Non-profit organisation The AI Forum is helping Kiwis learn about addressing climate change issues through the use of AI technology. Taking knowledge from their recent AI for the environment report, which will be released at TechWeek2022, the organisation has focused on five key environmental outcomes for Aotearoa where AI can deliver meaningful solutions from both modern science and matauranga Maori perspectives. The organisation says that while it's encouraging that results of a recent survey revealed one in five enterprises are using AI effectively in New Zealand, it was still concerning that only 7% are engaging in core practices supporting widespread AI adoption and 17% are not considering AI at all. "The majority of Kiwi companies are still in initial trial stages with just over one third taking their first steps in building AI capability," says The AI Forum NZ executive director Madeline Newman. "Most of these have run ad hoc pilots or applied AI to a single business process, which is a good cost effective start in understanding how and where to make best use of AI in business."

Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning. However, there is a lack of a systematic approach and a set of standardized evaluation criteria to facilitate direct comparison of both software and hardware implementations. In this paper, we formalize a set of standardized criteria and a publicly available synthetic dataset entitled Synthetic Simulations Of Extracellular Recordings (SSOER), which was constructed by aggregating existing synthetic datasets with varying Signal-To-Noise Ratios (SNRs). Furthermore, we present a benchmark for future comparison, and use our criteria to evaluate a simulated Resistive Random-Access Memory (RRAM) In-Memory Computing (IMC) system using the Discrete Wavelet Transform (DWT) for feature extraction. Our system consumes approximately (per channel) 10.72mW and occupies an area of 0.66mm$^2$ in a 22nm FDSOI Complementary Metal-Oxide-Semiconductor (CMOS) process.

A new Bayesian modelling framework is introduced for piece-wise homogeneous variable-memory Markov chains, along with a collection of effective algorithmic tools for change-point detection and segmentation of discrete time series. Building on the recently introduced Bayesian Context Trees (BCT) framework, the distributions of different segments in a discrete time series are described as variable-memory Markov chains. Inference for the presence and location of change-points is then performed via Markov chain Monte Carlo sampling. The key observation that facilitates effective sampling is that, using one of the BCT algorithms, the prior predictive likelihood of the data can be computed exactly, integrating out all the models and parameters in each segment. This makes it possible to sample directly from the posterior distribution of the number and location of the change-points, leading to accurate estimates and providing a natural quantitative measure of uncertainty in the results. Estimates of the actual model in each segment can also be obtained, at essentially no additional computational cost. Results on both simulated and real-world data indicate that the proposed methodology performs better than or as well as state-of-the-art techniques.

Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages. Taking the analogy between categories and languages seriously, this paper defines a probabilistic generative model of morphisms in free monoidal categories over domain-specific generating objects and morphisms. The paper shows how acyclic directed wiring diagrams can model specifications for morphisms, which the model can use to generate morphisms. Amortized variational inference in the generative model then enables learning of parameters (by maximum likelihood) and inference of latent variables (by Bayesian inversion). A concrete experiment shows that the free category prior achieves competitive reconstruction performance on the Omniglot dataset.

A couple of decades ago, on a backpacking trip in the Sierra Nevada, I was marching up a mountain solo under the influence of LSD. Halfway to the top, I took a break near a scrubby tree pushing up through the rocky soil. Gulping water and catching my breath, I admired both its beauty and its resilience. Its twisty, weathered branches had endured by wresting moisture and nutrients from seemingly unwelcoming terrain, solving a puzzle beyond my reckoning. I sensed a kind of wisdom in its conservation of resources.

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(Sivanantham and Seran, 2019). It is widely spoken in the southern state of Tamil Nadu in India, Combating offensive content is crucial for different Sri Lanka, Malaysia, and Singapore. Tamil is an entities involved in content moderation, which official language of Tamil Nadu, Sri Lanka, Singapore, includes social media companies as well as individuals and the Union Territory of Puducherry in (Kumaresan et al., 2021; Chakravarthi and India. Significant minority speak Tamil in the four Muralidaran, 2021). To this end, moderation is other South Indian states of Kerala, Karnataka, often restrictive with either usage of human content Andhra Pradesh, and Telangana, as well as the moderators, who are expected to read through Union Territory of the Andaman and Nicobar Islands the content and flag the offensive mentions (Arsht (Sakuntharaj and Mahesan, 2021, 2017, 2016; and Etcovitch, 2018). Alternatively, there are Thavareesan and Mahesan, 2019, 2020a,b, 2021).

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE. Additionally, our model is proven to be portable to new types of events effectively.

We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context involving only a subset of classes or a context where only limited compute resource is available. To solve this, we propose an efficient Bayesian framework to compress a given large network into much smaller size tailored to meet each contextual requirement. We employ a hypernetwork to parameterize the posterior distribution of weights given conditional inputs and minimize a variational objective of this Bayesian neural network. To further reduce the network sizes, we propose a new input-output group sparsity factorization of weights to encourage more sparseness in the generated weights. Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.

A small island in the South Pacific Ocean recently shot to fame by becoming the first territory on our planet to derive its energy needs from the Sun. Covering a small area of 10 square kilometers, Tokelau is a part of New Zealand and lies to the North of Samoan islands . Funded by the government of New Zealand, Tokelau spent about $7 million to put in place three solar grids that will now enable its 1500 residents to harness and utilize solar energy for their daily needs. Why spend $7 million for a power plant in the middle of nowhere you might ask! While the small island generates a small sum of $ 500,000 every year by selling agricultural produce, it spends over $2.8 million, most of which is spent of food and fuel.