YouTube has launched a new free-to-watch documentary series about artificial intelligence fronted by "Iron Man" star Robert Downey Jr. The YouTube Original series debuted on the platform on Wednesday, and is called "The Age of AI." Its stated aim is to demystify misconceptions around AI. One of the main focuses of the first episode is a New Zealand-based company called Soul Machines, which specialises in making digital avatars. Its founder Mark Sagar is an award-winning visual effects artist who's worked on films like "Rise of the Planet of the Apes" and "Avatar." Sagar is working on a project he calls "Baby X," in which he is using AI to simulate a human baby, modelled after his own daughter.

At the company's GPU Technology Conference (GTC) in Suzhou, China, Nvidia CEO Jensen Huang took to the stage to introduce Drive AGX Orin, the next generation SoC in the company's automotive portfolio. Orin follows Drive AGX Xavier, launched just under 2 years ago at CES 2018. Xavier is Nvidia's current flagship SoC for AI acceleration in vehicles. Orin, at 17 billion transistors, is almost double the size of Xavier, which had 9 billion, and it offers nearly 7x the performance (200 TOPS for INT8 data). Despite its size, Orin also offers 3x the power efficiency of Xavier, the company said.

The University of Adelaide plans to achieve substantial growth in its student population within five years, and one of the teams responsible for achieving this very aggressive goal has a new staff member this year: a chatbot. It helps answer the critical question, "Am I eligible to attend the university?" Catherine Cherry, the school's director of prospect management, is putting innovative technologies to work to help meet that goal. The University of Adelaide uses a chatbot to let prospective students know whether they're eligible to apply. Prior to the introduction of the chatbot, the university's admissions office couldn't easily answer the eligibility question for prospective students from outside of Australia who were curious about whether they could attend.

New Zealand's financial sector is primed for artificial intelligence, according to new national research. The AI Forum of New Zealand (part of the NZTech ecosystem) research study says the country's financial and insurance sectors are better prepared to incorporate and reap rewards from AI implementation than other industries. According to Emma Naji, AI Forum executor director, the report identifies New Zealand urgently needs to increase its focus on the core foundations needed to operate in an AI enabled future. This is especially important relating to investment, skills and talent, research, trusted data, ethics and regulation, she says. "The report shows how AI-driven solutions can be used to improve New Zealand's wellbeing, productivity and sustainability," says Naji. "Unsurprisingly, financial institutions have been quick to capitalise on the opportunities and new techniques that AI offers."

Like electricity or the internet, artificial intelligence (AI) is considered a general purpose technology with the potential to transform productivity, accelerate economic growth and improve wellbeing across the whole of society. It has started, and will continue to, drastically transform the way we work and live. At least, this is what the report'Towards Our Intelligent Future' published by New Zealand AI Forum earlier this year affirms. The report represents over nine months of collaborative work on parallel streams exploring AI adoption, policy and strategy in New Zealand and around the world. It highlights the value of AI for achieving New Zealand's wellbeing, sustainability and economic goals.

Systems powered by artificial intelligence are being developed to be more user-friendly by communicating with users in a progressively human-like conversational way. Chatbots, also known as dialogue systems, interactive conversational agents, or virtual agents are an example of such systems used in a wide variety of applications ranging from customer support in the business domain to companionship in the healthcare sector. It is becoming increasingly important to develop chatbots that can best respond to the personalized needs of their users so that they can be as helpful to the user as possible in a real human way. This paper investigates and compares three popular existing chatbots API offerings and then propose and develop a voice interactive and multilingual chatbot that can effectively respond to users mood, tone, and language using IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was evaluated using a use case that was targeted at responding to users needs regarding exam stress based on university students survey data generated using Google Forms. The results of measuring the chatbot effectiveness at analyzing responses regarding exam stress indicate that the chatbot responding appropriately to the user queries regarding how they are feeling about exams 76.5%. The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.

We consider distributed online convex optimization problems, where the distributed system consists of various computing units connected through a time-varying communication graph. In each time step, each computing unit selects a constrained vector, experiences a loss equal to an arbitrary convex function evaluated at this vector, and may communicate to its neighbors in the graph. The objective is to minimize the system-wide loss accumulated over time. We propose a decentralized algorithm with regret and cumulative constraint violation in $\mathcal{O}(T^{\max\{c,1-c\} })$ and $\mathcal{O}(T^{1-c/2})$, respectively, for any $c\in (0,1)$, where $T$ is the time horizon. When the loss functions are strongly convex, we establish improved regret and constraint violation upper bounds in $\mathcal{O}(\log(T))$ and $\mathcal{O}(\sqrt{T\log(T)})$. These regret scalings match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problem (for both convex and strongly convex loss functions). In the case of bandit feedback, the proposed algorithms achieve a regret and constraint violation in $\mathcal{O}(T^{\max\{c,1-c/3 \} })$ and $\mathcal{O}(T^{1-c/2})$ for any $c\in (0,1)$. We numerically illustrate the performance of our algorithms for the particular case of distributed online regularized linear regression problems.

In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introduced and proved in the binary case as an extension of Bordat's algorithm with the notion of strategies to select only some predecessors of each concept, avoiding the generation of unreasonably large lattices. The algorithm is then extended to any type of data in a generic way. It is inspired from pattern structure theory, where data are locally described by predicates independent of their types, allowing the management of heterogeneous data.

Artificial Intelligent systems are generally defined as computer systems which are to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation. In the context of Infrastructure Asset Management, there are many applications for AI which can replace or augment substantial levels of human effort in order to improve the performance of the Asset or extend its life. As many public organisations are making significant efforts to understand'best practice' asset management, often referring to ISO 550001 for guidance on the The goal of Machine Learning is to learn from data, ingesting a large volume of data a ML algorithm is predominantly focused on a certain task to maximize the performance of machine in performing that task. Artificial Intelligence, on the other hand, is primarily focused on decision making. So while ML allows a system to learn new things from data, and AI attempts to mimic human behavior in a circumstances.