Elon Musk has proclaimed himself to be a "free speech absolutist" though reports of the way employees of his companies have been treated when exercising their free speech rights to criticise him might indicate that his commitment to free speech has its limits. But as Musk's bid to takeover Twitter progresses in fits and starts, the potential for anyone to access and control billions of opinions around the world for the right sum should focus all our minds on the need to protect an almost forgotten right--the right to freedom of thought. In 1942 the U.S. Supreme Court wrote "Freedom to think is absolute of its own nature, the most tyrannical government is powerless to control the inward workings of the mind." The assumption that getting inside our heads is a practical impossibility may have prevented lawyers and legislators from dwelling too much on putting in place regulation that protects our inner lives. But it has not stopped powerful people trying to access and control our minds for centuries.

We propose a message passing algorithm, based on variational Bayesian inference, for low-rank tensor completion with automatic rank determination in the canonical polyadic format when additional side information (SI) is given. The SI comes in the form of low-dimensional subspaces the contain the fiber spans of the tensor (columns, rows, tubes, etc.). We validate the regularization properties induced by SI with extensive numerical experiments on synthetic and real-world data and present the results about tensor recovery and rank determination. The results show that the number of samples required for successful completion is significantly reduced in the presence of SI. We also discuss the origin of a bump in the phase transition curves that exists when the dimensionality of SI is comparable with that of the tensor.

Latent Dirichlet allocation (LDA) is widely used for unsupervised topic modelling on sets of documents. No temporal information is used in the model. However, there is often a relationship between the corresponding topics of consecutive tokens. In this paper, we present an extension to LDA that uses a Markov chain to model temporal information. We use this new model for acoustic unit discovery from speech. As input tokens, the model takes a discretised encoding of speech from a vector quantised (VQ) neural network with 512 codes. The goal is then to map these 512 VQ codes to 50 phone-like units (topics) in order to more closely resemble true phones. In contrast to the base LDA, which only considers how VQ codes co-occur within utterances (documents), the Markov chain LDA additionally captures how consecutive codes follow one another. This extension leads to an increase in cluster quality and phone segmentation results compared to the base LDA. Compared to a recent vector quantised neural network approach that also learns 50 units, the extended LDA model performs better in phone segmentation but worse in mutual information.

Roadheader is an engineering robot widely used in underground engineering and mining industry. Interactive dynamics simulation of roadheader is a fundamental problem in unmanned excavation and virtual reality training. However, current research is only based on traditional animation techniques or commercial game engines. There are few studies that apply real-time physical simulation of computer graphics to the field of roadheader robot. This paper aims to present an interactive physically-based simulation system of roadheader robot. To this end, an improved multibody simulation method based on generalized coordinates is proposed. First, our simulation method describes robot dynamics based on generalized coordinates. Compared to state-of-the-art methods, our method is more stable and accurate. Numerical simulation results showed that our method has significantly less error than the game engine in the same number of iterations. Second, we adopt the symplectic Euler integrator instead of the conventional fourth-order Runge-Kutta (RK4) method for dynamics iteration. Compared with other integrators, our method is more stable in energy drift during long-term simulation. The test results showed that our system achieved real-time interaction performance of 60 frames per second (fps). Furthermore, we propose a model format for geometric and robotics modeling of roadheaders to implement the system. Our interactive simulation system of roadheader meets the requirements of interactivity, accuracy and stability.

Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies. The virtual event, hosted by the AI Policy Forum (AIPF) -- an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing -- brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility. In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries -- most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own. Each of these developments represents a different approach to legislating AI, but what makes a good AI law?

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By defining the current limits (and thereby the frontiers), many boundaries are shaping, and will continue to shape, the future of Artificial Intelligence (AI). We must push on these boundaries to make further progress into what were yesterday's frontiers. They are both pliable and resilient--always creating new boundaries of what AI can (or should) achieve. Among these are technical boundaries (such as processing capacity), psychological boundaries (such as human trust in AI systems), ethical boundaries (such as with AI weapons), and conceptual boundaries (such as the AI people can imagine). It is within these boundaries that we find the construct of needs and the limitations that our current concept of need places on the future AI.

The Ukrainian army's astute use of drones has been a cornerstone of its defence against the powerful Russian invader, but experts say their role is beginning to fade as heavy artillery takes over. In the early phase of the war, Ukraine's sky seemed filled with the remote-controlled aircraft deployed by President Volodymyr Zelensky's army to spy on the enemy, or go on the attack. During Moscow's early advance on Kyiv "it would have been extremely challenging for Ukraine to block (Russian President Vladimir) Putin's army without drones", said Paul Lushenko, a US Army Lieutenant Colonel and PhD student at Cornell University. "They could compound or exacerbate Putin's strategic and logistical challenges," he told AFP. The Turkish-made Bayraktar drone, known as TB-2, already famous worldwide, added to its stellar reputation during the defence of Ukraine's capital. On top of providing intelligence on Russian movements, drones also helped Ukraine offset much of its air force's weakness compared to that of Russia.

Equity is not the same principle as equality. Within the social context they both relate to fairness; equality means treating everyone the same regardless of need, while equity means treating people differently depending on their needs. Consider vaccinations, if we based public health policy on equality, perhaps there would be a lottery system to decide who gets vaccinated first, giving everyone an equal chance. In practice, however, vaccinations are prioritized based on equity, those with the greatest risk, frontline healthcare workers and the elderly, understandably, are first in line. Assuming we understand the causal relationship between treatment and outcome, the question then is, how do we identify the subgroups who experience the greatest average causal effects, whether positive or negative.

Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?