Artificial intelligence (AI) is one of the signature issues of our time, but also one of the most easily misinterpreted. The prominent computer scientist Andrew Ng's slogan "AI is the new electricity"2 signals that AI is likely to be an economic blockbuster--a general-purpose technology3 with the potential to reshape business and societal landscapes alike. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.4 Such provocative statements naturally prompt the question: How will AI technologies change the role of humans in the workplaces of the future? An implicit assumption shaping many discussions of this topic might be called the "substitution" view: namely, that AI and other technologies will perform a continually expanding set of tasks better and more cheaply than humans, while humans will remain employed to perform those tasks at which machines ...
A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape. In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used in a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue. Privacy is a foremost ethical concern. AI learns to make predictions from large data sets -- specifically patient data, in the case of surgical systems -- and it's often described as being at odds with privacy-preserving practices.
Despite the recession, organisations have been hiring job roles for data science and analysts. In one of our previous articles, we discussed some of the ways that a data science enthusiast needs to do in order to get hired during the pandemic. In this article, we list down the 6 latest job openings for data scientists and analysts one must apply now. About: Being a Data Scientist at UnitedHealth Group, you will be working in teams addressing statistical, machine learning and data understanding problems. You will be contributing to the development as well as the deployment of machine learning models, operational research, semantic analysis, statistical methods, among others for finding structure in large data sets.
Brokerages who use artificial intelligence could find opportunities to upsell based on changes in a client's lifestyle, according to a software vendor executive. The more data you feed a machine learning model and the more you train it, the better it gets, said Kevin Deveau, managing director of FICO Canada, part of San Jose, Calif.-based Fair Isaac Corp., in a recent interview. Artificial intelligence (AI) is when technology mimics human cognition such as learning from experience, identifying patterns and deriving insights, said Mark Breading, a partner with Boston-based Strategy Meets Action. Machine learning is a type of AI in which computers act without being explicitly programmed, SAS Institute Inc. notes. Bigger brokerages with enough money to invest in AI and machine learning could use those technologies to build a "360-degree view" of a customer, said Deveau, in the context of how the COVID-19 pandemic is forcing companies to change the way they operate.
Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on artificial intelligence. As there are many startups working on various different applications, we want to share our insights with you. Here, we take a look at 5 promising genetic algorithm startups. For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 111 relevant solutions.
Mike Tyson famously said that "Everyone has a plan until they get punched in the mouth". Every company had a strategic plan coming into 2020. Then, Covid-19 walked into the ring. Insurance has been hit hard by Covid-19 and economic hardship. With many insurers focused on cash conservation, leading insurers can emerge from the crisis even stronger if they make smart investments in AI. Insurers' massive customer datasets and their famously manual processes create some'quick win' AI opportunities.
The mix of data, technology, and talent has made it feasible for the present smart systems to arrive at a basic point that drives exceptional development in AI investment. Funding for AI and machine learning startups has been developing at a yearly development pace of almost 60% since 2010 and the organizations are moving past a significant stretch of exploratory AI into a period of exponential AI. As specialists state, we are entering a "Race Against the Machine," and a "Fourth Industrial Revolution." As per Crunchbase, there are 8,705 startups and organizations today depending on AI and machine learning for their essential applications, products, and services. Practically 83% of AI and machine learning startups that Crunchbase tracks, had just three or fewer funding rounds, the most well-known being seed rounds, angel rounds, and early-stage rounds.
Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing. Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to Morbi-RSA models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction. We showed that the neural network outperformed the ridge regression as well as all Morbi-RSA models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes. In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes.
Data privacy has become one of the top concerns in machine learning with deep neural networks, since there is an increasing demand to train deep net models on distributed, private data sets. For example, hospitals are now training their automated diagnosis systems on private patients' data [LST 16, LS17, DFLRP 18]; and advertisement providers are collecting users' online trajectories to optimize their learning-based recommendation algorithm [CAS16, YHC 18]. These private data, however, are usually decentralized in nature, and policies such as the Health Insurance Portability and Accountability Act (HIPAA) [Act96] and the California Consumer Privacy Act (CCPA) [Leg18] restrict the exchange of raw data among distributed users. Various schemes have been proposed for privacy sensitive deep learning with distributed private data, where model updates [KMY 16] or hidden-layer representations [VGSR18] are exchanged instead of the raw data. However, recent research identified that even if the raw data are kept private, sharing the model updates or hidden-layer activations can still leak sensitive information about the input, which we refer to as the victim.