For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do--using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too--information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way? The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton's notion, called neural networks--which later became the groundwork for "deep learning" or "machine learning"--had already been disproven. In the late '50s, a Cornell scientist named Frank Rosenblatt had proposed the world's first neural network machine. It was called the Perceptron, and it had a simple objective--to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out "apple." The Perceptron ran on an IBM mainframe, and it was ugly.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, we're diving deeper into conversations with this year's winners, whom we honored recently at Transform 2021. Check out last week's interview with a winner of our AI responsibility and ethics award.

For many years, people have been skeptical about AI. So much so that the term "AI" has been derided variously as misleading, vague, or fantasy. I have been disappointed by AI chatbots since I first got interested in natural language processing as a child, but after chatting frequently with a GPT-3 over the course of many months, I'm convinced: It's time to drop our polite euphemisms for AI. It's time to admit that machines can be intelligent. We can admit that machines can learn how to tell if somebody on Twitter is angry or happy. Whether or not that photo is a cat. How to generate photorealistic images of people. But we're afraid to call any of these behaviors intelligent.

The IJCAI-2021 awards were announced during the opening ceremony of the International Joint Conference on Artificial Intelligence (IJCAI-21). The honours included the 2021 AIJ classic paper award, the AIJ prominent paper award, and the IJCAI-JAIR best paper prize. This award recognizes outstanding papers, exceptional in their significance and impact, that were published at least 15 years ago, in the journal Artificial Intelligence (AIJ). This paper brought partially observable Markov decision processes (POMDPs) from the field of operational research to the field of AI. It provides an excellent account of the theory behind POMDPs, which demystified the field for a generation of researchers, and popularised their use in both AI and robotics.

Grown men wearing tights like to yell terrible things at Fred DeJesus. DeJesus is an umpire in the outer constellations of professional baseball, where he's been spat on and, once, challenged to a postgame fight in a parking lot. He was born in Bushwick, Brooklyn, to Puerto Rican parents, stands five feet three, and is shaped, in his chest protector, like a fire hydrant; he once ejected a player for saying that he suffered from "little-man syndrome." Two years ago, DeJesus became the first umpire in a regular-season game anywhere to use something called the Automated Ball-Strike System. Most players refer to it as the "robo-umpire."

The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents. These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions. As deep neural networks are black-box models, their inner workings cannot be easily explained. At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making. While this has been known for some time, the issues this raises have been exacerbated by models increasing in size, and by EU legislation requiring models to be used for decision making to provide explanations, and, very recently, by legislation requiring online platforms operating in the EU to provide transparent reporting on their services. Despite this, current solutions for explainability are still lacking in the area of fact checking. This thesis presents my research on automatic fact checking, including claim check-worthiness detection, stance detection and veracity prediction. Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data. Finally, the thesis presents some first solutions for explainable fact checking.

Artificial intelligence (AI) is one of the hottest topics in marketing right now. Raj Venkatesan and Jim Lecinski recently published a book entitled "The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing". To better understand what an AI marketing canvas is, I sought insight from Raj Venkatesan, a professor at the Darden School of Business. In full disclosure, I work with Raj and find his research and work fascinating. Below is insight on the AI marketing canvas.

WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert.

In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein's amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind's Alphafold2 achieving remarkable accuracy. Baek et al. explored network architectures based on the DeepMind framework. They used a three-track network to process sequence, distance, and coordinate information simultaneously and achieved accuracies approaching those of DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and generate accurate models of protein-protein complexes. Science , abj8754, this issue p. [871][1] DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. [1]: /lookup/doi/10.1126/science.abj8754

Artificial intelligence (AI) and machine-learning (ML) have quickly grown beyond a few major tech companies and hardcore academic researchers. Every marketing organization can tap into the power of AI to streamline operations and grow the business. The new book The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing provides a growth framework for business and marketing leaders to implement AI using a five-stage model called the "AI Marketing Canvas." On this episode of Marketing Smarts, I speak with co-author Rajkumar Venkatesan about how he and his co-writer developed those stages by studying leading global brands. We cover examples of brands―including Google, Lyft and Coca-Cola―that have successfully woven AI into their marketing strategies. This is not a conversation about coding for AI models. Raj and I talk about how marketing leaders can go from "zero to hero" with AI in marketing, and what that means for your team and your company culture. Listen to the entire show now from the link above, or download the mp3 and listen at your convenience.