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As 5G standards mature more quickly and its pre-commercial tests are carried out around the globe, the pace of 5G deployment is speeding up and more innovative applications are made possible through 5G networks. In the era of 5G, telecom carriers are also faced with the challenges of network complexity, diverse services and personalized user experience.
Network complexity refers to complex site planning due to densely distributed 5G networks, complex configuration of large-scale antenna arrays, and complex global scheduling brought by SDN/NFV and cloud networks. Diverse services range from original mobile internet services such as voice and data to known and unknown services developed in IoT, industrial internet, and remote medical care. Personalized user experience means to offer differentiated and personal services to users and build user experience model in terms of full-life cycle, full-business process, and full-business scenario that are associated with service experience and marketing activities for smart operations. These challenges require networks to be maintained and operated in a smarter and more agile manner.
Artificial intelligence (AI) represented by machine learning and deep learning has done a remarkable job in the industries of internet and security protection. we believes that AI can also greatly help telecom carriers optimize their investment, reduce costs and improve O&M efficiency, involving precision 5G network planning, capacity expansion forecast, coverage auto-optimization, smart MIMO, dynamic cloud network resource scheduling, and 5G smart slicing (Fig. 1).
In smart network planning and construction, machine learning and AI algorithms can be used to analyze multidimensional data, especially the cross-domain data. For example, the 0-domain data, B-domain data, geographical information, engineering parameters, history KPI, and history complaints in a region, if analyzed by using AI algorithms, can help make reasonable forecast on business growth, peak traffic, and resource utilization in this region. Also multi-mode coverage and interference can be measured for optimization and parameter configuration can then be recommended to guide coordinated network planning, capacity expansion, and blind spot coverage in 4G/5G networks. In this way, operators make their regional network planning close to theoretical optimum and can significantly reduce labor cost in network planning and deployment.
AI technology can be used to identify the law of change in user distribution and forecast the distribution by analyzing and digging up historical user data. In addition, by learning the historical data, the correspondence between radio quality and optimal weights can be worked out. Based on the AI technology, when the scenario or user distribution changes or migrates, the system can automatically guide the MM site to optimize its weights. To achieve optimal combination and best coverage in a multi-cell scenario, interference among multiple MM sites should also be considered besides the intra-cell optimization. For example, when a stadium is used in different scenarios such as a sports event and a concert, its user distribution is quite different. In this case, MM sites in the stadium can automatically identify a different scenario and make adaptive optimization of the weights for the scenario so as to obtain best user coverage.
The application of AI in the telecom field is still in the early stage. The coming 5–10 years will be a critical period for smart transformation of carriers’ networks. With its gradual maturity, AI will be introduced in various telecom scenarios to help carriers transit from the current human management model to the self-driven automatic management mode and truly achieve smart transformation in network operation and maintenance.
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Machine Learning and Artificial Intelligence are the “Buzz topics” in every trending article of 2023, and rightfully so. It is much like how the internet emerged as a game-changer in everyone’s lifestyle, Artificial Intelligence and Machine Learning are poised to transform our lives which were unimaginable years ago.What are Artificial Intelligence and Machine Learning?
Artificial Intelligence (A.I.) is a simplified problem-solving process for humans. It empowers software to do jobs without being explicitly programmed. Also, it has neural networks and profound learning. It’s the larger notion of machines having the ability to do jobs how we’d think about.
And, Machine Learning is the app of Artificial Intelligence (AI) that enables machines to get data and allows them to learn how to execute these jobs. It uses algorithms and enables systems to discover concealed insights without being programmed.Why are A.I. and ML important?
Considering that the growing volumes and types of information readily available, the demand for computational processing is becoming crucial to supply deep-rooted information that is economical and readily available. With the support of both A.I. and Machine Learning, it is possible to automate versions that may analyze larger, complicated data to return faster and precise results.
Organizations are discovering profitable opportunities to cultivate their company by identifying the exact models to steer clear of unknown dangers. Using algorithms to construct a version is assisting businesses to bridge the difference between their products and consumers with greater choices and human intervention. Most businesses with enormous quantities of information have recognized that the significance of Machine Learning.
By gaining insights from this information, frequently in real-time, organizations are becoming more effective in their livelihood and gaining an edge over other competitors.
The Biggies such as Google, Facebook, and Twitter banks on Artificial Intelligence and Machine Learning to their potential expansion.
Sundar PichaiWho is using these technologies?
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The major industries where Machine Learning and Artificial Intelligence are used are:
6. Online Search
Gradually, human practitioners and machines will work in tandem to deliver improved outcomes
AI And Machine Learning are the New Future Technology Trends discuss how the latest technologies like blockchain are impacting India’s capital markets.
The repetitive tasks in an average DBA system provide an opportunity for AI technologies to automate processes and tasks.The Future of AI
In the post-industrialization era, people have worked to create a machine that behaves like a human. The thinking machine is AI’s biggest gift to humankind the grand entrance of the self-propelled machine has abruptly changed the surgical principles of business.
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Here are some predictions about Machine Learning, based on current technology trends and ML’s systematic progression toward maturity:
ML will be an integral part of all AI systems, large or small.
As ML assumes increased importance in business applications, there is a strong possibility of this technology being offered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).
Connected AI systems will enable ML algorithms to “continuously learn,” based on newly emerging information on the internet.
There’ll be a huge rush among hardware vendors to improve CPU power to adapt ML information processing. More correctly, hardware vendors will likely be forced to redesign their machines to do justice to the forces of ML.
Machine Learning will help machines to make better sense of the context and meaning of data.
Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.
Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency.
With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to:
Make devices smarter
Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.Detect Defects
AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.
Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.
Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time.
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Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.
Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.
General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.
High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.Optimize Processes
The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions.Make Devices Smarter
By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.
The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.
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The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes.
The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.
There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.
The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.
Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.
Indeed, even as AI innovation changes some aspects of cybersecurity, the crossing point of the two remains significantly human. In spite of the fact that it’s maybe unreasonable, humans are upfront in all pieces of the cybersecurity triad: the terrible actors who look to do hurt, the gullible soft targets, and the great on-screen characters who retaliate. Indeed, even without the approaching phantom of AI, the cybersecurity war zone is frequently hazy to average users and the technologically savvy alike. Including a layer of AI, which contains various innovations that can likewise feel unexplainable to many people, may appear to be doubly unmanageable as well as indifferent. That is on the grounds that in spite of the fact that the cybersecurity battle is once in a while profoundly personal, it’s once in a while pursued face to face. With an expected 3.5 million cybersecurity positions expected to go unfilled by 2023 and with security ruptures increasing some 80% every year, infusing human knowledge with AI and machine learning tools gets critical to shutting the talent availability gap. That is one of the recommendations of a report called Trust at Scale, as of late released by cybersecurity organization Synack and citing job and breach data from Cybersecurity Ventures and Verizon reports, individually. Indeed, when ethical human hackers were upheld by AI and machine learning, they became 73% increasingly proficient at identifying and evaluating IT risks and threats. In any case, while the conceivable outcomes with AI appear to be unfathomable, the possibility that they could wipe out the role of people in cybersecurity divisions is about as unrealistic as the possibility of a phalanx of Baymaxes supplanting the nation’s doctors. While the ultimate objective of AI is to simulate human functions, for example, problem-solving, learning, planning, and intuition, there will consistently be things that AI can’t deal with (yet), as well as things AI should not handle. The principal classification incorporates things like creativity, which can’t be viably instructed or customized, and therefore will require the guiding hand of a human. Anticipating that AI should viably and reliably decide the context of an attack may likewise be an unconquerable ask, at any rate for the time being, just like the idea that AI could make new solutions for security issues. At the end of the day, while AI can unquestionably add speed and exactness to tasks generally handled by people, it is poor at extending the scope of such tasks. As it were, AI’s impact on the field of cybersecurity is the same as its effect on different disciplines, in that individuals frequently terribly overestimate what AI can do. They don’t comprehend that AI often works best when it has a restricted application, similar to anomaly detection, versus a broader one, like engineering a solution to a threat. In contrast to people, AI needs inventiveness. It isn’t inventive. It isn’t cunning. It regularly neglects to consider context and memory, leaving it incapable to decipher occasions like a human mind does. In a meeting with VentureBeat, LogicHub CEO and cofounder Kumar Saurabh showed the requirement for human analysts with a kind of John Henry test for automated threat detection. “A few years ago, we did an examination,” he said. This included arranging a specific amount of information, a trifling sum for an AI model to filter through, yet a sensibly huge sum for a human analyst to perceive how teams utilizing automated frameworks would pass against people in threat detection.
In the United States alone, drug-related problems in patients account forAI Transforms Dosing and Gives Patients a Personalized Fit
The most compelling approach to solving this important problem to date is with the application of artificial intelligence to enable precision dosing. Precision dosing is an umbrella term that refers to the process of transforming a “one-size-fits-all” therapeutic approach into a targeted one, based on an individual’s demonstrated response to medication Precision dosing has been identified as a crucial method to maximize therapeutic safety and efficacy with significant potential benefits for patients and healthcare providers, and AI-powered solutions have so far proven to be among the most powerful tools to actualize precision dosing. In 2008, Dr. Donald M. Berwick, former Administrator of the Centers for Medicare and Medicaid Services,Better Decision Support in Dosing Achieved
Despite significant promise, applications of precision dosing have tended to be difficult to scale (5 Factors That Came Together to Make Now the Right Time for AI-Powered Dosing
Several factors have come together to create the necessary conditions to begin realizing the potential for AI-powered precision dosing: Public familiarity with artificial intelligence as an effective tool for solving complex problems makes physicians comfortable incorporating such tools in clinical settings. Reliable data is now available in electronic medical records and is standardized in a manner that is much more ingestible by algorithms as compared to free-form paper medical records. Big data analytics techniques have also made applying artificial intelligence and control algorithms to complex datasets much more practical and efficient. We can draw on data from millions of patients to design and test algorithms in silico to predict effectiveness and iterate quickly. This is a vast improvement on expert systems that are based on a clinician’s smaller number of patients, possibly in the thousands or hundreds, that are generally only possible to test in much more costly and risky clinical trials. Increasingly complex and powerful drugs have been developed that impact basic physiologic processes. Drugs that impact multiple physiologic processes and have a narrow therapeutic window (the “sweet spot” between toxicity and ineffective therapy) have become more prevalent. These are the types of drugs for which AI-powered drug dosing can provide the most benefit.Chronic Anemia Offers an Especially Powerful Opportunity to Apply AI-Powered Dosing
AI-powered precision dosing will likely be the standard of care for chronic disease management in the future. Artificial intelligence is a valuable tool that can enhance a physician’s ability to practice and make the best judgements possible, improving the cost of care and the quality of care itself. Dosing anemia drugs is only one, specific example of the impact that AI can have on medication prescribing. Dosis has already begun a trial of an AI-based intravenous iron dosing protocol, as an adjunct to Strategic Anemia Advisor. In addition, Dosis has developed a tool that informs the simultaneous dosing of three different types of medication that are used to manage mineral and bone disorder, a common comorbidity in kidney disease patients. This application will be the first of its kind, modelling three interdependent biological variables and three medications simultaneously that impact these values to return them to normal levels. Once AI for precision drug dosing is widely adopted, it will be extremely unlikely for the industry to revert back to previous dosing methods. The efficacy gap between AI-powered tools and legacy dosing methods will also only widen, as more data is incorporated into these tools. In 10 years, AI-driven dosing models will likely be the standard of care across the healthcare spectrum, used for a wide variety of drugs like warfarin, insulin, and immunosuppressives. Indeed, any drug that is administered chronically and has a narrow therapeutic range is a good candidate for AI-driven dosing. In addition, as more tools are developed and more opportunities to use those tools are identified, we will see exponential growth in the use of AI to drive therapies.Author
Understanding the Difference among Detection and Forecasting Models, and Predictive Analytics and Leveraging Them in Business.
We do not need a soothsayer to realize howUnderstanding the differences
While detection andHow can they help Business?
Detection Vs. Prediction A paper published by MIT states how detection can help businesses via a Forecasting vs. Prediction Coming to forecasting, Business leveraging Artificial Intelligence-based forecasting models, can figure out trends that shall dominate the market in the coming days. Forecasting relies on the input of base data to arrive at an outcome. The quality of this data affects the results, unlike prediction or predictive models that have no separate input or output variable. Typically, forecasting is all about the numbers and using level and trend and seasonality observations to predict outcomes; predictive analytics is more about understanding consumer behavior. Even though forecasting is considered as projective of predictive models, the former is based on temporal information. It is scientific and free from intuition and personal bias, whereas prediction is subjective, arbitrary, and fatalistic by nature. This is why we have weather forecast instead of weather prediction. We need to strike a balance when employing these algorithms in Business. For, e.g., forecasting can help inOutlook:
We do not need a soothsayer to realize how Artificial Intelligence (AI) has transformed our lives. From using machine learning for drug discovery to facial unlock ID using facial recognition, its’ application is everywhere. While AI may not say what the next reading on a dice (or magic 8) ball can be, it surely can predict the probability of getting 6 in the next roll of dice. The predictive aspect of AI has become more refined and accurate with time, thanks to deep learning and data analytics . However, the question is, can Artificial Intelligence do more than just prediction like forecasting or detection of a trend?While detection and forecasting may sound similar to predictive analytics or simply prediction, they are different. Detection refers to mining insights or information in a data pool when it is being processed. This can be the detection of objects, fraudulent behaviors, and practices, anomalies, etc. Whereas, forecasting is a process of predicting or estimating future events based on past and present data and most commonly by analysis of trends or data patterns. Unlike predictions, it is not vague and is defined by logic. Prediction or predictive analysis employs probability based on the data analyses and processing. Out of the three, it is the more uncertain, complicated, and expensive process.A paper published by MIT states how detection can help businesses via a smoke detector-crystal ball analogy. Here, smoke detector and crystal ball are metaphorically examples of how detection and prediction work. Smoke detectors issue warning signals of an impending fire hazard. They don’t predict the possibility of a fire accident. Based on early warning, we are presented options: whether to extinguish the fire/smoke source or escape the scene. Similarly, businesses can benefit from detecting issues quickly, even if they are unpredicted. By leveraging detection algorithms of AI, companies always have the chance to act and manage outcomes and other functions even when they might have missed the opportunity to prevent any shortcomings or bottlenecks. Detection always encourages action using multiple solutions. Further, it is always definite as they offer some value, unlike the uncertainty offset of predictive analytics . This can help to boost ROI at minimal costs. One use case is, instead of trying to predict which customers will churn, managers, can shift to detect better which customers are dissatisfied. The implications may be similar, but changes in satisfaction are measurable while customers who were going to leave but didn’t are. Also, detection models can be used in every stage of the business pipeline, just like smoke detectors in every flat in an apartment. They help us to make sense of the activities and business insights. These can be identifying where data signals are currently missing. Where data signals have poor quality? Where are data signals giving false alarms causing system fatigue? All these go in the long run in enlightening ways to augment and enhance the productivity channels.Coming to forecasting, Business leveraging Artificial Intelligence-based forecasting models, can figure out trends that shall dominate the market in the coming days. Forecasting relies on the input of base data to arrive at an outcome. The quality of this data affects the results, unlike prediction or predictive models that have no separate input or output variable. Typically, forecasting is all about the numbers and using level and trend and seasonality observations to predict outcomes; predictive analytics is more about understanding consumer behavior. Even though forecasting is considered as projective of predictive models, the former is based on temporal information. It is scientific and free from intuition and personal bias, whereas prediction is subjective, arbitrary, and fatalistic by nature. This is why we have weather forecast instead of weather prediction. We need to strike a balance when employing these algorithms in Business. For, e.g., forecasting can help in marketing and promotional planning, but predictions can help estimate sales for targeting chúng tôi bottom line is that businesses need to understand the key differences and use cases of predictive analytics, detection algorithms, and forecasting models of Artificial Intelligence. Then they can employ them as per their requirement to achieve brand goals.
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