Introducing the Zebra Technologies TC70 (Symbol)

| January 15, 2015

article image
Barcoding, Inc. offers the most efficient, accurate, and connected solutions available today - including the new TC70 from Zebra Technologies (Symbol). The TC70 enterprise touch computer will help you improve processes, reduce operational expenses, generate more revenue, and increase customer satisfaction. Find out more from www.barcoding.com

Spotlight

TP-LINK

Founded in 1996, TP-LINK has become one of the world's leading providers of SOHO & SMB networking products, offering both innovative and award winning solutions to the market. Ranked No. 1 provider of WLAN products, TP-LINK supply to over 120 countries, serving tens of millions of consumers worldwide.

OTHER ARTICLES

Basics of Artificial Intelligence and Machine Learning

Article | June 22, 2021

Lately, we all often come across two very hot buzzwords — Artificial Intelligence (AI) and Machine Learning (ML). Perhaps the impact of artificial intelligence and machine learning on today’s business world is more than our daily lives. According to a Bloomberg report, around $300 million were invested in 2014 to promote AI-powered startups. It was 300% more than the previous year’s investment in venture capital. It’s hard to deny the fact that artificial intelligence and machine learning are all around us. Whether it is about protecting confidential information at work or just playing your favourite games on PS5, AI and ML are there. Researchers, scientists, computer engineers, and analysts are working hard together to pass on human-like intelligence in machines so that they can think and act according to real-life scenarios. Businesses have changed their approach to AI keeping enterprise adoption in mind rather than treating it as just a research topic. Tech giants such as Google, Facebook, Microsoft have already invested billions in Artificial Intelligence and Machine Learning and already have started to reshape the customer experience. But the AI and ML incorporation we see today is just the tip of an iceberg. In the coming years, you will see them take over products and services one after another. What Is Artificial Intelligence and Machine Learning? It is nowadays common to see several companies marketing themselves as AI-powered startups even though their operations don’t really revolve around AI. To understand this type of gimmicky marketing, it is essential to first understand what Artificial Intelligence and Machine Learning are. Let’s be clear in the beginning about one fact — AI and ML are not the same things. If you think they are, kill this perception before it makes things very confusing. Both these terms crop up especially when the discussion is about the use of Artificial Intelligence in marketing, the use of Machine Learning in marketing, analytics, Big Data, and the modern-day tech that is transforming the world. To ease down the learning, here’s the best answer: Artificial Intelligence is a science used to develop systems that can mimic decision-making and behaviour like humans. In simple words, the main application of Artificial Intelligence is to make intelligent machines. Machine Learning is the subset of artificial intelligence that uses data to perform tasks. It involves designing and applying the data models or algorithms that can learn from their past experiences. There’s a subset of Machine Learning, too — Deep Learning. It counts on multilayered neural networks to perform tasks. Early Days of Artificial Intelligence The early mentions of AI trace back to Greek mythologies that have stories of a mechanical man that could mimic our own behaviour. Plus, the early computers were termed as “logical machines'' in Europe. These machines could solve arithmetic operations and even store memory. Scientists, fundamentally, were inspired by them to create mechanical brains. Over time, technology got more and more modern. And, our understanding of how the human mind works improved. Both these factors lead to the current AI revolution. Today, the use of AI is more focused on mimicking the decision-making process of humans rather than performing complex calculations. The prime motive of this is to allow machines to think and act more like humans. AI-powered machines that are designed to act intelligently come into two basic groups — General AI and Applied AI. General AIs are relatively less common and can theoretically handle any task. The most exciting improvements in the field of AI are happening in this specific area. In fact, it’s generalized AI that led to the rise of Machine Learning. On the other hand, applied AIs are designed to perform relatively smaller tasks like smartly trading shares and stocks, or guiding an autonomous vehicle to its destination, etc. The Rise of Machine Learning As mentioned earlier, Machine Learning is a subset of AI and can also be treated as the current state-of-the-art. It came into reality primarily because of the two major breakthroughs — the rise of the internet and human realization. In 1959, an American pioneer in the field of computer gaming and AI, Arthur Samual, realized that it can be possible to teach machines how to learn to perform tasks themselves rather than us telling them how to. As long as the emergence of the internet is concerned, that helped scientists with tons of digital information that could be analysed for the betterment of AI and eventually, ML. After these innovations, it was more efficient for scientists and engineers to program machines in a way that they learn to think like humans and then connect them to the internet so that they have all the needed information. Vertical AI And Horizontal AI No matter what kind of AI research it is, knowledge engineering is its essential part. Machines need plenty of information to think and act like humans. Therefore, AI needs access to objects, categories, properties, and relations between them to apply knowledge engineering. AI is responsible for generating analytical reasoning power, problem-solving abilities, and common sense in machines. And, it is not an easy task! The way AI serves us can be divided into two parts — Vertical AI and Horizontal AI. Vertical AI is used to perform single jobs such as automating repetitive tasks, scheduling meetings, etc. Vertical AI bots are so accurate in performing a single job that people often mistake them for human beings. Horizontal AI, on the other hand, can handle more than one task at the same time. The best examples of horizontal AI are Alexa, Siri, and Cortana. Different Types of Machine Learning ML can be best used to fix complex tasks such as enabling self-driving cars, face recognition, credit card fraud detection, etc. It uses huge, complex algorithms that keep on iterating frequently over big data sets. The following are the 3 major Machine Learning areas: ● Reinforcement Learning ● Unsupervised Learning ● Supervised Learning Reinforcement Learning In reinforcement machine learning, algorithms allow machines and software agents to automate ideal behaviour within a particular context to improve the performance of an overall system. It is characterised by learning problems rather than learning methods. If any method can solve a problem, it can be a reinforcement learning method. This Machine Learning technique assumes that the dynamic environment is connected to a software agent such as a computer program, bot, or robot. Ultimately, it chooses a specific action in order to rapidly deliver the most efficient result. Unsupervised Learning Due to the involvement of unclustered data, unsupervised machine learning is more complex than others. With it, the machine has to learn independently without any supervision. No fixed or correct solution is provided for any problem in this technique. The algorithm has to identify the data patterns and find the solution. The recommendation engines we see on several eCommerce websites and Facebook friend requests suggestions are the best examples of this sort of Machine Learning. Supervised Learning Training datasets are used in supervised learning. The algorithms are created in such a way that they can analyse the data patterns and develop an inferred function. The produced correct solution is then used to map new examples. The best example of supervised machine learning is credit card fraud detection. Final Words Artificial Intelligence and Machine Learning never fall short to surprise us with their exciting innovations. Their impact has reached all the industries including eCommerce, customer service, finance, education, healthcare, pharma, infrastructure security, and whatnot. Needless to say, all these industries are very keen on reaping all the benefits of Artificial Intelligence and Machine Learning. The human-like AI was an inevitable thing as most technologists thought. Today, we are indeed closer to this goal than ever. This exciting journey in the past couple of years is the result of how we predict AL and ML works. FAQs Why is AI Marketing important? With AI marketing, businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. ● A better understanding of your consumers ● Optimization of digital advertising campaigns ● Offer comprehensive customer profiles ● Allow real-time interactions with consumers ● Refined content delivery ● Reduced marketing costs ● Improved ROI Is artificial intelligence and machine learning the same? The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data. Does AI need machine learning? Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. Instead of such software agents, they get help from algorithms that can use their own intelligence to solve queries. These can be Machine Learning algorithms such as Deep Learning neural networks and Reinforcement Learning algorithms. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is AI Marketing important?", "acceptedAnswer": { "@type": "Answer", "text": "With AI marketing , businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. A better understanding of your consumers Optimization of digital advertising campaigns Offer comprehensive customer profiles Allow real-time interactions with consumers Refined content delivery Reduced marketing costs Improved ROI" } },{ "@type": "Question", "name": "Is artificial intelligence and machine learning the same?", "acceptedAnswer": { "@type": "Answer", "text": "The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data." } },{ "@type": "Question", "name": "Does AI need machine learning?", "acceptedAnswer": { "@type": "Answer", "text": "Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. Instead of such software agents, they get help from algorithms that can use their own intelligence to solve queries. These can be Machine Learning algorithms such as Deep Learning neural networks and Reinforcement Learning algorithms." } }] }

Read More

How 8 Trends Will Steer Infotech in 2020 and Beyond

Article | June 22, 2021

The tech industry is almost always playing the balancing act by continuing to drive innovations and at the same time grappling with the side effect of those innovations in the global economy. Though every industry faces this challenge as it becomes more mature, the challenge is unique for the tech industry with the scale that tech is able to achieve and the evolutionary aspect of mixing digital and physical worlds. Technology is evolving at such a rapid pace that it may go out of trend even before it is mentioned as a trending technology. But with the tremendous potential they bring with themselves, both for business and technology, it is time for the technology industry to make good use of it. While there are major questions around safety, privacy, sustainability, and trust, these questions can be answered by combining technical expertise with social awareness. We’ll discuss how the impact of latest trends in 2020 will support and progress the infotech industry. Table of Contents: - Tech-Washing Fades in Favor of Real Strategy - Growing Demand for Workforce Diversity - Redefining IT Infrastructure with the Internet of Things - Artificial Intelligence - Demand for Automation - Cybersecurity - Deep Fakes, 5G, and the Data Management Challenge - Changing Reality of Emerging Technologies 1. Tech-Washing Fades in Favor of Real Strategy With the vast influx of user friendly technologies it can now be said that every company is a tech company. Though the fact that technology is everywhere doesn’t necessarily change the underlying business model. A company cannot simply create new growth avenues by slapping a tech label on their product and expect to reap profits, which can be true for larger companies going public and struggling with the reality of the market.Then there are smaller businesses that are falling prey to marketing hyperbole. New trends like artificial intelligence and blockchain require significant investments and change to workflow. The smaller companies slowly realize the difference between buying new technologies and truly integrating them with their work culture. Businesses will show more intent to integrate a technology into their work culture for strategic returns rather than buying a technology to use it as a crutch. 2. Growing Demand for Workforce Diversity The technological workforce has been under the scanner for lack of diversity due to the unconscious bias along with other behaviour that is far more conscious, such as barrier to access for low-income students and even reports of outright abuse. In 2020, the call for improved diversity will continue to pay dividends, even if fully diverse and inclusive environments still lie further in the future. Going beyond the common conception of diversity, companies will also seek to bring in skill diversity. Companies are now increasingly seeking diversified expertise across all areas of IT framework – infrastructure, software development, cybersecurity and data. In addition, companies now look for individuals that have some degree of work experience which indicates unwillingness to hire freshmen. Business are also looking for professionals that can speak the language of business and collaborate with other departments in order to drive technology-fuelled business results. 3. Redefining IT Infrastructure with the Internet of Things The Internet of Things has emerged as one of the technological trends, along with cloud computing and mobile devices that will now be a permanent part of modern technology landscape. Digitization of environment and operations has gained pace due to the data value that comes with it. IoT is also bringing positive results for companies with both major and minor level IoT-related sales in the last year. Today, IoT as a managed services play is driving the most revenue in this category, but looking ahead to the next two years companies are predicting that analytics on data captured by IoT sensors – then shared with customers – holds the most financial promise. I&O must get involved in the early planning discussions of the IoT puzzle to understand the proposed service and support model at scale. This will avoid the cascade effect of unforeseen service gaps, which could cause serious headaches in future. - Ross Winser, Senior Research Director, Gartner The next wave of IoT will require expert understanding of digital BizOps. Business will have to treat IoT projects more like an expansion of infrastructure.This will dictate networking structures, storage options, data policies, and security decisions. The stage for IoT, as for cloud computing and mobile devices, is set and ready to bring in advance IoT digital transformation. 4. Artificial Intelligence Cloud computing lowered the barrierdeveloping software and distribution, while mobile devices extended the reach of software, thus increasing the software’s ability to drive activity. This created a new challenge in conducting said activity and acting on the data being collected. Artificial intelligence with software-driven routines and compute resources that can run advanced algorithms, takes software to another height. It is clear by now that AI needs a different kind of oversight compared to other software given the challenge of programming bias and unreasonable outputs. Like any other software, AI requires solid inputs and these inputs are often massive datasets rather than highly specific data points. But, there is still a need to maintain the data quality. AI opens up new opportunities for businesses as well as job roles as it continues to disrupt the infotech landscape. 5. Demand for Automation SMBs require automation mainly in the areas of integration of platforms, application and data, while the large corporation who also focus on integration, have more internal resourcesto lean on. But, whether the integration is in-house or outsourced, the next step is automation. Today’s automations open doors to new opportunities like cloud systems offers tools from the provider; IoT gathers inputs from varied sources, and AI suggests insight-driven actions. With a number of technologies disrupting the tech landscape, companies can build complex automation. But as automation goes more complex, AI will play more a monitoring role. 6. Cybersecurity Cybersecurity is not an emerging trends but it has been around for a while. Though the technologies revolving cybersecurity are upgrading as new threats keep appearing. Hackers keep finding news way to exploit the toughest of measures. The attitude towards cybersecurity has shifted drastically from defensive to a more aggressive approach. Another drastic change has come in enterprises now treating cybersecurity more as an important component of business and not another function of IT. At large enterprises, this usually takes the form of a CISO managing a team of resources, and the division is more clear. The first step, which many enterprise overlook, is to define the risk tolerance. The next steps is to fill the skill gap that exists due to a varied areas that come under the security umbrella. Finally, there must be metrics to measure the return on a more significant investment. READ MORE: HOW TO MITIGATE ROBOTIC PROCESS AUTOMATION IMPLEMENTATION WITH LOW-CODE DEVELOPMENT 7. Deep Fakes, 5G, and the Data Management Challenge Deep Fakes have the potential to wreak havoc on the society, personal lives, politics, careers, and beyond.Forging video and voice software appear to convincing people of doing things that they normal won’t do. Such software in the hands of bad elements would mean inviting trickeries and handing over important personal information to strangers. As long as deep fake applications exist – and they will continue to exist and proliferate – the need for sophisticated data management will skyrocket in the coming years. And data volume, already completely exponential, is only going to mushroom with the more expansive rollout of 5G networks next year and beyond. The entry of 5G will increase the absorption of data exponentially. In addition to bringing us all faster broadband speeds and more reliable mobile networks, the proliferation of 5G will also accelerate advancements in smart city, smart vehicle, smart manufacturing, and scores of IoT-intensive technologies hungry for 5G. Just about every industry that touches our daily lives will be transformed – for the better – by the technology evolution that will define 2020. - Daniel Newman, CEO,Broadsuite Media Group The two trends will bring new data management challenges. There will be increasing need to identify data with its true source as well assecure the mountains of information speeding along these networks. 8. Changing Reality of Emerging Technologies The excitement around emerging technologies is highand even though the impact on an operational levelhas been positive for business for building better practices for evaluating early-stage topics and accelerating adoption, its been chaotic at a tactical level. Companies have had less time to observe and evaluate which technology is profitable, while the constraints of resources and skill gap exacerbates. The trend has been that companies in the business of technology are starting to pull back on adopting new technology as part of their portfolio. This slight tap on the brakes suggests that classic situation where companies move too quickly into a new technology discipline or business model only to have a reality check in year two or three. Though companies are excited about new technologies including AI, IoT, 5G, drones, blockchain, and quantum computing, they reserve their excitement since new trends can still take off overnight. READ MORE: HOW TO LEVERAGE IOT IN MANUFACTURING TO USHER IN INDUSTRY 4.0

Read More

HOW REACT NATIVE APP DEVELOPMENT WORKS UNDER THE HOOD

Article | June 22, 2021

In this article, I’ll explain how React Native works to give you an understanding of whether it is the right choice for your software product. By describing the pros, cons, and capabilities of React Native, I’ll reveal nuances of this technology that we faced during cross-platform app development.It will be nothing new if I say that React Native is a framework for building cross-platform apps. The uniqueness of React Native is a single JavaScript codebase used for both platforms. React Native compiles the JavaScript code to native components, thus, using platform-specific APIs and modules. By using such native components as Images, Text, and View as building blocks, software developers can create new ones.

Read More

5 cool open source JavaScript libraries to refresh your custom app

Article | June 22, 2021

The Claris FileMaker 19 release includes one of the most requested features from the Claris Community - the ability to embed JavaScript directly in your custom apps!Now you can use packages from readily-available JavaScript libraries or your own custom code to modernize your apps with data visualization, maps, calendars and more.Here are 5 useful JavaScript libraries you can use to enhance your apps.

Read More

Spotlight

TP-LINK

Founded in 1996, TP-LINK has become one of the world's leading providers of SOHO & SMB networking products, offering both innovative and award winning solutions to the market. Ranked No. 1 provider of WLAN products, TP-LINK supply to over 120 countries, serving tens of millions of consumers worldwide.

Events