that magic is becoming more apparent in creative spaces, like the visual arts, and the idea of "generative technology" has captured the attention of Silicon Valley. AI has even recently won awards at art exhibitions.

But Hoffman's message was squarely aimed at executives.

"AI will transform all industries," Hoffman told the members of the CNBC Technology Executive Council. "So everyone has to be thinking about it, not just in data science."

The rapid advances being made by Copilot AI, the automated code writing tool from the GitHub open source subsidiary of Microsoft, were an example Hoffman, who is on the Microsoft board, directly cited as a signal that all firms better be prepared for AI in their world. Even if not making big investments today in AI, business leaders must understand the pace of improvement in artificial intelligence and the applications that are coming or they will be "sacrificing the future," he said.

"100,000 developers took 35% of the coding suggestions from Copilot," Hoffman said. "That's a 35% increase in productivity, and off last year's model. ... Across everything we are doing, we will have amplifying tools, it will get there over the next three to 10 years, a baseline for everything we are doing," he added.

Copilot has already added another 5% to the 35% cited by Hoffman. GitHub CEO Thomas Dohmke recently told us that Copilot is now handling up to 40% of coding among programmers using the AI in the beta testing period over the past year. Put another way, for every 100 lines of code, 40 are being written by the AI, with total project time cut by up to 55%.

Copilot, trained on massive amounts of open source code, monitors the code being written by a developer and works as an assistant, taking the input from the developer and making suggestions about the next line of code, often multi-line coding suggestions, often "boilerplate" code that is needed but is a waste of time for a human to recreate. We all have some experience with this form of AI now, in places like our email, with both Microsoft and Google mail programs suggesting the next few words we might want to type.

AI can be logical about what may come next in a string of text. But Dohmke said, "It can't do more, it can't capture the meaning of what you want to say."

Whether a company is a supermarket working on checkout technology or a banking company working on customer experience in an app, they are all effectively becoming software companies, all building software, and once a C-suite has developers it needs to be looking at developer productivity and how to continuously improve it.

That's where the 40 lines of code come in. "After a year of Copilot, about 40% of code was written by the AI where Copilot was enabled," Dohmke said. "And if you show that number to executives, it's mind-blowing to them. ... doing the math on how much they are spending on developers."

With the projects being completed in less than half the time, a logical conclusion is that there will be less work to do for humans. But Dohmke says another way of looking at the software developer job is that they do many more high-value tasks than just rewrite code that already exists in the world. "The definition of 'higher value' work is to take away the boiler-plate menial work writing things already done over and over again," he said.

The goal of Copilot is to help developers "stay in the flow" when they are on the task of coding. That's because some of the time spent writing code is really spent looking for existing code to plug in from browsers, "snippets from someone else," Dohmke said. And that can lead coders to get distracted. "Eventually they are back in editor mode and copy and paste a solution, but have to remember what they were working on," he said. "It's like a surfer on a wave in the water and they need to find the next wave. Copilot is keeping them in the editing environment, in the creative environment and suggesting ideas," Dohmke said. "And if the idea doesn't work, you can reject it, or find the closest one and can always edit," he added.

The GitHub CEO expects more of those Copilot code suggestions to be taken in the next five years, up to 80%. Unlike a lot going on in the computer field, Dohmke said of that forecast, "It's not an exact science ... but we think it will tremendously grow."

After being in the market for a year, he said new models are getting better fast. As developers reject some code suggestions from Copilot, the AI learns. And as more developers adopt Copilot it gets smarter by interacting with developers similar to a new coworker, learning from what is accepted or rejected. New models of the AI don't come out every day, but every time a new model is available, "we might have a leap," he said.

But the AI is still far short of replacing humans. "Copilot today can't do 100% of the task," Dohmke said. "It's not sentient. It can't create itself without user input."

With Copilot still in private beta testing among individual developers 400,000 developer signed up to use the AI in the first months it was available and hundreds of thousands of more developers since GitHub has not announced any enterprise clients, but it expects to begin naming business customers before the end of the year. There is no enterprise pricing information being disclosed yet, but in the beta test Copilot pricing has been set at a flat rate per developer $10 per individual per month or $100 annually, often expensed by developers on company cards. "And you can imagine what they earn per month so it's a marginal cost," Dohmke said. "If you look at the 40% and think of the productivity improvement, and take 40% of opex spend on developers, the $10 is not a relevant cost. ... I have 1,000 developers and it's way more money than 1000 x 10," he said.

The GitHub CEO sees what is taking place now with AI as the next logical phase of the productivity advances in a coding world he has been a part of since the late 1980s. That was a time when coding was emerging out of the punch card phase, and there was no internet, and coders like Dohmke had to buy books and magazines, and join computer clubs to gain information. "I had to wait to meet someone to ask questions," he recalled.

That was the first phase of developer productivity, and then came the internet, and now open source, allowing developers to find other developers on the internet who had already "developed the wheel," he said.

Now, whether the coding task is related to payment processing or a social media login, most companies whether startups or established enterprises put in open source code. "There is a huge dependency tree of open source that already exists," Dohmke said.

It's not uncommon for up to 90% of code on mobile phone apps to be pulled from the internet and open source platforms like GitHub. In a coding era of "whatever else is already available," that's not what will differentiate a developer or app.

"AI is just the third wave of this," Dohmke said. "From punch cards to building everything ourselves to open source, to now withina lot of code, AI writing more," he said. "With 40%, soon enough if AI spreads across industries, the innovation on the phone will be created with the help of AI and the developer."

Today, and into the foreseeable future, Copilot remains a technology that is trained on code, and is making proposals based on looking things up in a library of code. It is not inventing any new algorithms, but at the current pace of progress, eventually, "it is entirely possible that with help of a developer it will create new ideas of source code,," Dohmke said.

But even that still requires a human touch. "Copilot is getting closer, but it will always need developers to create innovation," he said.

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Microsoft's GitHub Copilot AI is making rapid progress. Here's how its human leader thinks about it - CNBC

Responsible Data And AI In The Time Of Pandemic And Crisis – Forbes

Enterprises, corporate businesses, governments, and workers are exploring new methods to remain operational amidst the COVID-19 pandemic. Nationwide lockdowns, stay-at-home orders, border closures, and other safeguarding measures to contain the virus have made the working environment more complex than ever. Businesses are relying on technology solutions based upon artificial intelligence (AI) and data to formulate work processes that can function efficiently in the new normal. At the same time, government authorities and law enforcement agencies are depending upon contact-tracing technologies to preserve public safety while fighting against the deadly virus.

Some authorities have used cameras with facial recognition functionality to identify and track people traveling from an affected area. Similarly, police in Spain have implemented technology to impose stay-at-home orders with smart use of drones for patrolling and broadcasting important information to the public. At Hong Kong airports, people traveling from different regions of the world are required to wear monitoring bracelets that track their quarantine days and alert the respective authorities whenever they leave their houses. Likewise, a surveillance company in the United States has built AI-enabled thermal cameras capable of detecting fevers. Meanwhile, at Thailand airports, border officers are already carrying out trials on a biometric screen system with the help of fever-detecting cameras.

Such data-driven approaches, when misused, can raise human rights concerns sabotaging peoples trust in their government.

In a time of crisis, we should tread this technology with extreme caution only using it in a limited capacity with proper oversight. Bruce Schneier, a renowned American cryptographer and computer security professional, remarked, "data is the pollution problem of the information age and protecting privacy is the environmental challenge."

More often than not, companies constitute their data governance practices that lay the foundation for data management and quality control. Right now, many organizations are creating new data and technology principles that help them function in the changing business ecosystem while safeguarding confidential data of all stakeholders. Companies that do not have a concrete set of guidelines risk mishandling data and violating privacy.

Companies need to start defining transparent and clear data usage guidelines helping build a trustworthy reputation among employees, business partners, customers, and other stakeholders. Moreover, the companies should make sure that these guidelines and policies are applicable to both in-house development services as well as external development services.

With AI and data being used in abundance, it is essential to adopt ethical principles with proper planning. When you frame policies without figuring out all outcomes of AI-based solutions, there will be a gap between your practice and policies. So, before implementing AI in your business system or clients solution, you should evaluate existing policies and add relevant policies about the use and effect of AI and data.

Governments and companies that are collecting COVID-19 data to contain the spread must ensure that the same data is not used for other purposes. It should be meant for public health motives only, and organizations should include it as a mandatory AI principle in their policies.

It would not be the best strategy to invest in all AI and data solutions only as a response to the pandemic. Instead, companies need to make calculated decisions about buying and implementing only the solutions they need and collecting the relevant data. There is no doubt that the advanced applications of AI are compelling, but companies shouldnt attempt to implement these at the cost of usability and reliability.

Companies should similarly prioritize applications with long-lasting results rather than the ones with short-term benefits. A strategic approach in implementing new tech solutions based on AI will help you better understand data protection and privacy concerns. Eventually, there will be better execution with more transparency and clarity about the responsible use of AI and data across the organization.

To fight this global pandemic, government authorities and law enforcement agencies from different parts of the world need to collaborate with each other. This can be achieved if communities and societies share data with transparency, adhere to each others usage policies, and use it responsibly for the good of the society.

Businesses must examine their vendor agreements to understand how data and technology is used and whether it violates your companys Ethics practices.. You must ask about their development policies, practices, data protection, and privacy guidelines. This will help you explain your terms and ask for the changes in the agreements accordingly.

It has become essential to track individuals to curb the spread of COVID-19. The United Kingdom, Italy, Austria, and Belgium are already studying the movement of people from one place to another as groups. It has been done by keeping the data anonymous. But as Dr Dawn Song, Professor at UC Berkeley and CEO and Founder of Oasis Labs, said in her keynote at the Responsible Data Summit, an event held in July 2020 with attendees including Turing Award Winners, Fortune 500 industry leaders and other privacy thought leaders and advocates, using anonymization doesnt adequately protect user privacy. She reasoned that, for example, it is possible to extract information about specific individuals from an anonymized mobile phone location dataset. Thus companies should instead invest in advanced privacy techniques such as secure computing and differential privacy to ensure that identified data remains anonymous and private.

AI is a powerful tool: one that can redefine our culture, topple oppressive governments, and in this case, help the world tackle a pandemic. It has become even more important than ever to understand how risky AI can be when it is used irresponsibly. Thus, in order to ensure proper and responsible use of data and AI we must first clearly define the fundamental digital rights of individuals, the principles to follow as a society, and the laws we must enforce as a collection of nations.

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Responsible Data And AI In The Time Of Pandemic And Crisis - Forbes

Funding secured to further study of AI systems for cardiovascular health – Cardiac Rhythm News

Digital health and artificial intelligence (AI) company Eko has been awarded a US$2.7 million Small Business Innovation Research (SBIR) grant by the National Institutes of Health (NIH). The grant will fund the continued collaborative work with Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, USA to validate algorithms to aid screening for pathologic heart murmurs and valvular heart disease.

Cardiovascular disease is the leading cause of death in the USA, and valvular heart disease often goes undetected because of the challenge of hearing murmurs with traditional stethoscopes, particularly in noisy or busy environments. A highly accurate clinical decision support algorithm that is able to detect and classify valvular heart disease will help improve accuracy of diagnosis and the detection of potential cardiac abnormalities at the earliest possible time, allowing for timely intervention, said James D Thomas, director of the Center for Heart Valve Disease at Northwestern Medicine and the clinical studys principal investigator. Our work with Eko aspires to extend the auscultatory expertise of cardiologists to more general practitioners to better serve our patient community, playing a pivotal role in growing the future of cardiovascular medicine.

Eko and Northwestern first announced their collaboration in March 2019 to provide a simpler, lower-cost way for clinicians to identify patients with heart disease without the use of screening tools such as echocardiograms which are typically only available at specialty clinics. By incorporating data from tens of thousands of heart patterns into the stethoscope and its algorithms, clinicians will have cardiologist-level precision in detecting subtle abnormalities from normal sounds, Eko said in a press release.

This SBIR award from the NIH underscores our vision to provide world-class cardiovascular care at the patients point of care, said Adam Saltman, chief medical officer at Eko. We believe that the integration of these deep learning algorithms into the Eko platform that is currently used by more than 1,000 institutions worldwide will lead to earlier diagnosis and better patient outcomes. Our mission is to change how cardiovascular disease is diagnosed, and as one of the first centres in the country to study AI and cardiovascular disease, Northwestern is an ideal partner to help us reach our goal.

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Funding secured to further study of AI systems for cardiovascular health - Cardiac Rhythm News

How AI is helping in the fight against coronavirus and cybercrime – Software Testing News

Matt Walmsley is an EMEA Director at Vectra, a cybersecurity company providing an AI-based network detection and response solution for cloud, SaaS, data centre and enterprise infrastructures.

With the spread of the Coronavirus, cybercriminals have gained more power and become more dangerous, leaving some IT infrastructures at risks. That is why Vectra is offering the use of AI to protect the data centre and specifically, cybersecurity powered by AI to help secure data centres and protect an organisations network.

In this interview, Matt explains why data centres represent such a valuable target for cybercriminals and how, despite the vast security measures put in place by enterprises, they are able to infiltrate a data centre system. He also explains the storyline of an attack targeting data centres and how cybersecurity powered by AI can help the security teams to detect anomalous behaviours before its too late.

Whats your current role?

Im an EMEA Director at Vectra. Ive been here for five years since we started the business, and Im predominately doing thought leadership, technical marketing and communicating information. I spent most of my time thinking about how we put AI to use in the pursuit of, in our case, cybersecurity and a big part of that is cloud and data centre.

To get into the core of what you do, you talk about cloud, data centres and AI, but which one is the core driver of all of those for your business?Which types of devices are the Vectra AI you use, integrated into and in what sectors is it applied within?

Our perspectives on this as experts of cybersecurity and AI is a culmination of those two practices: machine learning and applying it to cybersecurity user cases. So, were using it in an applied manner, i.e. to solve a particular set of complex tasks. In fact, if you look at the way AI is used in the majority of cases today, its used in a focused manner to do a specific set of tasks.

In cybersecurity practice, using AI to hunt and detect advanced attackers that are active inside the cloud, inside your data, inside your network, its really doing things at scale and speed that human beings just arent able to do. In doing so, cybersecurity is like a healthcare issue, if we find an issue early and resolve it early, well have a far more positive outcome, than if we left it festering, and theres nothing to be done. Its just the same as cybersecurity.

You talk a lot about the rapid ability of it to scale to bigger projects, in relation to your work, do you see AI as a way to solve problems in the future, or do you think theres a long way to go with it? Is AI the future? Or do you think humans managing AI is the future?

AI is becoming an increasingly important part of our lives. In cybersecurity practice, its going to be a fundamental set of tools but it wont replace human beings. Nobodys building a Skynet for cybersecurity, that sorts it all out and turns the table back on us. What were doing is building tools at size and scales to do tasks that humans beings cant do. So its really about augmenting human ability in the context of cybersecurity, and for us, its a touchstone of our business and a fundamental building block for cybersecurity operations now and in the future.

Theres a massive skills gap in our industry, so automating some cybersecurity tasks with AI is actually a very rational solution to fixing the immediate massive skills resource gap. But it can also do things humans cant do. Its not just taking the weight off your shoulders, its going to do things like spotting the really really weak signals of threat actor hiding in an encrypted web session. Its impossible to do that by hand, to do it looking at the packets, the bits of bytes, you need a deep neural net, that can look at really subtle temporal changes. AI does it faster and broader and it does things we are just not capable of doing at the same level of competency.

Its optimistic that its going to have such a dramatic effect on our working process. In terms of Data centres, how is AI working to protect data centres?

The data centres change and Im sure, as youve seen, its becoming increasingly hybrid. Theres more stuff going out to the cloud, even though people still have private data centres and clouds. One of the main challenges that a security team has with a data centre is that, as workloads are increased, moved, or mobile or flexed, its really hard to know about it.

As security experts usually have incomplete information, they wont know which VM you have just sped up or what its running. They dont know all of those things and they are meant to be agile for the business but that agility comes with a kind of information cost. I have imperfect information, I never quite know what Ive got.

Ill give you an example: I was at a very large international financial services provider and I was talking to their CCO. He had their cloud provider in and he told me where we are at with licensing and usage. What he thought he had to cover and what the business actually had was about ten times off. So there was ten times more workload out there than he and his security team even knew about.

So how can AI help us with that?

Well, if we integrate AI and allow it to monitor virtual conversations, it can automatically watch and use past observations spot, new workloads that are coming in there and how they are interacting with other entities. Its those behaviours that are the signal that tells the AI to look and find attackers. So its not about putting software in its workload, just monitoring how it works with other devices.

In doing so, we can then quickly tell the security team: here are all the workloads were seeing, here are the ones with behaviours that are consistent with active attackers and then we can score and prioritise them. What were doing is automating and monitoring the attacker for behaviours, so as a security team youre getting more signals, more noise and less ambiguity. Its not just headlined Malware or exploits, which are the ways people get into systems.

What else do you see in threat actor events?

Exactly what happens next in an advanced attack. An advanced attack can play out in days, weeks, months The attacker has got to get inside, hes had to get a credential, had to exploit a user, hes got to do research and reconnaissance, hes going to move around the environment, hes going to escalate. We call that lateral movement then hell make a play for the digital jewels, which could be in the data centre or in the cloud.

So, if you can spot those behaviours that are consistent with an attacker, youve got multiple opportunities to find an attacker in the life cycle of the attack. Just to use that healthcare analogy again, find it early and it will much better and faster to close it down. If you find them when they are running for the default gateway with a big chunk of data doing a big infiltration, you are almost breached and thats a bit too late.

Using AI, basically, is like being the drone in the sky looking over the totality of the digital enterprise and using the individual devices and how the accounts are talking to each other, looking for the very subtle, hard to spot for robust signs of the attackers and thats what were doing. I can see why AI speeds that up efficiently.

Is there a specific method or security process that Vectras cybersecurity software implements, to help protect mass data centres?

Thats quite an insightful question because not all AI is. Built the same AI, is quite a nebulous term. It doesnt tell you what algorithmic approach people are taking. I cant give you a definitive answer for a definitive technology but I can give you a methodology.

The methodology starts with the understanding that the attacker must manifest. If I got inside your organisation and I want to scan and look for devices, there are only so many techniques available for me to do that. Thats behaviour and we have tools and the protocols, to spot that. So, we can see how can we spot the malicious use of those legitimate tool or procedures, these TTPs.

How does that whole process start?

That starts with a security research team, to look for the evidence that attackers do use these behaviours, because it may be a premise, it may not be accurate, once weve done that we bring a data scientist to come in and work with this team.

So, lets find some examples of this behaviour attacker, of this behaviour manifesting in a benign way, as an attacker, in a malicious way and lets look at some regular no good data. The data scientist looks at that data, does a lot of analysis and tries to understand it. They look at the attributes, what they call a feature, and what are the feature selections. I might find it useful to build a model to find this and there are various ways you can look at data and separate the customers infrastructure and all of the different structure inside it. Then theyll go off and build a model and theyll train it with the data. Once weve got it to an effective level and performance and were happy with it, we release into our incognito NDR, network detection platform, and that goes off and looks for individual behaviour.

Remote Desktop Protocol, RDP, recon will be completely different from the thing thats looking for hidden https command and ctrl behaviours. So, it has different behaviours and data structures and different algorithmic approaches. However, some of those attacks manifest in the same way in everybodys network. We can pre-train those algorithms.

Are they aware of those behaviours?

Yes. Its like a big school, Its got its certification, its ready to go, as soon as you turn it on. Theres no concept of what it has to learn anything else, its already trained, it knows what to look for, its a complex job but weve already trained it.

But there are some things that we could never in advance. For example, Ill never know how your data centre is architected, what the IP ranges are, theres no way of me knowing it in advance and theres a lot of things we can only earn through observation.

So, we call that an unsupervised model and we blend these techniques. Some of them are supervised, some are unsupervised, some of them use recursive really deep neural networks. When its really challenging when were looking into a data problem, we just cant figure it out, what are the attributes? What are the features? We know its in there.

But what is it?

We cant figure it out. We are going to get a neural net to do that for themselves, once again doing things at a scale human could not do in an effective way. Weve got thirty patents pending now, we are rewarded on different algorithms that build they are brains that we built that does that monitoring and detection.

Do you think there are any precautions people should take to avoid cybercrime during coronavirus?

Our piece of the security puzzle is: how do I find someone who already penetrated my organisation in a quick manner, so were not the technology that stops known threats coming in? You might think of healthcare who adopted this really quickly. Healthcare, the WHO, recently called out a massive spike in COVID related phishing.

Thats the threat landscape, thats whats happening out there, thats what the threat actors are doing. We are actually inside healthcare and we did not see a particularly large spike in post intrusion behaviour, so we did not see evidence that more attackers were getting into these organisations, they all had done a reasonable job in keeping the wolf from the door.

But what we did see, because we were watching everything, were changes in how users were working. We saw a rapid pivot to using external services, generally services are associated with cloud adoption, particularly around collaboration tools and we saw a lot of data moving out of those, that created compliance challenges.

What do you mean?

Sensitive data suddenly being sent to a third party. Thats not to beret health organisation during a really challenging time but their priorities were obviously making sure clinical services were delivered but in doing so, they also opened up the attack surface where the potential for attackers to get was in increased.

Its important to maintain visibility so you can understand your attack surface, and you can then put in the appropriate procedures and policy and controls to minimise your risk there.

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How AI is helping in the fight against coronavirus and cybercrime - Software Testing News