The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC'22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia over low-power microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs). The dataset contains more than 38,000 5-second intracardiac electrograms (IEGMs) segments over 8 different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC'22, which is open to multi-person teams world-wide, attracted more than 150 teams from over 50 organizations. This paper first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.

AutoFarm releases AutoLabs to research, develop and integrate AI into AutoFarm's products We are thrilled to announce that Autofarm, the leading lowest fee multi-chain DEX & yield aggregator protocol, is set to integrate advanced Artificial Intelligence (AI) and Machine Learning (ML) technologies to revolutionise yield generation and scalability on the platform. AutoFarm has established AutoLabs, an in-house research division dedicated to exploring the integration of advanced AI/ML technologies. A specialised AI architecture team, comprising experts from various AI backgrounds, has been assembled within AutoLabs to lead this initiative. The goal of this internal research division is to empower Autofarm's products with the ability to analyse real-world data dynamically, identify profitable opportunities, and make autonomous decisions for optimal asset allocation across multiple blockchain networks. One key technique that Autofarm plans to implement is the use of oracles to bridge on-chain and off-chain data.

After early excitement about artificial intelligence (AI) in the late 1980s and early 1990s, followed by a couple of "AI winters" -- periods of reduced funding, interest and even disillusionment -- we now again see great enthusiasm about all things related to AI and machine learning (ML). It is no wonder that AI/ML is also being considered for network security, including distributed denial-of-service (DDoS) protection. It's not that AI/ML algorithms have changed so radically -- but they have matured. In network security, like in many other fields, the abundance of data and greater-than-ever processing power makes it feasible to implement new AI/ML algorithms in silicon or in the cloud, allowing us to teach machines to be more accurate and faster than humans are. With DDoS security, the problem is distinguishing "good" from "bad" traffic and minimizing the mitigative actions to reduce the effect on "good" traffic.

"We do not want schizophrenia researchers knowing a lot about software engineering," said Amy Winecoff, data scientist and Princeton's Centre for IT Policy. Research asserts that a basic understanding of machine learning and other software engineering principles might be a desirable trait for medical practitioners, but these skills should not come at the expense of expertise in domain knowledge. Many new startups and enterprises sell their products boasting about incorporating AI/ML techniques in the development. Though this is an issue in the developer and business market, the bigger worry is misapplied AI/ML algorithms in the field of science and healthcare as it causes real world consequences. Sayash Kapoor and Arvind Narayanan of Princeton University published a research paper--Leakage and the Reproducibility Crisis in ML-based Science, pointing out the problem of "data leakage" in various researches using pools of data to train and test their development's performance.

I attended an in-person customer event sponsored by Dataiku last week. Man, do I miss the provocative and enlightening discussions that occur in these face-to-face customer engagements. "In the marketplace, dynamics in the job marketplace will evolve, and data-savvy subject matter experts will be paid higher than data scientists." This is what I tell my students as part of my "Big Data MBA" class. I believe that the folks that will benefit the most from data and advanced analytics will be those who master the application of data and analytics to derive and drive new sources of customer, product, service, and operational value.

The Ericsson Intelligent RAN Automation portfolio, shown in Figure 1, features end-to-end network automation that includes centralized and distributed SON solutions and new capabilities that support the transformation to a more open environment enabled for AI/ML, which empowers innovation and support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks. The objective of RAN automation is to boost RAN performance and operational efficiency by replacing the manual work of developing, installing, deploying, managing, optimizing and retiring of RAN functions with automated processes. The AI's role is to unlock more advanced network automation performance to make RAN network functions more autonomous and replace manual processes with intelligent tools that augment humans. Furthermore, it makes both AI/ML powered RAN network functions and tools more robust for deployment in different environments. Ericsson AI and automation foundations gives service providers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands.

We believe Artificial Intelligence is a technology platform that will transform every industry across the global economy. Despite its promise, the technology is often misunderstood. Moreover, it is often portrayed as an existential threat to humanity. With AI Arena, we want to educate the world about AI, inspire the next generation of researchers, and prove that an AI powered world can enhance the richness of human experience. In short, we want to bring AI to the masses.

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

Supervised learning: You know how to classify the input data and the type of behavior you want to predict, but you need the algorithm to calculate it for you on new data Unsupervised learning: You do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you Reinforcement learning: An algorithm which learns by trial and error by interacting with the environment. You use it when you don't have a lot of training data; you cannot clearly define the ideal end state; or the only way to learn about the environment is to interact with it Reinforcement learning: An algorithm which learns by trial and error by interacting with the environment. You use it when you don't have a lot of training data; you cannot clearly define the ideal end state; or the only way to learn about the environment is to interact with it