A resposta é negativa. Mas ter televisão reduz em 6% a chance de ter existido relações sexuais.
Ou seja, não mata, mas é um produto substituto.
04 outubro 2018
Produto substituto
A resposta é negativa. Mas ter televisão reduz em 6% a chance de ter existido relações sexuais.
Ou seja, não mata, mas é um produto substituto.
Declínio da Inteligência Artificial
[...]
Underpinning much of the buzz over artificial intelligence in London and elsewhere is the implicit premise that AI is thetransformative technology of the moment, or maybe of the decade, or even of the century or, well, just about ever. Promises like the AI Summit’s claim that the technology goes “beyond the hype” to “deliver real value in business” only drives the corporate feeding frenzy among executives desperate not to be left behind.
But there is something else going on “beyond the hype,” something that ought to be disconcerting for AI boosters: Among those closest to the cutting edge of machine learning, there is a sense–perhaps a faint but creeping suspicion—that deep learning, the techniques which underpin most of what people think of as ground-breaking AI, may not deliver on its promise.
For one thing, there’s a growing consensus among AI researchers that deep learning alone will probably not get us to artificial general intelligence (the idea of a single piece of software that is more intelligent than humans at a wide variety of tasks). But there’s also a growing fear that AI may not create systems that are reliably useful for even a narrower set of real-world challenges, like autonomous driving or making investment decisions.
Filip Piekniewski, an expert on computer vision in San Diego who is currently the principal AI scientist for Koh Young Technology Inc, a company the builds 3D measurement devices, recently kicked off a debate with a viral blog post predicting a looming “AI Winter,” a period of disillusionment and evaporating funding for AI-related research. “(The field has experienced several such periods over the past half-century.)
Piekniewski’s evidence? The pace of AI breakthroughs seems to be slowing and those breakthroughs that are occurring seem to require ever-larger amounts of data and computer power. Several top AI researchers who had been hired by big tech firms to head in-house AI labs have left or moved into slightly less prominent roles. And most importantly, Piekniewski argues that the recent crashes of self-driving cars point to fundamental issues with the ability of deep learning to handle the complexity of the real world. Even more notable than the crashes, he says, is how often machines lose confidence in their ability to make safe decisions and cede control back to human drivers.
Piekniewski also references the work of New York University’s Gary Marcus, who earlier this year published a much-discussed paper critiquing the failings of today’s deep learning systems. This software, Marcus argues, can identify objects in images, but lacks any model of the real world. As a result, they often can’t handle new situations, even if they are very similar to the ones they’ve been trained to perform. For instance, the DeepMind algorithm that performs so well at the Atari game “Breakout”—and which the company often highlights in public presentations—does terribly if it is suddenly presented with a different-sized paddle, whereas a top human player would likely find the larger paddle wasn’t much of a handicap.
[..]
Fonte: aqui
Underpinning much of the buzz over artificial intelligence in London and elsewhere is the implicit premise that AI is thetransformative technology of the moment, or maybe of the decade, or even of the century or, well, just about ever. Promises like the AI Summit’s claim that the technology goes “beyond the hype” to “deliver real value in business” only drives the corporate feeding frenzy among executives desperate not to be left behind.
But there is something else going on “beyond the hype,” something that ought to be disconcerting for AI boosters: Among those closest to the cutting edge of machine learning, there is a sense–perhaps a faint but creeping suspicion—that deep learning, the techniques which underpin most of what people think of as ground-breaking AI, may not deliver on its promise.
For one thing, there’s a growing consensus among AI researchers that deep learning alone will probably not get us to artificial general intelligence (the idea of a single piece of software that is more intelligent than humans at a wide variety of tasks). But there’s also a growing fear that AI may not create systems that are reliably useful for even a narrower set of real-world challenges, like autonomous driving or making investment decisions.
Filip Piekniewski, an expert on computer vision in San Diego who is currently the principal AI scientist for Koh Young Technology Inc, a company the builds 3D measurement devices, recently kicked off a debate with a viral blog post predicting a looming “AI Winter,” a period of disillusionment and evaporating funding for AI-related research. “(The field has experienced several such periods over the past half-century.)
Piekniewski’s evidence? The pace of AI breakthroughs seems to be slowing and those breakthroughs that are occurring seem to require ever-larger amounts of data and computer power. Several top AI researchers who had been hired by big tech firms to head in-house AI labs have left or moved into slightly less prominent roles. And most importantly, Piekniewski argues that the recent crashes of self-driving cars point to fundamental issues with the ability of deep learning to handle the complexity of the real world. Even more notable than the crashes, he says, is how often machines lose confidence in their ability to make safe decisions and cede control back to human drivers.
Piekniewski also references the work of New York University’s Gary Marcus, who earlier this year published a much-discussed paper critiquing the failings of today’s deep learning systems. This software, Marcus argues, can identify objects in images, but lacks any model of the real world. As a result, they often can’t handle new situations, even if they are very similar to the ones they’ve been trained to perform. For instance, the DeepMind algorithm that performs so well at the Atari game “Breakout”—and which the company often highlights in public presentations—does terribly if it is suddenly presented with a different-sized paddle, whereas a top human player would likely find the larger paddle wasn’t much of a handicap.
[..]
Fonte: aqui
Quanto um influenciador ganha por postagem?
10 mil seguidores = 130 dólares para cada post
30 mil seguidores = 970 dólares para marca de moda ou beleza
1 milhão de seguidores = 13 mil dólares
Kylie Jenner (quem?) = 16 milhões de seguidores no Instagram, 25 milhões no Twitter e 21 milhões no Facebook. Ganha cerca de US $ 1 milhão por um único post .
Outros exemplos extremos:
Selena Gomez : 139 milhões de seguidores, US $ 800.000 por postagem.
Cristiano Ronaldo : 137 milhões de seguidores, US $ 750.000 por postagem.
Kim Kardashian West : 114 milhões de seguidores, US $ 720.000 por postagem.
Beyonc e Knowles : 116 milhões de seguidores, US $ 700.000 por postagem.
Mas o ganho depende de como é feita a postagem, o tipo de produto promovido e até a rede social (Instagram dá mais dinheiro que Facebook e Twitter, por exemplo).
Rir é o melhor remédio
03 outubro 2018
Futuro do contador nos EUA
A demanda por contadores nos EUA continuará alta pelos próximos 10 anos, segundo o Departamento de Estatística de Trabalho daquele país. O crescimento da demanda por contadores será maior que a média das demais profissões.
In 2016, the government counted 1,397,700 people working in the field as auditors and accountants. By 2026 they anticipate a growth of 10 percent or 139,900 jobs, bringing total employment to 1,537,600.
Over the next eight years many CPAs will retire, which is significant
growth because it’s growth over and above filling jobs as CPAs retire.
Also worth noting, for the first time the number of partners over 50
declined from 69.9 percent to 65.6 percent, due to retirements. The
national average is about 55.7 percent in smaller firms.
Why is the Profession Growing?
The BLS report gives four reasons for growth:
1. A Growing Economy. More new companies means more financial records to be analyzed and
reported.
2. More IPOs. Once a private company goes public, the amount of financial reporting increases substantially.
3. Globalization. Doing business overseas means complying with multiple tax codes. There are issues related to M&A and trade.
4. Technological Advancement. While routine accounting tasks will be more automated, the advisory and analytical role of the job will move to center stage.
Where’s the Demand for Accountants?
The BLS also reports the top five states for growth in the accounting profession will be California, Texas, New York, Florida and Pennsylvania.
The BLS has an overall job growth projection for all occupations as 7 percent over the 2016 – 2026 period. With 10 percent growth in the accounting sector and 19 percent growth in the financial manager sector, skills within this industry are in high demand.
Fonte: aqui
In 2016, the government counted 1,397,700 people working in the field as auditors and accountants. By 2026 they anticipate a growth of 10 percent or 139,900 jobs, bringing total employment to 1,537,600.
Why is the Profession Growing?
1. A Growing Economy. More new companies means more financial records to be analyzed and
reported.
2. More IPOs. Once a private company goes public, the amount of financial reporting increases substantially.
3. Globalization. Doing business overseas means complying with multiple tax codes. There are issues related to M&A and trade.
4. Technological Advancement. While routine accounting tasks will be more automated, the advisory and analytical role of the job will move to center stage.
Where’s the Demand for Accountants?
The BLS also reports the top five states for growth in the accounting profession will be California, Texas, New York, Florida and Pennsylvania.
The BLS has an overall job growth projection for all occupations as 7 percent over the 2016 – 2026 period. With 10 percent growth in the accounting sector and 19 percent growth in the financial manager sector, skills within this industry are in high demand.
Fonte: aqui
... e nem titular era
Com respeito a ganhadora do Nobel de Física, que não tinha verbete na Wikipedia, além disto ela não era professora titular da sua universidade (Waterloo, Canadá). Ela nunca se candidatou.
Cowen lembra que é trabalhoso candidatar a titular (compilar um dossiê, escrever documentos etc) para obter um aumento de US$1.500 de promoção, sem falar nos impostos. Além de responsabilidades administrativas.
Cowen lembra que é trabalhoso candidatar a titular (compilar um dossiê, escrever documentos etc) para obter um aumento de US$1.500 de promoção, sem falar nos impostos. Além de responsabilidades administrativas.
Frase
Um dia poderemos ter androides sofisticados o suficiente para fazer tudo o que os contadores humanos fazem agora; mas então o próprio conceito de “contador” terá mudado para além do reconhecimento. (Tim Harford)
02 outubro 2018
Razão para auditoria não encontrar as fraudes
Here are seven reasons why audits don’t typically find fraud.
1. Auditors are nice. Auditors are rule-followers who hold themselves accountable to high honesty and ethical standards. They wouldn’t dream of doing something the “wrong” way; therefore, they can’t comprehend that seemingly nice people would steal from their employer. If they spot something amiss during a routine audit, and in return were given documents or explanations that resolved the anomaly, it is more likely than not that they will proceed with the explanation without obtaining outside substantive documents or evidence to resolve the aberration. Because they presume honesty, auditors often take the explanations and documents at face value.
auditors
2. The nature of an audit isn’t designed to find fraud. During an audit, auditors look at the financial statements of the company (usually the assets and income figures) and calculate what’s called “materiality.” Materiality is the calculated threshold by which, if the information is withheld or misrepresented in the financial statements, it would impact the decisions of the stakeholders who rely on those documents to make economic decisions. Because of this, auditors look at transactions at or above the materiality level. While this is an effective method of making sure financial statements do not contain material errors, it does not lend itself to detecting fraud. Why? Because taken on an individual basis, fraudulent transactions are not typically at or above the materiality level.
3. A sample doesn’t tell the whole story. During an audit, auditors aren’t looking at everything. They gather random samples of transactions to verify that they were correctly recorded and that the internal controls were in place and working. The odds that a random sampling would include a fraudulent transaction among the thousands of available transactions is extremely small. Put simply: an arbitrary sample doesn’t tell a story or show a pattern. It’s akin to finding a needle in a haystack.
4. Fraudsters are clever. In the remote likelihood the auditor spots a questionable transaction, the oddity can often be easily explained away. That’s because fraudsters are clever: they can produce fake documents or pretend the paperwork has been misplaced, appeasing the busy auditor, who, as was pointed out earlier, assumes best intent, never imagining someone would be dishonest.
5. Fraud often looks like a simple error. Most of the time, fraud doesn’t even look like a “red flag.” It often looks like a regular bookkeeping or accounting error. Humans make mistakes, and it’s certainly not uncommon for a transaction to be entered incorrectly. So long as the number of errors doesn’t trigger the threshold of materiality, those inaccuracies will likely not be further investigated.
6. Auditors are constrained by time and budget. Many people are unaware that, in addition to testing transactions and checking internal controls, auditors are also required to conduct excessive due diligence, file copious paperwork, and conduct peer reviews of their audit processes. With competing deadlines and projects stacked back-to-back, in some cases, an auditor may green-light a company’s books even if something didn’t feel quite “right.” It’s not that the audit wasn’t thorough or inaccurate, but rather it’s the limitation of resources and demanding projects that lead to auditors not being as thorough as they might like to be.
7. The Scarlet A. In an ode to Nathaniel Hawthorne’s masterwork, it can be asserted that auditors unknowingly have an invisible scarlet A (for auditor) under their respective suits. Their status as an auditor creates a barrier between themselves and the very people they need to talk to, the whistleblowers. And, as mentioned earlier, auditors are nice. They view themselves as likable, approachable, and friendly. Unfortunately, people often view them as scary, unapproachable, and “out to get them.” The result is that auditors are rarely successful in gaining the trust of the very people who have information that can help them.
Most importantly, though, is the fact that fraudsters are always a company’s most liked and trusted employee. They’ve earned the respect and admiration of their coworkers and supervisors, and often have special privileges and access to company funds. Couple this dynamic with the fact that auditors assume honesty, and the general misunderstanding of what an audit’s purpose is, and it’s easy to see why audits rarely uncover fraud.
Despite this, CFOs need not feel defenseless against fraud. The fact is, management’s due diligence in identifying and assessing risk, addressing known risks with appropriate internal controls and oversight, implementing a hotline, and promoting a culture of honesty can deter or detect the fraudster in the company. (Por Tiffany Couch, via CFO)
Parece razoável os itens (4) e (7). Os demais, nem tanto.
1. Auditors are nice. Auditors are rule-followers who hold themselves accountable to high honesty and ethical standards. They wouldn’t dream of doing something the “wrong” way; therefore, they can’t comprehend that seemingly nice people would steal from their employer. If they spot something amiss during a routine audit, and in return were given documents or explanations that resolved the anomaly, it is more likely than not that they will proceed with the explanation without obtaining outside substantive documents or evidence to resolve the aberration. Because they presume honesty, auditors often take the explanations and documents at face value.
auditors
2. The nature of an audit isn’t designed to find fraud. During an audit, auditors look at the financial statements of the company (usually the assets and income figures) and calculate what’s called “materiality.” Materiality is the calculated threshold by which, if the information is withheld or misrepresented in the financial statements, it would impact the decisions of the stakeholders who rely on those documents to make economic decisions. Because of this, auditors look at transactions at or above the materiality level. While this is an effective method of making sure financial statements do not contain material errors, it does not lend itself to detecting fraud. Why? Because taken on an individual basis, fraudulent transactions are not typically at or above the materiality level.
3. A sample doesn’t tell the whole story. During an audit, auditors aren’t looking at everything. They gather random samples of transactions to verify that they were correctly recorded and that the internal controls were in place and working. The odds that a random sampling would include a fraudulent transaction among the thousands of available transactions is extremely small. Put simply: an arbitrary sample doesn’t tell a story or show a pattern. It’s akin to finding a needle in a haystack.
4. Fraudsters are clever. In the remote likelihood the auditor spots a questionable transaction, the oddity can often be easily explained away. That’s because fraudsters are clever: they can produce fake documents or pretend the paperwork has been misplaced, appeasing the busy auditor, who, as was pointed out earlier, assumes best intent, never imagining someone would be dishonest.
5. Fraud often looks like a simple error. Most of the time, fraud doesn’t even look like a “red flag.” It often looks like a regular bookkeeping or accounting error. Humans make mistakes, and it’s certainly not uncommon for a transaction to be entered incorrectly. So long as the number of errors doesn’t trigger the threshold of materiality, those inaccuracies will likely not be further investigated.
6. Auditors are constrained by time and budget. Many people are unaware that, in addition to testing transactions and checking internal controls, auditors are also required to conduct excessive due diligence, file copious paperwork, and conduct peer reviews of their audit processes. With competing deadlines and projects stacked back-to-back, in some cases, an auditor may green-light a company’s books even if something didn’t feel quite “right.” It’s not that the audit wasn’t thorough or inaccurate, but rather it’s the limitation of resources and demanding projects that lead to auditors not being as thorough as they might like to be.
7. The Scarlet A. In an ode to Nathaniel Hawthorne’s masterwork, it can be asserted that auditors unknowingly have an invisible scarlet A (for auditor) under their respective suits. Their status as an auditor creates a barrier between themselves and the very people they need to talk to, the whistleblowers. And, as mentioned earlier, auditors are nice. They view themselves as likable, approachable, and friendly. Unfortunately, people often view them as scary, unapproachable, and “out to get them.” The result is that auditors are rarely successful in gaining the trust of the very people who have information that can help them.
Most importantly, though, is the fact that fraudsters are always a company’s most liked and trusted employee. They’ve earned the respect and admiration of their coworkers and supervisors, and often have special privileges and access to company funds. Couple this dynamic with the fact that auditors assume honesty, and the general misunderstanding of what an audit’s purpose is, and it’s easy to see why audits rarely uncover fraud.
Despite this, CFOs need not feel defenseless against fraud. The fact is, management’s due diligence in identifying and assessing risk, addressing known risks with appropriate internal controls and oversight, implementing a hotline, and promoting a culture of honesty can deter or detect the fraudster in the company. (Por Tiffany Couch, via CFO)
Parece razoável os itens (4) e (7). Os demais, nem tanto.
Nobel de Física para uma mulher e a falha da Wikipedia
Donna Strickland ganhou hoje o Nobel de Física por seu trabalho com laser. Trata-se da única mulher viva que recebeu esta honraria. No passado Marie Curie e Maria Goeppert-Mayer também venceram, em um total de 209 premiados.
Mas o surreal é o seguinte: até o dia de hoje, Strickland não tinha um verbete na Wikipedia. Este fato já foi corrigido, mas em maio os gestores da enciclopedia rejeitaram um rascunho de um verbete sobre Donna, já com algumas das suas conquistas.
Mas o surreal é o seguinte: até o dia de hoje, Strickland não tinha um verbete na Wikipedia. Este fato já foi corrigido, mas em maio os gestores da enciclopedia rejeitaram um rascunho de um verbete sobre Donna, já com algumas das suas conquistas.
Big Four
"Eu não sei"
Há um mês, dois pesquisadores dos Estados Unidos publicaram um artigo com o seguinte título: “I don´t Know”. Só isto. O título é suficiente para atrair a atenção. Do que se trata? Do fato de especialistas terem uma reputação a defender ao ter que admitir que eles não sabem. Muitas vezes, em diversas questões que são feitas para um especialista, ele deve reconhecer sua limitação. Isto também ocorre com uma professor, quando um aluno faz uma pergunta que ele não sabem (muitas vezes “testando” o professor).
O problema é que muitos pareceristas são "avessos" a esta inovação. Lembro que há alguns semestres fui convidado para uma comissão de avaliação; o trabalho tinha o título "Mais que mil palavras". Genial. Já gostei do trabalho antes de ler. Outro exemplo: a tese de doutorado da Mariana Bonfim com o título "Honest Individuals, (dis) honest Groups?".
A criatividade de um trabalho deveria começar da primeira linha. Ou seja, do título.
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