AI Faces Speed Bumps and Potholes on Its Road From the Research Lab to Everyday Use

IEEE Spectrum  October 1, 2019 According to panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many. As the field is changing rapidly – there are new language models coming out every month, and new tools and the foundational elements are shifting so rapidly there aren’t any true experts at this point. And then there is the decision of where exactly machine learning should happen—on site, or in the cloud? Data scientists are building […]

Is AI the next big climate-change threat? We haven’t a clue

MIT Technology Review  July 29, 2019 Some predict that in the absence of significant innovation in materials, chip manufacturing and design, data centers’ AI workloads could account for a tenth of the world’s electricity usage by 2025 while others expect data center energy consumption to remain relatively flat over the next few years, in spite of a spike in AI-related activity. These widely diverging predictions highlight the uncertainty around AI’s impact on the future of large-scale computing and the ultimate implications for energy demand. But pessimistic forecasts ignore several important developments that could limit AI’s power grab. One of them […]

China has started a grand experiment in AI education. It could reshape how the world learns.

MIT Technology Review  August 2, 2019 According to one estimate, China led the way investing over $1 billion globally last year in AI education. Tech giants, startups, and education incumbents have all jumped in. Tens of millions of students now use some form of AI to learn. Three things have fueled China’s AI education boom. The first is tax breaks and other incentives for AI ventures, academic competition in China is fierce and Chinese entrepreneurs have masses of data at their disposal to train and refine their algorithms. Squirrel, one of the largest AI education companies in China, also opened […]

How a century-old tech giant is making a comeback with AI

MIT Technology Review   June 13, 2019 IBM relies on its research division with 3,000 researchers distributed across 12 locations, to stay on top of trends in emerging technology. For decades now, the company has engaged in an annual global technology outlook (GTO) process to create and adapt business units in light of what’s on the horizon. They decided AI is one of these technologies that’s on an exponential curve. Two years ago, IBM established the MIT-IBM Watson AI Lab. This collaboration has focused IBM research again on solving significant basic-science problems in AI…read more

What you may not understand about China’s AI scene

MIT Technology Review  April , 2019 Most Chinese researchers can read English, and nearly all major research developments in the Western world are immediately translated into Chinese, but the reverse is not true. Therefore, the Chinese research community has a much deeper understanding than the English-speaking one of what’s happening on both sides of the aisle. As China’s AI industry continues to grow, this could prove a major disadvantage for people in the West. Westerners have a hyped-up view of China’s AI capabilities. Westerners also lack a genuine understanding of the technical skills and capacity of Chinese companies. A few […]

China’s masses of data give it an edge in AI—but they may not forever

MIT Technology Review  March 5, 2019 How can the US outcompete China when the latter has far more people and the former cares more about data privacy? Is it, in other words, just a lost cause for the US to try to “win”? According to the President of MIT, state of the art changes with research. In other words, data may not always be king. Given that our brains themselves do not require a lot of data to learn, the better we come to understand its processes, the more closely we will be able to mimic it in new types […]

China’s Huawei has big ambitions to weaken the US grip on AI leadership

MIT Technology Review  March 4, 2019 Huawei plans to increase its investments in AI and integrate it throughout the company to “build a full-stack AI portfolio.” Officials from the company said last year that it planned to more than double annual R&D spending to between $15 billion and $20 billion. This could catapult the company to between fifth and second place in worldwide spending on R&D. According to its website, some 80,000 employees, or 45% of Huawei’s workforce, are involved in R&D. But Huawei is struggling to convince the Western world that it can be trusted. The company faces accusations […]

Global Artificial Intelligence Patent Survey

Inside Big Data  February 22, 2019 Corresponding to the rise of 4IR digital technologies, the number of international AI based patent filings has expanded rapidly over the last few years, mostly concentrated in the United States and Asia. According to a 2016 study, approximately 75% of all AI-related patent publications in the world come from three jurisdictions: China, Japan, and the United States. Although the majority of AI-related patents are filed in these countries, Europe is also seeing substantial increases in such patent filings…read more.

Causal disentanglement is the next frontier in AI

Phys.org  February 20, 2019 Complex behaviour emerges from interactions between objects produced by different generating mechanisms. Researchers in Sweden introduce a universal, unsupervised and parameter-free model-oriented approach, based on the seminal concept and the first principles of algorithmic probability, to decompose an observation into its most likely algorithmic generative models. They demonstrated its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are bit strings, space–time evolution diagrams, images or networks. Although this is mostly a conceptual contribution and an algorithmic framework, they have provided numerical evidence evaluating the ability of the methods to extract models from data […]

We analyzed 16,625 papers [from Arxiv] to figure out where AI is headed next

MIT Technology Review  January 25, 2019 Through their analysis, they found three major trends: a shift toward machine learning during the late 1990s and early 2000s, a rise in the popularity of neural networks beginning in the early 2010s, and growth in reinforcement learning in the past few years. The biggest shift was a transition away from knowledge-based systems by the early 2000s. Through the 1990s and 2000s, there was steady competition between all these methods. Then, in 2012, a pivotal breakthrough led to another sea change, deep learning. In the last few years, however, reinforcement learning, which mimics the […]