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) …
The Defense Department has officially adopted a set of principles to ensure ethical artificial intelligence adoption, but much work is needed on the implementation front, senior DOD tech officials told reporters Feb. 24. The five principles [see sidebar], which are based on the recommendations of the Defense Innovation Board's 15-month study on the matter, represent a first step and generalized intentions around AI use and adoption including being responsible, equitable, traceable, reliable, and governable. DOD released the principles during a news briefing Feb. 24. Those AI ethical guidelines will likely be woven into a little bit of everything, like cyber, from data collection to testing, DOD CIO Dana Deasy told reporters. "We need to be very thoughtful about where that data is coming from, what was the genesis of that data, how was that data previously being used and you can end up in a state of [unintentional] bias and therefore create an algorithmic outcome that is different than what you're actually intending," Deasy said.
Learning difficulties are not linked to differences in particular brain regions, but in how the brain is wired, research suggests. According to figures from the Department for Education, 14.9% of all pupils in England – about 1.3 million children – had special educational needs in January 2019, with 271,200 having difficulties that required support beyond typical special needs provision. Dyslexia, attention deficit hyperactivity disorder (ADHD), autism and dyspraxia are among conditions linked to learning difficulties. Now experts say different learning difficulties are not specific to particular diagnoses, nor are they linked to particular regions of the brain – as has previously been thought. Instead the team, from the University of Cambridge, say learning difficulties appear to be associated with differences in the way connections in the brain are organised.
"Although I was not directly involved in speeding up the video footage recognition I realised that I was still part of the kill chain; that this would ultimately lead to more people being targeted and killed by the US military in places like Afghanistan." The former Google engineer predicts autonomous weapons currently in development pose a far greater risk to humanity than remote-controlled drones. She outlined how external forces ranging from changing weather systems to machines being unable to work out complex human behaviour might throw killer robots off course, with potentially fatal consequences. She told The Guardian: "You could have a scenario where autonomous weapons that have been sent out to do a job confront unexpected radar signals in an area they are searching; there could be weather that was not factored into its software or they come across a group of armed men who appear to be insurgent enemies but in fact are out with guns hunting for food. "The machine doesn't have the discernment or common sense that the human touch has.
IDCC uniquely leverages optical character recognition, Utopia's advanced machine learning code, intelligent online web search, and document search. Beginning simply with only a photo of a manufacturer's nameplate, IDCC can produce complete and accurate material and asset information. Manufacturer and model data is organized in ISO-14224 standards and can be delivered via a variety of easy-to-integrate methods, including SAP Asset Intelligence Network . The cloud-based nature of IDCC enables cost-effective, rapid deployments by large and small organizations alike. IDCC can be deployed in pure cloud environments, such as SAP Intelligent Asset Management, or hybrid deployments using SAP Master Data Governance, enterprise asset management extension by Utopia.
Artificial intelligence (AI) is changing the way we live and work, and it has the potential to transform virtually every sector of the global economy – from healthcare and education to transport and energy systems. It holds tremendous promise for economic productivity, scientific advancements and sustainable development. Yet there are also anxieties around where the technology may lead us, including concerns that it may codify existing biases and discriminatory practices, or displace workers by accelerating automation. For governments, this presents a pressing question: how can we harness the potential of AI to improve well-being for all, while also mitigating the risks that it poses? The OECD AI Principles – the first intergovernmental standard on AI – provide a way forward.
Popular expressions, for example, "artificial intelligence", "machine learning" and "Big Data" have without question become a significant topic in the present tech scene and they are digging in for the long-term. However, the advancement power behind Artificial Intelligence and its related perspectives have additionally discovered its way to the core phase of our society. Can Artificial Intelligence be utilized for the more noteworthy benefit of society? Also, what job should organizations play in it? While AI is certainly not a silver bullet, it can help handle a lot of our general society's most challenging issues on a social, economic and environmental level.
The retail giant said today that it will deploy the generative chatbot as an aid to human agents for the time being but plans to eventually have it deal with customers directly. The company is also rolling out a separate consumer-facing chatbot that uses a neural network to better match human-authored response templates to customer queries. The project marks one of the first commercial tests of a state-of-the-art new natural language processing technology that researchers think has the potential to supercharge progress in the field. The model, which has also powered cutting-edge systems like OpenAI's GPT-2, draws on massive training datasets and predictive text to generate realistic-sounding copy or dialogue. "It is difficult to determine what types of conversational models other customer service systems are running, but we are unaware of any announced deployments of end-to-end, neural-network-based dialogue models like ours," wrote Jared Kramer, an applied-science manager on Amazon's Customer Service Tech team, in a blog post.
Self-driving cars, home automation, virtual assistants…it's clear we've already seen some outstanding technological advances and are on the brink of more significant breakthroughs. Alain Fiocco, CTO for OVHcloud, calls 2020 "a new era" for technology. But with all new advances, which will pull ahead in 2020? Here is a breakdown of the top five telecom trends to watch for in the year ahead. Right now, the world runs on 4G, also known as LTE.
Artificial intelligence has been used for the first time to instantly and accurately measure blood flow. A study led by researchers at University College London (UCL) and the Barts Health National Health Service Trust in the U.K. for the first time used artificial intelligence (AI) to measure blood flow, and forecast death, heart attack, and stroke. The AI system analyzed Cardiovascular Magnetic Resonance images from more than 1,000 patients at two U.K. hospitals, and precisely and instantly quantified blood flow to the heart muscle and transmitted the measurements to medical teams. Comparison of the AI-generated blood flow results with the health outcomes of each patient revealed that subjects with reduced blood flow were more likely to suffer negative health outcomes like death, heart attack, stroke, and heart failure. UCL's Kristopher Knott said, "As poor blood flow is treatable, these better predictions ultimately lead to better patient care, as well as giving us new insights into how the heart works."
In Part 3 of our series on how utilities are using artificial intelligence, we look at how AI amplifies analytics for grid operations. Duke Energy saved some $130 million in avoided costs by using predictive data analytics to identify problems before they caused equipment failures. A utility in Brazil estimates savings in the range of $420,000 USD each month through better, analytics-based theft detection. Because, as an article published by Forbes notes, "Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously." With these enhancements, data science will become more powerful than ever, and utilities stand to gain.