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) …
One in every four adults in America now owns a voice-activated smart speaker. While we love the convenience of talking to a gadget to play music, make calls, and such, most of us get a little creeped out when they "wake up" when they're not supposed to. I often trigger Siri when saying "seriously," or "Suli" – the name of my parents' dog. My friend Bill Keeshan says his Alexa connected device "gets triggered by my daughter saying'actually.'" All too familiar anecdotes aside, researchers at Northeastern University and the Imperial College of London spent the last six months streaming 125 hours of popular Netflix TV shows to a handful of voice-activated smart speakers.
Monitoring and troubleshooting distributed systems are notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today--logs, counters, and metrics--have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. Pivot Tracing does not correlate cross-component events using expensive global aggregations, nor does it perform offline analysis.
Sustainability--the capacity to endure--has emerged as a concern of central relevance for society. However, the nature of sustainability is distinct from other concerns addressed by computing research, such as automation, self-adaptation, or intelligent systems. It demands the consideration of environmental resources, economic prosperity, individual well being, social welfare, and the evolvability of technical systems.7 Thus, it requires a focus not just on productivity, effectiveness, and efficiency, but also the consideration of longer-term, cumulative, and systemic effects of technology interventions, as well as lateral side effects not foreseen at the time of implementation. Furthermore, sustainability includes normative elements and encompasses multi-disciplinary aspects and potentially diverging views. As a wicked problem (see the sidebar "Wicked Problems"), it challenges business-as-usual in many areas of engineering and computing research.
Robots and other artificial intelligence (AI) systems are transitioning from performing well-defined tasks in closed environments to becoming significant physical actors in the real world. No longer confined within the walls of factories, robots will permeate the urban environment, moving people and goods around, and performing tasks alongside humans. Perhaps the most striking example of this transition is the imminent rise of automated vehicles (AVs). They are expected to increase the efficiency of transportation, and free up millions of person-hours of productivity. Even more importantly, they promise to drastically reduce the number of deaths and injuries from traffic accidents.12,30 Indeed, AVs are arguably the first human-made artifact to make autonomous decisions with potential life-and-death consequences on a broad scale. This marks a qualitative shift in the consequences of design choices made by engineers. The decisions of AVs will generate indirect negative consequences, such as consequences affecting the physical integrity of third parties not involved in their adoption--for example, AVs may prioritize the safety of their passengers over that of pedestrians.
Many speakers have pointed to various challenging ethical and design dilemmas raised by AI technology--we will describe 10 of the most prominent ones in this column. The first few are mostly technical; they arise from seemingly impenetrable complexity of the new technology. The final few ethical and design dilemmas include strong social dimensions; they arise from the difficulty of resolving emotional value conflicts to everyone's satisfaction. The most common AI technology is the artificial neural network (ANN). An ANN consists of many layers of artificial neurons interconnected via weighted links.
Waverly Labs' Ambassador, an over-the-ear translation device, can support up to 20 languages and 42 dialects. The greatest obstacle to international understanding is the barrier of language," wrote British scholar and author Christopher Dawson in November 1957, believing that relying on live, human translators to accurately capture and reflect a speaker's meaning, inflection, and emotion was too great of a challenge to overcome. More than 60 years later, Dawson's theory may finally be proven outdated, thanks to the development of powerful, portable real-time translation devices. The convergence of natural language processing technology, machine learning algorithms, and powerful portable chipsets has led to the development of new devices and applications that allow real-time, two-way translation of speech and text. Language translation devices are capable of listening to an audio source in one language, translating what is being said into another language, and then translating a ...
Wind-generated electricity has expanded greatly over the past decade. In the U.S., for example, by 2018 wind was generating 6.6% of utility-scale electricity generation, according to the U.S. Energy Information Administration. The criteria for efficient design and reliable operation of the familiar horizontal-axis wind turbines have been well established through decades of experience, leading to ever-larger structures over time, both to intercept more wind and to reach faster winds higher up. As these gargantuan turbines are assembled into large wind farms, often spread over uneven terrain, complex aerodynamic interactions between them have become increasingly important. To address this issue, researchers have proposed protocols that slightly reorient individual turbines to improve the output of others downwind, and they are working with wind farm operators to assess their real-life performance.
Mind Foundry has been a pioneer in the development and use of'humble and honest' algorithms from the very beginning of its applications development. As Davide Zilli, Client Services Director at Mind Foundry explains, 'baked in' transparency and explainability will be vital in winning the fight against biased algorithms and inspiring greater trust in AI and ML solutions. Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons – and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023.
Given the intrinsic complex and dynamic nature of ML, the possibility of failure is not a surprise. There are many reasons why this can happen. One of them is bias in the training data and method (e.g. Another reason is that the ultimate scope of the ML is not well defined and transparent and does not match any specific business requirements. Further issues are linked to the machine learning techniques which are not able to inform us when the information is not clear, or they cannot effectively learn from the data.
"With more board configurations than there are atoms in the observable universe, the ancient Chinese game of'Go' has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined the Google DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and, ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?"