NVIDIA and Ericsson today announced they are collaborating on technologies that can allow telco operators to build high-performing, efficient and completely virtualized 5G radio access networks (RAN). These virtualized networks can enable faster and more flexible introduction of new AI and IoT services. The collaboration, announced by NVIDIA founder and CEO Jensen Huang at a keynote ahead of the start of MWC Los Angeles, brings together Ericsson's expertise in RAN technology with NVIDIA's leadership in GPU-powered accelerated computing platforms, as well as AI and supercomputing. Telcos are exploring alternative technologies and RAN architectures amid growing interest for virtualization, while securing the best possible user experience. A major industry challenge is how to virtualize the complete RAN solution in a cost-, size- and energy-efficient way, comparable with traditionally built RAN networks.

The government has unveiled the 16 centres set to deliver a £200m, five-year programme to train 2,000 PhD students in artificial intelligence specialisms. The initiative is a core part of the AI sector deal announced by Theresa May in 2018 and is supported by £100m from government, £78m from industry, and £23m from the participating universities. Google, AstraZeneca, Rolls-Royce and the NHS are among the organisations to have backed the programme. The sector deal was announced in April last year as the government faced criticism for failing to protect the UK's tech sector in the run up to Brexit. A report by the House of Lords had claimed the UK lagged behind Germany, Canada and South Korea when it came to AI, as well as world-leaders China and the US.

One need not understand the finer details of the Dark Web in order to respect the risk these black markets pose in an increasingly data-centric world. In nearly every aspect of our modern lives, cybersecurity is becoming a necessary part of the conversation. Even novice "hackers" can rent cloud-based botnets and orchestrate DDoS attacks for around $25 per hour according to data released by Kaspersky Labs. Fortunately, the classic adage remains true, even on the Dark Web - you get what you pay for. Low-priced DDoS attacks are easily thwarted by modern network security systems designed to recognize incoming threats and divert resources to squash attacks. Thanks to advancements in Artificial Intelligence (AI) and Machine Learning (ML) in the area of cybersecurity, small businesses and enterprise-level companies can stay focused and maintain a high level of digital trust from their customers while keeping overhead costs in check.

Who needs to use that delicate tiny sewing staple, when there's now a robot that can thread a needle for you? The XR-1 robot is powered by cloud artificial intelligence (AI)--one of the first of its kind--Sprint True Mobile 5G, and proprietary vision-controlled grasping tech, which means it not only can thread a needle, but can serve drinks and can be programmed to do other tasks, including manufacturing. The revolutionary XR-1 robot is a service robot, which also leverages human operator input for constant learning. "Overall, intelligent cloud robots paint the most vibrant picture of how 5G's ultra-low latency, exponentially faster speeds, and wider reach can dramatically improve response time and enable a new world of applications," said Bill Huang, founder and CEO of CloudMinds, in a release. CloudMinds' Virtual Backbone Network (VBN) combines high-performance, low-latency fixed, and mobile-network technology; blockchain technologies; and other innovations to manage cloud robotics through connectivity completely isolated from the internet, guaranteeing security.

SAN FRANCISCO, Oct. 24, 2019 (GLOBE NEWSWIRE) -- MICRON INSIGHT -- Micron Technology, Inc. (Nasdaq: MU), today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.

It's pretty easy to figure out what I mean if __ few symbols are missing from this sentence. According to a new paper by researchers from DeepMind and the University of Oxford's Faculty of Classics, AI can help restore, understand, and recreate ancient Greek texts that have been damaged and left with gaps that make them nearly impossible to understand. The work will be presented next month at the Empirical Methods in Natural Language Processing conference in Hong Kong. The researchers used an algorithm named after Pythia, the woman who in Greek mythology was a vessel for Apollo's prophecies. They found that it outperformed historians trained in restoring fragmented stone, clay, or metal tablets.

Tesla looks to Shanghai and Joker fans head to the Bronx, but first, today's cartoon: What's rarer than a unicorn? Here's the news you need to know, in two minutes or less. Want to receive this two-minute roundup as an email every weekday? Good news out of the Gigafactory: Tesla is back in black. On Wednesday, the electric carmaker announced a positive net profit in its quarterly report, the first record of earnings for the company since the end of 2018.

Artificial intelligence has replaced many skills in recent years – including the skills needed to do some human jobs. The tech revolution has not gone unnoticed by American workers. A 2018 Gallup poll revealed that 70% of Americans believe AI will eliminate more jobs than it creates. Democratic presidential candidate Andrew Yang has sounded the alarm, raising the prospect that millions are at risk for long-term joblessness. I'm an expert on labor markets in the U.S., and I believe that AI will undoubtedly change the future of U.S. labor--but Yang is also exaggerating the impact AI will have on the workforce.

Sustainability became the most important component of world development, as countries worldwide fight the battle against the climate change. To understand the effects of climate change, the ecological footprint, along with the biocapacity should be observed. The big part of the ecological footprint, the carbon footprint, is most directly associated with the energy, and specifically fuel sources. This paper develops a time series vector autoregression prediction model of the ecological footprint based on energy parameters. The objective of the paper is to forecast the EF based solely on energy parameters and determine the relationship between the energy and the EF. The dataset included global yearly observations of the variables for the period 1971-2014. Predictions were generated for every variable that was used in the model for the period 2015-2024. The results indicate that the ecological footprint of consumption will continue increasing, as well as the primary energy consumption from different sources. However, the energy consumption from coal sources is predicted to have a declining trend.

Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of partial orders. In such cases, the parameter of the model may not be even identifiable. In this paper, we focus on three popular structures of partial orders: ranked top-$l_1$, $l_2$-way, and choice data over a subset of alternatives. We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders). We also prove that under some combinations, including ranked top-$3$, ranked top-$2$ plus $2$-way, and choice data over up to $4$ alternatives, mixtures of two Plackett-Luce models are identifiable. Guided by our theoretical results, we propose efficient generalized method of moments (GMM) algorithms to learn mixtures of two Plackett-Luce models, which are proven consistent. Our experiments demonstrate the efficacy of our algorithms. Moreover, we show that when full rankings are available, learning from different marginal events (partial orders) provides tradeoffs between statistical efficiency and computational efficiency.