8 items tagged "Google"

  • 2016 wordt het jaar van de kunstmatige intelligentie

    Artificial-intelligence.jpg-1024x678December is traditiegetrouw de periode van het jaar om terug te blikken en oudjaarsdag is daarbij in het bijzonder natuurlijk de beste dag voor. Bij Numrush kijken we echter liever vooruit. Dat deden we begin december al met ons RUSH Magazine. In deze Gift Guide gaven we cadeautips aan de hand van een aantal thema’s waar we komend jaar veel over gaan horen.Eén onderwerp bleef bewust een beetje onderbelicht in onze Gift Guide. Aan de ene kant omdat het niet iets is wat je cadeau geeft, maar ook omdat het eigenlijk de diverse thema’s overstijgt. Ik heb het over kunstmatige intelligentie. Dat is natuurlijk niets nieuws, er is al ontzettend veel gebeurt op dat vlak, maar komend jaar zal de toepassing hiervan nog verder in een stroomversnelling raken.

  • Big Data on the cloud makes economic sense

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness

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    For almost 10 years, only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc.
    used data analytics on a scale that justified the idea of ‘big’ in Big Data. Now more and more firms are
    warming up to the concept. Photo: Bloomberg

    On 27 September, enterprise software company SAP SE completed the acquisition of Altiscale Inc.—a provider of Big Data as-a-Service (BDaaS). The news came close on the heels of data management and analytics company Cloudera Inc. and data and communication services provider CenturyLink Inc. jointly announcing BDaaS services. Another BDaaS vendor, Qubole Inc., said it would offer a big data service solution for the Oracle Cloud Platform.

    These are cases in point of the growing trend to offer big data analytics using a cloud model. Cloud computing allows enterprises to pay for software modules or services used over a network, typically the Internet, on a monthly or periodical basis. It helps firms save relatively larger upfront costs for licences and infrastructure. Big Data analytics solutions enable companies to analyse multiple data sources, especially large data sets, to take more informed decisions.

    According to research firm International Data Corporation (IDC), the global big data technology and services market is expected to grow at a compound annual growth rate (CAGR) of 23.1% over 2014-2019, and annual spending is estimated to reach $48.6 billion in 2019.

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness.

    MarketsandMarkets, a research firm, estimates the BDaaS segment will grow from $1.8 billion in 2015 to $7 billion in 2020. There are other, even more optimistic estimates: research firm Technavio, for instance, forecasts this segment to grow at a CAGR of 60% from 2016 to 2020.

    Where does this optimism stem from?

    For almost 10 years, it was only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc., that used data analytics on a scale that justified the idea of ‘big’ in Big Data. In industry parlance, three key attributes are often used to understand the concept of Big Data. These are volume, velocity and variety of data—collectively called the 3Vs.

    Increasingly, not just Google and its rivals, but a much wider swathe of enterprises are storing, accessing and analysing a mountain of structured and unstructured data. The trend is necessitated by growing connectivity, falling cost of storage, proliferation of smartphones and huge popularity of social media platforms—enabling data-intensive interactions not only among ‘social friends’ but also among employers and employees, manufacturers and suppliers, retailers and consumers—virtually all sorts of connected communities of people.

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    A November 2015 IDC report predicts that by 2020, organisations that are able to analyse all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.

    The nascent nature of BDaaS, however, is causing some confusion in the market. In a 6 September article onNextplatform.com, Prat Moghe, founder and chief executive of Cazena—a services vendor—wrote that there is confusion regarding the availability of “canned analytics or reports”. According to him, vendors (solutions providers) should be carefully evaluated and aspects such as moving data sets between different cloud and on-premises systems, ease of configuration of the platform, etc., need to be kept in mind before making a purchase decision.

    “Some BDaaS providers make it easy to move datasets between different engines; others require building your own integrations. Some BDaaS vendors have their own analytics interfaces; others support industry-standard visualization tools (Tableau, Spotfire, etc.) or programming languages like R and Python. BDaaS vendors have different approaches, which should be carefully evaluated,” he wrote.

    Nevertheless, the teething troubles are likely to be far outweighed by the benefits that BDaaS brings to the table. The key drivers, according to the IDC report cited above, include digital transformation initiatives being undertaken by a lot of enterprises; the merging of real life with digital identity as all forms of personal data becomes available in the cloud; availability of multiple payment and usage options for BDaaS; and the ability of BDaaS to put more analytics power in the hands of business users.

    Another factor that will ensure growth of BDaaS is the scarcity of skills in cloud as well as analytics technologies. Compared to individual enterprises, cloud service providers such as Google, Microsoft Corp., Amazon Web Services and International Businsess Machines Corp. (IBM) can attract and retain talent more easily and for longer durations.

    Manish Mittal, managing principal and head of global delivery at Axtria, a medium-sized Big Data analytics solutions provider, says the adoption of BDaaS in India is often driven by business users. While the need is felt by both chief information officers and business leaders, he believes that the latter often drive adoption as they feel more empowered in the organisation.

    The potential for BDaaS in India can be gauged from Axtria’s year-on-year business growth of 60% for the past few years—and there are several niche big data analytics vendors currently operating in the country (besides large software companies).

    Mittal says that the growth of BDaaS adoption will depend on how quickly companies tackle the issue of improving data quality.

    Source: livemint.com, October 10, 2016
     

     

  • DeepMind gaat algoritmes gebruiken om blindheid te voorspellen

    118628 c2f7304fDeepMind, een van de dochterbedrijven van zoekgigant Google, die onderzoek doet naar zelflerende computers, gaat helpen bij onderzoek naar blindheid. DeepMind gaat samenwerken met de Britse gezondheidsorganisatie NHS om zijn technologie te leren de eerste tekenen van blindheid op te sporen.

    Daartoe krijgt DeepMind 1 miljoen geanonimiseerde oogscans aangeleverd. De software gaat die scannen en op basis van meegeleverde informatie zou het moeten weten welke scans een oogziekte vertonen en welke niet. De bedoeling is dat de software uiteindelijk uit zichzelf de eerste tekenen van oogziektes leert te herkennen.

    Het gaat op dit moment om twee vormen van blindheid die relatief veel voorkomen: leeftijdsgebonden maculadegeneratie en diabetische retinopathie. Mensen met diabetes hebben bijvoorbeeld 25 keer zoveel kans om blind te worden als mensen zonder diabetes. Het vroeg herkennen van dit soort gevallen zou kunnen helpen blindheid te voorkomen.

    Het hoofd van de oogafdeling in het ziekenhuis, Professor Peng Tee Khaw, vertelt dat het kan helpen om snel oogziektes op te sporen bij patiënten. "Deze scans zijn ongelofelijk gedetailleerd, gedetailleerder zelfs dan alle andere scans die we van het lichaam hebben. We zien beelden op celniveau. Maar het probleem is tegelijkertijd juist dat het zoveel data biedt."

    Daar komt dan ook de oplossing om DeepMind te gebruiken vandaan. "Ik heb er alle ervaring uit mijn hele leven voor nodig om de geschiedenis van een patiënt te kunnen volgen. Maar patiënten vertrouwen op mijn ervaring om hun toekomst te voorspellen. Als we zelflerende technologie kunnen gebruiken, zouden we dit veel beter kunnen doen, want dan zou ik de ervaring van wel 10.000 levens hebben."

    Bron: Techzine.nl

  • European Union to Scrutinize Usage of Big Data by Large Internet Companies

    Competition Commissioner Margrethe VestagerThe European Union is considering whether the way large Internet companies, such as Alphabet Inc.’s Google or Facebook Inc., collect vast quantities of data is in breach of antitrust rules, the bloc’s competition chief said Sunday.

    “If a company’s use of data is so bad for competition that it outweighs the benefits, we may have to step in to restore a level playing field,” said Margrethe Vestager, European Commissioner for Competition, according to a text of her speech delivered at the Digital Life Design conference in Munich, Germany.

    “We continue to look carefully at this issue,” she said, adding that while no competition problems have yet been found in this area, “this certainly doesn’t mean we never will” find them in the future.

    Her comments highlight the increased focus that regulators give to the use of so-called big data—large sets of personal information that are increasingly important for digital businesses, even though people generally hand over the information voluntarily when they use free services.

    The data can help firms target ways to make business operations more efficient. Companies increasingly are also collecting more data as a greater range of devices—from fitness trackers, smoke detectors to home-heating meters—are being connected to the Web, a phenomenon known as the “Internet of Things.”

    “But if just a few companies control the data you need to satisfy customers and cut costs, that could give them the power to drive their rivals out of the market,” Ms. Vestager said.

    The concern is that huge data sets compiled by large Internet firms could give these companies an unfair advantage by essentially erecting barriers to new competition, some experts say. Incumbent firms would amass detailed profiles of their consumers that would allow them to target advertising with precision, while new rivals could find themselves too far behind to compete.

    This isn’t the first time Ms. Vestager has expressed interest into how companies use big data. On Sunday, she laid out some details about how the European Commission is looking into the issue.

    Ms. Vestager said the commission would be careful to differentiate between different types of data, since some forms of information can become obsolete quickly, making concerns of market dominance moot.

    She also said the EU would look into why some companies can’t acquire information that is as useful as the data that other competing firms have.

    “What’s to stop them [companies] from collecting the same data from their customers, or buying it from a data-analytics company?” she said.

    Lawyers representing U.S. tech firms have said previously that competition concerns over data are misguided. They said data isn’t exclusive since many different companies can hold the same information on people’s names, addresses and credit-card details, for example. It is also easy for consumers to switch between platforms, they said.

    As for how companies protect their consumers’ data, Ms. Vestager said that was beyond her scope and pointed to the new EU-wide data-privacy rules agreed late last year.

    Ms. Vestager also said she would publish a preliminary report in the middle of the year, as the next formal step in an investigation into whether Internet commerce companies, such as Amazon.com Inc., are violating antitrust rules by restricting cross-border trade.

    “With so much at stake, we need to act quickly when we discover problems,” she said, in reference to business contracts that aim to keep national markets separate.

    To start that debate, the commissioner said she would publish a paper before Easter outlining the views of relevant parties affected or involved in geo-blocking, a practice to discriminate via price or the range of goods a company offers based on a customer’s location.

    The commission in September launched a public questionnaire to gather more information about the practice of geo-blocking.

    Source: The Wall Street Journal

  • Google buys French image recognition startup Moodstocks

    524861120Two weeks after Twitter acquired Magic Pony to advance its machine learning smarts for improving users’ experience of photos and videos on its platform, Google is following suit. Today, the maker of Android and search giant announced that it has acquired Moodstocks, a startup based out of Paris that develops machine-learning based image recognition technology for smartphones whose APIs for developers have been described as “Shazam for images.”

    Moodstocks’ API and SDK will be discontinued “soon”, according to an announcement on the company’s homepage. “Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription,” the company noted.

    Terms of the deal were not disclosed and it’s not clear how much Moodstocks had raised: CrunchBase doesn’t note any VC money, although when we first wrote about the company back in 2010 we noted that it had raised $500,000 in seed funding from European investors. As a point of reference, Twitter paid a whopping $150 million in cash for its UK acquisition of Magic Pony the other week.

    While Magic Pony was young and acquired while still largely under the radar, Moodstocks has been around since 2008, all the while working around the basic premise of improving image recognition via mobile devices. “Our dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings,” the company writes in its acquisition/farewell/hello note.

    It looks like Moodstocks originally tried its hand at creating its own consumer apps, one of which was a social networking app of sorts: it let people snap pictures of media like books, and then add their own annotations about that media that would link up with other people’s annotations, by way of special image recognition behind the scenes that would match up the “fingerprint” in different people’s snaps.

    An interesting idea, but it didn’t take off, and so as the company pivoted to offering its tech to other developers, at least one of its apps, Moodstocks Scanner, turned into tools for testing the SDK before implementing it in your own app.

    Google doesn’t specify whether it will be launching its own SDK for developers to incorporate more imaging services into apps, or whether it will be incorporating the tech solely into its own consumer-facing services. What it does say is that it will be bringing Moodstocks’ team — the startup was co-founded by Denis Brule and Cedric Deltheil — and the company’s tech into its R&D operation based in France.

    In a short statement, Vincent Simonet, who heads up that center, says Google sees Moodstocks’ work contributing to better image searches, a service that is of course already offered in Google but is now going to be improved. “We have made great strides in terms of visual recognition,” he writes (in French), “but there is still much to do in this area.”

    It’s not clear if Moodstocks’ work will remain something intended for smartphones or if it will be applied elsewhere. There are already areas where Moodstocks’ machine learning algorithms could be applied, for example in Google’s searches, to “learn” more about how to find images that are similar and/or related to verbal search terms. Google also could potentially use the tech in an existing app like Photos.

    Or it could make an appearance in a future product that has yet to be launched, although the more obvious use case, for smartphones, is already here: on a small handset with a touchscreen, users are generally less inclined to enter text; and they may be using their own (poor quality) images to find similar ones: in both of these scenarios, having a stronger visual recognition tool (let’s say to snap a pic of something and then use it as a search ‘term’) could come in handy.

    Google has made other acquisitions in France, including FlexyCore (also for improving smartphone performance). It’s also made a number of acquisitions to improve its tech in imaging, such as JetPac and PittPatt for facial recognition. And other large tech companies are also buying up technology in talent in this area. Earlier this year, it emerged thatAmazon had quietly acquired Orbeus, a startup up that also develops photo recognition tech, with its service tapping AI and neural networks.

    Bron: Techcrunch.com

     

  • Google koopt Anvato ter versterking van cloudplatform

    Google heeft Anvato opgekocht. Dat bedrijf regelt de codering, editing, publicatie en distributie van uiteenlopende video's over meerdere platformen. De zoekgigant wil Anvato bij zijn cloudplatform voegen en de technologie implementeren in zijn eigen diensten. Hoe Google dit voor ogen heeft is niet bekend.


    Wel is bekend dat Amerikaanse televisiezenders als NBCUniversal, Fox Sports en MSNBC gebruik maken van de diensten van Anvato bij het maken en aanbieden van online video’s. Het is een dienst die Google’s eigen cloudplatform nog niet aanbiedt, dus vermoedelijk ligt hierin de reden voor de aankoop.

    "Onze teams gaan samenwerken om cloudoplossingen te bieden om bedrijven in de media en entertainmentindustrie te helpen hun video-infrastructuur te schalen en hoge kwaliteit live-video’s en on-demand content aan consumenten te bieden op elk apparaat – of dat nou een smartphone, tablet of smart-tv is", stelt Google’s senior productmanager Belwadi Srikanth in een statement.

    Het is niet bekend hoeveel Google betaald heeft voor het bedrijf. Bij zijn oprichting in 2007 haalde Anvato zo’n 2,5 miljoen dollar op in een investeringsronde, maar ondertussen zal de waarde van het bedrijf flink gegroeid zijn.

    Bron: Techzine nieuws 

  • Google wil telecomdiensten aanbieden

    Ggoogleoogle is van plan telecomdiensten aan te gaan bieden in de Verenigde Staten, via de mobiele netwerken van de telecombedrijven Sprint en T-Mobile US. Dat meldden ingewijden rond Google.

    Google zou al afspraken gemaakt hebben met Sprint en T-Mobile US over het gebruik van hun netwerken. Als Google inderdaad de telecommarkt opgaat, zal het bedrijf de concurrentie aangaan met aanbieders die mobieltjes met de Android-software van Google verkopen. De grootste telecombedrijven in de Verenigde Staten zijn AT&T en Verizon Communications, gevolgd door Sprint en T-Mobile US.

    Het is nog niet duidelijk hoe breed de telecomdiensten van Google zullen worden, wat de kosten zijn en wanneer de verkoop begint. Mogelijk kan Google eerst op beperkte schaal in een aantal Amerikaanse steden met de dienstverlening beginnen

    Automatiseringsgids, 22 januari 2015

  • Modern Information Management: Understanding Big Data at Rest and in Motion

    Big data is the buzzword of the century, it seems. But, why is everyone so obsessed with it? Here’s what it’s all about, how companies are gathering it, and how it’s stored and used.

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    What is it?

    Big data is simply large data sets that need to be analyzed computationally in order to reveal patterns, associations, or trends. This data is usually collected by governments and businesses on citizens and customers, respectively.

    The IT industry has had to shift its focus to big data over the last few years because of the sheer amount of interest being generated by big business. By collecting massive amounts of data, companies, like Amazon.com, Google, Walmart, Target, and others, are able to track buying behaviors of specific customers.

    Once enough data is collected, these companies then use the data to help shape advertising initiatives. For example, Target has used its big data collection initiative to help target (no pun intended) its customers with products it thought would be most beneficial given their past purchases.

    How Companies Store and Use It

    There are two ways that companies can use big data. The first way is to use the data at rest. The second way is to use it in motion.

    At Rest Data – Data at rest refers to information that’s collected and analyzed after the fact. It tells businesses what’s already happened. The analysis is done separately and distinctly from any actions that are taken upon conclusion of said analysis.

    For example, if a retailer wanted to analyze the previous month’s sales data. It would use data at rest to look over the previous month’s sales totals. Then, it would take those sales totals and make strategic decisions about how to move forward given what’s already happened.

    In essence, the company is using past data to guide future business activities. The data might drive the retailer to create new marketing initiatives, customize coupons, increase or decrease inventory, or to otherwise adjust merchandise pricing.

    Some companies might use this data to determine just how much of a discount is needed on promotions to spur sales growth.

    Some companies may use it to figure out how much they are able to discount in the spring and summer without creating a revenue problem later on in the year. Or, a company may use it to predict large sales events, like Black Friday or Cyber Monday.

    This type of data is batch processed since there’s no need to have the data instantly accessible or “streaming live.” There is a need, however, for storage of large amounts of data and for processing unstructured data. Companies often use a public cloud infrastructure due to the costs involved in storage and retrieval.

    Data In Motion – Data in motion refers to data that’s analyzed in real-time. Like data at rest, data may be captured at the point of sale, or at a contact point with a customer along the sales cycle. The difference between data in motion and data at rest is how the data is analyzed.

    Instead of batch processing and analyzation after the fact, data in motion uses a bare metal cloud environment because this type of infrastructure uses dedicated servers offering cloud-like features without virtualization.

    This allows for real-time processing of large amounts of data. Latency is also a concern for large companies because they need to be able to manage and use the data quickly. This is why many companies send their IT professionals to Simplilearn Hadoop admin training and then subsequently load them up on cloud-based training and other database training like NoSQL.

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    Big Data For The Future

    Some awesome, and potentially frightening, uses for big data are on the horizon. For example, in February 2014, the Chicago Police Department sent uniformed officers to make notification visits to targeted individuals they had identified as potential criminals. They used a computer-generated list which gathered data about those individuals’ backgrounds.

    Another possible use for big data is development of hiring algorithms. More and more companies are trying to figure out ways to hire candidates without trusting slick resume writing skills. New algorithms may eliminate job prospects based on statistics, rather than skillsets, however. For example, some algorithms find that people with shorter commutes are more likely to stay in a job longer.

    So, people who have long commutes are filtered out of the hiring process quickly.

    Finally, some insurance companies might use big data to analyze your driving habits and adjust your insurance premium accordingly. That might sound nice if you’re a good driver, but insurers know that driving late at night increases the risk for getting into an accident. Problem is, poorer people tend to work late shifts and overnights or second jobs just to make ends meet. The people who are least able to afford insurance hikes may be the ones that have to pay them.

    Source: Mobilemag

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