Nov
20
2020
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Onit acquires legal startup McCarthyFinch to inject AI into legal workflows

Onit, a workflow software company based in Houston, announced this week that it has acquired 2018 TechCrunch Disrupt Battlefield alum McCarthyFinch. Onit intends to use the startup’s AI skills to beef up its legal workflow software offerings.

The companies did not share the purchase price.

After evaluating a number of companies in the space, Onit focused on McCarthyFinch, which gives it an artificial intelligence component the company’s legal workflow software had been lacking. “We evaluated about a dozen companies in the AI space and dug in deep on six of them. McCarthyFinch stood out from the pack. They had the strongest technology and the strongest team,” Eric M. Elfman, CEO and co-founder of Onit told TechCrunch.

The company intends to inject that AI into its existing Aptitude workflow platform. “Part of what really got me excited about McCarthyFinch was the very first conversation I had with their CEO, Nick Whitehouse. They considered themselves an AI platform, which complemented our approach and our workflow automation platform, Aptitude,” Elfman said.

McCarthyFinch CEO and co-founder Whitehouse says the startup was considering whether to raise more money or look at being acquired earlier this year when Onit made its interest known. At first, he wasn’t really interested in being acquired and was hoping to go the partner route, but over time that changed.

“I was very much on the partner track, and was probably quite dismissive to begin with because I was quite focused on that partner strategy. But as we talked, all egos aside, it just made sense [to move to acquisition talks],” Whitehouse said.

The talks heated up in May and the deal officially closed last week. With Onit headquartered in Houston and McCarthyFinch in New Zealand the negotiations and meetings all happened on Zoom. The two companies’ principals have never met in person. The plan is for McCarthyFinch to stay in place, even after the pandemic ends. Whitehouse expects to make a trip to Houston whenever it is safe to do so.

Whitehouse says his experience with Battlefield has had a huge influence on him. “Just the insights that we got through Battlefield, the coaching that we got, those things have stuck with me and they’ll stick with me for the rest of my life,” he said.

The company had 45 customers and 17 employees at the time of the acquisition. It raised US$5 million along the way. Now it becomes part of Onit as the journey continues.

Nov
18
2020
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IBM is acquiring APM startup Instana as it continues to expand hybrid cloud vision

As IBM transitions from software and services to a company fully focussed on hybrid cloud management, it announced  its intention to buy Instana, an applications performance management startup with a cloud native approach that fits firmly within that strategy.

The companies did not reveal the purchase price.

With Instana, IBM can build on its internal management tools, giving it a way to monitor containerized environments running Kubernetes. It hopes by adding the startup to the fold it can give customers a way to manage complex hybrid and multi-cloud environments.

“Our clients today are faced with managing a complex technology landscape filled with mission-critical applications and data that are running across a variety of hybrid cloud environments – from public clouds, private clouds and on-premises,” Rob Thomas, senior vice president for cloud and data platform said in a statement. He believes Instana will help ease that load, while using machine learning to provide deeper insights.

At the time of the company’s $30 million Series C in 2018, TechCrunch’s Frederic Lardinois described the company this way. “What really makes Instana stand out is its ability to automatically discover and monitor the ever-changing infrastructure that makes up a modern application, especially when it comes to running containerized microservices.” That would seem to be precisely the type of solution that IBM would be looking for.

As for Instana, the founders see a good fit for the two companies, especially in light of the Red Hat acquisition in 2018 that is core to IBM’s hybrid approach. “The combination of Instana’s next generation APM and Observability platform with IBM’s Hybrid Cloud and AI technologies excited me from the day IBM approached us with the idea of joining forces and combining our technologies,” CEO Mirko Novakovic wrote in a blog post announcing the deal.

Indeed, in a recent interview IBM CEO Arvind Krishna told CNBC’s Jon Fortt, that they are betting the farm on hybrid cloud management with Red Hat at the center. When you combine that with the decision to spin out the company’s managed infrastructure services business, this purchase shows that they intend to pursue every angle

“The Red Hat acquisition gave us the technology base on which to build a hybrid cloud technology platform based on open-source, and based on giving choice to our clients as they embark on this journey. With the success of that acquisition now giving us the fuel, we can then take the next step, and the larger step, of taking the managed infrastructure services out. So the rest of the company can be absolutely focused on hybrid cloud and artificial intelligence,” Krishna told CNBC.

Instana, which is based in Chicago with offices in Munich, was founded in 2015 in the early days of Kubernetes and the startup’s APM solution has evolved to focus more on the needs of monitoring in a cloud native environment. The company raised $57 million along the way with the most recent round being that Series C in 2018.

The deal per usual is subject to regulatory approvals, but the company believes it should close in the next few months.

Nov
18
2020
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Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures  told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 

Nov
16
2020
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Computer vision startup Chooch.ai scores $20M Series A

Chooch.ai, a startup that hopes to bring computer vision more broadly to companies to help them identify and tag elements at high speed, announced a $20 million Series A today.

Vickers Venture Partners led the round with participation from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and several other unnamed investors. Today’s investment brings the total raised to $25.8 million, according to the company.

“Basically we set out to copy human visual intelligence in machines. That’s really what this whole journey is about,” CEO and co-founder Emrah Gultekin explained. As the company describes it, “Chooch Al can rapidly ingest and process visual data from any spectrum, generating AI models in hours that can detect objects, actions, processes, coordinates, states, and more.”

Chooch is trying to differentiate itself from other AI startups by taking a broader approach that could work in any setting, rather than concentrating on specific vertical applications. Using the pandemic as an example, Gultekin says you could use his company’s software to identify everyone who is not wearing a mask in the building or everyone who is not wearing a hard hat at a construction site.

With 22 employees spread across the U.S., India and Turkey, Chooch is building a diverse company just by virtue of its geography, but as it doubles the workforce in the coming year, it wants to continue to build on that.

“We’re immigrants. We’ve been through a lot of different things, and we recognize some of the issues and are very sensitive to them. One of our senior members is a person of color and we are very cognizant of the fact that we need to develop that part of our company,” he said. At a recent company meeting, he said that they were discussing how to build diversity into the policies and values of the company as they move forward.

The company currently has 18 enterprise clients and hopes to use the money to add engineers, data scientists and begin to build out a worldwide sales team to continue to build the product and expand its go-to-market effort.

Gultekin says that the company’s unusual name comes from a mix of the words choose and search. He says that it is also an old Italian insult. “It means dummy or idiot, which is what artificial intelligence is today. It’s a poor reflection of humanity or human intelligence in humans,” he said. His startup aims to change that.

Nov
12
2020
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Databricks launches SQL Analytics

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.

Nov
03
2020
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Intel has acquired Cnvrg.io, a platform to manage, build and automate machine learning

Intel continues to snap up startups to build out its machine learning and AI operations. In the latest move, TechCrunch has learned that the chip giant has acquired Cnvrg.io, an Israeli company that has built and operates a platform for data scientists to build and run machine learning models, which can be used to train and track multiple models and run comparisons on them, build recommendations and more.

Intel confirmed the acquisition to us with a short note. “We can confirm that we have acquired Cnvrg,” a spokesperson said. “Cnvrg will be an independent Intel company and will continue to serve its existing and future customers.” Those customers include Lightricks, ST Unitas and Playtika.

Intel is not disclosing any financial terms of the deal, nor who from the startup will join Intel. Cnvrg, co-founded by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from investors that include Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook estimates that it was valued at around $17 million in its last round. 

It was only a week ago that Intel made another acquisition to boost its AI business, also in the area of machine learning modeling: it picked up SigOpt, which had developed an optimization platform to run machine learning modeling and simulations.

While SigOpt is based out of the Bay Area, Cnvrg is in Israel, and joins an extensive footprint that Intel has built in the country, specifically in the area of artificial intelligence research and development, banked around its Mobileye autonomous vehicle business (which it acquired for more than $15 billion in 2017) and its acquisition of AI chipmaker Habana (which it acquired for $2 billion at the end of 2019).

Cnvrg.io’s platform works across on-premise, cloud and hybrid environments and it comes in paid and free tiers (we covered the launch of the free service, branded Core, last year). It competes with the likes of Databricks, Sagemaker and Dataiku, as well as smaller operations like H2O.ai that are built on open-source frameworks. Cnvrg’s premise is that it provides a user-friendly platform for data scientists so they can concentrate on devising algorithms and measuring how they work, not building or maintaining the platform they run on.

While Intel is not saying much about the deal, it seems that some of the same logic behind last week’s SigOpt acquisition applies here as well: Intel has been refocusing its business around next-generation chips to better compete against the likes of Nvidia and smaller players like GraphCore. So it makes sense to also provide/invest in AI tools for customers, specifically services to help with the compute loads that they will be running on those chips.

It’s notable that in our article about the Core free tier last year, Frederic noted that those using the platform in the cloud can do so with Nvidia-optimized containers that run on a Kubernetes cluster. It’s not clear if that will continue to be the case, or if containers will be optimized instead for Intel architecture, or both. Cnvrg’s other partners include Red Hat and NetApp.

Intel’s focus on the next generation of computing aims to offset declines in its legacy operations. In the last quarter, Intel reported a 3% decline in its revenues, led by a drop in its data center business. It said that it’s projecting the AI silicon market to be bigger than $25 billion by 2024, with AI silicon in the data center to be greater than $10 billion in that period.

In 2019, Intel reported some $3.8 billion in AI-driven revenue, but it hopes that tools like SigOpt’s will help drive more activity in that business, dovetailing with the push for more AI applications in a wider range of businesses.

Nov
02
2020
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AWS launches its next-gen GPU instances

AWS today announced the launch of its newest GPU-equipped instances. Dubbed P4d, these new instances are launching a decade after AWS launched its first set of Cluster GPU instances. This new generation is powered by Intel Cascade Lake processors and eight of Nvidia’s A100 Tensor Core GPUs. These instances, AWS promises, offer up to 2.5x the deep learning performance of the previous generation — and training a comparable model should be about 60% cheaper with these new instances.

Image Credits: AWS

For now, there is only one size available, the p4d.24xlarge instance, in AWS slang, and the eight A100 GPUs are connected over Nvidia’s NVLink communication interface and offer support for the company’s GPUDirect interface as well.

With 320 GB of high-bandwidth GPU memory and 400 Gbps networking, this is obviously a very powerful machine. Add to that the 96 CPU cores, 1.1 TB of system memory and 8 TB of SSD storage and it’s maybe no surprise that the on-demand price is $32.77 per hour (though that price goes down to less than $20/hour for one-year reserved instances and $11.57 for three-year reserved instances.

Image Credits: AWS

On the extreme end, you can combine 4,000 or more GPUs into an EC2 UltraCluster, as AWS calls these machines, for high-performance computing workloads at what is essentially a supercomputer-scale machine. Given the price, you’re not likely to spin up one of these clusters to train your model for your toy app anytime soon, but AWS has already been working with a number of enterprise customers to test these instances and clusters, including Toyota Research Institute, GE Healthcare and Aon.

“At [Toyota Research Institute], we’re working to build a future where everyone has the freedom to move,” said Mike Garrison, Technical Lead, Infrastructure Engineering at TRI. “The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours and we are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”

Nov
02
2020
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Coupa Software snags Llamasoft for $1.5B to bring together spending and supply chain data

Coupa Software, a publicly traded company that helps large corporations manage spending, announced that it was buying Llamasoft, an 18-year-old Michigan company that helps large companies manage their supply chain. The deal was pegged at $1.5 billion.

This year Llamasoft released its latest tool, an AI-driven platform for managing supply chains intelligently. This capability in particular seemed to attract Coupa’s attention, as it was looking for a supply chain application to complement its spend management capabilities.

Coupa CEO and chairman Rob Bernshteyn says when you combine that supply chain data with Coupa’s spending data, it can produce a powerful combination.

“Llamasoft’s deep supply chain expertise and sophisticated data science and modeling capabilities, combined with the roughly $2 trillion of cumulative transactional spend data we have in Coupa, will empower businesses with the intelligence needed to pivot on a dime,” Bernshteyn said in a statement.

The purchase comes at a time when companies are focusing more and more on digitizing processes across enterprise, and when supply chains can be uncertain, depending on the location of COVID hotspots at any particular time.

“With demand uncertainty on one hand, and supply volatility on the other, companies are in need of supply chain technology that can help them assess alternatives and balance trade-offs to achieve desired business results. LLamasoft provides these capabilities with an AI-powered cloud platform that empowers companies to make smarter supply chain decisions, faster,” the company wrote in a statement.

Llamasoft was founded in 2002 in Ann Arbor, Michigan and has raised more than $56 million, according to Crunchbase data. Its largest raise was a $50 million Series B in 2015 led by Goldman Sachs .

The company generated more than $100 million in revenue and has 650 big customers, including Boeing, DHL, Kimberly-Clark and GM, according to company data.

Coupa has been extremely acquisitive over the years, buying 17 companies, according to Crunchbase data. This deal represents the fourth acquisition this year for the company. So far the stock market is not enamored with the acquisition; the company’s stock price is down 5.20% at publication.

Nov
02
2020
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Leena AI nabs $8M Series A as it expands from chatbots to HR service platform

When we covered Leena AI as a member of the Y Combinator Summer 2018 cohort, the young startup was firmly focused on building HR chatbots, but in the intervening years it has expanded the vision to a broader HR policy platform. Today, the company announced an $8 million Series A led by Greycroft with help from several individual industry investors.

Company CEO and co-founder Adit Jain says that in 2018 the company was concentrating on building an intelligent virtual assistant for HR-related questions. It allowed employees to ask the bot questions like how many vacation days they have left or what holidays they have off this year.

Over the last couple of years since leaving Y Combinator, the company has moved into broader HR service delivery. “So I’m talking about having an intelligent case management, knowledge management and document management system, which is backing the virtual assistant as well,” Jain explained.

He says that users should think of it as an entire system where the chatbot is the user interface for employees to interact with the HR information on the back end. For example, he says that the knowledge management component is where the chatbots find the answers to questions, and as employees interact with the chatbot, it grows more intelligent based on the feedback from them.

The document management piece enables HR to write or import HR policies and the case management system comes into play when the situation is too complex for the chatbot to handle and it has to be escalated to a human HR representative.

When we spoke to Jain in September 2018 at the time of his startup’s $2 million seed round, he had 16 customers and hoped to have 50 in the next 12-18 months. Today the company has 100 enterprise customers with 300,000 employees using the platform worldwide.

In fact, the pandemic has fueled business with more than half of those customers coming on board this year. He says this is because companies are looking for ways to digitize processes like HR as employees are working from home more.

“This is a trend that’s going to continue as organizations have realized the value of doing things with more and more digital applications taking care of your processes […] especially mundane, repeatable tasks being handed over to technology more and more,” Jain said.

As the business has grown this year, the company has expanded from 30 to 75 employees and he hopes to double that number in the next year. As he does, he has discussed with his lead investor how to build a diverse and inclusive culture at Leena AI .

One thing he is trying to do is raise some money from a diverse group of investors, approximately $400,000, and his hope is that these diverse investors can help him build solid diversity programs as he adds employees to his growing company.

That the startup hasn’t only grown during these turbulent times, but thrived, shows that companies are looking to modernize every part of the enterprise technology stack, and that includes HR.

Oct
29
2020
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Intel acquires SigOpt, a specialist in modeling optimization, to boost its AI business

Intel has been doubling down on building chips and related architecture for the next generation of computing, and today it announced an acquisition that will bolster its expertise and work specifically in one area of future technology: artificial intelligence.

The semiconductor giant today announced that it has acquired SigOpt, a startup out of San Francisco that has built an optimization platform that can be used to run modeling and simulations (two key applications of AI tech) in a better way. Anthony described SigOpt as a startup built to “optimize everything” when we covered its Series A, but Intel specifically will be integrating the tech into its AI business, specifically into its AI Analytics Toolkit, a spokesperson tells me.

Terms of the deal were not disclosed, but SigOpt already counted a number of large enterprises — “SigOpt’s customer base includes Fortune 500 companies across industries, as well as leading research institutions, universities and consortiums using its products” — among its customers. The product was still in a closed beta, however. Notably, it had raised money from an interesting group of investors that included In-Q-Tel (the firm associated with the CIA that makes strategic investments) and Andreessen Horowitz, and Y Combinator, among others. It had raised less than $10 million.

The plan will be to continue providing services to existing users, and to continue building out the company’s platform — co-founders Scott Clark (CEO) and Patrick Hayes (CTO) and their team are joining Intel.

“We will continue to work with SigOpt’s existing customers and will also integrate the technology into our product road map,” a spokesperson confirmed.

While Intel is working hard on streamlining its business around next-generation chips to better compete against the likes of Nvidia (which itself is growing substantially with the acquisition of ARM) and smaller players like GraphCore, in part by divesting more legacy operations, it seems a strong opportunity in providing services for its customers alongside those chips, and these services specifically will help customers with the compute loads that they will be running on those chips.

The focus for Intel has been on the next generation of computing to offset declines in its legacy operations. In the last quarter, even as it beat expectations, Intel reported a 3% decline in its revenues, led by a drop in its data center business. It said that it’s projecting the AI silicon market to be bigger than $25 billion by 2024, with AI silicon in the data center to be greater than $10 billion in that period.

In 2019, Intel reported some $3.8 billion in AI-driven revenue, but it hopes that tools like SigOpt’s will help drive more activity in that business, dovetailing with the push for more AI applications in a wider range of businesses.

“In the new intelligence era, AI is driving the compute needs of the future. It is even more important for software to automatically extract the best compute performance while scaling AI models,” said Raja Koduri, Intel’s chief architect and senior vice president of its discrete graphics division. “SigOpt’s AI software platform and data science talent will augment Intel software, architecture, product offerings and teams, and provide us with valuable customer insights. We welcome the SigOpt team and its customers to the Intel family.”

While there could potentially be a number of applications for SigOpt’s tech, this is a signal of how bigger players will continue to consolidate specific services around their bigger business, giving the small startup a much bigger horizon in terms of potential business (even if it is all tied to customers that only use Intel hardware).

“We are excited to join Intel and supercharge our mission to accelerate and amplify the impact of modelers everywhere. By combining our AI optimization software with Intel’s decades-long leadership in AI computing and machine learning performance, we will be able to unlock entirely new AI capabilities for modelers,” said Clark in a statement.

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