Intel-backed startup Nyansa chases the total problem in the AI of network monitoring
There are many opinions about what matters most in machine learning. Some would say it’s the data, some would say it is the algorithms and equations used to train computers on that data. Still, others would say it is the formulation of the question itself that is most important in machine learning.
The last point of view is representative of a startup called Nyansa, composed of networking veterans and big data specialists who believe they have a better approach to network management than, say, Cisco Systems or Hewlett-Packard Enterprise.
The reason, according to chief technologist and co-founder Anand Srinivas, is because Nyansa figures out all the different parts of a system — not just the switches and wireless access points, but the applications as well, that can affect what an end user experiences.
“Our innovation is not inventing new machine learning algorithms, it is in terms of bringing machine-learning algorithms to a use case like networking,” Srinivas said in an interview with ZDNet on Monday. Srinivas holds a PhD in wireless networks and algorithms from MIT, and has held a number of industry positions, especially for software-defined networking, at firms such as Overture Networks, Plexxi, and Airvana.
Also: Network technologies are changing faster than we can manage them
Based in Palo Alto, four-and-half-year-old Nyansa sells tools to IT to monitor the health of the network, explain degradations of performance when they happen, and then propose solutions. Its tools run on Amazon’s AWS, though they can also be installed on-premise, with hooks back to the public cloud. The company has over 100 customers, representing over 10 million devices “under observation,” it says, across 200,000 access points from different vendors, on hundreds of production networks. Clients include Uber, Tesla, and Lululemon. It has been bankrolled by chip giant Intel’s investment arm, and Formation8, to the tune of $27 million.
Its machine learning tools are very simple, far less sophisticated or adventuresome than today’s deep learning neural network approaches. They include things such as logistic regression analysis, random forest searches, nearest-neighbor searches, and “cluster” analysis. “A lot of this is off-the-shelf stuff,” confesses Srinivas.
“It is more straightforward than deep learning; deep learning is not the right approach for us, not yet, not until we grab all the low-hanging fruit which the simpler kinds of machine learning algorithms can give us.”
Nyansa gathers petabytes of data from those millions of client devices and thousands of access points, and first establishes a baseline. How well does the network perform, in terms of things such as what percentage of users have a wireless connection issue, on average, or what percentage have a Citrix application connectivity issue? Some of these devices have no users, they are Internet-of-Things gadgets, such as a General Electric wireless patient monitor, or the wrenches used by Tesla on the shop floor in its Fremont facility. Telemetry data must be gathered from those devices as a baseline for performance.
“One way you can think of us is as a vertically-integrated Splunk,” says Srinivas, referring to the Big Data monitoring system that ingests and mines customer data. “We can take any type of data and tie it into our system, but we go one step further to solve customer use cases.”
Also: Cisco launches UCS system for AI, machine learning, deep learning
By having data from multiple customers in the cloud, says Srinivas, a baseline can be set not just for a given customer but across an industry. “What’s good performance,” he asks rhetorically. For a given industry, “if the baseline is a 30 percent network performance connection failure, then 5 percent for a given customer may be fine for them.”
Once a baseline is established, deviations can be detected in order to determine if a problem is, say, a network problem per se, or rather an application problem. And once deviations from the baseline are established, predictions can be made. “Based on what other customers have done, what is our prediction for actions that have been most beneficial,” is how he describes it. By then observing how recommendations are carried out, and the effects, the system can move beyond mere correlation, the focus of a lot of machine learning, to a sense of causality.
“By automatically learning a baseline, and learning it everywhere, and doing it in the exact same way, we can give you a recommendation, and it will have an impact.”
“At first, perhaps it’s 80 percent correlation. But when a customer takes that action, that baseline will tell you the truth of whether the action made a difference; if it [performance] gets better, the recommendation by definition is correct. That feedback loop gets you back into causation.”
Also: Why you need to learn about application performance monitoring TechRepublic
As to how they differ from Cisco or Hewlett, Srinivas sees the Nyansa system as more comprehensive in what it looks at than either one. “We don’t care who the vendor is for wireless [access points] or RADIUS or DNS or DHCP, we will take whatever data from whatever source, that is a fundamental difference.”
“Their data sources are limited to their own stuff.”
Srinivas offers the example of GE bedside wireless patient monitors in a hospital run by client Mission Health, a healthcare system serving North Carolina. It is not enough to say that a network is or is not performing at baseline. “The final thing that matters is, are those waveforms [of patient data from the monitors] getting back to the nurse, and is that nurse able to see the oxygen level on the screen. It doesn’t matter if the wireless signal is good, you can’t just baseline wireless, because the particular data source you care about, the monitor, has nothing to do with switches.”
Also: Facebook is using AI to curb exploitative and naked images of kids CNET
The answer, then, to machine learning, in its simpler form and perhaps even its more complex incarnations, is how engaging with the complexity of situations builds understanding. “Even with deep learning, the magic is in tuning the deep learning network, knowing how to turn the knobs,” observes Srinivas. “The crux of it is experience, over time knowing exactly how to tune things.”
Previous and related coverage:
What is AI? Everything you need to know
An executive guide to artificial intelligence, from machine learning and general AI to neural networks.
What is deep learning? Everything you need to know
The lowdown on deep learning: from how it relates to the wider field of machine learning through to how to get started with it.
What is machine learning? Everything you need to know
This guide explains what machine learning is, how it is related to artificial intelligence, how it works and why it matters.
What is cloud computing? Everything you need to know about
An introduction to cloud computing right from the basics up to IaaS and PaaS, hybrid, public, and private cloud.
The Experimental Honda Business Jet With A Strange Turbofan Design
While most small aircraft are made out of a combination of aluminum and fiberglass, the MH02 was the first ever all-composite jet, meaning that all structural components of the jet were made out of a carbon fiber-epoxy resin material. The carbon fiber wonder was just under 37 feet long and had a wingspan of over 36 feet. Its two aforementioned turbofans pumped out a combined 2,464 pounds of thrust, allowing it to reach speeds of 353 knots (or 406 miles per hour).
Unconventional design notwithstanding, the MH02 never saw the light of day or real production aside from the prototype. Honda never intended the MH02 to take to the sky as a production jet and its sole purpose was to act as a test bed for Honda’s flight-related projects. The MH02 wasn’t going to win many prizes in the looks department, but the data collected during its flight proved to be invaluable to the future HondaJet. It showed that the company responsible for making the Honda Accord was capable of making a feasible passenger jet, further cementing Honda’s reputation as the producer of just about anything that has an engine, turbofan or otherwise.
[Featured image by Morio via Wikimedia Commons | Cropped and scaled | CC BY-SA 3.0]
iPhone 15’s Potential Charging Limits May Bring Trouble For Apple
Given that Apple has yet to officially confirm or deny the possibility of its lower-priced iPhones getting slower charging speeds, the IMCO hasn’t discussed a possible regulatory intervention. IMCO’S major bone of contention is the possibility of Apple implementing a feature that would only allow official Apple USB-C accessories to be used with USB-C iPhones — thereby locking out competing products.
At this point, the IMCO seems unaware of Apple’s MFI (Made for iPhone) program, which allows third-party accessory makers to design and manufacture iPhone accessories that conform to Apple’s strict quality standards. Apple claims the MFI certification acts as a quality seal and prevents users from ending up with poor-quality devices that could potentially damage its products. However, Apple’s intentions behind the MFI program aren’t entirely noble, given that the company earns a small commission from the sale of each MFI-certified accessory.
At this point, the IMCO sees these rumored restrictions as an anti-competitive move that completely violates consumer rights. It remains to be seen if the two parties are able to settle these differences before the launch of the iPhone 15 series in September this year.
Hyundai And KIA To Offer Free Steering Wheel Locks To Combat Viral TikTok Thefts
The robberies started as a viral TikTok challenge where thieves, predominantly young boys who choose to be distinguished as the “KIA Boys,” have been hotwiring certain KIA and Hyundai car models using a USB cable. This is because the plagued models lack a crucial component called an ignition immobilizer, responsible for cutting off the fuel supply to the engine in case someone attempts to start the car without the actual key.
Due to the viral TikTok trend, several thieves have joined in to carjack the affected models and spread the word further. Most of these models affected by the flaw use mechanical keys and not smart key fobs.
Incidentally, many insurance companies “temporarily” stopped offering coverage for the affected models owing to their lack of anti-theft features. Despite warnings from several state and city police departments, there is no national tally of the number of robberies since the trend went viral. But in January 2023, Progressive, one of the leading insurance companies, told CNN that these vehicles were 20 times more likely to be stolen. It was one of the companies to limit the sale of new policies for the affected vehicles.
The Experimental Honda Business Jet With A Strange Turbofan Design
While most small aircraft are made out of a combination of aluminum and fiberglass, the MH02 was the first ever...
Google Bard gets better at homework with improved math and logic capabilities
Google Bard is getting a little smarter today with the addition of math and logic capabilities. Google employee Jack Krawczyk...
Hackers exploit WordPress plugin flaw that gives full control of millions of sites
Getty Images Hackers are actively exploiting a critical vulnerability in a widely used WordPress plugin that gives them the ability...
The 5 Greatest McLaren Racing Liveries, Ranked
2018 saw McLaren F1 cars return to an orange livery after many years of alternative color schemes. Many of these...
Twitter posts the code it claims determines which tweets people see, and why
Enlarge / Twitter has posted what it states is the code used by its algorithm to recommend tweets to its...
Social12 months ago
Web.com website builder review
Social3 years ago
CrashPlan for Small Business Review
Gadgets4 years ago
A fictional Facebook Portal videochat with Mark Zuckerberg – TechCrunch
Cars4 years ago
What’s the best cloud storage for you?
Social4 years ago
iPhone XS priciest yet in South Korea
Mobile4 years ago
Memory raises $5M to bring AI to time tracking – TechCrunch
Security4 years ago
Google latest cloud to be Australian government certified
Social4 years ago
Apple’s new iPad Pro aims to keep enterprise momentum