Writing has long been considered a hallmark of human intelligence and remains a pinnacle task for artificial intelligence (AI) due to the intricate cognitive processes involved. Recently, rapid advancements in generative AI, particularly through the development of Large Language Models (LLMs), have significantly transformed the landscape of writing assistance. However, underrepresented languages like Arabic encounter significant challenges in the development of advanced AI writing tools, largely due to the limited availability of data. This scarcity constrains the training of effective models, impeding the creation of sophisticated writing assistance technologies. To address these issues, we present Gazelle, a comprehensive dataset for Arabic writing assistance. In addition, we offer an evaluation framework designed to enhance Arabic writing assistance tools. Our human evaluation of leading LLMs, including GPT-4, GPT-4o, Cohere Command R+, and Gemini 1.5 Pro, highlights their respective strengths and limitations in addressing the challenges of Arabic writing. Our findings underscore the need for continuous model training and dataset enrichment to manage the complexities of Arabic language processing, paving the way for more effective AI-powered Arabic writing tools.

Stone Age humans were savvy hunters who devised long stone'runways' that could trap animals inside and make them easy prey to kill by the hundreds. These v-shaped structures, called'desert kites,' have been widely observed in the Middle East. Using laser scanning techniques, researchers in South Africa have confirmed these'desert kites' were used much further south in sub-Saharan Africa than previously believed. Found in Keimoes, South Africa, the desert kites are thousands of years newer than ones found in Israel and Syria and indicate a complex understanding of animal behavior and migratory patterns. Light Detection And Ranging, or LiDAR, technology illustrates where the stone walls of the desert kites' funnels were erected, guiding prey into a killing pit Desert kites were traps devised by Neolithic and Bronze Age hunter-gatherers to corner game like cattle, pig and deer.

Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to upload their private data to the cloud. This research aims to provide effective and efficient MLaaS such that the cloud server learns nothing about user data and the users cannot infer the proprietary model parameters owned by the server. This work makes the following contributions. First, it unveils the fundamental performance bottleneck of existing schemes due to the heavy permutations in computing linear transformation and the use of communication intensive Garbled Circuits for nonlinear transformation. Second, it introduces an ultra-fast secure MLaaS framework, CHEETAH, which features a carefully crafted secret sharing scheme that runs significantly faster than existing schemes without accuracy loss. Third, CHEETAH is evaluated on the benchmark of well-known, practical deep networks such as AlexNet and VGG-16 on the MNIST and ImageNet datasets. The results demonstrate more than 100x speedup over the fastest GAZELLE (Usenix Security'18), 2000x speedup over MiniONN (ACM CCS'17) and five orders of magnitude speedup over CryptoNets (ICML'16). This significant speedup enables a wide range of practical applications based on privacy-preserved deep neural networks.

The Gazelle.ai investment attraction platform has been designed from the ground up to help economic developers, trade, investors, and dealmakers generate leads, engage those leads, and ultimately attract investment. The Gazelle.ai investment attraction platform leverages the power of artificial intelligence to help you find the right companies at the right time.

The outcome is for more efficient security for cloud-based machine learning. The approach comes from the Massachusetts Institute of Technology and it is focused with securing data used in online neural networks. A secondary brief was to boost security while also avoiding significantly slowing down machine runtimes. A problem with many cybersecurity solutions is that they tend to slowdown the very device they aim to protect. The harnessing of machine learning with the cloud is important since more organizations are outsourcing machine learning.

The use of public clouds is on the rise with advisory firm Gartner forecasting that a whopping $186.4 billion will be spent on the services globally in 2018. Tech giants such as Amazon, Google and Microsoft have even launched cloud-based artificial intelligence (AI) platforms capable of conducting computation-heavy tasks through the use of convolutional neural networks. However, with great power comes great responsibility. As the cloud becomes ever more useful in ever more applications the potential for security breaches also increases. A study conducted in 2016 by McAfee-acquired security firm Skyhigh Networks on 30 million of its software users revealed that an average enterprise experiences an alarming 23.2 cloud-related threats per month.

Outsourcing machine learning is a rising trend in industry. Major tech firms have launched cloud platforms that conduct computation-heavy tasks, such as, say, running data through a convolutional neural network (CNN) for image classification. Resource-strapped small businesses and other users can upload data to those services for a fee and get back results in several hours. But what if there are leaks of private data? In recent years, researchers have explored various secure-computation techniques to protect such sensitive data.

A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data. Outsourcing machine learning is a rising trend in industry. Major tech firms have launched cloud platforms that conduct computation-heavy tasks, such as, say, running data through a convolutional neural network (CNN) for image classification. Resource-strapped small businesses and other users can upload data to those services for a fee and get back results in several hours.

Computers are playing spot the difference in the Serengeti. An image-recognition algorithm that can identify different species could make it easier to track animals in the wild. Using a database of 3.2 million photos taken by hidden camera traps in the Serengeti National Park in Tanzania, Jeff Clune at the University of Wyoming in Laramie and his colleagues trained the deep-learning system to distinguish between 48 animal species, such as elephants, giraffes and gazelles. In tests, it correctly identified the species present in an image 92 per cent of the time. Camera traps automatically take pictures of passing animals when triggered by heat and motion.