One week into shelter-in-place last year, Jeremy Bailenson was talking to a BBC reporter and had an epiphany. There's no need for us to be on Zoom," he thought. A phone call would have sufficed. This kernel of realization became an op-ed article that Bailenson penned in the Wall Street Journal titled, "Why Zoom Meetings Can Exhaust Us." Bailenson, a professor of communications and founder of the Virtual Human Interaction Lab at Stanford University, wanted to dig deeper. So he wrote an academic paper, published Tuesday in Technology, Mind, and Behavior, that boils down four underlying causes of videoconferencing fatigue.

Eleni Vasilaki is Professor of Computational Neuroscience and Neural Engineering and Head of the Machine Learning Group in the Department of Computer Science, University of Sheffield. Eleni has extensive cross-disciplinary experience in understanding how brains learn, developing novel machine learning techniques and assisting in designing brain-like computation devices. In this interview, we talk about bio-inspired machine learning and artificial intelligence. I am interested in bio-inspired machine learning. I enjoy theory and analysis of mathematically tractable systems, particularly they can be relevant for neuromorphic computation.

Alphabet, the parent company of Google GOOG, is the leading tech company that decided to invest a lot of resources and funding in artificial intelligence. So much so, that the WSJ recently announced that AI is central to Google's future. Not surprisingly, Google has been dealing with different challenges concerning its top AI executives and researchers. Activists shareholders are also showing interest in this. Recently, there is a rise in shareholder proposals calling on boards to ensure proper AI governance.

"Have you tried turning it off and on again? Okay, I'll hold while you do." Diana hit the mute icon on her iDesk and threw a screwed-up ball of paper at Mark, sitting opposite. It'd save so much effort." "I'd want a whole support phrasebook.

Edmund Melson Clarke, Jr., a celebrated American academic who developed methods for mathematically proving the correctness of computer systems, died on December 22, 2020 at the age of 75 from complications of COVID-19. Clarke was awarded the A.M Turing Award in 2008 with his former student E. Allen Emerson and the French computer scientist Joseph Sifakis, for their work on model checking. "I've never liked to fly, although I've done my share of it. I wanted to do something that would make systems like airplanes safer," Clarke said in a 2014 video produced by the Franklin Institute when he was awarded their 2014 Bower Award and Prize for Achievement in Sciencea "For his leading role in the conception and development of techniques for automatically verifying the correctness of a broad array of computer systems, including those found in transportation, communications, and medicine." Model checking is a practical approach for machine verification of mathematical models of hardware, software, communications protocols, and other complex computing systems.

On January 20th, while the world's attention was focussed on the Inauguration, David Lynch quietly turned seventy-five. He spent the day the way he's spent almost every day since the pandemic began: sheltered in his Los Angeles home, engaged with self-prescribed daily routines. "If you have a habit pattern," Lynch told me, over Zoom, "the more conscious part of your mind can concentrate on your work, and you can get ideas and do those things, and the rest sort of takes care of itself in the background." It sounded practical and wholesome, until Lynch related an example: a "famous criminal case" that he'd heard about, involving a man who hacked up his parents with an axe. The mother was killed in the act, but, Lynch said, "the father didn't die right away. He was wounded terribly, in the head, but, in the morning, when it was his time normally to wake up, covered in blood he got out of bed--he didn't even notice that his wife was dead right next to him--he just woke up and made his way down to do his habitual program. . . . Fixed breakfast, but he spilled his cereal all over the place. He made coffee, he made a mess of everything, but he knew the habits, he knew the routine. He went to get his paper, like he does every morning, and he came in with the paper and just bled out, right there in the foyer, and that was the end of him."

The task of link prediction for knowledge graphs is to predict missing relationships between entities. Knowledge graph embedding, which aims to represent entities and relations of a knowledge graph as low dimensional vectors in a continuous vector space, has achieved promising predictive performance. If an embedding model can cover different types of connectivity patterns and mapping properties of relations as many as possible, it will potentially bring more benefits for link prediction tasks. In this paper, we propose a novel embedding model, namely LineaRE, which is capable of modeling four connectivity patterns (i.e., symmetry, antisymmetry, inversion, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many) of relations. Specifically, we regard knowledge graph embedding as a simple linear regression task, where a relation is modeled as a linear function of two low-dimensional vector-presented entities with two weight vectors and a bias vector. Since the vectors are defined in a real number space and the scoring function of the model is linear, our model is simple and scalable to large knowledge graphs. Experimental results on multiple widely used real-world datasets show that the proposed LineaRE model significantly outperforms existing state-of-the-art models for link prediction tasks.

My brother-in-law Don Hunter, who has died aged 93, was a physicist who worked on some of the first electronic computers in the Rutherford Laboratory at Cambridge University and later helped set up one of the first major computer software companies in the UK. Don worked as a research assistant in the maths department of the Rutherford Laboratory from 1949 until 1952. There he was involved in pioneering work on the electronic delay storage automatic calculator (Edsac 1) computer. In 1955 he took up a research post at the Standard Telecommunication Laboratories (STL) in Harlow, Essex, where he was part of the design team for a computer called Step 1. Later, in a collaboration between STL and a Dutch subsidiary, he was involved in the development of Zebra, another early computer. During this time, Don worked in Paris, New York and Italy; he spoke fluent French and Italian.

For this week's ML practitioner's series, Analytics India Magazine(AIM) got in touch with Théo Viel, a Kaggle GM and a deep learning researcher at a French Startup called Damae Medical. In this interview, Théo shares his rich experiences from an impressive career in the world of algorithms. Théo: I recently graduated from a French Engineering school at which I studied applied Mathematics and Computer Science. I did the Ecole des Ponts ParisTech which is a Grande Ecole: it is a great school to do if you aspire to be a Data Scientist in France. I was introduced to Machine Learning at school, but mostly got into it during my internships, and doing Kaggle competitions.

We are introducing a monthly digest to keep you up-to-date with the latest happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. The big news from AIhub is that we recently launched our first ever focus series: "AI for good: UN sustainable development goals". Each month we will be concentrating on a different sustainable development goal (SDG) and bringing you a collection of work from people in the field. Our first topic was SDG number 3: good health and well-being.