Claudia Cruz, do Ideias Contábeis, fez um interessante (e engraçado) paralelo entre frases do livro O Pequeno Príncipe e conceitos contábeis. A seguir uma amostra:
“Foi o tempo que perdeste com tua rosa que fez tua rosa tão importante” => Formação do Capital Social
“É preciso que eu suporte duas ou três larvas se quiser conhecer as borboletas...” => Fase pré-operacional
“Só se vê bem com o coração, o essencial é invisível aos olhos” => Característica de Ativo intangível
“Se tu vens às quatro da tarde, desde as três eu começarei a ser feliz” => Existência de Ativo contingente
“O que dá beleza ao deserto é que esconde um poço de água em qualquer parte” => Reconhecimento de goodwill
“É bem mais difícil julgar a si mesmo que julgar os outros. Se consegues fazer um bom julgamento de ti, és um verdadeiro sábio” => Dificuldade para definição do valor em uso
Clique aqui para ver mais
03 outubro 2016
Dinâmica da Desigualdade de Renda
Resumo:
The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the longrun income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: “scale dependence” that may arise from changes in skill prices, and “type dependence,” i.e. the presence of some “high-growth types.” These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of “superstar” entrepreneurs or managers.
Fonte: The Dynamics of InequalityXavier Gabaix, Jean-Michel Lasry, Pierre-Louis Lions, and Benjamin Moll.2016
The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the longrun income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: “scale dependence” that may arise from changes in skill prices, and “type dependence,” i.e. the presence of some “high-growth types.” These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of “superstar” entrepreneurs or managers.
Fonte: The Dynamics of InequalityXavier Gabaix, Jean-Michel Lasry, Pierre-Louis Lions, and Benjamin Moll.2016
02 outubro 2016
01 outubro 2016
Deep Learning: passado, presente e futuro
Excelente reportagem da Fortune sobre o Deep Learning.
But what most people don’t realize is that all these breakthroughs are, in essence, the same breakthrough. They’ve all been made possible by a family of artificial intelligence (AI) techniques popularly known as deep learning, though most scientists still prefer to call them by their original academic designation: deep neural networks.
The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could. Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences.
In short, such computers can now teach themselves. “You essentially have software writing software,” says Jen-Hsun Huang, CEO of graphics processing leader Nvidia NVDA 1.66% , which began placing a massive bet on deep learning about five years ago. (For more, read Fortune’s interview with Nvidia CEO Jen-Hsun Huang.)
Neural nets aren’t new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and 1990s. What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well. “This is deep learning’s Cambrian explosion,” says Frank Chen, a partner at the Andreessen Horowitz venture capital firm, alluding to the geological era when most higher animal species suddenly burst onto the scene.
That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to theCB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch—with more than $6 billion of that coming since 2014. (In late September, five corporate AI leaders—Amazon, Facebook, Google, IBM, and Microsoft—formed the nonprofitPartnership on AI to advance public understanding of the subject and conduct research on ethics and best practices.)
Google had two deep-learning projects underway in 2012. Today it is pursuing more than 1,000, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. IBM’s IBM 0.47% Watson system used AI, but not deep learning, when it beat two Jeopardy champions in 2011. Now, though, almost all of Watson’s 30 component services have been augmented by deep learning, according to Watson CTO Rob High.
Venture capitalists, who didn’t even know what deep learning was five years ago, today are wary of startups that don’t have it. “We’re now living in an age,” Chen observes, “where it’s going to be mandatory for people building sophisticated software applications.” People will soon demand, he says, “ ‘Where’s your natural-language processing version?’ ‘How do I talk to your app? Because I don’t want to have to click through menus.’ ”
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Fato da Semana: Contas do setor público
Data: 30 setembro de 2016
Descrição do Fato
O déficit primário de agosto foi de 22 bilhões. Em doze meses o déficit é de 169 bilhões, fortemente influenciado pelo resultado de dezembro.
Explicações para o Déficit
Existem diversas causas para o déficit do setor público. Podemos citar entre elas:
A redução da receita do governo, em razão da recessão e da concessão de uma série de benefícios para empresas e setores, da frustração na venda de ativos e da redução dos dividendos das empresas estatais.
Durante os últimos anos os salários do pessoal do setor público cresceu bem acima da inflação e do setor privado. Além disto, aumentaram o número de pessoas contratadas. Na realidade, o setor público não consegue obter ganhos de produtividade.
A sociedade brasileira fez uma opção de participação do estado em setores cujo crescimento dos gastos é tradicionalmente superior aos outros setores. Vide os ensinamentos de Baumol sobre este assunto.
As empresas estatais são ineficientes e a participação do setor público na economia é desastroso, incentivando ineficiências e gastos questionáveis. (Isto inclui a nossa previdência)
Notícia boa para contabilidade? Não
A contabilidade pública parece não ajudar a sociedade a controlar o tamanho do estado, impedindo crises como esta. Pelo contrário, a nossa contabilidade pública é obscura, atrasada, legalista, pouco informativa, baseada numa teoria ultrapassada e com pouca perspectiva de mudança deste perfil.
Desdobramentos
Com respeito ao déficit, levaremos anos para corrigir os erros grosseiros cometidos nos últimos anos. Não consigo ver algo positivo sendo apresentado nos próximos anos.
Mas a semana só teve isto?
A crise do banco alemão Deustche, que não começou agora, mas está se agravando, talvez traga consequências serias para a economia mundial.
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