Redefining Technology with AI and Advanced Computing

Advanced computing and artificial intelligence (AI) have become the core of today’s technological development. This trend continues to expand, supporting the increasing complexity of both business and everyday life.

This article explores key insights about AI and advanced computing that you should know.

1. Agentic AI

Agentic AI refers to AI systems capable of acting autonomously to solve problems, make decisions, and perform tasks. It analyzes data in real time and supports operations across platforms and applications.

Examples of agentic AI applications include managing organizational resources and integrating with ERP systems. It helps businesses operate faster, more accurately, and more efficiently.

The following are types of AI used in ERP systems:

Machine Learning (ML): Analyzes data to predict business trends
Natural Language Processing (NLP): Powers AI chatbots and voice assistants

Steps to Apply AI to ERP Systems:

Assess Compatibility: Check whether your organization’s ERP system—such as SAP, Oracle, or Microsoft Dynamics—supports AI integration.
Identify Business Needs: Select AI solutions based on the specific needs of different departments, such as accounting, HR, or logistics.
Start Small: Begin with a pilot implementation on small tasks, then scale up gradually as the system proves effective.

In addition, agentic AI can respond to customer service inquiries, enabling faster response times. This reduces employee workload and enhances customer satisfaction.

The following are types of AI used in customer service:

Chatbot: Uses Natural Language Processing (NLP) to enable AI to automatically understand and interact with customers. It can answer general questions—such as product prices, order status, and warranties—and provide service 24/7.
Voice Assistant: Handles customer inquiries over the phone. AI helps route calls to the appropriate department, reducing wait times.
Sentiment Analysis: Analyzes customer tone, emotions, and messages. If a customer is dissatisfied, AI can immediately alert staff to take action.
Machine Learning: Analyzes customer behavior based on data and recommends products or services that better match customer preferences. It is commonly used in the e-commerce, banking, hospitality, and telecommunications industries.

2. AI Governance Platforms

The goal of AI governance platforms is to prevent the misuse of AI and to increase transparency in AI decision-making. These platforms aim to ensure that governance aligns with legal, ethical, and efficiency standards.
One use case is a platform for reviewing AI algorithms to prevent bias.

3. Integrating AI into Everyday Life (Ambient AI)

Ambient AI refers to the integration of artificial intelligence into everyday environments. It enables objects and systems to interact with users in a natural and intuitive way, using sensors and smart tags.

Examples of using Ambient AI:

Nest Thermostat: This smart home device connects to Wi-Fi and integrates with home automation systems to control heating, ventilation, and air conditioning (HVAC).
Warehouse Sensor System: Warehouses use this sensor system to manage inventory as soon as items are moved. It uses smart cameras, IoT sensors, RFID, and machine learning to detect movement and analyze behavior. It also enables real-time management of automated warehouses.

4. Development of Sustainable Computing Technology (Energy-Efficient AI)

Developers are evolving AI and computing systems to reduce energy consumption and utilize renewable energy to lower carbon dioxide emissions. They achieve this through hardware and software specifically designed with sustainability in mind. The following are some of the key technologies:

Optical Computing: A concept that uses light instead of electricity to process data. It relies on photons rather than electrons, as used in traditional computer circuits. This allows for faster data processing, lower energy consumption, and supports simultaneous computations.
Neuromorphic Chip: A computer chip designed to mimic the structure and function of the human brain. When used with AI, it enables faster processing of complex data, reduces energy consumption, and improves efficiency. This chip supports renewable energy use and helps reduce CO2 emissions. It can be applied in robotics for factories as well as in data centers.

5. Hybrid Computing

Hybrid computing is the integration of traditional computing mechanisms and quantum computing to create a more efficient system. The benefits of hybrid computing include:

Supporting the development of advanced AI: Engineers use hybrid computing to train deep learning models in the cloud, which require high computational power. They also use it to process data from sensors or devices such as cameras and smartphones, enabling immediate results without constantly sending data to the cloud. This approach reduces response time (real-time inference) and lowers data transmission costs.
Meeting the demand for large-scale data processing across industries: For example, the development of autonomous vehicles requires real-time, precise processing. Hybrid computing is also used in systems for simulating weather forecasts.

Conclusion

AI and advanced computing are not only transforming the way businesses operate but also enhancing people’s lives in many ways—through greater convenience, cost reduction, and improved energy efficiency. However, it is essential to develop and use AI ethically to ensure these technologies create long-term value for society.

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_{Source: Techsauce}

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