Most people talk about AI in very broad terms. The reality, however, is the artificial intelligence we have now is very different to the AI we see in the movies, i.e. AI where robots and machines are conscious, can deal with a myriad of situations, and have emotion. Specifically, the AI we have today is known as Narrow AI or Weak AI. The AI we want to get to in the future is General AI, also known as Strong AI.

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has transformed the role of digital technology in business operations. These cognitive technologies have boosted the value addition of computing and digital processing power, from pre-programmed process automation to turning data into actionable insights. AI is allowing businesses to be ultra-responsive in real-time, achieve system transparency and gain valuable insights into customers, markets and processes. The applications of AI are not only vast in themselves, but the technology can also be integrated with machines and human users through multiple interfaces. For instance, Audio AI allows humans to interact with machines using natural language processing algorithms.

Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' preferences. Personalized RS are successfully applied in platforms such as Netflix, Amazon or YouTube. However, they are missing in gastronomic platforms such as TripAdvisor, where moreover we can find millions of images tagged with users' tastes. This paper explores the potential of using those images as sources of information for modeling users' tastes and proposes an image-based classification system to obtain personalized recommendations, using a convolutional autoencoder as feature extractor. The proposed architecture will be applied to TripAdvisor data, using users' reviews that can be defined as a triad composed by a user, a restaurant, and an image of it taken by the user. Since the dataset is highly unbalanced, the use of data augmentation on the minority class is also considered in the experimentation. Results on data from three cities of different sizes (Santiago de Compostela, Barcelona and New York) demonstrate the effectiveness of using a convolutional autoencoder as feature extractor, instead of the standard deep features computed with convolutional neural networks.

Artificial intelligence (AI) systems, powered by massive data and sophisticated algorithms -- including but not limited to -- deep neural networks and statistical machine learning (ML)(support vector machines, clustering, random forest, etc.), are having profound and transformative impact on our daily lives as they make their way into everything from finance to healthcare, from retail to transportation. Netflix movie recommender, Amazon's product prediction, Facebook's uncanny ability to show what you may like, Google's assistant, DeepMind's AlphaGo, Stanford's AI beating human doctors. Machine learning is eating software. However, one of the common features of these powerful algorithms is that they utilize sophisticated mathematics to do their job -- to classify and segment an image, to arrive at the key decisions, to make a product recommendation, to model a complex phenomenon, or to extract and visualize a hidden pattern from a deluge of data. All of these mathematical processes are, quite simply, beyond the scope of a single human (or a team) to perform manually (even on a computer) or inside their head.

Think chatbots, intelligent virtual assistants, and digital employees. These and other related technologies enable computers to engage in dialogue with people in natural ways using conversational artificial intelligence (CAI). For banks, CAI makes it possible to respond to customers' questions more quickly, cost-effectively, and consistently than they could with a traditional workforce. Many banks have embarked on the CAI journey by launching chatbots. For example, HSBC and Bank of America have introduced digital financial assistants Amy and Erica, respectively.

Did you know 83% of business owners, executives and managers consider Artificial Intelligence (AI) a top priority for their business strategy, while 37% of organisations claim to have already implemented AI technology in some form? With every new AI technology or machine learning breakthrough, business leaders are increasingly recognising the strength of AI. Customer service departments are starting to choose AI virtual assistants that provide real-time support, while other companies are using AI to predict consumer behaviour to tailor offers and marketing materials more effectively. It's no secret that marketing is essential in helping businesses create and establish long-lasting relationships with their audiences, and marketing for technology businesses is no different. Technology is ever-changing, so it's crucial that technical companies adapt and focus on the long-term to keep up with the evolving market.

It is an hour past midnight and you are still wide awake. After scrolling through various social media feeds, you decide you need to talk to someone. The question is, who is actually available for a chat right now? Then you remember, your one-night owl buddy who is always up for a conversation the moment you say, "hey, are you awake?" Lo and behold, your buddy is awake, and even though he is busy eating a late night snack of pizzas, he is ready for some banter. You chat for a bit about things like favorite episodes of Stranger Things, how poorly rested he is and that it is getting late, and that you have work tomorrow morning so you say "good night" and fall back to bed.

Learning from non-stationary data remains a great challenge for machine learning. Continual learning addresses this problem in scenarios where the learning agent faces a stream of changing tasks. In these scenarios, the agent is expected to retain its highest performance on previous tasks without revisiting them while adapting well to the new tasks. Two new recent continual-learning scenarios have been proposed. In meta-continual learning, the model is pre-trained to minimize catastrophic forgetting when trained on a sequence of tasks. In continual-meta learning, the goal is faster remembering, i.e., focusing on how quickly the agent recovers performance rather than measuring the agent's performance without any adaptation. Both scenarios have the potential to propel the field forward. Yet in their original formulations, they each have limitations. As a remedy, we propose a more general scenario where an agent must quickly solve (new) out-of-distribution tasks, while also requiring fast remembering. We show that current continual learning, meta learning, meta-continual learning, and continual-meta learning techniques fail in this new scenario. Accordingly, we propose a strong baseline: Continual-MAML, an online extension of the popular MAML algorithm. In our empirical experiments, we show that our method is better suited to the new scenario than the methodologies mentioned above, as well as standard continual learning and meta learning approaches.

Increasingly complex and autonomous robots are being deployed in real-world environments with far-reaching consequences. High-stakes scenarios, such as emergency response or offshore energy platform and nuclear inspections, require robot operators to have clear mental models of what the robots can and can't do. However, operators are often not the original designers of the robots and thus, they do not necessarily have such clear mental models, especially if they are novice users. This lack of mental model clarity can slow adoption and can negatively impact human-machine teaming. We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well as facilitate the evaluation of operators' mental models.

Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.