Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with greater scale, reach, and specificity. New methods with inductive learning capacity will be needed to identify these novel operations before they indelibly alter public opinion and events. We develop an inductive learning framework which: 1) determines content- and graph-based indicators that are not specific to any operation; 2) uses graph learning to encode abstract signatures of coordinated manipulation; and 3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators$\unicode{x2013}$illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

ChatGPT-3 is a state-of-the-art language model developed by OpenAI. It is based on the GPT-3 (Generative Pre-trained Transformer 3) architecture and has been trained on a massive amount of text data. It has the ability to generate human-like text, answer questions, and complete various language-based tasks. It can also perform well on a wide range of natural language processing (NLP) tasks, such as text summarisation, translation, and text-to-speech. Additionally, it has the ability to generate text based on given prompt, which is unique for its large size and capability.

The graph represents a network of 7,377 Twitter users whose tweets in the requested range contained "chatbot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 July 2022 at 13:01 UTC. The requested start date was Friday, 01 July 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 7-day, 8-hour, 36-minute period from Thursday, 23 June 2022 at 15:20 UTC to Thursday, 30 June 2022 at 23:57 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 2,500 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 02 September 2020 at 13:06 UTC. The requested start date was Wednesday, 02 September 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 16-day, 4-hour, 48-minute period from Sunday, 16 August 2020 at 18:30 UTC to Tuesday, 01 September 2020 at 23:19 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

As artificial intelligence is entering all spheres of our lives, a lot of concern is arising about the possible white bias and patriarchy of the impending AI world. Research shows women are much more skeptical of and averse to innovation in comparison to men who embrace and triumph it. This fear of technological innovation has to do with the fact that society often views the role of women as replaceable by AI, which is visible in the abundance of women robots and female personal assistants, such as Alexa and Cortana. If we're coming to the point when most jobs are automated and robots become everyday reality in our lives, we'd better make sure those algorithms are beneficial for most people, be it an Afro-American woman or a Chinese man. As of today, 85% of the machine learning workforce is male.

As artificial intelligence is entering all spheres of our lives, a lot of concern is arising about the possible white bias and patriarchy of the impending AI world. Moreover, research shows women are much more skeptical of and averse to innovation in comparison to men who embrace and triumph it. This fear of technological innovation has to do with the fact that society often views the role of women as replaceable by AI, which is visible in the abundance of women robots and female personal assistants, such as Alexa and Cortana. If we're coming to the point when most jobs are automated and robots become everyday reality of our lives we'd better make sure those algorithms are beneficial for most people, be it an Afro-American woman or a Chinese man. As of today, 85% of the machine learning workforce is male.

What if we delegated so much to autonomous AI and intelligent machines that They passed a law that forbids humans to carry out a number of professions? We conceive the plot of a new episode of Black Mirror to reflect on what might await us and how we can deal with such a future.

If you listen at all to industry news, you can't escape the onslaught of marketing hype surrounding Artificial Intelligence (AI) and Machine Learning (ML). Is SkyNet ready to take over the world? Despite the optimism and promise, AI and ML largely remain in an immature stage where they suffer mistakes and biases from classic programming issues, which we will explore below: garbage-in, garbage-out and poor quality control. Still, with the potential for huge benefits on the horizon, we cannot simply reject security tools that have tried to incorporate these advances. Instead, we need to create a strategy for adoption that provides a safety net for failure through overlapping technology – just as we do for any other security product. AI will be eventually be good.

Elon Musk's comment that humans are underrated (Humans replace robots at flagging Tesla plant, 17 April) doesn't come as much of a surprise, even though his company is at the forefront of the technological revolution. Across industries, CEOs are wrestling with the balance between humans and increasingly cost-effective and advanced robots and artificial intelligence. However, as Mr Musk has discovered, the complexity of getting a machine to cover every possibility results in a large web of interconnected elements that can overcomplicate the underlying problem. This is why so many organisations fail when they try to automate everything they do. Three key mistakes I see time and again in these situations are missing the data basics, applying the wrong strategy, and losing the human touch. There are some clear cases where automation works well: low value, high repetition tasks or even complex ones where additional data will give a better outcome, for example, using medical-grade scanners on mechanical components to identify faults not visible to the human eye.

Korea Advanced Institute of Science and Technology demonstrates a robot designed for rescue missions or helping people with disabilities. The institute is facing a boycott from artificial intelligence researchers from nearly 30 countries over concerns that a new lab that has partnered with a leading defence company could lead to'killer robots' Subscribe to Guardian News http://bit.ly/guardianwiressub Guardian Science and Tech http://is.gd/guardiantech