AI and RPA are only beginning to transform how business is done in the insurance industry. We can expect to see burgeoning usage in operations, customer service, risk assessment, and mitigation and regulatory compliance. Insurance companies are only beginning to harness the potential of artificial intelligence (AI) and robotic process automation (RPA). AI refers to computer systems that can mimic human capabilities by learning and solving problems. RPA is an emerging form of business process automation technology based on using software robots or AI "workers."
Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap.
For many of these steps, there are no real short cuts to be taken. The only way to build a minimum viable product, for example, is to roll up your sleeves and start coding. However, in a few cases, tools exist to automate tedious manual processes and make your life much easier. In Python, this is the situation for steps 4, 8 and 10, thanks to the unittest, flake8 and sphinx packages. Let's look at each of these packages one by one.
Central Learning, a web-based coding assessment and education application, released the results of the 4th annual nationwide ICD-10 coding contest. Central Learning is part of the Pena4, Inc. suite of health information and revenue cycle technology solutions for healthcare organizations. Manny Peña, RHIA, Founder and CEO of Pena4, Inc., announced today that Kristin Iovino from Lexington, Massachusetts, received $1,000 for achieving the highest average accuracy and productivity scores for outpatient cases. This year's contest focused on outpatient coding performance to address some of the challenges associated with the surge in outpatient reimbursement, coding errors and claim denials, with the goal of helping HIM, coding and revenue cycle teams pinpoint opportunities for improvement. Four years of coding contests have resulted in over 10,000 real medical record cases using Central Learning, a real-time, online coder assessment tool for HIM.
Artificial intelligence (AI) can transform the productivity and GDP potential of the UK landscape. But, we need to invest in the different types of AI technology to make that happen. Our research shows that the main contributor to the UK's economic gains between 2017 and 2030 will come from consumer product enhancements stimulating consumer demand (8.4%). This is because AI will drive a greater choice of products, with increased personalisation and make those products more affordable over time. Labour productivity improvements will also drive GDP gains as firms seek to "augment" the productivity of their labour force with AI technologies and to automate some tasks and roles.
We all know Elon Musk to be a very ambitious guy. I mean, seriously, the guy has a company which specializes in electric car manufacturing, you've heard of Tesla, right? He also runs an aerospace manufacturing and space transportation services company called SpaceX. I am sure you've heard about it in the news or somewhere else. SolarCity, a solar energy company, now owned by Tesla.
As the US launches a cyberattack against Iranian weapons systems and escalates their infiltration of the Russian power grid in the same month, it's clear a new chapter of warfare is well and truly underway. Fueled by the same complex mix of diplomatic breakdowns, economic sanctions and historical grievances as regular conflicts, cyberwarfare is the new threat facing developing nations. The crisis faced by every technologically advanced state is highlighted in the World Economic Forum's Global Risks Report 2019 which ranked cyberattacks as the 5th global risk of our time. The US is certainly not alone in developing a cyberwarfare arsenal, as preemptive strikes, espionage and counter-attacks all require nations to develop cybersecurity defenses and demonstrate their clout. Here, we know exactly what the digital battleground looks like, and the attempts nations can take to develop defenses against the latest cyberwarfare threats.
Machine Learning helps your company create entirely new products to increase revenue. An example is new mobility services powered by self-driving cars, also called Robo-Taxi. Without Machine Learning, this new product is hard to create. In this case, Machine Learning allows the company to develop an entirely new product to increase revenue. The holy grail of Artificial Intelligence ("AI")-powered products is a product that enters the Virtuous Circle of AI.
No other technology has captured the world's imagination quite like AI, and there is perhaps no other that has been so disruptive. AI has already transformed the lives of people and businesses and will continue to do so in endless ways as more startups uncover its potential. According to a recent study, venture capital funding for AI startups in the UK increased by more than 200 percent last year, while a Stanford University study observed a 14-times increase in the number of AI startups worldwide in the last two years. As AI innovations and applications continue to advance, a growing number of technology companies including Microsoft, Salesforce and Uber have started to open-source their research and projects. These companies, which have substantial Research & Development (R&D) capabilities, are investing in or "giving back" to the open source community to reinforce the creation and improvement of AI & Machine Learning (ML) algorithms.
The #1 book that got the most votes is "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David. The book was first published in 2014 by Cambridge University aiming for students who want to learn the basics of Machine Learning and be familiar with all the important algorithms in this field.