On Jan. 18, the Association for Computing Machinery (ACM) announced its 2022 fellows, those it recognizes "for significant contributions in areas including cybersecurity, human-computer interaction, mobile computing, and recommender systems among many other areas." Included in the crop of new fellows were six distinguished scientists with ties to MIT. Constantinos Daskalakis, the Armen Avanessians (1982) Professor in the Department of Electrical Engineering and Computer Science (EECS), was honored "for contributions to the foundations of algorithmic game theory, mechanism design, sublinear algorithms, and theoretical machine learning." Daskalakis is a theoretical computer scientist who works at the interface of game theory, economics, probability theory, statistics, and machine learning. His current work focuses on multi-agent learning, learning from biased and dependent data, causal inference and econometrics.

Robocorp CMO Dave Dabbah is a full-stack marketing leader with a proven record of success driving growth and brand recognition. The ChatGPT revolution has officially gone into full unicorn mode, which theoretically should mean that marketers across the globe can now significantly increase their ability to produce high-quality content at scale. I'll let the cat out of the bag early in this article to say that I believe the underlying technology that makes ChatGPT so good will revolutionize how marketing teams produce content in the future. Although "revolutionize" is an overused term, the potentially far-reaching effects of an AI-powered service delivering high-quality content are among the most exciting developments in the tech industry that I have seen, reminiscent of the game-changing impact of Napster (in which I may have downloaded the entire Nirvana collection in under 60 seconds). I believe that the introduction of ChatGPT, which utilizes AI to deliver information, will be as impactful on content producers as the birth of the internet.

He is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. Pedro is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas; a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Pedro has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. Our speaker helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Question / problem: We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. Principal ideas/results: Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. Contribution: The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.

What can marketers bring to the mix when AI is so powerful? Don't miss a MarketingProfs podcast, subscribe to our free newsletter! Passion, for one thing, says Peter Prodromou of Boathouse. "If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product," he says on the latest episode of Marketing Smarts. "Think about Apple and Tesla; those are two brands that are very much about passion. Your ability to convey that is critically important." AI is just an algorithm, after all. "Everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it," Peter says.

The world faces a significant risk of passing a crucial global warming threshold earlier than scientists had suggested, possibly as soon as 2050, a paper published Monday found. The threshold is the point at which Earth's overall temperature has increased by 2.0 degrees Celsius, or 3.6 degrees Fahrenheit. If greenhouse gas emissions remain at high levels, there's a 50% probability the world could reach that catastrophic milestone before 2050, said co-author Noah Diffenbaugh, a climate scientist at Stanford University. The chance it could reach 2.0 degrees before 2058 is 84 in 100. Earlier estimates had put it closer to the end of the century.

A Northumbria University physicist has been awarded more than half a million pounds to develop artificial intelligence which will protect the Earth from devastating space storms. Activity from the Sun such as solar eruptions, known as Coronal Mass Ejections, results in plasma being fired towards Earth at supersonic speeds, which can result in serious disruption to power and communication systems. With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011. Northumbria's Dr Andy Smith has recently been awarded a Research Fellowship from the Natural Environment Research Council (NERC) to explore how physics-inspired machine learning could be used to forecast space weather more accurately and predict serious space storms. During the Next Generation, Physics-Inspired AI for Space Weather Forecasting project, Dr Smith and his team will analyse huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

How do you know I actually wrote this drivel? One reader wrote to me: "Your columns are so poorly written that I never read them. And when I do, I never finish them. And when I finish them, I wish I hadn't." Hey, everybody needs a little constructive criticism now and then.

Following two action-packed days of workshops, conferences, paper presentations, and tech talks, Machine Learning Developers Summit 2023 concluded by awarding 40 dynamic data scientists with the 40 Under 40 Data Scientists award. Aakash is a seasoned analytics leader with 15 years experience and has been instrumental in driving data and insight-led transformations. Over his career, he has worked closely with biz functions to drive revenue and achieve aggressive market growth by leveraging more than 50 analytical approaches. He also has experience in launching AI and tech-based solutions like Omni Channel Attribution, Customer Segmentation, Customer-360, Supply Chain Efficiency, Workforce Management and more at telecom, media, FMCG, retail, and ecommerce industries. Abhilash Surendran is assistant vice president, analytics, and data science at Merkle, leading the analytics practise for their high-tech portfolio. He comes with 15 years of experience in advanced analytics, data science, data visualisation and consulting.

Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produced by these methods suffer the problem of anisotropy. In this paper, we find that the generated representations are also not conversational, losing the conversation structure information during the context modeling stage. To this end, we identify two properties in dialogue modeling, i.e., locality and isotropy, and present a simple method for dialogue representation calibration, namely SimDRC, to build isotropic and conversational feature spaces. Experimental results show that our approach significantly outperforms current stateof-the-art models on three open-domain dialogue tasks with eight benchmarks across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our proposed approach. Dialogue modeling (Serban et al., 2016; Mehri et al., 2019; Liu et al., 2021) is to encode the raw text of the input dialogue to the contextual representations. Although the Transformer-based dialogue modeling methods (Hosseini-Asl et al., 2020; Liu et al., 2021) have achieved great success on various dialogue tasks, there are still some impediments in these methods that are not well explored nowadays. Specifically, recent studies (Ethayarajh, 2019; Su et al., 2022) have revealed that on dialogue generation tasks, the representations produced by existing dialogue modeling methods are anisotropic, i.e. features occupy a narrow cone in the vector space, thus leading to the problem of degeneration. To alleviate this problem, previous solutions (e.g. SimCTG) (Su et al., 2021; 2022) encourage the model to learn isotropic token embeddings by pushing away the representations of distinct tokens. While building the more discriminative and isotropic feature space, these methods still ignore learning dialogue-specific features, such as inter-speaker correlations and conversational structure information, in the dialogue modeling stage.