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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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Honda’s 2024 Prologue EV targets are difficult to believe

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Honda is setting aggressive sales goals for its upcoming all-electric Prologue SUV, though limited availability and concerns around EV subsidies could hamper those ambitions. A collaboration with GM, the Honda Prologue will be based on the Ultium battery-electric platform, though isn’t expected to go on sale until 2024.

Honda has been fairly miserly with details about the SUV, though the general promise is a distinctly Honda-esque vehicle that distinguishes itself from GM models based on the EV platform. An Acura version will follow shortly after that. Beyond Prologue, meanwhile, the automaker plans more EVs using its own e-Architecture platform.

That’s still in development, but Honda needs to get it right. The automaker is aiming for 70,000 annual sales of the Prologue when it arrives in 2024; by 2030, though, it’s anticipating BEV sales of 500,000 each year. Come 2040, Honda insists, it should only be selling electric vehicles. That’s a huge jump from where Honda is today, without a single all-electric model on sale in the US.

Demand for electrified vehicles, Honda insists, has been solid. Vehicles like the CR-V Hybrid and Accord Hybrid have helped make the first half of 2021 its best so far for electrified models, the automaker claims.

Still, it’s fair to say that Honda’s electric transition hasn’t been a straightforward one. Expectations were high for the Clarity series, a broad range of electrified vehicles that included pure-electric, plug-in hybrid, and hydrogen fuel cell models. All have since been discontinued, however, with questions in each case about the market competitiveness of each model.

In contrast, Honda has pushed ahead with regular hybrids: vehicles that combine combustion engines with battery-electric drive that is charged via excess ICE engine power or when the vehicle is braking. These can have a positive impact on fuel economy – the 2022 Insight, for example, is rated for up to 55 mpg in the city – but are far from zero-emissions.

Honda’s argument is that such hybrids offer drivers a reassuring taste of electrification. “We know customers who have a good experience with a hybrid vehicle are more likely to buy a battery electric vehicle in the future,” Dave Gardner, executive vice president of National Operations at American Honda Motor Co., Inc, points out. “Our strategy is focused on introducing a higher percentage of hybrids in core models in the near term, making a committed effort to achieve higher volume leading to the introduction of our Honda Prologue.”

The 2024 Prologue, though, won’t be a golden bullet to Honda’s EV problem. For a start, it’s going to be limited in availability, at least to begin with: just California and the ZEV states. The automaker argues that those regions would comprise the bulk of sales anyway, and that a broader release will follow later on.

Honda’s stance that the buying public needs that sort of convincing is at odds with many of its rivals. GM itself has been pushing ahead with Ultium, with the Cadillac Lyriq already opening for reservations, the GMC Hummer EV over-subscribed, and the promise of a Chevrolet Silverado EV in the relatively near future. Ford, meanwhile, has been even more aggressive, with the Mustang Mach-E proving a hit in the electric crossover segment, and the F-150 Lightning bringing an all-EV version of the best-selling pickup to market in spring 2022.

Even Honda management has conceded that its roadmap may not be as forceful as is required. The European Green Deal, revealed in July, paves the way for zero-emissions-only sales of vehicles in the EU by 2035; that’s five years ahead of the transition on Honda’s all-electric timeline. In the US, it also sees worrying implications around the proposed changes for EV subsidies.

Where the current federal incentive for electric vehicles promises up to $7,500, new proposals could increase that to as much as $12,500. However, in order to qualify for the full amount, automakers would need to not only produce their EVs in the US, but in unionized factories. Honda ticks the first of those boxes, but not the second.

“As with other automakers, Honda’s initial zero emission vehicle sales goals of 40 percent by 2030 are contingent upon fair and equitable access to state and federal EV incentives intended to encourage American consumers to purchase electric vehicles,” the automaker said today. “Honda has urged Congress to ensure that all vehicles made in America are treated equally.”

Tesla – which also operates US factories, but without a union workforce – has also been critical about the possible update to the incentives system. Final changes for the US EV tax credits have not been confirmed at this point.

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Tesla kills Referral program on all vehicles

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Tesla has announced that as of September 18, 2021, the referral program for all of its electric vehicles and solar panels has ended. Previously, the Referral program was a sales tool that Tesla used that gave those who referred buyers for Tesla vehicles or solar panels credits good for free Supercharging miles and opportunities to win an electric vehicle. The Referral program would also award users between $100 and $500 while giving those who referred buyers for solar products the opportunity to get Powerwall energy storage systems.

The elimination of the Referral program is happening globally, and the only product that is still eligible for the program is the Tesla Solar Roof. The referral award for that product is $500. For the Solar Roof, Tesla says that friends and family who order the product via the Referral link can earn $500 when they gain permission to operate.

The person who referred the Solar Roof buyer will receive $500 per referral. Tesla’s Referral program was a key sales tool to generate demand and sell its cars and other products because it relies on word-of-mouth. However, it is easy to imagine that it no longer needs the referral program to generate sales with the popularity of Tesla vehicles.

According to reports, some popular influencers were able to earn millions of miles of free Supercharging from the Referral program. Currently, Tesla is struggling to meet the demand for many of its vehicles, like many automakers. Interestingly, the message received by Referral program members indicates that the program has ended “until further notice.”

The “until further notice” statement seems to indicate there’s a chance the program could return in the future. Perhaps the program will return when Tesla has a new vehicle model it wants to promote. Reports have indicated that Tesla has its eyes on producing a smaller electric vehicle that could sell in the $25,000 range.

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Lotus Emira V6 First Edition starts at £75,995

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Lotus has confirmed the specifications and pricing for its new sports car called the Emira V6 First Edition. Pricing for the car is £75,995. It features a supercharged 3.5-liter V6 engine making 400 horsepower and 420Nm of torque when fitted with the manual transmission or 430 Nm with the automatic.

The standard transmission is a six-speed manual, but there is an option for a six-speed automatic with paddle shifters. Lotus is offering the First Edition in six different colors, with additional colors coming next year. Buyers get several options packs as standard.

The First Edition cars will arrive next spring, with the four-cylinder powered First Edition landing next fall. The Lotus Emira is a mid-engine premium sports car. It promises dynamic performance with best-in-class ride and handling along with aerodynamics and a driver-focused experience.

The car uses a new lightweight bonded aluminum chassis. The First Edition uses 20-inch ultra-lightweight V-spoke forged alloy wheels that are diamond cut with a two-tone finish. Buyers can choose silver or gloss black wheels at no additional cost. First Edition buyers also get two-piece brake discs, and Lotus branded calipers. The six available colors include Seneca Blue, Magma Red, Hethel Yellow, Dark Verdant, Shadow Gray, and Nimbus Gray. All versions get LED lights all around, titanium exhaust finisher, heated power-folding door mirrors, and rear parking sensors.

Lotus fits the cars with the Lower Black Pack as standard. Buyers can choose from seven interior color choices at no cost, with options in leather or Alcantara. The car includes heated seats with 12-way adjustability, climate control, cruise control, and keyless entry. Both Android Auto and Apple CarPlay are supported. Also standard are the Drivers Pack and Design Pack. Lotus also confirmed the entry-level Emira will launch sometime in 2023, priced starting at £59,995.

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