China and the U.S. are the top two countries for robot demand. It's true that more robots than ever are participating in the human labor force. Last year, businesses worldwide spent $26 billion employing some 70 million robots on manufacturing and service jobs, according to the World Robotics Report published by the International Federation of Robotics (IFR) on Wednesday. On the industrial side, 422,000 robots were shipped last year, a 6% increase from 2017. More than half of the robots were bought by automotive and electronics companies, which came as a surprise amid the tough market conditions facing these industries in 2018.

Artificial Intelligence (AI) is no longer the next big thing, it is now a big thing now in digital marketing. All digital marketing operations are now affected by AI-powered tools. From startups to large firms are opting for AI-powered digital marketing tools to enhance campaign planning & decision making. AI-based tools are now a flourishing market, with a drastic change in demand. According to most of the digital marketers AI enhancing all the areas where the predictive analysis, decision making & automation efforts required.

Choosing the right investment strategy is a very important step towards reaching your financial goal, whether it is accumulating enough to buy a cottage house or living the life of a millionaire after retirement. The way you build and manage your portfolio largely depends on your appetite for risks and the available funds, as well as your personal preferences and understanding of the market. Thus, it is no surprise that there are quite a few investment strategies out there, and while quite many of them have been showing good results, there is still room for improvement. The booming artificial intelligence technology has been a boon for the financial industry, and in this article, we will show why some of the most popular investment strategies and AI are a match made in heaven. The artificial intelligence has turned into a buzzword these days, to the point where it can at times be difficult to figure out what exactly this umbrella term means.

Machine-learning chatbot systems can be exploited to control what they say, according to boffins from Michigan State University and TAL AI Lab. "There exists a dark side of these models – due to the vulnerability of neural networks, a neural dialogue model can be manipulated by users to say what they want, which brings in concerns about the security of practical chatbot services," the researchers wrote in a paper (PDF) published on arXiv. They crafted a "Reverse Dialogue Generator" (RDG) to spit out a range of inputs that match up to a particular output. Text-based models normally work the other way, where outputs are generated after having been given an input. For example, given the sentence "Hi, how are you?", a computer learns to output a response like "Fine, thank you" as it learns that is one of the most common replies to that question in training data.

If you enjoy intellectual challenges and designing experiences that impact millions of people, enterprise UX could be right for you. In 2005, YouTube was born, Google had just acquired Android, Yahoo! was a popular search engine, and there was no Netflix, Twitter, or Spotify. In that same year, I was asked to build up and manage my first visual design team. In those still formative years of the internet and hence modern user experience (UX), it was unusual – at least in Europe – to find a visual designer trained in human-computer interaction. So, I hired what I could find: talented graphic designers, most of whom had experience creating work for print and the web, but who had no idea about designing software.

The recent revolution in genetic and molecular biological studies as well as imaging and artificial intelligence has contributed significantly to our understanding of AMD. However, many open questions are still to be addressed. We are approaching an era of new targets for therapy based on better differentiation, refined phenotyping and predictive markers of age-related macular disease. Four years after the last international Baden-Baden AMD meeting it appears timely to review emerging concepts. We look forward to welcoming you in Baden-Baden.

Welcome to the OCTLab, the OCT research group for OCT technology, Virtual Ophthalmology, Artificial Intelligence (AI) and Robotics of the Department of Ophthalmology at the University of Basel. Optical coherence tomography (OCT) is a new, non-invasive imaging technology that uses laser waves to detect pathologies in diseases as age related macular degeneration (AMD), glaucoma, diabetes, multiple sclerosis (MS), tumors of the eye or brain. The OCTlab does applied research and Quality Assurance Studies in the field of optical coherence tomography and targets to save human vision. The University of Basel was founded in 1460 and is the oldest University in Switzerland.

Around the world, millions of patients suffer from currently incurable vision loss. The new Institute of Molecular and Clinical Ophthalmology Basel (IOB) has been created to help them. At IOB, we accelerate the conversion of basic research into innovative treatments which change the field of ophthalmic therapy. Interdisciplinary teams of researchers and clinicians work hand in hand. These problem-solvers and innovators have in-depth knowledge of unmet medical needs, and daily exposure to patients.

As we saw in Minding the Gender Gap, women still lag far behind men in the tech field, both in terms of representations (which hovers around 25% in the United States), and in terms of pay, where the gap between men and women is close to 12%. While figures for pay disparity in tech don't focus on specialists in artificial intelligence (AI), female representation there is even lower. According to the report, Discriminating Systems: Gender, Race, and Power, conferences women make up only 18% of the represented authors at AI conferences and less than 20% of AI professors. They fare even worse in corporations where they make up only 15% of research staff positions at Facebook and a mere 10% at Google. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.

Technology has been blamed for a lot recently. Automation and artificial intelligence have supposedly led to substantial job losses, reduced bargaining power for workers and increased discrimination. It is even blamed for growing income and wealth inequality and, as a result, the presidency of Donald Trump, Brexit, the rise of far-right populism in Europe and the spectre of climate change. In response, calls are being made for global oversight and regulation of technology and there are attempts to slow down its spread through protectionist trade policies and political lobbying. But perhaps we should be careful about so readily blaming technological innovation for these social problems.