We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and personalized responses, while reducing workload of university staff. We employ retrieval-augmented generation to ground answers in university resources and to provide users with verifiable, contextually relevant information. We introduce a Frequently Asked Question (FAQ) retriever that maps user questions to knowledge-base entries, which allows administrators to steer retrieval, and improves over standard dense/hybrid retrieval strategies. The system is engineered for easy deployment in resource-constrained academic settings. We detail the system architecture, provide a technical evaluation of its components, and report insights from a real-world deployment.

So, while AI might change how humans work, the reality is it's unlikely to replace most jobs entirely. Dr Samer Al Moubayed, chief executive at Furhat Robotics, a conversational-AI social robotics startup that builds tech designed to interact with humans in a natural, fluid way, has overseen the conception of the firm's namesake robot that can speak, show emotions and maintain eye contact. The past few years have seen Moubayed work with the likes of human resources company Tengai to develop a robot that autonomously performs job interviews, scores the interview according to an established framework and summarises the output for a human recruiter. Moubayed is firm in his belief that such AI investments can support humans in their day-to-day jobs, working in harmony with the workforce instead of against it. As ad execs everywhere from Tokyo to Toronto shifted to remote working at the outset of 2020, Marcel hosted an internal job mobility platform that allowed people to change agencies, move to different markets and stretch their skillset.

The coronavirus crisis has given rise to fresh concerns about automation in labour markets. With people locked down and social distancing still in place, many businesses around the world have scaled up their investment in artificial intelligence (AI). AI's ability to identify and learn from data patterns, and translate them into useful technologies, has proven to be indispensable for many organisations, from healthcare providers to delivery subscription services, for example, in responding to the pandemic. Business applications of AI over the past 12 months have ranged from those designed to increase productivity and yield, through to customer-service functions. Robots have been rolling in to sanitise UK and US hospital corridors and deliver crucial supplies such as blood samples.

As with many other machine learning, or more generally, AI problems, RL can also be intimidating if one starts directly from the full problem and the formal mathematical definitions, so let us start by loosely defining RL as a collection of both problems and representations, meaning that, we have both RL problems and RL methods to solve that class of problems. More formally, when we are working on a reinforcement learning problem, we are trying to map specific situations to an action or a set of actions, and each of those actions will have a consequence or a "reward" which can be either positive, neutral, or negative, in fact, this can simply be a real number. For example, let's say that we have a pet monkey called Marcel and that he has a set of toys that he loves to play with, and let's say that we want to teach Marcel to pee in the toilet as opposed to on the floor, so to incentivize Marcel too choose the right action, we'll give him a new toy every time he pees in the toilet ( 1 toy) and we'll remove a toy from his collection (-1 toy) every time he pees on the floor. In this case, hopefully, Marcel (we can call him the "agent"), will learn to select an "action" (pee on the floor vs pee in the toilet) whenever he finds himself in a given situation or "state" -- when he feels the need to pee -- in a way to maximize the number of toys, namely the rewards, by choosing the right actions at that state. Now, I want to emphasize that while this example does a decent job describing the general idea of a reinforcement learning problem, there are many elements missing to fully describe the RL problem.

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers.

The coronavirus crisis has given rise to fresh concerns about automation in labour markets. With people locked down and social distancing still in place, many businesses around the world have scaled up their investment in artificial intelligence (AI). AI's ability to identify and learn from data patterns, and translate them into useful technologies, has proven to be indispensable for many organisations, from healthcare providers to delivery subscription services, for example, in responding to the pandemic. Business applications of AI over the past 12 months have ranged from those designed to increase productivity and yield, through to customer-service functions. Robots have been rolling in to sanitise UK and US hospital corridors and deliver crucial supplies such as blood samples.

"Talent and technology are the keys to unlocking our future in this industry-- finding ways for tech to come in and do a better job than people can in roles people have traditionally done," said MDC Partners global president Julia Hammond in explaining AI's value to her holding company. "The challenge with that is it's completely contradictory to the agency model, which has been built around people, so there's been a reluctance to build out AI and machine learning. We're actively pursuing it, in how we resource, how we scale and how we serve clients." Progress is being made elsewhere to find a happy middle ground. Last week, GroupM agency Wavemaker went public with its AI-driven media planning tool, Maximize, which the company claims is generating plans faster and more effectively than human planning teams alone. "It's a question of complexity of the problem solved.

According to Gartner, artificial intelligence (AI) is not defined by a single technology but by an array of capabilities and research, from advances in algorithms to abundant computing power and advanced analytical methods such as deep learning. It sounds like the voice that responds from a smart speaker when we ask about the weather or to tune into a podcast. A vast majority of CXOs are already relying on consumer technologies such as voice-activated digital assistants in their work, according to a recent survey by PwC. Imagine how much more powerful those virtual assistants could be with access to the collective knowledge of the enterprise and the ability to know individual users and what they need to work best. And imagine putting that power into the hands of users across the enterprise, in the meeting room, on a factory floor or in any device they use to do their jobs.

Recent years have seen the rise of artificial intelligence (AI) adoption in the marketing and media industries. While often a buzzword for marketers to make their work sound more exciting, the real benefits to brands center on the use of machines to carry out deep learning and make humans' jobs easier. AI is certainly growing in notoriety, with up to 85% of UK businesses said to be set to invest in the field by 2020. In addition, studies have shown the gradual uptake of soft robotics in the home – 23-32% of households in the US and 18% of households in the UK have at least one voice assistant, the most popular models being either Amazon's Alexa or Google Assistant. Moreover, Apple claimed in 2018 that a staggering 500 million of its users now frequently make use of Siri, its pre-installed voice assistant.

As artificial intelligence (AI) and chatbots start to infiltrate the digital workplace it's been interesting to watch the emergence of the "enterprise digital assistant" concept. While "digital assistant" may conjure up cute images of robot helpers, they are effectively apps that act as an interface with other systems to aid in task completion and search. In some cases, they include a chat interface and possibly even a little machine learning thrown in for good measure. The concept is persuasive -- who wouldn't want a friendly, convenient digital assistant that works quietly in the background to help you get things done? Offering an enterprise digital assistant can tick the "we are doing something about AI" box for potential new hires, who arguably might find this attractive.