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Exam Code: A2010-538 Practice exam 2022 by Killexams.com team
Assess: IBM Tivoli Monitoring V6.2.2 Implementation
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Gizmodo is 20 years old! To celebrate the anniversary, we’re looking back at some of the most significant ways our lives have been thrown for a loop by our digital tools.

In the revisionist history of the internet, we were all sold down the river by the “techno-optimists,” who assumed that once we “connected every person in the world” we’d enter a kind of post-industrial nirvana, a condition so wondrous that it behooved us all to “move fast,” even if that meant that we’d “break things.”

The problem is that this is the Facebook story, not the internet story, and notwithstanding decades of well-financed attempts at enclosure, “Facebook” is not “the internet” (also, it’s not even Facebook’s story: “connecting every person in the world” was always a euphemism for “spy on every person in the world in order to enrich our shareholders”).

20 years ago, the internet’s early adopters were excited about the digital world’s potential, but we were also terrified about how it could go wrong.

Take the Electronic Frontier Foundation, an organization that hired me 20 years ago, just a few months after the large record companies filed suit against the P2P file-sharing service Napster, naming its investors and their backers (giant insurance companies and pension funds) to the suit.

The Copyright Wars were kicking off. Napster showed that the record industry’s plan to capitalize on the internet with another “format shift” was doomed: we may have re-bought our vinyl on 8-track, re-bought our 8-tracks on cassette, and re-bought our cassettes on CD, but now we were in the driver’s seat. We were going to rip our CDs, make playlists, share the tracks, and bring back the 80 percent of recorded music that the labels had withdrawn from sale, one MP3 at a time.

The rate of Napster’s ascent was dizzying. In 18 short months, Napster attracted 52 million users, making it the fastest-adopted technology in history, surpassing DVD players. For comparison, this was shortly after the 2000 presidential elections, in which 50,999,897 votes were cast for the loser (the “winner” got 50,456,002 votes).

Napster’s fall was just as dizzying. In July 2001, the service shut down after a 25-month run. A month later, it declared bankruptcy. The labels’ attempt to drag Napsters’ VCs and their backers failed, but it didn’t matter –the investor class got the message, and P2P file-sharing became balance-sheet poison.

P2P users didn’t care. They just moved from “platforms” to “protocols”, switching to increasingly decentralized systems like Gnutella and BitTorrent – systems that, in turn, eliminated their own central points of failure in a relentless drive to trackerlessness.

P2P users interpreted the law as damage and routed around it. What they didn’t do, for the most part, was develop a political consciousness. If “P2P users” were a political party, they could have elected a president. Instead, they steered clear of politics, committing the original sin of nerd hubris: “Our superior technology makes your inferior laws irrelevant.”

P2P users may not have been interested in politics, but politics was interested in P2P users. The record industry sued 19,000 kids, singling out young P2P developers for special treatment. For example, there was the college Computer Science major who maintained a free software package called FlatLAN, that indexed the shared files on any local network. The labels offered him a settlement: if he changed majors and gave up programming computers, they wouldn’t seek $150,000 in statutory damages for each track in his MP3 collection.

This phase of the P2P wars was a race between civil disobedience and regulatory capture. Senate Commerce Chairman Fritz Hollings introduced a bill that would make it a crime to sell a computer unless it had a special copyright enforcement chip (wags dubbed this hypothetical co-processor the “Fritz Chip”) that would (somehow) block all unauthorized uses of copyrighted works. The Hollings bill would have required total network surveillance of every packet going in or out of the USA to block the importation of software that might defeat this chip.

The Hollings bill died, but the entertainment industry had a backup plan: the FCC enacted “the Broadcast Flag regulation,” a rule that would require all digital devices and their operating systems to be approved by a regulator who would ensure that they were designed to foil their owners’ attempts to save, copy, or manipulate high-definition digital videos. (EFF subsequently convinced a federal judge that this order was illegal).

Thus did the DRM Wars begin: a battle over whether our devices would be designed to obey us, or police us. The DRM Wars had been percolating for many years, ever since Bill Clinton signed the Digital Millennium Copyright Act into law in 1998.

The DMCA is a complex, multifaceted law, but the clause relevant to this part of history is Section 1201, the “anti-circumvention” rule that makes it a jailable felony to provide tools or information that would help someone defeat DRM (“access controls for copyrighted works”). DMCA 1201 is so broadly worded that it bans removing DRM even when it does not lead to copyright infringement. For example, bypassing the DRM on a printer-ink cartridge lets you print using third-party ink, which is in no way a violation of anyone’s copyright, but because you have to bypass DRM to do it, anyone who gives you a printer jailbreaking tool risks a five-year prison sentence and a $500,000 fine…for a first offense.

DMCA 1201 makes it illegal to remove DRM. The Hollings Bill and the Broadcast Flag would have made it a crime to sell a device unless it had DRM. Combine the two and you get a world where everything has DRM and no one is allowed to do anything about it.

DRM on your media is gross and terrible, a way to turn your media collection into a sandcastle that melts away when the tide comes in. But the DRM Wars are only incidentally about media. The real action is in the integration of DRM in the Internet of Things, which lets giant corporations dictate which software your computer can run, and who can fix your gadgets (this also means that hospitals in the middle of a once-in-a-century pandemic can’t fix their ventilators). DRM in embedded systems also means that researchers who reveal security defects in widely used programs face arrest on federal charges, and it means that scientific conferences risk civil and criminal liability for providing a forum to discuss such research.

As microprocessors plummeted in price, it became practical to embed them in an ever-expanding constellation of devices, turning your home, your car and even your toilet into sensor-studded, always-on, networked devices. Manufacturers seized on the flimsiest bit of “interactivity” as justification for putting their crap on the internet, but the true motivation is to be found in DMCA 1201: once a gadget has a chip, it can have a thin skin of DRM, which is a felony to remove.

You may own the device, but it pwns you: you can’t remove that DRM without facing a prison sentence, so the manufacturer can booby-trap its gizmos so that any time your interests conflict with its commercial imperatives, you will lose. As Jay Freeman says, DMCA 1201 is a way to turn DRM into a de facto law called “Felony Contempt of Business Model.”

The DRM Wars rage on, under many new guises. These days, it’s often called the “Right to Repair” fight, but that’s just a corner of the raging battle over who gets to decide how the digital technology that you rely on for climate control, shelter, education, romance, finance, politics and civics work.

The copyright maximalists cheered DRM on as a means to prevent “piracy,” and dismissed anyone who warned about the dangers of turning our devices into ubiquitous wardens and unauditable reservoirs of exploitable software bugs as a deranged zealot.

It’s a natural enough mistake for anyone who treats networked digital infrastructure as a glorified video-on-demand service, and not as the nervous system of 21st Century civilization. That worldview –that the internet is cable TV for your pocket rectangle– is what led those same people to demand copyright filters for every kind of online social space.

Filtering proposals have been there all along, since the days of the Broadcast Flag and even the passage of the DMCA, but they only came into widespread use in 2007, when Google announced a filtering system for YouTube called Content ID.

Google bought YouTube in 2006, to replace its failing in-house rival Google Video (Google is a buying-things company, not a making-things company; with the exception of Search and Gmail, all its successes are acquisitions, while its made-at-Google alternatives from Glass to G+ to Reader fail).

YouTube attracted far more users than Google Video – and also far more legal trouble. A bruising, multi-billion-dollar lawsuit from Viacom was an omen of more litigation to come.

Content ID was an effort to head off future litigation. Selected media companies were invited to submit the works they claimed to hold the copyright to, and Content ID scoured all existing and new user uploads for matches. Rightsholders got to decide how Content ID handled these matches: they could “monetize” them (taking the ad revenue that the user’s video generated) or they could block them.

Content ID is one of those systems that works well, but fails badly. It has three critical failings:

  1. YouTube is extremely tolerant of false copyright claims. Media companies have claimed everything from birdsong to Brahms without being kicked off the system.
  2. Content ID tolerates false positives. The designers of any audio fingerprinting system have to decide how close two files must be to trigger a match. If the system is too strict, it can be trivially defeated by adding a little noise, slicing out a few seconds of the stream, or imperceptibly shifting the tones. On the other hand, very loose matching creates a dragnet that scoops up a lot of dolphins with the tuna. Content ID is tuned to block infringement even if that means taking down non-infringing material. That’s how a recording of white noise can attract multiple Content ID claims, and why virtually any classical music performance (including those by music teachers) gets claimed by Sony.
  3. It is impossible for Content ID to understand and accommodate fair use. Fair use is a badly understood but vital part of copyright; as the Supreme Court says, fair use is the free expression escape-valve in copyright, the thing that makes it possible to square copyright (in which the government creates a law about who is allowed to publish certain phrases) with the First Amendment (which bars the government from creating such a law). There is no bright-line test for whether something is fair use; rather, there is a large body of jurisprudence and some statutory factors that have to be considered in their totality to determine whether a use is fair. Here are some uses that have been found fair under some circumstances: making copies of Hollywood blockbusters and bringing them to your friends’ house to watch at viewing parties; copying an entire commercial news article into an ad-supported message board so participants can read and discuss it; publishing a commercial bestselling novel that is an unauthorized retelling of another bestseller, specifically for the purpose of discrediting and replacing the original book in the reader’s imagination. Now, these were highly specific circumstances, and I’m not trying to say that all copying is fair, but Google’s algorithms can’t ever make the fine distinctions that created these exceptions, and it doesn’t even try. Instead, YouTube mostly acts as if fair use didn’t exist. Creators whose work is demonetized or removed can argue fair use in their appeals, but the process is beyond baroque and generally discourages fair usage.

Content ID resulted in billions of dollars in revenue for rightholders, but in no way ended copyright infringement on YouTube, as Big Content lobbyists frequently remind us. YouTube spent $100,000,000 (and counting) on the system, which explains why only the largest Big Tech companies, like Facebook, have attempted their own filters.

Copyright filters are derided as inadequate by rightsholder groups, but that doesn’t stop them from demanding more of them. In 2019, the EU erupted in controversy over the Article 13 of the Digital Single Market Act (DMSA), which requires platforms for user-generated content to prevent “re-uploading” of material that has been taken down following a copyright complaint. Artice 13 triggered street demonstrations in cities all over Europe, and a petition opposing Article 13 attracted more signatories than any petition in EU history.

The official in charge of the Article 13 push, a German politician named Axel Voss, repeatedly insisted that its goal of preventing re-uploading could be accomplished without automated filters – after all, the existing E-Commerce Directive banned “general monitoring obligations” and the General Data Protection Regulation (GDPR) bans “processing” of your uploads without consent.

Article 13 came up for a line-item vote in March 2019 and carried by five votes. Afterward, ten Members of the European Parliament claimed they were confused and pressed the wrong button; their votes were switched in the official record, but under EU procedures, the outcome of the (now losing) vote was not changed.

Almost simultaneously, Axel Voss admitted that there was no way this would work without automated filters. This wasn’t surprising: after all, this is what everyone had said all along, including lobbyists for Facebook and YouTube, who endorsed the idea of mandatory filters as a workable solution to copyright infringement.

European national governments are now struggling to implement Article 13 (renumbered in the final regulation and now called Article 17), and when that’s done, there’s a whole slew of future filter mandates requiring implementation, like the terror regulation that requires platforms to identify and block “terrorist” and “extremist” content and keep it down. This has all the constitutional deficiencies of Article 13/17, and even higher stakes, because the false positives that “terrorism filters” take down isn’t white noise or birdsong – it’s the war-crime evidence painstakingly gathered by survivors.

In the USA, the Copyright Office is pondering its own copyright filter mandate, which would force all platforms that allow users to publish text, audio, code or video to compare users’ submissions to a database of copyrighted works and block anything that someone has claimed as their own.

As with Article 17 (née Article 13), such a measure will come at enormous cost. Remember, Content ID cost more than $100 million to build, and Content ID only accomplishes a minute sliver of the obligations envisioned by Article 17 and the US Copyright Office proposal.

Adding more than $100 million to the startup costs of any new online platform only makes sense if your view of the internet is five giant websites filled with screenshots of text from the other four. But if you hold out any hope for a more decentralized future built on protocols, not platforms, then filtering mandates should extinguish it.

Which brings me back to 20 years ago, and the naivete of the techno-optimists. 20 years ago, technology activists understood and feared the possibilities for technological dystopia. The rallying cry back then wasn’t “this will all be amazing,” it was “this will all be great…but only if we don’t screw it up.”

The Napster Wars weren’t animated by free music, but by a free internet – by the principle that we should should be free to build software that let people talk directly to one another, without giving corporations or governments a veto over who could connect or what they could say.

The DRM Wars weren’t about controlling the distribution of digital videos, they were fought by people who feared that our devices would be redesigned to control us, not take orders from us, and that this would come to permeate our whole digital lives so that every appliance, gadget and device, from our speakers to our cars to our medical implants, would become a locus of surveillance and control.

The filter wars aren’t about whether you can upload music or movies – it’s about whether cops can prevent you from sharing videos of their actions by playing pop music in order to trigger filters and block your uploads.

From Napster to DRM to filters, the fight has always had the same stakes: will our digital nervous system be designed to spy on us and boss us around, or will it serve as a tool to connect us and let us coordinate our collective works?

But the techno-optimists – myself included – did miss something important 20 years ago. We missed the fact that antitrust law was a dead letter. Having lived through the breakup of AT&T (which unleashed modems and low-cost long-distance on America, setting the stage for the commercial internet); having lived through the 12-year IBM antitrust investigation (which led Big Blue to build a PC without making its own operating system and without blocking third-party clones of its ROMs); having lived through Microsoft’s seven-year turn in the antitrust barrel (which tamed Microsoft so that it spared Google from the vicious monopoly tactics that it used to destroy Netscape); we thought that we could rely on regulators to keep tech fair.

That was a huge mistake. In reality, by 1982, antitrust law was a dead man walking. The last major action of antitrust enforcers was breaking up AT&T. They were too weak to carry on against IBM. They were too weak to prevent the “Baby Bells” that emerged from AT&T’s breakup from re-merging with one another. They were even too weak to win their slam-dunk case against Microsoft.

That was by design. Under Reagan, the business lobby’s “consumer welfare” theory of antitrust (which holds that monopolies are actually “efficient” and should only be challenged when there is mathematical proof that a merger will drive up prices) moved from the fringes to the mainstream. Nearly half of all US Federal judges attended cushy junkets in Florida where these theories were taught, and afterwards they consistently ruled in favor of monopolies.

This process was a slow burn, but now, in hindsight, it’s easy to see how it drastically remade our entire economy, including tech. The list of concentrated industries includes everything from eyeglasses to glass bottles, shipping to finance, wrestling to cheerleading, railroads to airlines, and, of course, tech and entertainment.

40 years after the neutering of antitrust, it’s hard to remember that we once lived in a world that barred corporations from growing by buying their small, emerging competitors, merging with their largest rivals, or driving other businesses out of the marketplace with subsidized “predatory pricing.”

Yet if this regime had been intact for the rise of tech, we’d live in a very different world. Where would Google be without the power to gobble up small competitors? Recall that Google’s in-house projects (with the exception of Search and Gmail) have either failed outright or amounted to very little, and it was through buying up other businesses that Google developed its entire ad-tech stack, its mobile platform, its video platform, even its server infrastructure tools.

Google’s not alone in this – Big Tech isn’t a product-inventing machine, it’s a company-buying machine. Apple buys companies as often as you buy groceries. Facebook buys companies specifically to wipe out potential competitors.

But this isn’t just a Big Tech phenomenon. The transformation of the film industry – which is now dominated by just four studios – is a story of titanic mergers between giant, profitable companies, and not a tale of a few companies succeeding so wildly that their rivals go bust.

Here is an area where people with legitimate concerns over creators’ falling share of the revenues their labor generates and people who don’t want a half-dozen tech bros controlling the future have real common ground.

The fight to make Spotify pay artists fairly is doomed for so long as Spotify and the major labels can conspire to rip off artists. The fight to get journalists paid depends on ending illegal Google-Facebook collusion to steal ad-revenue from publishers. The fight to get mobile creators paid fairly runs through ending the mobile duopoly’s massive price-gouging on apps.

All of which depends on fighting a new war, an anti-monopoly war: the natural successor to the Napster Wars and the DRM Wars and the Filter Wars. It’s a war with innumerable allies, from the people who hate that all the beer is being brewed by just two companies to the people who are outraged that all the shipping in the world is (mis)managed by four cartels, to the people who are coming to realize that “inflation” is often just CEOs of highly concentrated industries jacking up prices because they know that no competitor will make them stop.

The anti-monopoly war is moving so swiftly, and in so many places, that a lot of combatants in the old tech fights haven’t even noticed that the battleground has shifted.

But this is a new era, and a new fight, a fight over whether a world where the line between “offline” and “online” has blurred into insignificance will be democratically accountable and fair, or whether it will be run by a handful of giant corporations and their former executives who are spending a few years working as regulators.

All the tech antitrust laws in the world won’t help us if running an online platform comes with an obligation to spend hundreds of millions of dollars to spy on your users and block their unlawful or unsavory speech; nor will reform help us if it continues to be illegal to jailbreak our devices and smash the chains that bind our devices to their manufacturers’ whims.

The Copyright Wars have always been premised on the notion that tech companies should be so enormous that they can afford to develop and maintain the invasive technologies needed to police their users’ conduct to a fine degree. The Anti-Monopoly Wars are premised on the idea that tech and entertainment companies must be made small enough that creative workers and audiences can fit them in a bathtub… and drown them.

Cory Doctorow is a science fiction author, activist and journalist. His next book is Chokepoint Capitalism (co-authored with Rebecca Giblin), on how Big Tech and Big Content rigged creative labor markets - and how to unrig them.

Mon, 08 Aug 2022 08:00:00 -0500 en-us text/html https://gizmodo.com/cory-doctorow-copyright-laws-tech-antitrust-1849376858
Killexams : Data tokenization: A new way of data masking

While researchers examined the pandemic in relation to how companies managed to keep afloat in such an unprecedented situation, auditors assessed the increased data vulnerability, lack of data compliance, and costs incurred by such events. As businesses were forced to adapt new styles of working and adapt technologies, they struggled to meet security compliance standards like the General Data Protection Regulation (GDPR) and lagged in responding to data breaches. An IBM report stated that data breaches now cost companies $4.24 million per incident on average – the highest cost in the 17-year history of the report.

Thus, enterprises need robust data security strategies to anonymize data for usage and to prevent potential data security breaches. Data tokenization is a new kind of data security strategy meaning that enterprises can operate efficiently and securely while staying in full compliance with data regulations. Data tokenization has grown to be a well-liked method for small and midsize businesses to increase the security of credit card and e-commerce transactions while lowering the cost and complexity of compliance with industry standards and governmental regulations.

Tokenization is the process of swapping out sensitive data with one-of-a-kind identification symbols that keep all of the data’s necessary information without compromising its security. Tokenization replaces the data by creating entirely random characters in the same format.

How does data tokenization work for an enterprise?

Tokenization masks or substitutes sensitive data with unique identification data while retaining all the essential information about the data. This equivalent unique replacement data is called a token. Tokenization is a non-destructive form of data masking wherein the original data is recoverable via the unique replacement data i.e., token. Two main approaches enable data encryption through data tokenization:

  1. Vault-based Tokenization
  2. Vault-less Tokenization

In the first instance, a token vault serves as a dictionary of sensitive data values and maps them to token values, which replace the original data values in a database or data store. Thus, an application or user can access the original value in the dictionary to its associated token which can be reversed. The token vault is the only place where the original information can be mapped back to its associated token.

The second data tokenization approach involves no vault. In the case of vault-less tokenization, tokens are stored using an algorithm instead of a secure database to protect private data. The original sensitive information is typically not kept in a vault if the token is reversible.

To understand better, here is an example of how tokenization with a token vault works.

A customer provides their credit card number for any transaction. In a traditional transaction, the credit card number is sent to the payment processor and then stored in the merchant’s internal systems for later reuse. Now, let’s see how this transaction takes place after the implementation of data tokenization.

  • As the customer provides their credit card number for any transaction, the card number is sent to a token system or vault instead of the payment processor.
  • The token system or vault replaces the customer’s sensitive information, i.e., the credit card number, with a custom, randomly created alphanumeric ID, i.e., a token.
  • Next, after a token has been generated, it is returned to the merchant’s POS terminal and the payment processor in a safe form in order to complete the transaction successfully.

With data tokenization, enterprises can safely transmit data across wireless networks. However, for effective implementation of data tokenization, enterprises must employ a payment gateway to store sensitive data securely. Credit card information is safely stored and generated by a payment gateway.

Why do you need data tokenization?

For an enterprise, the aim is to secure any sensitive payment or personal information in business systems and store such data in a secure environment. Data tokenization helps enterprises to achieve that by replacing each data set with an indecipherable token.

Here are five reasons why tokenization matters to businesses:

1.   Reduce the risk of data breaches and penalties

Tokenization helps protect businesses from the negative financial impacts of data theft. The process of tokenization does not shield personal data, thus, protecting it from any kind of data breach.               

Compromised security often translates to direct revenue loss for businesses as customers tend to switch to alternative competitors who are taking better care of their payment data.

Businesses may also incur losses after a data breach by being sued. For instance, Zoom had to set up an $85 million fund to pay cash claims to U.S. users after a series of cybersecurity breaches, including misleading end-to-end encryption. Also, noncompliance with many payment and security standards can lead to heavy business fines and penalties. For instance, non-compliance with PCI can result in monthly fines ranging from $5,000 to $100,000, imposed by credit card companies.

2.     Build customer trust

Tokenization helps companies to establish trust with their customers. Tokenization helps to keep online transactions secure for both customers and businesses by ensuring correct formatting and safe transmission of data. This makes the sensitive data significantly less vulnerable to cyberattacks and payment fraud.

3.   Meet compliance regulations

Tokenization helps in meeting and maintaining compliance with industry regulations, for instance, businesses accepting debit and credit cards as methods need to adhere to or compile with Payment Card Industry Data Security Standard (PCI DSS). Tokenization meets the PCI DSS regulation requirement of masking the sensitive cardholder information and safely managing its storage and deletion. Thus, tokenization governs the security of the sensitive data associated with the cards as well as cuts down any compliance-associated costs.

4.   Boost subscription-based purchases

Subscription-based purchases can be improved by faster and better customer experience during checkout. The faster checkout process requires customers to store their payment information safely. Tokenization helps to secure this financial data such as credit card information as a non-sensitive token. This token value remains undecipherable by hackers and creates a safe environment for recurring payments. Some of the major mobile payment gateways such as Google Pay and Apple Pay are already leveraging the benefits of data tokenization, thus making the user experience both seamless and more secure. Security assurance is also helping businesses to convince more users to sign up.

5.   Ensure safe data sharing

Businesses often utilize sensitive data for other business purposes, such as marketing metrics, analytics or reporting. With the implementation of tokenization, businesses can minimize the locations where sensitive data is allowed and ensure that tokenized data is accessible to users and applications conducting data analysis or any other business process. Tokenization can be used to achieve least-privileged access to sensitive data by ensuring that individuals only have access to the specific data they need to complete a particular task. Thus, the tokenization process maintains the security of the original sensitive data.

Conclusion

Any organization’s compliance obligation is somewhat proportionate to the size of its systems —the more applications using sensitive data, the greater the force to rethink or update their data compliance check. For this reason, using a tokenization platform is becoming popular. Tokenization platforms help businesses to secure sensitive information while taking care of security regulation compliance.

Replacing sensitive data with tokenization technologies offers numerous security and compliance advantages. Reduced security risk and audit scope are two advantages that decrease compliance costs and ease regulatory data handling obligations. Data tokenization platforms offer a dependable way to satisfy compliance needs both now and in the future, allowing businesses to concentrate resources on gaining market share in unpredictable economic times.

Mon, 25 Jul 2022 01:49:00 -0500 Author: Yash Mehta en-US text/html https://www.cio.com/article/403692/data-tokenization-a-new-way-of-data-masking.html
Killexams : Amazon, IBM Move Swiftly on Post-Quantum Cryptographic Algorithms Selected by NIST

A month after the National Institute of Standards and Technology (NIST) revealed the first quantum-safe algorithms, Amazon Web Services (AWS) and IBM have swiftly moved forward. Google was also quick to outline an aggressive implementation plan for its cloud service that it started a decade ago.

It helps that IBM researchers contributed to three of the four algorithms, while AWS had a hand in one. Google is also among those who contributed to SPHINCS+.

A long process that started in 2016 with 69 original candidates ends with the selection of four algorithms that will become NIST standards, which will play a critical role in protecting encrypted data from the vast power of quantum computers.

NIST's four choices include CRYSTALS-Kyber, a public-private key-encapsulation mechanism (KEM) for general asymmetric encryption, such as when connecting websites. For digital signatures, NIST selected CRYSTALS-Dilithium, FALCON, and SPHINCS+. NIST will add a few more algorithms to the mix in two years.

Vadim Lyubashevsky, a cryptographer who works in IBM's Zurich Research Laboratories, contributed to the development of CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon. Lyubashevsky was predictably pleased by the algorithms selected, but he had only anticipated NIST would pick two digital signature candidates rather than three.

Ideally, NIST would have chosen a second key establishment algorithm, according to Lyubashevsky. "They could have chosen one more right away just to be safe," he told Dark Reading. "I think some people expected McEliece to be chosen, but maybe NIST decided to hold off for two years to see what the backup should be to Kyber."

IBM's New Mainframe Supports NIST-Selected Algorithms

After NIST identified the algorithms, IBM moved forward by specifying them into its recently launched z16 mainframe. IBM introduced the z16 in April, calling it the "first quantum-safe system," enabled by its new Crypto Express 8S card and APIs that provide access to the NIST APIs.

IBM was championing three of the algorithms that NIST selected, so IBM had already included them in the z16. Since IBM had unveiled the z16 before the NIST decision, the company implemented the algorithms into the new system. IBM last week made it official that the z16 supports the algorithms.

Anne Dames, an IBM distinguished engineer who works on the company's z Systems team, explained that the Crypto Express 8S card could implement various cryptographic algorithms. Nevertheless, IBM was betting on CRYSTAL-Kyber and Dilithium, according to Dames.

"We are very fortunate in that it went in the direction we hoped it would go," she told Dark Reading. "And because we chose to implement CRYSTALS-Kyber and CRYSTALS-Dilithium in the hardware security module, which allows clients to get access to it, the firmware in that hardware security module can be updated. So, if other algorithms were selected, then we would add them to our roadmap for inclusion of those algorithms for the future."

A software library on the system allows application and infrastructure developers to incorporate APIs so that clients can generate quantum-safe digital signatures for both classic computing systems and quantum computers.

"We also have a CRYSTALS-Kyber interface in place so that we can generate a key and provide it wrapped by a Kyber key so that could be used in a potential key exchange scheme," Dames said. "And we've also incorporated some APIs that allow clients to have a key exchange scheme between two parties."

Dames noted that clients might use Dilithium to generate digital signatures on documents. "Think about code signing servers, things like that, or documents signing services, where people would like to actually use the digital signature capability to ensure the authenticity of the document or of the code that's being used," she said.

AWS Engineers Algorithms Into Services

During Amazon's AWS re:Inforce security conference last week in Boston, the cloud provider emphasized its post-quantum cryptography (PQC) efforts. According to Margaret Salter, director of applied cryptography at AWS, Amazon is already engineering the NIST standards into its services.

During a breakout session on AWS' cryptography efforts at the conference, Salter said AWS had implemented an open source, hybrid post-quantum key exchange based on a specification called s2n-tls, which implements the Transport Layer Security (TLS) protocol across different AWS services. AWS has contributed it as a draft standard to the Internet Engineering Task Force (IETF).

Salter explained that the hybrid key exchange brings together its traditional key exchanges while enabling post-quantum security. "We have regular key exchanges that we've been using for years and years to protect data," she said. "We don't want to get rid of those; we're just going to enhance them by adding a public key exchange on top of it. And using both of those, you have traditional security, plus post quantum security."

Last week, Amazon announced that it deployed s2n-tls, the hybrid post-quantum TLS with CRYSTALS-Kyber, which connects to the AWS Key Management Service (AWS KMS) and AWS Certificate Manager (ACM). In an update this week, Amazon documented its stated support for AWS Secrets Manager, a service for managing, rotating, and retrieving database credentials and API keys.

Google's Decade-Long PQC Migration

While Google didn't make implementation announcements like AWS in the immediate aftermath of NIST's selection, VP and CISO Phil Venables said Google has been focused on PQC algorithms "beyond theoretical implementations" for over a decade. Venables was among several prominent researchers who co-authored a technical paper outlining the urgency of adopting PQC strategies. The peer-reviewed paper was published in May by Nature, a respected journal for the science and technology communities.

"At Google, we're well into a multi-year effort to migrate to post-quantum cryptography that is designed to address both immediate and long-term risks to protect sensitive information," Venables wrote in a blog post published following the NIST announcement. "We have one goal: ensure that Google is PQC ready."

Venables recalled an experiment in 2016 with Chrome where a minimal number of connections from the Web browser to Google servers used a post-quantum key-exchange algorithm alongside the existing elliptic-curve key-exchange algorithm. "By adding a post-quantum algorithm in a hybrid mode with the existing key exchange, we were able to test its implementation without affecting user security," Venables noted.

Google and Cloudflare announced a "wide-scale post-quantum experiment" in 2019 implementing two post-quantum key exchanges, "integrated into Cloudflare's TLS stack, and deployed the implementation on edge servers and in Chrome Canary clients." The experiment helped Google understand the implications of deploying two post-quantum key agreements with TLS.

Venables noted that last year Google tested post-quantum confidentiality in TLS and found that various network products were not compatible with post-quantum TLS. "We were able to work with the vendor so that the issue was fixed in future firmware updates," he said. "By experimenting early, we resolved this issue for future deployments."

Other Standards Efforts

The four algorithms NIST announced are an important milestone in advancing PQC, but there's other work to be done besides quantum-safe encryption. The AWS TLS submission to the IETF is one example; others include such efforts as Hybrid PQ VPN.

"What you will see happening is those organizations that work on TLS protocols, or SSH, or VPN type protocols, will now come together and put together proposals which they will evaluate in their communities to determine what's best and which protocols should be updated, how the certificates should be defined, and things like things like that," IBM's Dames said.

Dustin Moody, a mathematician at NIST who leads its PQC project, shared a similar view during a panel discussion at the RSA Conference in June. "There's been a lot of global cooperation with our NIST process, rather than fracturing of the effort and coming up with a lot of different algorithms," Moody said. "We've seen most countries and standards organizations waiting to see what comes out of our nice progress on this process, as well as participating in that. And we see that as a very good sign."

Thu, 04 Aug 2022 10:39:00 -0500 en text/html https://www.darkreading.com/dr-tech/amazon-ibm-move-swiftly-on-post-quantum-cryptographic-algorithms-selected-by-nist
Killexams : A Description of the RC2(r) Encryption Algorithm Killexams : A Description of the RC2(r) Encryption Algorithm
Network Working Group
Request for Comments: 2268
Category: Informational
 

 R. Rivest
MIT Laboratory for Computer Science
and RSA Data Security, Inc.
March 1998

A Description of the RC2(r) Encryption Algorithm

Status of this Memo

This memo provides information for the Internet community. It does not specify an Internet standard of any kind. Distribution of this memo is unlimited.

Copyright Notice

Copyright (C) The Internet Society (1998). All Rights Reserved.

Table of Contents

1. Introduction
1.1 Algorithm description

2. Key expansion

3. Encryption algorithm
3.1 Mix up R[i]
3.2 Mixing round
3.3 Mash R[i]
3.4 Mashing round
3.5 Encryption operation

4. Decryption algorithm
4.1 R-Mix up R[i]
4.2 R-Mixing round
4.3 R-Mash R[i]
4.4 R-Mashing round
4.5 Decryption operation

5. Test vectors

6. RC2 Algorithm Object Identifier

1. Introduction

This memo is an RSA Laboratories Technical Note. It is meant for informational use by the Internet community.

This memo describes a conventional (secret-key) block encryption algorithm, called RC2, which may be considered as a proposal for a DES replacement. The input and output block sizes are 64 bits each. The key size is variable, from one byte up to 128 bytes, although the current implementation uses eight bytes.

The algorithm is designed to be easy to implement on 16-bit microprocessors. On an IBM AT, the encryption runs about twice as fast as DES (assuming that key expansion has been done).

1.1 Algorithm description

We use the term "word" to denote a 16-bit quantity. The symbol + will denote twos-complement addition. The symbol & will denote the bitwise "and" operation. The term XOR will denote the bitwise "exclusive-or" operation. The symbol ~ will denote bitwise complement. The symbol ^ will denote the exponentiation operation. The term MOD will denote the modulo operation.

There are three separate algorithms involved:

Key expansion. This takes a (variable-length) input key and produces an expanded key consisting of 64 words K[0],...,K[63].

Encryption. This takes a 64-bit input quantity stored in words R[0], ..., R[3] and encrypts it "in place" (the result is left in R[0], ..., R[3]).

Decryption. The inverse operation to encryption.

2. Key expansion

Since we will be dealing with eight-bit byte operations as well as 16-bit word operations, we will use two alternative notations

for referring to the key buffer:

For word operations, we will refer to the positions of the buffer as K[0], ..., K[63]; each K[i] is a 16-bit word.

For byte operations, we will refer to the key buffer as L[0], ..., L[127]; each L[i] is an eight-bit byte.

These are alternative views of the same data buffer. At all times it will be true that

K[i] = L[2*i] + 256*L[2*i+1].

(Note that the low-order byte of each K word is given before the high-order byte.)

We will assume that exactly T bytes of key are supplied, for some T in the range 1 <= T <= 128. (Our current implementation uses T = 8.) However, regardless of T, the algorithm has a maximum effective key length in bits, denoted T1. That is, the search space is 2^(8*T), or 2^T1, whichever is smaller.

The purpose of the key-expansion algorithm is to modify the key buffer so that each bit of the expanded key depends in a complicated way on every bit of the supplied input key.

The key expansion algorithm begins by placing the supplied T-byte key into bytes L[0], ..., L[T-1] of the key buffer.

The key expansion algorithm then computes the effective key length in bytes T8 and a mask TM based on the effective key length in bits T1. It uses the following operations:

T8 = (T1+7)/8; TM = 255 MOD 2^(8 + T1 - 8*T8);

Thus TM has its 8 - (8*T8 - T1) least significant bits set.

For example, with an effective key length of 64 bits, T1 = 64, T8 = 8 and TM = 0xff. With an effective key length of 63 bits, T1 = 63, T8 = 8 and TM = 0x7f.

Here PITABLE[0], ..., PITABLE[255] is an array of "random" bytes based on the digits of PI = 3.14159... . More precisely, the array PITABLE is a random permutation of the values 0, ..., 255. Here is the PITABLE in hexadecimal notation:

        0  1  2  3  4  5  6  7  8  9  a  b  c  d  e  f
   00: d9 78 f9 c4 19 dd b5 ed 28 e9 fd 79 4a a0 d8 9d
   10: c6 7e 37 83 2b 76 53 8e 62 4c 64 88 44 8b fb a2
   20: 17 9a 59 f5 87 b3 4f 13 61 45 6d 8d 09 81 7d 32
   30: bd 8f 40 eb 86 b7 7b 0b f0 95 21 22 5c 6b 4e 82
   40: 54 d6 65 93 ce 60 b2 1c 73 56 c0 14 a7 8c f1 dc
   50: 12 75 ca 1f 3b be e4 d1 42 3d d4 30 a3 3c b6 26
   60: 6f bf 0e da 46 69 07 57 27 f2 1d 9b bc 94 43 03
   70: f8 11 c7 f6 90 ef 3e e7 06 c3 d5 2f c8 66 1e d7
   80: 08 e8 ea de 80 52 ee f7 84 aa 72 ac 35 4d 6a 2a
   90: 96 1a d2 71 5a 15 49 74 4b 9f d0 5e 04 18 a4 ec
   a0: c2 e0 41 6e 0f 51 cb cc 24 91 af 50 a1 f4 70 39
   b0: 99 7c 3a 85 23 b8 b4 7a fc 02 36 5b 25 55 97 31
   c0: 2d 5d fa 98 e3 8a 92 ae 05 df 29 10 67 6c ba c9
   d0: d3 00 e6 cf e1 9e a8 2c 63 16 01 3f 58 e2 89 a9
   e0: 0d 38 34 1b ab 33 ff b0 bb 48 0c 5f b9 b1 cd 2e
   f0: c5 f3 db 47 e5 a5 9c 77 0a a6 20 68 fe 7f c1 ad

The key expansion operation consists of the following two loops and intermediate step:

for i = T, T+1, ..., 127 do

L[i] = PITABLE[L[i-1] + L[i-T]];

L[128-T8] = PITABLE[L[128-T8] & TM];

for i = 127-T8, ..., 0 do

L[i] = PITABLE[L[i+1] XOR L[i+T8]];

(In the first loop, the addition of L[i-1] and L[i-T] is performed modulo 256.)

The "effective key" consists of the values L[128-T8],..., L[127]. The intermediate step's bitwise "and" operation reduces the search space for L[128-T8] so that the effective number of key bits is T1. The expanded key depends only on the effective key bits, regardless

of the supplied key K. Since the expanded key is not itself modified during encryption or decryption, as a pragmatic matter one can expand the key just once when encrypting or decrypting a large block of data.

3. Encryption algorithm

The encryption operation is defined in terms of primitive "mix" and "mash" operations.

Here the expression "x rol k" denotes the 16-bit word x rotated left by k bits, with the bits shifted out the top end entering the bottom end.

3.1 Mix up R[i]

The primitive "Mix up R[i]" operation is defined as follows, where s[0] is 1, s[1] is 2, s[2] is 3, and s[3] is 5, and where the indices of the array R are always to be considered "modulo 4," so that R[i-1] refers to R[3] if i is 0 (these values are

"wrapped around" so that R always has a subscript in the range 0 to 3 inclusive):

R[i] = R[i] + K[j] + (R[i-1] & R[i-2]) + ((~R[i-1]) & R[i-3]);
j = j + 1;
R[i] = R[i] rol s[i];

In words: The next key word K[j] is added to R[i], and j is advanced. Then R[i-1] is used to create a "composite" word which is added to R[i]. The composite word is identical with R[i-2] in those positions where R[i-1] is one, and identical to R[i-3] in those positions where R[i-1] is zero. Then R[i] is rotated left by s[i] bits (bits rotated out the left end of R[i] are brought back in at the right). Here j is a "global" variable so that K[j] is always the first key word in the expanded key which has not yet been used in a "mix" operation.

3.2 Mixing round

A "mixing round" consists of the following operations:

Mix up R[0]
Mix up R[1]
Mix up R[2]
Mix up R[3]

3.3 Mash R[i]

The primitive "Mash R[i]" operation is defined as follows (using the previous conventions regarding subscripts for R):

R[i] = R[i] + K[R[i-1] & 63];

In words: R[i] is "mashed" by adding to it one of the words of the expanded key. The key word to be used is determined by looking at the low-order six bits of R[i-1], and using that as an index into the key array K.

3.4 Mashing round

A "mashing round" consists of:

Mash R[0] Mash R[1] Mash R[2] Mash R[3]

3.5 Encryption operation

The entire encryption operation can now be described as follows. Here j is a global integer variable which is affected by the mixing operations.

1. Initialize words R[0], ..., R[3] to contain the 64-bit input value.

2. Expand the key, so that words K[0], ..., K[63] become defined.

3. Initialize j to zero.

4. Perform five mixing rounds.

5. Perform one mashing round.

6. Perform six mixing rounds.

7. Perform one mashing round.

8. Perform five mixing rounds.

Note that each mixing round uses four key words, and that there are 16 mixing rounds altogether, so that each key word is used exactly once in a mixing round. The mashing rounds will refer to up to eight of the key words in a data-dependent manner. (There may be repetitions, and the genuine set of words referred to will vary from encryption to encryption.)

4. Decryption algorithm

The decryption operation is defined in terms of primitive operations that undo the "mix" and "mash" operations of the encryption algorithm. They are named "r-mix" and "r-mash" (r- denotes the reverse operation).

Here the expression "x ror k" denotes the 16-bit word x rotated right by k bits, with the bits shifted out the bottom end entering the top end.

4.1 R-Mix up R[i]

The primitive "R-Mix up R[i]" operation is defined as follows, where s[0] is 1, s[1] is 2, s[2] is 3, and s[3] is 5, and where the indices of the array R are always to be considered "modulo 4," so that R[i-1] refers to R[3] if i is 0 (these values are "wrapped around" so that R always has a subscript in the range 0 to 3 inclusive):

R[i] = R[i] ror s[i]; R[i] = R[i] - K[j] - (R[i-1] & R[i-2]) - ((~R[i-1]) & R[i-3]); j = j - 1;

In words: R[i] is rotated right by s[i] bits (bits rotated out the right end of R[i] are brought back in at the left). Here j is a "global" variable so that K[j] is always the key word with greatest index in the expanded key which has not yet been used in a "r-mix" operation. The key word K[j] is subtracted from R[i], and j is decremented. R[i-1] is used to create a "composite" word which is subtracted from R[i]. The composite word is identical with R[i-2] in those positions where R[i-1] is one, and identical to R[i-3] in those positions where R[i-1] is zero.

4.2 R-Mixing round

An "r-mixing round" consists of the following operations:

R-Mix up R[3]
R-Mix up R[2]
R-Mix up R[1]
R-Mix up R[0]

4.3 R-Mash R[i]

The primitive "R-Mash R[i]" operation is defined as follows (using the previous conventions regarding subscripts for R):

R[i] = R[i] - K[R[i-1] & 63];

In words: R[i] is "r-mashed" by subtracting from it one of the words of the expanded key. The key word to be used is determined by looking at the low-order six bits of R[i-1], and using that as an index into the key array K.

4.4 R-Mashing round

An "r-mashing round" consists of:

R-Mash R[3]
R-Mash R[2]
R-Mash R[1]
R-Mash R[0]

4.5 Decryption operation

The entire decryption operation can now be described as follows. Here j is a global integer variable which is affected by the mixing operations.

1. Initialize words R[0], ..., R[3] to contain the 64-bit ciphertext value.

2. Expand the key, so that words K[0], ..., K[63] become defined.

3. Initialize j to 63.

4. Perform five r-mixing rounds.

5. Perform one r-mashing round.

6. Perform six r-mixing rounds.

7. Perform one r-mashing round.

8. Perform five r-mixing rounds.

5. Test vectors

Test vectors for encryption with RC2 are provided below. All quantities are given in hexadecimal notation.

Key length (bytes) = 8
Effective key length (bits) = 63
Key = 00000000 00000000
Plaintext = 00000000 00000000
Ciphertext = ebb773f9 93278eff

Key length (bytes) = 8
Effective key length (bits) = 64
Key = ffffffff ffffffff
Plaintext = ffffffff ffffffff
Ciphertext = 278b27e4 2e2f0d49

Key length (bytes) = 8
Effective key length (bits) = 64
Key = 30000000 00000000
Plaintext = 10000000 00000001
Ciphertext = 30649edf 9be7d2c2

Key length (bytes) = 1
Effective key length (bits) = 64
Key = 88
Plaintext = 00000000 00000000
Ciphertext = 61a8a244 adacccf0

Key length (bytes) = 7
Effective key length (bits) = 64
Key = 88bca90e 90875a
Plaintext = 00000000 00000000
Ciphertext = 6ccf4308 974c267f

Key length (bytes) = 16
Effective key length (bits) = 64
Key = 88bca90e 90875a7f 0f79c384 627bafb2
Plaintext = 00000000 00000000
Ciphertext = 1a807d27 2bbe5db1

Key length (bytes) = 16
Effective key length (bits) = 128
Key = 88bca90e 90875a7f 0f79c384 627bafb2
Plaintext = 00000000 00000000
Ciphertext = 2269552a b0f85ca6

Key length (bytes) = 33
Effective key length (bits) = 129
Key = 88bca90e 90875a7f 0f79c384 627bafb2 16f80a6f 85920584 c42fceb0 be255daf 1e
Plaintext = 00000000 00000000
Ciphertext = 5b78d3a4 3dfff1f1

6. RC2 Algorithm Object Identifier

The Object Identifier for RC2 in cipher block chaining mode is

rc2CBC OBJECT IDENTIFIER

::= {iso(1) member-body(2) US(840) rsadsi(113549) encryptionAlgorithm(3) 2}

RC2-CBC takes parameters

RC2-CBCParameter ::= CHOICE {

iv IV,
params SEQUENCE {

version RC2Version,
iv IV

}

}

where

IV ::= OCTET STRING -- 8 octets
RC2Version ::= INTEGER -- 1-1024

RC2 in CBC mode has two parameters: an 8-byte initialization vector (IV) and a version number in the range 1-1024 which specifies in a roundabout manner the number of effective key bits to be used for the RC2 encryption/decryption.

The correspondence between effective key bits and version number is as follows:

1. If the number EKB of effective key bits is in the range 1-255, then the version number is given by Table[EKB], where the 256-byte translation table Table[] is specified below. Table[] specifies a permutation on the numbers 0-255; note that it is not the same table that appears in the key expansion phase of RC2.

2. If the number EKB of effective key bits is in the range 256-1024, then the version number is simply EKB.

The default number of effective key bits for RC2 is 32. If RC2-CBC is being performed with 32 effective key bits, the parameters should be supplied as a simple IV, rather than as a SEQUENCE containing a version and an IV.

        0  1  2  3  4  5  6  7  8  9  a  b  c  d  e  f

   00: bd 56 ea f2 a2 f1 ac 2a b0 93 d1 9c 1b 33 fd d0
   10: 30 04 b6 dc 7d df 32 4b f7 cb 45 9b 31 bb 21 5a
   20: 41 9f e1 d9 4a 4d 9e da a0 68 2c c3 27 5f 80 36
   30: 3e ee fb 95 1a fe ce a8 34 a9 13 f0 a6 3f d8 0c
   40: 78 24 af 23 52 c1 67 17 f5 66 90 e7 e8 07 b8 60
   50: 48 e6 1e 53 f3 92 a4 72 8c 08 15 6e 86 00 84 fa
   60: f4 7f 8a 42 19 f6 db cd 14 8d 50 12 ba 3c 06 4e
   70: ec b3 35 11 a1 88 8e 2b 94 99 b7 71 74 d3 e4 bf
   80: 3a de 96 0e bc 0a ed 77 fc 37 6b 03 79 89 62 c6
   90: d7 c0 d2 7c 6a 8b 22 a3 5b 05 5d 02 75 d5 61 e3
   a0: 18 8f 55 51 ad 1f 0b 5e 85 e5 c2 57 63 ca 3d 6c
   b0: b4 c5 cc 70 b2 91 59 0d 47 20 c8 4f 58 e0 01 e2
   c0: 16 38 c4 6f 3b 0f 65 46 be 7e 2d 7b 82 f9 40 b5
   d0: 1d 73 f8 eb 26 c7 87 97 25 54 b1 28 aa 98 9d a5
   e0: 64 6d 7a d4 10 81 44 ef 49 d6 ae 2e dd 76 5c 2f
   f0: a7 1c c9 09 69 9a 83 cf 29 39 b9 e9 4c ff 43 ab

A. Intellectual Property Notice

RC2 is a registered trademark of RSA Data Security, Inc. RSA's copyrighted RC2 software is available under license from RSA Data Security, Inc.

B. Author's Address

Ron Rivest
RSA Laboratories
100 Marine Parkway, #500
Redwood City, CA 94065 USA

Phone: (650) 595-7703
EMail: rsa-labs@rsa.com

C. Full Copyright Statement

Copyright (C) The Internet Society (1998). All Rights Reserved.

This document and translations of it may be copied and furnished to others, and derivative works that comment on or otherwise explain it or assist in its implementation may be prepared, copied, published and distributed, in whole or in part, without restriction of any kind, provided that the above copyright notice and this paragraph are included on all such copies and derivative works. However, this document itself may not be modified in any way, such as by removing the copyright notice or references to the Internet Society or other Internet organizations, except as needed for the purpose of developing Internet standards in which case the procedures for copyrights defined in the Internet Standards process must be followed, or as required to translate it into languages other than English.

The limited permissions granted above are perpetual and will not be revoked by the Internet Society or its successors or assigns.

This document and the information contained herein is provided on an "AS IS" basis and THE INTERNET SOCIETY AND THE INTERNET ENGINEERING TASK FORCE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION HEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

Thu, 29 Mar 2018 00:30:00 -0500 text/html https://www.ipa.go.jp/security/rfc/RFC2268EN.html
Killexams : Global Business Intelligence Market: 49% Businesses are Looking to Expand Business Intelligence Application

SkyQuest Technology Consulting Pvt. Ltd.

Global business intelligence (BI) market was valued at USD 24.90 billion in 2021, and it is expected to reach a value of USD 42.13 billion by 2028, at a CAGR of 7.6 % over the forecast period (2022-2028).

Westford, USA, Aug. 08, 2022 (GLOBE NEWSWIRE) -- Business intelligence is one of the most popular and rapidly growing areas of information technology. This growth is being driven by a number of factors, including the need for organizations to make better decisions faster and the increasing popularity of big data.  According to SkyQuest’s market analysis, demand for BI is broadly driven by growing focus of businesses on improving operational efficiency, enhanced decision making, improved customer interaction, and more rapid product iteration. The capabilities offered by BI tools can help organizations manage data more effectively, optimize processes and uncover insights that would otherwise go undetected. These capabilities can have a major impact on overall business performance and can provide significant cost savings for companies of all sizes across the global business intelligence market.

It has been observed that, over 43% of organizations already have or are deploying some form of BI solution. This includes both large and small organizations, as well as those specializing in different industries. In addition, nearly half of respondents at a latest survey said they would deploy more BI applications in the next 12 months to better understand their businesses.

SkyQuest has published a report on global business intelligence market that covers various market aspects such market detailed market analysis, demand, market trends, pricing analysis, leading providers of business intelligence solution, their market share analysis, value chain, and competitive landscape. The report would help the market players in understanding current market trends, growth opportunities, challenges and threat in the global market. To know more,

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AI is Shaping the Future of Business Intelligence Market

In latest years, artificial intelligence (AI) has been gaining more and more traction in the business world. This technology is helping to shape the future of business intelligence (BI), as AI can help speed up the process of extracting insights from data and providing actionable insights to managers.

One of the most significant applications of AI in global business intelligence market is its ability to automate complex analyses and decision-making processes. In some cases, AI can even take on the role of a data analyst, allowing companies to scale their data analysis capabilities without having to hire additional employees. In addition to automating tasks, AI also helps to identify patterns and trends that may be otherwise difficult to see. This can allow businesses to make smarter decisions faster, which can ultimately lead to improved profits.

One of the most popular BI tools is IBM’s Watson cognitive system. This tool can be used to create insights from data by analyzing text, images, videos and other sources. IBM has released several versions of Watson over the past few years, each with capabilities that have expanded beyond what was available in the previous version. And Microsoft’s Azure Cognitive Services offers a wide range of capabilities that can be used for BI and data management

As businesses shift away from manual data analysis and toward more automated systems, BI tools will continue to become increasingly important. Indeed, it seems likely that BI will become even more central to business operations.

Embedded Analytics Getting Popular Among Businesses

Embedded analytics is a technology that allows businesses to collect and analyze data without having to install separate software. As per SkyQuest analysis, this technology can be found in many different forms, such as web search engines, social media monitoring tools, and even embedded sensors in devices.

A large number of businesses are opting for embedded analytics to collect data from a wide range of sources in the global business intelligence market. Not only can embedded analytics be used in internet-based applications, but it can also be used in on-premises applications and even with mobile devices. Collecting data from a variety of sources means that businesses have more information available to them when they are trying to make decisions. As per latest study by IDC, four in five firms used more than 100 data sources and just under one-third had more than 1,000. Often, this data exists in silos.

In addition to collecting data, embedded analytics can also help businesses analyze the data. This analysis can help businesses determine how best to use the data and which pieces of the data are most important. Additionally, companies can use embedded analytics to predict future trends based on past data. This prediction is often referred to as “predictive modeling” and is one of the most powerful features of embedded analytics.

SkyQuest has studied the global business intelligence market and identified current market dynamics and trends. The report provides a detailed analysis of the embedded analytics and its increasing adoption among firms by their size. This will help the market participants understand consumer behavior and formulate growth strategies as per the current market opportunities. To get more insights,

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SkyQuest Analysis Suggests 49% Businesses are Planning to Expand BI Applications into their Businesses, But Pricing to Play Key Role  

With the rapid growth of businesses, it is no surprise that data analysis and intelligence are becoming increasingly important across business intelligence market. In a latest survey, 92% of respondents said that they use business intelligence to analyze their data. However, only 49% said that their organization has a clear strategy for using BI and are aiming to expand application of BI into their businesses. As a result, businesses are looking for ways to Excellerate their data-analysis capabilities.

These findings come from a survey conducted by SkyQuest Technology Consulting, which polled 239 business decision-makers in the United States Asia Pacific, and Europe about their perceptions of BI.

The survey on the global business intelligence market also found that 54% of respondents feel unhappy with their current role in BI and only 30% believe that their skills in BI are valuable to their organization. Furthermore, 43% of respondents say they have said that BI is becoming expensive for them to afford, while 32% cite its complexity as a reason for not using it. In fact, specifically, 56% of business executives said that this is a key obstacle to their BI implementation plans.

Interestingly, while only a 43% of businesses are currently using BI tools, most believe that this number will increase in the future. Sixty-seven percent of those surveyed believe that BI will become increasingly important over the next three years, while 49 percent think it will be critical within five years.

SkyQuest’s report on business intelligence market can be an essential tool for market players. This report would help to identify the growth potential, current trends, and future prospects of the business intelligence market. The report would also provide detailed analysis of each type of business intelligence product and service. This information would help market players to make informed decisions about their investments in this area.

Key Players in Global Business Intelligence Market

  • IBM Corporation (US)

  • Oracle Corporation (US)

  • Microsoft Corporation (US)

  • Google (US)

  • SAP SE (Germany)

  • Cisco Systems Inc. (US)

  • Information Builders (US)

  • Qlik Technologies Inc. (US)

  • SAS Institute Inc. (US)

  • Tableau Software Inc. (US)

  • TIBCO Software Inc. (US)

  • Domo Inc. (US)

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Related Reports in SkyQuest’s Library:

Global Machine Learning Market

Global Customer Data Platform Market

Global Cloud Managed Networking Market

Global Metaverse Infrastructure Market

Global Location Based Services Market

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Mon, 08 Aug 2022 02:09:00 -0500 en-GB text/html https://uk.news.yahoo.com/global-business-intelligence-market-49-140900327.html Killexams : Marian Underweiser, PhD Killexams : Marian Underweiser, PhD - IPWatchdog.com | Patents & Patent Law

Dr. Underweiser’s decades-long career in Intellectual Property Law spans a broad variety of leadership positions and technology areas.  She oversaw the intellectual property operations of IBM’s world-renowned research division,  leading a team of legal professionals charged with the procurement of patents, the development and implementation of intellectual property policies and the negotiation of commercial transactions in support of the global research organization. Prior to assuming that role, Marian was IBM’s Senior Counsel for IP Law Policy and Strategy, formulating policy on a broad spectrum of IP matters and developing and deploying IBM’s patent portfolio strategy.  Most recently, Marian was Managing IP Counsel at Waymo, where she provided IP support for clients working on aspects of self-driving technology.

The PTAB Reform Act Will Make the PTAB’s Problems Worse

Recently, we submitted comments for the record to the Senate Judiciary Committee’s IP Subcommittee in response to its June 22 hearing on the Patent Trial and Appeal Board (PTAB), titled: “The Patent Trial and Appeal Board: Examining Proposals to Address Predictability, Certainty and Fairness.” The hearing focused on Senator Leahy’s PTAB Reform Act, which among other changes, would eliminate the discretion of the Director to deny institution of an inter partes review (IPR) petition based on an earlier filed district court litigation involving the same patents, parties and issues. Here is the net of what we told them:

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Killexams : Bot Services Market Growing at a CAGR 33.2% | Key Player Microsoft, IBM, Google, Oracle, AWS
Bot Services Market Growing at a CAGR 33.2% | Key Player Microsoft, IBM, Google, Oracle, AWS

“Microsoft (US), IBM (US), Google (US), Oracle (US), AWS (US), Meta (US), Artificial Solutions (Sweden), eGain (US), Baidu (China), Inbenta (US), Alvaria (US), SAP (Germany), Creative Virtual (UK), Gupshup (US), Rasa (US), Pandorabots (US), Botego (US), Chatfuel (US), Pypestream (US), Avaamo (US), Webio (Ireland), ServisBOT (US).”

Bot Services Market by Service Type (Platform & Framework), Mode of Channel (Social Media, Website), Interaction Type, Business Function (Sales & Marketing, IT, HR), Vertical (BFSI, Retail & eCommerce) and Region – Global Forecast to 2027

The Bot Services Market size to grow from USD 1.6 billion in 2022 to USD 6.7 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 33.2% during the forecast period. Various factors such as rise in the need for 24X7 customer support at a lower operational cost, integration of chatbots with social media to augment marketing strategy, and innovations in AI and ML technologies for chatbots resulting in better customer experience are expected to drive the adoption of bot services.

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According to Microsoft, Azure Bot Service provides an integrated development environment for bot building. Its integration with Power Virtual Agents, a fully hosted low-code platform, enables developers of all technical abilities to build conversational AI bots without the need for any further coding. The integration of Azure Bot Service and Power Virtual Agents enables a multidisciplinary team with a range of expertise and abilities to build bots inside a single software as a service (SaaS) solution.

Healthcare and Life Sciences vertical to witness the highest CAGR during the forecast period

The segmentation of the bot services market by vertical includes BFSI, retail & eCommerce, healthcare & life sciences, media & entertainment, travel & hospitality, IT & telecom, government, and others (automotive, utilities, education and real estate). The healthcare industry is developing rapidly due to many major technological advancements to enhance the overall patients experience. Hospitals and other health institutions are increasingly adopting bot services to Excellerate the overall experience of patients, doctors, and other staff. Additionally, bot services can enhance patient experience and build patient loyalty, while improving organizational efficiency. Moreover, bots, also known as virtual health assistants, notify patients about their medication plan, address concerns, deliver diagnosis reports, educate them regarding certain diseases, motivate them to exercise, and personalize user experience.

Some major players in the bot services market include Microsoft (US), IBM (US), Google (US), Oracle (US), AWS (US), Meta (US), Artificial Solutions (Sweden), eGain (US), Baidu (China), Inbenta (US), Alvaria (US), SAP (Germany), CM.com (Netherlands), Creative Virtual (UK), Kore.ai (US), [24]7.ai (US), Gupshup (US), Rasa (US), Pandorabots (US), Botego (US), Chatfuel (US), Pypestream (US), Avaamo (US), Webio (Ireland), ServisBOT (US), Morph.ai (India), Cognigy (Germany), Enterprise Bot (Switzerland), Engati (US), and Haptik (US). These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the global bot services market.

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Artificial Solutions (Sweden) is a leading specialist in Conversational AI solutions and services. The solution offered by the company enables communication with applications, websites, and devices in everyday, human-like natural language via voice, text, touch, or gesture inputs. Artificial Solutions’ conversational AI technology makes it easy to build, implement, and manage a wide range of natural language applications, such as virtual assistants, conversational bots, and speech-based conversational UIs for smart devices. Artificial Solutions offers bot services and solutions to various industries, such as financial services, retail, automotive, telecom, energy and utilities, travel and leisure, and entertainment. Artificial Solutions has won several awards, such as the 2019 Stevie Awards for Sales and Customer Service, the 2018 Speech Industry Awards, and the 2018 AICONICS: Best Intelligent Assistant Innovation. The company’s major customers include AT&T, Shell, Vodafone, TIAA, Volkswagen Group, Deutsche Post, Widiba, Telenor Group, Accenture, KPMG, Cognizant, Wipro, and Publicis Sapient. It has development centers in Barcelona, Hamburg, London, and Stockholm and offices across Europe, Asia Pacific, and South America.

In the bot services market, it provides Teneo, a platform that enables business users and developers to collaborate to create intelligent conversational AI applications. These applications operate across 35 languages, multiple platforms, and channels in record time.

eGain (US) is a leading cloud customer engagement hub software supplier. eGain products have been used to Excellerate customer experience, streamline service processes, and increase revenue across the online, social media, and phone channels for over a decade. eGain helps hundreds of the worlds leading organizations turn their disjointed sales and customer service operations into unified customer engagement hubs (CEHs). In North America, Europe, the Middle East, Africa, and Asia Pacific, eGain Corporation develops, licenses, implements, and supports customer service infrastructure software solutions. It offers a unified cloud software platform to automate, augment, and orchestrate consumer interactions. It also provides subscription services, which supply users access to its software via a cloud-based platform, as well as professional services, including consultation, implementation, and training. The company caters to the financial services, telecommunications, retail, government, healthcare, and utilities industries.

In the bot services market, the company offers AI Chatbot Virtual Assistant software which improves customer engagement. The VA acts as a guide, helping customers navigate the website and taking them to the relevant places on a page. The virtual assistant provides answers to any queries, even helping in making shopping decisions.

Baidu (China) provides internet search services. It is divided into two segments: Baidu Core and iQIYI. The Baidu app helps customers to access search, feed, and other services through their mobile devices. Baidu Search helps users to access the companys search and other services. Baidu Feed gives users a customized timeline based on their demographics and interests. The company provides products, including Baidu Knows, an online community where users can ask questions to other users; Baidu Wiki; Baidu Healthcare Wiki; Baidu Wenku; Baidu Scholar; Baidu Experience; Baidu Post; Baidu Maps, a voice-enabled mobile app that provides travel-related services; Baidu Drive; Baijiahao; and DuerOS, a smart assistant platform. The company also provides online marketing services such as pay for performance, an auction-based service that enables customers to bid for priority placement of paid sponsored links and reach users searching for information about their products or services. Other marketing services offered by the company are display-based marketing services and other online marketing services based on performance criteria other than cost per click. The company offers a mobile ecosystem, which includes Baidu A, a portfolio of applications. Further, the company provides iQIYI, an online entertainment service, including original and licensed content; video content and membership; and online advertising services.

In the bot services market, Baidu offers Baidu Bot, a search bot software used by Baidu, which collects documents from the web to build a searchable index for the Baidu search engine.

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Killexams : AI Tech Stocks and the Growing Implementation in the Sports Market

Vancouver, Kelowna and Delta, British Columbia--(Newsfile Corp. - July 21, 2022) - Investorideas.com (www.investorideas.com), a global investor news source covering Artificial Intelligence (AI) stocks releases a sector snapshot looking at the growing AI tech implementation in the sports market, featuring AI innovator GBT Technologies Inc. (OTC Pink: GTCH).

Read the full article at Investorideas.com

As with so many other sectors, the sports industry is seeing increasing penetration of Artificial Intelligence (AI) related technologies as aspects of the medium become more and more digitized. A recently published report from Vantage Market Research finds that the global market for AI in Sports is projected to grow from $1.62 billion USD in 2021 to $7.75 billion by 2028, registering a compound annual growth rate (CAGR) of 29.7 percent in the forecast period 2022-28. According to a market synopsis from the report, AI is being leveraged by a number of firms to track player performance, Excellerate the player's health, and to Excellerate sports planning.

One such firm is GBT Technologies Inc. (OTC Pink: GTCH), an early stage technology developer in IoT and Artificial Intelligence (AI) Enabled Mobile Technology Platforms, which recently completed phase one of its intelligent soccer analytics platform through its 50 percent-owned joint venture GBT Tokenize Corp. (GTC). Given the internal codename of smartGOAL, the platform is "an intelligent, automatic analytics and prediction system for soccer game's results," which works by analyzing and predicting "possible outcomes of soccer games results according to permutations, statistics, historical data, using advanced mathematical methods and machine learning technology." GBT's CTO, Danny Rittman, explained:

"Considering the popularity of the game in the present world, we believe organizations will be interested in prediction systems for the better performance of their teams. As interesting as it may seem, prediction of the results of a soccer game is a very hard task and involves a large amount of uncertainty. However, it can be said that the result of football is not a completely random event, and hence, we believe a few hidden patterns in the game can be utilized to potentially predict the outcome. Based on the studies of numerous researchers that are being reviewed in our study as well as those done in the previous years, one can say that with a sufficient amount of data an accurate prediction system can be built using various machine learning algorithms. While each algorithm has its advantages and disadvantages, a hybrid system that consists of more than one algorithm can be made with the goal of increasing the efficiency of the system as a whole. There also is a need for a comprehensive dataset through which better results can be obtained. Experts can work more toward gathering data related to different leagues and championships across the globe which may help in better understanding of the prediction system. Moreover, the distinctive characteristics of a soccer player, as well as that of the team, can also be taken into consideration while predicting as this may produce a better result as compared to when all the players in a game are treated to be having an equal effect on the game. The more information the system is trained with, we believe the more accurate the predictions and analysis will be. One of our joint venture companies, GTC, aimed to evaluate machine learning-driven applications in various fields, among them are entertainment, media and sports. We believe smartGOAL is an intelligent application that has the ability to change the world's soccer field when it comes to analytics and game score predictions."

Elsewhere, Amazon Web Services (AWS), a subsidiary of tech giant Amazon announced a collaboration with Maple Leaf Sports & Entertainment (MLSE), a sports and entertainment company that owns a host of Toronto-based sports franchises, to innovate the creation and delivery of "extraordinary sports moments and enhanced fan engagement." This will see MLSE utilize AWS AI, machine learning (ML), and deep learning cloud services to support their teams, lines of business, and how fans connect with each other and experience games. Humza Teherany, Chief Technology & Digital Officer at MLSE, commented:

"We built Digital Labs at MLSE to become the most technologically advanced organization in sport. As technology advances and how we watch and consume sports evolves, MLSE is dedicated to creating solutions and products that drive this evolution and elevate the fan experience. We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition. Taking a cloud-first approach to innovation with AWS further empowers our organization to experiment with new ideas that can help our teams perform their very best and our fans feel a closer connection to the action."

Similarly, IBM, the "Official Technology Partner of The [tennis] Championships for the past 33-years, has recently, alongside the All England Lawn Tennis Club, unveiled "new ways for Wimbledon fans around the world to experience The Championships digitally, powered by artificial intelligence (AI) running on IBM Cloud and hybrid cloud technologies." Kevin Farrar, Sports Partnership Leader, IBM UK & Ireland, explained:

"The digital fan features on the Wimbledon app and Wimbledon.com, beautifully designed by the IBM iX team and powered by AI and hybrid cloud technologies, are enabling the All England Club to immerse tennis lovers in the magic of The Championship, no matter where they are in the world. Sports fans love to debate and we're excited to introduce a new tool this year to enable that by allowing people to register their own match predictions and compare them with predictions generated by Match Insights with Watson and those of other fans."

Another firm cited in the Vantage Market Research report on AI in Sports was sports performance tech firm Catapult Group International Limited, who recently reported a multi-year deal with the German Football Association (DFB-Akademie) to "capture performance data via video, track athlete performance via wearables, and Excellerate the analysis infrastructure at all levels of the German National Football Teams." Will Lopes, CEO of Catapult, commented:

"We strive every day to unleash the potential of every athlete and team, and we're proud to partner with the prestigious German Football Association to fulfill that ambition. We're looking forward to partnering with the DFB to unlock what even the best coaches in the world cannot see on film or from the sidelines. This technology will empower athletes at all levels with data and insights to perform at their best."

With the seemingly inexorable tendency toward digitization in the presentation and analysis of sports, the accompanying use of AI-related technologies seems equally inevitable as is already borne out by current industry trends.

For a list of artificial intelligence stocks on Investorideas.com visit here.

About GBT Technologies Inc.

GBT Technologies, Inc. (OTC Pink: GTCH) ("GBT") (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT's mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT's goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT's vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

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Disclaimer/Disclosure: Investorideas.com is a digital publisher of third party sourced news, articles and equity research as well as creates original content, including video, interviews and articles. Original content created by investorideas is protected by copyright laws other than syndication rights. Our site does not make recommendations for purchases or sale of stocks, services or products. Nothing on our sites should be construed as an offer or solicitation to buy or sell products or securities. All investing involves risk and possible losses. This site is currently compensated for news publication and distribution, social media and marketing, content creation and more. Disclosure is posted for each compensated news release, content published /created if required but otherwise the news was not compensated for and was published for the sole interest of our readers and followers. Contact management and IR of each company directly regarding specific questions. Disclosure: GTCH is a paid featured monthly AI stock on Investorideas.com More disclaimer info: https://www.investorideas.com/About/Disclaimer.asp Learn more about publishing your news release and our other news services on the Investorideas.com newswire https://www.investorideas.com/News-Upload/ Global investors must adhere to regulations of each country. Please read Investorideas.com privacy policy: https://www.investorideas.com/About/Private_Policy.asp

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Killexams : NLP in Healthcare and Life Sciences Market Analysis by Size, Share, Key Players, Growth, Trends & Forecast 2026

"Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelity (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US)."

NLP in Healthcare and Life Sciences Market by Component (Solutions and Services), NLP Type (Rule-based, Statistical, and Hybrid), Application (IVR, Predictive Risk Analytics), Organization Size, End User, and Region - Global Forecast to 2026

The global NLP in Healthcare and Life Sciences Market size to grow from USD 1.8 billion in 2021 to USD 4.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.0% during the forecast period. Factors such as growing need to analyze and extract insights from narrative text and huge amount of clinical data, increasing demand for improving EHR data usability to Excellerate healthcare delivery and outcomes and the rising urge of predictive analytics technology to reduce risks and Excellerate significant health concerns are driving the adoption of the NLP in healthcare and life sciences market across the globe.

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In the constant fight against COVID-19, pharmaceutical and healthcare organizations, government bodies, and the broader scientific communities worldwide are working to assess the impact of the COVID-19 virus and quickly develop accurate solutions. Few vendors in the market have observed that NLP provides faster, more systematic, and more comprehensive insight generation from unstructured text. Gaining key information from sources such as scientific literature, clinical trial records, pre-prints, internal sources, medical records, and social media and news reports, and synthesizing it into one evidence hub deepens the understanding for users, help accelerate outcomes. NLP is extensively used in different organizations to categorize sentiments, provide recommendations, and summarize information and topic. With the spread of COVID-19, communities, patients, and clinicians across the globe have all witnessed major disruptions in the way they work and how they engage with stakeholders across the ecosystem. Pharmaceutical and life science companies face immense pressure to provide essential medical products to support needy patients and ensure the development of new therapeutics and vaccines for COVID-19.

Scope of the Report

Report Metrics

Details

Market size available for years

2017–2026

Base year considered

2021

Forecast period

2022–2026

Forecast units

USD Million

Segments covered

Component, Deployment Mode, Organization Size, NLP Type, Application, End User, And Region

Geographies covered

North America, Europe, APAC, Latin America and MEA

Companies covered

Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelty (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US), Caption Health (US), ForeSee Medical (US), Press Ganey (US), Gnani.ai (India), Notable (US), Biofourmis (US), Babylon (UK), Flatiron (US), and Suki (US)

The services segment to hold higher CAGR during the forecast period

Based on components, the NLP in healthcare and life sciences market is segmented into solutions and services. The services segment has been further divided into professional and managed services. These services play a vital role in the functioning of NLP in healthcare and life sciences, as well as ensure faster and smoother implementation that maximizes the value of the enterprise investments. The growing adoption of NLP technology is expected to boost the adoption of professional and managed services. Professional service providers have deep knowledge related to the products and enable customers to focus on the core business, while MSPs help healthcare firms to Excellerate their business operations and reduce overall expenses.

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According to ForeSee Medical, NLP is the ability of computers to understand the latest human speech terms and text. It is used in current technology to support spam email privacy, personal voice assistants, and language translation applications. The adoption of NLP in healthcare and life sciences is rising because of its recognized potential to search, analyze, and interpret a mammoth amount of patient datasets. Using advanced NLP-based algorithms, healthcare and life sciences firms harness the relevant insights and concepts from the clinical data that was previously considered buried in the text form.

Some of the key players operating in the NLP in healthcare and life sciences market include Microsoft (US), Google (US), IBM (US), Cerner (US), 3M (US), AWS (US), Inovalon (US), Dolbey (US), Averbis (Germany), Linguamatics (an IQVIA Company) (UK), Apixio (US), Clinithink (US), Lexalytics (US), Apixio (US), Clinithink (US), Lexalytics (US), Health Fidelty (US), Wave Health Technologies (US), Corti (US), CloudMedx (US), Oncora Medical (US), Caption Health (US), ForeSee Medical (US), Press Ganey (US), Gnani.ai (India), Notable (US), Biofourmis (US), Babylon (UK), Flatiron (US), and Suki (US). These NLP in healthcare and life sciences vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global NLP in healthcare and life sciences market.

Microsoft (US) develops software, services, devices, and solutions to compete around intelligent cloud and intelligent edge. With continuous investments in the cloud, Microsoft enables its customers to digitalize their business processes. The company’s offerings include cloud-based solutions that provide customers with software, platforms, content and deliver solution support and consulting services. Its product offerings include Operating Systems (OS), cross-device productivity applications, server applications, business solution applications, desktop and server management tools, software development tools, and video games. Microsoft operates its business using three segments: Productivity and Business Processes, Intelligent Cloud, and More Personal Computing. The company’s platforms and tools help drive the productivity of small businesses, the competitiveness of large businesses, and the efficiency of the public sector. The company’s platform accelerates innovation across the spectrum of intelligent edge devices, from IoT sensors to gateway devices and edge hardware to build, manage, and secure edge workloads. Microsoft will invest USD 1 billion over the next four years in new technologies and innovative climate solutions. It has a geographical presence in more than 190 countries across North America, APAC, Latin America, MEA, and Europe. In response to the COVID-19 pandemic, Microsoft partnered with the Allen Institute for AI and leading research groups to prepare the COVID-19 Open Research Dataset. It is a free resource containing over 47,000 scholarly articles for use by the global research community. With Cognitive Search and Text Analytics, Microsoft developed the COVID-19 search engine, enabling researchers to more quickly evaluate and gain insights from the overwhelming amount of information about the COVID-19 pandemic.

3M (US) company was formerly known as Minnesota Mining and Manufacturing Company. 3M is a diversified global manufacturer, technology innovator, and marketer of a wide variety of products and services. 3M is a well-known provider of products, such as adhesives, laminates, dental products, orthodontic products, abrasives, and medical appliances. 3M is a diversified technology company with a worldwide presence and operates in segments including Safety and Industrial; Transportation and Electronics; Health Care, and Consumer. The company operates worldwide and caters to more than 65 nations. It delivers products through retailing and distributing partners in more than 200 nations. The company offers its products to verticals, such as healthcare, consumer and office; transportation and industry, safety, display and graphics, security and protection services, and electronics and communication. The company develops, manufactures, and markets its innovative products for the global market. 3M developed its NLP platform for email spam detection, personal assistants, and language translation apps. It further developed various healthcare-specific applications based on this platform for text processing and documentation. The company’s NLP platform automates the process of mining clinical concepts from unstructured data. 3M Health Information System (HIS) uses NLP to autosuggest codes, which helps coders turn clinical documentation into rich data sources, thus helping coders save time. 3M has also come up with an NLP software platform named CodeRyte CodeAssist. The platform helps capture the physician’s report and record diseases. All of this results in improved productivity, performance, and efficiency.

Browse Other reports:

Mobile Device Management Market by Component (Solutions (Device management, Application Management, Security management) and Services), Deployment Mode, Organization Size, Operating system Vertical and Region - Global Forecast to 2026

 

Speech Analytics Market by Component (Solutions and Services), Application (Risk & Compliance Management, Sentiment Analysis), Organization Size, Deployment Mode (Cloud, On-premises), Vertical and Region - Global Forecast to 2026

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Killexams : AI Tech Stocks and the Growing Implementation in the Sports Market

Vancouver, Kelowna and Delta, British Columbia--(Newsfile Corp. - July 21, 2022) - Investorideas.com (www.investorideas.com), a global investor news source covering Artificial Intelligence (AI) stocks releases a sector snapshot looking at the growing AI tech implementation in the sports market, featuring AI innovator GBT Technologies Inc. (OTC Pink: GTCH).

Read the full article at Investorideas.com

As with so many other sectors, the sports industry is seeing increasing penetration of Artificial Intelligence (AI) related technologies as aspects of the medium become more and more digitized. A recently published report from Vantage Market Research finds that the global market for AI in Sports is projected to grow from $1.62 billion USD in 2021 to $7.75 billion by 2028, registering a compound annual growth rate (CAGR) of 29.7 percent in the forecast period 2022-28. According to a market synopsis from the report, AI is being leveraged by a number of firms to track player performance, Excellerate the player's health, and to Excellerate sports planning.

One such firm is GBT Technologies Inc. (OTC Pink: GTCH), an early stage technology developer in IoT and Artificial Intelligence (AI) Enabled Mobile Technology Platforms, which recently completed phase one of its intelligent soccer analytics platform through its 50 percent-owned joint venture GBT Tokenize Corp. (GTC). Given the internal codename of smartGOAL, the platform is "an intelligent, automatic analytics and prediction system for soccer game's results," which works by analyzing and predicting "possible outcomes of soccer games results according to permutations, statistics, historical data, using advanced mathematical methods and machine learning technology." GBT's CTO, Danny Rittman, explained:

"Considering the popularity of the game in the present world, we believe organizations will be interested in prediction systems for the better performance of their teams. As interesting as it may seem, prediction of the results of a soccer game is a very hard task and involves a large amount of uncertainty. However, it can be said that the result of football is not a completely random event, and hence, we believe a few hidden patterns in the game can be utilized to potentially predict the outcome. Based on the studies of numerous researchers that are being reviewed in our study as well as those done in the previous years, one can say that with a sufficient amount of data an accurate prediction system can be built using various machine learning algorithms. While each algorithm has its advantages and disadvantages, a hybrid system that consists of more than one algorithm can be made with the goal of increasing the efficiency of the system as a whole. There also is a need for a comprehensive dataset through which better results can be obtained. Experts can work more toward gathering data related to different leagues and championships across the globe which may help in better understanding of the prediction system. Moreover, the distinctive characteristics of a soccer player, as well as that of the team, can also be taken into consideration while predicting as this may produce a better result as compared to when all the players in a game are treated to be having an equal effect on the game. The more information the system is trained with, we believe the more accurate the predictions and analysis will be. One of our joint venture companies, GTC, aimed to evaluate machine learning-driven applications in various fields, among them are entertainment, media and sports. We believe smartGOAL is an intelligent application that has the ability to change the world's soccer field when it comes to analytics and game score predictions."

Elsewhere, Amazon Web Services (AWS), a subsidiary of tech giant Amazon announced a collaboration with Maple Leaf Sports & Entertainment (MLSE), a sports and entertainment company that owns a host of Toronto-based sports franchises, to innovate the creation and delivery of "extraordinary sports moments and enhanced fan engagement." This will see MLSE utilize AWS AI, machine learning (ML), and deep learning cloud services to support their teams, lines of business, and how fans connect with each other and experience games. Humza Teherany, Chief Technology & Digital Officer at MLSE, commented:

"We built Digital Labs at MLSE to become the most technologically advanced organization in sport. As technology advances and how we watch and consume sports evolves, MLSE is dedicated to creating solutions and products that drive this evolution and elevate the fan experience. We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition. Taking a cloud-first approach to innovation with AWS further empowers our organization to experiment with new ideas that can help our teams perform their very best and our fans feel a closer connection to the action."

Similarly, IBM, the "Official Technology Partner of The [tennis] Championships for the past 33-years, has recently, alongside the All England Lawn Tennis Club, unveiled "new ways for Wimbledon fans around the world to experience The Championships digitally, powered by artificial intelligence (AI) running on IBM Cloud and hybrid cloud technologies." Kevin Farrar, Sports Partnership Leader, IBM UK & Ireland, explained:

"The digital fan features on the Wimbledon app and Wimbledon.com, beautifully designed by the IBM iX team and powered by AI and hybrid cloud technologies, are enabling the All England Club to immerse tennis lovers in the magic of The Championship, no matter where they are in the world. Sports fans love to debate and we're excited to introduce a new tool this year to enable that by allowing people to register their own match predictions and compare them with predictions generated by Match Insights with Watson and those of other fans."

Another firm cited in the Vantage Market Research report on AI in Sports was sports performance tech firm Catapult Group International Limited, who recently reported a multi-year deal with the German Football Association (DFB-Akademie) to "capture performance data via video, track athlete performance via wearables, and Excellerate the analysis infrastructure at all levels of the German National Football Teams." Will Lopes, CEO of Catapult, commented:

"We strive every day to unleash the potential of every athlete and team, and we're proud to partner with the prestigious German Football Association to fulfill that ambition. We're looking forward to partnering with the DFB to unlock what even the best coaches in the world cannot see on film or from the sidelines. This technology will empower athletes at all levels with data and insights to perform at their best."

With the seemingly inexorable tendency toward digitization in the presentation and analysis of sports, the accompanying use of AI-related technologies seems equally inevitable as is already borne out by current industry trends.

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About GBT Technologies Inc.

GBT Technologies, Inc. (OTC Pink: GTCH) ("GBT") (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT's mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT's goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT's vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

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