A team of researchers from Ben Gurion University and the University of Texas Southwestern Medical Center at Dallas have developed a method to determine which tumor cells are most likely to metastasize efficiently to distant sites in the body. An interdisciplinary research team headed by one of BGU's newest faculty members, Dr. Assaf Zaritsky of the Department of Software and Information Systems Engineering, and Gaudenz Danuser of the University of Texas Southwestern Medical Center (UTSW) at Dallas, has developed a groundbreaking method based on artificial intelligence to identify melanoma cells that are likely to metastasize to other parts of the body. The method, called "quantitative live cell histology", was presented at the American Society for Cell Biology / EMBO conference in San Diego last December. It was developed during Dr. Zaritsky's postdoctoral studies at UTSW. The approach calls for filming live cancer cells with microscopic cameras and using artificial intelligence to analyze the video sequence and identify cells' appearance and behavioral patterns that associate with metastatic potential.

In India the insurance industry has undergone several structural changes including embracing more advanced technologies like artificial intelligence/machine learning, together with bots, the Internet of Things and big data analytics. These digital transformation initiatives have been adopted by established insurance companies like Oriental, New India Assurance, LIC Of India, IFFCO TOKIO and ICICI Prudential. However, these firms remain vulnerable to losing some of the competitive space to startups. According to analysis by Brink Asia, these changes have been aided by a shifting economic landscape and government policies that have liberalized the insurance sector and encourage technological innovation. For example the National Health Protection Scheme under Ayushman Bharat has provided coverage to more than 100 million vulnerable families; the crop insurance scheme Pradhan Mantri Fasal Bima Yojana, which aided 47.9 million farmers in 2017-18; and the Pradhan Mantri Jeevan Jyoti Bima Yojana, a life insurance policy initiated by the central government to offer a life insurance policy to those who previously had no access to such services.

So there's been a lot of coverage by various websites, data science gurus, and AI experts about what 2019 holds in store for us. Everywhere you look, we have new fads and concepts for the new year. This article is going to be rather different. We are going to highlight the dark horses – the trends that no one has thought about but will completely disrupt the working IT environment (for both good and bad – depends upon which side of the disruption you are on), in a significant manner. So, in order to give you a taste of what's coming up, let's go through the top four (plus 1 (bonus) five) top trends of 2019 for data science: This single innovation is going to change the way machine learning works in the real world.

BEIJING: In a groundbreaking technological move, China's state-owned TV channel Xinhua has introduced the world's first ever Artificial Intelligence news anchor. On Tuesday, Xinhua announced that in collaboration with Sogou (search engine) has created the world's first female AI news anchor, known as Xin Xiaomeng. The anchor will be making her debut during the upcoming Two Sessions political meetings in March. The AI female news presented is to debut after the China unveiled the world's first male AI news anchor, Qiu Hao, during China's annual World Internet Conference held in November in the town of Wuzhen. Xinhua also went to on to add that they have developed an improved male anchor called Xin Xiaohao.

Speaking at a conference in Lisbon, Portugal shortly before his death, Stephen Hawking told attendees that the development of artificial intelligence might become the "worst event in the history of our civilization," and he had every reason for concern. Known as an artificial superintelligence (ASI) by AI researchers, ethicists, and others, it has the potential to become more powerful than anything this planet has ever seen and it poses what will likely be the final existential challenge humanity will ever face as a species. To better understand what concerned Stephen Hawking, Elon Musk, and many others, we need to deconstruct many of the popular culture depictions of AI. The reality is that AI has been with us for a while now, ever since computers were able to make decisions based on inputs and conditions. When we see a threatening AI system in the movies, it's the malevolence of the system, coupled with the power of a computer, that scares us.

I am a major sci fi fan. Well, at least I thought I was until I went to my first Star Trek convention in my 20s and realized that I was in the minority of people who did not speak Klingon or know episode numbers, titles or dates. Most recently, I have become inspired by Black Mirror, a show originally aired by the BBC and now offered on Netflix. The brainchild of Charlie Brooker, Black Mirror is the Twilight Zone for our times, giving us a glimpse as to how technology trajectories can be used to affect society in unintended ways in the coming decades. As Frederik Pohl used to say, 'A good science fiction story should be able to predict not the automobile but the traffic jam.' Metaphorically speaking, this show sure is predicting traffic jams.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

The field of Artificial Intelligence has made rapid progress in recent years, matching or in some cases, even surpassing human accuracy at tasks such as computer vision, natural language processing and machine translation. The intersection of artificial intelligence (AI) and GIS is creating massive opportunities that weren't possible before. AI, machine learning and deep learning are helping us make a better world by helping increase crop yield through precision agriculture, to fighting crime by deploying predictive policing models, to predicting when the next big storm will hit and being better equipped to handle it. Broadly speaking, AI is the ability of computers to perform a task that typically requires some level of human intelligence. Machine learning is one type of engine that makes this possible.

Walmart Labs, the technology unit of retail firm Walmart, has acqui-hired its third Indian startup, Dataturks, a Bengaluru-based Machine Learning (ML) -based Data Annotation Platform that do image bounding, document annotation, NLP and text annotations. The acquisition will help Walmart to use Dataturk's machine learning capabilities for content enrichment. Post acquisition, the 5-member team of Dataturks will be part of Wamart's merchant technology team for catalogue enrichment. Founded in last year, by Gajendra Dadheech and Mohan Gupta, Dataturks makes ML data annotations super easy by allowing teams to upload data, invite other team member to upload and build training/evaluation dataset in hours and find labeled datasets. In December 2018, Walmart Labs had acqui-hired Int.AI, a Bangalore-based mobile analytics aka MicroApps startup and in September last year it acquired Appsfly, a Bangalore-based micro-app startup.

When Bill Gates floated the idea of imposing a tax on robots a couple of years ago, Lawrence Summers, a former top economic adviser to President Barack Obama, called the Microsoft co-founder "profoundly misguided." How do you even define a robot to tax it? And taxing innovation is a sure way to make a country poorer. Europe has also rejected the idea. In 2017 the European Parliament soundly defeated a draft motion, proposed by its committee on legal affairs, that recommended considering a tax on the owners of robots to fund retraining programs for workers displaced by the machines and shore up the finances of their social security system.