"Talent and technology are the keys to unlocking our future in this industry-- finding ways for tech to come in and do a better job than people can in roles people have traditionally done," said MDC Partners global president Julia Hammond in explaining AI's value to her holding company. "The challenge with that is it's completely contradictory to the agency model, which has been built around people, so there's been a reluctance to build out AI and machine learning. We're actively pursuing it, in how we resource, how we scale and how we serve clients." Progress is being made elsewhere to find a happy middle ground. Last week, GroupM agency Wavemaker went public with its AI-driven media planning tool, Maximize, which the company claims is generating plans faster and more effectively than human planning teams alone. "It's a question of complexity of the problem solved.

Recently, transformer-based language models have stretched notions of what is possible with the simple self-supervised objective of language modeling, becoming a fixture in state of the art language technologies [Vaswani et al., 2017, Devlin et al., 2019, Brown et al., 2020]. However, the black box nature of these models combined with the complexity of natural language makes it challenging to measure how accurately they represent the world state underlying the text. In order to better measure the extent to which these models can capture the world state underlying the symbolic data they consume, we propose training and studying transformer language models for the game of chess. Chess provides a simple, constrained, and deterministic domain where the exact world state is known. Chess games can also be transcribed exactly and unambiguously using chess notations (Section 2). Most importantly, the form of chess notations allows us to probe our language models for aspects of the board state using simple prompts (Section 3) and without changing the language modeling objective or introducing any new classifiers.

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches.

Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed: How does an ML procedure derive the class for a particular entity? Why does a particular clustering emerge from a particular unsupervised ML procedure? What can we do if the number of attributes is very large? What are the possible reasons for the mistakes for concrete cases and models? For binary attributes, Formal Concept Analysis (FCA) offers techniques in terms of intents of formal concepts, and thus provides plausible reasons for model prediction. However, from the interpretable machine learning viewpoint, we still need to provide decision-makers with the importance of individual attributes to the classification of a particular object, which may facilitate explanations by experts in various domains with high-cost errors like medicine or finance. We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.

In a February 19 speech at the Munich Security Conference, delivered virtually from the White House, President Joe Biden declared, "We must shape the rules that will govern the advance of technologies and the norms of behavior in cyberspace, artificial intelligence, biotechnology, so they are used to lift people up, not used to pin them down." A few weeks earlier, during an address at the State Department's Truman Building, the president said, "Diplomacy is back at the center of our foreign policy." The Trump administration's undermining of years of work on internet diplomacy makes technology an ever more vital (and challenging) element of renewed US engagement abroad. Digital issues are no longer extricable from "traditional" foreign policy issues across trade, human rights, and security. And as the new White House starts to navigate these waters, one idea in particular has become a sort of bumper sticker for an overarching strategy: Unite democracies on technology. As the Chinese and Russian governments become more technologically assertive and undermine human rights, and as democracies grapple with how to appropriately implement rules and regulations for the likes of artificial intelligence systems, this work is essential.

Using artificial intelligence, Dutch photographer Bas Uterwijk released on Friday modern images of the Egyptian King Akhenaten (Amenhotep IV) and Queen Nefertiti. The pictures, which Uterwijk posted on his Twitter page, are based on old carvings and engravings that portrayed the two. The artist said in his post: "I don't claim to be a scientist. The historical portraits I make are based on artworks mostly made during the period of their subjects. With AI I filter out the sculpting styles of ancient portraiture and guide it to a credible outcome."

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Finding the right answer to this question can have a huge impact on revenue when you are dealing with hundreds of websites, thousands of ads, and millions of visitors. Fortunately (for the ad agencies, at least), reinforcement learning (RL), the branch of artificial intelligence that has become renowned for mastering board and video games, provides a solution. Reinforcement learning models seek to maximize rewards.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.

Speakers in tonight's Society, Robots and Us at 6pm PST Tuesday Feb 23 include Henry Evans, mute quadriplegic and founder of Robots4Humanity and Aaron Edsinger, founder of Hello Robot. We'll also being talking about robots for people with disabilities with Disability Advocate Adriana Mallozi, founder of Puffin Innovations and Daniel Seita, who is a deaf roboticist. The event is free and open to the public. As a result of a sudden stroke, Henry Evans turned from being a Silicon Valley tech builder into searching for technologies and robots that would improve his life, and the life of his family and caregivers, as the founder of Robots4Humanity. Since then Henry has shaved himself with the help of the PR2 robot, and spoken on the TED stage with Chad Jenkins in a Suitable Tech Beam.