Trending December 2023 # The Combination Of Humans And Artificial Intelligence In Cyber Security # Suggested January 2024 # Top 17 Popular

You are reading the article The Combination Of Humans And Artificial Intelligence In Cyber Security updated in December 2023 on the website Cattuongwedding.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 The Combination Of Humans And Artificial Intelligence In Cyber Security

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.

You're reading The Combination Of Humans And Artificial Intelligence In Cyber Security

The Future Of Artificial Intelligence In Manufacturing

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:

Detect defects

Predict failures

Optimize processes

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.

Also read: Top 6 Tips to Stay Focused on Your Financial Goals

Predict Failures

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.

Also read: The 15 Best E-Commerce Marketing Tools

What’s Next?

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.

Applications Of Artificial Intelligence And Machine Learning In 5G

Applications of artificial intelligence and machine learning in 5G

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.

The Promise Of Artificial Intelligence In Precision Medication Dosing

In the United States alone, drug-related problems in patients account for

AI 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

Future Of Artificial Intelligence And Machine Learning

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 Pichai

Who is using these technologies?

Also read: 10 Best Saas Marketing Tools And Platforms For 2023

The major industries where Machine Learning and Artificial Intelligence are used are:

6. Online Search

In Healthcare

Gradually, human practitioners and machines will work in tandem to deliver improved outcomes

Finance

AI And Machine Learning are the New Future Technology Trends discuss how the latest technologies like blockchain are impacting India’s capital markets.

Real Estate

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.

Also read: How to Calculate Your Body Temperature with an iPhone Using Smart Thermometer

The Future of Machine Learning

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.

Neuromorphic Chips: The Third Wave Of Artificial Intelligence

The age of traditional computers is reaching its limit. Without innovations taking place, it is difficult to move past the technology threshold. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The Moore’s law (named after Gordon Moore, in 1965) states that the number of transistors in a dense integrated circuit doubles about every two years while their price halves. But now the law is losing its validity. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. This may indicate the third wave of Artificial Intelligence. The first generation of AI was marked with defining rules and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second generation was populated by using deep learning networks to analyze the contents and data that were largely concerned with sensing and perception. The third generation is about drawing parallels to the human thought process, like interpretation and autonomous adaptation. In short, it mimics neurons spiking like the nervous system of humans. It relies on densely connected transistors that mimic the activity of ion channels. This allows them to integrate memory, computation, and communication, at higher speed, complexity, and better energy efficiency. Loihi is Intel’s fifth-generation neuromorphic chip. This 14-nanometer chip has a 60-millimeter die size and contains over 2 billion transistors, as well as three managing Lakemont cores for orchestration. It contains a programmable microcode engine for on-chip training of asynchronous spiking neural networks (SNNs). Total, it has 128 cores packs. Each core has a built-in learning module and a total of around 131,000 computational “neurons” that communicate with one another, allowing the chip to understand stimuli. On March 16, Intel and Cornell University showcased a new system, demonstrating the ability of this chip to learn and recognize 10 hazardous materials from the smell. And this can function even in the presence of data noise and occlusion. According to their joint profiled paper in Nature Machine Intelligence, this can be used to detect the presence of explosives, narcotics, polymers and other harmful substances like signs of smoke, carbon monoxide, etc. It can purportedly do this faster, more accurate than sniffer dogs thereby threatening to replace them. They achieved this by training it constructing a circuit diagram of biological olfaction. They drew this insight by creating a dataset by exposing ten hazardous chemicals (including acetone, ammonia, and methane) through a wind tunnel, and a set consisting of the activity of 72 chemical sensors collected the signals. This tech has multifold applications like identifying harmful substances in the airport, detecting the presence of diseases and toxic fumes in the air. The best part is, it constantly re-wires its internal network to allow different types of learning. The futuristic version can transform traditional computers into machines that can learn from experience and make cognitive decisions. Hence it is adaptive like human senses. And to put a cherry on top, it uses a fraction of energy than the current state of art systems in vogue. It is predicted to displace Graphics Processing Units (GPUs). Although Loihi may soon evolve into a household word, it is not the only one. The neuromorphic approach is being investigated by IBM, HPE, MIT, Purdue, Stanford, and others. IBM is in the race with its TrueNorth. It has 4096 cores, each having 256 neurons and each neuron having 256 synapses to communicate with others. Germany’s Jülich Research Centre’s Institute of Neuroscience and Medicine and UK’s Advanced Processor Technologies Group at the University of Manchester are working on a low-grade supercomputer called SpiNNaker. It stands for Spiking Neural Network Architecture. It is believed to stimulate so-called cortical microcircuits, hence the human brain cortex and help us understand complex diseases like Alzheimer’s.

Update the detailed information about The Combination Of Humans And Artificial Intelligence In Cyber Security on the Cattuongwedding.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!