Many industries are digitalizing their work processes. For large and complex projects such as those in mining, the availability of new technologies has enabled companies to better identify sustainable and cost-efficient methods for ore extraction.
Deswik is a leading provider of mine planning solutions, with a portfolio including software for computer-aided 3D mine design, scheduling, operations planning, mining data management and geological mapping.
Deswik software is used by a range of mining professionals, including mining engineers, geologists, surveyors and production superintendents for a range of tasks throughout the mine planning process.
Deswik’s integrated solution seamlessly links mine design and scheduling tasks. Data and workflows are streamlined across teams and systems, enabling management of design solids in the CAD platform. Any changes are dynamically reflected in their associated scheduling tasks in real-time.
The Mining Data Management solution (MDM) is also integrated with the CAD graphical platform, and assists in preserving data integrity and minimizing uncertainties by providing a single source of truth for the entire technical services team. By working with the same information, mines can better facilitate scheduling and shift planning to achieve the critical path.
Calliope Lalousis, Chief Operating Officer at Deswik
Calliope Lalousis, Chief Operating Officer at Deswik, explains that among the software’s strengths are the integration between Deswik’s core products and task-specific modules, along with powerful visualization tools and end-of-month compliance to plan reporting. “Our optimization tools enable users to rapidly generate and evaluate multiple scenarios to extract the highest possible value from the ore deposit, thereby minimizing risks and maximizing the Net Present Value,” she says.
Knowing how to plan for closure and manage waste from the early stages of the mining lifecycle can prove to be a huge advantage for managing risk
“An optimized plan allows for more sustainable and profitable operations with a more efficient extraction process. Good mine planning, however, is not possible unless considered within the context of final mine closure and relinquishment. Knowing how to plan for closure and manage waste from the early stages of the mining lifecycle can prove to be a huge advantage for managing risk, given the costs and environmental constraints involved in mining projects.”
Overall:
The information effectively highlights the crucial role of digitalization in mining, with a focus on Deswik’s leading mine planning solutions. It succinctly describes Deswik’s software portfolio, emphasizing integration capabilities and seamless linkage between mine design and scheduling tasks.
The piece provides a clear understanding of how Deswik’s software benefits various mining professionals throughout the planning process. It emphasizes the integration of the Mining Data Management solution with the CAD platform, highlighting data integrity and a single source of truth.
Also underscores the importance of mine closure planning and waste management from the early stages of the mining lifecycle, showcasing a forward-thinking perspective on industry challenges.
In summary, Deswik providing a concise and positive view of advancements in the mining sector through digitalization.
Emerging technologies are changing the way we live, work, and communicate. One such technology is 5G, the fifth generation of cellular network technology. 5G promises to revolutionize our communication by providing faster speeds, lower latency, and more reliable connectivity. However, like any new technology, 5G has its pros and cons. In this blog post, I will discuss the advantages and disadvantages of 5G technology.
Advantages of 5G Technology
Greater Transmission Speed: One of the most significant advantages of 5G technology is its greater transmission speed. The 5G network spectrum includes the millimeter-wave band, which is expected to be 100 times faster than Fourth Generation (4G) networks with transmission speeds up to 10 Gbps. This inevitably leads to faster transmission of images and videos. A high-resolution video that would normally take a long time to download can now be done in the blink of an eye using the 5G technology.
Lower Latency: Latency refers to the time interval between an order being received and the given instruction being executed. In 5G technology, the delay time is around 4-5 milliseconds (ms) and can be reduced to 1 ms, i.e., ten times less than the latency of 4G technology. This makes it possible for us to watch high-speed virtual reality videos with no interruptions. Due to this particular feature of 5G technology, it can be extremely helpful in fields other than IT, like medicine and construction fields.
Increased Connectivity: Since the 5G network uses more spectrum, it allows connection with a greater number of devices, a hundred times increase in traffic capacity, to be precise. This increased connectivity will enable more devices to connect to the internet simultaneously without any lag or delay.
Better Coverage: Anybody who has tried to get decent cellular service at a crowded concert or sports event knows that it can often be a challenge. Thousands of mobile phones competing for the same cellular service can overwhelm even the best Fourth Generation (4G)/Long-Term Evolution (LTE) networks. However, with 5G, more connectivity can be provided to these areas with lower latency and expanded access for larger groups who may need it.
Improved Communication: With its low latency and high speed, 5G is expected to enable faster and more efficient communication between people and devices. It will also provide ubiquitous connectivity to many more devices.
Disadvantages of 5G Technology
Costly: We need skilled engineers to install and maintain a 5G network. Additionally, the equipment required for a 5G network is costly, resulting in increased costs for arrangement and maintenance phases. Not to forget that 5G smartphones are costly too.
Development: The 5G technology is still under development, resulting in investing more time before it is fully operational without any issues such as security and privacy of the user.
Environmental Degradation: For establishing a 5G network, more towers and energy will be required for proper functioning, which will result in the degradation of forest land and resources, adding another cause to global warming.
Radiations: To establish a 5G network we require switching from Fourth Generation (4G) to Fifth Generation (5G) network which means both networks will operate together causing more radiation that will have long-lasting consequences on humans and wildlife.
Dangerous for Wildlife: Some studies have found that there are certain insects that absorb high frequencies used in Fourth Generation (4G) or Fifth Generation (5G) networks.
In conclusion, emerging technologies like 5G have their pros and cons. While they offer significant advantages like greater transmission speed, lower latency, increased connectivity, better coverage, and improved communication; they also pose significant risks like being costly, under development, environmental degradation due to increased towers and energy requirements for proper functioning; radiation causing long-lasting consequences on humans and wildlife; being dangerous for wildlife.
I decided to do more research about this topic rather than use AI-generated text. Bing AI was helpful with providing guidance to my research, however, it was less thorough when being asked prompts such as, “What speeds can new 5G technology perform at as compared to old 3G technology we had years ago?”. I didn’t necessarily agree with every article and the points that were being driven. Some argue that the rapid deployment of 5G infrastructure may pose environmental concerns due to increased energy consumption and electronic waste. Additionally, there are privacy and security concerns related to the vast amount of data transmitted through 5G networks, raising questions about data protection and surveillance. There are many mixed opinions about this topic and it is hard to trust a single source and outline where biases lay.
AI art production is a controversial topic that has sparked debates among artists, critics, and the general public. Some see AI as a powerful tool that can enhance human creativity and generate novel and original works of art. Others view AI as a threat that can undermine the value and meaning of human art and creativity. In this article, I will examine some of the arguments for and against AI art production, and offer my own perspective on this issue.
One of the main arguments in favor of AI art production is that it can expand the possibilities of artistic expression and exploration. AI can create images, music, text, and other forms of art that humans may not be able to imagine or produce on their own. AI can also learn from large datasets of existing art and generate new variations, combinations, and styles that can inspire human artists. For example, Dall-E 2, an AI image generator developed by OpenAI, can produce realistic and surreal images based on any text prompt, such as “a sea otter in the style of Girl with a Pearl Earring” or “Gollum from The Lord of the Rings feasting on a slice of watermelon” 1. Some of these images can be considered as artistic and creative, and may even evoke emotions and meanings in the viewers.
Another argument in favor of AI art production is that it can democratize the access and participation in art and culture. AI can lower the barriers of entry and cost for creating and consuming art, and allow more people to express themselves and enjoy art. AI can also enable collaboration and interaction between human and machine artists, and foster new forms of art and culture. For instance, Midjourney, an AI art platform, allows users to create and share AI-generated images using text prompts, and also edit, remix, and comment on other users’ creations 2. Midjourney claims that its mission is to “empower anyone to create and explore art” and that it is “building a community of creators who are passionate about AI and art” 2.
However, not everyone is enthusiastic about AI art production. Some of the main arguments against it are that it can diminish the quality and authenticity of art and creativity. AI can produce art that is superficial, derivative, and lacking in originality and intention. AI can also copy and exploit the work of human artists without their consent and recognition, and violate their intellectual property rights. For example, some AI art generators, such as Deep Dream Generator and Stable Diffusion, rely on databases of already existing art and text to create images from prompts 3. These databases may contain pirated or licensed images that belong to other artists, and the AI may not properly credit or compensate them. Some human artists, such as children’s illustrators, have expressed their concerns and frustrations about the legality and ethics of AI art generators, and launched an online campaign called #NotoAIArt 3.
Another argument against AI art production is that it can devalue and replace the role and skill of human artists and creatives. AI can generate art faster, cheaper, and more efficiently than humans, and may outperform and outsmart them in some tasks and domains. AI can also automate and standardize the process and outcome of art production, and reduce the need and demand for human art and creativity. For example, some AI tools, such as GPT-3, Imagen Video, and Lensa, can generate text, video, and audio content that can be used for various purposes, such as journalism, education, entertainment, and marketing 4. Some critics have predicted that AI will eventually eliminate creative jobs, undermine human creativity, and erode the cultural and social value of art 4.
My own view on AI art production is that it is neither a blessing nor a curse, but rather a challenge and an opportunity for human art and creativity. I think that AI can be a useful and powerful tool that can augment and complement human art and creativity, but not replace or surpass it. I think that AI can create art that is impressive and interesting, but not meaningful and expressive. I think that AI can learn from and collaborate with human artists, but not imitate or compete with them. I think that AI can democratize and diversify art and culture, but not trivialize or homogenize them.
Therefore, I think that the key to AI art production is not to reject or embrace it, but to regulate and integrate it. I think that we need to establish clear and fair rules and standards for the use and development of AI art tools, and protect the rights and interests of human artists and consumers. I think that we need to educate and empower human artists and creatives to use AI art tools effectively and responsibly, and enhance their skills and talents. I think that we need to appreciate and celebrate the diversity and uniqueness of human and machine art, and foster a culture of mutual respect and collaboration. I think that we need to recognize and embrace the potential and limitations of AI art production, and explore its implications and possibilities for the future of art and creativity.
China has unveiled a new supercomputer, the Sunway SW26010 Pro, which is four times faster than its predecessor. The chip is based on a new architecture that is designed for high-performance computing (HPC) and is expected to be used for a wide range of applications, including scientific research, national security, and artificial intelligence.
The Sunway SW26010-Pro processor and supercomputers based on it first became known back in 2021, but only this year at a high-performance computing conference did the SC23 developer publicly demonstrate this chip and talk about its architecture. The maximum FP64 performance of each Sunway SW26010-Pro is 13.8 teraflops – for comparison, the 96-core AMD EPYC 9654 is about 5.4 teraflops. Sunway SW26010-Pro is based on a completely new proprietary RISC architecture – it includes six core groups (CG) and a Protocol Processing Unit (PPU). Each CG cluster combines 64 computing cores (Compute Processing Elements – CPE) with a 512-bit vector engine, 256 KB of ultra-fast data cache and 16 KB of instructions; one management core (Management Processing Element – MPE) – superscalar out-of-order core with a vector engine, 32 KB L1 cache for data and instructions, 512 KB L2 cache; as well as a 128-bit DDR4-3200 memory interface.
Where it can be used?
This groundbreaking supercomputer promises to revolutionize a diverse range of fields, from scientific research and national security to artificial intelligence and drug discovery. Its immense computational power will empower scientists to tackle intricate scientific problems, such as molecular modeling and weather forecasting. In the realm of national security, the supercomputer’s capabilities will enhance intelligence gathering and threat analysis. And in the burgeoning field of artificial intelligence, the SW26010 Pro will serve as a powerful tool for developing advanced algorithms and training sophisticated AI models.
In summary, China’s entry into the field of high-performance computing (HPC) has attracted global attention and sparked discussions about the implications of this technological advancement. Advocates of China’s supercomputing capabilities emphasize the potential for scientific breakthroughs and technological innovation that this achievement could facilitate. They envision a future where China’s HPC capabilities contribute to the advancement of fields such as medicine, energy, and environmental protection. In my humble opinion, the rapid development of China’s high-performance computing capabilities is indeed remarkable and has the potential to significantly impact various scientific and technological domains. However, it’s important to ensure that ethical considerations, data privacy, and security are carefully addressed as this technology continues to advance.
We all know the existence of deepfake technology but recently there has been many articles about how people have misused deepfake. Some examples are: revenge, p0rnography, political agendas, spreading of misinformation, defamation, etc. Deepfake was a term coined for synthetic media in 2017 and its technology has only improved since then. So why is this technology still around and why is it not banned if there are so many disadvantages? Deepfake technology has the potential to revolutionize many industries and bring about a number of benefits. Here are some of the potential benefits of deepfakes:
1. Enhanced creativity and storytelling: Deepfakes can be used to create hyper-realistic content that would be impossible or impractical to produce using traditional methods. This can open up new possibilities for filmmakers, artists, and other creative professionals. For example, deepfakes could be used to create historical dramas featuring actors who are no longer alive, or to bring fictional characters to life in a more realistic way. 2. Personalized marketing and education: Deepfakes can be used to personalize marketing messages and educational materials. For example, a company could use deepfakes to create personalized video ads that feature a customer’s favorite celebrity endorsing the product. Or, a school could use deepfakes to create interactive simulations that help students learn about complex concepts. 3. Improved accessibility and inclusivity: Deepfakes can be used to make content more accessible to people with disabilities. For example, deepfakes could be used to sign language interpretation into videos, or to create audio descriptions of images and videos for people who are blind. Deepfakes could also be used to make content more inclusive, by allowing people to see themselves represented in a wider variety of roles and situations. 4. Enhanced language learning: Deepfakes can be used to create immersive language learning experiences. For example, a language learner could use deepfakes to watch a movie or TV show in their target language with the voices of actors they recognize. This could help them to learn the language more quickly and effectively. 5. Preservation of historical and cultural artifacts: Deepfakes can be used to preserve historical and cultural artifacts. For example, deepfakes could be used to restore old films and videos, or to create virtual reality experiences that allow people to visit historical landmarks.
Deepfake technology, while having potential benefits, also raises concerns about its potential misuse and negative impacts. Disadvantages of deepfakes include:
1. Misinformation and Manipulation: Deepfakes can be used to create and spread misinformation, making it difficult to distinguish between real and fake content. This can be used to manipulate public opinion, influence elections, and damage reputations. For instance, deepfakes could be used to create fake videos of politicians making false statements or celebrities endorsing products they have never used.
2. Erosion of Trust: As deepfakes become more sophisticated and difficult to detect, they can erode public trust in digital media, making it harder to verify the authenticity of information. This can lead to increased skepticism and cynicism, and a decline in the overall quality of online information. 3. Privacy Violations: Deepfakes can be used to create non-consensual pornography or other harmful content featuring individuals’ faces or voices without their permission. This can cause significant emotional distress and damage to individuals’ reputations. Deepfakes can also be used to impersonate individuals to access sensitive information or engage in fraudulent activities. 4. Criminal Activities: Deepfakes can facilitate criminal activities such as fraud, blackmail, and extortion. For example, deepfakes could be used to create fake videos of CEOs making false announcements to manipulate stock prices or to blackmail individuals with compromising images or videos. 5. Social Disruption: Deepfakes can be used to sow discord and social unrest by spreading misinformation, inciting violence, or undermining trust in institutions. For instance, deepfakes could be used to create fake videos of religious leaders making inflammatory statements or to spread false rumors about political figures.
It is crucial to develop safeguards and ethical guidelines to ensure that deepfakes are used responsibly and to minimize their potential for harm. This includes developing detection technologies, raising public awareness about deepfakes, and establishing clear legal and ethical frameworks for their use. It goes without saying that there are more regulatory boundaries we have to explore as the technology improves, which is evident in India, Virginia, United Kingdom. However, it is not enough to just ban the technology taking into account the benefits it presents. As a growing society it does not send a good message if we only ban new technology, this could hinder growth or even create more wayward behaviour in people.
Cloud security is a collection of procedures and technology designed to address external and internal threats to business security. Organizations need cloud security as they move toward their digital transformation strategy and incorporate cloud-based tools and services as part of their infrastructure.
The terms digital transformation and cloud migration have been used regularly in enterprise settings over recent years. While both phrases can mean different things to different organizations, each is driven by a common denominator: the need for change.
As enterprises embrace these concepts and move toward optimizing their operational approach, new challenges arise when balancing productivity levels and security. While more modern technologies help organizations advance capabilities outside the confines of on-premise infrastructure, transitioning primarily to cloud-based environments can have several implications if not done securely.
Striking the right balance requires an understanding of how modern-day enterprises can benefit from the use of interconnected cloud technologies while deploying the best cloud security practices.
What is cloud computing?
The “cloud” or, more specifically, “cloud computing” refers to the process of accessing resources, software, and databases over the Internet and outside the confines of local hardware restrictions. This technology gives organizations flexibility when scaling their operations by offloading a portion, or majority, of their infrastructure management to third-party hosting providers.
The most common and widely adopted cloud computing services are:
IaaS (Infrastructure-as-a-Service): A hybrid approach, where organizations can manage some of their data and applications on-premise while relying on cloud providers to manage servers, hardware, networking, virtualization, and storage needs.
PaaS (Platform-as-a-Service): Gives organizations the ability to streamline their application development and delivery by providing a custom application framework that automatically manages operating systems, software updates, storage, and supporting infrastructure in the cloud.
SaaS (Software-as-a-Service): Cloud-based software hosted online and typically available on a subscription basis. Third-party providers manage all potential technical issues, such as data, middleware, servers, and storage, minimizing IT resource expenditures and streamlining maintenance and support functions.
Why is cloud security important?
In modern-day enterprises, there has been a growing transition to cloud-based environments and IaaS, Paas, or SaaS computing models. The dynamic nature of infrastructure management, especially in scaling applications and services, can bring a number of challenges to enterprises when adequately resourcing their departments. These as-a-service models give organizations the ability to offload many of the time-consuming, IT-related tasks.
As companies continue to migrate to the cloud, understanding the security requirements for keeping data safe has become critical. While third-party cloud computing providers may take on the management of this infrastructure, the responsibility of data asset security and accountability doesn’t necessarily shift along with it.
By default, most cloud providers follow best security practices and take active steps to protect the integrity of their servers. However, organizations need to make their own considerations when protecting data, applications, and workloads running on the cloud.
Security threats have become more advanced as the digital landscape continues to evolve. These threats explicitly target cloud computing providers due to an organization’s overall lack of visibility in data access and movement. Without taking active steps to improve their cloud security, organizations can face significant governance and compliance risks when managing client information, regardless of where it is stored.
Cloud security should be an important topic of discussion regardless of the size of your enterprise. Cloud infrastructure supports nearly all aspects of modern computing in all industries and across multiple verticals.
However, successful cloud adoption is dependent on putting in place adequate countermeasures to defend against modern-day cyberattacks. Regardless of whether your organization operates in a public, private, or hybrid cloud environment, cloud security solutions and best practices are a necessity when ensuring business continuity.
What are some cloud security challenges?
Lack of visibility It’s easy to lose track of how your data is being accessed and by whom, since many cloud services are accessed outside of corporate networks and through third parties.
Multitenancy Public cloud environments house multiple client infrastructures under the same umbrella, so it’s possible your hosted services can get compromised by malicious attackers as collateral damage when targeting other businesses.
Access management and shadow IT While enterprises may be able to successfully manage and restrict access points across on-premises systems, administering these same levels of restrictions can be challenging in cloud environments. This can be dangerous for organizations that don’t deploy bring-your-own device (BYOD) policies and allow unfiltered access to cloud services from any device or geolocation.
Compliance Regulatory compliance management is oftentimes a source of confusion for enterprises using public or hybrid cloud deployments. Overall accountability for data privacy and security still rests with the enterprise, and heavy reliance on third-party solutions to manage this component can lead to costly compliance issues.
Misconfigurations Misconfigured assets accounted for 86% of breached records in 2019, making the inadvertent insider a key issue for cloud computing environments. Misconfigurations can include leaving default administrative passwords in place, or not creating appropriate privacy settings.
What types of cloud security solutions are available?
Identity and access management (IAM) Identity and access management (IAM) tools and services allow enterprises to deploy policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services. The core functionality of IAM is to create digital identities for all users so they can be actively monitored and restricted when necessary during all data interactions
Data loss prevention (DLP) Data loss prevention (DLP) services offer a set of tools and services designed to ensure the security of regulated cloud data. DLP solutions use a combination of remediation alerts, data encryption, and other preventative measures to protect all stored data, whether at rest or in motion.
Security information and event management (SIEM) Security information and event management (SIEM) provides a comprehensive security orchestration solution that automates threat monitoring, detection, and response in cloud-based environments. Using artificial intelligence (AI)-driven technologies to correlate log data across multiple platforms and digital assets, SIEM technology gives IT teams the ability to successfully apply their network security protocols while being able to quickly react to any potential threats.
Business continuity and disaster recovery Regardless of the preventative measures organizations have in place for their on-premise and cloud-based infrastructures, data breaches and disruptive outages can still occur. Enterprises must be able to quickly react to newly discovered vulnerabilities or significant system outages as soon as possible. Disaster recovery solutions are a staple in cloud security and provide organizations with the tools, services, and protocols necessary to expedite the recovery of lost data and resume normal business operations.
An overview of cloud security
Cloud security is a collection of procedures and technology designed to address external and internal threats to business security. Organizations need cloud security as they move toward their digital transformation strategy and incorporate cloud-based tools and services as part of their infrastructure.
The terms digital transformation and cloud migration have been used regularly in enterprise settings over recent years. While both phrases can mean different things to different organizations, each is driven by a common denominator: the need for change.
As enterprises embrace these concepts and move toward optimizing their operational approach, new challenges arise when balancing productivity levels and security. While more modern technologies help organizations advance capabilities outside the confines of on-premise infrastructure, transitioning primarily to cloud-based environments can have several implications if not done securely.
Striking the right balance requires an understanding of how modern-day enterprises can benefit from the use of interconnected cloud technologies while deploying the best cloud security practices. Learn more about cloud security solutions What is cloud computing?
The “cloud” or, more specifically, “cloud computing” refers to the process of accessing resources, software, and databases over the Internet and outside the confines of local hardware restrictions. This technology gives organizations flexibility when scaling their operations by offloading a portion, or majority, of their infrastructure management to third-party hosting providers.
The most common and widely adopted cloud computing services are:
IaaS (Infrastructure-as-a-Service): A hybrid approach, where organizations can manage some of their data and applications on-premise while relying on cloud providers to manage servers, hardware, networking, virtualization, and storage needs.
PaaS (Platform-as-a-Service): Gives organizations the ability to streamline their application development and delivery by providing a custom application framework that automatically manages operating systems, software updates, storage, and supporting infrastructure in the cloud.
SaaS (Software-as-a-Service): Cloud-based software hosted online and typically available on a subscription basis. Third-party providers manage all potential technical issues, such as data, middleware, servers, and storage, minimizing IT resource expenditures and streamlining maintenance and support functions.
Why is cloud security important?
In modern-day enterprises, there has been a growing transition to cloud-based environments and IaaS, Paas, or SaaS computing models. The dynamic nature of infrastructure management, especially in scaling applications and services, can bring a number of challenges to enterprises when adequately resourcing their departments. These as-a-service models give organizations the ability to offload many of the time-consuming, IT-related tasks.
As companies continue to migrate to the cloud, understanding the security requirements for keeping data safe has become critical. While third-party cloud computing providers may take on the management of this infrastructure, the responsibility of data asset security and accountability doesn’t necessarily shift along with it.
By default, most cloud providers follow best security practices and take active steps to protect the integrity of their servers. However, organizations need to make their own considerations when protecting data, applications, and workloads running on the cloud.
Security threats have become more advanced as the digital landscape continues to evolve. These threats explicitly target cloud computing providers due to an organization’s overall lack of visibility in data access and movement. Without taking active steps to improve their cloud security, organizations can face significant governance and compliance risks when managing client information, regardless of where it is stored.
Cloud security should be an important topic of discussion regardless of the size of your enterprise. Cloud infrastructure supports nearly all aspects of modern computing in all industries and across multiple verticals.
However, successful cloud adoption is dependent on putting in place adequate countermeasures to defend against modern-day cyberattacks. Regardless of whether your organization operates in a public, private, or hybrid cloud environment, cloud security solutions and best practices are a necessity when ensuring business continuity.What are some cloud security challenges?
Lack of visibility It’s easy to lose track of how your data is being accessed and by whom, since many cloud services are accessed outside of corporate networks and through third parties.
Multitenancy Public cloud environments house multiple client infrastructures under the same umbrella, so it’s possible your hosted services can get compromised by malicious attackers as collateral damage when targeting other businesses.
Access management and shadow IT While enterprises may be able to successfully manage and restrict access points across on-premises systems, administering these same levels of restrictions can be challenging in cloud environments. This can be dangerous for organizations that don’t deploy bring-your-own device (BYOD) policies and allow unfiltered access to cloud services from any device or geolocation.
Compliance Regulatory compliance management is oftentimes a source of confusion for enterprises using public or hybrid cloud deployments. Overall accountability for data privacy and security still rests with the enterprise, and heavy reliance on third-party solutions to manage this component can lead to costly compliance issues.
Misconfigurations Misconfigured assets accounted for 86% of breached records in 2019, making the inadvertent insider a key issue for cloud computing environments. Misconfigurations can include leaving default administrative passwords in place, or not creating appropriate privacy settings.
What types of cloud security solutions are available?
Identity and access management (IAM) Identity and access management (IAM) tools and services allow enterprises to deploy policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services. The core functionality of IAM is to create digital identities for all users so they can be actively monitored and restricted when necessary during all data interactions
Data loss prevention (DLP) Data loss prevention (DLP) services offer a set of tools and services designed to ensure the security of regulated cloud data. DLP solutions use a combination of remediation alerts, data encryption, and other preventative measures to protect all stored data, whether at rest or in motion.
Security information and event management (SIEM) Security information and event management (SIEM) provides a comprehensive security orchestration solution that automates threat monitoring, detection, and response in cloud-based environments. Using artificial intelligence (AI)-driven technologies to correlate log data across multiple platforms and digital assets, SIEM technology gives IT teams the ability to successfully apply their network security protocols while being able to quickly react to any potential threats.
Business continuity and disaster recovery Regardless of the preventative measures organizations have in place for their on-premise and cloud-based infrastructures, data breaches and disruptive outages can still occur. Enterprises must be able to quickly react to newly discovered vulnerabilities or significant system outages as soon as possible. Disaster recovery solutions are a staple in cloud security and provide organizations with the tools, services, and protocols necessary to expedite the recovery of lost data and resume normal business operations.
How should you approach cloud security?
The way to approach cloud security is different for every organization and can be dependent on several variables. However, the National Institute of Standards and Technology (NIST) has made a list of best practices that can be followed to establish a secure and sustainable cloud computing framework.
The NIST has created necessary steps for every organization to self-assess their security preparedness and apply adequate preventative and recovery security measures to their systems. These principles are built on the NIST’s five pillars of a cybersecurity framework: Identify, Protect, Detect, Respond, and Recover.
Another emerging technology in cloud security that supports the execution of NIST’s cybersecurity framework is cloud security posture management (CSPM). CSPM solutions are designed to address a common flaw in many cloud environments – misconfigurations.
Cloud infrastructures that remain misconfigured by enterprises or even cloud providers can lead to several vulnerabilities that significantly increase an organization’s attack surface. CSPM addresses these issues by helping to organize and deploy the core components of cloud security. These include identity and access management (IAM), regulatory compliance management, traffic monitoring, threat response, risk mitigation, and digital asset management.
Overall:
The breakdown of common cloud computing services (IaaS, PaaS, and SaaS) adds clarity, aiding understanding of modern enterprise models. Adeptly addresses challenges, including lack of visibility, multitenancy issues, access management complexities, compliance concerns, and misconfigurations, offering valuable insights for organizations.
The recommended cloud security solutions (IAM, DLP, SIEM, Business Continuity, and Disaster Recovery) provide a comprehensive approach to risk mitigation. The article’s inclusion of NIST principles and the emerging technology CSPM further enriches its content.
In summary, the article serves as a valuable resource for organizations navigating cloud security complexities. Its blend of informative content, practical solutions, and insights into emerging technologies makes it an effective guide.
As the world’s leading Internet television network with over 160 million members in over 190 countries, our members enjoy hundreds of millions of hours of content per day, including original series, documentaries and feature films. Of course, all our all-time favourites are right on our hands, and that is where machine learning has taken it’s berth on the podium. This is where we will dive into Machine Learning.
MONEY HEIST(2017)
Machine learning impacts many exciting areas throughout our company. Historically, personalization has been the most well-known area, where machine learning powers our recommendation algorithms. We’re also using machine learning to help shape our catalogue of movies and TV shows by learning characteristics that make content successful. Machine Learning also enables us by giving the freedom to optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network.
I believe that using machine learning as a whole can open up a lot of perspectives in our lives, where we need to push forward the state-of-the-art. This means coming up with new ideas and testing them out, be it new models and algorithms or improvements to existing ones.
Operating a large-scale recommendation system is a complex undertaking: it requires high availability and throughput, involves many services and teams, and the environment of the recommender system changes every second. In this we will introduce RecSysOps a set of best practices and lessons that we learned while operating large-scale recommendation systems at Netflix. These practices helped us to keep our system healthy:
1) reducing our firefighting time, 2) focusing on innovations and 3) building trust with our stakeholders.
RecSysOps has four key components: issue detection, issue prediction, issue diagnosis and issue resolution.
Within the four components of RecSysOps, issue detection is the most critical one because it triggers the rest of steps. Lacking a good issue detection setup is like driving a car with your eyes closed.
ALL YOUR FAVOURITE MOVIES AND TV SHOWS RIGHT HERE!
The very first step is to incorporate all the known best practices from related disciplines, as creating recommendation systems includes procedures like software engineering and machine learning, this includes all DevOps and MLOps practices such as unit testing, integration testing, continuous integration, checks on data volume and checks on model metrics.
The second step is to monitor the system end-to-end from your perspective. In a large-scale recommendation system there are many teams that often are involved and from the perspective of an ML team we have both upstream teams (who provide data) and downstream teams (who consume the model).
The third step for getting a comprehensive coverage is to understand your stakeholders’ concerns. The best way to increase the coverage of the issue detection component. In the context of our recommender systems, they have two major perspectives: our members and items.
Detecting production issues quickly is great but it is even better if we can predict those issues and fix them before they are in production. For example, proper cold-starting of an item (e.g. a new movie, show, or game) is important at Netflix because each item only launches once, just like Zara, after the demand is gone then a new product launches.
Once an issue is identified with either one of detection or prediction models, next phase is to find the root cause. The first step in this process is to reproduce the issue in isolation. The next step after reproducing the issue is to figure out if the issue is related to inputs of the ML model or the model itself. Once the root cause of an issue is identified, the next step is to fix the issue. This part is similar to typical software engineering: we can have a short-term hotfix or a long-term solution. Beyond fixing the issue another phase of issue resolution is improving RecSysOps itself. Finally, it is important to make RecSysOps as frictionless as possible. This makes the operations smooth and the system more reliable.
NETFLIX: A BLESSING IN DISGUISE
To conclude In this blog post I introduced RecSysOps with a set of best practices and lessons that we’ve learned at Netflix. I think these patterns are useful to consider for anyone operating a real-world recommendation system to keep it performing well and improve it over time. Overall, putting these aspects together has helped us significantly reduce issues, increased trust with our stakeholders, and allowed us to focus on innovation.
[1] Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, and D. Sculley. 2017. The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. In Proceedings of IEEE Big Data.Google Scholar
[2] Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett(Eds.). Curran Associates, Inc., 4765–4774.
Adobe Project Stardust is a new video and photo editing tool that is still under development. It is designed to be a more powerful and versatile tool than Adobe After Effects, and it has the potential to revolutionize the way that videos are edited, aimed at revolutionizing the way images are processed and manipulated within Adobe software suites, like Photoshop. Leveraging advanced AI and machine learning capabilities, the project aimed to offer users more efficient and intuitive editing tools, automating complex tasks and enabling users to achieve impressive results more effortlessly. Features speculated or announced earlier might include improved object selection, background removal, content-aware filling, and enhanced photo manipulation through smart algorithms. The integration of AI was expected to streamline workflows and enhance creativity for photographers and graphic designers.
Project Stardust was anticipated to bring several potential advantages to Adobe’s suite of photo editing tools:
Advantages: AI-Driven Efficiency: Project Stardust aimed to leverage artificial intelligence to automate complex editing tasks, making the editing process faster and more efficient. This could streamline workflows and save considerable time for photographers and designers. Enhanced Editing Capabilities: The AI-powered engine was expected to introduce advanced features like improved object selection, intelligent background removal, content-aware filling, 3D effects, motion graphics, visual effects, and other smart editing tools. These enhancements could empower users to achieve more sophisticated and polished results in their editing endeavors. User-Friendly Interface: By simplifying complex editing processes through AI-driven automation, Project Stardust might offer a more intuitive and user-friendly interface. This could potentially lower the barrier of entry for newcomers to photo editing while providing seasoned users with more powerful tools. Product Competitiveness: Stardust can render effects much faster than After Effects, which can save editors a lot of time. Additionally, Stardust is more stable than After Effects, and it is less likely to crash. This is important for editors who are working on complex projects with tight deadlines.
However, with any technological advancement, there might also be potential disadvantages: Disadvantages: Under Development: Project Stardust is still under development which means that there are some bugs and missing features. Additionally, Stardust can be difficult to learn, especially for editors who are not familiar with After Effects. Cost: It is more expensive than After Effects, and it is not available as part of the Creative Cloud subscription. This means that it may not be a good choice for editors who are on a budget, it is also not part of the Creative Cloud subscription Learning Curve: While the intention of AI-powered tools is to simplify the editing process, there might be a learning curve associated with understanding and effectively utilizing these new features. Users might need time to adapt to the changes and fully harness the capabilities of Project Stardust. Over-Reliance on Automation: Depending too heavily on automated tools might lead to a lack of creativity or personal touch in the editing process. Relying solely on AI-powered features might limit the creative expression of users who prefer a more hands-on approach to editing. Possible Errors or Inaccuracies: AI systems are not infallible and might occasionally make mistakes or produce inaccurate results. Users should be cautious and ready to manually intervene if the AI-powered tools generate unexpected or incorrect edits.
Overall, Adobe Project Stardust is a powerful and versatile video editing tool that has the potential to revolutionize the way that videos are edited. However, it is still under development, and it can be difficult to learn and expensive.
Artificial intelligence (AI) is rapidly transforming many industries, and customer service is no exception. AI-powered chatbots and virtual assistants are becoming increasingly sophisticated, and they are now able to handle a wide range of customer inquiries.
AI customer service offers a number of benefits for both businesses and customers. For businesses, AI can help to reduce costs, improve efficiency, and scale customer support operations. As for today 36% of businesses use a chatbot to generate more leads [1]. For customers, AI can provide 24/7 support, resolve issues more quickly, and personalize the customer experience. Ubisend survey stated that 48% of customers don’t care whether they get their information from bots or call centers.[2]
Use cases for AI customer service
AI customer service can be used in a variety of ways, including:
Answering customer questions: AI chatbots can be used to answer common customer questions about products, services, and policies. This can free up human agents to focus on more complex issues.
Resolving customer issues: AI can also be used to resolve customer issues, such as resetting passwords, troubleshooting technical problems, and processing returns.
Personalizing the customer experience: AI can be used to personalize the customer experience by recommending products and services based on the customer’s past purchase history and interests.
Benefits of AI customer service
AI customer service offers a number of benefits for both businesses and customers, including:
Reduced costs: AI can help to reduce customer service costs by automating tasks that would otherwise be performed by human agents. Cutting labor costs by reducing the reliance on human intervention leads to as much as 30% decline in customer support service fees[3].
Improved efficiency: AI can help to improve customer service efficiency by resolving issues more quickly and accurately. A study found that 62% of consumers would talk to a chatbot than wait for a human agent[4]
Scaled support: AI can help businesses to scale their customer support operations by providing 24/7 support and handling a high volume of inquiries.
24/7 support: AI chatbots and virtual assistants can provide customer support 24 hours a day, 7 days a week. This is especially beneficial for businesses that operate in multiple time zones or that have customers who live in different parts of the world.
Faster issue resolution: AI can help to resolve customer issues more quickly by automating tasks and providing access to a vast knowledge base.
Personalized experience: AI can be used to personalize the customer experience by recommending products and services based on the customer’s past purchase history and interests. According to chatbot.com 74% of internet users prefer using chatbots when looking for answers to simple questions[5]
While AI customer service offers a number of benefits, there are also some drawbacks to consider:
Lack of human touch: AI chatbots and virtual assistants may not be able to provide the same level of personalized and empathetic customer service that a human agent can.
Language and context understanding: AI systems can sometimes struggle to understand complex language structures or the context of certain queries. This can lead to misinterpretations and unsatisfactory responses.
Bias: AI systems can be biased, reflecting the biases of the data they are trained on. This can lead to unfair treatment of certain customers.
Job displacement: AI customer service could lead to job displacement, as some tasks currently performed by human agents are automated.
Here are some specific examples of how businesses are using AI to improve customer service:
Amazon: Amazon uses AI to power its Alexa virtual assistant, which allows customers to ask questions, place orders, and manage their accounts using only their voice.
Netflix: Netflix uses AI to recommend movies and TV shows to its customers based on their viewing history.
Spotify: Spotify uses AI to create personalized playlists for its users.
Zendesk: Zendesk offers AI-powered chatbots that can answer customer questions and resolve issues.
Salesforce: Salesforce offers AI-powered customer relationship management (CRM) software that can help businesses track and manage customer interactions.
If you are considering using AI to improve your customer service, there are a few things you should keep in mind:
Start small: Don’t try to implement AI solutions for all of your customer service needs at once. Start by identifying a specific area where AI can make a big impact, such as answering frequently asked questions or routing customer inquiries.
Choose the right AI solution: There are many different AI solutions available, so it is important to choose one that is right for your business. Consider your budget, your customer needs, and your existing customer service infrastructure.
Integrate AI with your existing systems: Make sure that your AI solution is integrated with your existing customer service systems, such as your CRM and help desk software. This will ensure that AI is able to access the data it needs to provide the best possible customer service.
Get feedback from your customers: Once you have implemented AI solutions, it is important to get feedback from your customers to see how they are working. This feedback will help you to identify areas where you can improve.
Conclusion
AI customer service is the future of customer support. By automating tasks, improving efficiency, and scaling support operations, AI can help businesses to reduce costs, improve customer satisfaction, and grow their business.
If you are not already using AI in your customer service operation, now is the time to start. There are a number of AI solutions available, and there is sure to be one that is right for your business.
„Please write techblog about ai customer service”, „please give me 5 different statistics from 5 different sources” and “include these to this blog post so it sounds naturally”
Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are two types of underwater robotic systems that play an increasingly significant role in ocean exploration, scientific research, and various industrial operations. Although both systems are designed to operate underwater, they differ in terms of how they are controlled and the tasks they are capable of performing. Collectively, both AUVs and ROVs are categorized as Unmanned Underwater Vehicles (UUVs).
Project Wilton Iver AUVs, courtesy of our partner, SeeByte
An AUV is an autonomous underwater vehicle that often (but not always) operates independently of direct human control. It is equipped with various sensors, instruments, and navigation systems that allow it to perform a range of tasks, including mapping the ocean floor, collecting environmental data, and conducting scientific surveys at sea. Ideally, AUVs are programmed to perform specific missions and have the ability to make decisions based on real-time data, making them a great candidate for conducting long-term, repetitive missions. However, due to the lack of remote off-grid power solutions, big-data transmissions, and edge-compute capabilities, the current generation of AUVs still have a limited operational reach and require interventions of human operators.
Subsea 7’s AIV performing a mid-water riser inspection using sonar, courtesy of our partner, SeeByte
Remotely Operated Vehicles (ROVs), on the other hand, are underwater robots that are often controlled by a human operator. Like their AUV counterparts, ROVs are also equipped with cameras, lights, and various sensors that allow them to perform tasks (such as inspections, maintenance, and repair on underwater structures and vessels). ROVs can also be equipped with sampling tools and other scientific instruments, making them useful for conducting research missions. ROVs play a very prominent role in deep-sea scientific missions for studying benthic ecosystems, such as during the EV Nautilus cruises. (More on this later in another post.) The main advantage of ROVs is that they allow for direct human control, which can be especially useful in situations where real-time decision-making is required. This makes ROVs ideal for missions that require a high degree of precision and control, such as the inspection of underwater pipelines, the repair of underwater communication cables, or the removal of debris from shipwrecks. Additionally, ROVs can be equipped with manipulator arms and other tools, making them capable of performing tasks that are (currently) not possible with AUVs.
Despite the differences between AUVs and ROVs, both systems play an important role in a variety of industries. In the oil and gas industry, for example, both types of underwater robots are used for exploration and production, as well as for monitoring and maintenance of underwater pipelines and platforms. In scientific research, both AUVs and ROVs are used for oceanographic surveys, as well as for monitoring ocean ecosystems and the effects of climate change.
As the blue tech industry continues to advance, it is likely that UUVs will play an even greater role in ocean exploration, scientific research, and industrial operations in the years to come, making them a pivotal component of the rapidly growing blue economy.
As for me the article is a clear and concise explanation of the differences between AUVs and ROVs, two types of underwater robotic systems that are widely used in the blue economy. It provides a brief overview of the main features, advantages, and disadvantages of each system, as well as some examples of how they are used in various industries and applications. The article also uses relevant images and links to illustrate the concepts and provide more information for the interested readers.
However, the article could also be improved in some ways. For instance, it could provide more details on the current challenges and limitations of AUVs and ROVs, such as the technical, operational, and regulatory issues that affect their performance and deployment. It could also discuss some of the emerging trends and innovations in the field of underwater robotics, such as the development of hybrid systems that combine the features of both AUVs and ROVs, or the use of artificial intelligence and machine learning to enhance the autonomy and capabilities of UUVs. It could also address some of the ethical and social implications of using UUVs in the ocean, such as the potential impacts on the marine environment and biodiversity, or the legal and moral responsibilities of the operators and users of UUVs.
Overall, the article is a good introduction to the topic of underwater robotics, but it could also go deeper and more critical in its analysis and discussion.
