Se o Google ainda fosse um cara... e o Bing.
30 junho 2016
29 junho 2016
Alienados
Os estadunidenses são considerados alienados. Não se preocupam com o que ocorre no mundo. Mas quais os países que mais despertam interesse dos gringos? Usando a ferramenta de pesquisa do Google é possível saber o grau de interesse deles. O primeiro colocado é uma surpresa, pois não se trata do parceiro histórico e origem do país, o Reino Unido, mas sim o polêmico, para eles, México. O segundo colocado na lista de pesquisa é mais surpreendente ainda: a Índia, que desperta mais interesse que o Canadá (terceiro), Japão (quinto) ou China (sexto). É também uma surpresa a Indonésia, em sétimo.
Brasil? Em 13º, na frente de França e Espanha. Eis a lista:
1 – México
2 – Índia
3 – Canadá
4 – Reino Unido
5 - Japão
6 - China
7 - Indonésia
8 - Itália
9 - Austrália
10 – Rússia
12 - Alemanha
13 – Brasil
14 – França
15 – Espanha
16 - Turquia
Brasil? Em 13º, na frente de França e Espanha. Eis a lista:
1 – México
2 – Índia
3 – Canadá
4 – Reino Unido
5 - Japão
6 - China
7 - Indonésia
8 - Itália
9 - Austrália
10 – Rússia
12 - Alemanha
13 – Brasil
14 – França
15 – Espanha
16 - Turquia
Inteligência Artificial pode melhorar a qualidade da Auditoria
[...]
One of the hottest areas of innovation today is the field of artificial intelligence (AI). AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Because AI technologies—also called cognitive technologies—extend the power of information technology to tasks traditionally performed by humans, they enable users to break prevailing tradeoffs between speed, cost, and quality.
Such technologies can enable auditors to automate tasks that have been conducted manually for decades, such as counting inventories or processing confirmation responses. And as a result, auditors can be liberated to focus on enhancing quality by evaluating advanced analytics, spending more time exercising their professional judgment, and providing greater insights.
One specific area in which auditors are taking advantage of the benefits of cognitive technologies is document review. Reading through stacks of contracts to extract key terms has traditionally been a time-consuming, manual process. Cognitive technologies are already being deployed by forward-thinking firms to largely automate this process. Natural language processing (NLP) technology reads and understands key concepts in the documents. And machine-learning technology makes it possible to train the system on a set of sample contracts so that it learns how to identify and extract key terms.
Contract review powered by cognitive technologies can now take a fraction of the time it used to. AI allows an auditor to review and assess larger samples—even up to 100% of the documents. And it makes it possible to do lightning-fast analytics—automating the separation of documents that contain escalation clauses from those that don’t, for instance, and visualizing the degree of variability from a standard form across a population of documents. This contributes to enhanced quality and delivery of insights to audit committees faster.
And this is just the beginning. We’re using information technology to accelerate and enhance many other parts of the audit process as well. And we see many opportunities to innovate and advance the profession with cognitive technologies to take audit quality and insight to the next level.
For instance, we are using workflow automation technology to streamline the confirmations process by providing stakeholders with a single digital environment to prepare, authorize, distribute, collect, manage, and evaluate the results of the confirmations process.
In the future, machine learning could be used to recognize, extract, and process values from the many supporting documents typically attached to a confirmation, automatically confirming the transaction without significant auditor intervention. And NLP could enable the system to handle anomalies and exceptions automatically by reading and understanding free-form textual responses from counterparties and recommending appropriate action.
Using cognitive technologies, auditors may soon be able to provide clients with new ways to uncover risk hiding in plain sight in financial statements. Today, our professionals use tools that parse financial statements automatically, making it easy to find footnotes and conduct thorough peer comparisons.
In the future, machine learning and natural language processing could make it possible to scan financial statements and suggest risks associated with the text while linking disclosures to Securities and Exchange Commission comment letters, analyst reports, and social media sentiment.
Researchers have already shown that it’s possible to predict the volatility of a company’s stock price by applying automated analysis, powered by natural language processing and machine learning, to a company’s financial statements. It’s not hard to imagine generalizing this approach to surface other types of risk.
Finally, consider how AI might change the inventory count process, a task that is typically as old-fashioned as it gets: visiting a client and taking inventory of materials and finished goods, clipboard in hand. Today, we are equipping staff with tablet and smartphone applications that collect and automatically consolidate inventory count results to the group auditor in real-time.
I can imagine turbocharging the inventory process by using smartphone cameras and computer vision to automatically identify and count items, spot patterns, and flag anomalies. This kind of application already exists in health care, where visiting nurses can snap a photo of a home-bound patient’s medications, automatically identifying them and ensuring the patient has what she needs.
Using cognitive technologies to evolve the audit process by making it smarter, more insightful, and more efficient is just one of the ways the audit profession is innovating. This isn’t self-driving cars. It is the future of the audit profession. And the users of financial statements deserve it.
Fonte: Jon Raphael is chief innovation officer at Deloitte & Touche LLP.
One of the hottest areas of innovation today is the field of artificial intelligence (AI). AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Because AI technologies—also called cognitive technologies—extend the power of information technology to tasks traditionally performed by humans, they enable users to break prevailing tradeoffs between speed, cost, and quality.
Such technologies can enable auditors to automate tasks that have been conducted manually for decades, such as counting inventories or processing confirmation responses. And as a result, auditors can be liberated to focus on enhancing quality by evaluating advanced analytics, spending more time exercising their professional judgment, and providing greater insights.
One specific area in which auditors are taking advantage of the benefits of cognitive technologies is document review. Reading through stacks of contracts to extract key terms has traditionally been a time-consuming, manual process. Cognitive technologies are already being deployed by forward-thinking firms to largely automate this process. Natural language processing (NLP) technology reads and understands key concepts in the documents. And machine-learning technology makes it possible to train the system on a set of sample contracts so that it learns how to identify and extract key terms.
Contract review powered by cognitive technologies can now take a fraction of the time it used to. AI allows an auditor to review and assess larger samples—even up to 100% of the documents. And it makes it possible to do lightning-fast analytics—automating the separation of documents that contain escalation clauses from those that don’t, for instance, and visualizing the degree of variability from a standard form across a population of documents. This contributes to enhanced quality and delivery of insights to audit committees faster.
And this is just the beginning. We’re using information technology to accelerate and enhance many other parts of the audit process as well. And we see many opportunities to innovate and advance the profession with cognitive technologies to take audit quality and insight to the next level.
For instance, we are using workflow automation technology to streamline the confirmations process by providing stakeholders with a single digital environment to prepare, authorize, distribute, collect, manage, and evaluate the results of the confirmations process.
In the future, machine learning could be used to recognize, extract, and process values from the many supporting documents typically attached to a confirmation, automatically confirming the transaction without significant auditor intervention. And NLP could enable the system to handle anomalies and exceptions automatically by reading and understanding free-form textual responses from counterparties and recommending appropriate action.
Using cognitive technologies, auditors may soon be able to provide clients with new ways to uncover risk hiding in plain sight in financial statements. Today, our professionals use tools that parse financial statements automatically, making it easy to find footnotes and conduct thorough peer comparisons.
In the future, machine learning and natural language processing could make it possible to scan financial statements and suggest risks associated with the text while linking disclosures to Securities and Exchange Commission comment letters, analyst reports, and social media sentiment.
Researchers have already shown that it’s possible to predict the volatility of a company’s stock price by applying automated analysis, powered by natural language processing and machine learning, to a company’s financial statements. It’s not hard to imagine generalizing this approach to surface other types of risk.
Finally, consider how AI might change the inventory count process, a task that is typically as old-fashioned as it gets: visiting a client and taking inventory of materials and finished goods, clipboard in hand. Today, we are equipping staff with tablet and smartphone applications that collect and automatically consolidate inventory count results to the group auditor in real-time.
I can imagine turbocharging the inventory process by using smartphone cameras and computer vision to automatically identify and count items, spot patterns, and flag anomalies. This kind of application already exists in health care, where visiting nurses can snap a photo of a home-bound patient’s medications, automatically identifying them and ensuring the patient has what she needs.
Using cognitive technologies to evolve the audit process by making it smarter, more insightful, and more efficient is just one of the ways the audit profession is innovating. This isn’t self-driving cars. It is the future of the audit profession. And the users of financial statements deserve it.
Fonte: Jon Raphael is chief innovation officer at Deloitte & Touche LLP.
28 junho 2016
Loterias
No vídeo em questão, Richard Lustig, ganhador por 7 vezes da loteria americana e escritor do livro “Learn how to increase your changes of winning the lottery”, aconselha sobre como ficar rico apostando em jogos de loteria. Lustig fornece três conselhos:
1º Conselho: Buy as many tickets as you can afford. Comprar tantos bilhetes de loteria quanto você consiga pagar.
Quando se aumenta a quantidade de bilhetes comprados, maior se torna a probabilidade de acertar os números sorteados. Entretanto, observando estatisticamente, a chance de ganhar na loteria seria insignificante, tendendo a zero. Considerando que no Powerball, loteria americana, a chance de ganho é de 1 em 292.201.338 e o custo da aposta é de US$ 3,00, para se obter 100% de certeza de ganho seria necessário um investimento de US$ 876.604.014,00, sendo que a maior aposta individual já paga pelo Powerball foi de US$ 370,9 milhões realizada por Gloria MacKenzie, ganhadora em 2013. Deve-se ter em mente também que, na tentativa de ganhar a loteria, muitos apostadores gastam recursos além do disponível, visando aumentar a probabilidade de ganho, afetando, por muitas vezes, seu orçamento pessoal, portanto um limite de gasto deve ser estabelecido. Sob a ótica de investimentos, a chance de retorno dos recursos alocados, estatisticamente, também se aproxima de zero. O que demonstra não ser uma boa estratégia de alocação de recursos, mas também observa-se que, por ser um investimento de alto retorno, as chances de acertar os números são baixas e, portanto, os riscos são altos.
2º Conselho: Don’t buy quick picks. Não terceirizar (quick picks) a escolha dos números, ou seja, não deixe o computador escolher seus números.
Segundo Lustig, cada vez que se utiliza os recursos de um computador para definir suas apostas o jogador esta esquecendo a sua estratégia de ganho, ou seja, não faz pesquisas ou analisar os resultados obtidos de forma a executar uma estratégia que possa levá-lo ao sucesso, portanto o computador vai oferecer as piores chances de ganhar. Observa-se que apesar dos números definidos por programas de apostas serem considerados aleatórios, a programação que executa a escolha é feita através de algoritmos pré-definidos e, também, que as chances de um bilhete fornecido por um computador e os feitos por qualquer pessoa individualmente têm a mesma probabilidade estatística de ganho.
3º Conselho: Pick numbers and never change them. Escolha os seus números e nunca os mude.
Você deverá escolher seus números, sempre jogar os mesmos números, nunca mudá-los e jogar em todos os concursos disponíveis, pois, em algum momento, eles serão sorteados, ou seja, um dos segredos é a persistência. Segundo Damodaran (2015) os ativos devem ser adquiridos com base em sua expectativa dos fluxos de caixa futuros e sua incerteza. Aplicando de forma restrita este conceito, dado o tamanho da incerteza contida na compra de um bilhete de loteria, tal ativo não deveria ser adquirido. A decisão de investimento envolve alocação de recursos para geração de fluxos de caixa positivos. O retorno dos fluxos de caixa decorrentes das apostas em loterias acontece ao acaso, o que inviabiliza sua análise pela ótica de investimentos. Realizar este tipo de investimento é apostar no acaso.
[Acredito que faltou um aspecto importante da questão: a moeda não possui memória. Ou seja, o sorteio de números no passado não garante o seu "não sorteio" no futuro]
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