Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need for an automated utterance generation. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm.

Artificial intelligence (AI) is no longer the realm of science fiction. AI is quickly becoming a powerful new technology and is set to disrupt any sector that deals with large amounts of data, and education is no different. The academic world is still considered one of the most human sectors -- the most human of the humanities -- but that doesn't mean there aren't ways in which teachers and school workers can benefit from implementing artificial intelligence. Just like any other industry, teachers deal with a huge amount of admin, and they are often having to spread their finite time between an ever-growing student body. As a result, the quality and relevance of education is becoming difficult to maintain.

Artificial Intelligence has already proven its transformative capability across diverse industries. Its impact on sales and marketing strategies promises to be optimistic. Both functional areas have been witnessed the rise of innovation in the last few years. By harnessing the power of AI, brands can improve decision-making and leave more resources for high-level human touch. AI-enabled technologies are becoming more prevalent in almost every sector. Technologies like machine learning and natural language processing (NLP) that not only automate repeatable and tedious tasks but also learn from exceptions in a process.

Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. When it comes to real-world scenarios such as recommendation system and online advertising, however, it is essential to consider the resource consumption of exploration. In practice, there is typically non-zero cost associated with executing a recommendation (arm) in the environment, and hence, the policy should be learned with a fixed exploration cost constraint. It is challenging to learn a global optimal policy directly, since it is a NP-hard problem and significantly complicates the exploration and exploitation trade-off of bandit algorithms. Existing approaches focus on solving the problems by adopting the greedy policy which estimates the expected rewards and costs and uses a greedy selection based on each arm's expected reward/cost ratio using historical observation until the exploration resource is exhausted. However, existing methods are hard to extend to infinite time horizon, since the learning process will be terminated when there is no more resource. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint. HATCH adopts an adaptive method to allocate the exploration resource based on the remaining resource/time and the estimation of reward distribution among different user contexts. In addition, we utilize full of contextual feature information to find the best personalized recommendation. Finally, in order to prove the theoretical guarantee, we present a regret bound analysis and prove that HATCH achieves a regret bound as low as $O(\sqrt{T})$. The experimental results demonstrate the effectiveness and efficiency of the proposed method on both synthetic data sets and the real-world applications.

Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability to quantify uncertainty in their predictions and avoid over-fitting, combined with high prediction accuracy. However, they have not been widely used on large-scale data because of their prohibitive computational cost. In recent work, efforts have been made to reduce the cost, both by improving the scalability of the BMF algorithm as well as its implementation, but so far mainly separately. In this paper we show that the state-of-the-art of both approaches to scalability can be combined. We combine the recent highly-scalable Posterior Propagation algorithm for BMF, which parallelizes computation of blocks of the matrix, with a distributed BMF implementation that users asynchronous communication within each block. We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.

Apple has acquired another artificial intelligence startup, according to Bloomberg. Voysis, a Dublin-based startup, will join Apple and aim to improve Siri's ability to understand a person's natural language. It focused on shopping-related voice commands, but Voysis' technology should grow under Apple's guidance to support general inquiries as Siri integrates with other apps. E-commerce companies used Voysis' technology to offer enhanced product search results through their apps. Bloomberg noted an example in which the user utters phrases such as "I need a new LED TV" and "my budget is $1,000."

The new decade of 2020 or the next stage of "digital evolution" welcomes the world with a promise of hyper intuitive cognitive capabilities and emotionally intelligent interfaces that will rebuild businesses in numerous unpredictable ways. As the tech community (for invested implementation) prepares itself for the new age of disruptive changes to arrive at it's matured stage, it becomes wise and necessary to have a look at these digital transformation trends. Conversational Artificial Intelligence- Siri and Google Assistant are always at swords for their accuracy in answers, but still they both lack in understanding the right intent. Applied conversational AI, fixes this disconnects as it understands the relevance and personalization within humans for successful computer interaction. Conversational AI has an automated speech recognition program that understands natural language and forms a response that exhibits a customized dialogue.

Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Apple Inc. acquired Voysis, an artificial intelligence startup that developed a platform for digital voice assistants to better understand people's natural language. Dublin, Ireland-based Voysis focused on improving digital assistants inside online shopping apps, so the software could respond more accurately to voice commands from users. A now-removed company webpage said the technology could narrow product search results by processing shopping phrases such as "I need a new LED TV" and "My budget is $1,000." Voysis provided this AI to other companies to incorporate it into their own apps and voice assistants. An Apple spokesman said the company "buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans."

We propose orthogonal inductive matrix completion (OMIC), an interpretable model composed of a sum of matrix completion terms, each with orthonormal side information. We can inject prior knowledge about the eigenvectors of the ground truth matrix, whilst maintaining the representation capability of the model. We present a provably converging algorithm that optimizes all components of the model simultaneously, using nuclear-norm regularisation. Our method is backed up by \textit{distribution-free} learning guarantees that improve with the quality of the injected knowledge. As a special case of our general framework, we study a model consisting of a sum of user and item biases (generic behaviour), a non-inductive term (specific behaviour), and an inductive term using side information. Our theoretical analysis shows that $\epsilon$-recovering the ground truth matrix requires at most $O\left( \frac{n+m+(\sqrt{n}+\sqrt{m})mn \sqrt{r}C}{\epsilon^2}\right)$ entries, where $r$ is the rank of the ground truth matrix. We analyse the performance of OMIC on several synthetic and real datasets. On synthetic datasets with a sliding scale of user bias relevance, we show that OMIC better adapts to different regimes than other methods and can recover the ground truth. On real life datasets containing user/items recommendations and relevant side information, we find that OMIC surpasses the state of the art, with the added benefit of greater interpretability.