Tech Research Online

What Is Artificial General Intelligence and How Does It Work?

Emily Lawson — Wed, 20 Mar 2024 12:21:33 +0000

Artificial General Intelligence (AGI) hasn’t become real yet. However, research into this type of artificial intelligence where machines think and learn as people continues in different parts of the world. The idea behind AGI is to have machines develop self-awareness and consciousness. These developments have already started manifesting in innovations like self-driving cars. Once developed fully, AGI can potentially blur the intellectual differences that currently exist between machines and humans.

Although it’s still too early to tell whether machines can simulate human intellectual capabilities fully, the concept of AGI is fascinating. In this article, we explore AGI further to help you understand how it differs from artificial intelligence (AI) and the technologies behind it.

What is Artificial General Intelligence?

Artificial General Intelligence is a theoretical form of AI that can learn, understand, and apply knowledge to perform intellectual tasks like humans. Although AGI isn’t a reality yet, its design incorporates adaptability, flexibility, and problem-solving skills. These skills will enable it to perform any intellectual task that a human can, or in some instances, outperform human abilities.

AGI is designed to address gaps in current AI systems. Currently, AI systems have limited scope. They cannot self-teach or complete tasks they are not trained to perform. AGI promises complete AI systems that utilize generalized human cognitive abilities to perform complex tasks across different domains. Artificial general intelligence examples already exist in self-driving cars.

Artificial General Intelligence vs Artificial Intelligence: What’s the Difference?

In decades past, computer scientists advanced machine intelligence to a point where machines perform specific tasks. For instance, AI text-to-speech tools use deep learning models to establish the link between linguistic elements and their acoustic features. These machine-learning models learn from huge volumes of audio and text data and then generate AI speech and voice patterns.

Today, AI systems are designed to perform specific tasks. They can’t be repurposed to work in other domains. Their computing algorithms and specifications are limited and they rely on real-time data for decision-making. This form of machine intelligence is considered narrow or weak AI.

AGI seeks to advance current AI capabilities. It seeks to diversify the tasks that machines can perform to enable them to solve problems in multiple domains instead of one. This makes AGI a hypothetical representation of a strong, full-fledged AI. Such AI will have general cognitive abilities that enable it to solve complex tasks, just like humans.

How Does General Artificial Intelligence Work?

The concept of AGI is based on the theory of mind that underpins the AI framework. This theory focuses on training machines to understand consciousness and learning as fundamental aspects of human behavior. Besides applying algorithms, AGI will incorporate logic into machine learning and AI processes to mirror human learning and development.

With a solid AI foundation, AGI is expected to learn cognitive abilities, make judgments, integrate learned knowledge in decision-making, manage uncertain situations, and even plan. General artificial intelligence will also facilitate machines to conduct imaginative, innovative, and creative tasks.

Technologies that Drive Artificial General Intelligence

The concept of AGI is still in the theoretical stage. Research on its viability and efforts to create AGI systems continue in different parts of the world. The following are the emerging technologies that will most likely characterize AGI:

1. Robotics

This is an engineering discipline that involves the creation of mechanical systems that automate physical tasks. In AGI, robotics facilitate the physical manifestation of machine intelligence. Robotics plays an important role in supporting the physical manipulation ability and sensory perception required by AGI systems.

2. Natural Language Processing

This AI branch enables machines to generate and understand human language. NLP systems convert language data into representations known as tokens using machine learning and computational linguistics.

3. Deep Learning

It’s an AI discipline that involves training multiple layers of neural networks to understand and extract complex relationships from raw data. Deep learning can be used to create systems that understand different types of information like audio, text, video, and images.

4. Computer Vision

A technology that supports extraction, analysis, and comprehension of spatial data from visual data. For instance, self-driving cars rely on computer vision models to analyze camera feeds in real time for safe navigation. Computer vision relies on deep learning technologies to automate object classification, recognition, and tracking among other image-processing tasks.

5. Generative AI

A subset of deep learning, this technology enables AI systems to generate realistic and unique content from knowledge learned. Generative AI models use huge datasets to train, which enables them to answer questions from humans in text, visuals, and audio formats that resemble natural human creations.

The Challenge Ahead

If it becomes a reality, there is no doubt artificial general intelligence will change how we work and live. But the journey to making it work isn’t smooth. In developing this emerging technology, computer scientists must find ways to make AGI models connect between domains the way humans do. Another challenge that needs to be overcome relates to emotional intelligence.

Neural networks cannot replicate the emotional thinking required to drive creativity and imagination. Humans respond to situations and conversations depending on how they feel. Considering the logic embedded in current AI models, replicating this ability and improving sensory perceptions to enable machines to respond and perceive the world the way humans do remains an uphill task.

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Implementing DevSecOps for Cloud Security

Noah Davidson — Wed, 13 Mar 2024 09:44:55 +0000

DevSecOps refers to development, security, and operations. It is a methodology that integrates security practices into the DevOps process, emphasizing the importance of security from the very beginning of software development.

DevSecOps is a relatively new idea. Cloud DevSecOps takes the Software Development Life Cycle (SDLC) one step further by integrating the security component, whereas DevOps merges development and operations in a continuous synchronized loop.

As a result, security is built right into the cloud application, saving a lot of time and money in the event of a cyberattack.

This strategy is necessary because DevSecOps in cloud security transpires to be a significant benefit for the widespread adoption of cloud computing. The method incorporates security testing and monitoring in addition to continuous development and deployment, making the cloud application secure from the start.

Understanding the breadth and advantages of cloud DevSecOps can be helpful if you intend to develop your software or application.

Importance of DevSecOps in Dealing with Cloud Security Challenges

While DevSecOps teams check on product security during development and launch, IT departments strive to guarantee the security of a company’s network and IT infrastructure.

Their responsibilities include process monitoring, risk analysis collection, security measure automation, and incident handling. DevSecOps helps businesses address cloud security concerns by assisting them to establish efficient risk management for cloud security.

1. Open Environment

DevSecOps’s introduction creates the framework for open dialogue between teams and business divisions. Once DevSecOps is the cornerstone of your development, tracking and monitoring complex initiatives—like cloud migration and security, for example—and keeping all stakeholders informed become non-issues.

Nonetheless, developing and implementing DevSecOps processes in a way that allows them to handle security threats and support the resilience of enterprises takes time.

2. Offering Cost-effective Security

Which is better, addressing a security breach’s consequences or trying to stop it from happening? Organizations that leverage DevSecOps neutralize such threats before they have a significant effect. Through the identification and prevention of events that may impact the internal IT environment, DevSecOps helps many firms reduce their business vulnerabilities.

It saves time and money by reducing the need to repeat a method to resolve security vulnerabilities thanks to DevSecOps’ quick and safe delivery. Because the integrated security code no longer includes pointless rebuilds or reviews, it is more secure. It is reasonably priced and effective.

3. Storing Data at A Single Location

Teams may quickly improve apps that are still in development by utilizing data management and the DevSecOps process suite to gather data from various sources and feed it back into the creative process. To put it briefly, the implementation of DevSecOps helps technical and operation teams to analyze collected data and transform it into meaningful insight.

The data insights are continuously improved under one roof, resulting in easy CI/CD that further helps save time during the product development cycle.

Strategies for DevSecOps that Can Transform Cloud Security

For a cloud DevSecOps deployment to be effective, the DevOps cloud security teams need to collaborate closely with other teams and monitor the code quality throughout the application’s lifetime. Here, we go over the six essential DevSecOps in cloud deployment techniques that have the potential to completely transform cloud security and cloud security solutions at your business:

1. Process Automation for Testing

Automated testing is unquestionably one of DevSecOps’ principles or best practices. It may be the impetus for cloud DevSecOps. Automated tests improve the testing process by repeating tests, documenting findings, and providing feedback to the team more quickly.

Automating tests throughout development can improve overall productivity by eliminating code errors. It is possible to expedite the entire cloud migration procedure, which facilitates the migration of more resources to the cloud.

2. Analysis of Code

Most businesses must be able to alter their software regularly to respond to changing market conditions. Short delivery cycles make traditional security models unsuitable, even though agile teams have adapted to this trend. As a result, they hinder your organization’s agile release cycles and expand software product offerings.

You may ensure excellent cloud security risk management and code production in short, frequent releases by using an agile approach to security operations within your teams. Using cloud technology for DevSecOps has two advantages: it incorporates code analysis into the quality control process and allows for rapid vulnerability screening.

3. Compliance Tracking

Cloud-based technologies are used to handle enormous volumes of data. Following stringent security laws such as HIPAA, GDPR, and SOC 2 could be difficult.

Adopting cloud DevSecOps could alter it and reduce the additional effort from regulatory audits. Every time new codes are generated or modified, development teams can gather instantaneous evidence of compliance. It would help the company be ready for any unanticipated circumstances.

4. Vulnerability Management

Finding, looking into, and fixing any threats or vulnerabilities that surface with every new code delivery is crucial to DevOps security. In addition to publishing and carrying out vulnerability checks, schedule regular, periodic security scans to aid in the discovery of new defects or vulnerabilities.

5. Change Management

It takes a deep comprehension of the change management procedure to implement a DecSecOps cloud computing approach. Giving your developers the knowledge and resources they need to recognize risks and take appropriate action before they become major problems enhances the effectiveness of change management.

Similarly, giving developers 24-hour clearance windows allows them to consistently propose proposals for security measures that are essential to the goal.

Conclusion

Cloud security protocols could undergo a revolution with the application of DevSecOps. Integrating security concerns into each software development lifecycle step allows organizations to guarantee the integrity of their cloud environments, protect sensitive data, and manage risks proactively.

Businesses may stay ahead of cyber risks and adhere to regulatory obligations by implementing DevOps managed services, which fosters cooperation, automation, continuous monitoring, and a security-first culture.

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Understanding the Dynamics of Digital Twin Technology

Olivia Carter — Wed, 28 Feb 2024 15:25:44 +0000

Digital twins are among the most sought-after business tools in the world today. One Internet-of-Things (IoT) analytics report shows that between 2020 and 2022, the digital twin technology market grew by 71%. The versatility that this technology offers, makes it attractive for varying businesses across industries. Digital twins are computer programs that utilize real-world data to develop simulations based on historical data and current conditions.

These programs can integrate with artificial intelligence, IoT, and software analytics to improve outputs. IoT sensors facilitate the transfer of real-world data needed to create virtual twins. Using the simulations, businesses can predict how a process or product will perform. But, virtual twins aren’t just about simulation. They span the entire product or process lifecycle and have service, engineering, and manufacturing use cases.

In this article, we delve deeper into the types of digital twin technology and its use cases.

What is Digital Twin Technology?

Digital twin refers to the technology that supports the creation of virtual representations of physical systems, processes, or objects. This digital twin is supported by various state-of-the-art technologies including artificial intelligence, IoT, big data, machine learning, IoT, and visualization technologies like augmented and virtual reality. Essentially, digital twins have three main components:

A virtual definition of their counterparts
Operational data of their components
Information model that gathers and presents data to inform decisions

Types of Digital Twin Technology

There are four types of digital twins namely:

1. Component Twins

Also referred to as part twins, component twins are the lowest levels of virtual twins. It corresponds with the tiniest elements within a specific part of a product or equipment like a switch or an IoT sensor. Component twins facilitate performance monitoring and allow for the simulation of real-world conditions for purposes of testing their efficiency, stability, or endurance.

2. Product/Asset Twins

Product twins feature several component twins or utilize the information that component twins generate to create complex assets like smart buildings, pumps, or engines. These twins analyze how well separate parts of a system perform and interact as part of the entire process or solution. Engineers use product twins to gather insights about equipment performance and identify potential flaws.

3. System Twins

These digital twins show how varying products combine to form functional units and duplicate product assets at the system level. They provide a large-scale view of plants or factories, allowing engineers to test varying systems for optimal effectiveness.

4. Process Twins

These constitute the highest level of virtual twins, connecting system twins into a single entity that supports synchronization and collaboration between systems. Process twins create solutions that offer a maximum view of workflows within manufacturing plants or factories for deeper and more versatile output analysis.

Key Industrial Use Cases of Digital Twin Technology

Virtual twinning has varying use cases. Below are the main use cases for this technology in businesses across industries:

1. Product Design

Digital twins allow companies to develop quality products, cities, processes, systems, or buildings. By simulating physical objects, product developers test varying designs, identify design flaws, and make adjustments to improve those flaws before commencing actual production.

2. Service Optimization

Companies can use digital twinning to identify service improvement opportunities and optimize the delivery of those services. Companies use virtual twinning to improve customer experience, optimize manufacturing processes, and improve operations.

3. Supply Chain Management

Industries can use digital twins to simulate and test scenarios that facilitate the identification of inefficiencies in supply chain management processes. This information enables them to optimize the flow of materials and products in supply chains. Digital twinning is also used to simulate supply chain disruptions like transportation problems or raw material delivery delays and their impact on the business. This allows suppliers to mitigate these issues before they occur.

4. Operations Management

In operations management, virtual twin technology facilitates real-time remote access and performance monitoring of systems, assets, and processes. This enables industries to test varying scenarios and find improvement opportunities. Digital twins also help companies identify and fix performance problems like energy consumption, uptime, and maintenance needs before they can result in production losses or downtime.

5. Entertainment

Another important digital twinning use is entertainment. Considering that mixed reality technologies are at the heart of virtual twins, the technology can be used to create immersive experiences for customers in the leisure industry.

One of the leading digital twin technology examples in the entertainment industry is the simulation of attractions and rides in amusement parks. This allows customers to experience the rides virtually even before they make actual visits to the parks. With this information, customers can plan their visits better and prioritize their preferred attractions for more satisfying experiences.

Final Thoughts

Digital twinning will continue to change the way industries build, design, and maintain systems and products. Its application will expand to new sectors beyond automobile, aerospace, health, and smart cities industries. Another trend we can expect in future is democratized access to the technology through the digital twin-as-a-service (DTaaS) model. Delivery of DTaaS via cloud-based solutions will make digital twin technology highly affordable and accessible to businesses of all sizes. Additionally, we’ll witness integration of virtual twinning technology with edge computing and the 5G network to support real-time analytics and facilitate flawless data transfer respectively.

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Risk-Based Authentication: How Duo Can Reduce Risks for Hybrid Work

Cisco — Mon, 26 Feb 2024 12:28:48 +0000

Risk-Based Authorization solves two challenges: it allows users to gain access from anywhere and ensures that access is secure. Find out how to frustrate attackers, not users with RBA in this guide.

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What Is Spatial Computing and How It Is Reshaping Industries?

Noah Davidson — Fri, 23 Feb 2024 15:40:31 +0000

The last few decades have been characterized by significant tech innovations like artificial intelligence, 3D printing, and driverless cars. As we progress into the current decade, another massive shift in computing is unfolding right before us – spatial computing. This new technology holds enormous potential to change how industries optimize their operations. In this article, we answer questions like what is spatial computing? What are the key elements of this technology, and how will it affect industries in future?

What is Spatial Computing?

This refers to digitization of activities that involve people, machines, objects, and environments where they take place. It merges the digital and real worlds to create a more immersive experience for users. Combining technologies like virtual reality (VR), augmented reality (AR), and mixed reality (MR), spatial analysis technology facilitates seamless interactions between computers and users in a 3-dimensional (3D) world.

What are the Main Elements of Spatial Computing?

Computing spatial analysis technologies have four main elements:

1. Augmented Reality (AR)

Space computing uses augmented reality to improve user perception and interaction by overlaying objects and digital information into the real world. AR applications leverage this technology to track user position and orientation for improved interaction with virtual objects.

2. Virtual Reality (VR)

Virtual reality immerses users in computer-generated environments. It’s ideal for simulating real-world activities. But, what is spatial computing technology with regard to virtual reality? Space computing utilizes VR to add natural interactions to virtual environments by tracking body and head movements.

3. 3-D Scanning

Computer spatial analysis leverages photogrammetry or 3D scanning to capture real-world objects and scenes. This results in the production of detailed 3D models with the right shape, texture, and color for immersive 3D user experiences.

4. 3-D Modeling

Mixed reality uses augmented reality to improve user perception and interaction by overlaying objects and digital information into the real world. AR applications leverage this technology to track user position and orientation to improve interactions with virtual objects.

Spatial Computing Use Cases that are Reshaping Industries

What is spatial computing enabling companies to do? We take an in-depth look at six use cases of computer vision spatial analysis and their application across industries:

1. Redefining Employee Training

Space computing is redefining employee training with its immersive and highly interactive capabilities. Using this technology, companies can develop virtual training environments that are very similar to scenarios in the real world. In such environments, employees learn by doing as opposed to observing. For example, pilots can acquire aircraft flying skills through virtual simulations that resemble real-life flight experiences. This hands-on, immersive approach accelerates employee learning and improves knowledge retention.

2. Improving Productivity

Mixed reality automates tedious tasks and optimizes workflows. Industries can use it to add intuitiveness to digital content interactions for enhanced efficiency and task completion. For example, the technology gives visual cues on AR glasses to enable factory workers to locate or identify the tools they need. In retail environments, technology can give buyers pleasant shopping experiences by leading them to the products they wish to buy. This blend between the physical world and digital data provides contextual information needed to fast-track decision-making and enhance productivity.

3. Virtual Product Prototyping

What is spatial computing to product development and how will it alter the way industries prototype their products? Companies incur huge costs conducting physical prototyping and testing on new products. Computer vision spatial analysis is changing this by enabling prototyping and testing of products virtually. Product designers and developers can develop, refine, and test product designs in digital environments before producing physical products. By facilitating simulation of real-world product usage and conditions, spatial computing technology allows companies to identify product design issues and resolve them early in the development process.

4. Improving Workplace Design

Companies in different industries can use spatial analysis computing to create workspaces that foster employee productivity and collaboration. Using this technology, organizations can simulate different office layouts, create digital replicas of real-world workspaces, and evaluate their impact on employee collaboration, workflow, and efficiency. Interior designers and architects use the spatial analysis technology to visualize workspace layouts in 3D, giving companies an office feel before implementing actual changes.

5. Fostering Collaboration

The immersive nature of computer spatial analysis technology gives buyers personalized experiences. For example, the technology can facilitate ‘try before purchase’ experiences for retail shoppers. Buyers can see how a dress would look on them or how furniture pieces would look in living spaces without trying or placing them physically.

6. Better Customer Service

Industrial enterprises can use spatial glasses to create shared digital spaces that allow teams to interact as if they were located in the same physical room. In such environments, teams can exchange digital objects, view each other’s avatars, and engage each other. This enables teams to build stronger relations and enhances collaboration, making it more immersive and highly interactive.

Final Thoughts

Industrial enterprises that understand space computing and its use cases can change their operations significantly. But, what is spatial computing in relation to extended reality technologies like augmented and virtual realities? Space computing leverages these technologies to recreate physical environments in digital spaces for immersive, highly engaging experiences. Industries can use this technology to enhance employee productivity, improve staff training and collaboration, and facilitate virtual product prototyping.

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Duo Makes Strong Security Feel Easy

Cisco — Fri, 23 Feb 2024 14:12:45 +0000

At a glance, discover how Cisco Duo combines robust authentication with a user-friendly experience to make strong security feel easier than ever.

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Duo Third Party Review

PeerSpot — Fri, 23 Feb 2024 13:54:07 +0000

As hybrid work solidifies itself in today’s business landscape and sophisticated threats hound unsuspecting users hoping to gain entry, organizations are looking for robust access management solutions to lock down their environments. But shopping for a comprehensive, end-to-end answer to your access management challenges can pose even more questions: What does a successful rollout look like? What benefits can we expect? Is this a worthwhile investment? To help answer these questions, unearth benefits, and envision what’s possible with more clarity, decision makers turn to real-world customer experiences. Discover what a logistics company gained after deploying Cisco Duo, a leading provider of trusted access solutions.

Read the PeerSpot PeerStudy and learn how you too can harness stronger security that’s user-friendly and cost-effective.

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Security Outcomes for Zero Trust: Adoption, Access, and Automation Trends

Cisco — Fri, 23 Feb 2024 13:30:28 +0000

At Cisco, we believe the promise of a zero trust security strategy should be securing access in a way that frustrates attackers and not users. Zero trust is not a new concept. But today’s rise in adoption reflects a fast-changing reality – boundaries that once existed to secure access to corporate data no longer exist. Business leaders adopting zero trust security are making progress towards building security resilience into their organizations. Despite some confusion over how to implement zero trust principles, many enterprises have made progress with material rewards as proof.

So, what are the secrets of their success?

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The State of Passwordless in the Enterprise Webinar

Cisco — Fri, 23 Feb 2024 12:48:58 +0000

76% of organizations experienced multiple account or credential compromises over the past 12 months, according to ESG’s 2023 research about the state of passwordless authentication in enterprise organizations. Recent prominent MFA-based breaches and friction in the end-user experience have reached the ears of IT and cybersecurity leadership. Now, IT security professionals are looking to address these risks by evaluating phishing-resistant and passwordless authentication methods but lack clarity on the uses cases this technology fulfills.

Join this engaging discussion with Cisco Duo Product Marketer Matt Brooks and ESG Senior Analyst Jack Poller as they unpack the research findings to understand the evolution of multi-factor authentication and passwordless technologies, and how IT security professionals are responding to the changing threats landscape.

You will walk away from this session with a deeper understanding of:

Risks and challenges of MFA and password-based authentication
Pitfalls and struggles of migrating to passwordless authentication
Passwordless authentication methods and best-fit use cases to adopt them

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How to Prevent Attacks that Bypass MFA Webinar

Cisco — Fri, 23 Feb 2024 12:24:32 +0000

The usual first step in establishing a zero trust environment is to deploy Multi-Factor Authentication (MFA) to control access to applications and services. MFA is popular because its protections — which combine what users know (such as a password) with something they have (a token or mobile phone) — make it hard for cybercriminals to gain access to applications and data. But MFA’s widespread use of mobile authenticators and push notifications has made MFA environments themselves a target of hackers. Attackers are increasingly using MFA Fatigue (or prompt bombing) to wear down a user’s patience and diligence with repeated fake MFA prompts, only to ultimately pose as a helpful IT support person who needs the user’s credentials to clear those nuisance alerts up. And then they’re in.

Fortunately, there are ways to defend against these attacks. MFA authentication is effective — but only if it’s properly protected. Knowing how to outsmart attacks designed to bypass MFA is as critical as deploying MFA itself. Watch this on-demand webinar to hear directly from an in-the-field researcher how to strengthen authentication to prevent attacks that try to bypass MFA. You’ll leave with proven best practices and new knowledge about where your MFA gaps may be — and how to fill them.

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