Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems can be designed in different ways, with black-box AI systems appearing as complex data-processing engines and white-box AI systems appearing as fully transparent data-processors. As such, it is reasonable to assume that these different design choices also affect user perception and thus their willingness to share data. To this end, we conducted a pre-registered, scenario-based online experiment with 240 participants and investigated how transparent and non-transparent data-processing entities influenced data-sharing intentions. Surprisingly, our results revealed no significant difference in willingness to share data across entities, challenging the notion that transparency increases data-sharing willingness. Furthermore, we found that a general attitude of trust towards AI has a significant positive influence, especially in the transparent AI condition, whereas privacy concerns did not significantly affect data-sharing decisions.

With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of support from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. The concept of hybrid intelligence is still at a nascent stage, and how learners can benefit from a symbiotic relationship with various agents such as AI, human experts and intelligent learning systems is still unknown. The emerging concept of hybrid intelligence also lacks deep insights and understanding of the mechanisms and consequences of hybrid human-AI learning based on strong empirical research. In order to address this gap, we conducted a randomised experimental study and compared learners' motivations, self-regulated learning processes and learning performances on a writing task among different groups who had support from different agents (ChatGPT, human expert, writing analytics tools, and no extra tool). A total of 117 university students were recruited, and their multi-channel learning, performance and motivation data were collected and analysed. The results revealed that: learners who received different learning support showed no difference in post-task intrinsic motivation; there were significant differences in the frequency and sequences of the self-regulated learning processes among groups; ChatGPT group outperformed in the essay score improvement but their knowledge gain and transfer were not significantly different. Our research found that in the absence of differences in motivation, learners with different supports still exhibited different self-regulated learning processes, ultimately leading to differentiated performance. What is particularly noteworthy is that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive laziness.

This study explores the impact of AI-generated digital self-clones on improving online presentation skills. We carried out a mixed-design experiment involving 44 international students, comparing self-recorded videos (control) with self-clone videos (AI group) for English presentation practice. The AI videos utilized voice cloning, face swapping, lip-sync, and body-language simulation to refine participants' original presentations in terms of repetition, filler words, and pronunciation. Machine-rated scores indicated enhancements in speech performance for both groups. Though the groups didn't significantly differ, the AI group exhibited a heightened depth of reflection, self-compassion, and a meaningful transition from a corrective to an enhancive approach to self-critique. Within the AI group, congruence between self-perception and AI self-clones resulted in diminished speech anxiety and increased enjoyment. Our findings recommend the ethical employment of digital self-clones to enhance the emotional and cognitive facets of skill development.

The arrangement echoes the $1 billion cash-for-computing investment that Microsoft made in OpenAI three years ago. In January, Microsoft announced a further "multiyear, multibillion-dollar" investment in OpenAI estimated at $10 billion. The deal cemented Microsoft's position as exclusive infrastructure provider to one of the world's leading AI startups. Chief executive Satya Nadella claimed that Microsoft had built a supercomputer to handle the OpenAI work, and that it could now handle some AI calculations at half the cost of its rivals. Reducing cost is key for the compute-intensive development of large language models: Estimates put the cost of running ChatGPT, assuming 10 million monthly users, at $1 million per day.

As the world waits for construction of the largest fusion reactor yet, called ITER, smaller reactors with similar designs are still running. These reactors, called tokamaks, help us test both hardware and software. The hardware testing helps us refine things like the materials used for container walls or the shape and location of control magnets. But arguably, the software is the most important. To enable fusion, the control software of a tokamak has to monitor the state of the plasma it contains and respond to any changes by making real-time adjustments to the system's magnets.

Andrew Ng knows a thing or two about artificial intelligence. The former head of Google Brain and prior chief scientist at Baidu, Ng also co-founded Coursera and regularly teaches popular courses on the technology online and at Stanford. And he runs Landing AI, which provides manufacturers (and soon, other industries) with an AI platform to help developers more easily build and deploy computer vision models. That experience has given Ng a deep understanding of the benefits that AI can produce -- and the limitations of the tech. As Ng expands his work outside of consumer internet companies, he's seeing a pattern: Organizations are setting their AI ambitions too high.

"Humans develop their own unique style. Machines don't – Yet!" Durtti is a PR and jobs platform exclusively for the 6000 screened members of The Artificial Intelligence Group on LinkedIn. With 500 new members joining every month, it is fast becoming one of the largest business networks in the world of Data Scientists, Machine Learning Engineers, Computer Vision Researchers, Roboticists, NLP Specialists, Cognitive Neuroscientists, Autonomous Driving Experts, Computer Science and Philosophy Professors, Lecturers and PhD Students. The name "Durtti" highlights the lack of clean water available to 748 million people in the world today – a problem that AI might one day solve?

Welcome to this user friendly group passionate about data science, machine learning, and artificial intelligence! Whether you are new to the subject or a veteran, our goal is to help you advance in your journey. The goal is to learn and we welcome all questions. We recognize this can be an intimidating subject so this group will try to explain the background concepts and demonstrate application. Big Data will be part of the subject area since we need to be able to analyze data regardless of volume or structure.

Amazon's digital brain Alexa is very skillful, now at more than 12,000 third-party capabilities, but it can sometimes be difficult to wade through them all and discover new skills, and the platform lacks a central voice search to help users find the right skill for a particular task. Paul Allen's Allen Institute for Artificial Intelligence wanted to fill that void, and it built a voice-activated search for Alexa skills. But one problem: Amazon said no. Here is the answer the Allen Institute team got: "Thank you for the recent submission of your skill, 'Skill Search'. Unfortunately, your skill has not been published on Amazon Alexa. We don't allow skills that recommend skills to customers at this time. We will contact you if this feature becomes available."

Intel announced late last week that it has formed a new AI group to consolidate a number of its programs and acquisitions. It's headed by Naveen Rao, the former head of Intel acquisition Nervana. This means Intel is making sure is has a major seat at the table as artificial intelligence and machine learning branch out to touch virtually everything -- from autonomous driving to IoT to enhancing corporate systems -- over the next 5-7 years. In the short term, the group will focus on research related to its software and hardware (Nervana, Xeon/Lakecrest chips and subsequent families) to deliver AI for drones and autonomous vehicles, smart cities, health care, personal appliances, etc. But I expect a longer-term play.