Intel invests $132mn in disruptive AI startups
Semiconductor chip giant Intel’s investment arm, Intel Capital, has announced it is investing in 11 technology startups.
Totalling $132mn total, the startups have interests in areas ranging from artificial intelligence, autonomous computing and chip design. The full list of companies includes Anodot, Astera Labs, Axonne, Hypersonix, KFBIO, Lilt, MemVerge, ProPlus Electronics, Retrace, Spectrum Materials and Xsight Labs. Intel said its investment arm was on course to invest between $300mn and $500mn in 2020.
One of the startups, San Francisco’s Lilt, is developing AI-powered language translation capabilities, combining neural networks and professional translators. Another, MemVerge, is developing software to provide large pools of persistent memory suited for data hungry applications such as AI, machine learning, financial market data analytics and high-performance computing. A third, Anodot, is using machine learning to autonomously monitor businesses, capable of sending out contextual alerts in real time to catch incidents such as drops in success rate or app performance.
Wendell Brooks, Intel senior vice president and president of Intel Capital, said in a press release: “Intel Capital identifies and invests in disruptive startups that are working to improve the way we work and live. Each of our recent investments is pushing the boundaries in areas such as AI, data analytics, autonomous systems and semiconductor innovation. Intel Capital is excited to work with these companies as we jointly navigate the current world challenges and as we together drive sustainable, long-term growth.”
While perhaps best known for building the processors powering most of the world’s PCs, a market it dominates alongside AMD, Intel has increasingly been exploring disruptive new technologies. Late last year, the company unveiled a quantum control chip which it said would speed up the development of quantum computers. Capable of operating in proximity to qubits at cryogenic temperatures, it is consequently more simple and scalable than other examples.
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