These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.
Billionaire hedge fund investor Steven Cohen's venture capital group announced this week it had co-led a $15 million investment round in a Silicon Valley startup that is developing artificial-intelligence software for driver assistance and autonomous driving. Mountain View, Calif.-based DeepScale supports automated driving with "deep neural network" software that uses low-power, automotive-grade chips to detect vehicles, pedestrians and other "objects of significance." By doing so, DeepScale officials said they aim to bring driver-assistance and autonomous driving to mass-produced vehicles at all price points. "We've been following (DeepScale co-founder and CEO) Forrest Iandola's research on efficient deep learning for a number of years," Sri Chandrasekar, a Point72 director, said in a statement. "Forrest's inventions … have already been a game-changer for putting deep learning onto smartphones.
Deep learning computer vision startup allegro.ai is set to showcase its latest product offering, hosted at the Intel partner booth (booth #307), during the Embedded Vision Summit which will take place in Santa Clara, California on May 20-May 23, 2019. The company's platform and product suite simplify the process of developing and managing deep learning-powered perception solutions - such as for autonomous vehicles, medical imaging, drones, security, logistics and other use cases. The platform enables engineering and product managers to get the visibility and control they need, while research scientists focus their time on research and creative output. The result is meaningfully higher quality products, faster time-to-market, increased returns to scale, and materially lower costs. The company's investors include Robert Bosch Venture Capital GmbH, Samsung Catalyst Fund, Hyundai Motor Company, and other venture funds.
As recently as 2013, the [deep learning] space saw fewer than 10 deals. Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. Captricity, which extracts information from hand-written data, has raised 49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.
Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to cofounder Rana el Kaliouby, the Waltham, Mass.-based company, wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the content or conversations in a video. The company's technology ingests digital images--including video in chat applications, livestreamed or recorded videos, or even GIFs--through typically the simplest web cams. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.