In the late sixties while working at Ericsson he invented both sequence diagrams and use cases, and in later years worked on the SDL, UML and the RUP. We caught up with Dr Ivar Jacobson to hear his thoughts on where the industry is today, and where it will head in the future. Builder AU: What do you think of the state of software engineering today? Ivar Jacobson: What I see when I travel and talk to customers, participate in conferences and have discussions with experts around the world is that software development is very much still an immature discipline. We still rely on too much old work. Personally I am convinced we will change that dramatically, but it is a very slow process. I have been working on process improvement and new technologies for many years now, starting with component based development and then adding to that object orientation and now aspect orientation. I've been involved with new technology since the sixties, and I've been more optimistic than most -- it's my nature, but we are still struggling with basic stuff for many reasons.

Will this year mark the first drone delivery, or the first time you encounter a robot at work or in your home? We saw significant advancements in robotics and artificial intelligence in 2015 (see "What Robots and AI Learned in 2015"). The world's largest economy has embarked on an audacious effort to fill its factories with advanced manufacturing robots. The government of China hopes this will help the country retain its vast manufacturing industry as workers' wages rise, and manufacturing becomes more efficient and technologically advanced around the world (see "China Wants to Replace Millions of Workers with Robots"). The project will require robots that are significantly more advanced and cost-efficient, and the economic and technological ripples could be felt around the world.

In a workshop at the Boston headquarters of Rethink Robotics, engineers are tending to a troop of eight bright red robots called Baxter. Each robot has a humanoid upper torso and a pair of friendly blue eyes on a small screen that track the robots' two arms as the engineers move them. Off to the side of the room, senior product manager Brian Benoit is assembling a new robot called Sawyer--Baxter's little brother. It has just one arm and is also smaller, faster, and more precise than Baxter. If things go as the company hopes, this new robot will find work alongside humans on many manufacturing production lines.

The behavior and interplay of two types of neurons in the brain helps give humans and other animals an uncanny ability to navigate by building a mental map of their surroundings. Now one robot has been given a similar cluster of virtual cells to help it find its own way around. Researchers in Singapore simulated two types of cells known to be used for navigation in the brain--so-called "place" and "grid" cells--and showed they could enable a small-wheeled robot to find its way around. Rather than simulate the cells physically, they created a simple two-dimensional model of the cells in software. The work was led by Haizhou Li, a professor at the Agency for Science, Technology and Research (A*STAR).

Few people ever need to deal with a stricken nuclear reactor, but that skill could turn out to be important for the evolution of smarter robots. In Pomona, California, this week, 25 of the world's most advanced humanoid robots will take part in a contest inspired by the challenge of stabilizing a nuclear reactor that's leaking dangerous radioactive material. Teams from universities across the U.S., as well as Japan, China, and Europe, are bringing robots that will try to walk across piles of rubble, climb ladders, operate power tools, and drive buggies, among other chores. Each challenge is inspired by something that might have helped stabilize the Fukushima Daiichi nuclear plant in Japan after it was damaged by an earthquake in 2011. Considerable academic kudos will go to whichever team completes the most tasks within the allotted time by the end of the contest.

The holy grail of artificial intelligence--creating software that comes close to mimicking human intelligence--remains far off. But 2014 saw major strides in machine learning software that can gain abilities from experience. Companies in sectors from biotech to computing turned to these new techniques to solve tough problems or develop new products. The most striking research results in AI came from the field of deep learning, which involves using crude simulated neurons to process data. Work in deep learning often focuses on images, which are easy for humans to understand but very difficult for software to decipher.

Some Web searches are easy to think of and describe, but complicated to conduct. If, for instance, you want to find "a nonstop flight from Las Vegas to San Diego next week on JetBlue," you have to fill out a bevy of fields on a travel site. SkyPhrase, a startup created by Nick Cassimatis, an associate professor at Rensselaer Polytechnic Institute, will soon offer software that lets companies turn natural language questions like the one above into a format that their databases can handle. Facebook's new search tool, Graph Search, highlights both the progress that's being made in natural language processing and the difficulties that remain. Unlike the old search bar, Graph Search lets users enter queries as they might speak them.

You might not have heard of iFlyTek. The company is hardly a household name in its domestic market of China, either. But it has a vice-like grip on over 80 percent of the speech technology market in the People's Republic, heading an ecosystem of over 10,000 partners and developers and with user numbers in the hundreds of millions. The company was founded in 1999 by Liu Qingfeng and five other students from the University of Science and Technology of China, widely recognized as one of the nation's preëminent research institutions. They took advantage of research conducted at the university's National Intelligent Computer R&D Center and the Human-Machine Speech Communication Laboratory.

To shush the music, he simply holds a finger up to his lips. And when he gets up from the couch and leaves the room, his TV screen pauses automatically. Banjara is a cofounder of PredictGaze, a startup that combines gaze detection, gesture recognition, and facial-feature recognition to create more natural ways to control everything from your TV to your car. While many people are just getting their hands on their first touch-screen gadget, PredictGaze is one of a slew of companies betting that touch-free controls will be the next big thing. With front-facing cameras being embedded in all sorts of gadgets, it's not hard to imagine.

The first time Yann LeCun revolutionized artificial intelligence, it was a false dawn. It was 1995, and for almost a decade, the young Frenchman had been dedicated to what many computer scientists considered a bad idea: that crudely mimicking certain features of the brain was the best way to bring about intelligent machines. But LeCun had shown that this approach could produce something strikingly smart--and useful. Working at Bell Labs, he made software that roughly simulated neurons and learned to read handwritten text by looking at many different examples. Bell Labs' corporate parent, AT&T, used it to sell the first machines capable of reading the handwriting on checks and written forms. To LeCun and a few fellow believers in artificial neural networks, it seemed to mark the beginning of an era in which machines could learn many other skills previously limited to humans. "This whole project kind of disappeared on the day of its biggest success," says LeCun. On the same day he celebrated the launch of bank machines that could read thousands of checks per hour, AT&T announced it was splitting into three companies dedicated to different markets in communications and computing. LeCun became head of research at a slimmer AT&T and was directed to work on other things; in 2002 he would leave AT&T, soon to become a professor at New York University.