Salesforce has officially unveiled Einstein, a set of artificial intelligence (AI) capabilities it says will help users of its platform serve their customers better. Billing the technology as "AI for everyone", Salesforce is putting Einstein's capabilities into all its clouds, bringing machine learning, deep learning, predictive analytics, and natural language processing into each piece of its customer relationship management platform. In Salesforce's Sales Cloud, for instance, machine learning will power predictive lead scoring, a new tool that can analyse all data related to leads -- including standard and custom fields, activity data from sales reps, and behavioural activity from prospects -- to generate a predictive score for each lead. The models will continuously improve over time by learning from signals like lead source, industry, job title, Web clicks and emails. Another tool will analyse CRM data combined with customer interactions such as inbound emails from prospects to identify buying signals earlier in the sales process and recommend next steps to increase the sales rep's ability to close a deal.

How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

The live-action adaptation of "Ghost in the Shell" starring Scarlett Johansson as the cyberpunk-fighting cyborg just dropped its first bit of footage. No doubt manga, anime and genre fans everywhere will have something to say about this brief glimpse of the new dystopia. The first footage from the feature film dropped during the finale of "Mr. Robot," because if a show about hackers taking down evil corporations entertains you, wait until you breathe in the cyberpunk horror that is "Ghost in the Shell." The movie takes place in a fictional, futuristic Japanese city and follows "The Major" (Johansson) and the members of a covert task force within the Japanese National Public Safety Commission made up of former detectives and military operatives.

Take a look at the tag on your shirt. If you are in the U.S., chances are it was made in a country like China or Thailand and then shipped overseas. Jonathan Zornow, the sole employee of a new startup called Sewbo, thinks the U.S. could bring garment manufacturing a little closer to home by automating the feeding of fabric into sewing machines--a step that to this day is done by hand. Zornow has created a process by which a robotic arm guides chemically stiffened pieces of fabric through a commercial sewing machine. Machines already play a large part in clothing manufacturing.

The scientific world is developing a knack for reading texts without opening them. Researchers in Israel and the US have conducted the first "virtual unwrapping" of a heavily damaged scroll, the En-Gedi scroll, to read its contents without destroying what's left. The team used a high-resolution volumetric scan to create a 3D model of the scroll, looked for bright pixels in the model (a sign of where the ink would be) and virtually flattened the scroll to make text segments readable. The process is slow, as you have to piece together segments and reconstruct lines of text that have been lost to the ages. However, the results were worth it in this case: the researchers discovered that this is the earliest known copy of a Pentateuchal book from the Bible (Leviticus) to be found in a Holy Ark, dating back "at least" 1,500 years.

A rather high profile area generating headlines this year has been connected vehicles. The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Vision is one critical factor; your car needs to be able to identify all road hazards as well as navigating from A to B. So, how can a car achieve that in an often over-crowded highway space? Computer vision can be described as graphics in reverse. Rather than us viewing the computer's world, the computer turns around to look at ours.

For Matt Osborne, finding exploited children typically starts with a walk on the beach, and it ends with hands cuffed behind his back. It's almost always the same--Osborne and a few friends travel somewhere that's known for sex tourism and walk along the beach or hang in area nightclubs, not to look for girls but to be seen themselves. A group of white American men is easy to spot in heavily-touristed resort towns in Asia, Central America, and South America, so it doesn't take long to make a connection. "They approach us," Osborne says. "At first, everything is innocuous. Want to go jet ski or parasailing? Buy a margarita or beer? They offer us drugs, and the conversation always turns to girls. And if you let them talk long enough and say, 'What else do you have? What else do you have?' Then sooner or later, they always offer us young girls."

That's how much content will be captured by 2020 by surveillance cameras across the globe. These one billion cameras -- twice today's number -- will be at traffic intersections, transit stations and other public areas, helping to make our cities safer and smarter. They'll be in retail stores, service centers, warehouses, and more, gathering information to boost sales, track inventory and improve service. But how to make sense of this staggering number of pixels? But a branch of artificial intelligence called deep learning offers a powerful, scalable method for extracting the most out of these vision systems.

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.

Like many aspiring software engineers and entrepreneurs, Haydé Martinez started her career working at large, established enterprises. In her case, she first worked at Intel as an intern -- then at Hewlett-Packard Enterprise as a software engineer. During these early days, she also had the opportunity to study artificial intelligence and machine learning at the renowned Kanazawa Institute of Technology in Japan. But this wasn't enough for Haydé -- she had big dreams and wanted to solve big challenges. When she was presented with the opportunity to work at Rever, she knew that the startup life was for her.