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) …
Yet another company enters the Destiny family as the company announces the acquisition of the Danish communication and collaboration company ipvision. The acquisition enhances Destiny's position as a leading European, SME-focused, secure cloud communication provider. Daan De Wever, CEO Destiny: "We are excited to welcome ipvision to the Destiny family. Obviously, this will strengthen our position on the Danish market and in the Nordics in general. Therefore, ipvision is a perfect match with the Destiny Group, reinforcing our position as an innovative, client-centric and market leading UCaaS provider for SMEs in Europe."
To increase their value in the fast-growing AI field, top Artificial Intelligence professionals will need to develop a few key skills that go beyond just technical expertise. According to'LinkedIn Jobs on the Rise: 15 opportunities that are in demand and hiring now', artificial intelligence (AI) is one of the fastest-growing occupations, with practitioners in great demand in 2021. The best AI/ML professionals and teams are well-rounded in their broad business understanding and ability to communicate, in addition to having expertise in Python, C, or Java and an aptitude for math. The next step of digital transformation is organization-wide adoption of AI/ML technologies; therefore a strong team of developers, programmers, and data scientists is essential for enhancing AI literacy from the top down. It is critical for IT leaders to emphasize that AI/ML is intended to improve, not completely replace the organization's teams.
The latest AI-powered cameras are clicking high-end pictures and help in face recognition in pictures and videos. In addition, the same application has found benefits in ease of video editing. From scanning text scripts to recognizing faces on videos, it can match the elements and automate the video editing task. If you have to do this daily, it is better to choose software that is artificial intelligence or AI-powered. The list below would help you go for the right one and make the most of it for editing purposes.
More companies are talking about AI ethics and its facets, but can they apply them? Some organizations have articulated responsible AI principles and values but they're having trouble translating that into something that can be implemented. Other companies are further along because they started earlier, but some of them have faced considerable public backlash for making mistakes that could have been avoided. The reality is that most organizations don't intend to do unethical things with AI. However, when something goes wrong, customers and the public care less about the company's intent than what happened as the result of the company's actions or failure to act.
Herein we use the term "machine-learned model" to refer to a model that has been created by running a supervised machine learning algorithm on a labelled data set. Machine-learned models are trained on specific data sets, known as their training distribution. Training data are typically drawn from specific ranges of demographics, country, hospital, device, protocol and so on. Machine-learned models are not dynamic unless they are explicitly designed to be, meaning that they do not change as they are used. Typically, a machine-learned model is deterministic, having learned a fixed set of weights (i.e., coefficients or parameters) that do not change as the model is run; that is, for any specific input, it will return the same prediction every time.
NeuralCoref is a pipeline extension for spaCy 2.1 which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client NeuralCoref-Viz, a web interface powered by a REST server that can be tried online.
MedTech Europe has called for the urgent clarification of a proposed artificial intelligence regulation because it uses an overly broad definition and is misaligned with existing regulatory frameworks. The European Commission outlined its plans to regulate AI, including medical devices and in vitro diagnostics that feature the technology, earlier this year. Under the proposal, the European Union would require high-risk AI systems to "comply with certain mandatory requirements" before coming to market. The Commission acknowledged a risk of overlap with existing regulations but envisioned the framework complementing requirements such as the Medical Devices Regulation. However, MedTech Europe contends the proposal falls short of that vision.
First, technology always creates winners and losers. While automation poses risks to some workers and firms, for others – especially in developing countries – it presents opportunities to upgrade quality, reach new export markets, and create productive employment. Policy should allow such firms to grow, for example by lowering labor market rigidities, which may help to offset any negative effects of declining firms and sectors. Second, our findings warn that growing trade protectionism may slow cross-border technology diffusion. It may constrain the ability of firms in developing countries to upgrade production processes, move into higher value-added activities and produce the high-quality products demanded by consumers.
Across a range of fields, individual careers are characterized by hot streaks, bursts of high-impact works clustered together in close succession. The hot streak highlights a specific period during which an individual's performance is substantially better than their typical performance. An example of a hot streak is Jackson Pollock's three-year period from 1947 to 1950, during which he created most of his famous artworks with his particular "drip technique". A few years ago, Lui and colleagues used AI to examine the work of scientists, artists, and film directors for hot streaks throughout their careers. They determined how impactful their work was by looking at output such as a scientist's most-cited papers over a 10-year period, auction prices for artwork, and IMDB.com movie ratings.