BRIAN KENNY: Artificial intelligence or AI for short is certainly creating a lot of buzz these days. And although it may seem like this amorphous thing that's somewhere off in our future, it's already very much in our midst. Navigation apps have turned printed maps into relics. Alexa, knows what you need from the grocery store before you do. Google Nest has the house at just the right temperature before you roll out from under the covers. And this is all great, but now you have to wonder if this intro is written by me or chat GPT. Which raises an important question.

One night in 1966, a twenty-three-year-old graduate student named Nicholas Humphrey was working in a darkened psychology lab at the University of Cambridge. An anesthetized monkey sat before him; glowing targets moved across a screen in front of the animal, and Humphrey, using an electrode, recorded the activity of nerve cells in its superior colliculus, an ancient brain area involved in visual processing. The superior colliculus predates the more advanced visual cortex, which enables conscious sight in mammals. Although the monkey was not awake, the cells in its superior colliculus were firing anyway, their activation registering as a series of crackles issuing from a loudspeaker. Humphrey seemed to be listening to the brain cells "seeing."

The demand for multimodal dialogue systems has been rising in various domains, emphasizing the importance of interpreting multimodal inputs from conversational and situational contexts. We explore three methods to tackle this problem and evaluate them on the largest situated dialogue dataset, SIMMC 2.1. Our best method, scene-dialogue alignment, improves the performance by ~20% F1-score compared to the SIMMC 2.1 baselines. We provide analysis and discussion regarding the limitation of our methods and the potential directions for future works. Our code is publicly available at https://github.com/holylovenia/multimodal-object-identification.

Finding out Taylor Swift was her 11th cousin twice-removed wasn't even the most shocking discovery Cher Scarlett made while exploring her family history. "There's a lot of stuff in my family that's weird and strange that we wouldn't know without Ancestry," says Scarlett, a software engineer and writer based in Kirkland, Washington. "I didn't even know who my mum's paternal grandparents were." In February 2022, the facial recognition search engine PimEyes surfaced non-consensual explicit photos of her at age 19, reigniting decades-old trauma. She attempted to get the pictures removed from the platform, which uses images scraped from the internet to create biometric "faceprints" of individuals.

David Auerbach is a writer and software engineer who has worked for Google and Microsoft. He also teaches the history of computation at the New Centre for Research & Practice in Seattle, US. His new book is Meganets: How Digital Forms Beyond Our Control Commandeer Our Daily Lives and Inner Realities. He argues that widespread concern about artificial intelligence is legitimate, but the problem is already all around us, with huge tech networks that no one – neither governments nor their owners – is able to control. Your book is concerned with the threat to social and economic stability represented by what you call meganets.

With the increasing complexity of robot systems, it is necessary to simulate them before deployment. To do this, a model of the robot's kinematics or dynamics is required. One of the most commonly used formats for modeling robots is the Unified Robot Description Format (URDF). The goal of this article is to understand how URDF is currently used, what challenges people face when working with it, and how the community sees the future of URDF. The outcome can potentially be used to guide future research. This article presents the results from a survey based on 510 anonymous responses from robotic developers of different backgrounds and levels of experience. We find that 96.8% of the participants have simulated robots before, and of them 95.5% had used URDF. We identify a number of challenges and limitations that complicate the use of URDF, such as the inability to model parallel linkages and closed-chain systems, no real standard, lack of documentation, and a limited number of dynamic parameters to model the robot. Future perspectives for URDF are also determined, where 53.5% believe URDF will be more commonly used in the future, 12.2% believe other standards or tools will make URDF obsolete, and 34.4% are not sure what the future of URDF will be. Most participants agree that there is a need for better tooling to ensure URDF's future use.

Meredith Broussard is unusually well placed to dissect the ongoing hype around AI. She's a data scientist and associate professor at New York University, and she's been one of the leading researchers in the field of algorithmic bias for years. And though her own work leaves her buried in math problems, she's spent the last few years thinking about problems that mathematics can't solve. Her reflections have made their way into a new book about the future of AI. In More than a Glitch, Broussard argues that we are consistently too eager to apply artificial intelligence to social problems in inappropriate and damaging ways. Her central claim is that using technical tools to address social problems without considering race, gender, and ability can cause immense harm.

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.

Looking back at 2022, it was obvious that Artificial Intelligence made incredible strides. These breakthroughs came in the form of NLP and Computer Vision to Generative AI and Explainable AI. We caught up with AI/ML experts from JP Morgan & Chase, UBS, University of Greenwich, Cornell University, and Fidelity Investments to find out about the most popular trends in Artificial Intelligence and Machine Learning in finance in 2023. Here's what they had to say: These leading experts will be joining us at the AI in Finance Summit New York on April 20-21, 2023, where they will be discussing the challenges of AI in Finance in more detail and how to overcome them. Standard Rate ticket sale for AI in Finance Summit New York ends on Friday, April 7, so secure your place today to save $200.

Broussard has also recently recovered from breast cancer, and after reading the fine print of her electronic medical records, she realized that an AI had played a part in her diagnosis--something that is increasingly common. That discovery led her to run her own experiment to learn more about how good AI was at cancer diagnostics. We sat down to talk about what she discovered, as well as the problems with the use of technology by police, the limits of "AI fairness," and the solutions she sees for some of the challenges AI is posing. The conversation has been edited for clarity and length. At the beginning of the pandemic, I was diagnosed with breast cancer.