Sobre as linguagens de programação de conhecimento do analista de sistemas Fábio, não é verdade que ele conhece as linguagens de programação R ou Python. Considere as seguintes afirmativas:

I. Fábio não conhece a linguagem de programação R, mas conhece a linguagem de programação Python.

II. Fábio não conhece a linguagem de programação Python.

III. Fábio só conhece a linguagem Python se não conhecer a linguagem R.

As afirmativas I, II e III são, respectivamente:

Moacir, Norberto, Orlando, Patrick e Sandro são estagiários no setor de tecnologia da informação de uma repartição pública. Em determinado dia, somente um deles propagou um vírus no sistema do setor que comprometeu o armazenamento de novas informações. Quando questionados pelo chefe sobre quem seria o culpado pela propagação do vírus, eles emitiram as seguintes declarações:

Moacir: Foi o Sandro.

Norberto: Eu não fui o culpado.

Orlando: Norberto diz a verdade.

Patrick: Foi o Moacir.

Sandro: Patrick está mentindo.

Se somente um dos estagiários está mentindo, quem foi o culpado pela propagação do vírus no sistema do setor?

A partir do discurso do Prefeito de uma cidade X, Ricardo destacou as seguintes proposições verdadeiras:

P1 = Todos os bairros da cidade X possuem inclusão digital.

P2 = Todo bairro com inclusão digital é desenvolvido.

Se o bairro Olaria não é desenvolvido, então é correto afirmar que o bairro Olaria

Anderson, Bráulio e César trabalham na PRODABEL e, em determinado dia, foram designados para promover a inclusão digital em três bairros de Belo Horizonte: Alto Vera Cruz, Lindeia e Padre Eustáquio. Cada profissional ficou responsável por um único bairro, diferente dos demais. Além disso, os bairros foram visitados em turnos distintos dentre os seguintes: manhã, tarde e noite. Sabe-se que:

• Um dos profissionais visitou o bairro Padre Eustáquio no turno da manhã.

• César visitou o seu bairro no turno da noite.

• Anderson visitou o bairro Lindeia.

Considerando essas informações, é correto afirmar que:

Considere as seguintes afirmações relacionadas entre si:

I. Se Bianca é técnica em informática, então Adriana é analista de sistemas.

II. Ou Daniela é engenheira de computação ou Cristiane é cientista de dados.

III. Cristiane é cientista de dados e Bianca é técnica em informática.

Se apenas a afirmação I é falsa, pode-se deduzir que é verdade:

Considering usage of the highlighted words, the item that offers compatible data is:

Computer engineering started as a specialization of electrical engineering before developing into a new discipline. As the field grew, computer engineering continued to adopt design fundamentals and theories from computer science, being distinct from the latter because it focuses on hardware and computer design. Computer engineers create and test hardware such as motherboards, routers, circuits, and other equipment. While a programmer focuses on the software side of computer systems, computer engineers specialize in the physical components of a computer, working in tandem with software developers to make sure any updates function as expected.They often research new technology and methods in the field to create innovative products and solutions. This role can involve heavy amounts of testing and experimenting with new designs during the development process. After creating a successful design, they may supervise the manufacturing process. As companies rely on their expertise to create modern processors and networking hardware, any tech companies that produce hardware need a qualified computer engeneering crew to make cutting-edge products and stay ahead of the competition. In a competitive market, employers will look for computer engineers with key soft skills and key hard skills, being some of the former: communication skills; analytical skills; problem-solving skills; and critical thinking.

(Available in: https://www.computerscience.org. Adapted.)

Having as reference the text that follows, there is consistent information in:

Mixed capital companies are registered companies whose capital belongs partly to a local public Administration or several local public Administrations and partly to a private partner or several private partners, with the main goal of managing a public service or an economic activity of general interest. The private partner often provides initial capital; becomes a minority shareholder; develops and implements the strategic and investment plans of the company (Business Plan); incorporates technologies and solutions of high added value; modernizes technical processes and management practices to improve the efficiency of the service (know-how and best practices); provides key high-level staff. Furthermore, the public partner keeps the majority of shares; nominates its members of the board of directors; controls, through a regulatory commission, the performance of the company and establishes the main lines. Some of the leverages taken are: long contract period; secure framework; known investment return; authority engagement; release of funds; efficient management; recurrent cash-flow to the original funds; flexibility to enlarge towards collateral services; good population perception. Nonetheless, drawbacks that ought to be averted are: risk of public involvement on management autonomy; oversizing of employees and investments; highmanagement costs; relatively complex establishment of legal framework; hassle to terminate the partnership in case of negligence or dissatisfaction.

(Available in: https://unece.org. Adapted.)

According to what text clues validate, proper content is NOT present in:

Do you trust your IoT device?

Do you know the security and privacy risks of your IoT device? If we want everyone to benefit from the potential of Internet- -connected devices, we need to ensure that they are safe and trusted. You can join people around the world and stand up for a safer and more secure connected world. While consumer IoT devices will undoubtedly be fun and may enhance our daily lives, they will also introduce a host of new privacy issues, and amplify existing ones. IoT devices will encroach upon traditionally private spaces such as the home, and extend the data collection practices of the online world into the offline world. The number and nature of sensors being introduced will bring data collection ever closer to our bodies and intimate spaces. The intimacy and ubiquity of IoT will raise issues of control, consent and transparency, and increasingly erode the boundary between the private and the public spheres.

Privacy is a key factor in trust relationships. When we disclose data to others, we are (implicitly or otherwise) trusting them not to use it in ways that conflict with our interests. In the context of IoT, privacy boils down to two things: either we trust third parties not to abuse the data generated by our use of connected objects, or we rely on the ability to control the collection and use of that data. Privacy therefore carries strong implications of trust, transparency and control:

• The ability for individuals to control how the information collected by their IoT devices is shared, and determine who has access to the data from devices in your home, in your car, and on your person. This means easy ways to blind and mute devices, and to have a say in how IoT data is analyzed or shared with third parties.

• Clarity about how information about people is collected, used, and shared with others. IoT devices and their applications should enable the user to find out what information is collected and shared, when and with whom.

• The ability to determine how identifiable one is when undertaking online or offline activities. IoT devices should have the option for pseudonymous or anonymous use.

• The ability to control one’s digital footprint, especially from IoT devices in intimate settings. The user should understand where information about them has gone, and how long it is kept.

(Available in: https://www.internetsociety.org. Adapted.)

After reading the text, indicate the idea conveyed by the featured “LIKEWISE”.

Venture capital investing by information technology companies: did it pay?

Abstract

While corporate venture capital programs offer prospects for direct financial returns and strategic benefits, there is little evidence regarding whether they deliver economically significant value to sponsoring firms. We take an initial step in addressing this question by evaluating direct returns of programs of U.S. information technology companies during 1990-2002. Direct gains (losses) were widely dispersed and bimodally distributed, based on IRR and net cash flow metrics. Timing of initiation within the venture capital cycle; program scale; and annual investment, write-down, and harvest behavior were associated with differences in returns. Likewise, we explore how program characteristics may relate to their attractiveness as platforms from which to pursue strategic benefits.

(Journal of Business Venturing, Volume 22, Issue 2, March 2007, Pages 262-282.)

Read the text, and analyse the assertives that are introduced.

The False Promise of ChatGPT (by Noam Chomsky)

Today our supposedly revolutionary advancements in artificial intelligence are indeed cause for both concern and optimism. OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney are marvels of machine learning. Roughly speaking, they take huge amounts of data, search for patterns in it and become increasingly proficient at generating statistically probable outputs – such as seemingly humanlike language and thought. These programs have been hailed as the first glimmers on the horizon of artificial general intelligence – that long-prophesied moment when mechanical minds surpass human brains not only quantitatively in terms of processing speed and memory size but also qualitatively in terms of intellectual insight, artistic creativity and every other distinctively human faculty. That day may come, but its dawn is not yet breaking, contrary to what can be read in hyperbolic headlines and reckoned by injudicious investments. However useful these programs may be in some narrow domains, they can be helpful in computer programming, for example, or in suggesting rhymes for light verse, we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: to be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism, possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered. Because these programs cannot explain the rules of English syntax, for example, they may well predict, incorrectly, that “John is too stubborn to talk to” means that “John is so stubborn that he will not talk to someone or other” rather than that “he is too stubborn to be reasoned with”. True intelligence is also capable of moral thinking. This means constraining the otherwise limitless creativity of our minds with a set of ethical principles that determines what ought and ought not to be (and of course subjecting those principles themselves to creative criticism). ChatGPT in its plethora was crudely restricted by its programmers from contributing anything novel to controversial issues as it sacrificed creativity for a kind of amorality: for all the seemingly sophisticated thought and language, the moral indifference born of unintelligence, exhibiting the banality of evil for its plagiarism, apathy and obviation. It summarizes the standard arguments in the literature by a kind of super-autocomplete, refuses to take a stand on anything, pleads not merely ignorance but lack of intelligence and ultimately offers a “just following orders” defense, shifting responsibility to its creators. In short, ChatGPT and its brethren are constitutionally unable to balance creativity with constraint. They either overgenerate (producing both truths and falsehoods, endorsing ethical and unethical decisions alike) or undergenerate (exhibiting noncommitment to any decisions and indifference to consequences). Given the amorality, faux science and linguistic incompetence of these systems, we can only laugh or cry at their popularity.

(The New York Times – March 8, 2023. Opinion-Guest essay. Adapted.)

I. Intelligence incrementally narrows down the prospects which might be sensibly pondered.

II. ChatGPT information is featured by amount patterns along with dodging divisive argument.

III. Human brains yield critical thought on the cornerstone of constraining ethical line tenets.

There is accuracy in what is stated in