Sitting in a purpose-built hut in the grounds of the historic Bletchley Park country estate, British officials believed they had pulled off a diplomatic coup. On stage in front of them was the UK's technology secretary, Michelle Donelan, and behind her were high-level representatives from the US and China, together for the first time to discuss the international regulation of artificial intelligence. Even better, both countries were among 28 signatories to the "Bletchley declaration", an agreement to work together on safety standards that may prevent AI systems turning on humanity. Rishi Sunak said on Thursday: "Some said we shouldn't even invite China; others said that we could never get an agreement with them. A serious strategy for AI safety has to begin with engaging all the world's leading AI powers, and all of them have signed the Bletchley Park communique."

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model.

Managing Partner and Co-Founder of Scale-Up VC, a Silicon Valley venture capital firm based in Palo Alto, California. Experts have warned against its potential misuse. It's now affecting aspects of our lives that many of us never anticipated: healthcare, education, employment and even national security. What could I be talking about? Artificial intelligence, or the "big AI," as I call it.

Imagine an underwater drone armed with nuclear warheads and capable of operating autonomously. Now imagine that drone has lost its way and wandered into another state's territorial waters. Russia aims to field just such a drone by 2027, CNBC reported last year, citing those familiar with a U.S. intelligence assessment. Known as Poseidon, the drone will be nuclear-armed and nuclear-powered. While the dynamics of artificial intelligence and machine learning, or ML, research remain open and often collaborative, the military potential of AI has intensified competition among great powers.

The logical consequence of the AI-reporting challenge is a new form of systemic risk. Jon Danielsson, et al., at the London School of Economics (LSE) recently studied the impact of AI on systemic risk and concluded that one of the impacts of the use of AI was pro-cyclicality. The report notes the link between pro-cyclicality and homogeneity in beliefs and actions--when people and/or machines all think in the same way, they are more likely to make the same errors and perpetuate the same dangerous practices. As data pools become more valuable for AI-driven businesses, market consolidation will likely shrink the number of companies that have access to them, reducing competition in the market and the diversity in decision-making AI. With one eye on the last financial crisis, financial regulators will be aware of the need to control new sources of systemic risk, but the challenge falls equally within the remit of competition regulators.

Artificial intelligence (AI) has been mainly the passion of data science labs and development shops. Lately, however, the implications of its potential impact on business -- in the form of enhanced customer service, expanded intelligent capabilities, and even society at large -- have become clearer. That means the time has come for business leaders to not only understand the implications of AI, but also step up and lead the way. That's because with the great power of AI comes great responsibility. "While AI is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole," write Roger Burkhardt, Nicolas Hohn, and Chris Wigley, all with McKinsey.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

The Cambrian explosion of deep-learning based startups that started a year or two ago has mostly continued unabated, even though the AI startup market is (arguably) showing signs of finally cooling down. Expectations, round sizes and valuations remain high, but we are certainly past the phase where big Internet companies would snap up very early AI startups at high prices just for the talent. The air is also clearing up a bit and revealing "real" AI startups, versus a number of other companies that were leveraging the hype. Some of the AI startups that were founded in the 2014-2016 time frame are starting to hit early scale, and many are offering increasingly interesting products across industries and verticals including health, finance, "industry 4.0" and back office automation. Deep learning will continue bringing a lot of value in real world applications for years to come, and vertical-focused AI startups have many great opportunities ahead of them.

Are the U.S., China, and Russia recklessly undertaking an "AI arms race"? Clearly, there is military competition among these great powers to advance a range of applications of robotics, artificial intelligence, and autonomous systems. So far, the U.S. has been leading the way. AI and autonomy are crucial to the Pentagon's Third Offset strategy. Its Algorithmic Warfare Cross-Functional Team, Project Maven, has become a "pathfinder" for this endeavor and has started to deploy algorithms in the fight against ISIS.