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Intel AI DFI MTH968 embedded system module (SOM)

Intel AI DFI MTH968 embedded system module SOM

DFI has unveiled a new embedded system module that is set to transform the way artificial intelligence (AI) is integrated into industrial computing. The MTH968, equipped with Intel’s latest Core Ultra processor, is a compact powerhouse designed to bring AI capabilities right to the edge of computing, where data is generated and collected. This new module is poised to meet the demands of an AI edge computing market that is expected to grow exponentially in the coming years.

Central to the MTH968’s design is the Intel Meteor Lake processor, a cutting-edge chip that combines a central processing unit (CPU), graphics processing unit (GPU), and a Neural Processor Unit (NPU) all in one. This combination is a significant step forward in terms of energy efficiency and computing power, offering substantial reductions in power usage while simultaneously enhancing the performance of both the CPU and GPU. This is particularly important for applications that require high levels of computing power but have strict energy consumption constraints.

Intel AI DFI MTH968 SOM

One of the most notable features of the MTH968 is its onboard NPU, which enables it to handle sophisticated AI tasks and intricate graphics processing right at the edge. This not only showcases the technological prowess of the module but also offers a cost-effective solution by potentially reducing reliance on cloud data storage, which can lower operational expenses. The MTH968 is designed with the future in mind, catering to the AIoT (Artificial Intelligence of Things) market with customizable options to meet the specific needs of different industries. It operates reliably in a wide range of temperatures and does so without the need for a cooling fan, ensuring consistent performance under various environmental conditions.

The MTH968 doesn’t stop at AI capabilities; it also supports the latest advancements in memory and storage technology, including DDR5 memory and NVMe SSD storage. This means that it can handle demanding computing tasks with impressive speed and efficiency, making it an ideal choice for sectors such as industrial automation, smart transportation, and smart agriculture. In these fields, having robust and responsive computing resources is essential.

The release of the MTH968 comes at a time when the industry is moving towards greater use of 5G, edge computing, and remote control technologies. These developments are changing the way data is processed and managed at the edge of networks, enabling real-time analytics and decision-making. The MTH968 is well-equipped to take advantage of these technologies, providing a powerful and efficient platform for the next generation of industrial applications.

With the introduction of the MTH968, DFI is offering a solution that stands to significantly enhance the capabilities of industrial PCs. By combining a powerful Intel Core Ultra processor with an integrated NPU and support for the latest memory and storage technologies, the MTH968 is set to make a meaningful impact on the AI edge computing market. As industries continue to evolve and adopt more intelligent, interconnected systems, the MTH968 is ready to meet these challenges and drive forward innovation.

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How to Clear System Data on Your Mac (Video)

macOS system data

Are you noticing your Mac running slower than usual, or is your storage space mysteriously disappearing? You’re not alone. Many users find themselves puzzled by the significant amount of system data consuming precious storage space, especially after upgrading to macOS Ventura. Fortunately, there’s a straightforward way to reclaim your Mac’s performance and storage, and it doesn’t require any third-party software.

Understanding the Culprit Behind Storage Woes

System data on a Mac can balloon to surprising sizes, in some cases nearing 130 GB. This data is essentially the cache files that macOS and installed applications use to speed up operations by storing temporary data. While this sounds beneficial, over time, these files can accumulate and take up a significant portion of your hard drive.

A Step-by-Step Guide to Decluttering Your Mac

The process to reduce this bloated system data is simpler than you might think, and it revolves around deleting unnecessary cache files. Here’s how you can do it:

  1. Accessing Cache Files: Use Finder’s “Go to Folder” feature by pressing Shift + Command + G, and type in ~/Library/Caches. This path leads you directly to where most cache files are stored.
  2. Identifying and Deleting Large Files: Within the cache folder, Adobe software cache files often contribute significantly to system data size. Locate these files and delete them. Don’t worry; this won’t affect your Adobe applications’ functionality or settings.
  3. Finalizing the Cleanup: After removing the unnecessary cache files, don’t forget to empty the bin to effectively free up the storage space. This action is what truly concludes the cleanup process.

Ensuring Software Integrity

Many might worry that deleting cache files could lead to loss of important data or settings, particularly with complex software like Adobe After Effects. However, it’s been confirmed that this cleanup method does not affect installed plugins or software settings. It’s a safe and efficient way to enhance your Mac’s performance without compromising on functionality.

Tips for Ongoing Maintenance

While manual cleanup is highly effective, it’s also beneficial to adopt habits that prevent system data from quickly ballooning again:

  • Regularly check and clear cache files, especially after extensive use of memory-intensive applications.
  • Consider using macOS built-in tools to manage storage, which can offer insights into what’s consuming space and how to optimize it.

By understanding the nature of system data and taking proactive steps to manage it, you can significantly improve your Mac’s performance and storage availability. This guide offers a practical approach to managing system data, tailored specifically for macOS Ventura users. By following these steps, you’ll not only reclaim space but also ensure your Mac runs more smoothly, allowing you to work and play without unnecessary hindrances.

Summary

Managing system data on your Mac doesn’t have to be a daunting task. With the right knowledge and a few minutes of your time, you can keep your system lean and efficient. Remember, the key to a well-performing Mac is regular maintenance and being mindful of the files and applications that contribute to system data. By taking control of your system data, you ensure that your Mac stays in top shape, ready to handle all your computing needs with ease.

Source & Image Credit:  Amir Bendabi

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Training AI to use System 2 thinking to tackle more complex tasks

Training AI LLM to use system 2 thinking to tackle more complex tasks

Artificial intelligence seems to be on the brink of another significant transformation nearly every week at the moment, and this week is no exception. As developers, businesses and researchers  dive deeper into the capabilities of large language models (LLMs) like GPT-4, we’re beginning to see a shift in how these systems tackle complex problems. The human brain operates using two distinct modes of thought, as outlined by Daniel Kahneman in his seminal work, “Thinking, Fast and Slow.” The first, System 1, is quick and intuitive, while System 2 is slower, more deliberate, and logical. Until now, AI has largely mirrored our instinctive System 1 thinking, but that’s changing.

In practical terms, System 2 thinking is what you use when you need to think deeply or critically about something. It’s the kind of thinking that requires you to stop and focus, rather than react on instinct or intuition. For example, when you’re learning a new skill, like playing a musical instrument or speaking a foreign language, you’re primarily using System 2 thinking.

Over time, as you become more proficient, some aspects of these skills may become more automatic and shift to System 1 processing. Understanding the distinction between these two systems is crucial in various fields, including decision-making, behavioral economics, and education, as it helps explain why people make certain choices and how they can be influenced or trained to make better ones.

AI System 2 thinking

Researchers are now striving to imbue AI with System 2 thinking to enable deeper reasoning and more reliable outcomes. The current generation of LLMs can sometimes produce answers that seem correct on the surface but lack a solid foundation of analysis. To address this, new methods are being developed. One such technique is prompt engineering, which nudges LLMs to unpack their thought process step by step. This is evident in the “Chain of Thought” prompting approach. Even more advanced strategies, like “Self-Consistency with Chain of Thought” (SCCT) and “Tree of Thought” (ToT), are being explored to sharpen the logical prowess of these AI models.

The concept of collaboration is also being examined as a way to enhance the problem-solving abilities of LLMs. By constructing systems where multiple AI agents work in concert, we can create a collective System 2 thinking model. These agents, when working together, have the potential to outperform a solitary AI in solving complex issues. This, however, introduces new challenges, such as ensuring the AI agents can communicate and collaborate effectively without human intervention.

Other articles you may find of interest on the subject of training large language models :

To facilitate the development of these collaborative AI systems, tools like Autogen Studio are emerging. They offer a user-friendly environment for researchers and developers to experiment with AI teamwork. For example, a problem that might have been too challenging for GPT-4 alone could potentially be resolved with the assistance of these communicative agents, leading to solutions that are not only precise but also logically sound.

What will AI be able to accomplish with System 2 thinking?

As we look to the future, we anticipate the arrival of next-generation LLMs, such as the much-anticipated GPT-5. These models are expected to possess even more advanced reasoning skills and a deeper integration of System 2 thinking. Such progress is likely to significantly improve AI’s performance in scenarios that require complex problem-solving.

The concept of System 2 thinking, as applied to AI and large language models (LLMs), involves the development of AI systems that can engage in more deliberate, logical, and reasoned processing, akin to human System 2 thinking. This advancement would represent a significant leap in AI capabilities, moving beyond quick, pattern-based responses to more thoughtful, analytical problem-solving. Here’s what such an advancement could entail:

  • Enhanced Reasoning and Problem Solving: AI with System 2 capabilities would be better at logical reasoning, understanding complex concepts, and solving problems that require careful thought and consideration. This could include anything from advanced mathematical problem-solving to more nuanced ethical reasoning.
  • Improved Understanding of Context and Nuance: Current LLMs can struggle with understanding context and nuance, especially in complex or ambiguous situations. System 2 thinking would enable AI to better grasp the subtleties of human language and the complexities of real-world scenarios.
  • Reduced Bias and Error: While System 1 thinking is fast, it’s also more prone to biases and errors. By incorporating System 2 thinking, AI systems could potentially reduce these biases, leading to more fair and accurate outcomes.
  • Better Decision Making: In fields like business or medicine, where decisions often have significant consequences, AI with System 2 thinking could analyze vast amounts of data, weigh different options, and suggest decisions based on logical reasoning and evidence.
  • Enhanced Learning and Adaptation: System 2 thinking in AI could lead to improved learning capabilities, allowing AI to not just learn from data, but to understand and apply abstract concepts, principles, and strategies in various situations.
  • More Effective Human-AI Collaboration: With System 2 thinking, AI could better understand and anticipate human needs and behaviors, leading to more effective and intuitive human-AI interactions and collaborations.

It’s important to note that achieving true System 2 thinking in AI is a significant challenge. It requires advancements in AI’s ability to not just process information, but to understand and reason about it in a deeply contextual and nuanced way. This involves not only improvements in algorithmic approaches and computational power but also a better understanding of human cognition and reasoning processes. As of now, AI, including advanced LLMs, primarily operates in a way that’s more akin to human System 1 thinking, relying on pattern recognition and rapid response generation rather than deep, logical reasoning.

The journey toward integrating System 2 thinking into LLMs marks a pivotal moment in the evolution of AI. While there are hurdles to overcome, the research and development efforts in this field are laying the groundwork for more sophisticated and dependable AI solutions. The ongoing dialogue about these methods invites further investigation and debate on the most effective ways to advance System 2 thinking within artificial intelligence.

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How to build an AI home security system using a Raspberry Pi mini PC

AI home security system using a Raspberry Pi mini PC

If you are looking for a project to keep you busy this weekend you might be interested in learning how to create your very own personal AI assistant by installing artificial intelligence onto a Raspberry Pi mini PC. The latest addition to the line of mini PC hardware is the Raspberry Pi 5, a small yet mighty computer, can now be the brains behind your home security and home automation system, thanks to advancements in artificial intelligence (AI). By combining the Raspberry Pi with the Coral AI Edge TPU, you can create a surveillance system that not only watches but understands what it sees.

AI home security system

To start, you’ll need to connect the Coral AI Edge TPU to your Raspberry Pi. This is done through a special accessory called the Pineberry AI hat, which fits onto the Raspberry Pi and uses the PCIe express bus for fast data transfer. This setup is crucial for handling the demands of AI applications. Once the hardware is in place, you’ll install the Raspberry Pi OS, setting the stage for your AI-powered security system.

The next step is to get the software ready. Docker is a tool that will help you run your AI models without any hiccups. It creates separate environments for each application, so they don’t interfere with each other. You’ll also need to set up MQTT, a messaging protocol that lets different parts of your AI system talk to each other seamlessly.

Running AI on a Pi

Here are some other articles you may find of interest on the subject of Raspberry Pi 5 :

For remote access, you’ll want to connect your Raspberry Pi to your Wi-Fi network and configure SSH. SSH allows you to securely log into your Raspberry Pi from another computer, which is incredibly handy for making updates or changes to your AI applications from anywhere.

When it comes to cameras, you have options. Webcams are a simple, cost-effective choice, while IP cameras offer higher quality and more features. Depending on your needs and budget, you can choose the best fit for your system. These cameras will work with Frigate, an open-source software that’s the heart of your AI surveillance system.

Frigate uses machine learning to analyze video footage in real-time. After installing Frigate on your Raspberry Pi, you’ll set it up with a configuration file written in YAML. This will kickstart your AI surveillance, allowing you to monitor your home with a smart, responsive system.

For those who want even more power, you can add more Edge TPUs to your Raspberry Pi. This means you can process several video streams at once, giving you a more comprehensive view of your home’s security. The Raspberry Pi’s PCIe slot isn’t just for AI, though. It can also be used for other upgrades, like adding NVMe storage, which can speed up your device for a range of projects.

By following these steps, you can transform your Raspberry Pi 5 into a cutting-edge tool for home surveillance. The combination of the Coral AI Edge TPU and the Pineberry AI hat opens up a world of possibilities for tech enthusiasts. This setup allows you to run complex AI models efficiently, making it a powerful and affordable choice for not just home security, but for any AI project you can dream up.

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Create a home automation system with Zigbee and Home Assistant

Using Home Assistant and Zima to create a home automation system

Imagine walking into your home and having everything respond to your needs without lifting a finger. Lights adjust to the perfect brightness, blinds open or close to keep your home at the ideal temperature, and your favorite music starts playing as you settle in. This isn’t a scene from a futuristic movie; it’s what today’s smart home technology can offer. At the forefront of this advancement is Home Assistant, a powerful automation platform that can turn your home into a responsive, connected environment.

Home Assistant is an open source home automation that puts local control and privacy first.  it is also perfect to run on a Raspberry Pi, local service or in this case a Zima board. Once set out Home Assistant can be the brain of your home, linking various smart devices and making them work together smoothly. To get started, you’ll need to install it, and many users choose a Zima board for this task. The Zima board is a sturdy piece of hardware that delivers excellent performance and is compatible with a wide range of devices. I found the installation process straightforward, especially after connecting a Sonoff Zigbee 3.0 USB dongle, which allowed my smart devices to communicate over the Zigbee protocol. Zigbee is known for being reliable and energy-efficient, which is ideal for a smart home setup.

When setting up a smart home, you’ll need to decide on a communication protocol. Zigbee and Z-Wave are two of the most popular choices. They both use mesh networks to keep your devices connected, but they operate on different frequencies and support different devices. I chose Zigbee because it supports a wider range of devices, which helps create a more unified smart home system.

Zigbee home automation system

Here are some other articles you may find of interest on the subject of automation and how you can use artificial intelligence to help make automated workflows even more effective :

Home Assistant the brains of your home automation system

The heart of Home Assistant is its central dashboard. This user-friendly interface brings all your smart devices together in one place. It makes it easy to add new devices and set up automation rules. With this dashboard, you have a powerful control panel for your entire smart home.

Home Assistant also offers a service called Home Assistant Cloud. This service allows you to access your smart home remotely and securely. It also includes features like emulated Hue integration, which means you can use voice commands with Amazon Alexa or Google Assistant, even if your devices don’t natively support these services.

While many people start their smart home journey with a Raspberry Pi, in the video above created by TechHut a x86 machines has been used to offer better performance and handle more devices and complex automations. This is important as you expand your smart home and add more devices. The Home Assistant platform is adaptable and powerful, and the possibilities for adding new devices and exploring advanced features are endless. Home Assistant is a robust choice for anyone interested in taking their home automation to the next level.

Zigbee

Zigbee is a specification for a suite of high-level communication protocols used to create personal area networks built from small, low-power digital radios. It’s based on the IEEE 802.15.4 standard, which defines the physical and media access control layers for low-rate wireless personal area networks (LR-WPANs). The key characteristics of Zigbee include its low power consumption, low data rate, and close proximity operation, typically within 10 to 100 meters.

Zigbee is often used in applications that require a low data rate, long battery life, and secure networking. It’s particularly popular in home automation, as it enables the wireless communication between devices like smart thermostats, lighting controls, and security systems. Zigbee networks are mesh networks, meaning each node in the network can communicate with others and relay data, allowing for greater range and reliability.

One of the main advantages of Zigbee is its ability to handle a large number of nodes — up to thousands in a single network. This makes it highly scalable and suitable for complex systems. However, its low data rate means it’s not ideal for applications requiring high bandwidth, such as video streaming.

Zigbee’s security features include 128-bit encryption, which is robust for most applications. However, like any wireless technology, it’s not immune to security vulnerabilities and requires proper setup and maintenance to ensure network security.

Home Assistant has transformed many home into a more intelligent and efficient space. Its straightforward installation, wide protocol support, and intuitive dashboard make it an excellent option for both experienced tech users and those new to smart home technology. If you’re interested in enhancing your home’s capabilities, Home Assistant provides a wealth of opportunities for customization and innovation.

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Building an AI automated appointment booking system

Building an AI automated appointment booking system

In the realm of modern business, where efficiency and customer service are paramount, the advent of AI agents for appointment scheduling marks a significant stride forward. Imagine a tool that not only schedules appointments but also integrates seamlessly with your existing systems, providing a smooth experience for both you and your clients. This is not a distant dream but a tangible reality that can elevate your business operations to new heights.

At the heart of this AI booking system lies an AI agent, a product of cutting-edge technology from Stammer.ai and Go High Level. These platforms are the bedrock upon which a sophisticated appointment booking and management system is built. The AI agent is adept at conversing with users, aligning appointments with real-time availability from a Google Calendar, and capturing essential lead information to feed directly into a CRM system. This integration ensures that every piece of data is where it needs to be, streamlining the management process and enhancing overall productivity.

Facebook AI booking system

If Facebook is not your go to platform check out our other projects and guides providing more information on building your own AI powered automation system to help cut out the mundane tasks in your life :

To set this system in motion, the AI agent is first connected to a Facebook page, which serves as the gateway for user interactions. Following this, an integration with Google Calendar is established through an API, ensuring that the scheduling remains accurate and up-to-date. Stammer.ai’s white-labeling feature is particularly beneficial for agencies, as it allows for the branding of the chatbot software. This means agencies can offer a personalized experience to their clients while leveraging the robust chatbot creation tools provided by Stammer.ai.

The process of creating workflows in Go High Level is designed to be straightforward, promoting the ease of automation. By automating responses and data collection, the AI agent not only becomes more responsive but also excels at managing information with remarkable efficiency. Before fully launching, it is essential to test the AI agent on your Facebook page. This step is crucial to evaluate its performance in real-time and to make any necessary adjustments. The goal is to ensure that the system operates flawlessly during actual user interactions.

The advantages of using an AI agent for appointment scheduling are significant. It takes over a task that traditionally consumes a considerable amount of time, freeing you to focus your attention on other critical aspects of your business. Moreover, by exploring this technology, you can gain valuable insights and knowledge that will aid you on your journey as an AI entrepreneur.

The AI agent we’ve discussed is a powerful tool that showcases the impactful role AI can play in contemporary business practices. By following the steps outlined, you can harness this technology to refine your operations, boost customer engagement, and secure a leading position in the competitive landscape of AI agencies. This AI agent is more than just a scheduling assistant; it’s a comprehensive solution that promises to transform the way you manage appointments and interact with your clients.

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Arduino Pro welcomes C3D to System Integrators Program

Arduino Pro welcomes C3D to System Integrators Partnership Program

In a significant development within the IoT and automation industry, Chilean engineering services firm C3D has joined Arduino Pro’s System Integrators Program at the Gold level. This move marks an important milestone for C3D, as it seeks to enhance its IoT and automation capabilities, leveraging Arduino Pro’s product line to accelerate the development of connected applications across key sectors.

The partnership between Arduino Pro and C3D is a strategic one, with both entities aiming to make the most of the burgeoning IoT market. A recent Gartner report reveals that IoT spending across key industries exceeded $268 billion in 2022, with a forecasted compound annual growth rate (CAGR) of 15% from 2021 to 2025. This exponential growth in the IoT market is primarily driven by increased connectivity and the integration of smart devices across various industries, including healthcare, manufacturing, transportation and logistics, smart homes, and smart cities.

Arduino Pro System Integrators Partnership Program

At the helm of C3D, CEO Juan Pablo Oyarzún Alcayaga expressed confidence in the partnership’s potential to expedite development processes and reduce non-recurring engineering fees. The integration of Arduino Pro’s product line into C3D’s operations is expected to streamline the development of IoT and automation applications, thereby speeding up time-to-market and reducing costs.

One of the key areas of interest for C3D is the integration of artificial intelligence (AI) in automation. The fusion of AI and IoT has given rise to a new era of intelligent automation, enabling smart decision-making and efficient process automation. C3D offers services that help businesses incorporate AI-driven automation into their workflows, thereby enhancing efficiency, reducing costs, and improving overall performance. This partnership with Arduino Pro will further strengthen C3D’s capabilities in this domain.

Additionally, C3D has shown a keen interest in the potential impact of Narrowband Internet of Things (NB-IoT) technology. NB-IoT is a specialized communication standard designed for the IoT, offering low power consumption, extended coverage, and support for a large number of devices. Such features make it ideal for applications in agriculture, utilities, asset tracking, and mining. Through its partnership with Arduino Pro, C3D aims to explore the potential of NB-IoT technology to enhance its service offerings and deliver more value to its clients.

The partnership between Arduino Pro and C3D signifies a strategic move to harness the potential of IoT and AI in automation. By joining Arduino Pro’s System Integrators Program, C3D aims to enhance its IoT capabilities, accelerate product development, and explore the potential of emerging technologies like NB-IoT. In a rapidly evolving market, such partnerships are crucial for companies to stay ahead of the curve and deliver innovative solutions that meet the changing demands of businesses across various sectors. Here are some other articles you may find of interest on the subject of Arduino :

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ZOOM VISS vulnerability impact scoring system announced

ZOOM VISS vulnerability impact scoring system announced

The digital landscape, ever expanding and evolving, has given rise to an increasing number of security vulnerabilities. To address this issue, a new open-source project called the Vulnerability Impact Scoring System (VISS) has been introduced. VISS is designed to enhance security measures by providing a unique assessment tool that measures the impact of vulnerabilities from a defender’s perspective. This innovative approach focuses on the actual impact of potential threats, rather than on their theoretical existence.

Since March 2023, Zoom, a leading video conferencing platform, has been utilizing VISS to assess reward disbursements within its Bug Bounty Program. This program encourages security researchers and product users to uncover and disclose security vulnerabilities, providing them with legal protection. The incorporation of VISS into this program has been instrumental in helping Zoom prioritize vulnerabilities that are most likely to impact them, thus allowing for more efficient use of resources.

The Vulnerability Impact Scoring System analyzes vulnerabilities based on 13 impact aspects. These aspects are categorized into three groups: platform, infrastructure, and data. The resulting score, ranging from 0 to 100, reflects the severity of the impact within a specific environment. This scoring system provides an objective measure of the potential damage a vulnerability could inflict, enabling organizations to prioritize their response efforts accordingly.

ZOOM VISS vulnerability impact scoring

VISS was put to the test during the HackerOne H1-4420 live-hacking event in London in 2023. The event demonstrated the effectiveness of VISS in improving resource allocation and focusing on addressing Critical and High severity vulnerabilities. The implementation of VISS led to a shift in vulnerability report submissions towards these higher severity categories, with a significant reduction observed in medium severity submissions.

This shift towards targeting higher severity vulnerabilities is a testament to the efficacy of VISS. By providing a clear, objective measure of the potential impact of a vulnerability, VISS enables organizations to focus their resources where they are most needed. This, in turn, leads to a more robust and secure digital environment.

VISS is not just a tool for individual organizations, but a global mission to enhance security measures. By providing a comprehensive and objective measure of vulnerability impact, VISS aims to enhance the capabilities of incident response and security teams across the globe. The open-source nature of the project invites contributions to its development, fostering a collaborative approach to improving digital security.

The development and implementation of the Vulnerability Impact Scoring System is a significant stride forward in the realm of digital security. By focusing on the actual impact of vulnerabilities, VISS offers a more realistic and effective approach to managing digital threats. The system’s successful use in Zoom’s Bug Bounty Program and the HackerOne H1-4420 live-hacking event highlights its potential to transform the way organizations respond to security vulnerabilities.

The VISS project is open for exploration and contribution under the GPL 3.0 license at https://github.com/zoom/viss. This open-source project is a testament to the collaborative spirit of the digital community, inviting all to contribute to the ongoing development and enhancement of this innovative security tool. With the continued development and implementation of VISS, the future of digital security looks promising.

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IBM System Two Quantum Computer unveiled crosses 1000 Qubits threshold

IBM System Two Quantum Computer

In the rapidly evolving world of quantum computing, IBM is making significant strides. Recently announcing that its latest quantum processor, the IBM Condor, which boasts 1,121 qubits, a significant increase from the previous 433-qubit chip. This development aligns with IBM’s projected quantum roadmap. Qubits, the fundamental units of quantum computers, enable significantly faster calculations than traditional computers when entangled. However, the sheer number of qubits is not the sole indicator of a quantum computer’s performance.

This cutting-edge field, once confined to theoretical research, is now seeing practical applications that could transform how we tackle complex problems. The IBM Quantum System Two, a new system that houses the Condor, is a marvel of engineering. Enclosed in a 15-foot structure, it operates in conditions that mimic the extreme cold of outer space. Initially, it will run on three 133-qubit Heron processors, but its design is future-proof, ready to integrate subsequent technological leaps.

IBM Quantum System Two computer

One of the most impressive features of the Quantum System Two is its modular architecture. This design is key to its ability to perform an astounding 100 million operations within a single quantum circuit. IBM isn’t stopping there; they have set their sights on scaling up to 1 billion operations by the year 2033.

To support the people who will develop the future of quantum computing, IBM has released Qiskit 1.0, a software development kit (SDK) that enhances the tools available to developers. This SDK makes it easier to compile quantum circuits with the help of artificial intelligence and introduces a batch mode that streamlines job execution. These improvements are designed to make the quantum computing workflow more user-friendly.

IBM is also focused on building a robust quantum computing ecosystem. They are doing this by developing resources like Qiskit Patterns and Quantum Serverless, which aid in the creation of algorithms and applications. Additionally, IBM is pioneering the integration of generative AI into quantum code programming through Watsonx, showcasing the synergy between artificial intelligence and quantum computing.

IBM Condor Qubit processor

At the forefront of this advancement is IBM’s latest creation, the IBM Condor, a powerful 1,121-qubit processor that is setting new benchmarks in computational capabilities. The IBM Condor’s large number of qubits is a clear indication of the progress IBM has made on their quantum computing roadmap. The power of a quantum computer comes from the entanglement of qubits, which allows for an exponential increase in computational capabilities. This means that quantum computers can address problems that are currently beyond the reach of classical computers.

Creating a quantum processor like the IBM Condor involves complex superconducting circuits that are etched onto silicon wafers. This is a crucial step in the advancement of quantum computing technology. However, it’s not just about having a large number of qubits. It’s also essential to achieve low error rates and maintain high fidelity in the operations of these qubits for them to be practically applied.

Although the qubit count of the IBM Condor is noteworthy, IBM has not yet shared detailed performance data for this new processor. The company has previously emphasized the importance of ‘quantum volume’ as a metric, which takes into account not only the number of qubits but also their quality, connectivity, and the error rates of operations. This metric has not been updated since 2020, leaving us waiting for more information on the processor’s capabilities.

1000 Qubits threshold crossed what does that mean?

The potential uses for the IBM Condor are still being explored. Experts in the field suggest that quantum computing will require millions of qubits to become commercially viable. This means that, despite the advancements the IBM Condor represents, there is still a long way to go before quantum computing can transform various industries. Here are some other articles you may find of interest on the subject of Quantum computing :

As we consider IBM’s latest development, it’s crucial to remember that the promise of quantum computing is not solely based on the number of qubits. It also includes the complexity of their interconnections and the accuracy with which they can be manipulated. The IBM Condor is a sign of the progress being made in quantum computing and signals the approach of a new era in this exciting field.

Quantum computing is an area of technology that has the potential to transform how we solve complex problems. Unlike traditional computers that use bits to process information, quantum computers use qubits, which can exist in multiple states simultaneously. This allows them to perform many calculations at once, providing a level of processing power that’s unattainable with current classical computers.

IBM’s unveiling of the IBM Condor quantum processor with 1,121 qubits is a testament to the rapid advancements in quantum technology. The IBM Condor represents a significant leap from IBM’s previous quantum processors and is a key milestone on their roadmap for the development of quantum computing.

The power of quantum computing lies in the ability of qubits to be entangled, which allows for an exponential increase in computational capabilities. This entanglement enables quantum computers to tackle problems that are currently unsolvable by traditional computers. The IBM Condor’s large number of qubits is a clear indication of the progress IBM has made in this area.

However, the number of qubits is not the only challenge in quantum computing. Achieving low error rates and maintaining high fidelity in qubit operations are also critical for the practical application of quantum processors. While the qubit count of the IBM Condor is impressive, IBM has yet to release detailed performance data for the processor. The company has previously highlighted ‘quantum volume’ as an important metric, which considers the number of qubits, their quality, connectivity, and the error rates of operations. This metric has not been updated since 2020, leaving us waiting for more information on the processor’s capabilities.

Looking ahead, IBM has laid out a comprehensive roadmap that extends to 2033. This plan includes a series of enhancements to their quantum computing systems, which will eventually feature processors with over 100 qubits. IBM is also forging partnerships with research institutions to explore quantum-powered applications.

IBM’s dedication to quantum computing is not just about technological prowess; it’s about providing enterprise solutions that are tailored to specific industries. As IBM’s quantum computing technology matures, it opens up possibilities for addressing some of the most challenging issues facing the world today. The advancements IBM is making today are paving the way for a future where quantum computing plays a pivotal role in solving complex problems and unlocking new opportunities.

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Arduino Pro welcomes new System Integrators Partner : Patti Engineering

Arduino Pro welcomes new System Integrators Partner Patti Engineering

It has been announced this week that Arduino Pro has teamed up with Patti Engineering to enhance the digital transformation of manufacturing processes. This collaboration is a key component of the Arduino Pro System Integrators Partnership Program, which is designed to incorporate Arduino technologies into professional projects, thereby elevating manufacturing productivity and quality.

Manufacturers are currently navigating the shift towards Industry 4.0, a movement characterized by the integration of digital technologies into all areas of business. With the guidance of Patti Engineering, manufacturers can leverage the expertise of both companies to implement cutting-edge digital solutions that streamline production and improve operational efficiency. This partnership is poised to assist manufacturers in adopting advanced technologies that are essential for staying competitive in today’s fast-paced market.

Industry 4.0 is transforming the way companies manufacture, improve and distribute their products. Manufacturers are integrating new technologies, including Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their production facilities and throughout their operations.

One of the key areas of focus for this partnership is the enhancement of production lines through the use of robotics. Patti Engineering’s proficiency in robotics is expected to lead to more efficient and reliable manufacturing operations. By integrating robotic systems, manufacturers can look forward to increased productivity and consistent product quality, which are critical factors for success in the industry.

Arduino Pro

Another crucial aspect of modern manufacturing is the control system that governs automated processes. Patti Engineering brings a wealth of experience in working with top manufacturers to optimize production through sophisticated control systems. The collaboration with Arduino Pro will provide tailored solutions that align with the specific needs of manufacturers, enhancing control and flexibility in their operations.

The issue of material visibility and tracking is also addressed through this partnership. Misplaced materials can result in significant costs for manufacturers. Patti Engineering’s asset tracking solutions offer improved visibility and traceability of materials throughout the production process. By employing the latest technologies, manufacturers can ensure accuracy and accountability, which are vital for maintaining high standards of production.

Patti Engineering

The strategic alliance between Arduino Pro and Patti Engineering is a concerted effort to drive innovation and technological advancement in the manufacturing sector. This collaboration is set to unlock new possibilities and establish benchmarks for manufacturing excellence. It represents a commitment to fostering a culture of innovation that can propel the industry forward.

“We believe that Patti Engineering’s wealth of experience and proficiency in Industry 4.0, robotics, control systems integration, and asset tracking will bring unparalleled value to our community,” Paul Kaeley, Strategic Sales Advisor to Arduino commented. “Together, we look forward to pushing the boundaries of what is possible and driving innovation in the world of technology and manufacturing.”

Furthermore, the expansion of the Arduino Pro System Integrators Partnership Program through the inclusion of Patti Engineering not only extends Arduino Pro’s reach but also reinforces its commitment to enabling the professional community to utilize technology for sophisticated and advanced applications. This program is a testament to Arduino Pro’s dedication to the professional application of its technologies across various projects.

The partnership between Arduino Pro and Patti Engineering marks a critical step towards achieving comprehensive Industry 4.0 capabilities for manufacturers. By drawing on the combined expertise of these leaders in technology and engineering, manufacturers are well-positioned to benefit from the ongoing evolution of the industry. The future of manufacturing is being shaped by increased connectivity, efficiency, and innovation, and this partnership is at the forefront of that transformation.

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