We propose a classifier that can identify ten common home network problems based on the raw textual output of networking tools such as ping, dig, and ip. Our deep learning model uses an encoder-only transformer architecture with a particular pre-tokenizer that we propose for splitting the tool output into token sequences. The use of transformers distinguishes our approach from related work on network problem classification, which still primarily relies on non-deep-learning methods. Our model achieves high accuracy in our experiments, demonstrating the high potential of transformer-based problem classification for the home network.

Juniper Networks has further expanded several AI solutions within its Mist portfolio. The improvements, such as the Mist WAN Assurance tool and an update to the Marvis AI engine, will make it easier for businesses to automatically detect network problems and resolve connectivity issues. Juniper Networks is strongly committing to AI for detecting problems within corporate networks and the underlying connections. The portfolio of Mist, which was acquired last year, has played a key role. With the tools of Mist, companies are able to automatically detect and solve network problems in (wireless) networks.

Next-generation network analytics driven by artificial intelligence and machine learning promise to revolutionize conventional infrastructure management models, simplifying operations, reducing costs, and providing fresh insights. Yet, as with many new technologies, AI-fueled analytics can only deliver its promised benefits if applied correctly to the proper problems. AI can be trained to pinpoint network failures and other shortcomings and bottlenecks, sometimes even before they happen. "It can diagnose the root cause of poor quality network streams to find if the problem is in the service provider's network, the backbone network or your ISP's network," said Shervin Shirmohammadi, a professor in the School of Electrical Engineering and Computer Science at the University of Ottawa, and an IEEE Fellow. "It can also solve network congestion (issues), provide bandwidth and delay estimation for better video or gaming experience, provide fair bandwidth allocation to users or within cloud data centers, fix insufficient network utilization and, in general, achieve a higher network performance and a happier customer," he added.

It's important to understand the distinction of machine learning versus other forms of automation analytics. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

If nothing else, AI continues to climb the technology hype curve. It was impossible to read the news, browse the web, attend a conference, or even watch television without seeing a reference to how AI is making our lives better. Since Alan Turing declared "what we want is a machine that can learn from experience" in a 1947 lecture to the London Mathematical Society, the imaginations of computer scientists and engineers have run wild with visions of a computer that can answer questions on par with a human. Today, almost everyone in business is looking at how to leverage AI, and there is no shortage of vendors looking to capitalize on the trend. Venture Scanner currently tracks more than 2,000 AI startups that have received more than $26 billion in funding.