Today, I will talk about Microsoft Edge, The New AI-Powered Browser for the Web & its new Ai features.
If you are looking for a browser that can do more than just display web pages, you might want to check out the new Microsoft Edge. The latest version of Edge is powered by a new AI model called Prometheus.
Which was developed in collaboration with OpenAI. Prometheus is designed to help you get more out of the web by providing better search, more complete answers, a new chat experience and the ability to generate content.
The new Edge integrates with the new Bing search engine, which uses Prometheus to deliver more relevant and comprehensive results for your queries. You can use the familiar search box to find information on simple things like sports scores, stock prices and weather, as well as more complex topics like travel plans, product reviews and recipes. You can also use the new sidebar to see more detailed answers and summaries for your questions, without leaving the web page you are on.
The new Edge and Bing can also provide you with complete answers for your queries, by reviewing results from across the web and summarizing them in a concise and clear way. For example, if you want to know how to substitute eggs for another ingredient in a cake you are baking, you can get detailed instructions right in the sidebar, without scrolling through multiple results. You can also see links to related web pages and videos for more information.
A New Chat Experience
For more complex searches that require more interaction and refinement, the new Edge and Bing offer a new chat experience. You can use the chat feature in the sidebar to ask follow-up questions, clarify details and get suggestions from Prometheus. The chat experience is interactive and conversational, and it can help you get the complete answer you are looking for. For example, if you want to plan a 5-day itinerary for a dream vacation to Hawaii, you can chat with Prometheus to get ideas on where to go, what to do and how to book your travel and accommodations.
A Creative Spark
There are times when you need more than an answer - you need inspiration. The new Edge and Bing can also generate content for you, based on your prompts and preferences. You can use the compose feature in the sidebar to ask Prometheus to help you write an email, a blog post, a resume, a quiz or anything else you can think of. You can specify the format, length and tone of the content you want, and Prometheus will generate it for you. You can also edit and refine the content as you like. The new Edge and Bing also cite all their sources, so you can see links to the web content they reference.
How does edge AI work?
Edge AI examples
Edge AI is the deployment of AI applications in devices that are close to where the data is generated or needed, rather than in a central cloud or data center. Edge AI can improve the speed, privacy and security of AI applications, as well as reduce the bandwidth and energy consumption.
Some examples of edge AI are:
- Speech recognition algorithms that transcribe speech on mobile devices
- Google’s new AI that automatically generates realistic background imagery to replace people or objects removed from a picture
- Wearable health monitors that assess heart rate, blood pressure, glucose levels and breathing locally using AI models developed in the cloud
- Facial recognition and real-time traffic updates on semi-autonomous vehicles
- Connected devices and smartphones that can perform tasks like image recognition, natural language processing and gesture control without internet connection
- Video games, robots, smart speakers, drones, wearable health monitoring devices and security cameras that use edge AI to enhance user experience and functionality
- Fraud detection and autonomous driving systems that use edge AI to analyze data and make decisions in real time
Benefits of Edge AI
- Data security and privacy: Edge AI can improve data security and privacy by performing data processing and analysis at the edge of the device or closer to the device, reducing the amount of data sent to the cloud or a central data center. This makes it harder for hackers to access or compromise the data.
- Real-time data processing: Edge AI can enable real-time data processing by reducing latency and providing high-performance data computation on edge devices. This can improve the responsiveness and accuracy of AI applications that require fast and reliable decision-making, such as autonomous driving, fraud detection and speech recognition.
- Lower internet bandwidth: Edge AI can conserve internet bandwidth by minimizing the data transmission between edge devices and the cloud or a central data center. This can reduce the cost and complexity of network infrastructure and improve the efficiency and scalability of AI applications.
- Lesser power consumption: Edge AI can reduce power consumption by optimizing the deployment and inference of AI models on edge devices. This can extend the battery life and performance of edge devices, especially for mobile and IoT applications that have limited power resources.
- Better responsiveness: Edge AI can enhance user experience and functionality by providing better responsiveness and personalization of AI applications on edge devices. This can improve customer satisfaction, engagement and loyalty for AI services that rely on user feedback, preferences and behavior, such as recommendation engines, intelligent content and ad services, home security systems and weather technologies.
Challenges of Edge AI
- Data loss: Edge AI devices may lose data due to network failures, device malfunctions, power outages or human errors. This can affect the accuracy and reliability of AI applications and require data backup and recovery mechanisms.
- Security: Edge AI devices may face security threats from hackers, malware, viruses or physical attacks. This can compromise the data privacy and integrity of AI applications and require encryption, authentication and protection techniques.
- Less compute power: Edge AI devices may have limited compute power compared to cloud or central data centers. This can constrain the complexity and scalability of AI models and require optimization, compression and pruning techniques.
- Machine variations: Edge AI devices may have different hardware specifications, software configurations and operating conditions. This can cause heterogeneity and inconsistency among edge devices and require standardization, coordination and synchronization techniques.