Most jobs in the future don't exist yet beyond a spark of the imagination – that's what nearly half of young people believe. The future will be full of outlandish problems no one has even thought of – but there will be more need than ever for people with the critical and creative-thinking skills to tackle them. "We already know centennials are well aware that industries are undergoing exponential change," says Professor Nick Colosimo, principal technologist at BAE Systems. Some 47 per cent of young people expect to work in industries that don't yet exist, research by BAE Systems shows, while nearly two thirds (63 per cent) think that job roles will be more exciting than those of their parents' era. Advances in areas such as artificial intelligence (AI), 3D printing and nanofabrication, automation and robotics will inevitably reshape the job market.

Morgan Stanley was another customer that showcased its work with IBM Power Systems at the event. Morgan Stanley executive director Marcelo Labre speaking with IBM's Sumit Gupta says that IBM Power Systems' computing power and AI-readiness is enabling the organization to explore new AI/ML use cases in finance, with the overall goal of increased efficiency and alignment with customer needs. For example, Morgan Stanley's Labre elaborated at THINK 2019 on how his organization is utilizing AI to challenge outdated risk models. Using AI to improve risk models is a common theme I hear over and over in the industry. You truly need big data to do this well and Power fits the bill.

Innovation is pivotal for businesses to remain globally competitive. Naturally then it has been a key talking point throughout the year. From the commercial opportunities of graphene to clear 3D printing and flat wine bottles, here are the top 10 innovation stories of 2018! Whether manufacturing personalised surgical guides, eye-catching consumer packaging, cutting-edge prototypes or anything in between, there are numerous advantages to 3D printing in transparent plastics. The new materials being designed, manufactured and supported have pushed clear printing to the boundaries of what's possible, offering ultra-high transparency, moisture and temperature resistance, biocompatibility, robustness and performance.

As artificial intelligence (AI) capabilities mature, enterprise leaders are continuously evaluating use cases that can transform their business. A key challenge that slows down AI adoption is the abundant but untamed data that is not ready for AI. There is a strong correlation between companies outperforming in AI adoption and the ones that have a robust data infrastructure aligned with their business architecture. According to the 2018 IBM Business Value survey, Shifting Toward Enterprise-grade AI, 65 percent of outperformers surveyed capture, manage and access business, technology and operational information on key corporate data with a high degree of consistency across the organization versus 52 percent of all others surveyed. IBM recently introduced IBM Cloud Private for Data (ICP4D), a data and analytics platform, to help make your data estate ready for AI.

Munich-based industrial equipment usage analytics startup Corrux has raised $3.1 million in a seed funding round led by German venture capital firm Target Partners, with participation from Josef Brunner, who is the CEO of Relayr and US venture investor Sean Dalton. This seed funding has taken place to implement artificial intelligence in the arena of industrial equipment and scale up its operations. Corrux is an industrial equipment usage analytics startup, which develops simple, powerful applications that let users understand heavy machinery ranging from excavators to track laying machines. The company provides construction managers and OEMs with software to monitor on-site operations and gain insights from data. Moreover, the company's analytics layer lets construction companies and OEMs understand their heavy machinery – ranging from excavators to track laying machines.

On the Starship Enterprise, replicators were devices that were used "to dematerialize matter and then reconstitute it in another form," according to Startrek.com. For Captain Picard's hungry crew, in particular, that usually meant nostalgically reconstituting meals on demand to appease a sudden craving. Though we remain a long way away from being able to transmogrify matter into a chocolate sundae on command, a team of real-life researchers has created a 3-D printer that can create entire objects simultaneously instead of creating them one painstaking layer at a time like most printing techniques. The new approach ---- known as Computer Axial Lithography (CAL) ---- carves an object out of a synthetic resin that solidifies when it comes into contact with particular patterns and intensities of light. Using a device dubbed "the replicator," researchers from University of California, Berkeley and the Lawrence Livermore National Laboratory used the technique to create tiny airplanes and bridges, copies of the human jaw, a screwdriver handle and minuscule copies of Rodin's Thinker.

A team of researchers from the University of North Carolina at Chapel Hill have unveiled a 3D printer that uses light to create an entire object at once. It's called the Replicator, named after the machines in the Star Trek universe that can synthesize food, water, air and various objects seemingly out of nothing. Before you get too excited, the researchers didn't quite create an exact replica of that fictional machine, but it still offers a new and promising 3D printing technique. According to the team's paper published in Science, the Replicator works like a reverse CT scan. When a patient undergoes the procedure, an X-ray tube rotates around their body to take multiple photos that a computer can use to create 3D images.

Experts warn ML should be used "for its correct purpose" – not for prying long-term strategies from sparse information Data is the latest of many hopes for banks and other investors looking for improved returns in a lacklustre environment. Several banks have begun to point their research teams at big data – using internal data, purchased databases or new research to collect huge quantities of data points, which can then be analysed using the new technology of machine learning (ML). UBS seems to be in the lead at present, but Morgan Stanley, BNP Paribas and many others are following. And this combination is being applied elsewhere as well; last week Risk looked at HSBC's client intelligence unit, which is aimed at using internal client data to generate new sales leads for existing customers. Standard Chartered's data analytics group earned a 2019 Risk Award for quant of the year for its head Alexei Kondratyev, based on the group's machine learning work.

John Deere has been to CES before. The company known for its dark green tractors with the yellow deer on them has rubbed shoulders with the smart TVs, smart light bulbs, smart cars, smart switches and smart toothbrushes for years. But 2019 was a bit different. "We do consider ourselves a technology company. We wanted to engage the tech company and tell the story of agriculture," said Julian Sanchez, John Deere's director of technology innovation.

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.