capital


China: Listed micro-loan provider works with InsurTech firm

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In a statement, CLDC says that it will work with Rui Xin to develop a consumer financial platform. CLDC expects to provide value-added consumer financial services to insurance consumers of Rui Xin and its partners. In addition, CLDC and Rui Xin will explore opportunities for collaboration in areas such as insurance consumer acquisition, development of insurance products, expansion of insurance business, and customisation of consumer financial solutions. Moreover, CLDC will benefit from Rui Xin and its partners' advanced technological capabilities in big data and artificial intelligence to improve its risk management and enhance its customer experience. In its turn, Rui Xin will be able to explore new business opportunities and increase its competency to eventually expand its customer base in the insurance industry by benefiting from CLDC's financial service expertise, bank credit facility resources, and client base in certain regional markets.


Artificial Intelligence-Focused Companies Advance Programming BioSpace

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The use of artificial intelligence and machine learning is almost commonplace in the biotech and pharma industry as multiple companies are harnessing the power to aid in drug discovery and development. This week more companies have announced advancements in their AI programming. This morning, San Francisco-based Notable Labs announced it secured $40 million in a Series B funding round to use its artificial intelligence platform to advance cancer drug development. The company's approach is aimed at predicting which types of patients are most likely to respond to a drug in as little as five days. The process is designed to help physicians make more informed decisions about which clinical trials will be effective with patients and can also benefit the likelihood of a trial's success by matching the right patients to the right trial.


The Periodic Table of Canadian Tech 2019 BetaKit

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Each Canada Day, PwC and CB Insights join forces to release the Periodic Table of Tech in Canada, which exhibits 150 of the most prominent Canadian venture-backed technology companies, investors, and exits. The country's anniversary is a fitting occasion to display and celebrate the abundant ambition and achievement in Canada's tech sector. Click to enlarge or download hi-res version. The 2019 Table is comprised of 108 venture-backed technology companies, 20 investors, and 22 exits. Those listed on the Table were selected using a methodology that focuses exclusively on the venture-backed ecosystem in Canada.


The case for taxing robots -- or not MIT Sloan

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Should your Roomba need a W-2? Probably not, but it's an amusing thought when debating the more serious topic of whether or not a robot should have to pay taxes -- and how to do it. During the June MIT Technology Review EmTech Next event, two experts argued both sides of the question before an audience at the MIT Media Lab in Cambridge, Massachusetts. Ryan Abbott, professor of law and health sciences at the University of Surrey, argued in favor of taxing robots, while Ryan Avent, economics columnist for The Economist, argued against the idea. Both agreed there needs to be a shift in tax burden from labor to capital. Avent, however, carried the most audience votes by the end of the debate.


Artificial intelligence

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Our Bionic Factory is a centre of expertise focused on AI, data engineering and intelligent automation. This centre will help you tap into additional forms of capital (Behavioral and Cognitive) as you move towards being a Bionic Organization and outperform others that rely solely on more traditional types of capital (Financial, Natural Resources, Human). SCALE.AI is a super cluster dedicated to building the next-generation supply chain and boosting industry performance through AI technologies. Close to 120 partners, including PwC Canada, have joined forces to create this Canadian innovation consortium.


New Artificial Intelligence Chips Lean Toward the Edge

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Few companies had enjoyed the sort of bull run AI chipmaker Nvidia (NVDA) had been on, returning more than 1200% between June 2015 and June 2018, eventually hitting a market cap of about $175 billion by September 2018. However, while many companies have bounced back, Nvidia has continued to languish, sitting at a valuation of about $88 billion, pretty much where it was circa May 2017 when we compared its AI chip technology against AMD (AMD). Now, over the last five years, the two chip manufacturers have returned almost identical value to investors, while a number of upstart startups have risen to also challenge Nvidia's supremacy with new artificial intelligence chips. In fact, it was exactly three years ago that we first introduced you to five startups building artificial intelligence chips, and then followed that up with 12 new AI chip makers in 2017. Last year, we noted that the Chinese are also gunning for Nvidia with their own homegrown artificial intelligence chips, as China seeks to dominate everything to do with AI and other emerging technologies.


Machine Learning Intro at @CloudEXPO Silicon Valley @BigDataTrunk #AI #IoT #BigData #MachineLearning #DeepLearning

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In his session at 23rd International CloudEXPO, Raju Shreewastava, founder of Big Data Trunk, will provide a fun and simple way to introduce Machine Leaning to anyone and everyone. Together we will solve a machine learning problem and find an easy way to be able to do machine learning without even coding. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Speaker Bio Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Big Data teams at Autodesk.


Killing The I-Bank: The Disruption Of Investment Banking - CB Insights Research

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Investment banking is seeing its historical profit centers eroded by technology and regulations. Core processes are being automated or commoditized. From IPOs, to M&A, to research and trading, investment banks are getting smaller, leaner, and scrambling to keep up with innovations. In 2006, investment banks were at the top of the finance world. With torrential growth and return on investment (ROI) driven largely by the trading of complex financial instruments, Lehman Brothers, Bear Stearns, Goldman Sachs and others achieved record profits and awarded unprecedented bonuses. Over the next two years, everything fell apart. Download the free report to learn how core processes of this financial service are being automated or commoditized. After the collapse of Lehman and Bear Stearns and the global financial crisis that ensued, the business models of the world's biggest investment banks needed to change. In the US, legislation emerged to forbid investment banks from prop trading, or trading with their own capital, and forcing them to keep more capital on hand. This regulation reduced trading profits and created a need to cut costs, spurring investment banks to spin off unprofitable divisions or eliminate them entirely. While the rules against prop trading have more recently been loosened, the restriction has still changed how investment banks operate. Moreover, as more and more companies raise large equity rounds they're also choosing to delay public offerings. And even when major tech companies do decide to go public, some, like Spotify and Slack, are doing so mostly without the help of banks. As a result, banks are facing dropping IPO profits: they generated just $7.3B in revenue in 2017 from equity capital markets, which includes IPOs, down an inflation-adjusted 43% since 2000's peak, according to the Wall Street Journal. At the same time, financial upstarts have built technologies that could eventually cut into the relationship-driven work that investment banks are used to doing. Instead of working with a bank to make an acquisition, you can use Axial -- the so-called "Tinder of M&A," for its algorithm-based approach to matching companies with potential buyers. In 2015, 26% of $1B mergers and acquisitions took place without the help of external financial advisors, up 13% from the year before, according to Dealogic.


Using AI, Machine Learning to optimize routes for last-mile operations - Geospatial World

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With the help of latest technology, this Spanish startup is working towards making logistics smarter. Scroll through their website and your attention is instantly drawn towards these lines: "You're in good company. Thanks to SmartMonkey, big corporations improve their logistic operations up to 30%. Below these lines are a reiteration of "up to 30% efficiency" and a list of companies that have benefitted from this Spanish startup. Formed in 2015 to make logistics smarter by using Machine Learning and Artificial Intelligence, the startup, in its own words, is "thriving to help companies optimize their distribution routes while learning from their clients' behaviors". "Our clever logistics products improve the companies' distribution operations, reduce the costs besides the operational risks by capturing the drivers' knowledge and transforming it into a new logistics data asset that helps the system learn and operate autonomously," explains SmartMonkey CEO Xavier Ruiz. The company's list of "satisfied" clients includes AGBAR and Heineken. But are clients the only source of revenue for them? "We have two main sources of funding: business angels and venture capital.


How This Entrepreneur Built A $1 Billion Business By Saving Lives With AI

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Using AI and lots of data Stefan Heck's startup Nauto is already saving lives on the road. While completely autonomous vehicles all the time may still be months away, this venture has found a way to apply the best in technology to make a difference today. Stefan Heck grew up between New York City and Austria. He learned to navigate ski slopes before he was three years old. Now he's helping large commercial fleets, taxi companies, automakers, and insurers navigate the applications of new technology on the roads.