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) …
Google has discontinued selling its artificial intelligence-powered camera device called'Clips'. The device, which was launched in 2017 at a price of $249, uses machine learning to learn and recognise faces and automatically records short motion images of things it finds "interesting". Google said it has begun integrating'Clips' technology into the'Photobooth' feature starting with its Pixel 3.
This is a guest post from Quenton Hall, AI System Architect for Industrial, Scientific and Medical applications. One of the AI demo highlights at XDF2019 in San Jose was a high-performance inference demo leveraging Alveo. If you are familiar with Alveo and ML Suite, this might at first glance not seem that novel. However, what was indeed very novel was that this demonstration leveraged a brand-new inference engine. Whereas past Alveo ML inference implementations have leveraged the xDNN engine architecture, this latest demo implements a new version of the Xilinx DPU IP, specifically optimized for the Alveo U280 and Xilinx SSIT devices.
The Allen Institute for Artificial Intelligence (AI2) is a non-profit research institute in Seattle founded by Paul Allen and headed by Professor Oren Etzioni. The core mission of (AI2) is to contribute to humanity through high-impact AI research and engineering. We are actively seeking post docs and Research Scientists at all levels who are passionate about AI and who can help us achieve this core mission by teaming to construct AI systems with reasoning, learning and reading capabilities. AI2 Research Scientists will have a primary focus in one of these specific areas but will also have the opportunity to contribute and engage in a variety of other areas critical to our research and mission. These include opportunities to participate in or lead select R&D projects, work with management to develop the long term vision for knowledge systems R&D, take a leading role in overseeing and implementing software systems supporting AI2's research, author and present scientific papers and presentations for peer-reviewed journals and conferences, and help develop collaborative and strategic relationships with relevant academic, industrial, government, and standards organizations.
"AI can only solve'toy' versions of real-life issues" However, researchers were not able to deliver on the lofty promises associated with AI. In 1973, the British parliament commissioned a thorough investigation of the state of research in AI. The resulting Lighthill report stated that AI was not able to achieve anything that could not also be achieved in other sciences. The report concluded by proclaiming that most successful AI algorithms would grind to a halt on real world problems and they were only suitable for solving'toy' versions of real-life issues. The UK subsequently significantly scaled back government funded AI research projects.
The multiple challenges of operating ever more complex environments are well known. The most common we hear when we are speaking with our customers and partners are based around the vast amount of data now being produced and the quality of it. These aren't new problems when it comes to network availability and performance monitoring. When I started working in this area in the late 1990s, Network Operations Centers (NOCs) were already drowning in the amount of data being produced. Back then, in the early days of systems and network management solutions, data would simply be discarded to avoid overloading the network management system.
John McCarthy, the father of Artificial Intelligence, describes it as "the science and engineering of making intelligent machines, especially intelligent computer programs". Artificial Intelligence is a way of making a computer and software related to computer which can think intelligently and autonomously, kind of similar to a human mind. In general understanding artificial intelligence is accomplished by studying how a human brain works while solving a problem and in what manner it learns and makes decisions, where outcomes of such kind of study are used as the basis of developing intelligent software and systems. Till now this field was dominated by quasi-artificial intelligent systems called "expert systems," which mainly used a rules-based decision-making process.1 In other words, we can interpret that these systems were not fully autonomous and, therefore, not truly intelligent, because they lacked the ability to learn and produce unpredictable results, and mostly they acted in a manner predetermined by their programming.2
We all know that feeling when the solution to a problem we have invested significant time into suddenly and unexpectedly reveals itself in a wondrous "a-ha" moment. Such an epiphany can be described as an enlightening realization that allows a problem or situation to be understood from a new and deeper perspective. Epiphanies, once deemed as insight from the divine, are relatively rare occurrences, but what if today's artificial intelligence (AI) tools can inspire and increase the frequency of epiphanies about the nature of very complex problems? BuildingIQ has set out to do exactly that --to move epiphanies out of the realm of the miraculous and into our everyday experience. We recently launched our powerful AI-driven inference engine, named Epiphany, which pulls together disparate data points within a given system; creates a virtualized network of that holistic system; and then learns how each point is connected and influenced by the other points in the network.
Standard computer programming has been around for decades; what is new (ish) is the propensity for computers to teach themselves how to spot patterns and progressively improve. Arup's Tim Chapman points the way forward for people planning a career, which is likely to last at least 45 years, and through which they will encounter unfathomable change. It is very easy to become fearful when reading press report after press report about the impact of artificial intelligence (AI) on our civilisation – all the jobs to be lost and what will become of us? First, it is worthwhile being clear about what AI actually is. Definitions can all too easily become conflated with the latest scary film – portraying robots with high intelligence and even human emotions – great films like'I Robot' and'Ex Machina' are wonderful stories and do show what may ultimately happen – but the level of technology is many decades away, if it ever happens.