Traditionally, African lenders use credit bureau scores, assessing, for instance, if a customer has a history of missed credit card payments. When no history exists, they evaluate social demographics: is the customer female – in which case they are likelier to repay – do they work in a stable job market and can they prove a regular income? But this can put those who are unbanked or informally employed at a disadvantage. Michele Tucci, chief product officer at fintech company Credolab said: "African lenders lack data to make good credit decisions and social demographic data can bring you only so far." He estimates that African lenders cannot obtain credit bureau scores for 70% of customers and simply reject them.
The avalanche of new data generated by these gadgets will enable carriers to understand their customers better, leading to new product categories, more tailored pricing, and increasingly real-time service delivery. FREMONT, CA: The disruption caused by COVID-19 shifted the timetables for AI adoption by considerably speeding up insurers' digitalization. The underlying AI technologies are already in use in the workplaces, homes, vehicles, and bodies. Organizations must react almost immediately to accommodate remote workers, extend their digital capabilities to facilitate distribution, and modernize their web channels. While most firms did not engage extensively in AI during the epidemic, the increased emphasis on digital technology and a more substantial openness to embracing change will enable them to integrate AI into their operations.
Contentsquare, which has developed a digital experience analytics platform that enables businesses to track online customer behavior, has acquired Upstride, a French startup specializing in improving machine-learning performance. Terms of the deal were not released. With the acquisition, Contentsquare gains Upstride's deep-learning experts to help it further drive innovation in ML and artificial intelligence. Fourteen Upstride engineers will join Contentsquare, bringing their experience of working for leading tech companies such as Facebook, Samsung, GoPro, and Nvidia. Meanwhile, Upstride CEO Gary Roth will fill a strategic role on Contentsquare's operations team.
Good connectivity between different pieces of equipment on the shop floor is important for their communication with each other, which eventually enable smart decision-making. This is one of the aspects of the fourth Industrial revolution, which is all set to re-map manufacturing businesses to deliver higher operational efficiency, better business outcomes and customer satisfaction through digital transformation. The digital landscape is continuously evolving for the manufacturing industry as businesses are adapting to the change and even anticipating changes before they occur. Fast-changing customer expectations and technological improvements that have brought a paradigm shift in other industries has begun to show the similar results in the manufacturing sector as well. Smart manufacturing is giving rise to smart factories Smart manufacturing is more than just automation, as it enables learning and adapting to the ever changing market conditions, delivering higher efficiency in quality control, than that performed by Quality Inspectors.
Two years ago, Open AI released Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. Safety Gym has use cases across the reinforcement learning ecosystem. The open-source release is available on GitHub, where researchers and developers can get started with just a few lines of code. In this article, we will explore some of the alternative environments, tools and libraries for researchers to train machine learning models. AI Safety Gridworlds is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
In general, trading is about making decisions on transactions with assets in order to make a profit. All technical analysis is based on statistical data, past market behavior, and reactions. Consequently, the analysis and search for some market patterns can be performed not only by person but by computer and artificial intelligence. It is no secret that trading robots have been working in the stock market for a long time, focusing on price movements in trends and within channels. According to a 2020 JPMorgan study, over 60% of trades over $10M were executed using algorithms.
Fero Labs, the only Explainable Machine Learning software solely dedicated to the industrial sector, today announced the closing of a $9 million Series A round led by Innovation Endeavors, with participation from Deutsche Invest VC. This funding will support Fero Labs in expanding its product offerings to new sectors and ultimately push the industrial manufacturing community forward. The industrial sector has just begun to implement technologies into its processes to reduce waste and increase efficiency and profits. More than half of leaders in the manufacturing and utilities sector expect artificial intelligence to control high-value assets such as industrial plants, equipment, machines and its processes in the next five years, according to Next-Gen Industrial AI - and due to the pandemic, the industry observed a steady increase in artificial intelligence and machine learning adoption across industries including energy, manufacturing, heavy industry, infrastructure, and transportation sectors. "At Fero Labs, we develop our technology around the needs of customers, delivering the best of machine learning, AI technologies and scalable automated infrastructure," said Berk Birand, CEO of Fero Labs.
If you've been studying data science for a while, you might know that in order to learn data science you need to learn math, statistics, and programming. This is a good start for anyone interested in data science, but do you know how to get even more exposure to data science? A project will help you put into practice all the knowledge you've acquired from math, statistics, and programming. So far you might've seen each of them individually, but after you finish a project, the concepts you've learned in each field will make more sense. In this article, I listed some end-to-end data science projects you can do with Python.
Altoida Inc. today announced a $6.3 million round of venture capital financing to bring its FDA-cleared and CE Mark-approved medical device and brain health data platform to patients, physicians and researchers around the globe. Led by a team of esteemed neuroscientists, physicians and computer scientists, Altoida uses digital biomarkers to drive better clinical outcomes for brain disease. The Series A round was led by M Ventures, the corporate venture capital arm of the science and technology company Merck KGaA, Darmstadt, Germany, with participation from Grey Sky Venture Partners, VI Partners AG, Alpana Ventures, and FYRFLY Venture Partners. The new capital will be used to further expand Altoida's global presence with an immediate focus on commercialization activities in the US and EU markets. "Altoida is at the forefront of a new era to leverage Artificial Intelligence and Machine Learning to assess brain health," said Alexander Hoffmann, Principal, New Businesses at M Ventures.