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Arm CEO Simon Segars discusses AI, data centers, getting acquired by Nvidia and more

Nvidia is in the process of acquiring chip designer Arm for $40 billion. Coincidentally, both companies are also holding their respective developer conferences this week. After he finished his keynote at the Arm DevSummit, I sat down with Arm CEO Simon Segars to talk about the acquisition and what it means for the company.

Segars noted that the two companies started talking in earnest around May 2020, though at first, only a small group of executives was involved. Nvidia, he said, was really the first suitor to make a real play for the company — with the exception of SoftBank, of course, which took Arm private back in 2016 — and combining the two companies, he believes, simply makes a lot of sense at this point in time.

“They’ve had a meteoric rise. They’ve been building up to that,” Segars said. “So it just made a lot of sense with where they are at, where we are at and thinking about the future of AI and how it’s going to go everywhere and how that necessitates much more sophisticated hardware — and a much more sophisticated software environment on which developers can build products. The combination of the two makes a lot of sense in this moment.”

The data center market, where Nvidia, too, is already a major player, is also an area where Arm has heavily focused in recent years. And while it goes up against the likes of Intel, Segars is optimistic. “We’re not in it to be a bit player,” he said. “Our goal is to get a material market share and I think the proof to the pudding is there.”

He also expects that in a few years, we’ll see Arm-powered servers available on all of the major clouds. Right now, AWS is ahead in this game with its custom-built Gravitron processors. Microsoft and Google do not currently offer Arm-based servers.

“With each passing day, more and more of the software infrastructure that’s required for the cloud is getting ported over and optimized for Arm. So it becomes a more and more compelling proposition for sure,” he said, and cited both performance and energy efficiency as reasons for cloud providers to use Arm chips.

Another interesting aspect of the deal is that we may just see Arm sell some of Nvidia’s IP as well. That would be a big change — and a first — for Nvidia, but Segars believes it makes a lot of sense to do so.

“It may be that there is something in the portfolio of Nvidia that they currently sell as a chip that we may look at and go, ‘you know, what if we package that up as an IP product, without modifying it? There’s a market for that.’ Or it may be that there’s a thing in here where if we take that and combine it with something else that we were doing, we can make a better product or expand the market for the technology. I think it’s going to be more of the latter than it is the former because we design all our products to be delivered as IP.”

And while he acknowledged that Nvidia and Arm still face some regulatory hurdles, he believes the deal will be pro-competitive in the end — and that the regulators will see it the same way.

He does not believe, by the way, that the company will face any issues with Chinese companies not being able to license Arm’s designs because of export restrictions, something a lot of people were worried about when the deal was first announced.

“Export control of a product is all about where was it designed and who designed it,” he said. “And of course, just because your parent company changes, doesn’t change those fundamental properties of the underlying product. So we analyze all our products and look at how much U.S. content is in there, to what extent are our products subject to U.S. export control, U.K. export control, other export control regimes? It’s a full-time piece of work to make sure we stay on top of that.”

Here are some excerpts from our 30-minute conversation:

TechCrunch: Walk me through how that deal came about? What was the timeline for you?

Simon Segars: I think probably around May, June time was when it really kicked off. We started having some early discussions. And then, as these things progress, you suddenly kind of hit the ‘Okay, now let’s go.’ We signed a sort of first agreement to actually go into due diligence and then it really took off. It went from a few meetings, a bit of negotiation, to suddenly heads down and a broader set of people — but still a relatively small number of people involved, answering questions. We started doing due diligence documents, just the mountain of stuff that you go through and you end up with a document. [Segars shows a print-out of the contract, which is about the size of two phone books.]

You must have had suitors before this. What made you decide to go ahead with this deal this time around?

Well, to be honest, in Arm’s history, there’s been a lot of rumors about people wanting to acquire Arm, but really until SoftBank in 2016, nobody ever got serious. I can’t think of a case where somebody actually said, ‘come on, we want to try and negotiate a deal here.’ And so it’s been four years under SoftBank’s ownership and that’s been really good because we’ve been able to do what we said we were going to do around investing much more aggressively in the technology. We’ve had a relationship with Nvidia for a long time. [Rene Haas, Arm’s president of its Intellectual Property Group, who previously worked at Nvidia] has had a relationship with [Nvidia CEO Jensen Huang] for a long time. They’ve had a meteoric rise. They’ve been building up to that. So it just made a lot of sense with where they are at, where we are at and thinking about the future of AI and how it’s going to go everywhere and how that necessitates much more sophisticated hardware — and a much more sophisticated software environment on which developers can build products. The combination of the two makes a lot of sense in this moment.

How does it change the trajectory you were on before for Arm?

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In conversation with European B2B seed VC La Famiglia

Earlier this month, La Famiglia, a Berlin-based VC firm that invests in seed-stage European B2B tech startups, disclosed that it raised a second fund totaling €50 million, up from its debut fund of €35 million in 2017.

The firm writes first checks of up to €1.5 million in European startups that use technology to address a significant need within an industry. It’s backed 37 startups to date (including Forto, Arculus and Graphy) and seeks to position itself based on its industry network, many of whom are LPs.

La Famiglia’s investors include the Mittal, Pictet, Oetker, Hymer and Swarovski families, industry leaders Voith and Franke, as well as the families behind conglomerates such as Hapag-Lloyd, Solvay, Adidas and Valentino. In addition, the likes of Niklas Zennström (Skype, Atomico), Zoopla’s Alex Chesterman and Personio’s Hanno Renner are also LPs.

Meanwhile, the firm describes itself as “female-led,” with founding partner Dr. Jeannette zu Fürstenberg and partner Judith Dada at the helm.

With the ink only just dry on the new fund, I put questions to the pair to get more detail on La Famiglia’s investment thesis and what it looks for in founders. We also discussed how the firm taps its “old economy” network, the future of industry 4.0 and what La Famiglia is doing — if anything — to ensure it backs diverse founders.

TechCrunch: You describe La Famiglia as B2B-focused, writing first checks of up to €1.5 million in European startups using technology to address a significant need within an industry. In particular, you cite verticals such as logistics and supply chain, the industrial space, and insurance, while also referencing sustainability and the future of work.

Can you elaborate a bit more on the fund’s remit and what you look for in founders and startups at such an early stage?

Jeannette zu Fürstenberg: Our ambition is to capture the fundamental shift in value creation across the largest sectors of our European economy, which are either being disrupted or enabled by digital technologies. We believe that opportunities in fields such as manufacturing or logistics will be shaped by a deep process understanding of these industries, which is the key differentiator in creating successful outcomes and a strength that European entrepreneurs can leverage.

We look for visionary founders who see a new future, where others only see fragments, with grit to push through adversity and a creative force to shape the world into being.

Judith Dada: Picking up a lot of signals from various expert sources in our network informs the opportunity landscape we see and allows us to invest with a strong sense of market timing. Next to verticals like insurance or industrial manufacturing, we also invest into companies tackling more horizontal opportunities, such as sustainability in its vast importance across industries, as well as new ways that our work is being transformed, for workers of all types. We look for opportunities across a spectrum of technological trends, but are particularly focused on the application potential of ML and AI.

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TinyML is giving hardware new life

The latest embedded software technology moves hardware into an almost magical realm

Aluminum and iconography are no longer enough for a product to get noticed in the marketplace. Today, great products need to be useful and deliver an almost magical experience, something that becomes an extension of life. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into that almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain.

Until now building machine learning (ML) algorithms for hardware meant complex mathematical modes based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML-related tasks were translated to the cloud, creating latency, consuming scarce power and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the edge slower, more expensive and less predictable.

But thanks to recent advances, companies are turning to TinyML as the latest trend in building product intelligence. Arduino, the company best known for open-source hardware is making TinyML available for millions of developers. Together with Edge Impulse, they are turning the ubiquitous Arduino board into a powerful embedded ML platform, like the Arduino Nano 33 BLE Sense and other 32-bit boards. With this partnership you can run powerful learning models based on artificial neural networks (ANN) reaching and sampling tiny sensors along with low-powered microcontrollers.

Over the past year great strides were made in making deep learning models smaller, faster and runnable on embedded hardware through projects like TensorFlow Lite for Microcontrollers, uTensor and Arm’s CMSIS-NN. But building a quality dataset, extracting the right features, training and deploying these models is still complicated. TinyML was the missing link between edge hardware and device intelligence now coming to fruition.

Tiny devices with not-so-tiny brains

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Monitoring is critical to successful AI

Companies often identify AI and ML performance issues after the damage has been done

Amit Paka

Krishna Gade

7 hours

Amit Paka
Contributor

Amit Paka is co-founder and chief product officer at Fiddler Labs, an explainable AI startup that enables enterprises to deploy and scale risk- and bias-free AI applications.

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RealityEngines launches its autonomous AI service

RealityEngines.AI, an AI and machine learning startup founded by a number of former Google executives and engineers, is coming out of stealth today and announcing its first set of products.

When the company first announced its $5.25 million seed round last year, CEO Bindu Reddy wasn’t quite ready to disclose RealityEngines’ mission beyond saying that it planned to make machine learning easier for enterprises. With today’s launch, the team is putting this into practice by launching a set of tools that specifically tackle a number of standard enterprise use cases for ML, including user churn predictions, fraud detection, sales lead forecasting, security threat detection and cloud spend optimization. For use cases that don’t fit neatly into these buckets, the service also offers a more general predictive modeling service.

Before co-founding RealiyEngines, Reddy was the head of product for Google Apps and general manager for AI verticals at AWS. Her co-founders are Arvind Sundararajan (formerly at Google and Uber) and Siddartha Naidu (who founded BigQuery at Google). Investors in the company include Eric Schmidt, Ram Shriram, Khosla Ventures and Paul Buchheit.

As Reddy noted, the idea behind this first set of products from RealityEngines is to give businesses an easy entry into machine learning, even if they don’t have data scientists on staff.

Besides talent, another issue that businesses often face is that they don’t always have massive amounts of data to train their networks effectively. That has long been a roadblock for many companies that want to see what AI can do for them but that didn’t have the right resources to do so. RealityEngines overcomes this by creating realistic synthetic data that it can then use to augment a company’s existing data. In its tests, this creates models that are up to 15 percent more accurate than models that were trained without the synthetic data.

“The most prominent use of generative adversarial networks  — GANS — has been to create deep fakes,” said Reddy. “Deepfakes have captured the public’s imagination by highlighting how easy it to spread misinformation with these doctored videos and images. However, GANS can also be applied to productive and good use. They can be used to create synthetic datasets which when then be combined with the original data, to produce robust AI models even when a business doesn’t have much training data.”

RealityEngines currently has about 20 employees, most of whom have a deep background in ML/AI, both as researchers and practitioners.

Source: TechCrunch

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4 High-Potential Sectors for AI and ML Startup Success

Today, the explosion of development in artificial intelligence (AI) and machine learning (ML) technology has created a market for which it appears there’s no limit. No matter the industry, if you name a reasonably-sized (or larger) company, there’s a good chance that they’re investing in AI and ML technology as a cornerstone of their strategic plans. With each passing day, it’s even becoming part of the small business equation, too. Here are four high-potential sectors for AI and ML startup success.

The takeaway is that there are as many ways for businesses to use machine learning as there are businesses. It’s the kind of burgeoning market that is perfect for fueling startup growth, and entrepreneurs have started to take notice. That’s why there’s been such a recent boom of startup activity in the sector – creating what many analysts are referring to as a 21st-century gold rush.

The problem is, like in the original gold rush in the late 1800s, there’s going to be a point where the majority of those rushing to stake their claims will see their odds of success dry up. That’s why it’s more critical than ever for entrepreneurs to understand which parts of the AI and ML space still have plenty of room for startup innovation, so they can mine the right vein and strike it rich.

Here’s a look at four of the parts of the market that show tremendous potential, to use as a guidepost.

Educational AI Systems

As AI and ML technology started their march into the business world, much of the attention paid to AI with respect to the education sector centered on producing the skilled worker’s businesses would need to operate their new technological platforms. Very little initial movement or investment went toward developing AI or ML solutions for the education sector.

In recent months, however, that has started to change. Education-focused platforms have been starting to roll out AI-powered tools and are increasingly viewing the technology as a game-changer for the industry. An analysis of spending by the education sector on AI and ML technology predicts that it will be the industry with the biggest spending growth by percentage through 2022. For an education-focused AI or ML startup, that’s a very encouraging sign.

Human Resources AI Technology

Another industry that’s been somewhat slow to adopt AI and ML technology is human resources (HR). The one exception has been in the adoption of applicant tracking systems (ATS) that use ML techniques to perform application screening for potential hires. That alone has spawned a cottage industry of AI-enhanced services meant to improve applicants’ chances of passing muster, as these machine-created resume examples should attest.

The thing is, the surge in ATS use is expected to be just a prelude to much wider adoption of AI and ML technologies in the realm of HR, with industry experts expecting adoption rates of the technologies to pick up significant steam in the coming years. That means it’s a great time to launch an HR-focused AI or ML startup now, to capitalize on the all-but-certain growth in the space.

AI-Powered Marketing Tools

hologram user interface
Futuristic infographics, online trading, e-commerce, hologram, user interface, internet.                Photo: lidiia/Adobe Stock

As the world edges closer and closer to an always-on internet-connected reality with the emergence of IoT technology, businesses everywhere are coming to grips with the fact that there are more marketing channels to manage than ever before. The only feasible solution is to turn the bulk of the work over to AI-powered marketing systems, using ML to adapt and evolve marketing efforts over time.

Already, such tools are cropping up in all phases of the marketing industry, from social media management to content marketing and all points in between. That, however, is just the beginning. Businesses that have already seen how AI-influenced marketing decisionmaking can help them grow are now looking for ways to turn more of their marketing efforts over to AI-powered solutions. A startup that focuses on delivering an AI solution to enable real-time marketing automation at scale could find itself well-positioned for long term success.

Financial AI Solutions

When startups are seeking an AI or ML market with solid growth prospects, their best bet is to go where the money is – which in this case means to the financial sector itself. AI and ML technology adoption in the world of finance has been so swift and complete that it spawned the whole new business category of fintech. In particular, asset managers are already going all-in on the technology as are hedge funds, financial advisors, and the entire banking sector.

It’s also an industry that has almost inexhaustible resources to pour into worthwhile AI and ML technology, which bodes well for any startup that looks to build solutions for the industry. The size and scope of the sector mean that there’s a near-limitless number of opportunities to be had in the space – and they’re all there for the taking for any savvy entrepreneur who finds an innovative way to capitalize on them.

Fools Rush In

The bottom line here is that there’s no shortage of opportunities to be had for AI and ML startups, as long as they choose their markets carefully. It’s not a coincidence that analysts are starting to call this the AI gold rush – they’re doing it because the stampede of development will eventually lead to an oversaturated market that can’t sustain the number of startups that it is spawning.

When that happens, only the entrepreneurs that made it a point to work within sectors that have long-term growth prospects will see their startups survive. When the bubble bursts, it won’t be because interest in the technologies has waned, it will be due to two factors – a systemic need to cull underperforming members of the startup herd, and a round of consolidations that will see the best of the bunch scooped up by larger entities.

Startups in the above four sectors will stand a good chance of being part of the latter group. As for those in the former group, I suggest they do some research into the end of the last gold rush for some insight into their ultimate fate.

Andrej Kovacevic

Andrej is a dedicated writer and digital evangelist. He is pursuing an ongoing mission to share the benefits of his years of hard-won expertise with business leaders and marketing professionals everywhere. He is a contributor to a wide range of technology-focused publications, where he may be found discussing everything from neural networks and natural language processing to the latest in smart home IoT devices. If there’s a new and exciting technology, there’s a good chance Andrej is writing about it somewhere out there.

Source: ReadWrite