From Figure 1(a), learning multiple linear sub-models and averaging the predictions (ensemble) is still a linear model, so it cannot tackleXOR problem. We compare the training cost of all methods from the two aspects;1). Thesub-model training enables themost adversarial attacks ofsub-models could be successfully defended. In particular, we train two kinds of models to defend against the attacks: 1). FromFigure2(a)and2(b),when0.01 ϵ 0.04, SoE without the collaboration training achieves a similar robustness compared with SoE.

Teleoperation of beyond-human-scale robotic manipulators (BHSRMs) presents unique challenges that differ fundamentally from conventional human-scale systems. As these platforms gain relevance in industrial domains such as construction, mining, and disaster response, immersive interfaces must be rethought to support scalable, safe, and effective human-robot collaboration. This paper investigates the control, cognitive, and interface-level challenges of immersive teleoperation in BHSRMs, with a focus on ensuring operator safety, minimizing sensorimotor mismatch, and enhancing the sense of embodiment. We analyze design trade-offs in haptic and visual feedback systems, supported by early experimental comparisons of exoskeleton- and joystick-based control setups. Finally, we outline key research directions for developing new evaluation tools, scaling strategies, and human-centered safety models tailored to large-scale robotic telepresence.

With the recent releases of large-language models, such as ChatGPT, artificial intelligence (AI) capability has leapfrogged, attracting intense attention around the globe. Inspired by the success of ChatGPT, many Chinese technology companies, such as Baidu, rushed to announce their own plans for developing a Chinese version of ChatGPT. However, to everyone's surprise, the Chinese government recently banned tech companies from offering ChatGPT-like services and will potentially impose more regulations on the development of AI. Since AI has gradually evolved into a foundational part of societal infrastructure essential to national interests, China may create a new state-owned enterprise (SOE) to monopolize AI foundation in China, similar to how SOEs monopolize the energy and telecommunication sectors. Traditionally, China's SOEs have controlled industries that are deemed essential to national interest and China's economy.

Travelling for business may sound glamorous, but it can actually be pretty stressful. Booking tickets and hotels, co-ordinating journey times, coping with queues and scrums for taxis, can all leave you frazzled before you've even entered the room to make your pitch. Booking.com research finds that more than nine in 10 business travellers suffer from stress. So wouldn't it be wonderful if technology could take a lot of these hassles away? From hotel concierge services offering online check in and room service at the touch of a button, to wireless Bluetooth padlocks for luggage, tech innovations promise to do just that. But it could be artificial intelligence (AI) that has the biggest impact.

Siummary: In 2017, AI and analytics M&A activity will accelerate, data lakes will finally become useful, and data monetization strategies will mature. These are some of the predictions Ramon Chen, CMO of data management innovator, Reltio, has for the coming year. Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications.

Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications. What's well understood is that AI needs a consistent foundation of reliable data upon which to operate. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017.