A Quick Guide to Analyzing Apache Logs on Alibaba Cloud Log Service

| March 23, 2018

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With Alibaba Cloud Log Service, there are several methods available for you to collect upstream data. You can use the built-in LogSearch and LogAnalytics functions, or you can deploy the more familiar ElasticSearch, Logstash, and Kibana (ELK) stack. In this article, we will discuss how you can build your own ELK stack on Alibaba Cloud Log Service to analyze and monitor Apache logs.

Spotlight

Computer Science at Howard University

The department offers a traditional B.S. degree in Computer Science, a Computer Science minor option for non-engineering disciplines at Howard, a graduate certificate course in Cybersecurity, a traditional M.S. degree in Computer Science, an accelerated 1-year M.S. degree in Computer Science, and a Ph.D in Computer Science...

OTHER ARTICLES

Effects of Artificial Intelligence on Software Development

Article | August 23, 2021

What’s the core of those drone-supported Amazon deliveries, online food orders, the ability to watch your favorite shows on Netflix, and virtually augmented monitoring of your upcoming trip to Disneyland? Software! They constitute a significant part of almost every evolution we see around us. But how are the developers managing to yield so much from computer programming? How are they able to enrich so many lives through their creations all over the world? The answer is simple — Artificial intelligence (AI). Undoubtedly, AI is one of the leading technologies now, and it has the power to transform every bit of any business’ functionality. The software industry is not behind in making the most of AI and delivering intelligent and intelligent software. On the contrary, modern enterprises are convinced to adopt an entirely new software development paradigm to stand out from the competition. Traditionally, machine learning was predominant in the Software Development Lifecycle (SLDC). Even though it could encode numerous tasks in a computer program, it took relatively more time to be finalized. It required developers to put the exact requirements together first and hand them over to engineers. And then, engineers programmed the code accordingly. However, AI came with its advantages. As a result, it is reshaping the modern world of automated testing, Agile test software, and ultimately the entire software development. So if you see bots accompanying computer programs to make software development even easier, faster, and smarter in the future, it will be because of AI. So if you are already thinking of potential changes AI will bring to your software development process and how you can reap all the benefits of AI software development, stay tuned! Area of AI Software Development Artificial intelligence has a significant impact on various aspects of software development, for example, software testing, coding, designs, etc. Let’s now discuss what role AI will play in the current and future of software technologies by reshaping the major software development areas. Software Design Process will Improve Designing software is one of the most complicated and error-prone stages of software development. Therefore, specialized skills and the right experience are crucial for designing and planning software development projects to come up with an absolute solution. Moreover, the software designs are mostly subjected to dynamic changes as clients may suggest changes in different stages of software development. AI-powered systems such as AIDA (Artificial Intelligence Design Assistant) can eliminate such complexities in the design process. Time & Money Saving Software Testing Traditionally, software testing takes a lot of time, especially when there are changes in the source code. Plus, it's costly, too! But in the end, it’s one of the essential software development stages as it ensures product quality. Therefore, there’s no room for error. Thankfully, there’s AI and a variety of software testing tools. Testers can utilize them to develop test cases and carry out regression testing. This kind of automated testing is relatively faster, smarter, and astonishingly time and money-saving. On top of all, it's error-free! Easy Data Gathering and Analysis Data gathering and data analysis are the most fundamental stage of any software development lifecycle and need a significant amount of human intervention. The project team has to come up with all the information necessary for the software development, and clients' input can be dynamic. Automated data gathering through various AI tools such as Google ML Kit can be the best option to ease the process. It can take care of specific data-gathering processes without the need for significant human intervention. Say Bye to Manual Code Generation Generating huge codes requires a lot of labor, time, and money. Therefore, simplifying the code generation process is significant because code writing is crucial for any software development life cycle. While traditional code generation can fall short in identifying the target goals effectively, automated code generation can be a game-changer. This is because AI tools typically generate snippets of reusable codes and write code lines as instructed. As a result, they save a substantial amount of money, labor, and time. Benefits of Artificial Intelligence in Software Development Incorporating artificial intelligence in software development can do wonders. Considering the incredible impact of AI on software development and the possibility of incredible transformations in the future software technologies due to AI, here are some promising benefits of AI software development. Enhanced accuracy in estimates Conceptual decision making Error-free end product Easy bugs and error detection Improved data security Conclusion The software development landscape is rapidly changing, and AI has a lot to do with it. Being an enterprise, you need to understand the benefits of AI and how it is enriching human lives worldwide. It's hard to deny the tremendous pressure on the current software development industry from the demand for applications. However, it’s one of the fastest-growing industries, and AI can simplify it with secure, unique, and scalable solutions. Unquestionably, AI software development is the future, and adopting it is the best decision enterprises can make. Frequently Asked Questions What are the things to consider when adopting AI for software development? It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining What are the real-world examples of integrating AI into software development? Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C What are the top machine learning and AI tools software developers should consider? Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the things to consider when adopting AI for software development?", "acceptedAnswer": { "@type": "Answer", "text": "It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining" } },{ "@type": "Question", "name": "What are the real-world examples of integrating AI into software development?", "acceptedAnswer": { "@type": "Answer", "text": "Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C" } },{ "@type": "Question", "name": "What are the top machine learning and AI tools software developers should consider?", "acceptedAnswer": { "@type": "Answer", "text": "Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio" } }] }

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AI TECH

CAN AI REPLACE ‘GUT FEELING’?

Article | August 23, 2021

Artificial Intelligence is empowering business leaders to make better, data-driven, and insightful decisions. It has undergone several evolutions since it burst into the business scene in the 1950s, to the point where several thinkers have already painted a machine that replaces human scenarios for the future. Our view on the future of work has evolved into a zero-sum game, where the result is an either-or. In my opinion, the view that AI will play a dominant role in the workplace is a little extreme. The fundamental assumption around AI replacing human workers is that humans and machines have the same characteristic. Totally untrue!. AI-based systems may be fast, consistently accurate, and rational, but they are not intuitive, emotional or culturally sensitive. Humans possess these qualities in abundance, and it is one of the reasons why we continue to surprise the world with our advancements. Intuition is the Mother of Innovation If we are living comfortable lives today, it’s because some business leaders chose their gut feeling over data analytics on numerous occasions. Some historical examples have been: 1: Henry Ford, facing falling demand for his cars and high worker turnover in 1914, doubled his employees’ wages, and it paid off. 2: Bill Allen was the CEO of Boeing in the 1950s, a company that manufactured planes for the defence industry. One day, he woke up to the idea of building commercial jets for a sector that was non-existent – civilian air travel. Allen convinced his board to risk $16 million on a new transcontinental airliner, the 707. The move transformed Boeing and air travel. 3: Travis Kalanick faced serious pushback when Uber instituted surge pricing. His move seemed to anger and alienate everyone. Travis stayed the course, and Uber modified its surge policy whenever appropriate. Now, dynamic pricing is an accepted aspect of this business and many others. So the question is, should a competent professional trust their gut feeling or make data-driven decisions? DATA V/S GUT Top professionals have repeatedly confirmed that gut feeling is one of the main reasons for their success. Leadership often gets associated with quick responses in unprecedented situations and lateral thinking. Experienced leaders are not only fearless about their instincts but are also proficient at making others feel confident in their judgment. Also, going with our instinct can help us make decisions quickly and more accurately since we tend to make choices based on experiences, values, and compassion. Malcolm Gladwell calls this ‘thin slicing’ in his book, “Blink”. Thin-slicing is a cognitive manoeuvre that involves taking a narrow slice of data, what you see at a glance, and letting your intuition do the work for you. However, he does warn that some decisions are exempt from this rule; it only applies to areas where you already have significant expertise. Artificial Intelligence and machine learning can support leaders to see complex patterns that can lead to new understanding in this fast-moving, digital era. The contention is that ‘human gut’ feeling can go hand in hand with AI – each supporting the other to achieve balanced outcomes. A Joint Venture Between Head and Heart Many see AI as an aid to human intelligence, not a replacement. To be one-step ahead in the AI era, professionals must learn to balance human and machine thinking. Organizations will have to showcase the ability to use the correct information at the right time and take action. It’s about using your instinct to take advantage of data and transforming that information into timely business decisions. AI is not yet ready to replace the human brain, but it has matured into an effective co-worker. Will intelligent machines replace human workers sometime soon? I guess not. Both have different abilities and strengths. The more important question is: Can human intelligence combine with AI to produce something experts are calling augmented intelligence? Augmented intelligence is collaborative, and at the same time, it represents a collaborative effort in the service of the human race. Figuring out how to blend the right mix with the best of data-driven deliberation and instinctive judgment could be one of the most significant challenges of our time.Enable GingerCannot connect to Ginger Check your internet connection or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×

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FUTURE TECH

4 Top Technology Trends That Will Dominate in 2022

Article | August 23, 2021

Technology trends have seen a dramatic change over the past decade. They have changed the way we live and will remain in the same state for the years to come. But, discovering the future is like going down the rabbit hole. And, when it’s about future technology innovations, it's even more challenging. Yes, there are many reports on top technology trends of the future. However, most of them fail to cover the compelling challenges faced by the technology industry. Although they present right-minded observations of the future of top technical trends, they are not enough. To address the loopholes, here’s a matter of fact about the four top technology trends in 2022. This report on top technology trends focuses on far-reaching innovations that will impact the technology landscape and actually make a difference for businesses, entrepreneurs, data scientists, software professionals, and more. These four convincing changes in the tech domain are powerful enough to revolutionize the strategic technology platforms and ultimately help enterprises and businesses to make more money and deliver what their customers are hungry for. “The advance of technology is based on making it fit in so that you don't really even notice it, so it's part of everyday life.” Bill Gates The Big Four | Best Future Technology Innovations Traditions are indeed important. But if the companies like Apple, Facebook, Google, or Microsoft remained old-fashioned, they wouldn’t be dictating the global industry today. So, if you are still into playing retro regarding your company operations, you shouldn’t be proud of it. During an interview with Media 7, Tom Raftery, Global VP at SAP, shared his views on embracing innovative culture as technology companies. As he says, technology influencers should encourage potential technological solutions for driving environmental sustainability. Being said, digitization isn’t just something to learn about, but it is the need of an hour! To clarify the future, one must reflect on the past first. The same applies to the top technology trends in 2022. The world has seen how the Internet of Things (IoT) reshaped Volksvagon’s car-sharing services, how augmented reality made IKEA the most prominent furniture company in the world, and how Capital One, An American credit card giant, amplified its revenue by making all its applications and systems cloud-based. Based on such factors, it’s now time to analyze the best digital technology trends that will dominate in 2022 and over the next few years. So, without further ado, let's dive right into it! Cloud Services As a business, you want to save your valuable time and manpower in local database management and maintenance. At the same time, it is critical to keep cyberattacks and data leakages at bay. Cloud services are an excellent option for all these tasks as they make data more flexible and accessible. Companies are going cloud-native and solutions like adopting managed cloud services and serverless architectures. This huge push will continue to flourish in the future to make the most of the cloud’s faster time to value and lower operational overhead. Internet of Things (IoT) The Internet of Things is indeed one of the most hyped buzzwords in the tech world and smart machines have the power to unleash modern-day opportunities to a wide range of industries. As the digital transformation shifts, IoT will become one of the most potent superpowers across all the industries and large-scale IoT deployments in vastly different use cases will be a usual thing. However, this transformation will need a bit of help from Artificial Intelligence (AI) and Machine Learning (ML). AI and ML will be critical for eliminating complexities related to building and managing IoT implementation. As a result, the tech world will see a gradual upsurge in AI-powered IoT platforms in the next few years. Augmented Reality (AR) Do you know why megacorps like Apply, Google, and Amazon are investing heavily in AR? It’s because AR-based apps are influencing more and more people worldwide. “When we get to this [AR] world, a lot of the things we think about today as physical objects, like a TV, will be $1 apps in an AR app store” Mark Zukerberg Yes, there’s still a lot of room for improvement in the AR sector. However, we will see more companies implementing AR into their business environment to improve the lives of their customers. Augmented reality will slowly but surely spread, and its future will redefine the future of the tech world. Quantum Computing The next-gen supercomputers, aka quantum computers, are a major technology breakthrough that can influence all the major sectors like marketing, finance, healthcare, etc. A complex computational task that requires thousands of years for a supercomputer to finish can be performed in mere three minutes by a quantum computer. Well, that answers why huge corporations like Honeywell, IBM, Google, etc., are already past the initial stages of quantum computing deployment. Frequently Asked Questions How to keep up with the top technology trends in 2022? Even though this is a daunting task, you can follow the below-mentioned steps to keep up with the landscape of new technology in 2020. Prioritize learning and share your ideas with peers Be prepared for experimenting Attend tech events and industry conferences Listen to the podcasts and utilize social media What technologies should we learn in 2022 apart from the ones mentioned above? This is a century of technological advancement. Almost all the industries are adopting new technology trends to stay updated and ultimately grow their businesses. If you wish to do the same, you should be well-versed with not only the trends we just discussed but also the following ones: Data Science Edge Computing Cyber Security Blockchain 5G How can organizations be ready for the best future technology? The digital landscape changes rapidly, thus making it difficult for organizations to keep up. The best way to tackle this problem is to enhance digital skills across all departments. You can develop a skills improvement routine from the baseline and move up or start working with vendors who offer on-demand training on new technologies. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How to keep up with the top technology trends in 2022?", "acceptedAnswer": { "@type": "Answer", "text": "Even though this is a daunting task, you can follow the below-mentioned steps to keep up with the landscape of new technology in 2020. Prioritize learning and share your ideas with peers Be prepared for experimenting Attend tech eventsand industry conferences Listen to the podcasts and utilize social media" } },{ "@type": "Question", "name": "What technologies should we learn in 2022 apart from the ones mentioned above?", "acceptedAnswer": { "@type": "Answer", "text": "This is a century of technological advancement. Almost all the industries are adopting new technology trends to stay updated and ultimately grow their businesses. If you wish to do the same, you should be well-versed with not only the trends we just discussed but also the following ones: Data Science Edge Computing Cyber Security Blockchain 5G" } },{ "@type": "Question", "name": "How can organizations be ready for the best future technology?", "acceptedAnswer": { "@type": "Answer", "text": "The digital landscape changes rapidly, thus making it difficult for organizations to keep up. The best way to tackle this problem is to enhance digital skills across all departments. You can develop a skills improvement routine from the baseline and move up or start working with vendors who offer on-demand training on new technologies." } }] }

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AI TECH

5 AI Trends Profoundly Benefiting Business Bottom Lines

Article | August 23, 2021

Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind. In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before. AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.” “To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner. However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines. “To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization. Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability. Manifesting new market opportunities Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus. Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians. Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof. Reconcile emotions with actualities Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages. “Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.” Scale statistical models for actionable models We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike. Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board. Future-proof, anti-fragile data supply chains From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights. It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered. “Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.” Multimedia monetization Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization. Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind. Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson. In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate. “All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.” As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia. “New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.” For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.” “Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.” Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.

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Spotlight

Computer Science at Howard University

The department offers a traditional B.S. degree in Computer Science, a Computer Science minor option for non-engineering disciplines at Howard, a graduate certificate course in Cybersecurity, a traditional M.S. degree in Computer Science, an accelerated 1-year M.S. degree in Computer Science, and a Ph.D in Computer Science...

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