Robots have histories that extend far back into the past. Artificial servants, autonomous killing machines, surveillance systems, and sex robots all find expression from the human imagination in works and contexts beyond Ovid (43 BCE to 17 CE) and the story of Pygmalion in cultures across Eurasia and North Africa. This long history of our human-machine relationships also reminds us that our aspirations, fears, and fantasies about emergent technologies are not new, even as the circumstances in which they appear differ widely. Situating these objects, and the desires that create them, within deeper and broader contexts of time and space reveals continuities and divergences that, in turn, provide opportunities to critique and question contemporary ideas and desires about robots and artificial intelligence (AI).

Ever wondered how the news channel predicts the weather conditions accurately? The answer is because of data science. It always works in the background in the whole process of weather prediction. For all individuals and organizations, it is a great deal to know the accurate situation of the weather. Many businesses are directly or indirectly linked with climatic conditions.

Rapid advances in artificial intelligence ("AI") are unlocking enhanced capabilities for machine learning, data interpretation and innovation, whilst also increasingly becoming useful in our everyday lives. AI now plays a key role in drug discovery, the advertisements we see recommended to us online, route suggestions for online mapping platforms, and auto-generated digital content. Recently, this has raised questions for traditional thinking around intellectual property law, with particular implications for patent ownership and invention. The question is – could AI be capable of being considered an inventor? An additional step, that an inventor must be human, was recently put to the test.

Learning dynamical models from data plays a vital role in engineering design, optimization, and predictions. Building models describing dynamics of complex processes (e.g., weather dynamics, or reactive flows) using empirical knowledge or first principles are onerous or infeasible. Moreover, these models are high-dimensional but spatially correlated. It is, however, observed that the dynamics of high-fidelity models often evolve in low-dimensional manifolds. Furthermore, it is also known that for sufficiently smooth vector fields defining the nonlinear dynamics, a quadratic model can describe it accurately in an appropriate coordinate system, conferring to the McCormick relaxation idea in nonconvex optimization. Here, we aim at finding a low-dimensional embedding of high-fidelity dynamical data, ensuring a simple quadratic model to explain its dynamics. To that aim, this work leverages deep learning to identify low-dimensional quadratic embeddings for high-fidelity dynamical systems. Precisely, we identify the embedding of data using an autoencoder to have the desired property of the embedding. We also embed a Runge-Kutta method to avoid the time-derivative computations, which is often a challenge. We illustrate the ability of the approach by a couple of examples, arising in describing flow dynamics and the oscillatory tubular reactor model.

A daily power walk or bike ride in old age may cut the risk of developing Alzheimer's disease, a study has claimed. Research has long shown exercise in middle age and beyond can cut the chance of dementia -- which is most commonly caused by Alzheimer's -- by up to 40 per cent. Now researchers from the University of California say the disease can be prevented if people exercise in later life as well. Exercise is thought to help stave off the disease because it improves cognitive function, keeps bodyweight low and prevents plaque forming in the arteries -- a key cause of vascular dementia. But the latest study also suggests exercise in later life can reduce inflammation in the brain, which can cause Alzheimer's to develop.

Current trends in AI are nothing if not remarkable. Day after day, we hear stories about systems and machines taking on tasks that, until very recently, we saw as the exclusive and permanent preserve of humankind: making medical diagnoses, drafting legal documents, designing buildings, and even composing music. Our concern here, though, is with something even more striking: the prospect of high-level machine intelligence systems that outperform human beings at essentially every task. This is not science fiction. In a recent survey the median estimate among leading computer scientists reported a 50% chance that this technology would arrive within 45 years.

Thanks to advancements in speech and natural language processing, there is hope that one day you may be able to ask your virtual assistant what the best salad ingredients are. Currently, it is possible to ask your home gadget to play music, or open on voice command, which is a feature already found in some many devices. If you speak Moroccan, Algerian, Egyptian, Sudanese, or any of the other dialects of the Arabic language, which are immensely varied from region to region, where some of them are mutually unintelligible, it is a different story. If your native tongue is Arabic, Finnish, Mongolian, Navajo, or any other language with high level of morphological complexity, you may feel left out. These complex constructs intrigued Ahmed Ali to find a solution.

Artificial intelligence (AI) was once the stuff of science fiction. It is used in mobile phone technology and motor vehicles. But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Google's Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies.

Unmanned aerial, ground and underwater vehicles are increasingly being used for national security in Japan and other countries, with the lack of training requirements and risk to human life seen as major benefits. Autonomous vehicles are seen as indispensable to Japan, which has a rapidly aging population and low birthrate. In the National Defense Program Guidelines adopted in late 2018, the government pledged to promote the Self-Defense Forces' use of artificial intelligence and other technological innovations for "automation and manpower-saving," with accelerating population declines now making the recruitment of SDF members a pressing issue. The Defense Ministry has launched a project to develop unmanned aircraft to escort the new fighter jet Japan plans to deploy as the successor to the Air Self-Defense Force's existing F-2s in fiscal 2035 at the earliest. Equipped with AI, the planned unmanned aircraft would be able to detect enemy fighters and missiles, fire missiles, stage electronic attacks and serve as a decoy to disorient enemy missiles.

Artificial neural networks have successfully tackled a large variety of problems by training extremely deep networks via back-propagation. A direct application of back-propagation to spiking neural networks contains biologically implausible components, like the weight transport problem or separate inference and learning phases. Various methods address different components individually, but a complete solution remains intangible. Here, we take an alternate approach that avoids back-propagation and its associated issues entirely. Recent work in deep learning proposed independently training each layer of a network via the information bottleneck (IB). Subsequent studies noted that this layer-wise approach circumvents error propagation across layers, leading to a biologically plausible paradigm. Unfortunately, the IB is computed using a batch of samples. The prior work addresses this with a weight update that only uses two samples (the current and previous sample). Our work takes a different approach by decomposing the weight update into a local and global component. The local component is Hebbian and only depends on the current sample. The global component computes a layer-wise modulatory signal that depends on a batch of samples. We show that this modulatory signal can be learned by an auxiliary circuit with working memory (WM) like a reservoir. Thus, we can use batch sizes greater than two, and the batch size determines the required capacity of the WM. To the best of our knowledge, our rule is the first biologically plausible mechanism to directly couple synaptic updates with a WM of the task. We evaluate our rule on synthetic datasets and image classification datasets like MNIST, and we explore the effect of the WM capacity on learning performance. We hope our work is a first-step towards understanding the mechanistic role of memory in learning.