At first glance, it seems counterintuitive. To learn more about how cells go awry causing disease, it seems logical to focus on the minutiae of a cell's molecular components: the specific genes, proteins, and small molecules that change over time leading to a disease state. Instead, Ernest Fraenkel, MIT associate professor in the Department of Biologic Engineering, first takes a macro view for finding new ways to understand and cure diseases. "When you are faced with making any sense of the 10,000 or 20,000 molecules that are present within a cell and evolve during disease, you need an entirely new approach to figure out what is really important among all the changes you see," Fraenkel explains. He develops computational and laboratory experimental methods to uncover the molecular pathways that go awry in disease and search for new strategies and intervention targets.

Universitat Pompeu Fabra seeks for a PhD candidate to work on the topic of cell segmentation and tracking. The PhD will partially be carried out in the context of the Bioimage Analysis project, which is supported by the Maria de Maeztu Programme for Centres/Units of Excellence in R&D (https://portal.upf.edu/web/mdm-dtic/). The PhD project will also be in collaboration with Institut de Robòtica i Informàtica Industrial. The candidate is expected to have a good background in computer vision and machine learning. Knowledge in advanced microscopy imaging is a plus.

Sephora is doubling down on augmented reality and artificial intelligence sales tactics by enabling shoppers to virtually try on false lashes, watch tutorials using their own image and engage via a chatbot to trial and purchase lip color. With Sephora's customers virtually trying on more than 70 million lip shades using the Virtual Artist in-app functionality that was introduced earlier this year, false lashes are being added to expand the program. Users of the Sephora application can also now experience live step-by-step makeup application tutorials using their own uploaded images and augmented reality technology. "This is a significant expansion because we are adding elements that we know will help empower and educate our clients' purchase making decisions, and they're done in a way that is fun and engaging," said Bridget Dolan, vice president of Sephora Innovation Lab. "The new Live Tutorials especially are a game changer for our users," she said.

As a futurist, an entrepreneur, and a lawyer, I always get asked, 'What do you think is the next big thing in the legal world?' I always begin my response with a catch-all reply: 'The next big thing is anything that helps you attract and keep a client. No client equals no business. A bit of a cliché, I know. But you must constantly rethink how to do things better and be more efficient by using the latest research, thinking, and innovations.'

In one of the year's most telegraphed tech reveals, Facebook announced in April that it was opening its Messenger APIs so brands could deploy chatbots to create rich, automated customer engagement on the app. But barely 24 hours passed before users discovered that the first chatbots on Messenger could not understand some simple questions, were slow to respond, and did not interact in much of a conversational manner. In other words, they were more chatbust than chatbot. Weather app Poncho took the lion's share of the tribal frustration by giving quippy, off-topic responses to questions it didn't understand. And it clearly was not understanding much.

It could change the workplace, our culture, our sense of humanity, and our relationship not only to one another, but to machines. We are joined by Lucy Suchman, professor of Anthropology of Science and Technology at Lancaster University. Also joining us are Nathaniel Popper a business reporter at the New York Times, and Zeynep Tufecki an associate professor at the University of North Carolina and a contributing opinion writer at the New York Times.

We all know about the incredible progress that deep learning has made in recent years. In just 5 years, we went from near-unusable speech recognition and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to beating a world champion. We went further than anybody could have foreseen --if you went back to 2010 and told AI researchers about the things we can do today, most likely no one would believe you. And we keep on making remarkable progress on a month-to-month basis.

In robotics, the Artificial Intelligence (AI) is probably the most exciting field today. Yes, we all think that a robot can work on an assembly line, but there is no harmony that a robot can ever be intelligent. Though the functionality and universality of Internet and computers have outranked the myths about technology advancements and usefulness in everyday life. It's true that AI is changing our lives since decades, but the presence of AI everywhere today was not felt ever like this. Nearly, every scientist has the different opinion about the future of Robotics and AI and about the change which will happen due to the combo of these two.

The consumer sentiment analysis of this one's pretty easy, but will they be compensated? When a person feels sufficiently wronged to lodge a complaint with the Consumer Financial Protection Bureau (CFPB), there's likely to be some negative sentiment involved. But is there a connection between the language they use and the likelihood they will be compensated by the offending company? At the upcoming Sentiment Analysis Symposium, I will discuss how machine learning and rule-based sentiment analysis can support each other in a complementary analysis, and produce actionable information from large amounts of free form text. In this case, machine learning and sentiment analysis could improve and evolve the CFPB's ability to assess consumer complaints.

In three months (as of June 2016) the New Orleans Saints will play a football game against the Atlanta Falcons. I want to know who will win. I ask my friend and he says the Saints. Technically this is a predictive model, but it's probably not worth much. I can improve upon this model by asking other people who they think will win.