You're probably already somewhat familiar with chatbots, or have at least seen one pop up in the lower right-hand corner of your computer screen while browsing online. But what exactly is a chatbot, and why are so many brands scrambling to add them to their websites? Keep reading to learn the answers to both of these questions, along with a few tools you can use to start using chatbots as part of your marketing and sales strategies. A chatbot is a computer program powered by either rules or artificial intelligence (or both!) that interacts with human users via a chat interface. For example, Pizza Hut has a Facebook Messenger chatbot that lets its customers learn about specials and promotions, then place orders for delivery or pickup. The bot mostly relies on multiple-choice menus and basic input to help customers, but is extremely user-friendly and serves its purpose of helping users place online orders effectively. And bots like this are only the beginning.

Over the last century, scientists have developed methods to map the structures within the Earth's crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves -- those that occur at low frequencies of around 1 hertz -- that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth's noisy seismic hum, and are therefore difficult to pick up with current detectors.

Our new deep learning architectures substantially improve prediction within complex chemical industrial processes. We can predict the outcome of complex industrial processes several hours in advance, allowing for better-informed predictive analytics to drastically increase the efficiency of industrial processes. Better decision-making improves performance and saves millions of dollars in energy costs.

Our zombie solar cells could power indoor devices without sunlight by Marina Freitag, Newcastle University Internet connected devices need power. That either means connecting them to the grid, which limits what we can use them for, or using batteries. To avoid this, my colleagues and I are helping develop a new type of smart solar cell that can adapt to the amount of available light. Last week, that all died. Sidewalk Labs canceled the Quayside project on May 7. More Unknown Devices on Corporate Networks A report published this week by Sepio Systems suggests the number of devices being attached to corporate networks since the start of the COVID-19 pandemic began has increased sharply.

It is useful, for the forthcoming discussion, to have a better understanding of some key terms used in RL. Agent: A software/hardware mechanism which takes certain action depending on its interaction with the surrounding environment; for example, a drone making a delivery, or Super Mario navigating a video game. The algorithm is the agent. Action: An action is one of all the possible moves the agent can make. An action is almost self-explanatory, but it should be noted that agents usually choose from a list of discrete possible actions.

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.

Exploration of the high-dimensional state action space is one of the biggest challenges in Reinforcement Learning (RL), especially in multi-agent domain. We present a novel technique called Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment. It can be combined with arbitrary off-policy MARL algorithms and is applicable to either homogeneous or heterogeneous environments. We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments. In the best performing scenario, the MADDPG with experience augmentation reaches to the convergence reward of vanilla MADDPG with 1/4 realistic time, and its convergence beats the original model by a significant margin. Our ablation studies show that experience augmentation is a crucial ingredient which accelerates the training process and boosts the convergence.

For many of us, our microwaves and dishwashers aren't the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way. But we may soon be rethinking that, thanks to new research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called "Sapple," analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter. Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used. For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns.

It has been observed by many that we are at the dawn of the next industrial revolution: The Artificial Intelligence (AI) revolution. The benefits delivered by this intelligence revolution will be many: in medicine, improved diagnostics and precision treatment, better weather forecasting, and self-driving vehicles to name a few. However, one of the costs of this revolution is going to be increased electrical consumption by the data centers that will power it. Data center power usage is projected to double over the next 10 years and is on track to consume 11% of worldwide electricity by 2030. Beyond AI adoption, other drivers of this trend are the movement to the cloud and increased power usage of CPUs, GPUs and other server components, which are becoming more powerful and smart.

Combined with complexity of a rapidly changing energy sector where digital technologies, the drive for greener energy and demand for more consumer-centric services are putting shareholder returns at risk and reconfiguring policy mandates, industry players are forced to make a significant re-evaluation of energy value chains, assets and operations. The way we produce and consume oil & gas is shifting. Renewable energy sources, such as wind and solar, are growing exponentially and are expected to account for nearly 70% of global electricity production in 2050. Transport is being electrified, with 50% of all new cars sold globally forecasted to be electric by 2033. Advances in digital technology are enabling these dramatic changes to our energy system and digitalization will play an important role in enabling the energy transition.