Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, require large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations. This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.

Making living cells blink fluorescently like party lights may sound frivolous. But the demonstration that it's possible could be a step toward someday programming our body's immune cells to attack cancers more effectively and safely. That's the promise of the field called synthetic biology. While molecular biologists strip cells down to their component genes and molecules to see how they work, synthetic biologists tinker with cells to get them to perform new feats -- discovering new secrets about how life works in the process. Listen on Apple Podcasts, Spotify, Google Podcasts, Stitcher, TuneIn or your favorite podcasting app, or you can stream it from Quanta. Steve Strogatz (00:03): I'm Steve Strogatz, and this is The Joy of Why, a podcast from Quanta Magazine that takes you into some of the biggest unanswered questions in science and math today. In this episode, we're going to be talking about synthetic biology. Simply put, we could say that synthetic biology is a fusion of biology, especially molecular biology, and engineering. The distinctive thing about it is that it treats cells as programmable devices. It's a kind of tinker toy approach that builds circuits, but not out of wires and switches like we're used to, but rather out of biological components, like proteins and genes. But also, the approach holds promise for illuminating how life works at the deepest level. It's one thing to strip cells apart to see how they work. But it's another thing to tinker with cells to try to get them to perform new tricks, which is something that my guest, Michael Elowitz, does. For example, a while back, he engineered cells to blink on and off like Christmas lights. Michael Elowitz is a professor of biology and biological engineering at Caltech and Howard Hughes Medical Institute. It's great to be here. Strogatz (01:53): So let's talk about the foundational idea of synthetic biology. I mentioned it in the intro, that's -- that living cells, we could think of as programmable devices. The field, synthetic biology, it seems like you guys have this philosophy that you can learn about cells by building functionality into cells yourself.

Prompts may well be the new oil. Writing the text strings that instruct AI systems like ChatGPT and DALL-E 2 to generate essays, articles, images and more has become a veritable profession, commanding salaries well into the six-figure range. Anyone can come up with prompts, of course. But only certain prompts (e.g. "Create a watercolor of a solider standing in the middle of a field, in the style of John Singer Sargent) accomplish very specific, desirable (or undesirable) things. Prompt writing requires skill and dedication, owing to the black box and unpredictable nature of today's bleeding-edge AI systems. Complicating matters further, the systems are frequently changing and responding to malicious prompts, bypassing the guardrails that their makers put in place. But not every company or developer has the budget to hire a so-called prompt engineer. Prompt marketplaces, or e-commerce portals where users can buy, sell or give away prompts "designed" for various AI systems, are a growing industry. When we first profiled prompt marketplaces last July, there was only one major player. But since then, the landscape has expanded dramatically. Even a cursory Google search turns up a dozen or more prompt marketplaces, with new ones added on a monthly basis. ChatX, for instance, offers prompts tuned to ChatGPT as well as popular image-generating systems like DALL-E 2, Midjourney and Stable Diffusion. NeutronField's prompts for sale cover a slightly wider range of AI systems, including Disco Diffusion and Craiyon. Many of the marketplace operators, like NeutronField's Miroslav Kostic, have no background in AI or even data science. They were hobbyists to start, experimenting with systems like Stable Diffusion but running into hurdles unlocking their full potential. "I've been playing with AI text-to-image models since Disco Diffusion first appeared in September 2021," Kostic told TechCrunch in an email interview. "I spent countless hours trying to bring to life the ideas that had been in my head for years -- dystopian sci-fi landscapes and otherworldly spacescapes.

This paper considers the problem of safely coordinating a team of sensor-equipped robots to reduce uncertainty about a dynamical process, where the objective trades off information gain and energy cost. Optimizing this trade-off is desirable, but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planners based on coordinate descent lose their performance guarantees. Furthermore, methods that handle non-monotonicity lose their performance guarantees when subject to inter-robot collision avoidance constraints. As it is desirable to retain both the performance guarantee and safety guarantee, this work proposes a hierarchical approach with a distributed planner that uses local search with a worst-case performance guarantees and a decentralized controller based on control barrier functions that ensures safety and encourages timely arrival at sensing locations. Via extensive simulations, hardware-in-the-loop tests and hardware experiments, we demonstrate that the proposed approach achieves a better trade-off between sensing and energy cost than coordinate-descent-based algorithms.

To celebrate International Women's Day, we take a look back over the past year and highlight some of the women we've interviewed, written about, chatted to, and featured on AIhub. Rose Nakasi is a Lecturer of Computer Science and a Research Scientist at the Makerere Artificial Intelligence Lab, in Makerere University, Uganda. She holds a PhD in Computer Science from Makerere University. Her research interests are in artificial intelligence and data science, and particularly in the use of these for developing improved automated tools and techniques for microscopy diagnosis of diseases like malaria in low-resourced but highly endemic settings. We spoke to Rose Nakasi about her work developing machine learning techniques to aid diagnosis of microscopically diagnosed diseases: Interview with Rose Nakasi: using machine learning and smartphones to help diagnose malaria.

The humble broadcast-radio host, whether a disc jockey or interviewer or reporter, has been going through it for decades now. The 1996 Telecommunications Act fueled the consolidation of local stations, decimating their staffs. The explosion of online radio, music and video streaming, and podcasting have upended ratings for shows on public airwaves. Funding for public radio is notoriously unreliable. On top of all that, your local DJ was already on the losing end of the artificial-intelligence revolution. Before the A.I. hype from last year, and even before the COVID recession demolished media ad markets, broadcast networks were gutting on-air talent at the both the national and collegiate level to trim budgets and automate programming: syndicating well-known shows and brands, prerecording and prearranging late-night broadcasts, training a roboticized voice to fill in the space when needed.

If you haven't already, please study our free Bootstrapping course and the Investor Introductions page. This is a fantastic discussion on how to cold start an AI company, build it to scale, etc. Also, excellent guidance on white spaces around which to build new AI companies. Sramana Mitra: Let's start by introducing our audience to yourself. Tell us a bit about your background and all also introduce us to People.ai. Oleg Rogynskyy: I am the CEO and Founder of People.ai. I have been doing startups all my life. My previous company Semantria was also an AI company. I started it in 2011 and then sold it in 2014. People.ai was started in 2016 when I moved out here to Silicon Valley. I have been working on it ever since. Sramana Mitra: Where did you move from? Oleg Rogynskyy: Montreal. I am originally from Slovenia. I worked for one of the first AI companies called Nstein Technologies from 2006 to 2010. We sold it to Open Text. Our technology now is the Open Text AI. I

There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.

Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and entail the time-consuming processes of data and parameter tuning. To overcome these challenges, we propose an easily implemented framework that can directly handle heterogeneous data sources for classification tasks. Our data-versus-data approach automatically quantifies distinctive differences in distributions in a high-dimensional space via kernel two-sample testing between two sets extracted from multimodal data (e.g., images, sounds, haptic signals). We demonstrate the effectiveness of our technique by benchmarking against expertly engineered classifiers for visual-audio-haptic surface recognition due to the industrial relevance, difficulty, and competitive baselines of this application; ablation studies confirm the utility of key components of our pipeline. As shown in our open-source code, we achieve 97.2% accuracy on a standard multi-user dataset with 108 surface classes, outperforming the state-of-the-art machine-learning algorithm by 6% on a more difficult version of the task. The fact that our classifier obtains this performance with minimal data processing in the standard algorithm setting reinforces the powerful nature of kernel methods for learning to recognize complex patterns.

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.