Having AIs Train Robot Dogs to Balance Makes Them a Lot Cheaper Jeremy Tsu New Scientist "An AI has been used to train a small robot dog to perform cleaning tasks. The hardware cost a total of $6300, which is less than a tenth of the price tag of the well-known robot dogs built by US tech firm Boston Dynamics. This type of self-taught robotic body coordination relies on an AI training regimen that could pave the way for affordable robot dogs and possibly even humanoid robots that could be used as helpers in homes and workplaces." Google Plans Giant AI Language Model Supporting World's 1,000 Most Spoken Languages James Vincent The Verge "i'The way we get to 1,000 languages is not by building 1,000 different models. Languages are like organisms, they've evolved from one another and they have certain similarities. And we can find some pretty spectacular advances in what we call zero-shot learning when we incorporate data from a new language into our 1,000 language model and get the ability to translate [what it's learned] from a high-resource language to a low-resource language,' says [Zoubin Ghahramani, vice president of research at Google AI]. Genetically Modified Mosquitoes Cut the Insect's Number by 96 Percent Miriam Fauzia New Scientist "Although not a permanent fix, periodically releasing such mosquitoes could reduce the burden of infections including dengue, malaria, and Zika.

Elon Musk is wasting no time making extremely deep cuts at Twitter, calving off many teams doing essential work at the company in the process. News of layoffs swept the platform on Friday, showing that Twitter's billionaire owner is painting in broad strokes when it comes to trimming down the team by half. The same day that Musk complained about supposed activists impacting Twitter's ad revenue, he cut some departments outright -- actions that are sure to make advertisers all the more skittish about Musk's ability to steer a ship with a skeleton crew. As he's only owned the company for a single week, it's impossible to imagine that such sweeping layoffs won't lead to dysfunction at Twitter, from the content moderation policies sure to prove crucial for Tuesday's U.S. midterm elections to product teams keeping the platform humming. Former Twitter employees affected by the layoffs describe a chaotic situation with little official communication beyond abruptly receiving a termination letter or seeing their access to internal tools like Slack or databases suddenly revoked.

A new report from UN Climate Change says that the world might be on track for 2.5 C of warming by the end of the century, a potentially catastrophic level of warming that's far above the 1.5 C target of the 2015 Paris Agreement. I think it is time to seriously consider a specific solution in which AI can play a meaningful role: Climate geoengineering via stratospheric aerosol injection. Stratospheric aerosol injection involves spraying fine particles that reflect sunlight high in the atmosphere. By increasing the reflectivity (or albedo) of the planet, we can slow down the rate at which sunlight warms it, and thereby buy more time to reduce carbon emissions and develop mitigations. Harvard Professor David Keith explains the science behind this idea is in his book, A Case for Climate Engineering. At the current 1.1 C of warming, the world is already experiencing increased climate-related crises.

A gaggle of new AI projects are coming soon from Google, including disaster monitoring tools and a service that uses machine intelligence to generate custom videos. The company announced the array of initiatives at its AI@ event this week. The most practical development: Google is expanding its AI-powered disaster tracking and response systems. The company rolled out a wildfire tracking tool during the apocalyptic 2020 fire season. The tool aims to track wildfire movements in real time using satellite imagery, on-the-ground data, and AI predictions.

In 2015, General Qassem Suleimani, the architect of Iran's foreign military strategy, travelled to Moscow to see President Vladimir Putin. He had a pitch--and a plea. During their two-hour meeting, Suleimani rolled out large maps of the battlefield in Syria, which was in the fifth year of a civil war with disparate rebel groups and had lost control of a third of its territory to ISIS jihadis. The stakes for Russia were high. For decades, Syria had been its only steadfast ally among the twenty-two Arab countries.

Somewhere in Mabushi, a crossroad area between the inner and outer districts of Abuja, Nigeria, Uniccon Group, a two-year-old Nigerian technology firm, has built a humanoid: a 6-foot-tall multilingual human-like robot called Omeife. From an idea that was conceptualised in 2020 to a back-and-forth construction--slow wins and quick-succession learning--that stretched across two years, Omeife, built as a female Igbo character that understands and speaks eight different languages, is now a product ready to meet the world. Powered by sophisticated artificial intelligence algorithms developed in-house by the company's team of scientists, Omeife has a deep understanding of African culture and behavioural patterns. Speaking to TechCabal about the project over a call, Chuks Ekwueme, who founded the company in 2020 and serves as its CEO, revealed that the humanoid also has a real time understanding of its environment including active listening and the ability to focus on a specific conversation thread as it's happening. "It's not just multilingual, it has the ability to switch languages and interact with specific gestures--hand illustrations, smile and other bodily gestures--that match the tone of the conversation," said Ekwueme.

In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise understanding of the present phrase or sentence. However, the standard attention mechanisms typically generate weights using query and key but ignore context, forming a Bi-Attention framework, despite their great success in modeling sequence alignment. This Bi-Attention mechanism does not explicitly model the interactions between the contexts, queries and keys of target sequences, missing important contextual information and resulting in poor attention performance. Accordingly, a novel and general triple-attention (Tri-Attention) framework expands the standard Bi-Attention mechanism and explicitly interacts query, key, and context by incorporating context as the third dimension in calculating relevance scores. Four variants of Tri-Attention are generated by expanding the two-dimensional vector-based additive, dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the tensor operations for Tri-Attention. Extensive experiments on three NLP tasks demonstrate that Tri-Attention outperforms about 30 state-of-the-art non-attention, standard Bi-Attention, contextual Bi-Attention approaches and pretrained neural language models1.

The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to re-train the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as 'One-Shot Learning', whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify attack-classes based only on one example is evaluated using three datasets. Results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representation.

Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study's contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges. Specifically, we conducted a series of experiments with these objectives; $a)$ generating a realistic synthetic dataset, $b)$ simulating data missingness, $c)$ recovering the missing data, and $d)$ analyzing imputation performance. Our methodology used a gaussian mixture model whose parameters were learned from a cleaned subset of a real demographic and health dataset to generate the synthetic data. We simulated various missingness degrees ranging from $10 \%$, $20 \%$, $30 \%$, and $40\%$ under the missing completely at random scheme MCAR. We used an integrated performance analysis framework involving clustering, classification and direct imputation analysis. Our results show that models trained on synthetic and imputed datasets could make predictions with an accuracy of $83 \%$ and $80 \%$ on $a) $ an unseen real dataset and $b)$ an unseen reserved synthetic test dataset, respectively. Moreover, the models that used the DAE method for imputed yielded the lowest log loss an indication of good performance, even though the accuracy measures were slightly lower. In conclusion, our work demonstrates that using our methodology, one can reverse engineer a solution to resolve missingness on an unseen dataset with missingness. Moreover, though we used a health dataset, our methodology can be utilized in other contexts.

Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities. This research introduces an ask-to-generate approach that automatically generates NER datasets by asking questions in simple natural language to an open-domain question answering system (e.g., "Which disease?"). Despite using fewer in-domain resources, our models, solely trained on the generated datasets, largely outperform strong low-resource models by an average F1 score of 19.4 for six popular NER benchmarks. Furthermore, our models provide competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by an F1 score of 5.2 on three benchmarks and achieve new state-of-the-art performance.