Listas: 20 livros acadêmicos mais influentes de todos os tempos

The top 20

A Brief History of Time by Stephen Hawking
A Vindication of the Rights of Woman by Mary Wollstonecraft
Critique of Pure Reason by Immanuel Kant
Nineteen Eighty-Four by George Orwell
On the Origin of Species by Charles Darwin
Orientalism by Edward Said
Silent Spring by Rachel Carson
The Communist Manifesto by Karl Marx and Friedrich Engels
The Complete Works by William Shakespeare
The Female Eunuch by Germaine Greer
The Making of the English Working Class by EP Thompson
The Meaning of Relativity by Albert Einstein
The Naked Ape by Desmond Morris
The Prince by Niccolò Machiavelli
The Republic by Plato
The Rights of Man by Thomas Paine
The Second Sex by Simone de Beauvoir
The Uses of Literacy by Richard Hoggart
The Wealth of Nations by Adam Smith
Ways of Seeing by John Berger

Fonte: aqui

Inteligência artificial: nem tão inteligente assim

Many technologists who are not also neuroscientists would like us to believe that human-like artificial intelligence—or something close to it—is right around the corner. Just a discovery or two away. Less attention is given to the actual gulf between our current knowledge and capabilities and that actual future.

We're to assume instead that it's trivial, at least in the sense that it will soon be bridged and that this bridging is inevitable. And so concepts like machine intelligence and neural networks are tossed around like sci-fi props. Luke Hewitt, a doctoral student at the MIT Department of Brain and Cognitive Sciences, is particularly concerned about the "unreasonable reputation" of neural networks. In a post at MIT's Thinking Machines blog, he argues that there are good reasons to be more skeptical.

Hewitt's central point is that by becoming proficient in a single task, it's very easy for a machine to seem generally intelligent, when that's not really the case.

"The ability of neural networks to learn interpretable word embeddings, say, does not remotely suggest that they are the right kind of tool for a human-level understanding of the world," Hewitt writes. "It is impressive and surprising that these general-purpose, statistical models can learn meaningful relations from text alone, without any richer perception of the world, but this may speak much more about the unexpected ease of the task itself than it does about the capacity of the models. Just as checkers can be won through tree-search, so too can many semantic relations be learned from text statistics. Both produce impressive intelligent-seeming behaviour, but neither necessarily pave the way towards true machine intelligence."

"If they have succeeded in anything superficially similar, it has been because they saw many hundreds of times more examples than any human ever needed to."


That said, Hewitt is far from a neural networking detractor. He notes that neural networking techniques—in which webs of nodes function as information processing units in ways similar to biological neurons—are immensely powerful when it comes to learning patterns from very large datasets. This is their utility in otherwise computationally prohibitive tasks like text and speech recognition. That's one of the brain's superpowers: finding meaning within relentless floods of sensory data. The brain's auditory and visual centers must take vast amounts of input in the form of waves and pixels, turn it all into data, and then capture the meaning, the statistical regularities, in that data.


But only a starting point:

The many facets of human thought include planning towards novel goals, inferring others' goals from their actions, learning structured theories to describe the rules of the world, inventing experiments to test those theories, and learning to recognise new object kinds from just one example. Very often they involve principled inference under uncertainty from few observations. For all the accomplishments of neural networks, it must be said that they have only ever proven their worth at tasks fundamentally different from those above. If they have succeeded in anything superficially similar, it has been because they saw many hundreds of times more examples than any human ever needed to.

It's easy to get swamped by Singularity noise and science fictional grand stands against gun-toting machine intelligence, so well-reasoned AI reality checks like Hewitt's are worth spotlighting. The reality is often so, so far from the hype. Deep learning, whether it's our brains contending with floods of sensory input or algorithms reading handwriting, is necessary for intelligence, but it's not intelligence in itself.

Fonte: aqui

Rir é o melhor remédio

Fonte: Aqui

Curso de Contabilidade Básica: Divulgando o balanço de maneira incompleta

Numa atitude rara no mercado, o banco BTG Pactual divulgou as informações referentes ao exercício de 2015 no dia 19 de janeiro. Entretanto, a instituição financeira divulgou somente parte das informações e sem o parecer de auditoria.

Em novembro de 2015 o então principal executivo do banco foi preso na operação lava-jato. O envolvimento provocou um grande volume de saques dos clientes, colocando em risco a instituição. A gestão do BTG reagiu, vendendo ativos e obtendo recursos de fundo criado para as situações de crises nas instituições financeiras. O cronograma encontra-se a seguir:

O resultado divulgado apresentou basicamente os seguintes números (comparando o exercício findo em 31/12 com o findo em 30/setembro): aumento nas receitas de R$2,56 bilhões para R$3,52; redução no lucro de R$1,51 bilhão para R$1,23; e aumento no patrimônio líquido, de R$22,1 bilhões para R$22,5 bilhões. A instituição não apresentou o balanço, nem DFC ou DMPL e somente algumas contas da demonstração do resultado. O parecer de auditoria também não foi apresentado.

A principal razão para que uma grande instituição financeira apresente parcialmente algumas poucas informações contábeis é acalmar os clientes e financiadores. A diretoria, ao fazer esta divulgação, está reduzindo os possíveis boatos sobre a saúde financeira do BTG. É como estivesse dizendo: “sobrevivemos aos problemas”.

É obvio que a divulgação parcial pode gerar alguns problemas. A entidade não informou, por exemplo, quanto tem em caixa ou a distribuição do lucro. Pode, em certas circunstâncias, gerar dúvidas. Mas no caso do BTG o recado foi dado com bastante clareza.

Curso de Contabilidade Básica - Editora Atlas - César Augusto Tibúrcio Silva e Fernanda Fernandes Rodrigues

4 maiores desafios de 2016

What are the biggest economic challenges facing the world in 2016? We put this question to four winners of the Nobel Memorial Prize in Economic Sciences, all of whom will be attending this year's Annual Meeting in Davos. Here's what they had to say.

Fonte: aqui

Links

O primeiro brasileiro indicado ao Global Teacher Prize

Amigos íntimos, melhores amigos, bons amigos, amigos, conhecidos e conhecidos de vista (para assinantes)

Adoção de tecnologia militar e estabilidade política: adoção do arco na Inglaterra, mas não na França e Escócia, que usavam a besta

Mr Bean e Cinquenta tons de Cinza (vídeo)

Descoberto o nono planeta do sistema solar: Phattie

Metrô de New York tem material com mais de cem anos de vida (E a depreciação?)

Sorte

Recentemente comentamos sobre o papel da sorte nas finanças pessoais. Um texto de Frick para Harvard Business Review destaca a relevância deste fator no sucesso de executivos:

Quando você pergunta a Lars Serensen da Novo Nordisk qual a força que impulsionou ele ao top do ranking de 2015 da HBR de melhor desempenho de executivo no mundo, ele cita algo muito diferente: sorte. (...) Uma série de textos recentes ajuda a responder a questão, quantificando o papel da sorte, habilidade e experiência no sucesso do CEO. Juntos eles sugerem duas conclusões: primeiro, nenhuma peculiaridade ou habilidade parece explicar o desempenho do CEO e, segundo, a sorte tem um grande papel.