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For over 23 years, Larry Collins worked as a toll collector on the Carquinez Bridge in San Francisco. He loved his job -- every day, he would come to work and greet drivers, provide directions, answer questions, and collect toll fees. Over the years, although the toll price had changed tremendously, his job was always in a stable condition. But, this all changed during March of 2020. In the midst of the coronavirus pandemic, Collins was suddenly informed that his tollbooth was getting shut down and replaced by an artificial intelligence-based toll collector machine. Collins was not the lone victim of industrial automation unemployment, just in the Northern California region, 185 other toll booths were also shut down and replaced by technological alternatives (Semuels). As the 21st-century technological advances continue, applications of artificial intelligence are expected to expand exponentially. Slowly but surely, artificial intelligence is automating a multitude of manual jobs, causing widespread unemployment around the world (Peterson). There is clear uncertainty about the future of artificial intelligence. A recent report from the conference on Computers, Privacy, and Data Protection suggested that the European Commission (EU), is strongly "considering the possibility of legislating for Artificial Intelligence". This legislation would explore a number of nuances that come with future artificial intelligence job automation and will consider the implementation of a novel regulatory framework (MacCarthy). On the other hand, organizations such as Deltec, an international financial research institute, are in support of artificial intelligence automation and don't want regulation as it would hinder humanity's ability to research and solve problems in an efficient manner (Trehan). Currently, there has been no clear conclusion to this ongoing debate -- experts have varying opinions but agree that a full-proof solution is direly needed.
Imagine you wanted to design a drug for a new disease, 'Disease X', about which little is known. Imagine then that you have a machine that could use all the available data in the world about Disease X to identify a potential mechanism of disease and use this to predict which molecules within this mechanism could make suitable targets for drugs against the disease. Then, a machine would virtually design a drug targeting these optimal molecules, building it bit by bit and continuously checking with the target's structure to ensure activity at the desired binding site. Once the drug was "built", it could then be synthesised and, following various rounds of in vitro, in vivo, and clinical testing to validate its efficacy, the drug could be used in clinical practice. Although a machine like this does not yet exist, advocates of artificial intelligence (AI) propose that AI has the potential to revolutionise drug design, turning this imaginary scenario -- at least in part -- into a reality.
TOKYO -- Zipline, an American company that specializes in using autonomously flying drones to deliver medical supplies, has taken off in Japan. Other parts of Japan may follow, including urban areas, although the biggest needs tend to be in isolated rural areas. Zipline, founded six years ago, already is in service in the U.S., where it has partnered with Walmart Inc. to deliver other products at the retail chain as well as drugs. It is also delivering medical goods in Ghana and Rwanda. Its takeoff in Japan is in partnership with Toyota Tsusho, a group company of Japan's top automaker Toyota Motor Corp. "You can totally transform the way that you react to pandemics, treat patients and do things like home health care delivery," Zipline Chief Executive Keller Rinaudo told The Associated Press.
As data volumes grow rapidly, so does the time spent on processes to clean and organize that data. This means that the potential of smart automation features driven by AI will more and more become key differentiators for the Collibra software platform. As the Senior AI Engineering Manager, you will collaborate with a diverse team of ML practitioners, product managers, architects, etc. to help shape Collibra's AI roadmap and vision, and execute that vision. We strive to provide all Collibrians with competitive and cost-efficient benefits that are aligned to our company values. As a high-growth company, our goal is to offer flexibility and choice with our benefits programs to support the evolving needs of our changing workforce.
It has been estimated that 1.7 million people die from Tuberculosis (TB), and more than 10.4 million new cases are reported every year worldwide. The global'End TB' strategy aims to eliminate the disease by 2030. However, realizing this goal would be challenging if there were to be a gap in treatment adherence to prescribed medication. In the context of TB and HIV coinfection, non-adherence to the medication has been associated with the incidence of drug resistance, prolonged infection, unsuccessful treatments, and death. Africa experiences a severe shortage of healthcare workers, making delivering proper healthcare difficult.
The past few years have seen significant advancements in information technologies such as artificial intelligence, and the adaptation of such technologies were only further necessitated and accelerated as a result of the coronavirus pandemic. Applications for artificial intelligence technology are no longer exclusive to the tech industry, and today there are an increasing number of new products and use cases that allow businesses to leverage the wealth of digital insights the technology can provide. The insurance industry is no exception to this. Those developing the technology have identified solutions to some of the most common pain points, and by embracing artificial intelligence, insurance providers large and small have the ability to create new efficiencies in their operations. From automating form-filling processes to assessing vehicle damage, artificial intelligence has the ability to help organizations dramatically reduce costs and time.
As the CEO of one of the top global AI-powered biotechnology companies, I regularly get to see some of the world's most innovative techno parks and biotechnology hubs that are popping up all over the world. Over the past couple of years, I traveled to several such centers in the US, Canada, China, Singapore, and the Middle East. We even established one of our R&D centers at the Hong Kong Science and Technology Park. All of these centers have their advantages and disadvantages that often go in line with the government policies and I will try to cover some of these centers in my future posts and make a comparison. So far, some of the most impressive biotechnology hubs are in China and in Singapore.
A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types. Full-rank estimation of all interactions is often infeasible in empirical settings. Models that specialize on a spatiotemporal application alleviate this obstacle by exploiting spatial locality, allowing the dyadic relationships between events to depend only on separation in time and relative distances in real Euclidean space. Here we generalize this framework to any multivariate Hawkes process, and harness it as a vessel for embedding arbitrary event types in a hidden metric space. Specifically, we propose a Hidden Hawkes Geometry (HHG) model to uncover the hidden geometry between event excitations in a multivariate point process. The low dimensionality of the embedding regularizes the structure of the inferred interactions. We develop a number of estimators and validate the model by conducting several experiments. In particular, we investigate regional infectivity dynamics of COVID-19 in an early South Korean record and recent Los Angeles confirmed cases. By additionally performing synthetic experiments on short records as well as explorations into options markets and the Ebola epidemic, we demonstrate that learning the embedding alongside a point process uncovers salient interactions in a broad range of applications.
Performances of machine learning models are obtained by testing them. We use many statistical tests but also one thing that we all are aware of is that no statistical test is perfect. Some errors in models are easy to understand but hard to capture. The base rate fallacy can be considered an easy to understand but hard to find error. The concept of base rate fallacy is taken from behavioral science.
Fake faces created by artificial intelligence (AI) are considered more trustworthy than images of real people, a study has found. The results highlight the need for safeguards to prevent deep fakes, which have already been used for revenge porn, fraud and propaganda, the researchers behind the report say. The study – by Dr Sophie Nightingale from Lancaster University in the UK and Professor Hany Farid from the University of California, Berkeley, in the US – asked participants to identify a selection of 800 faces as real or fake, and to rate their trustworthiness. After three separate experiments, the researchers found the AI-created synthetic faces were on average rated 7.7% more trustworthy than the average rating for real faces. This is "statistically significant", they add.