Credits : Yourstory

A decade ago, when people spoke about connected vehicles, they thought it was just another fad. And today, we can see connected vehicles already plying on the roads with cars having algorithms that can take real-time decisions to make driving safer.

Increasing urbanisation and the growth of mega cities is set to change the way people move around very soon. Technological innovations such as autonomy, electrification, connectivity, and sharing are forcing the auto industry to rethink the way people commute.

“The software component in cars is going to be a trillion-dollar opportunity in the next decade. Each car will have a supercomputer talking to the infrastructure and other cars on the road,” says Elmar Degenhart, CEO of Continental AG.

When this happens, the future of mobility is going to be viewed very differently. If India can leapfrog these technologies, there can certainly be a revolution in mobility. However, one thing is certain, all automotive technology – at least the software component – will be built in India for the world. According to data from Continental the software part will be a $1 trillion opportunity by 2030. Currently, it stands slightly over $250 billion.

Here are some of the technologies that will be part of the future of mobility across the world.

Robo-Taxis

For large cities that are increasingly suffocating due to traffic congestion, robo-taxis offer an effective way of tackling the challenges of urban mobility.

Robo-taxis were introduced to help reduce traffic jams, accidents, air pollution, and to address the issue of parking spaces in cities.

According to a study by consulting firm Roland Berger, around one quarter of transportation tasks could be carried out by driverless vehicles by 2030.

After all, it is much smarter to operate less driverless vehicles on a near-continuous basis than to have countless private cars, which often sit in a parking space for long hours.

In addition, on campuses, amusement parks, and shopping malls, autonomous vehicles such as the “CUbE”, developed by German automaker Continental, could be used to reduce walking distances and to transport people.

To further advance the development of driverless mobility, Continental acquired a minority stake in the French company EasyMile SAS, a leading producer of driverless technologies and intelligent mobility solutions, in 2017. Continental is currently working on such mobility systems in the USA and Japan.

Similarly, Bosch and Daimler, which have a partnership to bring out autonomous vehicles in the next three years, have just been given a go-ahead by German authorities to test a fully autonomous parking valet technology. Both the companies are also working on robo-taxis.

Early this year, serial tech entrepreneur and Founder of Tesla Elon Musk also outlined his plans of launching robo-taxis next year. If Musk is to be believed, his company will be putting at least a million self-driving robo-taxis on the road in some parts of the US by 2020.

Blockchain-powered cars

Ethereum-based blockchain tokens are very popular with those who use crypto to trade items. The primary use case for blockchain is transparency, consensus, and a system of records. Above all, this works on decentralisation.

Now, companies such as Continental, Hewlett Packard Enterprise, and Crossroad.io have built a blockchain for data sharing with car companies.

So, here’s how it works. If you are driving through a new city, and don’t have required information of a particular route, you can make use of blockchain technology to connect to the cloud service of car companies operating in the area. These companies will then pull data from their customers driving on the particular route, and provide you with the details.

Individuals or drivers, who are fine with sharing their data, will provide details such as traffic jams and location landmarks. The data will be shared with a company like Continental, which will beam the data back to the person who has requested for it.

The payment made for subscribing that data goes in the form of rewards tokens to the drivers who provide the data. The drivers can then redeem these tokens on a blockchain exchange for normal or fiat currency. 

“Sharing of vehicle data across vendors can solve some of the toughest traffic problems and improve driver experience by leveraging the power of swarm intelligence,” says Phil Davis, President, Hybrid IT, Chief Sales Officer, HPE.

“Together with Continental, we provide the key to unlock the value of this data treasure by not taking control of the data by ourselves, but by giving control to the drivers and car manufacturers,” he adds.

Apart from this, Bosch is working with an energy supplier, EnBW, on a prototype that uses blockchain technology to improve the electric car recharging process. The idea is to streamline and tailor the entire process to customers’ needs, so they can select, reserve, and pay for recharging services as they see fit.

For example, the operator can use the software to offer customers transparent pricing models, with options varying in real time, and according to the availability of charging stations.

The entire transaction – reservation and payment – will be a fully automated blockchain operation. This service can factor other customer preferences into the equation. For example, a customer who has kids and likes coffee could opt for a charging station with a playground and cafés nearby. Initial trials with this new system are underway.

A car that pays its own parking fees

To make parking less of a chore, Bosch and Siemens are jointly developing a second application, a smart parking-management system, based on blockchain. By making use of distributed ledger technology (DLT), cars will be able to communicate directly with parking facilities in their vicinity and negotiate the best terms.

As soon as the car reaches the entrance of a parking garage, it will identify itself at the entry barrier, which will then be raised without the driver having to remove a ticket from the dispenser. The driver will also be able to leave the parking garage without further ado, since the vehicle will have already communicated with the exit barrier and settled the parking fee in a virtual transaction.

At present, the prototype has been installed at Bosch’s Renningen research campus and at the Siemens campus in Munich.

Distributed structures 

Distributed structures means data is decentralised. Rather than a few platform providers storing data in their data centers, here it is spread across numerous servers.

“To build trust in digital ecosystems, we need open platforms in which users have the power to decide for themselves,” says Volkmar Denner, CEO of Bosch.

This will ultimately benefit people. If users are “captive,” a web platform provider can change its terms of use at will. By gaining independence from the big internet players, users no longer have to blindly accept such changes.

“We are building trust in internet platforms with these distributed structures. They enable many players to participate,” says Michael Bolle, board of management member and CDO/CTO, Bosch.

Distributed platforms operated by an ecosystem encompassing numerous equal partners are also better protected against external attacks.

LED lights– Illumination to communication

While many people regard autonomous vehicles and electric mobility as the future of automotive industry, the automotive lighting market is also fast catching up.

For instance, Continental is exploring the future of modern lighting systems with its new joint venture – Osram Continental GmbH. While Osram supplies state-of-the-art lighting technology, Continental takes care of the electronics and software

“We have created a new company that will rethink the future of automotive lighting,” says Dirk Linzmeier, CEO of Osram Continental.

The first product to emerge from the development pipeline includes the Smartrix modules, which enable glare-free high beam light and dynamic low beam light, and laser headlights with a reach of 600 meters.

Another product is a system that can project warning messages while driving on the road. For example, if there is an alert telling the driver about an uncovered drain on the road, people can avoid driving through the drain, and also avert any accident.

Commenting about the future of mobility, Elmar says: “The future is already moving from electric vehicle technology to fuel cells, and we are looking at the impact of those technologies by 2030.”

A fuel cell uses chemical reactions to produce energy rather than using metals like lithium or lead that enable current battery technologies.

At least what is real is the software component that enables the bridge between all these technologies, which is a trillion-dollar opportunity according to all automobile companies.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Forbes

Artificial intelligence (AI) and machine learning (ML) aren’t academic research subjects anymore. Businesses, especially the disruptors and those who are entrenched in digital transformation, have been setting a trend for the adoption of these principles and applying them to yield rich dividends. This trend is now rampant, and it is clearly a favorite when it comes to enriching customer experiences and using data to arrive at smarter decisions, faster deliveries and sustainable businesses.

But here’s the burning question: In a race to get their hands on new-age technology, are technology businesses overlooking the other perks of AI that can accelerate the speed of their IT operations and impact their entire software development life cycle? AI is not limited to automating workflows. When used well by your executive staff and system administrators, AI can make all lives stress-free.

Automating And Augmenting Your IT Department

Now, imagine having sophisticated monitoring and management tools in place that can enable a self-service IT infrastructure. Having infrastructure templates ready for configuration can give you the confidence to scale on demand to support an ever-increasing volume, variety and velocity of deployments. In that scenario, deployments will be as fun as making chocolates out of silicon molds. With AI, you can implement various tools to fit your varied needs, including those for data input and more. HCL Technologies, an Indian multinational company, said their ElasticOps applies AIOps to maintain their managed cloud infrastructure service (a 50,000-instance environment) with 30 engineers.

To name a few tools that can aid AIOPs, I’d start with the cloud. With AI, you can build an automated scaling solution for your cloud platform for future flexibility before taking it live. Monitoring tools can extract utilization metrics of live instances via APIs. Even further, incident management tools can trigger alerts and, in certain instances, pass a percentile threshold, causing pre-scripted response and escalation patterns to be applied, according to the situation. Through all this time, your metric analytics and visualization suite can generate reports based on actionable data. And, these tools don’t even cover half of what can be done with AI and ML during software development.

Managing Your Customer Experience

In addition, there are tools to implement that will help you manage your customer experience. This is particularly helpful, considering the deluge of data that flows in and out of your systems every second — with social media reactions, helpdesk complaints and more. Forward-thinkers can have an APM (application monitoring system) installed to provide real-time insights that help IT teams and the company to avoid revenue-impacting outages.

A few years ago, Netflix found a way to put several experimental machine learning algorithms to good use and started automatically recommending personalized content to subscribers. Their attempts to redeem viewership and constantly gain a new set of subscribers using AI and ML technologies have never let them down. Apparently, the world’s favorite video streaming platform saved and earned big bucks with all these initiatives.

Along the same lines, Amazon acquired Kiva to automate the picking and packing process in their warehouse. According to them, their click-to-ship time went from a peak of 75 minutes to 15 minutes.

To yield such best results, companies should strive for a conversational model that can drive self-service operations to a point where operations professionals can switch their focus to other strategic elements with the enterprise. With a proactive APM, along with automated remediation and declarative provisioning and deployment, employees can address build-level failures, manage pipelines and releases and apply guided code fixes.

Automation: Is It All Or Nothing?

All of this said, the rule of thumb here is that you should never try to automate functions if they aren’t (at least) 80% stable and unchanging. Otherwise, you’d need human interference each time there is a new scenario that requires a change in your scripts. This is not nearly as productive as you’d like it to be, as script maintenance is a huge cost to your company.

When there is a shortage of talent that can draft clear and precise test cases, when there are not enough datasets to train your algorithms continuously, when buying or building the required AI system costs more than the anticipated value or when your functions specifically need general intelligence to address emotional factors, it is nearly useless to bring in AI.

An ideal set of AI solutions will automate your mundane tasks, recognize serious issues at hand, streamline interactions between your various teams and altogether magnify your return on investment. But adopting and investing in these mechanisms is as much a business decision as a technical one. The trick lies in drafting as-is and to-be business process maps, identifying where time is wasted in the current system and focusing on the value that new adoption can bring into the picture. With the right automation tools in place, your workforce can focus on elements that need human intelligence, not artificial intelligence.

Financing AI solutions and machine learning without monitoring and tracking your value stream is a straight path toward failure. To make the transition smoother, aim for incrementalism — slowly adopting one solution at a time. Your executive staff and major stakeholders should familiarize themselves with each solution and its potential and performance within the delivery pipeline. Conducting a value stream mapping exercise can help you identify the waste and the value that come along with each solution, which is especially important if you’re building the solution in-house and will incur development costs.

AI is already working its magic for various e-commerce, retail, health care, banking, logistics and social media giants. It can certainly keep your software development business armed to survive in an automated world.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Forbes

In the early 2000s, a courageous security initiative saved a major software company. At the time, this company was beleaguered with security vulnerabilities. Their products were being regularly hacked and ridiculed in the marketplace. It seemed like they had become a poster child for insecurity, and it was damaging their business. How did they respond? Did they start legal action against hackers? Did they attempt to blame victims? Did they suppress the bad press?

No. This company made the choice to do better. They made security their “highest priority” — a fight they knew they could win. They stopped development on all their products, fixed weaknesses and put their developers through security training. They designed new security controls, set new standards, created new processes and even wrote their own security tools. And it seems to have worked.

That company was Microsoft, and their “Trustworthy Computing” initiative was a huge success. Over the ensuing decade, I saw them reclaim their reputation, take back the market and reestablish their industry leadership. As Bill Gates said in 2002: “all those great features won’t matter unless customers trust our software. So now, when we face a choice between adding features and resolving security issues, we need to choose security.”

Today, the fight is on your doorstep.

Today, your company faces an existential challenge. You’ve turned everything distinctive about your company into code. Meanwhile, the hacking game has moved up the stack — from the operating system to your application layer. Hackers may be able to easily access your web applications and web APIs, which are likely full of valuable data and capabilities and rife with vulnerabilities. Many organizations simply don’t include software risk in their decision process — this is the dangerous seduction of automation.

If you’re like the typical Fortune 1000 financial, insurance or health care company, you have thousands of these web applications and web APIs, both “internal” and “external” (as if that distinction means anything anymore). Web applications can include millions of lines of custom code, open source libraries and configuration files, and I’ve seen that web flaws are a common cause of breaches. We’re not talking about super-complex, unique vulnerabilities that require specialized hacking skills to discover. Instead, they’re basic “blocking and tackling” problems that we’ve understood for many years, such as SQL injection, path traversal, cross-site scripting, weak access control and using libraries with known vulnerabilities.

Given all this, it’s not surprising that we have so many breaches. And remember, we may not hear about the vast majority of breaches — breach disclosure laws only apply in very narrow circumstances.

Are you abusing your customers’ trust?

Consider the trust that you put in the websites you use every day. Why do you trust these websites? What evidence do you have that they are safe? Relying on something without evidence is simply blind trust. Many organizations have the same myopia about their own software. They’ve convinced themselves that they are doing good security despite decades of vulnerabilities and breaches.

As Michal Zalewski said in The Tangled Web, “[Risk management] introduces a dangerous fallacy: that structured inadequacy is almost as good as adequacy and that underfunded security efforts plus risk management are about as good as properly funded security work.”

You can make a conscious choice, as Bill Gates did in 2002, to build trust with consumers over time. This isn’t about cost, as practicing strong security is likely to save you money over time. The challenge is moving your culture away from compliance, risk management, and “structured inadequacy” and toward continuous, transparent and convincing assurance.

If you think your company can’t produce a compelling argument that its applications are secure, consider whether abusing the trust of your customers is a good long-term business strategy.

Which company will step up?

Which company in your sector is going to dominate your market by creating trust? Which of your competitors is going to justify the trust people put in their web applications and APIs? Which will share the evidence showing how their code defends against the threats that matter?

One powerful way to share your security argument is in the form of a story. This is a structured approach that shows:

• You understand your application’s threat model

• You have the right security controls to counter your threats

• Your security controls are correct and effective

• You monitor your software for attacks and prevent vulnerabilities from being exploited (something my company helps with but that organizations can do independently)

The top half of your argument should be a set of claims you structure around your threat model. You can probably reverse-engineer it by simply asking “why” about the defenses you already have in place. The bottom half provides evidence justifying those claims. Your evidence can come from a variety of sources, but direct evidence that you generate from the running application is often the most compelling. Use this approach to focus on what matters so you can streamline your security work and avoid the tremendous potential for waste in the traditional “managing insecurity” approach. Ideally, you can generate the evidence to support your story by using a security as code approach.

Note that achieving trustworthy software doesn’t imply any particular organizational structure or engineering method. I believe the focus should be on achieving outcomes, not on trying to force your organization to follow a maturity model. Perhaps a team of experts does the work, or maybe it is fully automated, done once a year or outsourced entirely. The method you choose should match your engineering culture. Still, beware of “shifting left” by simply dumping security tools and activities on development.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Itprotoday

Software testing is as old as software itself. However, the strategies, tools and processes that software delivery teams use to assure the quality of software are always changing. If you haven’t taken a look at the latest types of software testing, you might be missing out on some important strategies for making testing and QA faster and more efficient. Here’s a primer on modern software testing practices.

What Is Software Testing?

As anyone who has ever written code knows, software is a tricky thing. For a variety of reasons, code often does unexpected things when you run it. Your code could contain bugs that cause an application behavior problem. Your compiler might do something unexpected when it builds the code. There could be unexpected environment variables that cause strange behavior.

Software testing is as old as software itself. However, the strategies, tools and processes that software delivery teams use to assure the quality of software are always changing. If you haven’t taken a look at the latest types of software testing, you might be missing out on some important strategies for making testing and QA faster and more efficient. Here’s a primer on modern software testing practices.

What Is Software Testing?

As anyone who has ever written code knows, software is a tricky thing. For a variety of reasons, code often does unexpected things when you run it. Your code could contain bugs that cause an application behavior problem. Your compiler might do something unexpected when it builds the code. There could be unexpected environment variables that cause strange behavior.DO YOU WANT ACCESS TO2019 TECHNOLOGY SALARY SURVEY FINDINGS?YES, UNLOCK ACCESSNO, NOT RIGHT NOW

Software testing is the art and science of testing software to check for these and other problems that could cause software to behave in an unexpected or unacceptable way. In most cases, the main purpose of software tests is to ensure that IT teams discover problems within their applications before they impact end users.

There are many different types and categories of software tests, from performance and usability testing to security and load testing. Generally speaking, the software testing trends described below apply to all of these types of testing.

Shift-Left and Shift-Right Testing

One recent trend in software testing is so-called shift-left and shift-right testing.

Traditionally, software testing was performed near the “middle” of the software delivery pipeline: after your application had been built, but before it was released into production.

With shift-left testing, systematic tests begin earlier, as soon as code is written. And with shift-right testing, testing continues once software is in production in order to identify performance or usability problems that may be impacting your end users.

These software testing strategies build off of the broader shift-left and shift-right concepts associated with DevOps.

QAOps

Speaking of DevOps, another important trend in software testing in recent years has been the embrace of so-called QAOps.

Whereas DevOps emphasizes close collaboration between developers and IT Ops departments, QAOps brings software test engineers into the fold by encouraging them, too, to coordinate with developers and ITOps engineers. The goal of QAOps is to make software testing (and quality assurance more generally) a fully integrated part of the software delivery pipeline, rather than a “siloed” operation.

QAOps hasn’t gained as large a following as some of the other DevOps offshoots, like DevSecOps. But it does represent an important new strategy for optimizing quality assurance operations.

Test Automation

Test automation is not an entirely new idea within the world of software testing. Test automation frameworks like Selenium have been around since the mid-2000s. What has changed today, however, is that automation has become the primary end goal for most QA teams.

This is true for two main reasons. First, the past decade has seen the explosion of automated testing frameworks designed to make it easy to write and run tests automatically, instead of having to have human engineers execute each one manually. Second, the demand for ever-faster software delivery ushered in by the DevOps movement means that, in many cases, automation is the only way for QA processes to keep pace with the rest of the software delivery pipeline.

It’s worth noting that few organizations achieve complete test automation. For most, automating something like 70%  of tests is a realistic goal. Certain tests, such as usability tests that involve monitoring how users interact with an application or react to a new interface, are best performed manually.

AI and Software Testing

AI is everywhere these days, and software testing is no exception. While there are a number of potential ways to apply AI to software tests, two stand out as approaches that are increasingly being adopted in the real world.

First is AI-powered “self healing” for automated test scripts. Using AI tools, QA teams are writing automated tests that can reconfigure themselves automatically to make a failed test run successfully, or respond to a configuration change within the test environment.

Second, AI-driven analytics are becoming more and more important for interpreting test data. That’s only natural: As automated testing makes it possible to run more and more tests at once, and as applications become ever-more complex, interpreting test results manually is less and less feasible. In response, software testing vendors are now beginning to offer tools that use AI to help understand test data.

Integration of Different Types of Software Testing

As I noted earlier, software testing can be broken down into several distinct categories, like performance testing, usability testing and security testing.

Traditionally, different teams, tools and methodologies were associated with each type of testing. But, today, the lines between the various testing disciplines are blurring. Given the enormous complexity and degree of dependency of modern applications, it often does not make sense to try to perform each type of test in isolation.

For example, load testing (which involves testing how well your application responds to heavy demand) goes hand-in-hand with protecting your organization from DDoS attacks (a type of cyberattack that attempts to overwhelm applications with traffic or other requests). Thus, load testing and security testing are converging around this area.

As another example, it’s hard to separate performance testing from usability testing–users don’t like applications that don’t perform adequately.

For these reasons, software testing as a whole is becoming a more integrated affair. Instead of specializing in one type of testing, QA engineers are now responsible for covering it all.

Conclusion

DevOps, AI and other important trends of the past decade have exerted significant impact on software testing. IT teams today are automating more tests than ever, while also striving to test earlier and more often. It’s important to consider all types of software testing to determine which one (or ones) will work best for your organization.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Barrons

As we head into the second-quarter earnings season, it’s worth taking a moment to recognize the remarkable performance of software stocks in 2019. The SPDR S&P Software & Services ETF (ticker: XSW) is up 36% year to date.

Microsoft (MSFT), the world’s most valuable public company, has rallied 36% this year, sports a market cap of $1.05 trillion, and trades near an all-time high. If the market likes the company’s June quarter and fiscal year-end financial results on Wednesday afternoon, the stock could very well go higher still.

Microsoft is no anomaly. Oracle (ORCL), Workday (WDAY) , SAP (SAP) and VMware (VMW) are all up more than 25% in 2019; ServiceNow (NOW) is up 65%. And it isn’t just the large cap names either. Shopify (SHOP), Coupa Software (COUP), Anaplan (PLAN), Okta (OKTA), and Zscaler (ZS) have all more than doubled year to date; MongoDB (MDB), CyberArk (CYBR), Veeva (VEEV), and Paycom (PAYC) all sport gains for the year of at least 70%.

And of course, there have been strong public market debuts in 2019 by software firms like CrowdStrike (CRWD), Pager Duty (PD), Zoom Video (ZM), and Slack (WORK).

The investor Marc Andreessen once famously declared that software will eat the world; now software seems to be absorbing vast swaths of investor portfolios.

As a group, software companies enter the second-quarter earnings period trading at record valuations. Macquarie Capital analyst Sarah Hindlian finds that the average software stock is trading for a record 7.1 times next fiscal year’s projected revenues. The one-year average valuation on that basis is 5.6 times, she reports. The five-year average is 4.4 times and the 10-year average is 3.9.

In a research note Tuesday, Hindlian wrote that average multiples are elevated by a combination of persistently low interest rates and highly valued new issues.

But there are other issues at play, as well. The widespread adoption of cloud-based software is shifting the dynamics of the software industry, spreading the reach of enterprise-class applications to smaller businesse and reducing the costs involved in creating, selling, and supporting applications.

The prevalent old model involved selling packaged software through quota-carrying armies of swaggering, Armani-clad salespeople to the white-coated rulers of corporate-hosted data centers, which came with complex installation and maintenance issues. The new model is increasingly self-service and flexible, hosted by Amazon Web Services , Google Cloud or Microsoft Azure, with the ability to cast a wide net both geographically and down market. The companies are reaching wider markets, and generating better gross margins—even while many of the younger players forego profitability to focus on growth.

The obvious result of that can be found in newly public software stocks trading at 15 times to 25 times projected revenue, and sometimes higher, defying historical conventions about what enterprise technology companies are worth. But valuations are also growing for more established companies. Microsoft is trading at 7.5 times fiscal 2020 projected revenue—that’s a premium to the broader software group, with consensus forecasts expecting continued double-digit sales growth—all for a stock that has already more than tripled in five years.

But the rules are changing for software stocks—the market may simply be “re-rating” them to reflect the industry’s shifting business model. We’ll know more in the coming weeks.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Gadgets

Communication app Truecaller on Tuesday announced the global launch of its software development kit (SDK) solution exclusively for the mobile Web platforms.

Truecaller SDK would support all the key mobile platforms across Android, iOS, React and now mobile Web including “Progressive Web App” support.

“Our vision has always been to enable the developer community by providing them with solutions that help them to build user-focused, trust-based and growth-oriented products,” Priyam Bose, Global Head, Developer Platform And Relations, Truecaller, said in a statement.

“User onboarding and verification continues to be one of the critical use cases for developers as it is crucial in creating a first impression for their users in terms of building a seamless and secure product experience,” he added.

In emerging markets like India, mobile Web-based experiences on smartphones are proving to be the first point of discovery for users trying to access products and services online.

One of the key challenges in these markets has been to on-board users using email or other modes and getting verified using the inefficient OTP process.

The SDK solution on mobile Web aims to simplify this process for developers through its OTP-less and free to use phone number-based verification solution, allowing users to securely access services using their Truecaller credentials.

In February, the app crossed 100 million daily users mark in India, from where the company attracts over 60 per cent of its global user base.

Headquartered in Stockholm, Sweden, the company was founded in 2009 by Alan Mamedi and Nami Zarringhalam.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Itprotoday

Artificial intelligence offers real value, but recognizing its limitations is critical for actually capitalizing on that value.

Artificial intelligence, or AI, is one of the most intriguing topics in software development today. It is also one of the most widely misunderstood. For software developers and IT teams, AI offers an array of tantalizing possibilities for making applications faster, more scalable and more efficient. However, in many cases, the hype surrounding AI doesn’t line up with the reality of what is actually possible or practical. Toward that end, here’s a look at five common AI myths related to software development and deployment.  

1. Artificial inteligence is a new technology.

AI has gained major attention from the media in just the last few years. Likewise, most software products that pitch AI as a key feature (like AIOps-driven monitoring tools) are still very new. But, AI is not a new technology at all. The concept of machine learning and artificial intelligence in general stretches back many centuries (see, for example, the Brazen Head). And software applications have been using AI to do things like play checkers since the 1950s.

Artificial intelligence, or AI, is one of the most intriguing topics in software development today. It is also one of the most widely misunderstood. For software developers and IT teams, AI offers an array of tantalizing possibilities for making applications faster, more scalable and more efficient. However, in many cases, the hype surrounding AI doesn’t line up with the reality of what is actually possible or practical. Toward that end, here’s a look at five common AI myths related to software development and deployment.  

1. Artificial inteligence is a new technology.

AI has gained major attention from the media in just the last few years. Likewise, most software products that pitch AI as a key feature (like AIOps-driven monitoring tools) are still very new. But, AI is not a new technology at all. The concept of machine learning and artificial intelligence in general stretches back many centuries (see, for example, the Brazen Head). And software applications have been using AI to do things like play checkers since the 1950s.DO YOU WANT ACCESS TO2019 TECHNOLOGY SALARY SURVEY FINDINGS?YES, UNLOCK ACCESSNO, NOT RIGHT NOW

Thus, if AI seems like a relatively new technology in the software world, or one that has only become practically usable in the past few years, that is only because it took the media and marketers a long time to catch up. The reality is that AI has been an established field of computer science for more than half a century.

2. AI is smarter than humans.

Some AI advocates would have you believe that AI-powered applications are “smarter” than humans, in the sense that they can solve problems or develop ideas more creatively and effectively than human minds. But the reality is that AI-powered software applications don’t outthink humans. They simply think faster than humans.

And when it comes to use cases that require nuanced understanding of reasoning and human expression, AI fares particularly poorly, as IBM’s recent experiment with AI-powered debate software showed.

There is a chance that this could change in the future. Someday, AI might become so sophisticated that AI-driven applications are genuinely smarter than humans. But that day remains beyond the horizon.

3. AI will lead to smaller IT teams.

Many marketers of AI-powered software tools sell their products as a way for companies to reduce the size (and, by extension, cost) of their IT teams. By using AI to automate IT decision-making and operations, they say, companies can do more with fewer staff members.

Some observers go so far as to claim that AI, combined with other innovations, is edging us closer to a world of “NoOps,” wherein IT operations teams are fully replaced by software.

It’s certainly true that AI can help to increase automation and reduce the manual effort required to perform certain tasks. However, the idea that AI will remove the need for human engineers entirely is fantastical. Someone still has to set up and manage the AI-powered tools that do the operations work.

Plus, there is an argument to be made that AI is not making IT operations simpler; it is merely helping IT Ops teams keep up with the ever-increasing complexity of new software and infrastructure. Deploying and managing containers and microservices requires much more work than dealing with virtual machines or bare-metal servers. In this sense, AI is simply helping IT teams to maintain the status quo; it is not empowering them to gain new ground.

4. AI software is “set and forget.”

On its face, AI tools can seem like a type of “set it and forget it” wonder. If data-powered algorithms and machine learning allow AI tools to make all the decisions they need, then humans don’t have to do any work beyond the initial set up and data training, right?

Well, no. There are lots of reasons why even the best-designed AI tools need to be managed actively and continuously. They need to be constantly retrained with up-to-date data in order to make accurate decisions about ever-changing conditions. The quality of the data that they rely on must be carefully managed to ensure that it delivers the level of accuracy and clarity that the tools require. Humans may need to help provide ethical guidancefor AI algorithms.

5. AI will destroy the world.

The four AI myths that I have discussed above involve hype or an excess of confidence in the abilities of AI and machine learning. Now, I’d like to approach things from the opposite perspective by pointing out that AI is not at all a bad or useless technology.

Sure, AI has many shortcomings, and AI tools in many cases are not likely to live up fully to the promises behind them. But that doesn’t mean that AI is the bane of our existence, or that software teams should not use it at all.

This is important to note because the conversation surrounding AI has so far tended to be bipolar in nature. On one side are technologists and futurists promising us that AI will lead us into utopia. On the other are fierce AI critics worried about an AI-driven dystopia marked by all manner of dehumanizing, unethical automations.

Neither of these views represents reality. AI will not fully replace humans, but it will make their jobs easier. AI won’t completely remove the need to perform manual tasks, but it will reduce it. AI won’t prove smarter than human beings, but it can provide insights that help them make smarter decisions.


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Credits : Techaeris

As technology becomes a more prominent and instrumental element of our daily lives, so does the underpinning software and platforms powering these systems.

Just as a computer needs an operating system to function to its full potential, IoT and network devices need firmware and all electronics need some form of software.

The growth of technology has introduced a concurrent increase in demand for better and more efficient software. This side-by-side growth has also pushed new professional opportunities, as more companies and operations look to bolster their software development teams.

But that doesn’t mean the market isn’t competitive  quite the contrary. Anyone who wishes to remain competitive in today’s landscape must keep their fingers on the pulse. New and innovative trends play a role in software development. Understanding where the market is going and what professionals can be doing to advance in the field is not just beneficial  it’s essential.

Here’s a look at some of the more influential trends making an impact in the software development field today.

1. THE RISE OF ROBOTICS AND TELE-INTERFACES IS IMMINENT

The rise of robotics and advanced automation solutions is no secret. So many are discussing the idea that they might lose work to these new technologies and systems, which goes to show just how prevalent they are in today’s landscape. Experts project the industrial robotics market alone to grow by 175% over the next decade.

An often overlooked element of this whole situation, however, is that the software and control interfaces used to manage these technologies don’t yet match the demand. The lack of infrastructure becomes even more glaring concerning remote computing.

Think of it like this. Every robot or hardware system needs underpinning software to power its operation. But they also require nuanced controls, which property or facility managers will use to keep the equipment running optimally or making adjustments. That might be a local system, powered via on-site computers and servers, or it might be off-site systems — reliant on cloud computing and remote technologies. Then there’s “smart” or app-based controls accessible via mobile. Those kinds of control interfaces need someone to build them as well.

Any way you cut it, the rise of these technologies puts increased demand on software development circles. And that’s before even considering maintenance and continual improvement requirements. All these software solutions will need support long into the future through bug fixes, security updates, and general improvements.

2. MIXED-REALITY SOLUTIONS ARE GROWING MORE COMMON

Virtual reality (VR) and augmented reality (AR) technologies are becoming more popular as their capabilities increase. The worldwide AR and VR markets will likely grow over seven times their current size between 2018 and 2022.

These solutions offer many applications, particularly when it comes to hands-on or virtual training, instructional tasks or even research and development. Workers can don a headset to immerse themselves in an entirely virtual experience using VR tech. On the other side of the coin, they can bring digital information and content into the real world using AR tech.

For example, imagine a plumber or electrical technician being able to see all the wiring and pipelines hidden behind solid surfaces by wearing a pair of AR-enabled goggles. But as with all forms of modern technology, developers must create the software powering these devices and experiences.

3. THE INDUSTRY IS TURNING TO IOT OR IIOT FOR SMARTER OPERATIONS

McKinsey predicts the IoT market will be worth a whopping $581 billion for information and communications technology-based spending by 2020, at a compound annual growth rate between 7 and 15% during that same period.

The Internet of Things — or Industrial Internet of Things — is more of a network of similar devices, all designed to collect and transmit varying streams of information. They also present new, more efficient opportunities, like the ability to control related devices from afar or enable more informed automation processes.

Software developers and engineers will have to create all digital facets of these technologies, from the firmware used to power the tech to the interfaces and software used to control them.

Of course, when you’re talking about more vulnerable and internet-ready devices, the overall requirements are much more complex, too. Information security and high levels of privacy are necessary when it comes to the transmission and collection of all data. It’s not safe to exchange highly sensitive information openly, where competing operations or unscrupulous parties could harvest it.

Software developers will not only be responsible for creating these solutions but managing all aspects of them as well, including continued security and efficiency. Some longstanding professionals may even move into managerial or advisement positions using their accrued experience, talents and knowledge to further corporate interests. This shift is particularly notable for information security, as high-profile hacks and data breaches are incredibly common these days.

4. LOW-CODE DEVELOPMENT WILL PICK UP SPEED

Coding and language requirements continue to lessen as time goes on, with the industry pushing towards more streamlined opportunities. In a survey involving 3,300 IT professionals, 41% saidtheir organization is already using a low-code platform.

For those unfamiliar, low-code development involves the use of drag-and-drop style interfaces, mitigating the need to understand programming and coding.

That doesn’t necessarily mean there’s a decrease in demand for professionals with programming backgrounds, especially since developers still need to engineer low-code environments in the first place. But it does help to show where the industry is heading in the next few years. More and more development operations will focus on using such platforms, so gaining familiarity with them is desirable.

It also shows software developers will need to interact more and more with non-coding types or inexperienced developers, which requires a certain finesse.

THE SOFTWARE DEVELOPMENT INDUSTRY IS ALWAYS CHANGING

While the trends discussed here can and will have a significant impact on the future of the industry, they are not the only changes happening.

Additional trends include the rise of microservices and perpetual offerings, the spread of conversational UI and increased emphasis on AI and automation, as well as more capable remote and cloud computing technologies. Any of these trends could replace the others in priority and importance. It’s difficult to say with any degree of certainty what trends will rise to the surface.

One thing is always certain, no matter what trend or pattern is prevalent: The software development industry is continually evolving, and that’s never going to fade away.

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Credits : Fxstreet

South Korean technology behemoth Samsung announced the release of its blockchain and decentralized application (DApp) Software Development Kit (SDK) in a recent post on its website.

Per the announcement, the Samsung Blockchain SDK allows for account management and backup, payment and digital signature facilitation, Samsung Keystore and other cold wallet support. The page dedicated to the SDK also explains that it is a superset of all SDKs, including the Samsung Blockchain Keystore SDK.

As Cointelegraph reported in February, the new Samsung smartphone, the Galaxy S10, includes storage for private cryptocurrency keys. In May, rumors started circulating that the tech giant would also be rolling out blockchain-enabled features to its budget smartphone models.

More recently, in June, Samsung’s IT subsidiary announced that it is launching three new products aimed at addressing clients worries about blockchain. The products aim to make integrating blockchain with other platforms easier for entities that are attempting to adopt the technology. 

The president and CEO of Samsung SDS has also revealed that the firm is including blockchain as one of the key technologies for its “Digital Transformation Network” in May.

Also in May, Samsung competitor and consumer electronics giant High Tech Computer (HTC) announced the Exodus 1S smartphone with Bitcoin (BTC) full node capability, and rolled out in-wallet cryptocurrency trading for users of its Exodus 1 smartphone.

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credits : Forbes

There’s no doubt that it’s a new era for digital marketing. The irruption of new technologies in the form of automation, artificial intelligence (AI)-powered tools and big data analytics has changed the marketing landscape.

In fact, I can’t recall a more favorable moment for marketing in my 10-plus years managing my software-focused IT company. With the aid of software development and new technological advancements, companies of all sizes can redefine their marketing strategies and boost their marketing team’s performance. However, doing so requires a shift in the understanding of what digital marketing is and can be, as well as a careful approach to incorporating new technologies.

Understanding Modern Marketing Trends

Knowing what modern marketing trends can bring to the table will help you determine whether they’ll work for your company. I think all of these will somewhat impact your brand in the near future, so it’s good to learn about them now to see what you can expect.

• Artificial intelligence (AI): AI is all over the place now and for a reason. With its aid, you can analyze consumer behavior and identify patterns through the use of data coming from social media and your website.

Working with AI software also allows you and your marketing team to make more informed decisions when it comes to defining the best channels for your communications, your sales outreach and the influence of your digital advertising.

A good example of AI supporting marketing happened when Samsung launched its S8 model. The South Korean company used social listening tools to monitor the reactions to the device by scanning words, hashtags and phrases of interest. The AI then explored the sentiment surrounding them and pulled actionable insights.

• Automation: Marketing automation is something you’ve surely heard about. It refers to software that helps you prioritize and do your marketing-related tasks more efficiently, saving time.

Automation can make a difference when it comes to communicating with your customers. Delivering predefined actions such as emails and offers based on specific behaviors can enhance your relationship with clients.

• Big data analytics: Modern marketing software allows you to delve into the data you gather through all channels. You can gain insight into your customers’ thoughts and behavior. It also provides a centralized platform where all of your data is collected, aggregated and stored for quick access for team members. That way, you can adjust your overall strategy to offer a more personalized experience.

Efficient software can monitor key performance indicators (such as unique visitors, leads, generation costs and return on investment (ROI)) and identify patterns and potential growth opportunities. Take H&M as an example. The fashion retailer uses data insights to improve its supply chain, while detecting trends that impact its inventory and defining prices. It also uses big data to tailor the merchandise for its stores.

• Personalization: Another big player in today’s marketing world, personalization through custom software lets you use all your available data to address each customer in a more tailored way.

You’ve surely had an experience with personalized marketing by now: special offers based on your purchase history, deals tied to your specific location and recommendations in accordance with your preferences. The possibilities for delivering personalized actions are endless.

Making The Shift To Marketing Based On Software

Simply being informed about the trends that are available isn’t enough, though. It is important to decide which ones will work for your particular needs. Not just that — you also have to manage your expectations around these trends and consider your own timing to ultimately decide if you are ready to embrace them.

• Understand your overall strategy and goals: One of the things I think every marketing team should do once in a while is to review its overall strategy and objectives to check if there’s a need to adjust them. For instance, you might feel tempted to automate marketing tasks, from email marketing to lead generation. However, this can possibly lead you to actions that won’t fit with your target audience.

• Think about how marketing software impacts your audience:Though your goals might be to get more leads, make more sales or improve internal tasks, you have to consider how marketing software can impact your customers. There might be a gap between what the software can give you and what your clients want.

• Be certain about what these trends can offer: Time and time again, I’ve spoken with people who were disappointed with one of these new trends that felt like the next big thing. Believing that automating marketing, gathering large datasets or personalizing your experience will boost your sales or increase your brand awareness just because you’re using them is plain wrong. Implementing these technologies into your marketing requires hard work, analysis and continuing adjustments for them to deliver on their promises.

• Analyze where you are standing right now: Another thing many advocates of these trends try to instill in everyone who’s listening is a sense of urgency. “You need to embrace these tech solutions for your marketing right now!” they cry, and many believe them — even if they aren’t ready for the switch. Don’t get caught up in a false sense of urgency.

Some Final Words

Using automated tools, AI-powered solutions, big data analytics and a personalized approach provide marketing teams with a variety of benefits. More efficient time-management, easier data control and access, deep insights available for decision-making and more autonomy for the team are just some of them.

However, all of these benefits are promises that are only fulfilled for companies that implement those tools with careful attention at the right time. Understanding what they can give you is only half of the journey — you need to devise how these trends will fit in your overall marketing strategy and how they will impact the way you do things.

Don’t rush into this new age of digital marketing simply because you don’t want to be “left behind.” It takes a lot of hard work and effort to truly understand the new landscape and how can you fit in it.

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