"With great power comes great responsibility." You don't have to be a Marvel buff to recognize that quote, popularized by the Spider-Man franchise. And while the sentiment was originally in reference to superhuman speed, strength, agility, and resilience, it's a helpful one to keep in mind when making sense of the rise of generative AI. While the technology itself isn't new, the launch of ChatGPT put it into the hands of 100 million people in the span of just 2 months, something that for many felt like gaining a superpower. But like all superpowers, what matters is what you use them for. Generative AI is no different.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The rapid growth in machine learning (ML) capabilities has led to an explosion in its use. Natural language processing and computer vision models that seemed far-fetched a decade ago are now commonly used across multiple industries. We can make models that generate high-quality complex images from never before seen prompts, deliver cohesive textual responses with just a simple initial seed, or even carry out fully coherent conversations.

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.

Elon Musk's Neuralink touts its brain chip as a way to help people suffering with mobility issues regain control of their lives, but has also proposed using the technology to merge humans with computer. The move would provide the average person with super-human intelligence that hooks their brain up to the cloud where memories can be stored, thoughts can be exchanged and experiences can be had. Although the abilities of an implanted chip may sound limitless, such wonders come with great responsibilities that Musk, scientists and other companies need to address – specifically privacy. 'If the widespread use becomes hooking us to the cloud, not as therapies, and merge humans with AI the economic model will be to sell our data,' Dr. Susan Schneider, the founding director of the new Center for the Future Mind, told Daily Mail. 'Our inner most thoughts would be sold to the highest bidder.

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.

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.

The organisation of tomorrow will be built around data using emerging technologies. Big data analytics empowers consumers and employees. This will result in real-time decision making and a better understanding of the changing environment. Blockchain enables peer-to-peer collaboration and trustless interactions governed by cryptography and smart contracts. Meanwhile, artificial intelligence allows for new and different levels of intensity and involvement among human and artificial actors. When big data analytics, blockchain and AI are combined, it will change collaboration among individuals, organisations and things.

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.