The idea of using machine learning to teach programs how to automatically write or modify code has always been tempting for computer scientists. The feat would not only greatly reduce engineering time and effort, but could also lead to the creation of novel and advanced intelligent agents. In a new paper, Google Brain researchers propose using neural networks to model human source code editing. Effectively this means treating code editing as a sequence and having a machine learn how to "write code" like in a natural language model -- by analysing a short paragraph of editing, the model can extract intent and leverage that to generate subsequent edits. To understand the intent behind developers' source code editing actions, the main challenge was how to learn from earlier editing sequences in order to predict upcoming edits.
This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. In the first chapter we are going to talk about the basic graph algorithms.
The paper is a half-way between the agent technology and the mathematical reasoning to model tactical decision making tasks. These models are applied to air defense (AD) domain for command and control (C2). It also addresses the issues related to evaluation of agents. The agents are designed and implemented using the agent-programming paradigm. The agents are deployed in an air combat simulated environment for performing the tasks of C2 like electronic counter counter measures, threat assessment, and weapon allocation. The simulated AD system runs without any human intervention, and represents state-of-the-art model for C2 autonomy. The use of agents as autonomous decision making entities is particularly useful in view of futuristic network centric warfare.
In this work, we formulate the process of generating explanations as model reconciliation for planning problems as one of planning with explanatory actions. We show that these problems could be better understood within the framework of epistemic planning and that, in fact, most earlier works on explanation as model reconciliation correspond to tractable subsets of epistemic planning problems. We empirically show how our approach is computationally more efficient than existing techniques for explanation generation and also discuss how this particular approach could be extended to capture most of the existing variants of explanation as model reconciliation. We end the paper with a discussion of how this formulation could be extended to generate novel explanatory behaviors.
PolyAI, a London startup founded by experts in the field of "conversational AI" -- including CEO Nikola Mrkšić, who was previously the first engineer at Apple-acquired VocalIQ -- has raised $12 million in Series A funding to deploy its tech in customer support contact centres. The round was led by Point72 Ventures, with participation from Sands Capital Ventures, Amadeus Capital Partners, Passion Capital and Entrepreneur First (EF). PolyAI's founders are graduates of EF, although they didn't meet during the company building program but already knew each other from their time at Cambridge's Dialog Systems Group, part of the Machine Intelligence Lab at the University of Cambridge. "We started PolyAI in 2017, straight after submitting our PhD theses," Mrkšić tells me. "At Cambridge, we developed state-of-the-art conversational technology, and starting a company was the best way to get this tech used in the real world. We brought many of our Cambridge colleagues with us and started building the commercial version of our conversational platform."
A two-year study from McKinsey Global Institute predicts that that by 2030, intelligent agents and robots could eliminate up to 30% of the world's human labor, rivaling the industrial revolution. This could mean the displacement of between 400–800 million jobs globally. So, should marketers be worried about the inevitable day their Siri's evolve from helping them to find the nearest Walmart to actually one day taking their job?"The growth of AI in marketing means collecting more precise data and automating menial and repetitive tasks," explains Guo. "AI can help generate more robust datasets. Although the advent of new technology may be daunting for marketers, AI will never fully replace them.
Advancement in artificial intelligence is picking up pace at a substantial level. Entering humans in to an era where decision making will be at least machine consulted, if not machine governed. Since, these intelligent machines or agents do not experience the same emotions and experiences as humans do. Their suggestions or outputs will more likely be calculated decisions, which sometimes are not appropriate from a human standpoint. At this stage it is essential that such intelligent agents are programmed so that their suggestions or outputs coincide with the human ethics and traditions.
Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to increase performance, but do not generally look at how information can be shared between agents to address the curse of dimensionality in MARL. We posit that a multiagent problem can be decomposed into a multi-task problem where each agent explores a subset of the state space instead of exploring the entire state space. This paper introduces a multiagent actor-critic algorithm and method for combining knowledge from homogeneous agents through distillation and value-matching that outperforms policy distillation alone and allows further learning in both discrete and continuous action spaces.
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.
Expert programmers' eye-movements during source code reading are valuable sources that are considered to be associated with their domain expertise. We advocate a vision of new intelligent systems incorporating expertise of experts for software development tasks, such as issue localization, comment generation, and code generation. We present a conceptual framework of neural autonomous agents based on imitation learning (IL), which enables agents to mimic the visual attention of an expert via his/her eye movement. In this framework, an autonomous agent is constructed as a context-based attention model that consists of encoder/decoder network and trained with state-action sequences generated by an experts' demonstration. Challenges to implement an IL-based autonomous agent specialized for software development task are discussed in this paper.