"We needed to invest in a building of that type of size and scale so we can deliver the convenience, in terms of delivery speed, to the Australian customer base."Mr Fuller said while the centre would likely improve Amazon's delivery times across most of its Australian customers, the retailer would not know the material benefits of the centre until its completion in 2021.When we launched in Australia there were lots of unknowns…we had to learn the nuances of the Australian marketplaceCraig Fuller, Amazon Australia's director of operationsWhile Amazon operates around 30 robotic fulfilment centres internationally, this will be its first in Australia. The centre will still use humans to pick and pack items, but instead of workers walking to the shelves to pick the items, robotic units take the shelves to them, improving fulfilment time and reducing the amount of walking workers have to do.Amazon has faced criticism in the past over the treatment of its distribution centre workers, who have described working conditions at its Melbourne centre as a "hellscape" due to allegedly unrealistic performance targets.New South Wales Premier Gladys Berejiklian said the jobs created by the new centre come at a time the Australian economy " …

Artificial Intelligence (AI) has a market full of hype, with vendors, customers, and media speaking non stop about the abilities of AI on worldwide and their contributions individually. Blockchain is also generally hyped in the market, with technology providers and clients claiming all sorts of abilities that may or may not be possible. Combining AI and blockchain can obtain double the hype? On the other hand, AI is implemented real, the actual value in many endless ways we talk about every day. Likewise, blockchain is starting to show value across a variety of applications and businesses.

XGBoost is a popular and efficient machine learning (ML) algorithm for regression and classification tasks on tabular datasets. It implements a technique known as gradient boosting on trees and performs remarkably well in ML competitions. Since its launch, Amazon SageMaker has supported XGBoost as a built-in managed algorithm. For more information, see Simplify machine learning with XGBoost and Amazon SageMaker. As of this writing, you can take advantage of the open-source Amazon SageMaker XGBoost container, which has improved flexibility, scalability, extensibility, and Managed Spot Training.

LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.

Tesla CEO Elon Musk is criticizing rivaling billionaire and Amazon CEO, Jeff Bezos, on Twitter after the e-tailing giant's splashy acquisition of a self-driving startup. In a tweet after the news, Musk called Bezos a'copy cat' for Amazon's decision to acquire Zoox, a self-driving technology company, for $1billion. 'Jeff Bezos is a copy [cat emoji] haha,' said Musk in the tweet which also linked a report from the Financial Times about Amazon's acquisition of Zoox. Musk's critical tweet no doubt references the CEO's own ventures with the electric vehicle company, Tesla, which has been making its own self-driving vehicles and software for some time. In addition to developing self-driving technology, Zoox, much like Tesla, also develops its own vehicles and aims to use those autonomous cars to let people order driverless rides from their phones.

This week brought good sales on Apple and Amazon devices, as well as some intriguing gaming deals. The Apple Watch Series 5 dropped to $299 again after WWDC kicked off earlier this week and Amazon still has some of its Echo speakers on sale (including the handy Echo Dot with clock). You can grab some extra storage for your Nintendo Switch for less at Newegg and Steam's Summer Sale has just begun. Here are the best deals we found this week that you can still get today. The latest Apple Watch has dropped to its lowest price ever again at Amazon and Walmart.

Amazon.com Inc. has reached an agreement to acquire autonomous-car developer Zoox, the two companies said Friday. The Wall Street Journal reported in May that the Seattle-based e-commerce giant was in advanced talks to buy Zoox, at a price lower than the $3.2 billion valuation Zoox had achieved in a previous fundraising round. Zoox was founded in 2014 and grew quickly amid expanding interest in autonomous vehicles and ride hailing but has more recently struggled to raise funding.

When it comes to smart assistants, we like Google Assistant over Amazon's Alexa here at WIRED. It's easier to set up and is just better at answering voice questions, hands-free. A growing number of smart speakers and smart displays support it, too. It was $574 and dropped down to $300 around March. Now it's the lowest we've seen, and the Amazon price is about $150 cheaper than other major retailers like B&H.

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.

We observe that a crucial inventory management problem ('phantom inventory'), that by some measures costs retailers approximately 4% in annual sales can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. State of the art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that non-anomalous entries be observed with vanishingly small noise (which is not the case in our problem, and indeed in many applications). So motivated, we propose a conceptually simple entry-wise approach to anomaly detection in low-rank Poisson matrices. Our approach accommodates a general class of probabilistic anomaly models. We extend recent work on entry-wise error guarantees for matrix completion, establishing such guarantees for sub-exponential matrices, where in addition to missing entries, a fraction of entries are corrupted by (an also unknown) anomaly model. We show that for any given budget on the false positive rate (FPR), our approach achieves a true positive rate (TPR) that approaches the TPR of an (unachievable) optimal algorithm at a min-max optimal rate. Using data from a massive consumer goods retailer, we show that our approach provides significant improvements over incumbent approaches to anomaly detection.