A major focus of Computer Weekly's technology and ethics coverage in 2022 was on working conditions throughout the tech sector, from the issue of forced labour and slavery throughout technology supply chains, to UK Amazon workers staging spontaneous "wildcat" strikes in response to derisory pay rises and warehouse conditions. Other stories in this vein included coverage of accusations that "soft union-busting" tactics were used by app-based food delivery firm Deliveroo to scupper its workers' grassroots organising efforts, and the ongoing court case against five major tech firms for their alleged role in the maiming and deaths of people extracting raw materials in the Democratic Republic of Congo. Artificial intelligence (AI) also featured heavily in Computer Weekly's technology and ethics coverage in 2022, with stories published on the tech sector's lacklustre commitment to "ethical" AI, as well as on the pitfalls and challenges of auditing AI-powered algorithms. Police technology was another major focus of 2022, as policing bodies continue to push ahead with new tech deployments such as live facial recognition (LFR) despite serious concerns about its effectiveness, proportionality and efficacy. Other stories focused on how technology is developed and deployed, and the underlying power dynamics at play.

Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace, mechanical, and civil engineering. Generative adversarial networks (GANs) have recently emerged as a popular alternative to traditional iterative topology optimization methods. However, these models are often difficult to train, have limited generalizability, and due to their goal of mimicking optimal structures, neglect manufacturability and performance objectives like mechanical compliance. We propose TopoDiff - a conditional diffusion-model-based architecture to perform performance-aware and manufacturability-aware topology optimization that overcomes these issues. Our model introduces a surrogate model-based guidance strategy that actively favors structures with low compliance and good manufacturability. Our method significantly outperforms a state-of-art conditional GAN by reducing the average error on physical performance by a factor of eight and by producing eleven times fewer infeasible samples. By introducing diffusion models to topology optimization, we show that conditional diffusion models have the ability to outperform GANs in engineering design synthesis applications too. Our work also suggests a general framework for engineering optimization problems using diffusion models and external performance with constraint-aware guidance. We publicly share the data, code, and trained models here: https://decode.mit.edu/projects/topodiff/.

Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models themselves do not consider any relative importance of each frequency component. Therefore, to incorporate the inductive bias for image data, we propose a novel generative process that synthesizes images in a coarse-to-fine manner. First, we generalize the standard diffusion models by enabling diffusion in a rotated coordinate system with different velocities for each component of the vector. We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds. Specifically, the proposed blur diffusion consists of a forward process that blurs an image and adds noise gradually, after which a corresponding reverse process deblurs an image and removes noise progressively. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets. Code is available at https://github.com/sangyun884/blur-diffusion.

Artificial intelligence and machine learning have become vital to many aspects of financial services, from powering chatbots to improving fraud detection. But one area where AI has not gained as much traction, particularly with community financial institutions, is in sales and marketing. Its use has been steadily ramping up, though: SouthState Bank and Eglin Federal Credit Union are among those using AI to parse data in ways that have resulted in much greater impact for their marketing efforts. Rather than targeting people based on simple demographics like age or gender, they combine internal data with information available beyond their own databases to surface a much more telling -- read: predictive -- combination of details. The key benefit has been the ability to more accurately identify when customers are ready to buy a particular financial product and deliver the appropriate messaging to them.

Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous for general tabular problems, where datapoints are typically represented by vectors of heterogeneous features. The inherent heterogeneity of tabular data makes it quite challenging for accurate modeling, since the individual features can be of completely different nature, i.e., some of them can be continuous and some of them can be discrete. To address such data types, we introduce TabDDPM -- a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. We extensively evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority over existing GAN/VAE alternatives, which is consistent with the advantage of diffusion models in other fields. Additionally, we show that TabDDPM is eligible for privacy-oriented setups, where the original datapoints cannot be publicly shared.

Most organizations realize that using data to better understand customer needs and preferences is vital to creating consistently great customer experiences. The challenge many face is how to put all of the data they're collecting to work toward that goal. We asked the CIO Experts Network, a community of IT professionals, industry analysts, and other influencers, how businesses can make better use of their data to improve customer experiences. Here are four key takeaways from their responses. Positive customer experience is good for business.

When I think of elevator operators, I think of health care. Now, it's not likely that many people think about elevator operators very often, if ever. Many have probably never seen a elevator operator. The idea of a uniformed person standing all day in a elevator pushing buttons so that people can get to their floors seems unnecessary at best and ludicrous at worse. But once upon a time they were essential, until they weren't.

Tens of thousands of times a year, a technician places a drop of blood on a slide and peers at it under a microscope, searching for malaria parasites. Making a definitive diagnosis requires the technician to look at up to 300 different fields of view over roughly half an hour. This process is repeated over and over, day after day, on every continent except Antarctica. It's tedious work, but it saves lives. Malaria parasites infect over 200 million people and kill 400,000 every year, mostly children in Africa. Trained and experienced malaria microscopists are rare, however.

About 10 undisclosed companies in Europe, Canada and the U.S. are using Centricity's software platform in sectors such as grocery, nonfood retail, apparel and consumer electronics, said Chief Executive Michael Brackett, who founded the company in late 2019. Centricity employs about 50, up from less than 10 last April, and its planned fundraise could bring total venture-capital investment to $12.5 million. Startups such as Centricity, which build software and services aimed directly at large enterprise customers, have been capitalizing on the increased demand for their services during the coronavirus pandemic, as companies have been forced to accelerate their digital initiatives to remain competitive. Companies use Centricity's AI-based insights to help predict what customers will want to buy in around one to three months, depending on the client, so they can stock their shelves accordingly. Its technology can also be used by research and development divisions at companies interested in launching new products.

You want a conversational artificial intelligence (AI) platform? No problem--you just need to choose one. But don't stop now: There are hundreds of options (from Kore.ai to SAP to Cisco's MindMeld to etc. etc.). Rasa's approach just might stand out. "We think that infrastructure for conversational interfaces in the long run will be open source," said Tyler Dunn, a product manager at Rasa.