Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average).
We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing high-level concepts across domains and learning complex procedures are key challenges in lifelong learning. We show that a program synthesis approach that combines gradient descent with combinatorial search over programs can be a more effective response to these challenges than purely neural methods. Our framework, called HOUDINI, represents neural networks as strongly typed, differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a symbolic program synthesizer that performs a type-directed search over parameterized programs, and decides on the library functions to reuse, and the architectures to combine them, while learning a sequence of tasks; and (2) a neural module that trains these programs using stochastic gradient descent.
In this work we aim at extending theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that the tasks are sampled i.i.d. Instead we study two scenarios when lifelong learning is possible, even though the observed tasks do not form an i.i.d. In the first case we prove a PAC-Bayesian theorem, which can be seen as a direct generalization of the analogous previous result for the i.i.d. For the second scenario we propose to learn an inductive bias in form of a transfer procedure.
Continuous learning and applying our knowledge can be powerful and critical success factors for achieving our professional goals. The Cognitive Class AI offers a wide variety of professional learning paths, as free of charge, to learners globally. In this article, I provide you with some prominent learning path samples with links so that you commence achieving your 2020 professional education and career development goals. I also provide you with a list of sample industry badges that you can earn by undertaking these online training courses. The badges can help you promote your knowledge, skills, experience, and expertise globally hosted in a centralised industry recognised digital program governance organisation such as Credly's Acclaim which is the world's largest network of individuals and organizations using verified achievements to unlock opportunities. You can join millions of professionals in sharing your achievements online with a simple link.
TED talks are simply fascinating. They provide tightly knit stories in short doses with mind-blowing information and experiences. It is amazing how much knowledge has been shared in this world using this simple and powerful medium. With Artificial Intelligence and Machine Learning getting so much attention in the spheres of research and business, I started looking out for TED talks on Artificial Intelligence in particular. I was in for such a treat – information treat to be precise.
The researchers' findings confirm what employers have reported in recent months: The demand for soft skills will only increase as automation takes hold. In a Cengage survey released earlier this year, employers said they most needed workers who could listen, pay attention to detail, communicate effectively, think critically, demonstrate interpersonal skills and learn new skills. Similarly, a recent Workhuman report concluded that, despite tech's ever-increasing presence in the workplace, the future of work will be people-focused, not machine centered. In fact, that future will create more opportunities for employers to "leverage the previously untapped creativity and innovation of people -- to prioritize humanity and emotional intelligence at work," Workhuman said. Employers may well be recognizing that opportunity: they identified "soft skills" as their top training priority in a LinkedIn poll last year.
Sign in to report inappropriate content. Hava Siegelmann, Microsystems Technology Office Program Manager DARPA, gives a keynote at the Human-Level AI Conference in Prague in August 2018. The conference combined three major conferences AGI, BICA, and NeSy and was organized by AI research and development company GoodAI.
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.
Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting an array of desirable properties, such as non-forgetting, concept rehearsal, forward transfer and backward transfer of knowledge, few-shot learning, and selective forgetting. Previous approaches to lifelong machine learning can only demonstrate subsets of these properties, often by combining multiple complex mechanisms. In this Perspective, we propose a powerful unified framework that can demonstrate all of the properties by utilizing a small number of weight consolidation parameters in deep neural networks. In addition, we are able to draw many parallels between the behaviours and mechanisms of our proposed framework and those surrounding human learning, such as memory loss or sleep deprivation. This Perspective serves as a conduit for two-way inspiration to further understand lifelong learning in machines and humans.
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