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Global Big Data Conference

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Fraym is using artificial intelligence and machine learning to help aid organizations in Africa and South Asia identify populations at risk due to Covid-19 using new geospatial visualizations. Fraym identifies high-risk populations and how to best communicate with them – making it an invaluable tool for more than 40 organizations and governments fighting the pandemic, including the Nigerian CDC, Kenyan presidential office, Zambian public health policymakers and aid organizations in Pakistan. Fraym has mapped communities based on concentrations of common transmission variables and then combined this with data from household surveys and remote sensing data, to then understand how these individuals consume news at a hyper-local level. The company is providing this information, which is at a 1-square kilometer level, for free to help fight the spread of Covid19. Since March 2020, Fraym has produced more than 300 COVID-19 related data layers in nearly 20 different countries.



Top 10 Big Data Startups in the United States to Watch In 2020

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Data is growing by leaps and bounds, the convergence of extremely large data sets both structured and unstructured define Big Data. The increasing awareness of the Internet of Things (IoT) devices among organizations and volume, variety, velocity and veracity at which data is generated have caught the attention of the enterprise in a bid to enhance digital technologies and guide digital transformation. Analytics Insights eliminates that the big data market size will grow at a CAGR of 10.9%, globally from US$ 193.5 billion in 2020 to US$ 301.5 billion by 2023. This region is witnessing significant developments in the big data market gaining remarkable traction in the BFSI industry vertical. Numerai is the world's first hedge fund, to predict the stock market.


Defining data science, machine learning, and artificial intelligence

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With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they even be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.


Global Big Data Conference

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On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.


Global Big Data Conference

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The world is in the midst of a historical turning point. The COVID-19 pandemic has effectively halted life as we once knew it, and left the open question, "what will our world look like when'normal' life resumes?" While we don't have a crystal ball that allows us to peer into the future, history has given us a template on what to expect. Past pandemics have shaped politics, crashed economies, purred revolutions and produced other profound societal transformations. In the 14th century, the bubonic plague killed more than 60 percent of Europe's population – a dramatic population decline that actually improved living standards for the survivors and marked the decline in serfdom.


Global Big Data Conference

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Artificial intelligence is beginning to be usefully deployed in almost every industry from customer call centers and finance to drug research. Yet the field is also plagued by relentless hype, opaque jargon and esoteric technology making it difficult for outsiders identify the most interesting companies. To cut through the spin, Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create our second annual AI 50, a list of private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways. To be included, companies had to be privately-held and focused on techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see"). The list was compiled through a submission process open to any AI company in the U.S. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency).


What's Next After Machine Learning?

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Recently, there is increasing attention towards machine learning and its application within personal and business contexts. "The study of computer algorithms that improve automatically through experience" Machine learning leverages mathematical models and big data to solve business problems. This requires two key support areas of data engineering and data science. Within data engineering, the evolution of cloud computing has allowed big data to be stored inexpensively. Within data science, the rise of data scientists and data science tools have allowed better ease of model building and exploration. However, machine learning is NOT the summit of a data analytics evolution journey.



Council Post: Where Insights Meet Privacy: Privacy-Preserving Machine Learning

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Artificial intelligence (AI) and machine learning (ML) have the power to deliver business value and impact across a wide range of use cases, which has led to their rapidly increasing deployment across verticals. For example, the financial services industry is investing significantly in leveraging machine learning to monetize data assets, improve customer experience and enhance operational efficiencies. According to the World Economic Forum's 2020 "Global AI in Financial Services Survey," AI and ML are expected to "reach ubiquitous importance within two years." However, as the rise and adoption of AI/ML parallels that of global privacy demand and regulation, businesses must be mindful of the security and privacy considerations associated with leveraging machine learning. The implications of these regulations affect the collaborative use of AI/ML not only between entities but also internally, as they limit an organization's ability to use and share data between business segments and jurisdictions.