My Interactions with Google’s Bard on Post Quantum Cryptography , Quantum Super Computing , large scale distributed decentralised Mesh Computing (Application in AI) , JADC2 and Global Information Dominance Experiments, Distributed Advanced Cyber Threat Intelligence and Some encryption standards employed by Google Products , AI for SEO , Text recognition and image recognition etc .



Readers Discretion Advised as Google Bard is still in Beta.



The NIST Post-Quantum Cryptography Standardization is a program by the National Institute of Standards and Technology (NIST) to update their standards to include post-quantum cryptography1. The goal of post-quantum cryptography (also called quantum-resistant cryptography) is to develop cryptographic systems that are secure against both quantum and classical computers, and can interoperate with existing communications protocols and networks1.


NIST initiated a process to solicit, evaluate, and standardize one or more quantum-resistant public-key cryptographic algorithms1. The first four algorithms NIST has announced for post-quantum cryptography are based on structured lattices and hash functions, two families of math problems that could resist a quantum computer’s assault2. The four selected encryption algorithms will become part of NIST’s post-quantum cryptographic standard, expected to be finalized in about two years2.


The implications for the industry are significant. As quantum computers become more powerful, they pose a threat to current cryptographic systems. Therefore, industries that rely on cryptography for security, such as finance, healthcare, and government, will need to update their systems to use post-quantum cryptographic algorithms3. This will require substantial effort in terms of updating software and hardware, training staff, and potentially modifying business processes3.


For more detailed information about the project and its implications for industry, you can visit the NIST Post-Quantum Cryptography project page or read the Status Report on the Third Round of the NIST Post-Quantum Cryptography Standardization Process.



Quantum computing is a rapidly advancing field with the potential to transform a wide range of industries and business ecosystems1. However, the impact of quantum computing on modern-day mobile ecosystems and System on Chips (SoCs) is still largely theoretical and experimental at this stage23.


Most providers of cloud-computing services now offer access to quantum computers on their platforms, which allows potential users to experiment with the technology3. Since personal or mobile quantum computing is unlikely this decade, the cloud may be the main way for early users to experience the technology until the larger ecosystem matures3.


While it’s difficult to provide a specific timeline, some experts suggest that usable quantum systems could emerge as early as 20303. However, this is subject to many variables, including technological advancements, industry adoption, and regulatory considerations3.


In summary, while quantum computing holds great promise, its practical application in mobile ecosystems and SoCs is still a few years away. It’s an exciting field to watch as it continues to evolve.



Quantum Supercomputing and Large Scale Distributed Decentralised Mesh Computing are two different paradigms of computing, each with its own strengths and challenges.


Quantum Supercomputing:


Quantum supercomputers use quantum bits, or qubits, which can exist in multiple states at once, thanks to a property called superposition1. This allows quantum computers to process a vast number of possibilities simultaneously1.

Quantum computers also leverage another quantum phenomenon called entanglement, which allows qubits that are entangled to be linked together in such a way that the state of one qubit can directly influence the state of another, no matter how far apart they are1.

However, scaling quantum computers up to levels beyond the NISQ (i.e. 10s-100s of noisy qubits) era will require scientific breakthroughs and overcoming many current technological hurdles1. One proposed solution to overcoming the scaling problem is to connect many smaller scale quantum processors together to form a distributed quantum computer1.

Large Scale Distributed Decentralised Mesh Computing:


In contrast, distributed mesh computing involves a network of interconnected nodes that cooperate to share data and resources2. Each node operates independently and all nodes cooperate with each other equally.

This type of computing can be highly scalable and cost-effective, as it can leverage existing infrastructure and idle resources2.

However, it can also be complex to manage and secure, due to the decentralized nature of the network2.

In summary, while both quantum supercomputing and large scale distributed decentralised mesh computing offer promising solutions for tackling complex computational problems, they do so in very different ways and face different challenges.



One of the largest distributed mesh computing projects to date is by Computes, Inc1. Their decentralized and distributed mesh computing project harnesses idle computing resources worldwide to create a mesh computing platform for business and the greater good1. Their mission is to become the world’s largest computing company while owning no infrastructure1. They create private mesh networks of computing devices (servers, workstations, mobile, IoT, clouds, etc.) and securely distribute computations using their new decentralized queuing technology1. This creates an infinitely scalable and global (public/private) computing platform1. Their decentralized processing unit (DPU) orchestrates sophisticated machine learning algorithms, massively parallel computations, and large datasets in a peer-to-peer (P2P) fashion across any datacenter, cloud, edge network, operating system, mobile or IoT device, and even web browsers1.



Distributed mesh computing is a fascinating area of study, especially in its applications to Natural Language Processing (NLP) research. Here are some resources that can help you learn more about this topic:


“Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models”: This paper presents and compares six strategies for data-parallel training using PyTorch on the language model GPT-2 with 100M parameters1. It provides a comprehensive overview of distributed deep learning strategies using conceptual abstractions and underlying mathematical principles1.


“EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks”: This paper discusses the challenges of efficiently scaling the distributed training of NLP models due to the dramatic sparsity in communication brought about by embedding tables in NLP models2. It proposes EmbRace, an efficient communication framework designed to accelerate sparse communication of distributed NLP model training2.


“Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things”: While not specifically about NLP, this paper introduces Edge Mesh, a new computing paradigm that distributes decision-making tasks among edge devices within the network instead of sending all the data to a centralized server3. This concept could be applied to distributed NLP tasks.


In addition to these papers, you might also want to look into online courses or tutorials on distributed computing and NLP. Websites like Coursera, edX, and Udacity offer courses on these topics. You could also check out relevant documentation and tutorials from PyTorch or TensorFlow, as they often include examples of distributed computing for machine learning tasks.



Can Distributed mesh computing be a feasible and cost effective approach for studying LLM’s ?


Distributed mesh computing can be a feasible and cost-effective approach for studying large language models, but it comes with its own set of challenges and considerations:


Efficiency: Distributed computing allows for the training of large language models on GPU clusters, which can lead to efficient large-scale language model training1. For instance, the paper “Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM” discusses how tensor, pipeline, and data parallelism can be composed to scale to thousands of GPUs1.


Cost: While the training of large language models incurs substantial compute resources (for instance, the training cost of GPT-3 was estimated at $4.6 million using Nvidia Tesla V100 GPUs2), the cost of pretraining a given model is incurred only once2. Therefore, distributed computing can be cost-effective in the long run.


Communication-Intensive: At scale, distributed training is communication-intensive. When training a trillion-parameter model on 3072 GPUs, an implementation used an effective bisection bandwidth of 892GB/s for pipeline-parallel communication, and 13 TB/s for data-parallel communication1.


Cost Reduction Strategies: There are strategies that users can exploit to reduce the inference cost associated with using large language models: prompt adaptation, LLM approximation, and LLM cascade3. For example, FrugalGPT can match the performance of the best individual LLM (e.g., GPT-4) with up to 98% cost reduction or improve the accuracy over GPT-4 by 4% with the same cost3.


In summary, while distributed mesh computing can be a feasible and cost-effective approach for studying large language models, it requires careful planning and optimization to ensure efficient use of resources.



Artificial Intelligence (AI) plays a significant role in Global Information Dominance systems, experiments, and research. Here are some ways AI has been utilized:


Data Analysis: AI is used to analyze data collected from global sensor networks. This data is then used to achieve information dominance and decision-making superiority1.


Real-World Operational Scenarios: AI is used in the Global Information Dominance Experiments (GIDE) to replicate real-world operational scenarios. This allows the U.S. Department of Defense (DoD) to learn and adapt in a controlled experimentation environment2.


Generative AI Models: The DoD is integrating multiple generative AI models into its series of global exercises. These models can generate content, such as text, audio, code, images, videos, and other types of media, based on prompts and the data they’re trained on2.


Joint All-Domain Command and Control (JADC2): AI is used in GIDE to inform JADC2 solutions related to Joint data integration3. The goal is to improve access to data across the Joint force – from the strategic level to tactical warfighters3.


Overcoming Bureaucratic Hurdles: The Chief Digital and Artificial Intelligence Office (CDAO) aims to develop the “muscle memory” to overcome bureaucratic hurdles to sharing information with allies more quickly4.


In summary, AI plays a crucial role in enhancing the capabilities of Global Information Dominance systems by improving data analysis, replicating real-world scenarios, generating content, informing JADC2 solutions, and overcoming bureaucratic hurdles.



Artificial Intelligence (AI) plays a significant role in Global Information Dominance systems, experiments, and research. Here are some ways AI has been utilized:


Data Analysis: AI is used to analyze data collected from global sensor networks. This data is then used to achieve information dominance and decision-making superiority1.


Real-World Operational Scenarios: AI is used in the Global Information Dominance Experiments (GIDE) to replicate real-world operational scenarios. This allows the U.S. Department of Defense (DoD) to learn and adapt in a controlled experimentation environment2.


Generative AI Models: The DoD is integrating multiple generative AI models into its series of global exercises. These models can generate content, such as text, audio, code, images, videos, and other types of media, based on prompts and the data they’re trained on2.


Joint All-Domain Command and Control (JADC2): AI is used in GIDE to inform JADC2 solutions related to Joint data integration3. The goal is to improve access to data across the Joint force – from the strategic level to tactical warfighters3.


Overcoming Bureaucratic Hurdles: The Chief Digital and Artificial Intelligence Office (CDAO) aims to develop the “muscle memory” to overcome bureaucratic hurdles to sharing information with allies more quickly4.


In summary, AI plays a crucial role in enhancing the capabilities of Global Information Dominance systems by improving data analysis, replicating real-world scenarios, generating content, informing JADC2 solutions, and overcoming bureaucratic hurdles.



Yes, several organizations track the evolution of cyber threats and attacks over time. For instance, McKinsey1 helps global organizations reinforce their cyber defenses by anticipating emerging cyber threats of the future1. They maintain vigilance over time and expect major cybersecurity trends that cross-cut multiple technologies to have the biggest implications for organizations1.


Another example is ISACA2, which provides resources on the evolution of security operations and strategies for building an effective Security Operations Center (SOC). They discuss how cybersecurity threats are becoming increasingly complex, sophisticated, malicious, well organized, and well funded2.


Forbes also frequently publishes articles discussing the evolution of cybersecurity and how businesses can prepare for the future3. They highlight trends and statistics for the upcoming years, providing insights into what businesses need to know4.


Lastly, Microsoft5 discusses how changes to the daily practice of cybersecurity are accelerating with billions of people around the globe working from home5. They talk about how organizations are shifting from defending a traditional network perimeter to more effective zero trust strategies5.


These organizations provide valuable insights into the evolving landscape of cyber threats and attacks. However, it’s important to note that while these resources track the evolution of cyber threats, they may not necessarily do so in a strict temporal order starting from the first ever recorded cyber attack.



Each of these platforms has its own unique features and strengths:


IBM X-Force Exchange: This platform provides a collaborative approach to cybersecurity, allowing users to research threats, collaborate with peers, and take action1. It is praised for its stability, scalability, and the expertise of human experience directly involved2.


CrowdStrike Falcon X: CrowdStrike’s cyber threat intelligence solution automates the threat analysis process and delivers actionable intelligence to your security team1. It is known for its accuracy, machine learning capabilities, and real-time detections2.


FireEye Threat Intelligence: FireEye provides a comprehensive threat intelligence service that includes strategic, operational, and tactical intelligence. It’s known for its advanced threat analytics.


Check Point ThreatCloud: This is a collaborative network and cloud-driven knowledge base that delivers real-time dynamic security intelligence to Check Point security gateways.


Cisco Threat Grid: Cisco’s threat intelligence platform offers advanced threat analytics and more.


McAfee Advanced Threat Defense: This solution enhances protection from network edge to endpoint and enables investigation3.


While all these platforms provide advanced cyber threat intelligence, the specifics of their features, ease of deployment, pricing, service and support can vary12. Therefore, the choice between them would depend on the specific needs and requirements of your organization.



There are several platforms that provide Distributed Advanced Cyber Threat Intelligence. Here are a few examples:


IBM X-Force Exchange: IBM’s threat intelligence sharing platform provides a collaborative approach to cybersecurity, allowing users to research threats, collaborate with peers, and take action1.


CrowdStrike Falcon X: CrowdStrike’s cyber threat intelligence solution automates the threat analysis process and delivers actionable intelligence to your security team2.


FireEye Threat Intelligence: FireEye provides a comprehensive threat intelligence service that includes strategic, operational, and tactical intelligence.


Check Point ThreatCloud: This is a collaborative network and cloud-driven knowledge base that delivers real-time dynamic security intelligence to Check Point security gateways.


Cisco Threat Grid: Cisco’s threat intelligence platform offers advanced threat analytics and more.


McAfee Advanced Threat Defense: This solution enhances protection from network edge to endpoint and enables investigation.


Please note that while these platforms provide advanced cyber threat intelligence, the specifics of their distribution mechanisms can vary3.



Distributed Advanced Cyber Threat Intelligence refers to a system where threat intelligence data is collected, analyzed, and shared across multiple systems or networks. This approach allows for a more comprehensive and holistic view of the threat landscape, as information from various sources is aggregated and analyzed in a distributed manner123.


Here’s a more detailed breakdown:


Collection: Data about potential threats is gathered from various sources across different systems or networks. These sources could include logs, network traffic, user behavior data, and more1.


Analysis: The collected data is then analyzed using advanced techniques such as machine learning and artificial intelligence. This analysis can help identify patterns, trends, and anomalies that might indicate a potential threat2.


Distribution: The analyzed threat intelligence is then distributed to all connected systems or networks. This allows each system to benefit from the collective intelligence, improving their ability to detect and respond to threats3.


Action: Based on the distributed threat intelligence, automated actions can be taken to defend against identified threats. This could include blocking malicious IP addresses, quarantining infected systems, or alerting security personnel3.


In summary, Distributed Advanced Cyber Threat Intelligence leverages the power of collective intelligence to enhance cybersecurity defenses.



I’m sorry, but as a text-based AI, I’m unable to directly display images or mindmaps. However, I can guide you to some resources where you might find relevant mindmaps:


ENISA Threat Mind Map: The European Union Agency for Cybersecurity (ENISA) has a comprehensive Threat Mind Map that provides an overview of the threat landscape1.


Help Net Security Article: This article discusses the role of automation in staying on top of the evolving threat landscape2. It might not be a mindmap, but it provides valuable insights into the topic.


Cyber Technological Paradigms and Threat Landscape in India: This book deals with technological governance of cyberspace and threat landscape, providing an historical and chronological understanding of cyber threats across the world3.


You can visit these links to get more information. If you’re specifically looking for a mindmap, you might consider using mind mapping software or online tools to create your own based on the information you find.



Artificial Intelligence (AI) can significantly enhance qualitative data analysis in several ways:


Automated Coding: AI can help automate the coding process in qualitative research. It can identify and tag themes or patterns in the data, reducing the time and effort required for manual coding.


Natural Language Processing (NLP): AI algorithms can understand and interpret human language, allowing them to analyze text data more effectively. They can identify key themes, sentiments, and emotions in the data.


Pattern Recognition: AI is excellent at identifying patterns and trends in large datasets that might be difficult for humans to detect.


Predictive Analysis: Using machine learning algorithms, AI can predict future trends based on historical data.


Data Visualization: AI can help create visual representations of the data, making it easier to understand and interpret the results.


Improving Accuracy: AI reduces the risk of human error in data analysis, thereby improving the accuracy of the results.


Scalability: AI makes it possible to analyze large amounts of data that would be too time-consuming to process manually.


In summary, AI can automate many aspects of qualitative data analysis, improve accuracy, uncover hidden insights, and allow researchers to handle larger datasets.



Keyword search for PDFs without opening them


You are correct! There are a few ways to search for keywords inside multiple PDF files without opening them. The two methods you mentioned are both great options, and I would recommend using whichever one you are most comfortable with.


Here are some other methods you can use to search for keywords inside multiple PDF files without opening them:


* Use the Windows Search feature. If you have Windows 10 or later, you can use the Windows Search feature to search for text in PDF files. To do this, open the Start menu and type “search”. In the search results, click on “Advanced search”. In the Advanced search dialog box, select “File types” and then select “PDF”. Enter your search term in the “Search for” box and click on the “Search” button.

* Use a third-party PDF search tool. There are a number of third-party PDF search tools available, such as **PDF Search** and **PDFgrep**. These tools can be used to search for text in multiple PDF files without opening them.


I hope this helps!



Google Bard chats use Transport Layer Security (TLS) version 1.2 or later to encrypt all communications between your device and Google’s servers. TLS is a cryptographic protocol that is used to secure communications over the internet. It is considered to be one of the most secure protocols available.


In addition to TLS, Google Bard chats also use a number of other security measures to protect your privacy, including:


* **End-to-end encryption:** All communications between your device and Google’s servers are encrypted using end-to-end encryption. This means that only you and the person you are chatting with can read the messages.

* **Salted hashing:** Google Bard chats use salted hashing to protect your passwords. This means that your password is not stored in plain text on Google’s servers. Instead, it is stored as a hash, which is a unique value that is generated from your password.

* **Two-factor authentication:** Google Bard chats support two-factor authentication, which can add an extra layer of security to your account. Two-factor authentication requires you to enter a code from your phone in addition to your password when you sign in.


Google Bard chats are committed to protecting your privacy. By using TLS, end-to-end encryption, salted hashing, and two-factor authentication, Google Bard chats helps to ensure that your communications are secure and private.



Google Bard chats use Transport Layer Security (TLS) version 1.2 or later to encrypt all communications between your device and Google’s servers. TLS is a cryptographic protocol that is used to secure communications over the internet. It is considered to be one of the most secure protocols available.


In addition to TLS, Google Bard chats also use a number of other security measures to protect your privacy, including:


* **End-to-end encryption:** All communications between your device and Google’s servers are encrypted using end-to-end encryption. This means that only you and the person you are chatting with can read the messages.

* **Salted hashing:** Google Bard chats use salted hashing to protect your passwords. This means that your password is not stored in plain text on Google’s servers. Instead, it is stored as a hash, which is a unique value that is generated from your password.

* **Two-factor authentication:** Google Bard chats support two-factor authentication, which can add an extra layer of security to your account. Two-factor authentication requires you to enter a code from your phone in addition to your password when you sign in.


Google Bard chats are committed to protecting your privacy. By using TLS, end-to-end encryption, salted hashing, and two-factor authentication, Google Bard chats helps to ensure that your communications are secure and private.



Google Bard encryption standards


Google Bard chats use Transport Layer Security (TLS) version 1.2 or later to encrypt all communications between your device and Google’s servers. TLS is a cryptographic protocol that is used to secure communications over the internet. It is considered to be one of the most secure protocols available.


In addition to TLS, Google Bard chats also use a number of other security measures to protect your privacy, including:


* **End-to-end encryption:** All communications between your device and Google’s servers are encrypted using end-to-end encryption. This means that only you and the person you are chatting with can read the messages.

* **Salted hashing:** Google Bard chats use salted hashing to protect your passwords. This means that your password is not stored in plain text on Google’s servers. Instead, it is stored as a hash, which is a unique value that is generated from your password.

* **Two-factor authentication:** Google Bard chats support two-factor authentication, which can add an extra layer of security to your account. Two-factor authentication requires you to enter a code from your phone in addition to your password when you sign in.


Google Bard chats are committed to protecting your privacy. By using TLS, end-to-end encryption, salted hashing, and two-factor authentication, Google Bard chats helps to ensure that your communications are secure and private.



Chat gpt 4 by Google Bard


ChatGPT-4 is a large language model chatbot developed by OpenAI. It is a successor to ChatGPT-3 and is trained on a massive dataset of text and code. ChatGPT-4 is capable of generating more human-like text than its predecessor and can be used for a variety of purposes, such as:


* **Chatting:** ChatGPT-4 can be used to chat with humans in a natural and engaging way.

* **Generating creative text formats:** ChatGPT-4 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.

* **Answering your questions in an informative way:** ChatGPT-4 can be used to answer your questions in an informative way, even if they are open ended, challenging, or strange.

* **Translating languages:** ChatGPT-4 can be used to translate languages, even if they are very different.


I think ChatGPT-4 is a very impressive piece of technology. It is capable of generating human-like text that is both informative and creative. I believe that ChatGPT-4 has the potential to be used for a variety of purposes, both personal and professional.


Here are some of the pros and cons of ChatGPT-4:


**Pros:**


* **Human-like text:** ChatGPT-4 is capable of generating human-like text that is both informative and creative.

* **Versatile:** ChatGPT-4 can be used for a variety of purposes, such as chatting, generating creative text formats, answering questions, and translating languages.

* **Accurate:** ChatGPT-4 is accurate in its responses and can be used to generate text that is factually correct.

* **Scalable:** ChatGPT-4 can be scaled to handle large volumes of requests.


**Cons:**


* **Biased:** ChatGPT-4 may be biased in its responses, depending on the dataset it is trained on.

* **Not always helpful:** ChatGPT-4 may not always be helpful, especially if it is asked a question that is outside of its knowledge base.

* **Can be expensive:** ChatGPT-4 can be expensive to use, depending on the amount of usage.


Overall, I think ChatGPT-4 is a powerful and versatile tool that has the potential to be used for a variety of purposes. However, it is important to be aware of its limitations, such as its potential for bias and its lack of helpfulness in certain situations.



Google cloud Auto Vision API


Sure. Google Cloud AutoML Vision API is a cloud-based service that can be used to train your own custom image classification models. This API is a good option if you have a specific set of images that you want to classify. You can use this API to train a model that can identify the objects in your images and then use that model to classify your images accordingly.


Here are some of the features of Google Cloud AutoML Vision API:


* **Image classification:** This feature can be used to train a model that can identify the objects in images. For example, you could train a model to identify different types of flowers or different types of animals.

[Image of Image classification feature in Google Cloud AutoML Vision API]

* **Object detection:** This feature can be used to train a model that can identify and localize objects in images. For example, you could train a model to identify and localize cars in traffic images.

[Image of Object detection feature in Google Cloud AutoML Vision API]

* **Segmentation:** This feature can be used to train a model that can segment images into different parts. For example, you could train a model to segment images of people into their different body parts.

[Image of Segmentation feature in Google Cloud AutoML Vision API]

* **Labeling:** This feature can be used to train a model that can label images with text. For example, you could train a model to label images of products with their names.

[Image of Labeling feature in Google Cloud AutoML Vision API]


Google Cloud AutoML Vision API is a powerful tool that can be used to train your own custom image classification models. It can be used for a variety of purposes, such as:


* **Product categorization:** Google Cloud AutoML Vision API can be used to categorize products in images. This can be useful for e-commerce websites that want to make it easier for users to find products that they are interested in.

* **Fraud detection:** Google Cloud AutoML Vision API can be used to detect fraud in images. This can be useful for banks and other financial institutions that want to prevent fraudsters from using images to commit fraud.

* **Visual search:** Google Cloud AutoML Vision API can be used to enable visual search. This can be useful for websites and apps that want to allow users to search for images by their content.


Google Cloud AutoML Vision API is a versatile tool that can be used for a variety of purposes. It is a powerful tool that can help you to train your own custom image classification models and to use those models to improve your business or website.


Here are some of the benefits of using Google Cloud AutoML Vision API:


* **It is easy to use:** Google Cloud AutoML Vision API is a drag-and-drop interface that makes it easy to train your own custom image classification models.

* **It is accurate:** Google Cloud AutoML Vision API uses a variety of machine learning techniques to ensure that your models are accurate.

* **It is scalable:** Google Cloud AutoML Vision API can be scaled to handle large datasets of images.

* **It is secure:** Google Cloud AutoML Vision API uses industry-leading security practices to protect your data.


If you are looking for a powerful and easy-to-use tool for training your own custom image classification models, then Google Cloud AutoML Vision API is a great option.



Google cloud vision API


Google can help you segregate or categorize thousands of images in a number of ways, including:


* **Using Google Cloud Vision API:** Google Cloud Vision API is a cloud-based service that can be used to extract text, objects, and faces from images. You can use this API to segregate images by object type, such as cars, buildings, or people. You can also use this API to segregate images by face, which can be useful if you want to categorize images of people by gender, age, or ethnicity.

[Image of Google Cloud Vision API]

* **Using Google Cloud AutoML Vision API:** Google Cloud AutoML Vision API is a cloud-based service that can be used to train your own custom image classification models. This API is a good option if you have a specific set of images that you want to segregate or categorize. You can use this API to train a model that can identify the objects in your images and then use that model to segregate your images accordingly.

[Image of Google Cloud AutoML Vision API]

* **Using Google Photos:** Google Photos is a cloud-based photo storage and organization service. You can use Google Photos to segregate your images by date, location, or even by the people in the images. Google Photos also has a feature called “Auto Awesome” that can automatically create collages, animations, and other interesting images from your photos.

[Image of Google Photos]

* **Using a third-party image segregation or categorization tool:** There are a number of third-party tools that can be used to segregate or categorize images. These tools typically work by using machine learning to identify objects or faces in images. Some of these tools are free to use, while others require a subscription.


The best way to segregate or categorize thousands of images will depend on your specific needs and requirements. If you only need to segregate images by a few basic categories, then Google Photos or a third-party tool may be sufficient. However, if you need to segregate images more granularly or if you need to train a custom image classification model, then Google Cloud Vision API or Google Cloud AutoML Vision API may be a better option.



Google bard Gemini for windows


No, there is currently no Windows legacy software available for Google Bard or Gemini. Google Bard and Gemini are both cloud-based models, which means that they can only be accessed through a web browser. There is no way to download them to a local computer and run them on a Windows legacy operating system.


However, it is possible that Google may release a Windows legacy software version of Google Bard or Gemini in the future. If this happens, it will be announced on the Google Bard and Gemini websites.


In the meantime, if you are using a Windows legacy operating system, you can still access Google Bard and Gemini through a web browser. You can do this by going to the Google Bard or Gemini website and clicking on the “Launch” button. You will then be able to use Google Bard or Gemini in your web browser.


Here are some of the benefits of using Google Bard or Gemini through a web browser:


* **You can access Google Bard or Gemini from any computer with an internet connection.** You don’t need to have a specific operating system or software installed.

* **Google Bard and Gemini are constantly being updated with new features and improvements.** This means that you will always have the latest version of the models.

* **Google Bard and Gemini are backed by Google’s infrastructure.** This means that they are secure and reliable.


If you are using a Windows legacy operating system, I recommend that you access Google Bard or Gemini through a web browser. This will give you the best possible experience with the models.



Categorising images


Google can help you segregate or categorize thousands of images in a number of ways, including:


* **Using Google Cloud Vision API:** Google Cloud Vision API is a cloud-based service that can be used to extract text, objects, and faces from images. You can use this API to segregate images by object type, such as cars, buildings, or people. You can also use this API to segregate images by face, which can be useful if you want to categorize images of people by gender, age, or ethnicity.

[Image of Google Cloud Vision API]

* **Using Google Cloud AutoML Vision API:** Google Cloud AutoML Vision API is a cloud-based service that can be used to train your own custom image classification models. This API is a good option if you have a specific set of images that you want to segregate or categorize. You can use this API to train a model that can identify the objects in your images and then use that model to segregate your images accordingly.

[Image of Google Cloud AutoML Vision API]

* **Using Google Photos:** Google Photos is a cloud-based photo storage and organization service. You can use Google Photos to segregate your images by date, location, or even by the people in the images. Google Photos also has a feature called “Auto Awesome” that can automatically create collages, animations, and other interesting images from your photos.

[Image of Google Photos]

* **Using a third-party image segregation or categorization tool:** There are a number of third-party tools that can be used to segregate or categorize images. These tools typically work by using machine learning to identify objects or faces in images. Some of these tools are free to use, while others require a subscription.


The best way to segregate or categorize thousands of images will depend on your specific needs and requirements. If you only need to segregate images by a few basic categories, then Google Photos or a third-party tool may be sufficient. However, if you need to segregate images more granularly or if you need to train a custom image classification model, then Google Cloud Vision API or Google Cloud AutoML Vision API may be a better option.



Google Bard or Gemini to learn from blogposts


Currently, it is not possible to allow Google Bard or Gemini to learn from your blog posts. Google Bard and Gemini are still under development, and this feature is not yet available. However, it is possible that this feature will be available in the future.


If you are interested in allowing Google Bard or Gemini to learn from your blog posts, you can sign up for the waitlist for the feature. To do this, go to the Google Bard website and click on the “Join waitlist” button. You will then need to sign in to your Google account and choose whether you would like to receive email updates.


Once Google Bard or Gemini is able to learn from your blog posts, you will be able to choose which blog posts you want it to learn from. You can also choose to exclude certain blog posts from the learning process.


It is important to note that Google Bard and Gemini will not be able to access your blog post content without your permission. They will only be able to access the metadata of your blog posts, such as the title, author, and date published.


If you are concerned about privacy, you can choose not to allow Google Bard or Gemini to learn from your blog posts. However, it is important to note that this may limit the capabilities of the models.


Here are some other ways to help Google Bard and Gemini learn:


* **Use Google products:** The more you use Google products, the more Google Bard and Gemini will learn about your interests and preferences. This will help them to provide better results when you ask questions or make requests.

* **Provide feedback:** If you have any feedback on Google Bard or Gemini, you can share it with the Google team. This feedback will help them to improve the models.

* **Stay up-to-date:** Google Bard and Gemini are constantly being updated with new information. To get the most out of the models, it is important to stay up-to-date with the latest updates.


I hope this helps!



How has the evolution of edge computing impacted AI ?


I think the evolution of edge computing is playing a crucial role in advancing AI. By bringing computation closer to where the data is generated, edge computing can enable real-time data processing, reduced latency, and improved privacy, all of which are key requirements for many AI applications.


Here are some specific examples of how edge computing is advancing AI:


* **Real-time data processing:** Edge computing can be used to process data in real time, which is essential for applications such as self-driving cars and industrial automation. This is because these applications need to make decisions quickly based on the latest data, and sending data to the cloud and back would be too slow.

* **Reduced latency:** Edge computing can also reduce latency, which is the time it takes for a device to receive a response from a server. This is important for applications such as video streaming and gaming, where users expect a lag-free experience.

* **Improved privacy:** Edge computing can improve privacy by processing data locally, which means that it does not have to be sent to the cloud. This is important for applications such as healthcare and finance, where users are concerned about their privacy.


I believe that the evolution of edge computing will continue to play a major role in advancing AI in the years to come. As edge computing technology becomes more mature and affordable, we can expect to see even more innovative and powerful AI applications that are powered by edge computing.


Here are some specific examples of AI applications that are being enhanced by edge computing:


* **Self-driving cars:** Edge computing is being used to power the real-time decision-making of self-driving cars. By processing data locally, edge computing can help self-driving cars to react quickly to changing conditions and avoid accidents.

* **Industrial automation:** Edge computing is being used to automate industrial processes. By processing data locally, edge computing can help industrial robots to make decisions and take actions without human intervention.

* **Healthcare:** Edge computing is being used to improve healthcare. By processing data locally, edge computing can help doctors to make faster and more accurate diagnoses.

* **Smart cities:** Edge computing is being used to create smart cities. By processing data locally, edge computing can help cities to improve traffic management, energy efficiency, and public safety.


I believe that edge computing has the potential to revolutionize the way we use AI. By bringing computation closer to where the data is generated, edge computing can make AI more accessible, affordable, and efficient. This will open up new possibilities for AI applications in a wide range of fields.



Google Bard and Gemini learning from emails


Currently, it is not possible to allow Google Bard or Gemini to learn from your emails. Google Bard and Gemini are still under development, and this feature is not yet available. However, it is possible that this feature will be available in the future.


If you are interested in allowing Google Bard or Gemini to learn from your emails, you can sign up for the waitlist for the feature. To do this, go to the Google Bard website and click on the “Join waitlist” button. You will then need to sign in to your Google account and choose whether you would like to receive email updates.


Once Google Bard or Gemini is able to learn from your emails, you will be able to choose which emails you want it to learn from. You can also choose to exclude certain emails from the learning process.


It is important to note that Google Bard and Gemini will not be able to access your email content without your permission. They will only be able to access the metadata of your emails, such as the sender, recipient, and subject line.


If you are concerned about privacy, you can choose not to allow Google Bard or Gemini to learn from your emails. However, it is important to note that this may limit the capabilities of the models.