The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. \textit{Sensitivity} and \textit{privacy budget} are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.

Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. However, existing studies are not efficient or effective enough for practical use. They suffer either from the inefficiency due to the usage of costly data transformations such as secure sharing and homomorphic encryption, or from the low model accuracy due to differential privacy designs. In this paper, we study a practical federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. We prove that our framework is secure without exposing the original record to other parties, while the computation overhead in the training process is kept low. Our experimental studies show that, compared with normal training with the local data of each owner, our approach can significantly improve the predictive accuracy, and achieve comparable accuracy to the original GBDT with the data from all parties.

Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.

The US government funding in artificial intelligence has fallen short and the country needs to invest in research, train an AI-ready workforce and apply the technology to national security missions, a government-commissioned panel led by Google's former CEO said in an interim report on Monday. The National Security Commission on Artificial Intelligence (NSCAI), created by Congress last year, raised concerns about the progress China has made in this area. It also said the US government still faces enormous work before it can transition AI from "a promising technological novelty into a mature technology integrated into core national security missions." The commission thinks an allied effort on AI in the realm of national security is important, Robert Work, vice chairman of the NSCAI and a former deputy secretary of defense, told reporters. The NSCAI has spoken with Japan, Canada, the United Kingdom, Australia and the European Union, Work said. The challenge US officials face is that American industry and academic leaders have said that any such restrictions would harm the US economy.

Birdsong is the world's finest music. Yet in New Zealand, where birds are regarded as taonga or precious things, a unique ecological conundrum exists. For an estimated 70 million years, New Zealand was isolated by water from the rest of the world, and its birds evolved free from predator mammals. As a result, many of New Zealand's birds, including the iconic kiwi, lost the ability to fly. Unfortunately, predators such as rats, possums, and stoats have gradually been introduced and are increasing.

Latest Strategic Study Released on Global Software for Autonomous Cars Market with forecast till 2025, the report comprises of historical data and estimation of the Global Software for Autonomous Cars Market. The following Industry is shown to progress with a noteworthy rise in the Compound Annual Growth Rate (CAGR) during the forecast period owing to various factors driving the market. Some of the key players mentioned in this research are "Alphabet, Delphi Automotive, Intel, NVIDIA, QNX Software Systems, Tesla, Apple, Autotalks, Cisco, Cohda Wireless, Covisint, DeepMap & Nauto", etc. Rapid Growth Factors In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor that's helping the market grow faster than usual is the tough competition. Business Strategies Key strategies in theGlobal Software for Autonomous Cars Market that includes product developments, partnerships, mergers and acquisitions, etc discussed in this report.

By now you've seen the big news about the world's largest fast-food chain by revenue. It was a bit shocking that it took this long to happen, but McDonald's (MCD) is finally embracing advanced technologies like artificial intelligence to boost sales. The guy credited with the turnaround at the 65-year-old restaurant behemoth since 2015, Steve Easterbrook, was behind the technological push that saw McDonald's not only embrace digital innovation but acquire two AI companies this year. This is big news for a few reasons. Before this year, it had been nearly two decades since the company made its last acquisition of a mediocre fast-food chain called Boston Chicken.

And while we don't always know the full impact of such technological advancement, with Artificial Intelligence (AI) it's becoming increasingly clear that AI will disrupt nearly every industry in one way or another. While many people fear a future in which robots have eliminated human employees from the workforce, AI technology is not nearly that sophisticated yet. Sure, an autonomous train is already helping a company automate its mining operations in Australia, Google is making music with AI and a robot named Flippy has a job flipping burgers in Southern California, but the technology has quite a ways to go before it'll free us from all mundane and repetitive tasks. While experts argue whether AI will ultimately create more jobs than it destroys, we know it is already being used to help humans do their jobs better and augment the number of tasks they can handle concurrently. The technology has been around longer than most people realize.

Newcrest Mining has deployed Microsoft cloud, AI and IoT technologies at Australia's largest underground, block cave mine to monitor and manage crushed ore bin levels. The soft sensor delivered a return in investment within the first three months of operation. The technology, developed by Newcrest in association with Microsoft and its partner Insight Enterprises, has been rolled out at Newcrest's Cadia Valley gold mine in NSW. The challenge facing Newcrest at Cadia was managing the levels in the underground crushed ore bins. If the bins overfill, they have to be manually emptied introducing lengthy and expensive production delays.

The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. The data was extracted from Twitter based on pre-defined keywords (i.e. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers.