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) …
Deep learning models rely on numerical vectors to'understand' the input words. We can think of the numerical vectors as high dimensional features representing the input words. In this high dimensional space, words are located close together or far away from each other. Word representation is built by finding the proper numerical vector representations for all the words in a given corpus. The quality of word representation relies on the corpus. This can be easily understood in the way that two human beings can have a different understanding of the same word, depending on whether he likes to spend time reading the modern newspaper or Shakespeare's literature. Besides, the quality of word representation heavily relies on the methods to find numerical vector representations for all the words. There are several methods to generate word representation by learning from the words' context.
Deep neural networks have been responsible for much of the advances in machine learning over the last decade. These restrictions not only raise infrastructure costs but also complicate network implementation in resource-constrained contexts like mobile phones and smart devices. Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time. The goal of neural network pruning is to convert a large network to a smaller network with equivalent accuracy. Here in this article, we will discuss the important points related to neural network pruning. The major points to be covered in this article are listed below.
The World Economic Forum has published a new study on how artificial intelligence (AI) can be used to accelerate a more equitable energy transition and build trust for the technology throughout the industry. As the impacts of climate change become more visible worldwide, governments and industry face the urgent challenge of transitioning to a low-carbon global energy system. Digital technologies – particularly AI – are key enablers for this transition and have the potential to deliver the energy sector's climate goals more rapidly and at lower cost. Written in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German Energy Agency, Harnessing Artificial Intelligence to Accelerate the Energy Transition reviews the state of play of AI adoption in the energy sector, identifies high-priority applications of AI in the energy transition, and offers a road map and practical recommendations for the energy and AI industries to maximize AI's benefits. The report finds that AI has the potential to create substantial value for the global energy transition.
You might remember the fist versions of assistants like siri, but you have to admit that in the last 10 years they had a big leap of development. Now not only phones have the abilities to support you daily through talking, but also speakers that can do things like playing music, teaching, shopping and other similar things. Since I needed a new music Speaker and i am a fan of simplicity and easiness I bought the 40$ Amazon Echo Dot 4 with my coupons that were left from Christmas. The day the package shipped I was nervous and happy at the same time. Since this was my first Smart Speaker, i didn't know what to expect but i was purely happy to finally receive a new gadget that could change my life for good.
While artificial intelligence (AI) is already effectively assisting human developers at every level of the development process, software development will only get better as it is about to undergo a huge change. Artificial intelligence is revolutionizing the way developers work, resulting in significant productivity, quality and speed increases. Everything -- from project planning and estimation to quality testing and the user experience -- can benefit from AI algorithms. AI will undoubtedly impact how developers create applications and how users interact with them in the modern environment. As organizations become more interested in AI technologies, artificial intelligence will certainly affect the future of software development.
Several use cases for AI in fraud detection and management were discussed in the report. For example, AI can improve the accuracy of transaction monitoring. The analysts described how financial services provider FIS worked with Brighterion, an AI company owned by Mastercard, to improve its anti-money laundering capabilities. The provider now uses AI to vet risk when onboarding new vendors, for example. In other use cases, AI can improve the efficiency of fraud investigations by streamlining and prioritizing alerts.
The European Commission believes that its proposed Artificial Intelligence Act should become the global standard if it is to be fully effective. The upcoming AI treaty that is being drafted by the Council of Europe might help the EU achieve just that. In April the European Commission launched its proposal for an Artificial Intelligence Act (AIA). Structured around a risk-based approach, the regulation introduces tighter obligations in proportion to the potential impact of AI applications. Commissioner Thierry Breton argued that "one should not underestimate the advantage of the EU being the first mover" and emphasised that the EU is the main "pacemaker" in regulating the use of AI on a global scale. In a similar vein, the Commission's director-general for communications networks, content and technology, Roberto Viola said that "equilibrium is key to have a horizontal risk-based approach in which many voices are heard to avoid extremism and create rules that last.
Iran's top nuclear scientist woke up an hour before dawn, as he did most days, to study Islamic philosophy before his day began. That afternoon, he and his wife would leave their vacation home on the Caspian Sea and drive to their country house in Absard, a bucolic town east of Tehran, where they planned to spend the weekend. Iran's intelligence service had warned him of a possible assassination plot, but the scientist, Mohsen Fakhrizadeh, had brushed it off. Convinced that Fakhrizadeh was leading Iran's efforts to build a nuclear bomb, Israel had wanted to kill him for at least 14 years. But there had been so many threats and plots that he no longer paid them much attention. Despite his prominent position in Iran's military establishment, Fakhrizadeh wanted to live a normal life. And, disregarding the advice of his security team, he often drove his own car to Absard instead of having bodyguards drive him in an armored vehicle. It was a serious breach of security protocol, but he insisted. So shortly after noon on Friday, Nov. 27, he slipped behind the wheel of his black Nissan Teana sedan, his wife in the passenger seat beside him, and hit the road.
New Zealand Police has recruited an unusual new officer to the force: an AI cop called Ella. Ella is a life-like virtual assistant that uses real-time animation to emulate face-to-face interaction in an empathetic way. Its first day of work will be next Monday, when Ella will be stationed in the lobby of the force's national headquarters in Wellington. Its chief duties there will be welcoming visitors to the building, telling staff that they've arrived, and directing them to collect their passes. It can also talk to visitors about certain issues, such as the force's non-emergency number and police vetting procedures. After three months on the job, Ella's future on the force will be evaluated.