Self-driving vehicles have the potential to reduce the cost of congestion on Australia's roads by more than a quarter over the next decade if there is a quick take-up of the technology, new modelling shows. The cost of congestion to the nation would, by 2030, drop to $27 billion a year from $37 billion if automated vehicles made up 30 per cent of the kilometres travelled, according to analysis of a "fast-penetration scenario" by the Bureau of Infrastructure, Transport and Regional Economics. Drawing on the analysis, federal Cities and Urban Infrastructure Minister Alan Tudge will tell a conference on Monday that potential benefits from self-driving vehicles would be the equivalent of spending tens of billions of dollars on boosting the size of roads and railways. A trial of an automated shuttle bus has been under way at Sydney Olympic Park since late 2017. "There are several ways that automated vehicles can reduce congestion, but the main one is that it would allow cars to safely travel more closely together," he will say in a speech to the Cities Symposium in western Sydney.

Data will be refreshed once a week.Also check out Artificial Intelligence Videos from Best 10 Artificial Intelligence Youtube Channels. If your blog is selected in this list, you have the honour of displaying this Badge (Award) on your blog. Boston, MA About Blog AI Trends is the leading industry media channel focused on the business and technology of AI. It is designed to keep executives ahead of the curve. Jason started this blog because he is passionate about helping professional developers to get started and confidently apply machine learning to address complex problems.

Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches.

In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence. In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position. To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks. As the first step in this direction, we evaluate our proposed method on the text summarization task. Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.

Brown-Forman, whose brands include Old Forester and Woodford Reserve bourbon, has spent the past three years taking inventory and integrating diverse pools of consumer, production and sales data across its global operations, as part of a broader effort to update an aging technology stack, Mr. Nall said. That was no small task. Founded nearly 150 year ago, Brown-Forman today has some 4,800 employees and operates in more than 170 countries world-wide. Since becoming CIO in 2015, Mr. Nall has led a gradual strategic shift in the role of the company's enterprise information-technology hub, from a backroom tech support service to a business partner aligned with marketing and sales teams, as well as other corporate and global production functions. That shift has seen data scientists and other IT pros increasingly working across the entire business on efforts to drive efficiencies and generate revenue: "Technology is interwoven into the whole process," he said. Nowhere is the need for a more business-oriented IT model more clear than with the emerging powers of artificial intelligence and machine learning to supercharge corporate decision-making, he said.

Naval technology is developing so rapidly that Australia's new $50 billion fleet of submarines may one day have to face deadly underwater drones, an expert has warned. Earlier this month, the federal government announced the signing of the Attack class submarine Strategic Partnering Agreement with French shipbuilder Naval Group. It will build 12 attack submarines to replace the Royal Australian Navy's ageing Collins class vessels, with the first one scheduled to be delivered in the early 2030s, the federal government said. But Russia has already provided a glimpse of underwater autonomous – or drone - weaponry. The Russian Ministry of Defence released testing footage of its'Poseidon' – a high-speed nuclear torpedo. Naval chiefs said the weapon is capable of carrying both conventional and nuclear warheads and will have a maximum speed of 200 km/h.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

"We are not talking about Terminator. We're talking about much simpler technologies, which are at best a few years away, and in fact, many of which you can see under development today in every theater of the war." He spoke February 14th as part of discussion called Killer Robots: Technological, Legal and Ethical Challenges at a meeting of the American Association for the Advancement of Science. "And so these are systems that are using sensors and software processing on their own to determine what constitutes a target and then applying lethal force to that, without supervision or meaningful human control." Another speaker, Peter Asaro, co-founder of the International Committee for Robot Arms Control, has participated in U.N. talks on autonomous weapons.

If you shop at Westfield, you've probably been scanned and recorded by dozens of hidden cameras built into the centres' digital advertising billboards. The semi-camouflaged cameras can determine not only your age and gender but your mood, cueing up tailored advertisements within seconds, thanks to facial detection technology. Westfield's Smartscreen network was developed by the French software firm Quividi back in 2015. Their discreet cameras capture blurry images of shoppers and apply statistical analysis to identify audience demographics. And once the billboards have your attention they hit record, sharing your reaction with advertisers.