Showing posts with label make. Show all posts
Showing posts with label make. Show all posts

Updated Divorce Laws to Make Men Equal

Courts favor females too much when they make decisions on financial separation matters and who looks after the offspring. The current environment is toxic with the fairer sex being not so dainty. ➤ ➤ ✿ ✿ ➽ ➽ ⬢ updated f laws f to f make f men f equal f children f marriage f women f assets ⬢ ◉ updated e divorce e laws e to e make e men e equal e children e marriage e women e assets e maintenance e years e law ◉ ⧭ updated d divorce d laws d to d make d men d equal d children d marriage d women d assets d maintenance d years d law d married d work d wife ⧭ ▩ updated c divorce c laws c to c make c men c equal c children c marriage c women c assets c maintenance c years c law c married c work c wife c man c time c house c short c case ▩ ⏏ updated b divorce b laws b to b make b men b equal b children b marriage b women b assets b maintenance b years b law b married b work b wife b man b time b house b short b case b marries b appeal b family b career b awarded b costs b marriages b poor b couple b matrimonial b b division b easy b parliamentary b judges ⏏ ⦿ children a marriage a women a assets a maintenance a years a law a married a work a wife a man a time a house a short a case a marries a appeal a family a career a awarded a costs a marriages a poor a couple a matrimonial a a division a easy a parliamentary a judges a sister a sisters a clergyman a well-off a mother a amount a top a workforce a claim a year a employment a left a white a woman a mothers a single a lasted a female a favour a divorced a wealthier a put a awards a rate a making a pay a funds a high a society a certainty a welfare a couples a father a order a legal a wealthy a sum a notion a attitudes a european a parties a position a occupation a carer a payable a claimant a care a aim a older a state a breakdown a reform a ruth ⦿ ∎ children marriage women assets maintenance years law married work wife man time house short case marries appeal family career awarded costs marriages poor couple matrimonial home division easy parliamentary judges sister sisters clergyman well-off mother amount top workforce claim year employment left white woman mothers single lasted female favour divorced wealthier put awards rate making pay funds high society certainty welfare couples father order legal wealthy sum notion attitudes european parties position occupation carer payable claimant care aim older state breakdown reform ruth ∎ || ||
Equality of the sexes

Algorithms the Basis of Artificial Intelligence Make Biased Decisions

Lost handwriting skills
Programmers have biases and they create AI software. consequently, so called smart machines are faulty. ➽ Algorithms i the i Basis i of i Artificial i Intelligence i Make i Biased i Decisions i data i bias i company ➽ ▶ Algorithms h the h Basis h of h Artificial h Intelligence h Make h Biased h Decisions h data h bias h company h humans h hiring h business ▶ ⬢ Algorithms g the g Basis g of g Artificial g Intelligence g Make g Biased g Decisions g data g bias g company g humans g hiring g business g people g female g algorithm ⬢ ✿ Algorithms f the f Basis f of f Artificial f Intelligence f Make f Biased f Decisions f data f bias f company f humans f hiring f business f people f female f algorithm f process f making f women f simply ✿ ⧭ Algorithms e the e Basis e of e Artificial e Intelligence e Make e Biased e Decisions e data e bias e company e humans e hiring e business e people e female e algorithm e process e making e women e simply e candidates e models e outcomes e good e machine e learning e o’neil e companies ⧭ ▩ Algorithms d the d Basis d of d Artificial d Intelligence d Make d Biased d Decisions d data d bias d company d humans d hiring d business d people d female d algorithm d process d making d women d simply d candidates d models d outcomes d good d machine d learning d o’neil d companies d broader d manager d world d applicants d learn d words d candidate d math d management d talent d apply d job d based d language d human d time d reveal d organizations ▩ ⏏ Algorithms c the c Basis c of c Artificial c Intelligence c Make c Biased c Decisions c data c bias c company c humans c hiring c business c people c female c algorithm c process c making c women c simply c candidates c models c outcomes c good c machine c learning c o’neil c companies c broader c manager c biases c world c applicants c learn c words c candidate c math c management c talent c apply c job c based c language c human c time c reveal c organizations c potential c increasingly c mathematical c patterns c banks c design c train c issue c postings c gender c sophisticated c find c social c input c baldridge c cognitive c recommendations c address c transparency c support c alert c complex ⏏ ⦿ Algorithms b the b Basis b of b Artificial b Intelligence b Make b Biased b Decisions b data b bias b company b humans b hiring b business b people b female b algorithm b process b making b women b simply b candidates b models b outcomes b good b machine b learning b o’neil b companies b broader b manager b biases b world b applicants b learn b words b candidate b math b management b talent b apply b job b based b language b human b time b reveal b organizations b potential b increasingly b mathematical b patterns b banks b design b train b issue b postings b gender b sophisticated b find b social b input b baldridge b cognitive b recommendations b address b transparency b create b support b alert b complex b sense b fact b suitable b information b talk b amounts b simple b markets b conscious b objective b assumptions b future b decision b objectively b hope b assumption b decision-making b counter b current b pattern b demographics b variables b demographic b code b drive b book b mind b standards b leader b free b discriminatory b feed b engineer b haystack b suited b unconscious b discrimination b professors b black b race b improve b spotting b predictive b modeling b determine b point b qualified b cost b context b set b accurate b relevant b trained b company’s b sources b analyze b media b deliberate b standard b english b slang b word b sick b takes b obvious b attention b avoid b positive b impact b benefits b analytics b technology b schubmehl b users b knowing b legally b fair b competitive b research b fairness b program b university b step b industry b ensure b eliminate b groups b scientists b risk b evaluate ⦿ ∎ Algorithms a the a Basis a of a Artificial a Intelligence a Make a Biased a Decisions ➤ XXXXXXXXXX ➤ a humans a hiring a business a people a female a algorithm a process a making a women a simply a candidates a models a outcomes a good a machine a learning a o’neil a companies a broader a manager a biases a world a applicants a learn a words a candidate a math a management a talent a apply a job a based a language a human a time a reveal a organizations a potential a increasingly a mathematical a patterns a banks a design a train a issue a postings a gender a sophisticated a find a social a input a baldridge a cognitive a recommendations a address a transparency a create a support a alert a complex a sense a fact a suitable a information a talk a amounts a simple a markets a conscious a objective a assumptions a future a decision a objectively a hope a assumption a decision-making a counter a current a pattern a demographics a variables a demographic a code a drive a book a mind a standards a leader a free a discriminatory a feed a engineer a haystack a suited a unconscious a discrimination a professors a black a race a improve ➤ a data a bias a company ➤ a spotting a predictive a modeling a determine a point a qualified a cost a context a set a accurate a relevant a trained a company’s a sources a analyze a media a deliberate a standard a english a slang a word a sick a takes a obvious a attention a avoid a positive a impact a benefits a analytics a technology a schubmehl a users a knowing a legally a fair a competitive a research a fairness a program a university a step a industry a ensure a eliminate a groups a scientists a risk a evaluate ∎
HIV survivor

You Need Yeast to Make Vegemite beer

Vegemite beer could be good for society.
There is a myth about Vegemite. It is assumed by many Australians to be unique. This isn't the case. Marmite and Bovril are the same product. It is yeast residue, the stuff at the bottom of the container when you brew your own beer with a lot of salt added.
Prison Vegemite beer
Of course, the yeast is dormant and can still be activated. This is the case despite Vegemite spread being sterilized. The tasty "treat" is banned in Victorian prisons because it can be used to make an alcoholic concoction loosely called beer. Scientists actually made beer with Vegemite to prove that this is possible. Vegemite acted as a booster in the fermentation process. This made it possible for basic raw materials to be used to make beer.

There is one proviso: some natural yeast has To be obtained from somewhere else. Yeast naturally occurs on the skin of fruit and berries, so it is easy to get.  Just get some flour and add water.  It will start fermenting in a few days, so the prison ban is meaningless.

Ironically, a liter of Vegemite beer costs $0.24 to make. This compares to $1.28 for commercial beer. There could be a whole new market out there for an entrepreneur to begin manufacturing Vegemite beer for the ordinary consumer.
vegemite, beer, prisons, victorian, banned, make, scientists, booster, brew, yeast articles news politics economics society anthropology historiography history sociology people nations country asia europe africa u.s. south america central Mediterranean eastern western interesting unique technology free news sex

Antimalarial Drug Artemisinin Made in Green Process

There is a new drug for malaria called artemisinin. It will be synthesized in a chemical process and the procedure is "green". The French pharmaceutical company Sanofi  produces a third of the world's demand annually. Unfortunately, the synthetic drug is expensive.
Sanofi company new chemical process produces antimalarial artemisinin
Click to enlarge.
Most of artemisinin is extracted from sweet wormwood. This is simple but the synthetic version involves the conversion of glucose into artemisinic acid: then three oxygen atoms are bonded on to produce artimsinin. It is complex.

The use of toxic dichloromethane is not used in the new process. It was discovered, ironically, that water greatly assists the synthesis. It packs molecules together. Water-soluble photocatalyst is indeed clean and green. The result is a product that is purer than artemisinin taken directly from wormwood.
Chemistry by Ty Buchanan
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