I started 2019 by saying it would be the year of AI. As we witness how this game-changing technology is dominating the agendas of leading organisations and nations, I can say I was right. At Microsoft, we have helped organisations in Thailand harness artificial intelligence as the core of their digital transformation strategy, offering them a competitive advantage. Whether employed to improve operational efficiency or create entirely new business models, making AI part of your core strategy translates into growth and added value. To help understand how AI is shaping the future for Thai businesses, Microsoft partnered with the research firm IDC to survey readiness for AI adoption among business leaders and workers.

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than $\epsilon$-greedy exploration.

The ability to generate appropriate verbal and non-verbal backchannels by an agent during human-robot interaction greatly enhances the interaction experience. Backchannels are particularly important in applications like tutoring and counseling, which require constant attention and engagement of the user. We present here a method for training a robot for backchannel generation during a human-robot interaction within the reinforcement learning (RL) framework, with the goal of maintaining high engagement level. Since online learning by interaction with a human is highly time-consuming and impractical, we take advantage of the recorded human-to-human dataset and approach our problem as a batch reinforcement learning problem. The dataset is utilized as a batch data acquired by some behavior policy. We perform experiments with laughs as a backchannel and train an agent with value-based techniques. In particular, we demonstrate the effectiveness of recurrent layers in the approximate value function for this problem, that boosts the performance in partially observable environments. With off-policy policy evaluation, it is shown that the RL agents are expected to produce more engagement than an agent trained from imitation learning.

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in the logistics industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. So, let's take a look at promising machine learning solutions disrupting warehousing. For our 4 picks of machine learning startups, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 4 interesting examples out of 157 relevant solutions.

As language learning makes a sea change to online, users will come to expect personalized learning experiences. China's AI-powered online education market alone reached $568 million in 2017 and is expected to surpass $26 billion in 2022. At Sana Labs, we build AI technologies to power these learning experiences through easy to integrate APIs. This means that machine learning models for personalization as well as pronunciation, grammar, and overall fluency feedback can be production ready in days, not months. In this article, I'll highlight why deep learning will power this shift.

At the University of North Carolina Charlotte (UNCC), I recently attended the debut of an exciting new machining-related event where machining had to be explained to some attendees. The inaugural meeting of the Consortium for Self-Aware Machining and Metrology (CSAM) brought together manufacturing experts with mathematicians who had little basic familiarity with machining operations, all with the goal of advancing the development of, in the words of meeting organizer Dr. Tony Schmitz, "production systems with the ability to know their own state and respond." In short, this was a conference entirely focused on uniting machining with artificial intelligence (AI). Of course, the manufacturing people in attendance needed basic instruction also. The hope of applying AI to manufacturing is still in its early stages, and one of the first steps is just to figure out what the one might mean for the other.

At the University of North Carolina Charlotte (UNCC), I recently attended the debut of an exciting new machining-related event where machining had to be explained to some attendees. The inaugural meeting of the Consortium for Self-Aware Machining and Metrology (CSAM) brought together manufacturing experts with mathematicians who had little basic familiarity with machining operations, all with the goal of advancing the development of, in the words of meeting organizer Dr. Tony Schmitz, "production systems with the ability to know their own state and respond." In short, this was a conference entirely focused on uniting machining with artificial intelligence (AI). Of course, the manufacturing people in attendance needed basic instruction also. The hope of applying AI to manufacturing is still in its early stages, and one of the first steps is just to figure out what the one might mean for the other.