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.

As the decade wraps up, many of the most evident shifts in technology have already taken place in the AI, DL, and ML landscape. Billions in venture financing have been raised to chase AI opportunities, and media outlets now have dedicated beat reporters focused on the market. Though the space has come a long way, it's also clear that there's still a long way to go. While reflecting on the past year, I started thinking about what 2020 may bring. I wanted to share some predictions on what will shape the industry landscape and the work I do at Determined AI.

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.

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.

Descartes Labs uses artificial intelligence to detect wildfires, and they can correctly spot one in a record-breaking time of nine minutes.

"Global Artificial Intelligence in Chatbot Security Market Size, Status and Forecast 2019-2026"offers a primary overview of the Artificial Intelligence in …

Amazon Web Services debuted a keyboard called DeepComposer this week, claiming it's "the world's first musical keyboard powered by generative AI." It has 32 keys, costs $99, and connects to a software interface that uses machine learning and cloud computing to generate music based on what you play. It's been unclear who this is for, and many have latched on to the fact that the music it creates just sounds bad. It looks like a consumer product, and Amazon used an over-the-top presentation to hype it, which included what AWS claimed was "the first hybrid AI human pop acoustic collaboration." But actually, the keyboard is intended to be a beginning tool for developers to get into machine learning and music.

Year 2020 does have a sci-fi ring to it. Perhaps that's one reason AI has so many people nervous. From your smart phone to your Google search to your Netflix and Spotify recommendations, AI shapes modern US work and life in many ways. Despite its growing ubiquity, fear of AI remains. The 2019 Artificial Intelligence--American Attitudes and Trends report from the Future of Humanity Institute at the University of Oxford found that "more Americans think that high-level machine intelligence will be harmful to humanity" than those who think it will be beneficial.