Artificial intelligence has been a trending topic for quite some time, which comes as no surprise as the use of it is on the rise in every industry. According to a report by Salesforce, 51% of marketing leaders state that they currently use AI in some scope, with 27% planning to start using it in the next two years. As it continues to grow, it will progressively impact how our society functions and transform the way we work as marketers. As with any transformative technology, AI has marketing professionals on their toes; leaving them to wonder where the fate of their jobs lies in the era of AI. So, should you anticipate a massive disruption of AI threatening the security of your job?

It surveys the general formulation, terminology, and typical experimental implementations of reinforcement learning and reviews competing solution paradigms. In order to compare the relative merits of various techniques, this survey presents a case study of the Linear Quadratic Regulator (LQR) with unknown dynamics, perhaps the simplest and best studied problem in optimal control. The manuscript describes how merging techniques from learning theory and control can provide non-asymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. This survey concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and controls might be combined to approach these challenges.

A little over a week after the fervor surrounding Google's involvement in the Department of Defense's Project Maven, an autonomous drone program, showed signs of abating, another machine learning controversy returned to the headlines: local law enforcement deploying Amazon's Rekognition, a computer vision service with facial recognition capabilities. In a letter addressed to Amazon CEO Jeff Bezos, 19 groups of shareholders expressed concerns that Rekognition's facial recognition capabilities will be misused in ways that "violate [the] civil and human rights" of "people of color, immigrants, and civil society organizations." And they said that it set the stage for sales of the software to foreign governments and authoritarian regimes. Amazon, for its part, said in a statement that it will "suspend … customer's right to use … services [like Rekognition]" if it determines those services are being "abused." It has so far declined, however, to define the bright-line rules that would trigger a suspension.

A recent MIT Technology Review article titled "The Dark Secret at the Heart of AI" warned: "No one really knows how the most advanced algorithms do what they do. That could be a problem." Thanks to this uncertainty and lack of accountability, a report by the AI Now Institute recommended that public agencies responsible for criminal justice, health care, welfare and education shouldn't use such technology. Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a "black box" -- seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the "dark" about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that's seemingly impossible -- and certainly too complicated -- for us to understand.

Like any field of empirical science, AI may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and tacit assumptions in deep learning research. Deep Learning methodology seeks to overcome limitations in traditional machine learning research as it combines facets of model richness, generality, and practical applicability. The methodology so far has produced outstanding results due to a productive synergy of function approximation, under plausible assumptions of irreducibility and the efficiency of back-propagation family of algorithms. We examine these winning traits of deep learning, and also observe the various known failure modes of deep learning. We conclude by giving recommendations on how to extend deep learning methodology to cover the postulates of general-purpose AI including modularity, and cognitive architecture. We also relate deep learning to advances in theoretical neuroscience research.

C-3PO and R2-D2 are an odd couple in the Star Wars universe. C-3PO is a cowardly droid who obeys pre-defined protocols and routine tasks, while R2-D2 is a curious and adventurous robot who learns from previous problems, uses logical thinking and larger concepts to solve new problems. But together they do things they could not do alone. Similarly, RPA (Robotic Process Automation) and Advanced Analytics are an odd but very complementary combination of new business technologies. Like the diligent but unimaginative C-3PO, RPA follows precise rules to execute repetitive business processes; and like the curious and adaptable R2-D2, Advanced Analytics learns to make complex judgments when faced with new situations.

We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. Model sizes can be reduced by a factor of 4 by quantizing weights to 8-bits, even when 8-bit arithmetic is not supported. This can be achieved with simple, post training quantization of weights.We benchmark latencies of quantized networks on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations compared to floating point on CPUs. Speedups of up to 10x are observed on specialized processors with fixed point SIMD capabilities, like the Qualcomm QDSPs with HVX. Quantization-aware training can provide further improvements, reducing the gap to floating point to 1% at 8-bit precision. Quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing convolutional networks and review best practices for quantization-aware training to obtain high accuracy with quantized weights and activations. We recommend that per-channel quantization of weights and per-layer quantization of activations be the preferred quantization scheme for hardware acceleration and kernel optimization. We also propose that future processors and hardware accelerators for optimized inference support precisions of 4, 8 and 16 bits.

IAGON is the first decentralized Artificial Intelligence Blockchain-enabled Supercomputing Grid Technology for harnessing the storage capacities and processing power of multiple smart devices over a decentralized Blockchain/Tangle grid. The platform creates an AI decentralized architecture that manages and optimizes spare/idle distributed computing power and storage around the world, creating a truly decentralized Global Smart Computing Network solution for web-centric and decentralized applications. Utilizing a new consensus mechanism known as Proof-of Utilitarian (PoUW), it promotes decentralized cloud services, where multiple grid miners are rewarded by conducting decentralized parallel computing tasks and storing user's files. IAGON represents a new wave of High Performance Computing powered by Artificial Intelligence, Blockchain, BigData and wrapped in a sophisticated Encryption/Decryption philosophy catering for both individual and corporate clients. About IAGON IAGON is Global Supercomputing powered by Artificial Intelligence, BigData and Blockchain Technology that harnesses the storage capacities and processing power of multiple smart devices over a decentralized network Blockchain/Tangle grid. It's design philosophy is simple in that enables storing of BigData and repositories, as well as smaller scales of files, and carries out complex computational processes through a smart computation grid such as those needed for Artificial Intelligence and Machine Learning operations. IAGON operates a fully secure and encrypted platform that integrates Multiple Blockchain Support/Tangle Technologies, AI-Based Computational Processing, Smart Computational Grid and Secure Lake Technologies in an intuitive and user-friendly environment. Under IAGON's platform you can imagine a world where anyone can profit by joining a massive processing grid. IAGON will provide a fully automated platform for carrying out the storage and processing tasks of users on the basis of unutilized storage and processing capacities that are contributed by participating nodes or "miners".

We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn to control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are the way forward, then it should be possible to learn to interact with a wide variety of objects, and not just rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.

The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to play an important role in applied and theoretical machine learning. Restricted Boltzmann machines carry a rich structure, with connections to geometry, applied algebra, probability, statistics, machine learning, and other areas. The analysis of these models is attractive in its own right and also as a platform to combine and generalize mathematical tools for graphical models with hidden variables. This article gives an introduction to the mathematical analysis of restricted Boltzmann machines, reviews recent results on the geometry of the sets of probability distributions representable by these models, and suggests a few directions for further investigation.