Locates ethical analysis of artificial intelligence in the context of other modes of normative analysis, including legal, regulatory, philosophical, and policy approaches Interrogates artificial intelligence within the context of related fields of technological innovation, including machine learning, blockchain, big data, and robotics Broadens the conversation about the ethics of artificial intelligence beyond computer science and related fields to include many other fields of scholarly endeavour, including the social sciences, humanities, and the professions (law, medicine, engineering, etc.) Invites critical analysis of all aspects of-and participants in-the wide and continuously expanding artificial intelligence complex, from production to commercialization to consumption, from technical experts to venture capitalists to self-regulating professionals to government officials to journalists to the general public Broadens the conversation about the ethics of artificial intelligence beyond computer science and related fields to include many other fields of scholarly endeavour, including the social sciences, humanities, and the professions (law, medicine, engineering, etc.)
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
A recent virtual event addressed another such issue: the potential impact machines, imbued with artificial intelligence, may have on the economy and the financial system. The event was organised by the Bank of England, in collaboration with CEPR and the Brevan Howard Centre for Financial Analysis at Imperial College. What follows is a summary of some of the recorded presentations. The full catalogue of videos are available on the Bank of England's website. In his presentation, Stuart Russell (University of California, Berkeley), author of the leading textbook on artificial intelligence (AI), gives a broad historical overview of the field since its emergence in the 1950s, followed by insight into more recent developments.
The development and advancements of technology are rapidly changing the nature of work and the workforce. The relentless growing connectivity and cognitive technologies are making it possible where humans and machines can work side-by-side at a shared workplace, enhancing the abilities of the human workforce. Today, as the workplaces are evolving to a flexible workforce driven by technology advances, software, automation, IoT, robotics, and artificial intelligence, among others, almost every job in every discipline is being revived. With the introduction of new technology, companies now have opportunities to power the workplace and augment their workforce to perform tasks effortlessly. The increasing proliferation of intelligent automation into the workplace is taking away a lot of tedious, repetitive works that used to overwhelm workflow, freeing up employees to focus on more valuable tasks.
Artificial intelligence (AI) is quickly becoming a critical component in how government, business and citizens defend themselves against cyber attacks. Starting with technology designed to automate specific manual tasks, and advancing to machine learning using increasingly complex systems to parse data, breakthroughs in deep learning capabilities will become an integral part of the security agenda. Much attention is paid to how these capabilities are helping to build a defence posture. But how enemies might harness AI to drive a new generation of attack vectors, and how the community might respond, is often overlooked. Ultimately, the real danger of AI lies in how it will enable attackers.
The question of whether artificial beings or machines could become self-aware or consciousness has been a philosophical question for centuries. The main problem is that self-awareness cannot be observed from an outside perspective and the distinction of whether something is really self-aware or merely a clever program that pretends to do so cannot be answered without access to accurate knowledge about the mechanism's inner workings. We review the current state-of-the-art regarding these developments and investigate common machine learning approaches with respect to their potential ability to become self-aware. We realise that many important algorithmic steps towards machines with a core consciousness have already been devised. For human-level intelligence, however, many additional techniques have to be discovered.
This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot.
From machine learning to smart sensors, today, social as well as economic ecosystems are surrounded by the dynamics of Artificial Intelligence (AI). Furthermore, the presence of robotics is perceived as a powerful catalyst for industrial productivity and economic growth. Artificial Intelligence in association with the other path-breaking technologies of the present times is increasing the efficiency of people as well as machines in every sector, and prominently in the education sector. Today, Artificial Intelligence has already made long strides in the academic world, transforming the traditional methods of imparting knowledge into a comprehensive system of learning using simulation and augmented reality (AR) tools. Interactive study material comprising text as well as media files can be shared very easily among the interest groups and with the help of smart devices, they can utilise the study material rather effectively as per their convenience.
The rapid development of artificial intelligence technologies around the globe has led to increasing calls for robust AI policy: laws that let innovation flourish while protecting people from privacy violations, exploitive surveillance, biased algorithms, and more. But the drafting and passing of such laws has been anything but easy. "This is a very complex problem," Luis Videgaray PhD '98, director of MIT's AI Policy for the World Project, said in a lecture on Wednesday afternoon. "This is not something that will be solved in a single report. This has got to be a collective conversation, and it will take a while. It will be years in the making."