Artificial Intelligence is poised to disrupt the entire eCommerce industry. An interesting convergence is taking place; one that will have enormous implications in the way retailers sell their products and services and the way consumers buy them. Artificial Intelligence capabilities and applications are attempting to solve real-world issues that eCommerce industry are facing. How Artificial Intelligence in e-commerce can play an important and game changing role, moving beyond customer segmentation to help them achieve the best possible results? The visual Search engine is one of the most exciting trends of Artificial Intelligence in eCommerce.

We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: - For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; - For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; -If the noise is adversarial, no non-trivial approximation guarantee can be obtained.

Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period, the {\em long-term causal effects}, are not captured by the classical methodology even though they clearly are more indicative of the value of the new policy. Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.

Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.

IBM Watson Virtual Agent is a set of preconfigured cognitive components based on the IBM Watson Conversation service. By configuring the virtual agent with your company's information, you can quickly implement an automated chat bot that enables your customers to achieve their goals. The established model of creating a digital or virtual agent requires experienced developers with a highly specific skill set to create complex systems that rely on custom – and often cumbersome – rules. Watson Virtual Agent allows businesses to simply build and deploy conversational agents. Watson Virtual Agent helps accelerate users' ability to deploy bots, including pre-trained cross-industry content, with minimal configuration, simplifying the process for both seasoned developers or users without formal technical training.

Watson Virtual Agent is a new way to provide automated services to your customers. It offers a cognitive, conversational self-service experience that can provide answers and take action. You can easily customize your Watson Virtual Agent to fit your specific business needs, provide custom content and match your business brand. Additionally, deep analytics provide insights on your customer's engagement with the Watson Virtual Agent and help with the understanding of your constantly changing customer's needs.

As artificial intelligence improves, machines will soon be equipped with intellectual and practical capabilities that surpass the smartest humans. But not only will machines be more capable than people, they will also be able to make themselves better. That is, these machines will understand their own design and how to improve it – or they could create entirely new machines that are even more capable. The human creators of AIs must be able to trust these machines to remain safe and beneficial even as they self-improve and adapt to the real world. This idea of an autonomous agent making increasingly better modifications to its own code is called recursive self-improvement.

Technology is advancing at record speed as innovations that were a foggy prediction came to life one after the other. This passing month, I decided to explore "future studies" and browsed the web for the latest advancements in the tech world, and especially AI, IT, and SaaS. An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor -- outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. People have started noticing self-driving Uber cars in downtown San Francisco, fueling speculation the ridesharing company could soon be deploying autonomous vehicles for commercial use right where it all started, in the Bay Area.

The society of intelligent agents now has a new member. His name is Ozlo, from the Palo Alto, California-based company of the same name. According to co-founder and CEO Charles Jolley, he's the only independent intelligent agent left, now that Samsung has scooped up Viv. But the key differentiator, Jolley told me, is that Ozlo is "the only assistant that can link together competing sources of information." As an example, Jolley recalled that he wanted "some steak and live music" while on a recent trip to Las Vegas.

With the increasing volume of interactions with customers over social media channels, KLM Royal Dutch Airline is the first airline to test how artificial intelligence could assist customer service agents. "We have 100,000 mentions a week on social media," says Tjalling Smit, senior vice president of Digital at KLM Royal Dutch Airlines. "We handle around 15,000 customer service cases a week and we answer our customers 24/7 in 10 different languages." As social channels proliferate, KLM makes sure it is present where its customers live online. "We were the first airline to allow customers to get their boarding passes and flight confirmation through Facebook Messenger," says Smit. KLM is piloting DigitalGenius' GPU-accelerated AI system that is integrated directly into KLM's Customer Relationship Management tool, and provides a layer of deep learning and artificial intelligence to service agents in real-time.