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02 dezembro 2018

Amigo Secreto: Dicas de quem eu tirei [Isabel]

Já estamos na primeira semana de dezembro e o espírito natalino paira no ar. Assisti a alguns filmes, montei a árvore e cansei o meu som com as músicas de natal, mas ainda estou encalhada quanto ao presente do meu amigo oculto.

Nós estamos no meio do nosso terceiro amigo secreto dos blogs de contabilidade (2013 e 2017). Nos anos passados tirei a querida amiga Claudinha, do Ideias Contábeis, e foi maravilhoso. O meu dilema era escolher uma dentre as tantas coisas com as quais a queria presentear.

(Curiosamente, houve um certo clube da Luluzinha nos anos anteriores. Foi Polyana -> Isabel -> Cláudia em 2017 e Isabel -> Cláudia -> Polyana em 2013. O Felipe estava alegando indícios fraude...!!! rsrsrs)

Este ano tirei alguém com quem tenho menos contato... É, inclusive, um blogueiro que não conheço pessoalmente, mas por quem tenho muito respeito e carinho.

Sabe qual é a parte mais legal? É alguém que conheci por causa desta brincadeira, o que só ressalta o quão especial ela é.

Com essas dicas ficou fácil, então não vou falar muito mais para não entregar tudo de uma só vez.
Mas lá vou eu, stalkear o meu amigo para ter inspiração para o presente.

Balanço da Lava-jato

Durante o Congresso da UnB foi apresentado um balanço atual da operação lava-jato. Os números são impressionantes:

Valor total do ressarcimento solicitado (incluindo multas) = 39,9 bilhões de reais
Crimes já denunciados envolvem propinas = 6,4 bilhões de reais
Bens dos réus bloqueados = 3,2 bilhões de reais
Acusações criminais = 82
Número de pessoas envolvidas = 347
Pessoas já sentenciadas = 46
Número de condenações = 215 contra 140 pessoas
Soma das penas = 2036 anos

Grandes Limitações da Inteligência Artificial

[...]

As someone who has worked in A.I. for decades, I’ve witnessed the failure of similar predictions of imminent human-level A.I., and I’m certain these latest forecasts will fall short as well. The challenge of creating humanlike intelligence in machines remains greatly underestimated. Today’s A.I. systems sorely lack the essence of human intelligence: understanding the situations we experience, being able to grasp their meaning. The mathematician and philosopher Gian-Carlo Rota famously asked, “I wonder whether or when A.I. will ever crash the barrier of meaning.” To me, this is still the most important question.



The lack of humanlike understanding in machines is underscored by recent cracks that have appeared in the foundations of modern A.I. While today’s programs are much more impressive than the systems we had 20 or 30 years ago, a series of research studies have shown that deep-learning systems can be unreliable in decidedly unhumanlike ways.

Programs that “read” documents and answer questions about them can easily be fooled into giving wrong answers when short, irrelevant snippets of text are appended to the document. Similarly, programs that recognize faces and objects, lauded as a major triumph of deep learning, can fail dramatically when their input is modified even in modest ways by certain types of lighting, image filtering and other alterations that do not affect humans’ recognition abilities in the slightest.
One recent study showed that adding small amounts of “noise” to a face image can seriously harm the performance of state-of-the-art face-recognition programs. Another study, humorously called “The Elephant in the Room,” showed that inserting a small image of an out-of-place object, such as an elephant, in the corner of a living-room image strangely caused deep-learning vision programs to suddenly misclassify other objects in the image.

[...]

These are only a few examples demonstrating that the best A.I. programs can be unreliable when faced with situations that differ, even to a small degree, from what they have been trained on. The errors made by such systems range from harmless and humorous to potentially disastrous: imagine, for example, an airport security system that won’t let you board your flight because your face is confused with that of a criminal, or a self-driving car that, because of unusual lighting conditions, fails to notice that you are about to cross the street.
Even more worrisome are recent demonstrations of the vulnerability of A.I. systems to so-called adversarial examples. In these, a malevolent hacker can make specific changes to images, sound waves or text documents that while imperceptible or irrelevant to humans will cause a program to make potentially catastrophic errors.
The possibility of such attacks has been demonstrated in nearly every application domain of A.I., including computer vision, medical image processing, speech recognition and language processing. Numerous studies have demonstrated the ease with which hackers could, in principle, fool face- and object-recognition systems with specific minuscule changes to images, put inconspicuous stickers on a stop sign to make a self-driving car’s vision system mistake it for a yield sign or modify an audio signal so that it sounds like background music to a human but instructs a Siri or Alexa system to perform a silent command.

These potential vulnerabilities illustrate the ways in which current progress in A.I. is stymied by the barrier of meaning. Anyone who works with A.I. systems knows that behind the facade of humanlike visual abilities, linguistic fluency and game-playing prowess, these programs do not — in any humanlike way — understand the inputs they process or the outputs they produce. The lack of such understanding renders these programs susceptible to unexpected errors and undetectable attacks.
What would be required to surmount this barrier, to give machines the ability to more deeply understand the situations they face, rather than have them rely on shallow features? To find the answer, we need to look to the study of human cognition. 

Our own understanding of the situations we encounter is grounded in broad, intuitive “common-sense knowledge” about how the world works, and about the goals, motivations and likely behavior of other living creatures, particularly other humans. Additionally, our understanding of the world relies on our core abilities to generalize what we know, to form abstract concepts, and to make analogiesin short, to flexibly adapt our concepts to new situations. Researchers have been experimenting for decades with methods for imbuing A.I. systems with intuitive common sense and robust humanlike generalization abilities, but there has been little progress in this very difficult endeavor.
A.I. programs that lack common sense and other key aspects of human understanding are increasingly being deployed for real-world applications. While some people are worried about “superintelligent” A.I., the most dangerous aspect of A.I. systems is that we will trust them too much and give them too much autonomy while not being fully aware of their limitations. As the A.I. researcher Pedro Domingos noted in his book “The Master Algorithm,” “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

Fonte: aqui

Rir é o melhor remédio


Fonte: Aqui

01 dezembro 2018

Curso Prático de Contabilidade


Na quarta, durante do Congresso da UnB, apresentamos o livro Curso Prático de Contabilidade. A seguir o índice da obra, que possui mais de 600 páginas:
O livro possui diversos recursos didáticos, como o "iniciando a conversa", "objetivos do capítulo", "prática", "Pequena e Média Empresa" ...

... "ética", "antes de prosseguir", "exercício de revisão", "um exemplo mais completo"...

... "usando a informação contábil", "resumo dos objetivos", "decisão", "dicionário" ...

... "problema demonstração, "questões de múltipla escolha", "questões para revisão",  "exercícios breves", "problemas" ...
 A seguir, um exemplo de uma página do livro:

 Para preparar a apresentação, fizemos uma contagem dos exercícios no livro. São mais de 700 !!

Você pode comprar o livro aqui ou em outra livraria.


Investigação e Incerteza na informação

Três empresas dos Estados Unidos estão sendo investigadas: General Electric, Tesla e Snap. Em um texto do New York Times, um resumo dos casos. As empresas afirmam que estão cooperando com o governo, mas não fornecem muita informação sobre a situação, até o momento que elas passam a saber do que está ocorrendo. E as investigações contábeis podem levar meses ou até anos para serem finalizadas.

O texto chama a atenção para a incerteza

That leaves investors with uncertainty as investigators pore over corporate records and try to build a case — and little hobbles a company’s stock price more than investor uncertainty.

A participação de mais de uma entidade na investigação, no caso o Departamento de Justiça, aumenta ainda mais a pressão.

Rir é o melhor remédio

Quando o herói tem um bebê:






Fonte: Aqui