Un trajet simplifié grace à la réalité augmentée dans Google Maps

Twitter – Google Maps

Google ne cesse de créer le buzz avec ses innovations toujours plus poussées. L’objectif reste la simplification du quotidien pour les utilisateurs. Et cette fois, c’est Google Maps qui est visé par une invention incroyable en lien avec la réalité augmentée. La première version bêta de cette nouveauté sera disponible dans quelques jours !

Une nouvelle version de Google Maps

Qui ne se sert pas de Google Maps pour se déplacer en voiture ? Il faut bien avouer que c’est très pratique et que les trajets sont quand même plus simples. D’autant plus que l’application fonctionne en temps réel et vous indique même les embouteillages ! Mais c’est une toute nouvelle version de Google Maps qui va débarquer dans quelques jours.

Cette fois, Google va vous proposer de la réalité augmentée. En effet, on parle de « Live View », une nouvelle option qui permettra de mieux vous repérer dans l’espace. On a tout simplement l’impression d’être dans la réalité. Les plans sont ultra réalistes et les itinéraires largement simplifiés. De quoi ravir ceux qui font beaucoup de route !

Google Maps

@googlemaps

Google Maps meets AR.

Rolling out to Pixel phones, starting today.

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Un trajet simplifié

Cette innovation proposée par Google Maps ne sera pas disponible sur tous les appareils. En effet, le géant américain a publié une liste complète des appareils qui seront compatibles à cette nouvelle version de Google Maps. La version bêta sera disponible simultanément dans toutes les langues et sur tous les marchés.

C’est donc bel et bien l’arrivée de la réalité augmentée dans votre quotidien. Et ça pourrait bien être une véritable révolution dans la manière de vous guider ! Désormais, l’application utilisera l’écran du smartphone pour ajouter une couche d’informations par-dessus le monde réel. Cette nouvelle fonctionnalité est tout simplement fascinante !

Surveillez bien vos smartphones, une mise à jour de Google Maps devrait être disponible très bientôt !

Former Google CEO predicts the internet will split in two — and one part will be led by China

  • Speaking at a private event hosted by Village Global VC yesterday night, tech luminary and former Google CEO Eric Schmidt predicted that the internet will bifurcate into Chinese-led and US-led versions within the next decade.
  • Under Sundar Pichai’s leadership, Google has explored the potential to launch a censored version of its search engine in China, stirring up controversy internally and outside the company.

Former Google CEO claims internet will split between U.S. & China  

Eric Schmidt, who has been the CEO of Google and executive chairman of its parent company, Alphabet, predicts that within the next decade there will be two distinct internets: one led by the U.S. and the other by China.

Schmidt shared his thoughts at a private event in San Francisco on Wednesday night convened by investment firm Village Global VC. The firm enlists tech luminaries — including Schmidt, Jeff Bezos, Bill Gates and Diane Green — as limited partners, then invests their money into early-stage tech ventures.

At the event, economist Tyler Cowen asked about the possibility of the internet fragmenting into different sub-internets with different regulations and limited access between them in coming years. “What’s the chance, say, 10 to 15 years, we have just three to four separate internets?”

Schmidt said:

“I think the most likely scenario now is not a splintering, but rather a bifurcation into a Chinese-led internet and a non-Chinese internet led by America.

If you look at China, and I was just there, the scale of the companies that are being built, the services being built, the wealth that is being created is phenomenal. Chinese Internet is a greater percentage of the GDP of China, which is a big number, than the same percentage of the US, which is also a big number.

If you think of China as like ‘Oh yeah, they’re good with the Internet,’ you’re missing the point. Globalization means that they get to play too. I think you’re going to see fantastic leadership in products and services from China. There’s a real danger that along with those products and services comes a different leadership regime from government, with censorship, controls, etc.

Look at the way BRI works – their Belt and Road Initiative, which involves 60-ish countries – it’s perfectly possible those countries will begin to take on the infrastructure that China has with some loss of freedom.”

The Belt and Road is a massive initiative by Beijing to increase China’s political and economic influence by connecting and facilitating all kinds of trade, including digital trade, between China and countries in Europe, Africa, the Middle East and Asia.

Schmidt’s predictions come at a time when his successor at Google, CEO Sundar Pichai, has stirred up controversy around the company’s strategy in China.

Reportedly, Google has been developing “Project Dragonfly,” a censored version of its search engine that could appease authorities in China. The project allegedly included a means to suppress some search results, booting them off the first page, and a means to fully block results for sensitive queries, for example, around “peaceful protests.”

What's next for Schmidt?

What’s next for Google’s Eric Schmidt? Sree Sreenivasan weighs in  

In recent weeks, hundreds of Google employees lobbied Pichai for more transparency and signed a letter saying that the reported plans raised “urgent moral and ethical issues.”

Pichai has said that Google has been “very open about our desire to do more in China,” and that the team “has been in an exploration stage for quite a while now,” and considering “many options,” but is nowhere near launching in China.

In a separate discussion last night between Schmidt and several start-up founders, he lauded Chinese tech products, services and adoption, especially in mobile payments. He noted that Starbucks in China don’t feature a register. Customers order ahead online and pay with their phones before picking up their lattes.

A business development leader with Facebook, Ime Archebong, asked Schmidt if large tech companies are doing enough good in the world.

Schmidt replied: “The judge of this is others, not us. Self-referential conversations about ‘Do I feel good about what I’m doing?’ are not very helpful. The judge is outside.”

At several points in the private discussion, Schmidt urged entrepreneurs to build products and services that are not merely addictive, but valuable. He also said not enough companies “measure the right things.” Too many focus on short-term revenue growth and satisfying shareholders, rather than what’s best for their users, society and the long-term health of their companies.

Schmidt was the CEO of Google from 2001, when he took over from co-founder Larry Page, through 2011, when Page reclaimed the reins. He remained as executive chairman of Google and then Alphabet until earlier this year.

Correction: Eric Schmidt did not specify a date by which he believed the internet would bifurcate. He was responding to a question from Tyler Cowen which specified “in the next 10 to 15 years.”

The 10 tech companies that have invested the most money in AI of the tech giants. Google is the biggest investor in AI by billions.

  • Google has invested the most in artificial intelligence (AI) out of the tech giants, according to research from RS Components.
  • Since the first acquisition in 1998, tech giants have spent nearly $8.6 billion on AI startups.v2-AI-innovations

Google has invested the most money in artificial intelligence (AI), according to research from RS Components. Tech giants have disclosed nearly $8.6 billion in acquisitions since 1998.

The company has spent nearly $3.9 billion in disclosed deals since 2006, with the bulk of that spent in its 2014 acquisition of Nest Labs for $3.2 billion. The Nest Labs purchase was the single largest disclosed investment on RS Components’ list, which includes 103 startup purchases across 15 tech giants.

Here are the top 10 tech companies based on how much they’ve spent acquiring AI startups where the price was disclosed.

1. Google – $3.9 billion

2. Amazon – $871 million

3. Apple – $786 million

4. Intel – $776 million

5. Microsoft – $690 million

6. Uber – $680 million

7. Twitter – $629 million

8. AOL – $191.7 million

9. Facebook – $60 million

10. Salesforce – $32.8 million

Google continued its domination in total number of acquired startups, investing in 29 since its first, Neven Vision, in 2006. Apple grabbed second with 14, and Microsoft was third with nine.

Microsoft was the first to invest in AI, spending $40 million on Firefly Network in 1998. Google was next to invest, but didn’t do so for another eight years.

Here are the eight single biggest disclosed investments in AI startups to date.

1. Nest Labs – $3.2 billion

2. Kiva Systems – $775 million

3. Otto – $680 million

4. Deep Mind – $500 million

5. TellApart – $479 million

6. Movidius – $400 million

7. Nervana – $350 million

8. SwiftKey – $250 million

The pace and price of startup acquisitions are unlikely to drop as AI continues to grow as a technology.

Artificial intelligence and machine learning: What are the opportunities for search marketers? (Author: Albert Gouyet)

Did you know that by 2020 the digital universe will consist of 44 zettabytes of data (source: IDC), but that the human brain can only process the equivalent of 1 million gigabytes of memory?

Source: https://searchenginewatch.com/2018/01/02/artificial-intelligence-and-machine-learning-what-are-the-opportunities-for-search-marketers/

The explosion of big data has meant that humans simply have too much data to understand and handle daily.

For search, content and digital marketers to make the most out the valuable insights that data can provide, it is essential to utilize artificial intelligence (AI) applications, machine learning algorithms and deep learning to move the needle of marketing performance in 2018.

In this article, I will explain the advancements and differences between artificial intelligence (AI), machine learning and deep learning while sharing some tips on how SEO, content and digital marketers can make the most of the insights – especially from deep learning – that these technologies bring to the search marketing table.

I studied artificial intelligence in college and after graduating took a job in the field. It was an exciting time, but our programming capabilities, when looking back now, were rudimentary. More than intelligence, it was algorithms and rules that did their best to mimic how intelligence solves problems with best-guess recommendations.

Fast forward to today and things have evolved significantly.

The Big Bang: The big data explosion and the birth of AI

Since 1956, AI pioneers have been dreaming of a world where complex machines possess the same characteristics as human intelligence.

In 1996, the industry reached a major milestone when the IBM’s Deep Blue computer defeated a chess grandmaster by considering 200,000,000 chessboard patterns a second to make optimal moves.

Between 2000 and 2017, there were many developments that enabled great leaps forward. Most important were the geometric increases in the amount data collected, stored, and made retrievable. That mountain of data, which came to be known as big data, ushered in the advent of AI.

And it keeps growing exponentially: in 2016 IBM estimated that 90% of the world’s data had been generated over the last few years.

When thinking about AI, machine learning and deep learning, I find it helps to simplify and visualize how the 3 categories work and relate to each other –  this framework also works from a chronological, sub-set development and size perspective.

Artificial intelligence is the science of making machines do things requiring human intelligence. It is human intelligence in machine format where computer programs develop data-based decisions and perform tasks normally performed by  humans.

Machine learning takes artificial intelligence a step further in the sense that algorithms are programmed to learn and improve without the need for human data input and reprogramming.

Machine learning can be applied to many different problems and data sets. Google’s RankBrain algorithm is a great example of machine learning that evaluates the intent and context of each search query, rather than just delivering results based on programmed rules about keyword matching and other factors.

Deep learning is a more detailed algorithmic approach, taken from machine learning, that uses techniques based on logic and exposing data to neural networks (think human brain) so that the technology trains itself to perform tasks such as speech and image recognition.

Massive data sets are combined with pattern recognition capabilities to automatically make decisions, find patterns, emulate previous decisions, etc. Self-learning comes from here as the machine gets better from the more data that it is supplied.

Driverless cars, Netflix movie recommendations and IBMs Watson are all great examples of deep learning applications that break down tasks to make machine actions and assists possible.

Organic search, content and digital performance: Challenge and opportunity

Organic search (SEO) drives 51% of all website traffic and hence in this section it is only natural to explain the key benefits that deep-learning brings to SEO and digital marketers.

Organic search is a data-intensive business. Companies value and want their content to be visible on thousands or even millions of keywords in one to dozens of languages. Search best practices involve about 20 elements of on-page and off-page tactics. The SERPs themselves now come in more than 15 layout varieties.

Organic search is your market-wide voice of the customer, telling you what customers want at scale. However, marketers are faced with the challenge of making sense of so much data, having limited resources to mine insights and then actually act on the right and relevant insight for their business.

To succeed in highly demanding markets against your competitors’ many brands now requires the expertise of an experienced data analyst, and this is where machine learning and deep learning layershelp recommend optimizations to content.

Connecting the dots with deep learning: Data and machine learning

The size of the organic data and the number of potential patterns that exist on that data make it a perfect candidate for deep learning applications. Unlike simple machine learning, deep-learning works better when it can analyse a massive amount of relevant data over long periods of time.

Deep learning and its ability to identify or prioritize material changes in interests and consumption behavior allows organic search marketers to gain a competitive advantage, be at the forefront of their industry, and produce the material that people need before their competitors, boosting their reputation.

In this way, marketers can begin to understand the strategies put forth by their competitors. They will see how well they perform compared to others in their industry and can then adjust their strategies to address the strengths or weaknesses that they find.

  • The insights derived from deep learning technologies blend the best of search marketing and content marketing practices to power the development, activation, and automated optimization of smart content, content that is self-aware and self-adjusting, improving content discovery and engagement across all digital marketing channels.
  • Intent data offers in-the-moment context on where customers want to go and what they want to know, do, or buy. Organic search data is the critical raw material that helps you discover consumer patterns, new market opportunities, and competitive threats.
  • Deep learning is particularly important in search, where data is copious and incredibly dynamic. Identifying patterns in data in real-time makes deep learning your best first defense in understanding customer, competitor, or market changes – so that you can immediately turn these insights into a plan to win.

To propel content and organic search success in 2018 marketers should let the machines does more of the leg work to provide the insights and recommendations that allow marketers to focus on the creation of smart content.

Below are a just a few examples of the benefits for the organic search marketer:

Site analysis

Pinpoint and fix critical site errors that drive the greatest benefits to a brand’s bottom line. Deep learning technology can be used to incorporate website data, detect anomalies tying site errors to estimated marketing impact so that marketers can prioritize fixes for maximum results.

Without a deep learning application to help you, you might be staring at a long list of potential fixes which typically get postponed to later.

Competitive strategy

Identifying patterns in real-time makes deep learning a brands’ best first defense in understanding customer, competitor, or market changes– so that marketers can immediately turn these insights into a plan to win.

Content discovery

Surface high-value topics that target different content strategies, such as stopping competitive threats or capitalizing on local demand.

Deep learning technology can be used to assess the ROI of new content items and prioritize their development by unveiling insights such as topic opportunity, consumer intent, characteristics of top competing content, and recommendations for improving content performance

Content development

Score the quality and relevance of each piece of content produced. Deep learning technology can help save time with automated tasks of content production, such as header tags, cross-linking, copy optimization, image editing, highly optimized CTAs that drive performance, and embedded performance tracking of website traffic and conversion.

Content activation

Deep learning technology can help ensure that each piece of content is optimized for organic performance and customer experience—such as schema for structure, AMP for better mobile experiences, and Open Graph for Facebook. Technology can help marketers can amplify their content in social networks for greater visibility.

Automation

Automation helps marketers do more with less and execute more quickly. It allows marketers to manage routine tasks with little effort, so that they can focus on high-impact activities and accomplish organic business goals at scale.

Note: To make the most of the insights and recommendations from deep learning marketers need to take action and make the relevant changes to web page content to keep website visitors engaged and ultimately converting.

Additionally, because the search landscape changes so frequently, deep learning fuels the development of smart content and can be used to automatically adjust to changes in content formats and standards.

Deep learning in action

An example of deep learning in organic search is DataMind. BrightEdge (disclosure, my employer) Data Mind is like a virtual team of data scientists built into the platform, that combines massive volumes of data with immediate, actionable insights to inform marketing decisions.

In this case the deep learning engine analyzes huge, complex, and dynamic data sets (from multiple sources that include 1st and 3rd party data) to determine patterns and derive the insights marketers need. Deep learning is used to detect anomalies in a site’s performance and interpret the reasons, such as industry trends, while making recommendations about how to proceed.

Conclusion

Think of deep learning applications as your own personal data scientist – here to help and assist and not to replace. The adoption of AI, machine learning and now deep learning technologies allows faster decisions, more accurate and smarter insights.

Brands compete in the content battleground to ensure their content is optimized and found, engages audiences and ultimately drives conversions and digital revenue. When armed with these insights from deep learning, marketers get a new competitive weapon and a massive competitive edge.

Google launches a new directory to help you discover Assistant actions (Source: TechCrunch)

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source: https://techcrunch.com/2018/01/08/google-launches-a-new-directory-to-help-you-find-assistant-action/?ncid=rss&utm_source=tcfbpage&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&sr_share=facebook

Google says you can now perform more than a million actions with the Google Assistant. Those range from looking up photos with Google Photos to starting a meditation session from Headspace. But one problem with voice assistants is that it’s very hard to discover which actions you actually can perform. For many users, that means they use their Google Home or Alexa to set a few timers and maybe play music, without ever realizing what else they can do.

To help its users a bit, Google is launching a new directory page for the Google Assistant today. This is part of a slew of Assistant-related announcements at CES today; while it’s probably not the most important (those smart displays sure look nice, after all), it’s nevertheless a useful new tool, especially for new users.

It’s been almost exactly a year since Google enabled third-party actions, and while Google can’t boast the same numbers of third-party support as Amazon, there’s clearly a lot of developer interest in building these actions. And to make talking about them a bit easier, Google is also now calling its first-party actions… wait for it… “actions.”

FEATURED IMAGE: BLOOMBERG/GETTY IMAGES

Search engines are weakening Amazon’s hold on product search

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Source: http://www.businessinsider.fr/us/google-search-engines-weaken-amazon-hold-on-product-search-2017-12/

Where US Consumers start product searches

BI Intelligence

Amazon’s share of initial product searches dropped from 55% in 2016 to 49% in 2017, and search engines like Google appear to be responsible, according to a survey from Survata as cited by Bloomberg.

Search engines’ share went from 28% to 36% between 2016 and 2017, reversing the drop they saw between 2015, when they had 34%, and 2016.

The rise of mobile commerce (m-commerce) may be responsible for search engines’ turnaround.

Search engines are the most popular option for mobile shopping, with consumers favoring them over retailers’ websites and apps, which includes Amazon’s. M-commerce is estimated to have grown from 19% of US e-commerce sales in 2016 to 23% in 2017, so search engines’ mobile advantage may be helping it gain on Amazon in product search. Mobile shopping is projected to make up nearly half of all US e-commerce by 2021, so search engines would be wise to invest in their mobile shopping search experience going forward.

Winning back search is necessary when competing with Amazon because of its strong conversion rate. Amazon has a tremendous ability to convert searchers to purchasers, which blocks competitors from having an opportunity to steal them away. If search engines and retailers want to take back more control of search, they should note the reasons consumers gave for starting their searches on Amazon — navigation, product selection, prices, and shipping capabilities — and then look to improve in those areas.

Voice shopping and social commerce may be the next battlefields for e-commerce search.

  • BI Intelligence estimates that 31% of US adults will use voice to make a payment by 2022, up from 8% this year, opening up a new field for search. Amazon is already well established in the space, but Google is working with retailers in the hopes of overtaking it and claiming more of voice search for itself. And as the industry develops, there are sure to be even more players.
  • Social media is heavily influential on younger generations of shoppers, so many of them are likely to search for products there. Those trying to control product search will need to find ways to use social media to stay competitive in search, as Amazon is trying to do with its social platform Spark.