Multimodal language analysis is a burgeoning field of NLP that aims to simultaneously model a speaker's words, acoustical annotations, and facial expressions. In this area, lexicon features usually outperform other modalities because they are pre-trained on large corpora via Transformer-based models. Despite their strong performance, training a new self-supervised learning (SSL) Transformer on any modality is not usually attainable due to insufficient data, which is the case in multimodal language learning. This work proposes a Transformer-Based Speech-Prefixed Language Model called TEASEL to approach the mentioned constraints without training a complete Transformer model. TEASEL model includes speech modality as a dynamic prefix besides the textual modality compared to a conventional language model. This method exploits a conventional pre-trained language model as a cross-modal Transformer model. We evaluated TEASEL for the multimodal sentiment analysis task defined by CMU-MOSI dataset. Extensive experiments show that our model outperforms unimodal baseline language models by 4% and outperforms the current multimodal state-of-the-art (SoTA) model by 1% in F1-score. Additionally, our proposed method is 72% smaller than the SoTA model.

Within the national innovation system literature, empirical analyses are severely lacking for developing economies. Particularly, the low- and middle-income countries (LMICs) eligible for the World Bank's International Development Association (IDA) support, are rarely part of any empirical discourse on growth, development, and innovation. One major issue hindering panel analyses in LMICs, and thus them being subject to any empirical discussion, is the lack of complete data availability. This work offers a new complete panel dataset with no missing values for LMICs eligible for IDA's support. I use a standard, widely respected multiple imputation technique (specifically, Predictive Mean Matching) developed by Rubin (1987). This technique respects the structure of multivariate continuous panel data at the country level. I employ this technique to create a large dataset consisting of many variables drawn from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can thus be used for comparative analyses of national absorptive capacities and development, transition, and convergence analyses among LMICs.

Did you know Regent Honeyeaters of Australasia are actually forgetting how to talk? This song bird's habitat has been so badly affected that its numbers are slowly going down. And the worst scenario is that the adult population of these birds is scattered so far that they cannot teach the younger birds how to sing for a mate and this means they cannot even speak their own language. Let's know what AI can do to help unlock animal communication in this article. Now you know why there is a gradual loss of the Honeyeater song right!

We lived in this world for many thousand years. But in the last couple of centuries, we've started to ruin the earth ruthlessly. We filled oceans with plastics, destroyed forests, killing animals for sport, and whatnot. And the impact is arduous to recover from. The plastic we talk about is invisible to the naked eye that floats in the ocean, where big ships sometimes go missing.

The big picture: A US judge ruled this week that an artificial intelligence cannot be listed as the inventor of a patent. This ruling is the latest on an issue that has come before judges in multiple countries. A court in Alexandria, Virginia, ruled that inventions can only be patented under the name of a "natural person." The decision was made against someone who tried to list two designs under the name of an AI as part of a broader project to gain worldwide recognition of AI-powered inventions. Imagination Engines, Inc. CEO Stephen Thaler built an AI called DEBUS, which independently designed a new kind of drink holder and flashing light (used to get someone's attention). The name "DEBUS," along with "Invention generated by artificial intelligence," was used in the attempted patent filing for the inventions.

Deep learning is on the frontline in a new age of piracy, outwitting attacks with pre-emptive tech, explains Yarden Gross, CEO and co-founder of Orca AI. Almost a decade has passed since piracy raged off Somalia, and yet the danger posed by maritime hijackings is as present as ever. The global pandemic last year sparked a resurgence of attacks, with piracy incidents doubling across Asia, in a worrying uptick also seen in the Gulf of Mexico and West Africa. The fallout from coronavirus, including the loss of key security personnel, turned quarantined vessels into easy targets. This wave has since receded a little, with the International Maritime Bureau reporting a 44 per cent YoY dip in piracy and armed robbery incidents in 2021.

When U.S. President Joe Biden told an exhausted nation on Aug. 31 that the last C-17 cargo plane had left Taliban-controlled Kabul, ending two decades of American military misadventure in Afghanistan, he defended the frantic, bloodstained exit with a simple statement: "I was not going to extend this forever war." And yet the war grinds on. As Biden drew the curtain on Afghanistan, the CIA was quietly expanding a secret base deep in the Sahara, from which it runs drone flights to monitor al-Qaida and Islamic State group militants in Libya, as well as extremists in Niger, Chad and Mali. The military's Africa Command resumed drone strikes against the Shabab, an al-Qaida-linked group in Somalia. The Pentagon is weighing whether to send dozens of Special Forces trainers back to Somalia to help local troops fight militants.

AI 2041: Ten Visions for Our Future is an unusual book. Each chapter consists of a short story, penned by science fiction writer Chen Qiufan, and a related analysis piece from Kai-Fu Lee, CEO of Sinovation Ventures and author of the nonfiction bestseller AI Superpowers. Chen, who also is founder of Thema Mundi, a content development studio, spoke with Fast Company on the eve of the release of AI 2041 about his collaboration with Lee, his own experiences with artificial intelligence, and what machine learning will mean for artists and writers. This interview was edited for length and clarity. Fast Company: How did this project come about?

I found really interesting a study from 2020, titled "Legal liability for Autonomous Robotics", made by Dr. Safaa Fatouh Gomaa, Member of the Faculty of Law of the Egyptian Mansoura University, a study related to legal issues regarding liability related to Artificial Intelligence products, but more specifically, in the production of autonomous robotics. According to the European resolutions of 2017 and 2018, according to Gomaa, the liability rules cover cases where the cause of the robot's actions or missteps can be attributed to a specific human agent such as the manufacturer, the machinist, the holder or the manager, and where this representative could have foreseen and circumvented the robot's dangerous conduct. He also adds that since digital technologies are constantly evolving, due to, patches, updates and software extensions, influencing the behaviour of all mechanisms of the system, it is crucial to identify responsibilities among the different actors in the AI supply chain. Given the complexity of the topic to be covered, the researcher has divided the paper into three sections; section 1 is the historical, international and legal framework for Robots; section 2 is about identifying the legal responsibility for autonomous industrial robotics; finally, section 3 gives an overview of his conclusions. Robot concepts began as legends.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.