Apple HomePod release date: Siri speaker delayed until early 2018, company says

The Independent

Apple has delayed the HomePod, its next big new release. The Siri-enabled speaker is intended to be Apple's response to the increasing popularity of gadgets like the Amazon Echo, Google Home and the Sonos One. But it will have to wait a while before taking them on, since it has been delayed until "early 2018". "We can't wait for people to experience HomePod, Apple's breakthrough wireless speaker for the home, but we need a little more time before it's ready for our customers," a statement from Apple said. "We'll start shipping in the US, UK, and Australia in early 2018."


HomePod Release Date Delayed: Apple Speaker Will Launch Next Year

International Business Times

Apple promised to release the HomePod in December, but the company now says it won't start shipping them until next year. Apple revealed the HomePod at its Worldwide Developers Conference in June and had said it would come out for the holiday season, but that won't be the case anymore. "We can't wait for people to experience HomePod, Apple's breakthrough wireless speaker for the home, but we need a little more time before it's ready for our customers. We'll start shipping in the US, UK and Australia in early 2018." The delayed release date means that people who wanted to buy the HomePod for the holidays won't be able to anymore.


AIOps tools portend automated infrastructure management

#artificialintelligence

Automated infrastructure management took a step forward with the emergence of AIOps monitoring tools that use machine learning to proactively identify infrastructure problems. Orchestration tools are becoming increasingly popular as part of the DevOps process as they allow admins to focus on more critical tasks, rather than the routine steps it takes to move a workflow along. Our experts analyze the top solutions in the market, namely: Automic, Ayehu, BMC Control-M, CA, Cisco, IBM, Micro Focus, Microsoft, ServiceNow, and VMware. You forgot to provide an Email Address. This email address doesn't appear to be valid.


Machine Learning: Don't Let Weather Fluctuations Take Your Supply Chain by Storm

#artificialintelligence

Weather can cause significant fluctuations in consumer demand, and because of the bullwhip effect, it can produce unnecessarily high fluctuations on the supply side as well. And these types of variations typically turn into costs. Prepare too extensively, and you'll end up breaching the capacity limitations at every level of your supply chain and increasing your fresh goods spoilage, but failing to prepare sufficiently leads to significant lost sales. What's more, lost sales do not only apply to products that go out of stock, especially during extreme weather conditions when customers are more likely to make their decision on which store to visit based on the availability of a key product, for example bottled water, snow shovels, quality barbecue meats or candles. So how can retailers optimally prepare for weather-related fluctuations?


miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

#artificialintelligence

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed. We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites.


AI chip startup Graphcore raises $50 million to battle Nvidia and Intel

#artificialintelligence

With a focus on chips and artificial intelligence, U.K.-based Graphcore can now be considered one of Europe's hottest startups. Today, the company announced it has raised a $50 million round of funding led by Silicon Valley's Sequoia Capital, a firm not known for investing much in Europe. This follows the $60 million that Graphcore had already raised over the last 18 months. In a blog post, Graphcore cofounder Nigel Toon wrote that the company's partnership with Sequoia is an indication that it intends to remain independent as it seeks to compete in the surging AI chip market. "So over the last few weeks, Graphcore and Sequoia Capital have worked together on a scale-up business plan and on a funding plan which will allow us to grow more quickly and to support our prospective customers more deeply as we bring products to market," Toon wrote.


How AI-driven Search Empowers the Digital Workplace

#artificialintelligence

If finding information in the workplace is a manual hunt-and-peck exercise, and you keep adding more digital information to the mix, your employees are getting frustrated, and even worse, disengaged. After all, they are used to easy, intuitive search experiences in their personal lives with tools like Google, Alexa, and Siri. Yet when it comes to the workplace, the systems don't deliver that ease of use. Get the eBook How AI-driven Search Empowers the Digital Workplace to learn how AI technologies and cognitive search deliver a personalized, highly relevant experience for information access in the enterprise, and help you engage a modern workforce.


Stop Doing Fragile Research

@machinelearnbot

Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more generaland robust method, you have made the problem easier.


Google launches TensorFlow Lite for machine learning on mobile devices

@machinelearnbot

TensorFlow Lite for machine learning on mobile devices was first announced by Dave Burke, VP of engineering of Android at the Google I/O 2017. TensorFlow Lite is a lightweight version of Google's TensorFlow open source library that is mainly used for machine learning application by researchers and developers. Now, the search giant has launched the developer preview of a new machine learning toolkit designed specifically for smartphones and embedded devices and will be available for both Android and iOS app developers. This platform will allow developers to deploy AI on mobile devices. It enables on-device machine learning inference with low latency and a small binary size.


Apple delays HomePod release until 2018

Mashable

Looks like Apple lovers are going to have to wait a little longer for smart, room-filling sound. Per a report from CNBC, Apple announced on Friday it will delay the release of the HomePod, a smart speaker set to directly compete with the Amazon Echo, until early 2018. "We can't wait for people to experience HomePod, Apple's breakthrough wireless speaker for the home, but we need a little more time before it's ready for our customers," Apple wrote in an email to Mashable. "We'll start shipping in the U.S., UK and Australia in early 2018." Apple debuted the device in June at their Worldwide Developers Conference (WWDC), and originally planned to release the HomePod in December of 2017.