Artificial intelligence and machine learning have the potential to contribute to the resolution of some of the most intractable problems of our time. Examples include climate change and pandemics. But they have the capacity to cause harm too. And they can, if not used properly, perpetuate historical injustices and structural inequalities. To mitigate against their potential harms, the world needs frameworks for the governance of data that are economically enabling and that preserve rights.

Natural language communication is core to intelligence, as it allows ideas to be efficiently shared between humans or artificially intelligent systems. The generality of language allows us to express many intelligence tasks as taking in natural language input and producing natural language output. Autoregressive language modelling -- predicting the future of a text sequence from its past -- provides a simple yet powerful objective that admits formulation of numerous cognitive tasks. At the same time, it opens the door to plentiful training data: the internet, books, articles, code, and other writing. However this training objective is only an approximation to any specific goal or application, since we predict everything in the sequence rather than only the aspects we care about. Yet if we treat the resulting models with appropriate caution, we believe they will be a powerful tool to capture some of the richness of human intelligence. Using language models as an ingredient towards intelligence contrasts with their original application: transferring text over a limited-bandwidth communication channel. Shannon's Mathematical Theory of Communication (Shannon, 1948) linked the statistical modelling of natural language with compression, showing that measuring the cross entropy of a language model is equivalent to measuring its compression rate.

This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and Environmental Harms. The first area concerns the perpetuation of stereotypes, unfair discrimination, exclusionary norms, toxic language, and lower performance by social group for LMs. The second focuses on risks from private data leaks or LMs correctly inferring sensitive information. The third addresses risks arising from poor, false or misleading information including in sensitive domains, and knock-on risks such as the erosion of trust in shared information. The fourth considers risks from actors who try to use LMs to cause harm. The fifth focuses on risks specific to LLMs used to underpin conversational agents that interact with human users, including unsafe use, manipulation or deception. The sixth discusses the risk of environmental harm, job automation, and other challenges that may have a disparate effect on different social groups or communities. In total, we review 21 risks in-depth. We discuss the points of origin of different risks and point to potential mitigation approaches. Lastly, we discuss organisational responsibilities in implementing mitigations, and the role of collaboration and participation. We highlight directions for further research, particularly on expanding the toolkit for assessing and evaluating the outlined risks in LMs.

Message passing neural networks (MPNN) have provable limitations, which can be overcome by universal networks. However, universal networks are typically impractical. The only exception is random node initialization (RNI), a data augmentation method that results in provably universal networks. Unfortunately, RNI suffers from severe drawbacks such as slow convergence and high sensitivity to changes in hyperparameters. We transfer powerful techniques from the practical world of graph isomorphism testing to MPNNs, resolving these drawbacks. This culminates in individualization-refinement node initialization (IRNI). We replace the indiscriminate and haphazard randomness used in RNI by a surgical incision of only a few random bits at well-selected nodes. Our novel non-intrusive data-augmentation scheme maintains the networks' universality while resolving the trainability issues. We formally prove the claimed universality and corroborate experimentally -- on synthetic benchmarks sets previously explicitly designed for that purpose -- that IRNI overcomes the limitations of MPNNs. We also verify the practical efficacy of our approach on the standard benchmark data sets PROTEINS and NCI1.

Hand-encoding PDDL domains is generally accepted as difficult, tedious and error-prone. The difficulty is even greater when temporal domains have to be encoded. Indeed, actions have a duration and their effects are not instantaneous. In this paper, we present TempAMLSI, an algorithm based on the AMLSI approach able to learn temporal domains. TempAMLSI is based on the classical assumption done in temporal planning that it is possible to convert a non-temporal domain into a temporal domain. TempAMLSI is the first approach able to learn temporal domain with single hard envelope and Cushing's intervals. We show experimentally that TempAMLSI is able to learn accurate temporal domains, i.e., temporal domain that can be used directly to solve new planning problem, with different forms of action concurrency.

Saudi Arabia has appealed to the United States and its allies in Europe and the Gulf for resupplies of ammunition it uses to defend the kingdom against drone and missile attacks, the Wall Street Journal reported on Tuesday (paywall), citing US and Saudi officials. Riyadh has been using its Patriot surface-to-air missile system over the past several months to thwart weekly ballistic missile and drone attacks launched by Houthi rebels based in Yemen, the officials told the WSJ. But the kingdom's stock of Patriot missiles to intercept aerial attacks has run dangerously low. The call for resupplies comes after the US has scaled back a large of portion its military presence in the Middle East that shored up the kingdom's security as the administration of President Joe Biden pivots to counter China's growing prowess on the global stage. Though the US is expected to approve the Saudi request for more Patriot interceptors, Saudi officials told the Journal they are concerned that insufficient stocks could result in a successful missile or drone attack, costing lives in the kingdom or harming the Saudi economy by damaging its critical oil infrastructure.

Recruitment has been changing rapidly. At the root of all this change is technology advancement as the ultimate change maker. Some recruiters wonder about the future of recruitment and what role is left for them to fulfil. What if you could articulate yourself without saying a word? The truth is, we already can.

How often do we link terms like data science, artificial intelligence (AI), and machine learning with futuristic advancement, such as highly sophisticated robots and space ships as public transport? Why do we not associate them with a greener area, cleaner air, or flourishing biodiversity? Fourth Industrial Revolution technologies such as AI are enabling humanity to harness information and data to revolutionise education, energy, healthcare, agriculture, transportation, and many other service areas. AI helps us makes the world a better place, from traffic management in urban mobility to enhancing the efficiency of renewable energies to predict crop needs and other innovative solutions in smart agriculture. AI is becoming a key tool for facilitating a circular economy and building smart cities that use their resources efficiently.

On this episode of the eeDesignIt Podcast, we're joined by Dhonam Pemba to explore artificial intelligence (AI) and his new company KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA. His latest company, KidX, is in the AI edtech space, where he has built NLP and Voice assessment to serve China's leading robotics company with 4M users.

Artificial intelligence operates on a different plane than human reason. As described by various futurists and technologists, AI is literally an "alien mind." In advanced artificial neural networks, the modes of cognition--the logical steps behind any given conclusion--are completely incomprehensible, even to their creators. In the coming years, this nonhuman intelligence will change everything about our personal lives, our social organization, and how we think. That's the premise of two books published back-to-back this year--one from the West, the other from the East. The Age of AI: And Our Human Future is a primer on technetronic civilization for Western policy makers.