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) …
Colorectal cancer is the third-most common cancer worldwide, and it spreads to the liver in about half the patients. Kazemier, who specializes in liver surgery, says the best way to treat this type of cancer is to remove it. But some tumors are too large to be removed, and these patients must undergo systemic therapy, such as chemotherapy to shrink the tumors. After a period of treatment, tumors are manually evaluated using computerized tomography (CT) scans. At that time, medical professionals can see if a tumor shrunk or changed in appearance.
Artificial Intelligence (AI) as an idea seems to have caught the imagination of both industry and academia alike. Although AI-related academic research has been in place since the late nineties but it is recently that products and services inspired by AI have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, speech recognition, various voice response systems like Alexa and Google assistant, every single one of these products has some form of AI at its core. Undoubtedly, ever-increasing processing speeds and storage capacities along with possibilities of machine to machine (M2M) communication have let the cat out of the bag. Today we produce more data in a single day then possibly we did in the entire year in the eighties.
Not nearly enough thought has gone into the tremendous potential AI holds for decision support in governance. One hears a lot of worried talk about the potential of future robots or AGIs "taking over the world." However, while working to avoid negative outcomes is certainly worthwhile, it's equally important to think imaginatively and practically about positive potentials. We humans are not doing a tremendously great job of running our own world at present. The biggest risks concerning AI are situated at the intersection of the current sociopolitical system (wracked as it is with conflict, confusion, and unfairness) with advanced narrow AIs and early-stage AGIs.
'Alexa, what are the early signs of a stroke?' GPs may no longer be the first port of call for patients looking to understand their ailments. 'Dr Google' is already well established in patients' minds, and now they have a host of apps using artificial intelligence (AI), allowing them to input symptoms and receive a suggested diagnosis or advice without the need for human interaction. And policymakers are on board. Matt Hancock is the most tech-friendly health secretary ever, NHS England chief executive Simon Stevens wants England to lead the world in AI, and the prime minister last month announced £250m for a national AI lab to help cut waiting times and detect diseases earlier. Amazon even agreed a partnership with NHS England in July to allow people to access health information via its voice-activated assistant Alexa.
Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of guidelines for human-AI interaction design.
According to IBM's survey of 6,000 executives, 66% of CEOs believe that cognitive computing can drive significant value in the Human Resource domain. About half of the HR executives back that up, saying that they recognize that cognitive computing and AI has the power to transform various crucial areas of Human Resource. Also, 54% of HR executives believe that AI or cognitive computing will affect their key roles in the HR organization. The Human Resources Professional Association (HRPA) reported in a survey that about 52% of respondents indicated their businesses were unlikely to adopt AI or cognitive computing in their HR departments in the next 5 years or so. Also, approximately 36% believe their company was too small to do it, while 28% said that their senior leadership didn't see the need for such technology in the near future.
The Radiological Society of North America (RSNA) is organizing a challenge intended to show the application of machine learning and artificial intelligence on medical imaging and the ways in which these emerging tools and methodologies may improve diagnostic care. The RSNA Pediatric Bone Age Machine Learning Challenge addresses a familiar image analysis activity for pediatric radiologists: assessment of bone age from hand radiographs of pediatric patients used to evaluate growth and diagnose developmental disorders. The Challenge uses a dataset of hand radiographs provided by a consortium of leading research institutions -- Stanford University, the University of California, Los Angeles and the University of Colorado -- that have associated bone age assessments provided by multiple expert observers. Participants in the challenge will be judged by how well the bone age evaluations produced by their algorithms accord with the expert observers' evaluations. Participants will have the opportunity to directly compare their algorithms in a structured way using this carefully curated dataset.
I've spent the last few years applying data science in different aspects of business. Some use cases are internal machine learning (ML) tools, analytics reports, data pipelines, prediction APIs, and more recently, end-to-end ML products. I've had my fair share of successful and unsuccessful ML products. There are even reports of ML product horror stories where the developed solutions ended up failing to address the problems they were supposed to solve. To a large extent, the gap can be filled by properly managing ML products to ensure that it ends up being actually useful to users. Given the difficulties in the ML workflow and our resource constraints (e.g.
First proposed by Professor John McCarthy at Dartmouth College in the summer of 1956,1 Artificial Intelligence (AI) – human intelligence exhibited by machines – has occupied the lexicon of successive generations of computer scientists, science fiction fans, and medical researchers. The aim of countless careers has been to build intelligent machines that can interpret the world as humans do, understand language, and learn from real-world examples. In the early part of this century, two events coincided that transformed the field of AI. The advent of widely available Graphic Processing Units (GPUs) meant that parallel processing was faster, cheaper, and more powerful. At the same time, the era of'Big Data' – images, text, bioinformatics, medical records, and financial transactions, among others – was moving firmly into the mainstream, along with almost limitless data storage.