The salesperson hawking Brother printers on Taobao works hard--like, really hard. At any time of the day, even when there's no audience on the Chinese ecommerce platform, the same woman wearing a white shirt and black skirt is always livestreaming, boasting about the various features of different office printers. She has a phone in one hand and often checks it as if to read a sales script or monitor the viewer comments coming in. "My friends, I've gotta plug this game-changing office tool that can double your workplace efficiency, " the salesperson said during one recent broadcast, trying to achieve the delicate balance between friendliness and precision that has come to define the billion-dollar livestream ecommerce industry in China. Occasionally, she greeted the invisible audience.

Hedge funds utilising artificial intelligence capabilities have shown a competitive edge over investors that didn't use AI, according to new research. The coronavirus pandemic has given partial proof of the effectiveness of the application of artificial intelligence as a predictive tool in fund management; reveals the latest issue of The Cerulli Edge―Global Edition. An examination by Cerulli Associates of the assets under management (AUM) of various funds and net new flows of Europe-domiciled AI-enabled funds from 2013 to April this year reveals substantial AUM growth from 2016 to 2019. The aggregate return of AI-led hedge funds was almost three times higher than that of the overall hedge fund during this time: 33.9% compared to 12.1%. Despite this, AI-powered hedge funds' net new flows dropped somewhat last year, before dropping sharply mid-January and April.

Interfacing memory with behaviour is crucial for building systems that self-learn. In reinforcement learning, an agent can be an on-policy learner, which can only learn the value of its direct actions, or an off-policy learner, which can learn about optimal actions even when not performing those actions – e.g., it might be taking random actions, but can still learn what the best possible action would be. Off-policy learning is therefore a desirable property for agents, helping them learn the best course of action to take while thoroughly exploring their environment. Combining off-policy learning with memory is challenging because you need to know what you might remember when executing a different behaviour. For example, what you might choose to remember when looking for an apple (e.g., where the apple is located), is different to what you might choose to remember if looking for an orange. But if you were looking for an orange, you could still learn how to find the apple if you came across the apple by chance, in case you need to find it in the future.

Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.

With the digital world of data becoming the focal point of discussions and innovation, there is unparalleled hype over what it takes to be a digital enterprise in this day and age. Data sits at the center of the digital revolution, and companies that have determined the best possible way to extract meaning out of data are well on their way to glory. An organization takes its first steps into the digital world of change when it realizes and utilizes the importance of cloud based technologies like AI and IoT. These services are used to better manage data and to generate the best possible insights from it on a real-time basis. The insights generated from your data through cloud based services like IoT and AI can help improve business processes, automate tasks, design new products and manage operations in an efficient manner.