The widely observed'benign overfitting phenomenon' in the neural network literature raises the challenge to the `bias-variance trade-off' doctrine in the statistical learning theory.Since the generalization ability of the'lazy trained' over-parametrized neural network can be well approximated by that of the neural tangent kernel regression,the curve of the excess risk (namely, the learning curve) of kernel ridge regression attracts increasing attention recently.However, most recent arguments on the learning curve are heuristic and are based on the'Gaussian design' assumption.In this paper, under mild and more realistic assumptions, we rigorously provide a full characterization of the learning curve in the asymptotic senseunder a power-law decay condition of the eigenvalues of the kernel and also the target function.The learning curve elaborates the effect and the interplay of the choice of the regularization parameter, the source condition and the noise.In particular, our results suggest that the'benign overfitting phenomenon' exists in over-parametrized neural networks only when the noise level is small.

We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly rapid pace, changing the greedy action in a large fraction of states within a handful of learning updates (in a typical deep RL set-up such as DQN on Atari). We characterise the phenomenon empirically, verifying that it is not limited to specific algorithm or environment properties. A number of ablations help whittle down the plausible explanations on why churn occurs to just a handful, all related to deep learning. Finally, we hypothesise that policy churn is a beneficial but overlooked form of implicit exploration that casts $\epsilon$-greedy exploration in a fresh light, namely that $\epsilon$-noise plays a much smaller role than expected.

The widely observed'benign overfitting phenomenon' in the neural network literature raises the challenge to the bias-variance trade-off' doctrine in the statistical learning theory.Since the generalization ability of the'lazy trained' over-parametrized neural network can be well approximated by that of the neural tangent kernel regression,the curve of the excess risk (namely, the learning curve) of kernel ridge regression attracts increasing attention recently.However, most recent arguments on the learning curve are heuristic and are based on the'Gaussian design' assumption.In this paper, under mild and more realistic assumptions, we rigorously provide a full characterization of the learning curve in the asymptotic senseunder a power-law decay condition of the eigenvalues of the kernel and also the target function.The learning curve elaborates the effect and the interplay of the choice of the regularization parameter, the source condition and the noise.In particular, our results suggest that the'benign overfitting phenomenon' exists in over-parametrized neural networks only when the noise level is small.

Connectionism challenges a basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipulations. Connectionism replaces symbol structures with distributed representations in the form of weights between units. For problems close to the architecture of the underlying machines, connectionist and symbolic approaches can make different representational commitments for a task and, thus, can constitute different theories. For complex problems, however, the power of a system comes more from the content of the representations than the medium in which the representations reside. The connectionist hope of using learning to obviate explicit specification of this content is undermined by the problem of programming appropriate initial connectionist architectures so that they can in fact learn.

Alt,hough cognitive AI is not generally viewed as being "scieutific" in the same, strong sense as is physics, it shares a number of the properties of the natural sciences, especially biology Certain of the special themes of biology, notably the principles of bistoricity and of structure-function relations, are applicable in AI research From a biologist's viewpoint, certain principles of cognitive AI research emerge It typically gets mixed reviews-some critics raise their hands in horror and say, "This is not how things are done. You arc violating the canons of drama, and I just, don't like it." Others are swept along by the excitement of the play. Some of these friendlier critics may like what they see even t,hough it runs counter to principles they have previously espoused, but most like the new play in part because it does fit into their intellectual framework. It is in this latter spirit that I view the science of AI.

To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.

One of the greatest common misconceptions about many of today's Artificial Intelligence (AI) systems is that they possess something called "Generalised intelligence," in other words, many people today can be forgiven for thinking that today's AI's are good at lots of things, hence "general," when in matter of fact, like humans, different AI's are good at different tasks. While there are a couple of companies trying to create Artificial General Intelligence (AGI) platforms, like Google's DeepMind who last year published the world's first AGI architecture, which they based on hierarchies of interconnected neural networks, Facebook is coming at the problem from a different angle. And in a nod to the growing sub-field of reinforcement learning, which is one of the DeepMind team's favourite training methods, the Facebook team also notes that AGI should resemble a human's ability to master new tasks with "decreasing explicit rewards," and that these new AGI's should be able to communicate and express themselves in a variety of ways – depending on the situation they find themselves in at the time. Facebook considers these capabilities to be more of a prerequisite to assess whether or not a platform has in fact achieved "true" AGI than the Turing test, which was designed in the 1950's by Alan Turing and which is still today's preferred, and only, method of comparing machine intelligence with human intelligence.

Let me use this space to discuss about some of the main topics of my PhD thesis: "Big Data, Cognitive Extension, Self-organizing Processes and Economic Development". My research was born as an effort to improve my comprehension of the emerging phenomenon "Big Data" and its potential impacts on the Economy, in particular Economic Development and fight against poverty. The first part explores how the phenomenon of Big Data may fit within Economic Theory. A new analytical framework is defined that will allow to link Big Data, human's cognitive extension, self-organizing processes and Economic Development.

Representing the pronoun we have Jacque Derrida and his creation Deconstructionism, our adverb is the thinker Ludwig Wittgenstein and the idea of'Language games'. Moreover, one sees this core value, or use of the pronoun – as being a very suitable metaphor for the Post-structuralist French philosopher Jacque Derrida's work. In his book On Grammatology, Derrida writes, 'Descartes's analyticism is intuitionist, that of Leibniz points beyond mani-fest evidence, toward order, relation, point of view' [5]. Especially when faced with another fact, We humans are the things that create meaning – meaning is not derived from the things we have created.

Machine learning, and more particularly learning with neural networks, can be viewed as just such a phenomenon. Frequently remarkable performance is obtained by training networks to perform relatively complex AI tasks. The need for a fuller theoretical analysis and understanding of their performance has been a major research objective for the last decade. Neural Network Learning: Theoretical Foundations reports on important developments that have been made toward this goal within the computational learning theory framework.