doc2016
1日前
You said:
supermicro computer's telecom partners are?
ChatGPT said:
Supermicro collaborates with a diverse array of partners to enhance its offerings in the telecommunications sector. Notable partnerships include:
NVIDIA: Together, they deliver AI-powered infrastructure tailored for telecommunications companies, enhancing operational efficiency, network management, and customer experiences.
SUPERMICRO.COM
Mettle Networks: This partnership focuses on pre-testing and validating Mettle's Virtual Broadband Network Gateway (vBNG) on Supermicro platforms, ensuring reliability and performance in business-critical deployments.
METTLENETWORKS.COM
Converge ICT Solutions: Collaborating to establish AI-ready data centers in the Philippines, Supermicro provides advanced servers with liquid cooling technology to support intensive AI computing tasks.
TELECOMTV.COM
World Wide Technology (WWT): This partnership delivers advanced technology solutions across various sectors, including telecommunications, focusing on innovations in 5G Telco/Edge IT infrastructure.
WWT.COM
SK Telecom: Supermicro collaborates with SK Telecom to develop AI data centers, enhancing the telecom company's capabilities in artificial intelligence and data processing.
PULSE.MK.CO.KR
Napatech and Saviah Technologies: In this collaboration, Supermicro provides optimized server platforms for private 5G networks, enhancing performance and efficiency in network edge locations.
NAPATECH.COM
These partnerships underscore Supermicro's commitment to delivering robust and efficient solutions for the evolving needs of the telecommunications industry.
Sources
You said:
dell telecom cloud and use of microsoft teams?
ChatGPT said:
Dell Technologies collaborates with Microsoft to enhance telecom cloud environments and offers comprehensive services for Microsoft Teams integration.
Transforming Telecom Cloud Environments
In partnership with Microsoft, Dell is redefining the deployment and operation of multicloud, disaggregated networks for communications service providers (CSPs). This collaboration focuses on:
Network Modernization: Utilizing cloud technology to modernize and monetize networks, aiming to lower total cost of ownership, enhance operational efficiency, and improve security.
Simplified Deployment: Dell plans to develop engineered systems that streamline telecom cloud network deployment and management, reducing operational expenses and accelerating service delivery.
DELL.COM
Microsoft Teams Integration Services
Dell provides a range of services to assist organizations in adopting and optimizing Microsoft Teams:
Consulting Services: Guidance on transitioning to a chat-centric workspace, deploying Teams clients, and optimizing security and compliance.
DELLTECHNOLOGIES.COM
Onboarding and Adoption: Support for seamless integration of Teams into existing workflows, ensuring effective adoption and change management.
APPSOURCE.MICROSOFT.COM
Calling and Meeting Services: Assistance in implementing voice and video communication features within Teams, enhancing collaboration capabilities.
APPSOURCE.MICROSOFT.COM
By leveraging its expertise and strategic partnership with Microsoft, Dell aims to provide robust solutions that address the evolving needs of the telecommunications industry and enhance organizational collaboration through Microsoft Teams
doc2016
3週前
chatgpt says, "can a customer directly connect to openai without 8x8?
ChatGPT said:
Yes, a customer can directly connect to OpenAI without using 8x8. OpenAI provides APIs that allow you to build AI-powered chatbots, virtual agents, call transcription services, and more—all without relying on 8x8.
How to Directly Use OpenAI for Customer Interactions
If you don’t need 8x8’s telephony, contact center, or messaging platform, you can integrate OpenAI into your own system for:
AI Chatbots:
Use GPT-4 Turbo for AI-powered customer support.
Integrate with your website, mobile app, or messaging platforms (WhatsApp, Messenger, etc.).
AI Voice Assistants:
Use Whisper (speech-to-text) to transcribe customer calls.
Use GPT-4 to process and understand the conversation.
Use Text-to-Speech (TTS) to generate voice responses.
Call Transcription & Summarization:
Record calls via your VoIP provider.
Use Whisper to convert speech to text.
Use GPT to summarize calls and extract key insights.
Agent Assist & CRM Integration:
AI can suggest responses for live agents.
Integrate OpenAI with Salesforce, Zendesk, HubSpot, or your internal CRM.
How to Connect Directly to OpenAI
To build a chatbot, voice bot, or AI assistant, follow these steps:
1. Setup OpenAI API
Sign up at platform.openai.com
Get your API key
2. Choose Your AI Features
Text-Based Chatbot ? Use GPT-4 Turbo
Voice-Based AI ? Use Whisper + TTS
Call Summarization ? Use Whisper + GPT
3. Develop Your AI Application
For chatbots (Web, WhatsApp, Slack, etc.)
Use Python, Node.js, or cloud functions to handle OpenAI API requests.
For voice AI (IVR, call handling, transcription)
Use Twilio, Vonage, or another VoIP provider for call routing.
Use Whisper to transcribe calls and GPT to generate responses.
4. Deploy Your AI System
Host on AWS, Azure, or Google Cloud
Use serverless functions (AWS Lambda) or Docker containers
Connect to a CRM or ticketing system for case tracking
Comparison: 8x8 vs. Direct OpenAI Connection
Feature Using 8x8 + OpenAI Direct OpenAI Connection
Voice & Call Handling ✅ Built-in (VoIP, IVR, Contact Center) ❌ Needs separate VoIP provider (e.g., Twilio)
Chatbots & AI Support ✅ Integrated with 8x8 Messaging ✅ Directly build & deploy
Call Transcription ✅ 8x8 provides transcription + AI ✅ Use Whisper for full control
Agent Assist ✅ Built into 8x8 platform ✅ Custom-built, integrates into any CRM
Customization 🔄 Limited to 8x8 ecosystem 🎯 Full control over AI logic
When to Use 8x8 vs. Direct OpenAI
✅ Use 8x8 + OpenAI if
You need a VoIP or contact center solution.
You want integrated messaging, IVR, and agent workflows.
You prefer a pre-built solution instead of coding.
✅ Use OpenAI Directly if
You don’t need 8x8’s contact center and just want AI chat/voice bots.
You want full control over customization and deployment.
You already have VoIP services (Twilio, Vonage, Cisco, etc.) for calls.
'
doc2016
3週前
geminiai pro is better on this than copilot.:
"ai model optimization and dynamic routing
AI model optimization and dynamic routing are powerful techniques used together to enhance the performance, efficiency, and scalability of AI applications, especially those involving large language models (LLMs) or complex AI tasks.
AI Model Optimization
This refers to the process of improving the performance of an AI model by adjusting its parameters, architecture, or training data. Optimization techniques can include:
Hyperparameter Tuning: Finding the best settings for the model's parameters (e.g., learning rate, batch size) to achieve optimal accuracy and efficiency.
Model Architecture Optimization: Modifying the structure of the model (e.g., number of layers, types of layers) to improve its performance for a specific task.
Data Augmentation and Preprocessing: Enhancing the training data by adding variations or preprocessing it to improve the model's ability to learn and generalize.
Quantization and Pruning: Reducing the size and complexity of the model by quantizing weights or pruning unnecessary connections, making it more efficient to run.
Dynamic Routing
Dynamic routing involves intelligently routing requests to the most suitable resources based on real-time conditions and model characteristics. This is particularly important for AI inference, where a trained model is used to make predictions or decisions on new data.
Factors considered in dynamic routing for AI optimization:
Server Load: Distribute requests evenly across available servers or GPUs to prevent overload and ensure responsiveness.
Model Complexity: Route requests to resources with sufficient processing power to handle the computational demands of the AI model.
Request Characteristics: Consider the specific input data and desired output format to route requests to specialized resources if needed.
Network Conditions: Account for network latency and bandwidth to minimize delays and ensure efficient communication.
Benefits of Combining AI Model Optimization and Dynamic Routing
Improved Performance: Optimized models and efficient routing lead to faster and more responsive AI applications.
Increased Efficiency: Minimizes resource usage and avoids bottlenecks, leading to cost savings and energy efficiency.
Enhanced Scalability: Allows AI applications to scale seamlessly by dynamically adjusting resource allocation based on demand.
Fault Tolerance: Reroutes requests to healthy resources in case of failures, ensuring high availability.
Key Takeaways:
AI model optimization focuses on improving the model itself, while dynamic routing optimizes how the model is deployed and used.
Combining these techniques can lead to significant performance gains, increased efficiency, and better scalability for AI applications.
This approach is particularly valuable for real-time AI applications, large-scale deployments, and microservices architectures.
By strategically combining AI model optimization and dynamic routing, organizations can maximize the effectiveness and efficiency of their AI solutions, unlocking new possibilities for innovation and automation."
doc2016
1月前
swtich library of virtual bpx associations
It seems like you're asking about changing the virtual switch configuration for a virtual machine, likely in VirtualBox. This usually involves changing the network mode of the virtual network adapter. Here's a breakdown of common scenarios and how to change them, along with some things to keep in mind:
Understanding Virtual Switches
Virtual switches in VirtualBox act like physical network switches, controlling how your virtual machines connect to networks (your host computer, the internet, or other VMs). There are a few main types:
NAT: (Network Address Translation) This is the default. Your VM gets a private IP address and accesses the internet through your host machine's connection. It's good for basic internet access but isolates the VM from your local network.
Bridged Adapter: Your VM acts like a completely separate device on your physical network, getting its own IP address from your router. This is useful if you want your VM to be directly accessible to other devices on your network.
Internal Network: This creates a network isolated to your host machine. VMs with this setting can only communicate with each other.
Host-only Adapter: Similar to Internal Network, but your host machine can also communicate with the VMs.
Changing the Virtual Switch
Open VirtualBox Settings: Shut down the virtual machine you want to modify. Right-click on it in the VirtualBox Manager and select "Settings."
Go to Network: In the settings window, navigate to the "Network" tab.
Select the Adapter: Choose the network adapter you want to change (usually Adapter 1).
Change "Attached to": This is where you select the virtual switch type (NAT, Bridged Adapter, etc.).
Configure Adapter Settings (if needed):
Bridged Adapter: You might need to select the specific physical network adapter on your host machine to bridge to.
Host-only Adapter: You might need to choose or create a host-only network interface.
Click OK: Save the changes.
Important Notes
Guest OS Network Configuration: After changing the virtual switch, you'll likely need to reconfigure the network settings inside your virtual machine's operating system to match the new network mode. This might involve setting a static IP address, changing the DNS server, or enabling/disabling DHCP.
Network Conflicts: Switching to Bridged Adapter mode can sometimes cause IP address conflicts on your network if the VM's assigned IP address is already in use by another device.
Troubleshooting: If you have trouble with network connectivity after changing the switch, double-check the network settings both in VirtualBox and within your guest operating system.
Need More Help?
If you're still having trouble or need more specific guidance, please provide the following information:
Virtualization Software: (e.g., VirtualBox, VMware, Hyper-V)
Guest Operating System: (e.g., Windows, Linux, macOS)
Desired Network Setup: What do you want your VM to be able to do on the network? (e.g., access the internet, connect to other VMs, be accessible from other devices on your local network)
doc2016
2月前
does drive os send realtime data to the learning module during the course of operation? answer from geminiai
Yes, absolutely! DRIVE OS is designed to send real-time data to the learning module during operation. This is a crucial aspect of how autonomous vehicles (AVs) learn and improve their driving capabilities.
Here's a breakdown of how it works:
DRIVE OS and its Components
Operating System: DRIVE OS is NVIDIA's operating system specifically designed for autonomous vehicles. It provides a platform for running the complex software and AI models required for self-driving.
Sensor Input: AVs are equipped with various sensors, including cameras, lidar, radar, and ultrasonic sensors, which constantly gather data about the vehicle's surroundings.
Perception: DRIVE OS processes the raw sensor data to perceive the environment, identifying objects, lanes, traffic signals, and other relevant information.
Planning and Control: Based on the perceived environment, DRIVE OS plans the vehicle's path and controls its actions, such as steering, acceleration, and braking.
Learning Module: The learning module is responsible for training and improving the AI models that power the AV's perception, planning, and control systems.
Real-Time Data Flow
Sensor Data Acquisition: Sensors continuously collect data about the environment.
Data Processing: DRIVE OS processes the raw sensor data in real-time, extracting relevant features and information.
Data Transmission: This processed data is transmitted to the learning module, which might be located on the vehicle itself or in the cloud.
Model Training and Updates: The learning module uses this real-time data to train and update the AI models, improving their accuracy and performance.
Deployment of Updated Models: The updated models are then deployed back to the vehicle, enhancing its driving capabilities.
Types of Data Sent to the Learning Module
Camera Images: Images from the vehicle's cameras, along with annotations or labels identifying objects and features.
Lidar Point Clouds: 3D point cloud data from lidar sensors, representing the shape and distance of objects.
Radar and Ultrasonic Data: Information about the speed and distance of objects detected by radar and ultrasonic sensors.
Vehicle Data: Data from the vehicle's internal sensors, such as speed, acceleration, steering angle, and GPS location.
Driver Inputs: In some cases, data about the driver's actions, such as steering corrections or braking, might also be sent to the learning module.
Benefits of Real-Time Data
Continuous Learning: Allows the AV to continuously learn and adapt to new situations and environments.
Improved Performance: Real-time data helps improve the accuracy and performance of the AI models, leading to safer and more reliable autonomous driving.
Faster Development: Accelerates the development and validation of AV systems by providing a constant stream of real-world data.
Overall
The ability of DRIVE OS to send real-time data to the learning module is a crucial aspect of its functionality. This enables continuous learning, improved performance, and faster development of autonomous driving systems. By leveraging real-world data, AVs can become more intelligent, adaptable, and safe over time.
Sources and related content
doc2016
2月前
for the combined classical and quantum network to do the job of generative ai,
unified messaging/communications has to be maintained throughout the defined network, such as msft fabric. api/instructions/communications/answers/questions /parts of algorithms going to different parts involved in the parallel processing of the query need to be orchestrated and done is a reliable way so that the probability of their validity is obtained in the most reliable manner possible and in the time frame needed/desired. guesses.
timing, time, clocks and a multitude of clocks might be involved , just one example is qite, or more simpler word processing apps, especially, if any latency of their parallel work is encountered, might need a file system for storing the work until 'done'..guesses.
so, the different components of the system/architecture need to be ready to receive event driven messaging, data;perhaps, notified by sms?
and their partial work placed into a file until completed and then the app notifies the integrator, next step.
from another company:"Method and system for automatically downloading and storing markup language documents into a folder based data structure
Patent number: 9405736" from justia.
patent application 2013 and grant 2016....long live this patent, which would seem crucial to cuda-q, etc.
8 could use it to interoperate with the various apps and their timing?
doc2016
2月前
"Focus: Fabric focuses on providing a unified platform for data and analytics, while .NET is a general-purpose development platform for building various types of applications.
Deployment: Fabric is a cloud-based SaaS offering, while .NET applications can be deployed on-premises, in the cloud, or in hybrid environments.
Technology Stack: Fabric leverages various technologies within the Azure ecosystem, while .NET is a specific framework for building applications on the Microsoft platform."
one platform for unified communications is 8 mantra.....
this .NET is a specific framework for building applications on the msft platform.
now, is this an arbitrary object platform in the manner of vcsy or borland delphi, which is willy nilly? the tools for building might say msft on them, but are they manifestations of v ip patents?
the key word for the presentation is do they use arbitrary in the manner of vcsy or pure chance.......from my readings on quantum stuff, there is no pure chance . everything is by design/rules of order/probabilities......
doc2016
2月前
is microsoft fabric vcsy ips?
i asked gemini ai 1.5 pro :"is microsoft fabric a new .net platform?
No, Microsoft Fabric is not a new .NET platform. It's actually a unified analytics platform that brings together various data and analytics tools into a single, integrated environment.
Here's a breakdown of what Microsoft Fabric is and how it relates to .NET:
Microsoft Fabric:
Unified Analytics Platform: Fabric combines components from Azure Synapse Analytics, Azure Data Factory, Power BI, and other services into a single platform. It provides a comprehensive suite of tools for data integration, engineering, warehousing, science, real-time analytics, and business intelligence.
SaaS Offering: Fabric is delivered as a Software as a Service (SaaS) solution, meaning it's hosted and managed by Microsoft in the cloud.
Key Features:
OneLake: A multi-cloud data lake that provides a centralized repository for all your data.
Data Factory: A data integration service for creating ETL (Extract, Transform, Load) pipelines.
Synapse Analytics: A platform for big data analytics, data warehousing, and data science.
Power BI: A business intelligence tool for data visualization and reporting.
Target Users: Data engineers, data scientists, data analysts, and business users.
Relationship to .NET:
Not a .NET Platform: Fabric is not built on or directly related to the .NET platform. It's a cloud-based SaaS offering that leverages various technologies and services within the Microsoft Azure ecosystem.
Potential for Integration: While Fabric itself is not a .NET platform, it's possible to integrate .NET applications with Fabric. For example, you could use .NET to build custom data connectors, process data, or interact with Fabric APIs.
.NET for Data Science: .NET can be used for data science tasks within the Fabric environment, particularly with tools like ML.NET.
Key Differences:
Focus: Fabric focuses on providing a unified platform for data and analytics, while .NET is a general-purpose development platform for building various types of applications.
Deployment: Fabric is a cloud-based SaaS offering, while .NET applications can be deployed on-premises, in the cloud, or in hybrid environments.
Technology Stack: Fabric leverages various technologies within the Azure ecosystem, while .NET is a specific framework for building applications on the Microsoft platform.
In Summary:
Microsoft Fabric is a powerful analytics platform that provides a comprehensive suite of tools for data professionals and business users. While it's not directly related to the .NET platform, there are opportunities for integration and using .NET for specific tasks within the Fabric environment. However, it's important to understand that Fabric is a distinct offering with its own focus and capabilities.
Sources and related content
"
doc2016
2月前
does microfrontend carry it's own time stamp?
It's not quite accurate to say that a microfrontend itself carries its own timestamp. However, timestamps can play a crucial role in managing and updating microfrontends within a larger web application.
Here's how timestamps relate to microfrontends:
1. Independent Deployments:
Microfrontends are designed to be independently deployable. This means that each microfrontend can be updated and released without affecting other parts of the application.
Timestamps can be used to track the version and deployment time of each microfrontend. This helps ensure that the latest version of each microfrontend is loaded and displayed in the application.
2. Cache Busting:
Browsers often cache static assets (like JavaScript and CSS files) to improve performance. However, this can cause problems when a microfrontend is updated, as the browser might still load the old cached version.
Timestamps can be used as query parameters in the URLs of microfrontend assets. This forces the browser to re-fetch the assets from the server, ensuring that the latest version is loaded.
Example: https://my-app.com/microfrontend-1.js?t=1678886400 (where t is a timestamp).
3. Version Control and Rollbacks:
Timestamps can be used in conjunction with version control systems to track changes to microfrontends and enable rollbacks to previous versions if necessary.
This helps maintain the stability and reliability of the application, especially when multiple teams are working on different microfrontends concurrently.
4. Dependency Management:
Microfrontends often have dependencies on shared libraries or resources.
Timestamps can be used to track the versions of these dependencies and ensure that compatible versions are loaded.
5. Build Processes:
Timestamps can be embedded into the build artifacts of microfrontends during the build process. This helps track the build time and identify potential issues related to specific builds.
Important Considerations:
Timestamp Granularity: The granularity of timestamps (e.g., seconds, milliseconds) should be chosen based on the frequency of updates and the desired level of precision.
Synchronization: In a distributed microfrontend architecture, ensuring that timestamps are synchronized across different servers and clients can be important.
Alternative Versioning Strategies: While timestamps can be useful, other versioning strategies, such as semantic versioning or content hashing, might be more robust and reliable in certain situations.
In Summary:
While microfrontends themselves don't inherently carry timestamps, timestamps can be a valuable tool for managing their deployment, versioning, and integration within a larger application. By using timestamps effectively, developers can ensure that the latest versions of microfrontends are loaded, manage dependencies, and maintain the stability of the application.
Sources and related content
doc2016
6月前
Device drivers interact with the kernel primarily through a well-defined set of interfaces and mechanisms:
Kernel APIs and System Calls:
Drivers use kernel-provided functions (APIs) to access kernel resources, manage memory, schedule tasks, and communicate with other parts of the system.
Drivers also implement specific entry points (system calls) that the kernel invokes to perform device-specific operations requested by user applications or other parts of the system.
Interrupts and DMA:
Drivers use interrupts to signal the kernel when a device requires attention, such as when data is ready to be transferred or an error has occurred.
Drivers may also use Direct Memory Access (DMA) to transfer data directly between a device and memory without involving the CPU, improving performance.
Device Registration and Management:
Drivers register themselves with the kernel during system initialization, providing information about the devices they support and the operations they can perform.
The kernel maintains a registry of loaded drivers and handles device discovery, configuration, and resource allocation.
Kernel Modules:
In many modern operating systems, drivers are implemented as loadable kernel modules, allowing them to be added or removed from the running kernel without requiring a system reboot.
doc2016
7月前
the role of cpaas in generative ai
Show drafts
CPaaS (Communications Platform as a Service) plays a crucial role in enabling and enhancing the integration of generative AI into communication channels.
Here's how:
Seamless Integration: CPaaS platforms provide the necessary infrastructure and APIs to easily integrate generative AI models into various communication channels, such as voice, SMS, chat, and video. This allows businesses to leverage the power of AI to enhance customer interactions, automate processes, and personalize experiences.
Real-Time Communication: CPaaS enables real-time communication between generative AI models and customers, allowing for instant responses and dynamic conversations. This is essential for applications like AI-powered chatbots, virtual assistants, and voice assistants.
Scalability: CPaaS platforms are designed to be scalable, allowing businesses to easily add or remove communication channels and AI capabilities as needed. This ensures that the generative AI-powered communication solution can adapt to changing business needs and growing customer demand.
Omnichannel Experience: CPaaS enables businesses to provide a consistent and seamless omnichannel experience for their customers. Generative AI models can be integrated across multiple channels, allowing customers to interact with the same AI assistant across different platforms, such as website chat, SMS, or voice calls.
Data Collection and Analysis: CPaaS platforms can collect and analyze data from customer interactions with generative AI models. This data can be used to train and improve the AI models, personalize customer experiences, and gain insights into customer behavior and preferences.
Security and Compliance: CPaaS platforms often provide built-in security features and compliance measures to protect customer data and ensure privacy. This is crucial when dealing with sensitive information that may be shared during interactions with generative AI models.
Use Cases of CPaaS in Generative AI:
AI-Powered Chatbots: CPaaS enables businesses to deploy AI-powered chatbots that can handle customer inquiries, provide support, and even complete transactions.
Virtual Assistants: CPaaS can be used to create virtual assistants that can schedule appointments, send reminders, and perform other tasks for customers.
Voice Assistants: CPaaS allows businesses to develop voice assistants that can answer questions, provide directions, and control smart home devices.
Personalized Marketing: Generative AI models integrated with CPaaS can create personalized marketing messages and recommendations based on customer data and preferences.
Conclusion:
CPaaS plays a critical role in bridging the gap between generative AI and communication channels. By providing the necessary infrastructure, APIs, and features, CPaaS enables businesses to leverage the power of AI to transform customer interactions, improve efficiency, and drive growth.
Sources and related content
doc2016
9月前
from the quarterly:"These are the early indicators that our efforts to build a high-performance go-to-market organization are working. Given the nature of enterprise sales cycles and our ratable revenue growth, there's a lag before our progress is evident in our revenue growth. But I am confident that the changes we've made will drive improved performance and accelerating growth by the end of the current fiscal year. As we look to the future, our strategy is clear. We will continue to drive innovation, intentionally engineered solutions that deliver superior business outcomes for our customers. I encourage you to watch a short video case study of the San Diego Zoo's implementation of our integrated solution as an example of our solution approach and rapid time to value.
Working in partnership with the internal team at the zoo, we implemented a full service contact center with an AI-based self-service option for inbound calls in less than 45 days. We have intentionally engineered our products and solutions for this level of agility and rapid time to value. We can leverage this advantage to develop our own purpose-built solutions as well. For example, 2024 is a big year for elections in both the US and UK. Using the power of conversational AI and automation, our UK team built a library of out-of-the-box templates to support local government and improve citizen access to important information for upcoming elections. To drive awareness and adoptions of our innovations, we are committed to continued improvement in our go-to-market activities."
doc2016
9月前
yahoo seems to have deleted my posts. but i guess investorhub is freedom from tyranny and manipulation.
8 and aws;
sap and aws for multiple chat apps in an app? sap and aws pr today.
ec2 of aws allows users to rent virtual servers . to use such and the services they provide you might also need to use the 8 patent for user designated dynamic dns mapping of virtua server and virtual services desired.
a review of the sap/aws pr shows there are 7 ai's available : AIw1 labs, anthropic, cohere,meta, mistral ai, stabiity ai, and amazon.
i'm wondering if apple will use some aws for their ai. i believe apple used ibm, which was on vz, which is now on aws.
small world...
doc2016
2年前
natural language prompts and ability to translate and transcribe. etl........
now i wonder if a prompt is calling for the correct virtual server/virtual service to do you work?
one for financial, another for football team, another for medical, another for chemistry/physics/,
another for medical specialties like eye, ent, cardiac, endocrinology or across the board medicine?
another for dna sequencing/synthetic construction.
etc.
doc2016
2年前
google is down today because of rumored faulty information for a search on james webb telescope. the ai said james webb was the first to take pictures of exoplanets, but it was done earlier . However, a search of nasa and james webb disclosed that james webb would be taking/took pictures of exoplanets in never before seen wavelengths which would yield new information. so, not exactly right but not wrong. the meta data should have indicated what kind of pictures...new wavelength pictures. perhaps, like any other ml/ai , the more you use it, the better it gets?
doc2016
2年前
hp bought neoware and now poly. poly used 8x8 network. new partner of 8x8 hp.
hp partner of vmware and msft and cisco and intel and amd and google and ect.
vista equity and citrix which is to be integrated with tibco.
citrix client to msft os/pc's. for connection to server/services/and other organizations.
8x8 and cloud ip. including nodes and networked nodes.
connectivity via ethereum blockchain, merge proof of stake.
contracts at the client side determine the functionality/routing/etc.
data about corporate interactions analyzed in realtime.
sales force organization can be redone with significantly less effort than before. this would facilitate mergers and acquisitions and reorganizations.
we all remember 1-800 flowers, or some of us do.
adhoc working groups now becoming de rigueur in many organizations due to job, goals, employee attendance, etc.
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glenn1919
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