Intel Goes Heavy On Disruption, Invests $132 M In 11 AI Startups
Intel has been on a shopping spree this week. After a $900M acquisition of Moovit, today Intel’s investment wing, Intel Capital has announced that they are investing $132 M in 11 AI startups. Last year, at the Intel Capital Summit, CEO, Bob Swan described Intel Capital’s role as a combination of strategic, financial and cultural. He said Intel can provide insights that companies may not get from other venture capitalists. In exchange, he explained, Intel will benefit from the outside perspective on technology trends. “We view our portfolio companies as one of those ways culturally that we can stay extremely contemporary with those that are doing massive disruptive things out in the tech world.”-Bob Swan, Intel CEO Why Are These Startups Important The companies that are joining Intel Capital’s portfolio are Anodot, Astera Labs, Axonne, Hypersonix, KFBIO, Lilt, MemVerge, ProPlus Electronics, Retrace, Spectrum Materials and Xsight Labs. Although most of these startups are largely unheard of, Intel Capital sees great value in them and claims that they are on track to invest close to $500 M in startups this year. The value and the disruption that Intel observes in these companies becomes clear to outsiders if they are seen as a collective force covering all departments that Intel excels in or is on a path to. Semiconductor Manufacturing (smarter chip design etc.) Astera Labs is a semiconductor company that develops purpose-built connectivity solutions for data-centric systems that can handle compute-intensive workloads related to AI and ML ProPlus Electronics, a Chinese electronic design automation company that specialises in advanced device modelling and fast circuit simulation solutions. ProPlus software helps design chips faster Spectrum Materials is another Chinese company that supplies a high-purity speciality gas and material for semiconductor industries Xsight Labs is an Israeli company that develops technology for accelerating the next-generation, cloud-based, data-intensive workloads such as machine learning by offering new chipset designs that enhance scalability, performance and efficiency. AI (self-driving cars/healthcare etc.) Axonne develops next-generation high-speed network connectivity solutions for autonomous driving that require a high degree of functional reliability and electric vehicle-friendly power efficiency KFBIO’s uses big data, cloud computing and AI to quickly and reliably scan and digitise images, making them easier to share with experts to improve the speed and accuracy of diagnosis MemVerge’s software offers the next-gen Big Memory computing, providing petabyte-size pools of shared persistent memory and powerful data services for applications such as AI, machine learning, financial market data analytics and high-performance computing Lilt combines adaptive neural machine translation technology and software to provide accurate, localised and cost-effective translation and enables organisations to use language translation to scale their localisation programs, accelerate go-to-market strategies and improve the global customer experience Retrace applies AI in its predictive analytics platform that uses real-time data to improve dental decision-making Customer Analytics (success rate, app performances etc.) Anodot uses machine learning to drive the future of analytics — autonomous business monitoring across telco, finance and digital sectors sending them contextual alerts to tap the moments or events that impact revenue and costs, including drops in success rate, app performance and other business metrics Hypersonix provides an AI-powered autonomous analytics platform designed for decision-making such as consumer industries like retail, restaurants, hospitality and e-commerce. Decisions that matter, decisions that drive profitability, productivity and customer engagement through simple voice and text search, data visualisation and interpretation The phrase, “if you can’t beat them, join,” cannot be more precise if one looks at the acquisition strategies of top companies like Intel or NVIDIA. Intel already has made billion-dollar acquisitions to fortify their place in future. All the 11 companies listed above are a testament to Intel’s tactics to involve themselves on all the foundational pillars of the future — AI, healthcare, self-driving cars etc. Intel has been pioneering chip design for the past 50 years. They inarguably have led the digital revolution, and now as they stand at the threshold of transition towards the next-gen AI-based technologies, their strategy has leaned more on collaborations then burdening themselves with building from scratch. Intel seems to have found the formula to marry their in-house expertise with innovations of the up and coming startups. In 2019, Intel Capital invested $466 million to bring resources and expertise to accelerate and realise potentially world-changing technologies in 36 new investments. In 2020, Intel Capital claims that it is already on track to invest between $300 million and $500 million in companies that deal with artificial intelligence, intelligent edge and network transformation. These technologies, believes Intel, will bring broad transformations to industries such as healthcare, automotive, and consumer goods.
Intel has been on a shopping spree this week. After a $900M acquisition of Moovit, today Intel’s investment wing, Intel Capital has announced that they are investing $132 M in 11 AI startups.  Last year, at the Intel Capital Summit, CEO, Bob Swan described Intel Capital’s role as a combination of strategic, financial and cultural. […]
In 2019, Intel Capital invested $466 million to bring resources and expertise to accelerate and realise potentially world-changing technologies.
["Intel"]
["Go", "machine learning", "artificial intelligence", "AI", "cloud computing", "R", "ML", "Aim", "analytics", "predictive analytics", "Intel"]
["AI", "artificial intelligence", "machine learning", "ML", "analytics", "Aim", "predictive analytics", "cloud computing", "R", "Go"]
I have a master's degree in Robotics and I write about machine learning advancements.