Artificial intelligence (AI) techniques such as deep learning play a key role in enabling self-driving vehicles – for example, helping with feature extraction and object classification. AI can turn a fusion of camera, LiDAR, and automotive radar data into meaningful navigation information. But there are other tools that can help the decision-making process, such as game theory for autonomous vehicles. Game theory may be in the shadow of recent breakthroughs in AI, but its automotive future could turn out to be a very bright one indeed. Groups around the world have been busy looking at game theory for autonomous vehicles, and the list of potential applications is a long one.

Artificial Intelligence (AI) is one of the fastest-growing fields in technology, with researchers and developers working tirelessly to create ever more advanced machines. One of the most exciting developments in recent years has been the rise of generative AI, which has quickly captured the imagination of tech enthusiasts and industry experts alike. This new technology promises to revolutionize the way we interact with computers and has the potential to change many aspects of our lives. One of the most significant areas of development in generative AI has been the creation of AI chatbots. These chatbots are capable of answering questions, completing tasks, and even engaging in conversation with humans.

"Robots will not take over the world," said the world's most realistic humanoid robot, Ameca. But is this going to be the actual reality? The question most people ask is whether AI is becoming conscious or whether it is going to be rogue and wipe out human civilization. These thoughts have been greatly influenced by the predictive programming in our media, mostly in movies and comics. Should we be worried about an army of killer robots patrolling the streets with heat sensors and giant lasers to find and exterminate humans?

OpenAI CEO Sam Altman said that he was "a little bit scared" of ChatGPT and admitted that his technology would likely destroy "a lot of current jobs." The CEO of the company behind ChatGPT, likely the world's most famous AI chatbot, admitted that he was "a little bit scared" of his company's creation during an interview with ABC News. "We've got to be careful here," OpenAI CEO Sam Altman said during an interview Thursday. That's because the technology itself, he explained, was extremely powerful and could be dangerous. "I think people should be happy that we are a little bit scared of this," the 37-year-old tech guru said.

Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing. In this paper, we extend this task to resolving coreferences in long-form narrations of visual scenes. First we introduce a new dataset with annotated coreference chains and their bounding boxes, as most existing image-text datasets only contain short sentences without coreferring expressions or labeled chains. We propose a new technique that learns to identify coreference chains using weak supervision, only from image-text pairs and a regularization using prior linguistic knowledge. Our model yields large performance gains over several strong baselines in resolving coreferences. We also show that coreference resolution helps improving grounding narratives in images.

Subgraph-wise sampling -- a promising class of mini-batch training techniques for graph neural networks (GNNs -- is critical for real-world applications. During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i.e., the exponentially increasing dependencies of nodes with the number of MP iterations. However, discarding messages may sacrifice the gradient estimation accuracy, posing significant challenges to their convergence analysis and convergence speeds. To address this challenge, we propose a novel subgraph-wise sampling method with a convergence guarantee, namely Local Message Compensation (LMC). To the best of our knowledge, LMC is the first subgraph-wise sampling method with provable convergence. The key idea is to retrieve the discarded messages in backward passes based on a message passing formulation of backward passes. By efficient and effective compensations for the discarded messages in both forward and backward passes, LMC computes accurate mini-batch gradients and thus accelerates convergence. Moreover, LMC is applicable to various MP-based GNN architectures, including convolutional GNNs (finite message passing iterations with different layers) and recurrent GNNs (infinite message passing iterations with a shared layer). Experiments on large-scale benchmarks demonstrate that LMC is significantly faster than state-of-the-art subgraph-wise sampling methods.

With the rise of big data, artificial intelligence (AI), and data mining techniques, group analysis has increasingly become a powerful tool in many applications, ranging from policy-making, direct marketing, education, to healthcare. For example, an important analysis strategy is group profiling, which extracts and describes the characteristics of groups of people [40]; it has been commonly used for customized recommendations to overcome sparse and missing personal data [25]. The same strategy is also used for mining social media, educational, and healthcare data to understand the shared characteristics of online communities or student/patient cohorts [15, 51, 100]. While it may help to support public and private services or product creations that are better tailored to different communities, group profiles resulted from mathematical inference are typically not valid for every individual regarded as a member in the group (this is known as non-distributive group profiles) [40]. The shared group characteristics extracted from data can have social ramifications such as stereotyping, stigmatization, or lead to pernicious consequences in decision making because individuals might be judged by group characteristics they do not posses [24, 56, 58].

What would Albert Einstein think of AI? We may never know for sure, but it's fascinating to imagine. Some believe that he would have been a strong advocate for the technology, while others contend that he would have been more cautious about its implementation. No matter where you stand on this debate, one thing is for sure: AI is here to stay. And with its ever-growing presence in our lives, it's important to consider Einstein's potential thoughts on the matter.

MIT engineers have designed a walking lunar robot cleverly inspired by the animal kingdom. The "mix-and-match" system is made of worm-like robotic limbs astronauts could configure into various "species" of robots resembling spiders, elephants, goats and oxen. The team won the Best Paper Award last week at the Institute of Electrical and Electronics Engineers (IEEE) Aerospace Conference. WORMS (Walking Oligomeric Robotic Mobility System) is one team's vision of a future where astronauts living on a moon base delegate activities to robotic minions. However, to avoid "a zoo of machines" with various robots for every task imaginable, the modular WORMS would allow astronauts to swap out limbs, bases and appendages for the task at hand.

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.