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Intel-backed startup Nyansa chases the total problem in the AI of network monitoring

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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.


Nyansa, a network monitoring startup competing with Cisco and Hewlett-Packard Enterprise, believes it wins the day for clients such as Uber not by the complexity of its artificial intelligence tools, which are fairly routine, but by its understanding of the problem of network performance. (Image: Nyansa)

“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.

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2022 BMW M8 Competition range revealed with bigger screens and better lights

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German automaker BMW has updated its 2022 M8 Competition sport-luxury car. You can still get an M8 Competition in three body styles (2-door Coupe, 2-door Cabriolet, and 4-door Gran Coupe), sharing the same 4.4-liter twin-turbocharged V8 engine with 617 horsepower and 553 pound-feet of torque.

Images: BMW AG
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Tesla Cybertruck delayed again plus Elon Musk squashes $25k EV rumors

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Tesla closed out 2021 with a bumper year, besting Q4 estimates and pushing EV deliveries past 300,000, though Elon Musk tempered hopes for the arrival of the Cybertruck and more affordable models. Revenue in the year as a whole grew 71%, Tesla announced, describing 2021 as “a breakthrough year” for the automaker, but some of the most anticipated electric vehicles are still some way out.

Tesla

No Tesla Cybertruck until 2023

The most conspicuous project that Tesla has underway is undoubtedly the Cybertruck. The oddly-shaped all-electric pickup proved controversial when Elon Musk first revealed it, and glimpses of development prototypes in the intervening years haven’t dimmed its ability to polarize opinion. Undoubtedly the most frequently-asked question, however, is when Tesla actually might put the Cybertruck into production.

Tesla

Tesla’s investor deck continues with the same, vague timeline as has been stated in previous releases. “We are making progress on the industrialization of Cybertruck, which is currently planned for Austin production subsequent to Model Y,” the automaker says.

Speaking on the investor call, however, Musk confirmed that the Cybertruck wouldn’t go into production this year. The primary focus for Tesla, the CEO explained, would be ramping production of its existing models, like the popular Model 3 and Model Y. They’re still in strong demand, with orders for some configurations of Model Y not expected to be delivered until August 2022.

Tesla Cybertruck pricing

Tesla screenshot by SlashGear

For the Cybertruck, there are still technological hurdles to be worked through, Musk admitted. The automaker is also still trying to figure out how to make it affordable: there was widespread surprise when Tesla announced the full-size electric pickup would have a starting price of around $40,000 when it began taking reservations in late 2019. For the moment, Musk said, the hope is that production can begin sometime in 2023.

Don’t expect the Tesla Roadster any time soon, either

What goes for the Cybertruck, also goes for Tesla’s rebooted Roadster. Also the spur of no shortage of reservation deposits – or the full $250,000 apiece in advance for those wanting one of the first 1,000 “Founder’s Series” cars – the Roadster was originally intended to go into production in mid to late 2021. That was delayed to 2022, and then to 2023.

Tesla Roadster

Tesla

The good news is that it’s still, apparently, on track for that timescale, though as Tesla feels the impact of the supply chain issues affecting the whole auto industry that could still change in the meantime. Chip constraints were name-checked by Musk as being a primary bottleneck for 2021 production of its cars, arguing that if Tesla tried to introduce new models in 2022 it would only have the overall impact of cutting total production output. The need to assign resources to new models would take away from the ability to build cars like the Model 3 and Model Y, he pointed out.

Engineering and tooling-up for the upcoming Tesla models may still begin in 2022. However they won’t go into production until 2023 at the earliest.

The $25,000 Tesla isn’t happening

Tesla line-up

Tesla

Though Tesla hasn’t been affected by the “market adjustments” that have seen dealers of other brands add thousands or even tens of thousands to the sticker price of a new car, it’s clear that the EV-maker is still focused on the trims with the biggest profit margins. Despite previous chatter of a $25,000 Tesla that could undercut even the most affordable Model 3, Musk says that’s simply not on the cards.

“We have too much on our plate,” the CEO said during the investor call.

The reality is, while Tesla has been surprisingly well placed for dealing with the supply chain crunch – including making admirable use of existing chip supplies by reprogramming its software to suit – like most car companies it can’t build as many as it would like to. Focusing on maximizing the return on each vehicle is the inevitable result, not only by prioritizing the more expensive configurations, but on post-sale software enhancements too. Indeed, “over time, we expect our hardware-related profits to be accompanied with an acceleration of software-related profits,” the investor deck points out.

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This carbon 3D-printed Rolls-Royce Cullinan is a $500,000 upgrade

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The Cullinan is the Rolls-Royce of SUVs, so what does this make 1016 Industries’ carbon-fiber, 3D-printed Cullinan? You can call it anything you like, but it is indeed a dignified way to go sporty. We highly prefer it over the quirky Mansory Rolls-Royce Cullinan unveiled last year for the 50th founding anniversary of the United Arab Emirates, and it’s all thanks to the crafty use of 3D printing for the details.

Images: 1016 Industries
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