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How AI proptech can instantly value properties around APAC

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Property technology, often abbreviated as proptech, is becoming increasingly commonplace around the Asia Pacific (APAC) in recent years, and certain factors are set to see proptech truly takeoff. An excellent example of proptech disrupting traditional property investor tools is the AI-powered platform, OfficeBlocks. According to the Urban Land Institute and PwC Emerging Trends in Real Estate Asia Pacific (APAC) 2020 report, awareness of and investment in proptech strategies are growing rapidly. And the recent world events have made things difficult for the property investor market, with travel restrictions and other restrictions limiting the possibilities. But using the Market Intelligence App within the OfficeBlocks platform, a photo of a property can be uploaded, and the industry-first AI and big data tool will send out the rental estimates and valuation of the property to an email address within minutes.


Global Big Data Conference

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We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI. Credible pilots and use cases have succeeded in many different sectors.


Global Big Data Conference

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It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.


Augmented Analytics Evolves to Make AI, BI Easier in 2021 - InformationWeek

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Augmented analytics is entering the mainstream in 2021, which means more enterprise organizations will be able to take advantage of its benefits to accelerate business intelligence, machine learning, and other forms of artificial intelligence in their organizations, whether that means more production projects or faster insights for decision makers. But just what is augmented analytics? But it is the idea of leveraging technologies such as machine learning and analytics to help automate the entire data management pipeline from data preparation to generating insights to assisting with building models and operationalizing them. That's crucial because data science and machine learning are complex and difficult. That's why just a few years ago so many organizations were struggling to hire "unicorn" data scientists who were experienced in three different areas: statistics, coding, and a specific business domain.


Are big data and machine learning methods enough? Part 1

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Sir David Hand gave a brilliant plenary talk and set the stage for a great panel discussion by cautioning us to remember that thinking is required and to be aware of all the dark data out there -- the data that we don't see, but that we need to take into account. Dark Data: Why What You Don't Know Matters is his latest book (see a blog post about it; if you haven't read it, you can get a sample excerpt). The panelists included Cameron Willden, statistician at W.L. Gore, who supports engineers and scientists across many different product lines; Sam Gardner, founder of Wildstats Consulting, with more than 30 years of experience doing statistical problem solving for government and industry; and JMP's Jason Wiggins, a 20-year US Synthetic veteran with expertise in process optimization, measurement systems analysis and predictive modeling/data mining. We ran out of time before we could answer all the questions from the livestream audience, but our panelists have kindly agreed to provide answers to many of them, further sharing the wisdom from their collective experiences. The questions are grouped by topic -- there were so many, we are doing two posts.


Black Women In Artificial Intelligence - Beyond The Lab - Elizabeth M. Adams

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Join us for the world premiere of Black Women In Artificial Intelligence - Beyond the Lab with our special guest author Elizabeth M. Adams a technology integrator, working at the intersection of Cyber Security, AI Ethics and AI Governance, focused on Ethical Tech Design. She also passionately teaches, advises, consults, speaks and writes on the critical subjects within Diversity & Inclusion in Artificial Intelligence, such as racial bias in Facial Recognition Technology, Video Surveillance, Predictive Analytics and Children's Rights. Beyond the Lab she has written several children's books including "Little Miss Minnesota", "Little A.I. and Peety" and the soon to be released book "I'm Beautiful".


The dawn of AI is upon Insurance industry - ET CIO

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We've all experienced pesky calls from strangers trying to sell us insurance. If you've noticed, such calls are already on the decline and it is rare to get unsolicited emails, messages etc for an insurance offering for a car when you don't even own one. No, the insurance companies haven't stopped doing business, they've just got smarter, all thanks to artificial intelligence, which is helping them understand you better, sell in more innovative ways and serve the customers better. AI is also helping improve claims disbursements and fraud detection. Take for instance, Max Life Insurance, which is using a number of AI technologies including vision, speech and NLP to develop a host of predictive models and cognitive applications.


Using Artificial Intelligence (AI) Capabilities in Data Analytics – Techieno

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Data analytics is nothing new and neither is Artificial Intelligence (AI). Over the next few years, the impact of data analytics on the world will ramp up remarkably. In fact, the global market for data analytics is expected to be valued at over USD 77.64 billion, expanding at a CAGR of 30.08% by 2023. This is primarily because of the increased data generation and the ability to use statistical algorithms and the latest machine learning approaches to deliver actionable insights. Data analytics can be used at a business scale to drive revenue, provide solutions to emerging trends, optimize marketing, and improve overall efficiency to create a competitive advantage.


Util Launches AI-based ESG Analytics Solution to Assess Company Impact on UN SDGs - ESG Today

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London-based fintech company Util announced today the release of an AI-based analytics solution designed to measure companies' impact on the 17 United Nations Sustainable Development Goals (SDGs). According to Util, the new solution aims to fill a gap in the market for data that can keep investors informed of the true impact of their investments, as demand for sustainable funds grows, while the effectiveness of ESG-themed products remains inconsistent. The company noted, for example, that only 6.7% of European funds labelled as'sustainable' explicitly screen out or reduce exposure to fossil fuels. "The disparity between growing demand and inadequate supply is a recipe for greenwashing at best, a bubble at worst. It has led to investments being mis-sold as sustainable, when in reality, they're inconsistent with investors' values."


Big data, machine learning shed light on Asian reforestation successes

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Since carbon sequestration is such an important factor for mitigating climate change, it's critical to understand the efficacy of reforestation efforts and develop solid estimates of forest carbon storage capacity. However, measuring forest properties can be difficult, especially in places that aren't easily reachable. Purdue University's Jingjing Liang, an assistant professor of quantitative forest ecology and co-chair of the Forest Advanced Computing and Artificial Intelligence (FACAI) Laboratory in the Department of Forestry and Natural Resources, led an international team to measure forest carbon capacity in northeast Asia. Their research, which blends remote sensing, field work and machine learning, offers the most up-to-date estimates of carbon capture potential in reclusive North Korea and details the benefits of reforestation efforts over the last two decades in China and South Korea. "Because there is historically scant data from North Korea, people know little about how much carbon is stored in this region," said Liang, whose findings were published in the journal Global Change Biology.