capital


CloudFactory raises $65 million to prep and process data sets

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AI and machine learning algorithms require data. But the bulk of that data is of no use if it isn't first labeled by human annotators. This predicament has given rise to a cottage industry of startups, including Scale AI, which recently raised $100 million for its extensive suite of data labeling services. That's not to mention Mighty AI, Hive, Appen, and Alegion, which together occupy a data annotation tools segment that's anticipated to be worth $1.6 billion by 2025. CloudFactory is yet another vying for attention.


The Age of Thinking Machines

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We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.


The Age of Thinking Machines

#artificialintelligence

We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.


6 ways machine learning has changed asset management London Business School

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Machine learning is making inroads into every aspect of business life and asset management is no exception. Here are six ways in which machine learning has transformed the field – from the feel of the trading floor to the ideal skillset. Most flow trading done by banks has already been fully automated. While 20 years ago such products as cash equities or foreign exchange were mostly traded by humans, often with hundreds of traders occupying the trading floors, shouting "buy" or "sell" orders, currently most market makers rely on the algorithmic execution and automated inventory management. In fact, many institutional orders are not executed by hand either; they are routinely sent to the algorithms ensuring optimal execution that would minimize their market impact or trading costs.


Stradigi AI raises $40.3 million to develop business AI solutions

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Stradigi AI, a Montréal-based AI solutions provider and research lab founded in 2014, today announced that it has raised $53 million CAD ($40.3 million) in a series A round led by Canadian institutional funds Investissement Québec and Fonds de solidarité FTQ, with participation from Holdun Family Office, Segovia Capital, Cossette, and company cofounders Basil Bouraropoulos and Curtis Gavura. CEO Bouraropoulos said the influx of capital will accelerate Stradigi's North American expansion, which will include new offices in the U.S., with 50 new positions in research, software, sales, and marketing. Additionally, he says it will bolster development of the firm's freshly unveiled AI platform, Kepler, on the heels of a recently announced partnership with professional services network KPMG. "Investissement Québec and the Fonds de solidarité FTQ, in addition to all the other amazing investors that contributed to this financing, are great partners for Stradigi AI," said Bouraropoulos. "As two of the most respected institutional funds in Canada, with diverse portfolios and deep experience with preparing companies for international growth, IQ and the Fonds will bring tremendous value as we execute our strategy to become one of the top three leading platforms in North America." It's built on an adaptable environment that leverages a software-meets-service model, where guidance from Stradigi's research scientists is provided in tandem with solutions deployed via a secure service.


Artificial Intelligence in Preclinical Design and Execution: Investors and Startups

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The growing demand for ML/AI technologies, as well as for ML/AI talent, in the pharmaceutical industry is driving the formation of a new interdisciplinary field: data-driven drug discovery/healthcare. Consequently, there is a growing number of AI driven startups offering technology solutions for drug discovery/development. In drug development, preclinical phase (in vitro and in vivo), also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials, and during which important feasibility, iterative testing and drug safety data are collected. According to a detailed mind-map prepared by Pharma Division of Deep Knowledge Analytics (updated Q1 2019): the AI for Drug Discovery, Biomarker Development and Advanced R&D Industry Landscape counts so far 400 investors, 170 companies and 50 corporations. This article focuses only on the AI startups and the AI investors trying to overcome the above 4 challenges during design and execution of the preclinical phase.


Big Data and AI solutions for Drug Development

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The healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year.


Highlights in AI for Drug Discovery Q2 2019

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There's been significant growth in the AI for Drug Discovery sector this year. In the last quarter, the number of R&D centers increased by 5, companies increased by 20, collaborations increased by 30, and investors increased by 50. There are now 400 investors investing in AI for Drug Discovery including Google Ventures, Tencent, WuXi, Andreessen Horowitz, Khosla Ventures, and Sequoia Ventures. This week, Deep Knowledge Analytics Pharma Division published Landscape of AI for Drug Discovery and Advanced R&D Q2 2019. This report is the most comprehensive analytical report on the AI for Drug Discovery sector to date.


Motor City stakes claim to be capital of autos' future

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The Motor City's historic strength in manufacturing is enabling it to become the center for the future of the automotive industry. Just a few years ago, conventional thinking assumed Silicon Valley's tech heavyweights held the upper hand in producing the next generation of vehicles. That was before the extensive problems experienced by electric-vehicle start-up Tesla Inc. in building EVs at its California plant, among other challenges to the tech-will-prevail thesis. "There was this thinking that Silicon Valley was going to crush Detroit, that they knew how to do it better," said Michelle Krebs, an analyst with Cox Automotive. "Well, reality has set in" that Detroit knows is how to make cars.


GLOBALFOUNDRIES and SiFive to Deliver Next Level of High Bandwidth Memory on 12LP Platform for AI Applications

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SANTA CLARA, Calif. and HSINCHU, Taiwan, Nov. 5, 2019 – GLOBALFOUNDRIES (GF) and SiFive, Inc. announced today at GLOBALFOUNDRIES Technology Conference (GTC) in Taiwan that they are working to extend high DRAM performance levels with High Bandwidth Memory (HBM2E) on GF's recently announced 12LP FinFET solution, with 2.5D packaging design services to enable fast time-to-market for Artificial Intelligence (AI) applications. In order to achieve the capacity and bandwidth for data-intensive AI training applications, system designers are challenged with squeezing more bandwidth into a smaller area while maintaining a reasonable power profile. SiFive's customizable high bandwidth memory interface on GF's 12LP platform and 12LP solution will enable easy integration of high bandwidth memory into a single System-on-Chip (SoC) solutions to deliver fast, power-efficient data processing for AI applications in the computing and wired infrastructure markets. As a part of the collaboration, designers will also have access to SiFive's RISC-V IP portfolio and DesignShare IP ecosystem, which will leverage GF's 12LP Design Technology Co-Optimization (DTCO), enabling them to significantly increase silicon specialization, improve design efficiency and deliver differentiated SoC solutions quickly and cost-effectively. "Extending SiFive's reference IP platform, with HBM2E, on GF's best-in-class performance 12LP solution delivers new levels of performance and integration for next generation SoCs and accelerators," said Mohit Gupta, vice president and general manager, IP Business Unit at SiFive.