Few trends in technology have caused the level of panic and uncertainty in the job market as artificial intelligence (AI). The impending "robot revolution" has brought questions about what jobs, if any, will be replaced by bots, and when it will happen. While some have posited that robots will replace nearly all jobs, and free up humans to work on more creative endeavors, others have been more reserved in their predictions. A new report for Forrester Research claims that, by 2021, "intelligent agents and related robots" will only have eliminated 6% of jobs. "By 2021, AI within intelligent agents will evolve significantly beyond today's relatively simple machine learning and natural language processing (NLP)," the report said.

Artificial intelligence is making fast progress by the day. A report by Forrester indicates that in the next five years, robots and intelligent agents will eliminate 6% of jobs. The intelligent agents include chat bots and digital assistants like Alexa of Amazon, Siri of Apple, GoogleNow of Alphabet, and Messenger bots of Facebook. They will find more ground in developing robots and intelligent agents which learn better from users and handle more complex scenarios than they do now.

By 2021, robots will have eliminated 6% of all jobs in the US, starting with customer service representatives and eventually truck and taxi drivers. That's just one cheery takeaway from a report released by market research company Forrester this week. These robots, or intelligent agents, represent a set of AI-powered systems that can understand human behavior and make decisions on our behalf. Current technologies in this field include virtual assistants like Alexa, Cortana, Siri and Google Now as well as chatbots and automated robotic systems. For now, they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs like self-driving cars.

Hayles: In my view, machines are "real things," so I don't see an engagement with machines as in any way antithetical to contemporary reality. I do think it is important not to elide the very real differences that exist between humans and machines, especially the different embodiments that humans and machines have. Certainly I think that Albert is correct in insisting that virtual reality will never displace the three-dimensional world in which our perceptual systems evolved; the richness, diversity, and spontaneity of this immensely complex environment makes even the most sophisticated computer simulation look like a stick world by comparison. Where I differ, perhaps, is in seeing the situation not as a dichotomy between the real and virtual but rather as space in which the natural and the artificial are increasing entwined. I foresee a proliferation of what Bruno Latour calls "quasi-objects," hybrid objects produced by a collaboration between nature and culture--genetically engineered plants and animals, humans who have had gene thereapy, humans with cybernetic implants and explants, intelligent agent systems with evolutionary programs who have evolved to the point where they can converse in a convincing fashion with humans, and so forth.

By 2021, robots will have eliminated 6% of all jobs in the US, starting with customer service representatives and eventually truck and taxi drivers. That's just one cheery takeaway from a report released by market research company Forrester this week. These robots, or intelligent agents, represent a set of AI-powered systems that can understand human behavior and make decisions on our behalf. Current technologies in this field include virtual assistants like Alexa, Cortana, Siri and Google Now as well as chatbots and automated robotic systems. For now, they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs like self-driving cars.

Graph aggregation is the process of computing a single output graph that constitutes a good compromise between several input graphs, each provided by a different source. One needs to perform graph aggregation in a wide variety of situations, e.g., when applying a voting rule (graphs as preference orders), when consolidating conflicting views regarding the relationships between arguments in a debate (graphs as abstract argumentation frameworks), or when computing a consensus between several alternative clusterings of a given dataset (graphs as equivalence relations). In this paper, we introduce a formal framework for graph aggregation grounded in social choice theory. Our focus is on understanding which properties shared by the individual input graphs will transfer to the output graph returned by a given aggregation rule. We consider both common properties of graphs, such as transitivity and reflexivity, and arbitrary properties expressible in certain fragments of modal logic. Our results establish several connections between the types of properties preserved under aggregation and the choice-theoretic axioms satisfied by the rules used. The most important of these results is a powerful impossibility theorem that generalises Arrow's seminal result for the aggregation of preference orders to a large collection of different types of graphs.

The role purpose is to establish and lead the Applied Intelligence in Health group within the wider Health Informatics group, the aim of which is the development and deployment of innovative applications of computer science to improve patient care and medical and biological knowledge discovery. The role will build and evaluate Intelligent reasoning systems and autonomous multi-agent ecosystems to serve as key use-cases in the infrastructures of partner hospitals. Objectives of the role include: • Establishing novel methodologies for temporal representation, reasoning and management of medical knowledge and use the methodologies to create and evaluate measures of patient profile similarity based on mined temporal patterns in longitudinal patient records, using the resulting measures in personalised clinician assistant recommender systems. Successful candidates will have knowledge & skills in Artificial Intelligence, bioinformatics, and health/clinical informatics. Experience in translational research delivery, temporal medical knowledge management, bioinformatics, temporal representation and reasoning, multi-agent systems, graphical representations and machine learning is essential.

This work addresses decentralized online optimization in non-stationary environments. A network of agents aim to track the minimizer of a global time-varying convex function. The minimizer evolves according to a known dynamics corrupted by an unknown, unstructured noise. At each time, the global function can be cast as a sum of a finite number of local functions, each of which is assigned to one agent in the network. Moreover, the local functions become available to agents sequentially, and agents do not have a prior knowledge of the future cost functions. Therefore, agents must communicate with each other to build an online approximation of the global function. We propose a decentralized variation of the celebrated Mirror Descent, developed by Nemirovksi and Yudin. Using the notion of Bregman divergence in lieu of Euclidean distance for projection, Mirror Descent has been shown to be a powerful tool in large-scale optimization. Our algorithm builds on Mirror Descent, while ensuring that agents perform a consensus step to follow the global function and take into account the dynamics of the global minimizer. To measure the performance of the proposed online algorithm, we compare it to its offline counterpart, where the global functions are available a priori. The gap between the two is called dynamic regret. We establish a regret bound that scales inversely in the spectral gap of the network, and more notably it represents the deviation of minimizer sequence with respect to the given dynamics. We then show that our results subsume a number of results in distributed optimization. We demonstrate the application of our method to decentralized tracking of dynamic parameters and verify the results via numerical experiments.

A pair of artificial intelligence experts from Cornell University have joined a nationwide effort to ensure the nightmare science fiction scenarios -- the ones involving corrupted human-killing computers -- don't become a reality. "We are in a period in history when we start using these machines to make judgments," researcher Bart Selman, a professor of computer science at Cornell, explained in a news release. Joseph Halpern, a professor of computer science at Cornell and also a "decision theory" expert, says providing an artificial intelligent agent with as much information as possible will make these difficult decisions more manageable. Scientists at Georgia Tech have been working to instill human values by teaching robots fairy tales.

I cannot think more than 2 hours without thinking how to introduce AI techniques into what I'm thinking about. The last time it happened was super interesting and stay with me to see how I used graph theories to crawl reddit and make a knowledge base about Magic the Gathering card relations. Long story short, I was browsing magiccardmarket.eu to check which cards to buy when I found a guy selling a 9 card for 6 . The card spiked over the week-end and I jumped on reddit to check out the reason. Is there a new deck using it?