The UneeQ, based in the United States and New Zealand, published a video of its artificial intelligence project Digital Einstein that has the father of relativity theory chat with a fictional version of his human Sofia. Users of UneeQ technology will be able to chat with the iconic Nobel Prize in Physics, who will answer their questions. The idea of this long-term project is to teach and accompany people who feel lonely, especially seeing the effects of quarantines around the world due to the COVID-19 pandemic. The company said in a statement that "Digital Einstein, among other digital humans, can communicate with people in a more natural way: using conversation, human expressions and emotional responses to provide the best daily interactions that we hope will make a difference in people's lives ".

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.

Jensen Huang, Nvidia's chief executive, says the company's next step is creating a'metaverse', artificially created environments where companies can simulate the future before acting on it. Mr Huang said the company wanted to "create the future" by creating a virtual world that is thousands of times larger than the physical world. This digital space would be recreations of New York City and Shanghai, Mr Huang predicts, with "digital twin[s]" of "every single factory and every single building". "Engineers and software programmers could simulate new software that will ultimately run in the physical version of the car, the physical version of the robot, the physical version of the airport, the physical version of the building", Mr Huang said in an interview with Time magazine. "All of the software that's going to be running in these physical things will be simulated in the digital twin first, and then it will be downloaded into the physical version".

Large scale projects increasingly operate in complicated settings whilst drawing on an array of complex data-points, which require precise analysis for accurate control and interventions to mitigate possible project failure. Coupled with a growing tendency to rely on new information systems and processes in change projects, 90% of megaprojects globally fail to achieve their planned objectives. Renewed interest in the concept of Artificial Intelligence (AI) against a backdrop of disruptive technological innovations, seeks to enhance project managers cognitive capacity through the project lifecycle and enhance project excellence. However, despite growing interest there remains limited empirical insights on project managers ability to leverage AI for cognitive load enhancement in complex settings. As such this research adopts an exploratory sequential linear mixed methods approach to address unresolved empirical issues on transient adaptations of AI in complex projects, and the impact on cognitive load enhancement. Initial thematic findings from semi-structured interviews with domain experts, suggest that in order to leverage AI technologies and processes for sustainable cognitive load enhancement with complex data over time, project managers require improved knowledge and access to relevant technologies that mediate data processes in complex projects, but equally reflect application across different project phases. These initial findings support further hypothesis testing through a larger quantitative study incorporating structural equation modelling to examine the relationship between artificial intelligence and project managers cognitive load with project data in complex contexts.

Most of us now encounter AI on a daily basis without noticing it. Social media feeds which show you the posts you are likely to engage with, music streaming platforms which suggest new music you may enjoy listening to, or chatbots which help renew insurance policies are all using a form of AI. We are now seeing what is commonly defined as "weak AI"; systems programmed with algorithms to reach conclusions and predict future behaviour by learning from data patterns. The more data fed to the system, the more accurate the system becomes in predicting future behaviours. The Scottish Government has flagged the food and drink industry, worth around £14 billion each year, as a key growth sector in its economic strategy.

Poppy Gustafsson runs a cutting-edge and gender-diverse cybersecurity firm on the brink of a £3bn stock market debut, but she is happy to reference pop culture classic the Terminator to help describe what Darktrace actually does. Launched in Cambridge eight years ago by an unlikely alliance of mathematicians, former spies from GCHQ and the US and artificial intelligence (AI) experts, Darktrace provides protection, enabling businesses to stay one step ahead of increasingly smarter and dangerous hackers and viruses. Marketing its products as the digital equivalent of the human body's ability to fight illness, Darktrace's AI-security works as an "enterprise immune system", can "self-learn and self-heal" and has an "autonomous response capability" to tackle threats without instruction as they are detected. "It really does feel like we're in this new era of cybersecurity," says Gustafsson, the chief executive of Darktrace. "The arms race will absolutely continue, I really don't think it's very long until this [AI] innovation gets into the hands of attackers, and we will see these very highly targeted and specific attacks that humans won't necessarily be able to spot and defend themselves from. "It's not going to be these futuristic Terminator-style robots out shooting each other, it's going to be all these little pieces of code fighting in the background of our businesses.

With the current pandemic accelerating the revolution of AI in healthcare, where is the industry heading in the next 5-10 years? What are the key challenges and most exciting opportunities? To answer those questions, DeepLearning.AI and Stanford Institute for Human-Centered Artificial Intelligence (HAI) are proud to present our virtual event, Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng, at 10am PT on April 29. What's special about this event is that you get to decide what our speakers talk about. If you'd like to submit and upvote questions for our speakers, please sign up for the Q&A General access ticket.

It all started with an odd pile of shells: a pile that, upon closer inspection, fell apart like a flower losing its petals, introducing a burned-out nature documentarian named Craig Foster--and, in time, the world--to the octopus hiding cleverly inside. Known simply as "her," she would become the star of My Octopus Teacher, the Oscar-nominated Netflix documentary and surprise pandemic hit that told the story of Foster's unlikely relationship with that eight-armed mollusk. Released in September 2020, it arrived at the perfect moment. Audiences exhausted by lockdowns and unrelenting 2020-ness were primed for escape into the undersea fantasia of South Africa's kelp forests, where Foster met her. Best-selling books like The Soul of an Octopus and Other Minds: The Octopus, the Sea, and the Deep Origins of Consciousness had whetted public curiosity about these uncannily intelligent creatures with whom humans last shared a common ancestor 600 million years ago. Yet while most writing about octopuses emphasizes their ostensibly alien, unknowable nature,1 and serious, science-minded nature documentaries elevate concern about biodiversity over sentiment for a single animal, My Octopus Teacher defied convention. It embraced Foster's feelings for the octopus, which over the course of a year evolved from curiosity to care--even to love. And though her own feelings were left for viewers to interpret, the film's indelible impression was of nature populated by species who are not only beautiful and exquisitely evolved and ecologically important, but highly sentient, too. Nautilus talked to Foster about his octopus teacher and how getting to know her changed the way he thinks about nature. I write a lot about nature and biology and ecology, but in the last few years I've focused on the minds of animals and how we think about them.

Assessing an AI agent that can converse in human language and understand visual content is challenging. Generation metrics, such as BLEU scores favor correct syntax over semantics. Hence a discriminative approach is often used, where an agent ranks a set of candidate options. The mean reciprocal rank (MRR) metric evaluates the model performance by taking into account the rank of a single human-derived answer. This approach, however, raises a new challenge: the ambiguity and synonymy of answers, for instance, semantic equivalence (e.g., `yeah' and `yes'). To address this, the normalized discounted cumulative gain (NDCG) metric has been used to capture the relevance of all the correct answers via dense annotations. However, the NDCG metric favors the usually applicable uncertain answers such as `I don't know. Crafting a model that excels on both MRR and NDCG metrics is challenging. Ideally, an AI agent should answer a human-like reply and validate the correctness of any answer. To address this issue, we describe a two-step non-parametric ranking approach that can merge strong MRR and NDCG models. Using our approach, we manage to keep most MRR state-of-the-art performance (70.41% vs. 71.24%) and the NDCG state-of-the-art performance (72.16% vs. 75.35%). Moreover, our approach won the recent Visual Dialog 2020 challenge. Source code is available at https://github.com/idansc/mrr-ndcg.

Claro Enterprise Solutions, a leading global technology services company, today announced that its Hospital Asset Management Solution has been selected as a winner in the Business Intelligence Group's Artificial Intelligence Excellence Awards program. Lost and stolen equipment costs the healthcare industry millions annually. Nurses, meanwhile, can spend more than an hour a day looking for equipment and supplies. Claro Enterprise Solutions' award-winning solution addresses this challenge by leveraging AI-enabled video analytics, geo-fencing and beacons to accurately identify and monitor the location and movement of equipment within a healthcare facility. Artificial Intelligence capabilities allow video cameras to identify the type of assets within a facility, while beacons and sensors monitor the location of stationary assets as well as assets in motion.