Jabra's new, shockingly slim over-and on-ear headsets feature an everyday design that can transition effortlessly from professional to personal use. We may earn revenue from the products available on this page and participate in affiliate programs. I'm in the lobby of a Las Vegas hotel and casino, where roller bag wheels hiss across marble and slot-machine chimes compete for oxygen. Everything is auditioning for my attention, but I'm transfixed by someone reaching into a purse and pulling out headphones carrying cases so slim I assume they're empty. But these are no hollow accessories.

Chinese porcelain holds immense historical and cultural value, making its accurate classification essential for archaeological research and cultural heritage preservation. Traditional classification methods rely heavily on expert analysis, which is time-consuming, subjective, and difficult to scale. This paper explores the application of DL and transfer learning techniques to automate the classification of porcelain artifacts across four key attributes: dynasty, glaze, ware, and type. We evaluate four Convolutional Neural Networks (CNNs) - ResNet50, MobileNetV2, VGG16, and InceptionV3 - comparing their performance with and without pre-trained weights. Our results demonstrate that transfer learning significantly enhances classification accuracy, particularly for complex tasks like type classification, where models trained from scratch exhibit lower performance. MobileNetV2 and ResNet50 consistently achieve high accuracy and robustness across all tasks, while VGG16 struggles with more diverse classifications. We further discuss the impact of dataset limitations and propose future directions, including domain-specific pre-training, integration of attention mechanisms, explainable AI methods, and generalization to other cultural artifacts.

This p aper explore s the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the s ecurit y and s afet y lands cape. A s AI models become increasingly prevalent, understanding their potential risks and vulnerabilitie s is crucial. We review the current s ecurit y and s afet y s cenarios while highlighting challenge s such as tracking issue s, remediation, and the app arent abs ence of AI model lifecycle and ownership proce ss e s. Comprehensive strategie s to enhance s ecurit y and s afet y for both model developers and end-us ers are propos ed. This p aper aims to provide s ome of the foundational piece s for more standardized s ecurit y, s afet y, and transp arency in the development and operation of AI models and the larger open ecosystems and communitie s forming around them. Generative AI, a branch of artificial intelligence focus ed on AI produc tion of content such as text, image s and video, has s een significant advancement s since the introduc tion of generative advers arial net works (GANs) in 2014 (Goodfellow et al., 2014), which improved data generation but faced issue s like training instabilit y. The development of transformers and s elf at tention mechanisms in 2017 (Vaswani et al., 2017) facilitated further improvement s in natural language proce ssing, leading to large language models (LLMs) like GPT (Radford et al., 2018) with highly advanced text generation cap abilitie s. Dif fusion models (S ohl-Dickstein et al., 2015) have als o s een rapid advancement in image and video generation. This rapid advancement in technology cap abilit y has been matched by an equally rapid uptake in adoption. A s with any new technology, it is worth noting that the industr y is still identif ying new and valuable us e s for AI and the s e market predic tions may fluc tuate as us e cas e s are te sted in real world environment s with real world problems. For the purpos e of clarit y we shall be using the term public model, for a model which is publicly available for download and us e. LLMs are the next evolution of data s cience, a field focus ed on math and data. Unlike traditional systems and applications which rely on logic and programming for a specified outcome, large language model development t ypically consist s of architec ture re s earch and de sign, which is then coded.

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.

Glaive is a state-space planner based on Hoffmann and Nebel's Fast-Forward which solves the narrative planning problem defined by Riedl and Young -- to construct a plan which achieves the author's goals out of steps which are clearly motivated and goal-oriented toward individual character goals. Glaive reasons about how characters cooperate and conflict based on causal structures and possible worlds. By leveraging the unique constraints of narrative planning, Glaive reduces its branching factor and calculates a more accurate heuristic. We evaluate it on 8 narrative planning problems and demonstrate that it can solve certain non-trivial problems in under 1 second.

The typical goal of an experience manager in an interactive narrative is to create a sense of shared authorship that lends the player freedom to personalize the experience while still meeting the author's constraints on structure. This can be difficult when the player and author only communicate with one another through their actions. Each new action causes new questions to arise, assumptions to be made, and old questions to be answered. In this paper, I propose a technique called Mutual Implicit Question Answering, or MIQA, designed to allow an experience manager to both perceive and influence the momentum of an interactive story. It combines a generative model of narrative planning with analytical models of question answering and salience. I also present the results of a small, qualitative study of how people construct interactive narratives that lends insight for the eventual evaluation of a MIQA experience manager.

PALO ALTO, Calif., July 22, 2020 – QC Ware, provider of enterprise software and services for quantum computing, announced a significant breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. QC Ware's algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware's Forgecloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators. "QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines."

Cloud computing is becoming increasingly popular as a platform for distributed training of deep neural networks. Synchronous stochastic gradient descent (SSGD) suffers from substantial slowdowns due to stragglers if the environment is non-dedicated, as is common in cloud computing. Asynchronous SGD (ASGD) methods are immune to these slowdowns but are scarcely used due to gradient staleness, which encumbers the convergence process. Recent techniques have had limited success mitigating the gradient staleness when scaling up to many workers (computing nodes). In this paper we define the Gap as a measure of gradient staleness and propose Gap-Aware (GA), a novel asynchronous-distributed method that penalizes stale gradients linearly to the Gap and performs well even when scaling to large numbers of workers. Our evaluation on the CIFAR, ImageNet, and WikiText-103 datasets shows that GA outperforms the currently acceptable gradient penalization method, in final test accuracy. We also provide convergence rate proof for GA. Despite prior beliefs, we show that if GA is applied, momentum becomes beneficial in asynchronous environments, even when the number of workers scales up.

It's January, which means that Las Vegas, or the bit that pretends not to be Las Vegas for tax reasons, will play host to CES. The Consumer Electronics Show is the event that kicks off the technology world's annual calendar, and 2018 will see thousands of companies descend upon Nevada to show off their wares. Many will claim to have the solution to whatever problem you may have, but we'll be on the ground to peer through their flashy promises. If last year's CES had a theme, then it was an attempt to broaden its horizons beyond smartphones, tablets and TVs. Technology companies have mined every last drop of good ideas from the traditional gadget world, which is why many chose to try something new.

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.