We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of complicated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the agent knows to be codes of computable ordinals. We prove that if one agent knows certain things about another agent, then the former necessarily has a higher intelligence level than the latter. This allows our intelligence notion to serve as a stepping stone to obtain results which, by themselves, are not stated in terms of our intelligence notion (results of potential interest even to readers totally skeptical that our notion correctly captures intelligence). As an application, we argue that these results comprise evidence against the possibility of intelligence explosion (that is, the notion that sufficiently intelligent machines will eventually be capable of designing even more intelligent machines, which can then design even more intelligent machines, and so on).

We study learning control in an online lifelong learning scenario, where mistakes can compound catastrophically into the future and the underlying dynamics of the environment may change. Traditional model-free policy learning methods have achieved successes in difficult tasks due to their broad flexibility, and capably condense broad experiences into compact networks, but struggle in this setting, as they can activate failure modes early in their lifetimes which are difficult to recover from and face performance degradation as dynamics change. On the other hand, model-based planning methods learn and adapt quickly, but require prohibitive levels of computational resources. Under constrained computation limits, the agent must allocate its resources wisely, which requires the agent to understand both its own performance and the current state of the environment: knowing that its mastery over control in the current dynamics is poor, the agent should dedicate more time to planning. We present a new algorithm, Adaptive Online Planning (AOP), that achieves strong performance in this setting by combining model-based planning with model-free learning. By measuring the performance of the planner and the uncertainty of the model-free components, AOP is able to call upon more extensive planning only when necessary, leading to reduced computation times. We show that AOP gracefully deals with novel situations, adapting behaviors and policies effectively in the face of unpredictable changes in the world -- challenges that a continual learning agent naturally faces over an extended lifetime -- even when traditional reinforcement learning methods fail.

In the vision and language navigation task, the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. We propose an interactive learning framework to endow the agent with the ability to ask for users' help in such situations. As part of this framework, we investigate multiple learning approaches for the agent with different levels of complexity. The simplest model-confusion-based method lets the agent ask questions based on its confusion, relying on the predefined confidence threshold of a next action prediction model. To build on this confusion-based method, the agent is expected to demonstrate more sophisticated reasoning such that it discovers the timing and locations to interact with a human. We achieve this goal using reinforcement learning (RL) with a proposed reward shaping term, which enables the agent to ask questions only when necessary. The success rate can be boosted by at least 15% with only one question asked on average during the navigation. Furthermore, we show that the RL agent is capable of adjusting dynamically to noisy human responses. Finally, we design a continual learning strategy, which can be viewed as a data augmentation method, for the agent to improve further utilizing its interaction history with a human. We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.

In the past year, the field of Natural Language Processing (NLP) has seen the rise of pretrained language models such as as ELMo (Peters et al., 2018), ULMFiT (Howard and Ruder, 2018) and BERT (Devlin et al., 2019). These approaches train a deep - learning language model on large volumes of unlabeled text, which is subsequently fine - tuned for particular NLP tasks. Applying these models to th e General Language Understanding Evaluation (GLUE) benchmark introduced by Wang et al. (2018) has achieved the best performance to date on tasks ranging from sentiment classification to question answering (Devlin et al., 2019). The benefit of these models has also been demonstrated in specialized NLP domains. BioBERT (Lee et al., 2019), a version of BER T trained exclusively on biomedical text, was able to significantly increase performance on biomedical named entity recognition. Further refining this model on clinical text produced an increase in performance in medical natural language inference (Alsentz er et al. 2019). While large pretrained models offer significantly increased performance, they come with their own constraints, as the number of parameters in the classic BERT - base model exceeds 100 million. As such, their computational cost can thus be p rohibitively high at both training and prediction time (Devlin et al., 2019). More recent work has addressed this challenge by'distilling' the models, training smaller versions of BERT which reduce the number of parameters to train by 40% while retaining more than 95% of the full model performance and even outperforming it on two out of eleven GLUE tasks (Sanh et al., 2019).

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From Siri, the virtual assistant in Apple mobile devices, to self-driving cars, artificial intelligence (AI) is progressing rapidly, outperforming humans at some tasks. As with the majority of the changes happening globally, there will be positive and negative impacts as AI continues to shape the world we live in. Every single one of us will have to reckon with our ability to balance the human way of life and the transition to the AI cosmos. According to a report by the technology research group IDC, spending on AI is expected to reach US$46 billion by 2020 with no signs of slowing down. AI is definitely on the rise in both business and life in general. The question is, will humans eventually lose control as machines become super-intelligent?

What is the difference between humans and robots? What are the dangers of artificial intelligence? I'll discuss when AI began and how it relates to art. Author Jeff Krimmel states, "AI began with the calendar and abacus." The word'automata' (pleural for automaton) is from the Greek word meaning'self-acting'.

New Delhi, Nov 30 (PTI) There is a need for degree courses and professional training programmes in Artificial Intelligence (AI) with the changing technology landscape, according to industry and academic experts. While the Central Board of Secondary Education (CBSE) has already introduced AI as an optional subject in schools, no full fledged degree courses are available in the area in the country besides few short term courses. 'In the digital era and rapidly-evolving business landscape, AI is influencing a range of industries and altering the job roles. The world is looking at AI for its widespread applications in almost every industry and is considered to be the next big technological shift in industrial and smartphone revolution. The need of the hour is to make AI education more focused and easily available,' said Varun Dhamija, Vice President, Pearson Professional Programs (PPP). 'According to our recent survey, 60 pc Indians believe that the world is shifting to a model where people participate in education over a lifetime which makes it age agnostic.

How will technological change affect the quantity and quality of future jobs? It is difficult to know. The issue of whether there will be enough jobs in the future receives substantial attention, mainly because of the well-publicized experiences of manufacturing workers whose jobs have been displaced by the introduction of robotics. It also reflects a long-held societal anxiety that machines will replace us. The consensus among most scholars, however, is that jobs are not going away.

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