If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT). IoT devices, such as in-car sensors, smartphones, and smart appliances, can send insurers data on product usage and driving habits among other behaviors. In turn, this data could be fed into AI algorithms that may allow insurers to offer risk-based pricing and other popular services.
There are more than 1.9 billion users logged in to YouTube every single month who watch over a billion hours of video every day. With this number of users, activity, and content, it makes sense for YouTube to take advantage of the power of artificial intelligence (AI) to help operations. Here are a few ways YouTube, owned by Google, uses artificial intelligence today. In the first quarter of this year, 8.3 million videos were removed from YouTube, and 76% were automatically identified and flagged by artificial intelligence classifiers. More than 70% of these were identified before there were any views by users.
An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using Computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist. Automating this process has increased accuracy and saved time to ensure patient survival. Whether it's image analysis to detect cancer or other diseases immediately, predicting the number of patients to ensure the right number of doctors and hospital beds are available or using natural language processing (NLP) to understand lengthy patients reports – the potential for technological enhancement in healthcare is colossal.
Robert Chang, a Stanford ophthalmologist, normally stays busy prescribing drops and performing eye surgery. But a few years ago, he decided to jump on a hot new trend in his field: artificial intelligence. Doctors like Chang often rely on eye imaging to track the development of conditions like glaucoma. With enough scans, he reasoned, he might find patterns that could help him better interpret test results. That is, if he could get his hands on enough data.
Machine learning has grown to have a significant impact on our daily lives: From Amazon's home assistant Alexa collecting and analyzing information to anticipate our needs, or Facebook suggesting who we should friend, to applications protecting us from credit card fraud and improving online shopping experiences. Organizations want their data to do the heavy lifting for them, driven by the desire to save on costs, improve consistency and streamline operations. While ML technologies were previously perceived as an excessive expenditure, today they are seen as an investment in the business' future and a competitive revenue driver. In order to stay competitive and successful, organizations have to invest in the right technologies and intelligently use the skills and data systems that they already have. The following three tips will help enterprises evaluate ML benefits and investments and make the most of the technology they already have.
AI is already on its way to transforming healthcare delivery and improving patient outcomes. However, while AI, Machine Learning, and Robotics are all designed to reduce human error and increase the predictability of patient care, they also create new risks across the healthcare liability landscape. In a situation where a healthcare provider uses AI to treat a patient who has a less than a desired outcome (or even simply an unanticipated one), we anticipate liability suits against those healthcare providers, healthcare systems, AI software companies, and robotic device manufacturers. In this post, we will consider what happens when lawsuits get ahead of science, insurance considerations in this new liability landscape, and possible modifications to legal doctrine to address this new science. What makes AI so compelling is its use of predictive, learning algorithms (Machine Learning) to improve the precision of the practice of medicine.
The Matrix reached US cinemas just over 20 years ago and articulated society's fear of the power of artificial intelligence (AI) and its potential to overpower the human. The film taps into ongoing human anxiety around technology and our ability to control it, best epitomised by Mary Shelley's 19th century trope of the Frankenstein's Monster-- the notion that we may well lose control of our own creations as we strive to push the boundaries of science. The human relationship with technology remains a fraught one, but there is little question that AI has the potential to be revolutionary. The McKinsey Global Institute Study reported that in 2016 alone, between $8bn and $12bn was invested in the development of AI worldwide, and Goldstein Research predicts that by 2023, AI will be a $14bn industry. While few of us yet use driverless cars and interact regularly with the animated robots of another science fiction story, I Robot, AI is nonetheless beginning to affect our daily life.
Today microcontrollers can be found in almost any technical device, from washing machines to blood pressure meters and wearables. Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable. A wide variety of software solutions currently exist for machine learning, but as a rule they are only available for the PC and are based on the programming language Python.
There's been plenty of hype over artificial intelligence, no question. But there are highly practical ways that CFOs can use AI right now to bring new efficiencies to the enterprise. Here are five of them. The CFO sits at the center of customer data flows: sales data, pricing information, receivables updates -- the list goes on. This puts the CFO in a powerful position to link predictive analytics with customer behavior.