In July 1950, a small group of American soldiers called Task Force Smith were all that stood in the way of an advance of North Korean armor. The soldiers' only anti-armor weapons were bazookas left over from World War II. The soldiers of Task Force Smith quickly found themselves firing round after round of bazooka ammunition into advancing North Korean T-34s only to see them explode harmlessly on the heavily armored tanks. Within seven hours, 40 percent of Task Force Smith were killed or wounded, and the North Korean advance rolled on.1 The shortcomings of the bazooka were no surprise. However, budget cutbacks after World War II scuttled adoption of an improved design.

In the past few years, there's been a lot of buzz around artificial intelligence (AI) in cybersecurity. Can AI really help businesses improve their security posture? How can we determine which solutions actually use AI versus which ones make hyped-up claims? For solutions that can help, how do they help? Obtaining clarity around this subject will help us understand the areas in which AI can help and what value it can add, which will, in turn, help us make more informed decisions.

The Brookings Institution last week published a report from global economy expert Indermit Gill prophesying that the AI leader in 2030 will go on to rule the planet until at least 2100. The territories in the running include the US, China, and the European Union. Economists appear to have reached a general consensus that artificial intelligence is among the four great "general purpose technologies" to come along since the 1800s. Gill argues that AI, like steam power, electricity, and information systems technology, will directly impact the way business is conducted at the global scale by 2030. Related: Here's what AI experts think will happen in 2020 Technological leadership will require big digital investments, rapid business process innovation, and efficient tax and transfer systems.

After a draft white paper about the EU's position on AI regulation was leaked earlier this month, Google chief Sundar Pichai, on Monday (20 January), warned the bloc about imposing its own regulations and called for an "international alignment" on the core values of the future laws of the sector. However, ahead of what is expected to be the fourth industrial revolution, the European Commission wants to ensure an "appropriate" ethical and legal framework for the development of AI, which promises to boost innovation while making EU citizens' rights a priority. In November, commission chief Ursula von der Leyen pledged to develop AI legislation similar to the General Data Protection Regulation (GDPR), an EU law on privacy. "It is not about damming up the flow of data, it is about making rules that define how to handle data responsibly," she told MEPs back then. "For us, the protection of a person's digital identity is the overriding priority," she added. According to Ursula Pachl, deputy director-general at the European Consumer Organisation (BEUC), an NGO in Brussels, "it is important that the European Commission has announced a legislative framework and is vocal about the ambition to become a global standard-setter in this area, much like it has done with the GDPR on data protection".

Yes, companies use AI to automate various tasks, while consumers use AI to make their daily routines easier. But governments–and in particular militaries–also have a massive interest in the speed and scale offered by AI. Nation states are already using artificial intelligence to monitor their own citizens, and as the U.K.'s Ministry of Defence (MoD) revealed last week, they'll also be using AI to make decisions related to national security and warfare. The MoD's Defence and Security Accelerator (DASA) has announced the initial injection of £4 million in funding for new projects and startups exploring how to use AI in the context of the British Navy. In particular, the DASA is looking to support AI- and machine learning-based technology that will "revolutionise the way warships make decisions and process thousands of strands of intelligence and data."

Sign up here to receive the Davos Diary, a special daily newsletter that will run from Jan. 20-24. IBM called for rules aimed at eliminating bias in artificial intelligence to ease concerns that the technology relies on data that bakes in past discriminatory practices and could harm women, minorities, the disabled, older Americans and others. As it seeks to define a growing debate in the U.S. and Europe over how to regulate the burgeoning industry, IBM urged industry and governments to jointly develop standards to measure and combat potential discrimination. The Armonk, New York-based company issued policy proposals Tuesday ahead of a Wednesday panel on AI to be led by Chief Executive Officer Ginni Rometty on the sidelines of the World Economic Forum in Davos. The initiative is designed to find a consensus on rules that may be stricter than what industry alone might produce, but that are less stringent than what governments might impose on their own. "It seems pretty clear to us that government regulation of artificial intelligence is the next frontier in tech policy regulation," said Chris Padilla, vice president of government and regulatory affairs at International Business Machines Corp.

In 2016, a third of surveyed Americans told researchers they believed the government was concealing what they knew about the "North Dakota Crash," a conspiracy made up for the purposes of the survey by the researchers themselves. This crash never happened, but it highlights the flaws humans carry with them in deciding what is or is not real. The internet and other technologies have made it easier to weaponize and exploit these flaws, beguiling more people faster and more compellingly than ever before. It is likely artificial intelligence will be used to exploit the weaknesses inherent in human nature at a scale, speed, and level of effectiveness previously unseen. Adversaries like Russia could pursue goals for using these manipulations to subtly reshape how targets view the world around them, effectively manufacturing their reality.

Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In this research, a test-proven deep learning architecture is applied to predict the performance under various restrictions and search for better shapes by optimizing the learned prediction function. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. To remedy this drawback, a Frequentist active learning is used to explore regions of the output space that DNN predicts promising. This operation reduces the number of data samples demanded from ~8000 to 625. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity. Flood fill is used to define a boundary area function so that the optimal shape does not bypass the minimum area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the ultimate shape is optimized while circumventing the required area. Jointly, shapes with extremely low drags are found explored by a practical user interface with no human domain knowledge and modest computation overhead.

In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso if it is tuned correctly and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation and the tuning parameter satisfies some further restrictions. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.

New technologies have always provoked panic about workers being replaced by machines. In the past, such fears have been misplaced, and many economists maintain that they remain so today. Yet in A World Without Work, Daniel Susskind shows why this time really is different. Advances in artificial intelligence mean that all kinds of jobs are increasingly at risk. Susskind will argue that machines no longer need to reason like us in order to outperform us.