BOULDER, Colo.--(BUSINESS WIRE)--Today Tractica announced the launch of its new Artificial Intelligence Advisory Service, a subscription-based market research and analysis suite that provides independent and objective market intelligence and strategy insights for companies engaged in the rapidly evolving artificial intelligence (AI) market. As part of the service, Tractica's global industry analyst team provides strategic and quantitative analysis focused on the market opportunity for AI technologies in enterprise, consumer, and government markets. Research reports include an in-depth examination of AI business models, use cases, technology issues, and key industry players in addition to detailed market sizing, segmentation, and forecasts. Tractica's Artificial Intelligence Advisory Service examines use cases and business models for the application of artificial intelligence technologies in enterprise, consumer, and government markets.

BOULDER, Colo.--(BUSINESS WIRE)--Today Tractica announced the launch of its new Artificial Intelligence Advisory Service, a subscription-based market research and analysis suite that provides independent and objective market intelligence and strategy insights for companies engaged in the rapidly evolving artificial intelligence (AI) market. As part of the service, Tractica's global industry analyst team provides strategic and quantitative analysis focused on the market opportunity for AI technologies in enterprise, consumer, and government markets. "Artificial intelligence technologies are already beginning to have a disruptive effect on established business models across virtually every industry, while simultaneously enabling new business processes that were not previously possible," says managing director Clint Wheelock. "Rapid advances in AI technologies like deep learning, machine learning, computer vision, and natural language processing (NLP) are being enabled by more powerful hardware, sophisticated algorithms, and a virtually limitless ocean of data to analyze and interpret." As part of its Artificial Intelligence service, Tractica's industry analysts offer timely and actionable market insights, covering specific technology and industry sectors as well as overall market conditions and trends.

Researchers at Uppsala University have used computer modelling to study how brain tumours arise. The study, which is published today in the journal EBioMedicine, illustrated how researchers in the future will be able to use large-scale data to find new disease mechanisms and identify new treatment targets. The last ten years' progress in molecular biology has drastically changed how cancer researchers work. Instead of almost exclusively using different biological models, like cells, today large-scale statistical analyses are increasingly used to understand tumour diseases and find new therapies. Researchers at Uppsala University, together with colleagues at the University of Gothenburg, Chalmers University of Technology and University of Freiburg, have developed a new algorithm, aSICS, that uses large amounts of data to suggest hypotheses about "what causes what" in a cancer cell.

Much ink has been spilled on the subject of how the jobs market is being impacted by artificial intelligence (AI) and robotics. The well-known study by economists Frey and Osborne published in 2013, which predicts that 47% of all currently existing jobs in the United States will come under threat over the next twenty years, is regularly brought out of the cupboard as a terrifying spectre. Other far more optimistic studies, based on longer time-frames, have delivered a riposte to this – largely unfounded – scaremongering, which has in fact been repeated many times over throughout our history. However, the potential impact of AI on general education and vocational skills training – two means of preparing people for the labour market – is still being largely disregarded. The model whereby you learn during the first half of your life and spend the remaining years applying what you have learned in the world of work has held up pretty well.

Artificial Intelligence, or AI, is all around us and we might not even realize it! As a technology that imitates human decision making, it is present in a lot of what we use every day. AI can be found in Amazon.com Facebook tells you whom you could know and want to be friends with. Cars can automatically park for you.

While Star Trek-style teleportation is still a long way off, researchers have revealed a major breakthrough in the field of quantum travel. Two separate teams have transferred quantum information over several miles of fibre optic networks in an urban network. The results could lead to more secure bank accounts, a faster internet and possibly even open the door to the controversial idea of human teleportation. One of the potential applications for quantum teleportation is a network of quantum computers (illustrated) and a'quantum internet' that is far faster and much more secure than current networks When atoms are'entangled' the measurement of one of the atoms will not only cause it to'pick' one state, but it will also instantaneously cause the atoms it is entangled with to do the same, even if that atom has not been measured itself. This means we automatically know information about all the atoms that are entangled at once, just by measuring one, and it does not matter how far apart in space the two entangled atoms are.

We've all had that feeling that somebody is watching us – even if we're not looking directly at their eyes. Sometimes we even experience a feeling of being watched by someone completely outside our field of vision. But how can we explain this phenomenon without resorting to psuedoscientific explanations like extrasensory perception - or a'sixth sense'? Dr Harriet Dempsey-Jones, a Postdoctoral Researcher in Clinical Neurosciences at the University of Oxford explains how our minds can play tricks on us. Sometimes we experience a feeling of being watched by someone completely outside our field of vision.

In a previous tutorial series I went over some of the theory behind Recurrent Neural Networks (RNNs) and the implementation of a simple RNN from scratch. That's a useful exercise, but in practice we use libraries like Tensorflow with high-level primitives for dealing with RNNs. With that using an RNN should be as easy as calling a function, right? In this post I want to go over some of the best practices for working with RNNs in Tensorflow, especially the functionality that isn't well documented on the official site. Check out the tf.SequenceExample Jupyter Notebook here! RNNs are used for sequential data that has inputs and/or outputs at multiple time steps.

Salesforce is really laying on the jargon when it comes to Einstein, an artificial-intelligence addition to the CRM giant's entire product line set to be announced Monday. "Einstein's models will be powered by advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery," according to a Salesforce press release. But AI is red-hot right now, and it must be said that Salesforce has paid dearly for the right to use such language. It has spent 650 million buying a dozen companies to acquire their staff and technology, chairman and CEO Marc Benioff said last week. The word "Einstein" will be appended to each of Salesforce's eight product offerings. Details will be fully shared at the annual Dreamforce users' conference Oct. 4-7 in San Francisco, but for now, here's a partial list of what Salesforce says that will mean in practice.

Artificial intelligence is changing the world in many ways and the medical field is one that has been revolutioanised by AI. While some reports have been arguing that it may take over from human doctors soon, it's already proving helpful in detection and diagnosis of several conditions. A recent study has suggested that Artificial Intelligence can be instrumental in improving accuracy of diagnosis for lung diseases. It does so by using algorithms that can perform predictive analysis of data and learn from that. Researchers developed an algorithm process in addition to clinical variables of smoking history and body mass.