Regularization techniques play a crucial role in preventing overfitting and improving the generalization performance of neural networks. Dropout, a widely used regularization technique, randomly deactivates units during training to introduce redundancy and prevent co-adaptation among neurons. Despite its effectiveness, dropout has limitations, such as its static nature and lack of interpretability. In this paper, we propose a novel approach to regularization by substituting dropout with Conway's Game of Life (GoL), a cellular automata with simple rules that govern the evolution of a grid of cells. We introduce dynamic unit deactivation during training by representing neural network units as cells in a GoL grid and applying the game's rules to deactivate units. This approach allows for the emergence of spatial patterns that adapt to the training data, potentially enhancing the network's ability to generalize. We demonstrate the effectiveness of our approach on the CIFAR-10 dataset, showing that dynamic unit deactivation using GoL achieves comparable performance to traditional dropout techniques while offering insights into the network's behavior through the visualization of evolving patterns. Furthermore, our discussion highlights the applicability of our proposal in deeper architectures, demonstrating how it enhances the performance of different dropout techniques.

This paper focuses on a data-centric approach to machine learning applications in the context of Conway's Game of Life. Specifically, we consider the task of training a minimal architecture network to learn the transition rules of Game of Life for a given number of steps ahead, which is known to be challenging due to restrictions on the allowed number of trainable parameters. An extensive quantitative analysis showcases the benefits of utilizing a strategically designed training dataset, with its advantages persisting regardless of other parameters of the learning configuration, such as network initialization weights or optimization algorithm. Importantly, our findings highlight the integral role of domain expert insights in creating effective machine learning applications for constrained real-world scenarios.

We explore the hypothesis that LLMs, such as GPT-3.5 and GPT-4, possess broader cognitive functions, particularly in non-linguistic domains. Our approach extends beyond standard linguistic benchmarks by incorporating games like Tic-Tac-Toe, Connect Four, and Battleship, encoded via ASCII, to assess strategic thinking and decision-making. To evaluate the models' ability to generalize beyond their training data, we introduce two additional games. The first game, LEGO Connect Language (LCL), tests the models' capacity to understand spatial logic and follow assembly instructions. The second game, the game of shapes, challenges the models to identify shapes represented by 1s within a matrix of zeros, further testing their spatial reasoning skills. This "show, don't tell" strategy uses games instead of simply querying the models. Our results show that despite their proficiency on standard benchmarks, GPT-3.5 and GPT-4's abilities to play and reason about fully observable games without pre-training is mediocre. Both models fail to anticipate losing moves in Tic-Tac-Toe and Connect Four, and they are unable to play Battleship correctly. While GPT-4 shows some success in the game of shapes, both models fail at the assembly tasks presented in the LCL game. These results suggest that while GPT models can emulate conversational proficiency and basic rule comprehension, their performance in strategic gameplay and spatial reasoning tasks is very limited. Importantly, this reveals a blind spot in current LLM benchmarks that we highlight with our gameplay benchmark suite ChildPlay (https://github.com/child-play-neurips/child-play). Our findings provide a cautionary tale about claims of emergent intelligence and reasoning capabilities of LLMs that are roughly the size of GPT-3.5 and GPT-4.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. "Game of Thrones" may be over, but winter is still coming. That means the dreaded cold and flu season is right around the corner. "A visit with a clinician has become increasingly common for upper respiratory symptoms since the COVID pandemic," Mark Fendrick, M.D., a general internist at the University of Michigan, who is based in Ann Arbor, Michigan, told Fox News Digital.

For weeks, Jay Deitcher has been on the hunt for a specific Miles Morales: Spider-Man toy from Spidey and His Amazing Friends. "The thing that makes the toy special is Miles's mask flips up to show his face," Deitcher says. "My son is Black, and it would be great to have a Spider-Man figure that looks like him." But even though the father of two from Albany, New York, started shopping for Hanukkah earlier than usual, he has yet to track down the elusive toy, which is sold out at many retailers. "We were already expecting a shortage, so we got him most of his other presents," he says.

The phrase "data science" is used every day, including in this very publication. We feel like we have an idea what it is. But what exactly is it? For one answer, we turn to Jeffrey Ullman, who won the Turing Award in 2020. "Where does data science come from?" asked Ullman, a Stanford University computer science professor, during his keynote address at the 27th ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) conference on Monday.

That's because we are encumbered in this world to have limited computational capabilities. We need abstractions and generalizations to navigate the complexities of this world. But along the way in developing a way to simplify the complex world, we discovered recurring patterns that have infinite reach. The models that we have discovered also allowed us to reason about many more different systems and to create universal computational machines. Obscured from our intuitive understanding of this world is that fundamental reality that everything is of computational origin.

This paper is both an introduction and an invitation. It is an introduction to CARLE, a Life-like cellular automata simulator and reinforcement learning environment. It is also an invitation to Carle's Game, a challenge in open-ended machine exploration and creativity. Inducing machine agents to excel at creating interesting patterns across multiple cellular automata universes is a substantial challenge, and approaching this challenge is likely to require contributions from the fields of artificial life, AI, machine learning, and complexity, at multiple levels of interest. Carle's Game is based on machine agent interaction with CARLE, a Cellular Automata Reinforcement Learning Environment. CARLE is flexible, capable of simulating any of the 262,144 different rules defining Life-like cellular automaton universes. CARLE is also fast and can simulate automata universes at a rate of tens of thousands of steps per second through a combination of vectorization and GPU acceleration. Finally, CARLE is simple. Compared to high-fidelity physics simulators and video games designed for human players, CARLE's two-dimensional grid world offers a discrete, deterministic, and atomic universal playground, despite its complexity. In combination with CARLE, Carle's Game offers an initial set of agent policies, learning and meta-learning algorithms, and reward wrappers that can be tailored to encourage exploration or specific tasks.

Fleet tracking, asset tracking, autonomous vehicles, manufacturing automation and warehousing are all areas in which artificial intelligence-embedded chip technologies can offload network data-carrying loads. They can do this while providing frontline, real-time information. Many of these on-the-go processes need lots of data to be activated. At the same time, they need this data in real time, and in transit, to take place. Instead these processes benefit most from edge computing, which brings compute, networking and other resources directly to the devices and data that need them. By activating artificial intelligence (AI0 processing loads at the level of a system-on-a-chip (SOC), IT can expand its options for distributing and offloading data-processing loads to different layers of enterprise architecture (e.g., cloud, a central data center, or the edge itself).

The U.S. Food and Drug Administration on Thursday convened a public meeting of its Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices. "Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients. As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency – potentially leading, in turn, to an undermining of patient trust in devices. Medical device innovation has already ramped up in response to the COVID-19 crisis, with Center for Devices and Radiological Health Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA. It's imperative, said Shuren, that patients' needs be considered as part of the creation process.