Vamos mostrar que a razão do mais forte é sempre a melhor. Um cordeiro matava sua sede numa corrente de água pura, quando chega um lobo cuja fome o levava a buscar caça.

– Que atrevimento é esse de sujar a água que estou bebendo? – diz enfurecido o lobo. – Você será castigado por essa temeridade.

– Senhor – responde o cordeiro –, que vossa majestade não se encolerize e leve em conta que estou bebendo vinte passos mais abaixo que o senhor. Não posso, pois, sujar a água que está bebendo.

– Você a suja, sim – diz o cruel animal. – Sei que você falou mal de mim no ano passado.

– Como eu poderia tê-lo feito, se não havia sequer nascido? – responde o cordeiro. – Eu ainda mamo.

– Se não foi você, foi seu irmão.

– Eu não tenho irmãos.

– Então, foi alguém dos seus, porque todos vocês, inclusive pastores e cães, não me poupam. Fiquei sabendo disso e, portanto, preciso vingar-me.

Sem fazer nenhuma outra forma de julgamento, o lobo pegou o cordeiro, estraçalhou-o e devorou-o.

La Fontaine. O lobo e cordeiro. In: SAVIOLI, Francisco Platão; FIORIN, José Luiz.Lições de texto: leitura e redação. 5. ed. São Paulo: Ática, 2006. p. 125. (Adaptado).

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time.

In health care, artificial intelligence relies on the power of computers to sift through and make sense of reams of electronic data about patients—such as their ages, medical histories, health status, test results, medical images, DNA sequences, and many other sources of health information. AI excels at the complex identification of patterns in these reams of data, and it can do this at a scale and speed beyond human capacity. The hope is that this technology can be harnessed to help doctors and patients make better health-care decisions.

One of the first applications of AI in patient care that we currently see is in imaging, to help improve the diagnosis of cancer or heart problems, for example. There are many types of imaging tests —X-rays, CT scans, MRIs and echocardiograms. But the underlying commonality in all those imaging methods is huge amounts of high-quality data. For AI to work well, it's best to have very complete data sets—no missing numbers, so to speak—and digital images provide that. Plus, the human eye is often blind to some of the patterns that could be present in these images—subtle changes in breast tissue over several years of mammograms, for example. There has been some interesting work done in recognizing early patterns of cancer or early patterns of heart failure that even a highly trained physician would not see.

In many ways, we already have very simple forms of AI in the clinic now. We've had tools for a long time that identify abnormal rhythms in an EKG, for example. An abnormal heartbeat pattern triggers an alert to draw a clinician's attention. This is a computer trying to replicate a human being understanding that data and saying, "This doesn't look normal, you may need to address this problem." Now, we have the capacity to analyze much larger and more complex sources of data, such as the entire electronic health record and perhaps even data pulled from daily life, as more people track their sleep patterns or pulse rates with wearable devices, for example.

It's important to emphasize that these tools are never going to replace clinicians. These technologies will provide assistance, helping care providers see important signals in massive amounts of data that would otherwise remain hidden. But at the same time, there are levels of understanding that computers still can't and may never replicate. To take a treatment recommendation from an AI, even an excellent recommendation, and decide if it's right for the patient is inherently a human decision-making process. What are the patient's preferences? What are the patient's values? What does this mean for the patient's life and for his or her family? That's never going to be an AI function. As these AI systems slowly emerge, we may start to see the roles of physicians changing—in my opinion, in better ways. Doctors' roles may shift from being data collectors and analyzers to being interpreters and councilors for patients as they try to navigate their health.
Right now, the challenges we need to address as we try to bring AI into medical practice include improving the quality of the data that we feed into AI systems, developing ways to evaluate whether an AI system is actually better than standard of care, ensuring patient privacy and making sure not only that AI doesn't disrupt clinical work flow but in fact improves it. But if doctors do their jobs right and build these systems well, much of what we have described will become so ingrained in the system, people won't even refer to it separately as informatics or AI. It will just be medicine.

Disponível em: https://medicalxpress.com/news/2018-12-artificial-intelligence-future-medicine.html. Acesso em: 02 maio 2019.

Technology is often blamed for destroying traditional working-class jobs in sectors like manufacturing and retail. But blue collar jobs aren't the only ones at risk on an imminent future: white collar jobs are going to be affected by technology as well.

The legal profession is on the cusp of a transformation in which artificial-intelligence (AI) platforms might dramatically affect how legal work gets done. Those platforms will mine documents for evidence that will be useful in litigation, to review and create contracts, raise red flags within companies to identify potential fraud and other misconduct or do legal research and perform due diligence before corporate acquisitions. Those are all tasks that — for the moment at least — are largely the responsibility of flesh-and-blood attorneys.

Increasing automation of the legal industry promises to increase efficiency and save client’s money, but could also cut jobs in the sector as the technology becomes responsible for tasks currently performed by humans.

Advocates of AI, however, argue there could actually be an increase in the sector's labor force as the technology drives costs down and makes legal services more affordable to greater numbers of people. It's like the beginning for a future changing in legal profession with AI-powered platform which can perform almost all mechanical work such as creating a new contract or reviewing it for clients and companies.

One question raised by the introduction of AI legal platforms is how well they do their jobs compared to a flesh-and-blood lawyer, who has years of experience under his belt. Supporters of this new technology defend that AI platform can search documents for relevant information to lawsuits and other litigation as well as experienced lawyers. Here are some of AI advantages:
Keywords: human beings are not very good at keyword searches. There's a fallacy that human beings looking at documents is the gold standard which cannot be, because human may miss things.
Database: the explosion in the amount of electronic data generated today makes it hard for human workers to keep up. This so much more data nowadays need these technologies find relevant material for lawyers. Also the AI could not just look at the text of a document or email, it can look at the tone of the conversation, who sent it, to check if the item should be flagged for review in litigation.
Restless: computers don't get tired, they don't get hungry, they don't sleep in and all of the things that are biological problems that can happen to a human being can't happen to computers.
An example of this technology is ROSS - it is a legal research platform based on IBM's cognitive computing system Watson. This technology is being used by a number of law firms, which state that the legal sector has being changing along the years. Firms, particularly larger ones, begin to see the advantage of AI, and their legal future possibly will completely change, with lawyers working from office, home office and other possibilities.

Capitalism as we know it is over. So suggests a new report commissioned by a group of scientists appointed by the UN secretary general. The main reason? We’re transitioning rapidly to a radically different global economy, due to our increasingly unsustainable exploitation of the planet’s environmental resources and the shift to less efficient energy sources .

Climate change and species extinctions are accelerating even as societies are experiencing rising inequality, unemployment, slow economic growth, rising debt levels, and impotent governments. Contrary to the way policymakers usually think about these problems these are not really separate crises at all.

These crises are part of the same fundamental transition. The new era is characterized by inefficient fossil fuel production and escalating costs of climate change. Conventional capitalist economic thinking can no longer explain, predict or solve the workings of the global economy in this new age.

Those are the implications of a new background paper prepared by a team of Finnish biophysicists who were asked to provide research that would feed into the drafting of the UN Global Sustainable Development Report (GSDR), which will be released in 2019.

For the “first time in human history”, the paper says, capitalist economies are “shifting to energy sources that are less energy efficient.” Producing usable energy (“exergy”) to keep powering “both basic and non-basic human activities” in industrial civilisation “will require more, not less, effort”.

At the same time, our hunger for energy is driving what the paper refers to as “sink costs.” The greater our energy and material use, the more waste we generate, and so the greater the environmental costs. Though they can be ignored for a while, eventually those environmental costs translate directly into economic costs as it becomes more and more difficult to ignore their impacts on our societies.

Overall, the amount of energy we can extract, compared to the energy we are using to extract it, is decreasing across the spectrum – unconventional oils, nuclear and renewables return less energy in generation than conventional oils, whose production has peaked – and societies need to abandon fossil fuels because of their impact on the climate.

Whether or not this system still comprises a form of capitalism is ultimately a semantic question. It depends on how you define capitalism.

Economic activity is driven by meaning – maintaining equal possibilities for the good life while lowering emissions dramatically – rather than profit, and the meaning is politically, collectively constructed. Well, this is the best conceivable case in terms of modern state and market institutions. It can’t happen without considerable reframing of economic-political thinking, in short words: rethinking capitalism as it is nowadays.

Leia o texto a seguir para responder à questão.

Não há para mim uma ruptura entre o saber empírico e o saber científico, mas uma superação. Essa superação se dá na medida em que a curiosidade ingênua, sem deixar de ser curiosidade, se torna crítica. Ao se fazer crítica, tornando-se então curiosidade epistemológica, metodicamente rigorosa na sua aproximação com o objeto, promove achados de maior exatidão.

Na verdade, a curiosidade ingênua está associada ao saber do senso comum. É a mesma curiosidade que, se tornando crítica, passa a ser curiosidade epistemológica. Muda de qualidade, mas não de essência. A curiosidade de camponeses com quem tenho dialogado ao longo de minha experiência político-pedagógica é a mesma curiosidade com que cientistas ou filósofos acadêmicos admiram o mundo. Porém, os cientistas e os filósofos superam a ingenuidade da curiosidade do camponês ao aplicarem rigor metódico à sua curiosidade.

A curiosidade como inquietação indagadora, como inclinação ao desvelamento de algo, como pergunta, como procura de esclarecimento, faz parte da vida. Não haveria criatividade sem a curiosidade que nos move e que nos põe pacientemente impacientes diante do mundo que não fizemos, acrescentando a ele algo que fazemos.

Como manifestação da vida, a curiosidade humana vem sendo histórica e socialmente construída e reconstruída, precisamente porque a passagem da ingenuidade para a criticidade não se dá automaticamente. Uma das tarefas precípuas da prática educativa-progressista é exatamente o desenvolvimento da curiosidade crítica, insatisfeita, indócil. Curiosidade com que podemos nos defender dos "irracionalismos" decorrentes do ou produzidos pelo excesso de "racionalidade" de nosso tempo altamente tecnologizado.

FREIRE, Paulo. Pedagogia da autonomia: saberes necessários à prática educativa. 55. ed. São Paulo: Paz e Terra, 2017. p. 32-33. (Adaptado).