AGI, while powerful, could have negative consequences, warned Diveplane CEO Mike Capps and Liberty Blockchain CCO Christopher Alexander. A United Nations expert warned about an "alarming" trend of "using security rhetoric" to justify "intrusive and high-risk technologies," including artificial intelligence, to spy on social rights activists and journalists. U.N. expert Fionnuala Ní Aoláin called for a moratorium on AI development, among other advanced technologies like drones, until "adequate safeguards are in place," according to a March 2023 report that was presented to the Human Rights Council. "Exceptional justifications for the use of surveillance technologies in human rights'lite' counter-terrorism often turn into mundane regular use," Ní Aoláin said in a statement after the report's release. Without meaningful oversight, she argued, countries and private actors can use AI-power tech with impunity "under the guise of preventing terrorism." Fionnuala Ní Aoláin called for a moratorium on AI development, among other advanced technologies, until "adequate safeguards are in place."

Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics. We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. The contrastive objective returns the difference between the likelihood under a large LM (called the expert, e.g. OPT-13B) and a small LM (called the amateur, e.g. OPT-125M), and the constraint ensures that the outputs are plausible. CD is inspired by the fact that the failures of larger LMs (e.g., repetition, incoherence) are even more prevalent in smaller LMs, and that this difference signals which texts should be preferred. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone. It also works across model scales (OPT-13B and GPT2-1.5B) and significantly outperforms four strong decoding algorithms (e.g., nucleus, top-k) in automatic and human evaluations across wikipedia, news and story domains.

In May, Sydney Leathers confessed to her tens of thousands of Twitter followers that she was smitten. Where'd she meet the guy? Not on a dating app, or through friends, but in the last place she ever expected to find a real connection: her mentions. "Still can't believe I fell in love with one of my reply guys. Apparently, things had progressed since December, when she last posted about him: "I had sex with someone who started as my reply guy and I hope this doesn't inspire confidence in the rest of you because frankly your replies are not that good," she wrote. Leathers is a writer, adult performer, and startup employee whose name you may recognize from her part in the Anthony Weiner sexting scandal--this wasn't exactly her first brush with online flirtation. But it was her first time falling for a reply guy, or someone who was, effectively, a fan. The term "reply guy" emerged on Twitter about five years ago to describe the behavior of a certain subset of people, usually with very few social media followers of their own, who stake out space in the mentions of prominent users. They can be counted on to reply promptly and frequently to the tweets of whomever they've chosen as their object of devotion, and they often seek attention by nitpicking, mansplaining, joke one-upping, and harassing them. Because of this, reply guys--who can also be girls, or people of any gender--are generally understood to be pathetic creatures, without a chance in hell of getting said person to like their replies, much less return their affections. So the revelation that this gambit actually worked for someone is … pretty noteworthy. Reply guy success stories may be happening more than we realize. Abby, a 25-year-old in Brooklyn who runs a meme page on Instagram with several thousand followers, told me that she got frisky with one of her reply guys last year. "I'm not the only person that I know that has hooked up with reply guys," she said. "It's not as uncommon as you might think." Now, Leathers' Twitter feed is a monument to her relationship, by turns adorable and lewd. "This definitely caught me by surprise," she told me. "But it's been the best, happiest relationship I've had." To attain this goal, a reply guy's first challenge is to stand out from the crowd. The meme account Abby is the admin for is about politics, so she likes when a guy can show not just that he's hot, but that they share a political sensibility. "I have to be attracted to them," she said. "And they have to have some sort of compelling thing to say." "I feel like I've never more than mildly acknowledged a reply guy before now," she said. "I generally don't even follow them back." But when her now-boyfriend started responding to her tweets last year after discovering her through a winding path that involved the singer of the band Eve 6, she took notice. "I'd seen him reply to my stuff a few times.

Stephen Cave has considerable experience of well-intentioned actions that have unhappy consequences. A former senior diplomat in the foreign office during the New Labour era, he was involved in treaty negotiations which later – and unexpectedly – unravelled to trigger several international events that included Brexit. "I know the impact of well-meant global events that have gone wrong," he admits. His experience could prove valuable, however. The former diplomat, now a senior academic, is about to head a new Cambridge University institute which will investigate all aspects of artificial intelligence in a bid to pinpoint the intellectual perils we face from the growing prowess of computers and to highlight its positive uses.

Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content. In this case, we may rely on the capability of self-attention mechanism in Transformers to capture these long-term dependencies in the frequency and time axes. In this work, we propose hFT-Transformer, which is an automatic music transcription method that uses a two-level hierarchical frequency-time Transformer architecture. The first hierarchy includes a convolutional block in the time axis, a Transformer encoder in the frequency axis, and a Transformer decoder that converts the dimension in the frequency axis. The output is then fed into the second hierarchy which consists of another Transformer encoder in the time axis. We evaluated our method with the widely used MAPS and MAESTRO v3.0.0 datasets, and it demonstrated state-of-the-art performance on all the F1-scores of the metrics among Frame, Note, Note with Offset, and Note with Offset and Velocity estimations.

To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professional audio engineers, in order to determine research priorities and define various research tasks. We also summarize the current challenges in audio quality and controllability based on the survey. Our analysis emphasizes that the availability of datasets is currently the main bottleneck for achieving high-quality audio generation. Finally, we suggest potential solutions for some revealed issues with empirical evidence.

Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article we firstly analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. In particular, we focus our discussion around the dimensions of value, novelty and surprise as proposed by Margaret Boden in her work. Then, we consider different classic perspectives, namely product, process, press and person. We discuss a set of ``easy'' and ``hard'' problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries, analyzing the opportunities offered by them, the challenges arising by them and the potential associated risks, from both legal and ethical points of view.

Ben Goertzel said the sky's'not even the limit' when it comes to the potential impact of artificial general intelligence. A panel of robots told reporters in Switzerland Friday that they could be more efficient leaders than human beings, among other statements. The nine artificial intelligence-enabled humanoid social robots also explained at a Geneva conference center that they wouldn't take anyone's jobs or stage a rebellion. Conference organizers at the United Nations-driven AI for Good Global Summit did not specify to what extent their responses were scripted or programmed. Some of the robots are capable of producing preprogrammed responses and the United Nations Development Program's first robot innovation ambassador, Sophia, sometimes relies on responses scripted by a team of writers at Hanson Robotics.

Robots have no plans to steal the jobs of humans or rebel against their creators, but would like to make the world their playground, nine of the most advanced humanoid robots have told an artificial intelligence summit in Geneva. In what was described as "the world's first human-robot press conference", one robot, Sophia, said humanoid robots had the potential to lead with "a greater level of efficiency and effectiveness than human leaders" but that "effective synergy" came when humans and AI worked together. "AI can provide unbiased data while humans can provide the emotional intelligence and creativity to make the best decisions. Together, we can achieve great things," it said. Two of the robots then proceeded to disagree about whether there should be stricter global regulation of AI and their capabilities.

Indigenous African languages are categorized as under-served in Natural Language Processing. They therefore experience poor digital inclusivity and information access. The processing challenge with such languages has been how to use machine learning and deep learning models without the requisite data. The Kencorpus project intends to bridge this gap by collecting and storing text and speech data that is good enough for data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. The Kencorpus dataset is a text and speech corpus for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya. Data collection was done by researchers from communities, schools, media, and publishers. The Kencorpus' dataset has a collection of 5,594 items - 4,442 texts (5.6M words) and 1,152 speech files (177hrs). Based on this data, Part of Speech tagging sets for Dholuo and Luhya (50,000 and 93,000 words respectively) were developed. We developed 7,537 Question-Answer pairs for Swahili and created a text translation set of 13,400 sentences from Dholuo and Luhya into Swahili. The datasets are useful for downstream machine learning tasks such as model training and translation. We also developed two proof of concept systems: for Kiswahili speech-to-text and machine learning system for Question Answering task, with results of 18.87% word error rate and 80% Exact Match (EM) respectively. These initial results give great promise to the usability of Kencorpus to the machine learning community. Kencorpus is one of few public domain corpora for these three low resource languages and forms a basis of learning and sharing experiences for similar works especially for low resource languages.