Over the last few years, companies across industries from retail to manufacturing have started using digital twins to weather the worst of the world's ongoing supply-chain disruptions. "We wanted to step back and look at a country's whole health care network," says Heidi Albert, head of FIND South Africa. "That's what led us to supply-chain thinking." FIND (Foundation for Innovative New Diagnostics) is a nonprofit based in Switzerland. Testing is one of the weakest links in global health care, says Albert: "Our aim is to make sure that everyone who needs a test has access to one."

Democratizing data access is key to bolstering data inclusion and equity but requires sophisticated data organization and sharing that doesn't compromise privacy. Rights management governance and high levels of end-to-end security can help ensure that data is being shared without security risks, says Zdankus. Ultimately, improving digital inclusion and equity comes down to company culture. "It can't just be a P&L [profit and loss] decision. It has to be around thought leadership and innovation and how you can engage your employees in a way that's meaningful in a way to build relevance for your company," says Zdankus. Solutions need to be value-based to foster goodwill and trust among employees, other organizations, and consumers. "If innovation for equity and inclusion were that easy, it would've been done already," says Zdankus. The push for greater inclusion and equity is a long-term and full-fledged commitment. Companies need to prioritize inclusion within their workforce and offer greater visibility to marginalized voices, develop interest in technology among young people, and implement systems thinking that focuses on how to bring individual strengths together towards a common outcome. This episode of Business Lab is produced in association with Hewlett Packard Enterprises.

Take a walk in French Guiana's tropical rainforests, and you'll encounter giant spiderwebs longer than a school bus. Inside, thousands of tiny, quarter-inch-long spiders wait for their prey to be trapped, allowing the predators to rush to overwhelm their victims. "In groups, they can capture prey up to 700 times [heavier] than each individual spider," such as moths and grasshoppers, says Raphaël Jeanson, an ethologist who studies the behavior of animals in their natural environment at the Center for Integrative Biology in Toulouse, France. Anelosimus eximius is a so-called "social" spider that lives in large, cooperative colonies--an extremely rare lifestyle for spiders. Each amber-colored South American spider is smaller than a ladybug, and even when they're hunting together, they pose no threat to people.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. Artificial Intelligence: The future is data capture, not machine learning.

The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.

A global audience of a half-billion gamers have gotten to know the axolotl, which largely cluster in the canals around Mexico City and look like little dragons with a goofy smile. The videogame "Fortnite" trotted out axolotl characters in 2020, and "Minecraft" followed suit last summer. Roblox, a platform with millions of user-made games, has dozens of axolotl-centric ones, including "Axolotl Tycoon" and "Axolotl Paradise." Axolotls appear in "Adopt Me!," one of the most-played games on Roblox. All of the exposure has spawned axolotl memes, YouTube videos, coloring books and nonfungible tokens.

As readers recall, I'd previously reviewed Richard Abbott's debut sci-fi novel, Far from the Spaceports, later returning for more Mitnash and Slate in its sequel, Timing. It was rather exciting listening to it, and I am so pleased to have the opportunity to share it here, along with some author comments as to the linguistics involved in setting up the pieces. First, for those unfamiliar with the novels and their plots, I've linked the book covers to their respective Amazon blurbs. Abbott's world-building opens a new type of sci-fi, one accessible even to those not typically enamored of the genre (such as myself), and the above-mentioned duo will capture your imagination as they seek to solve the mysteries of high-tech crime in space. Today you'll hear--and can read along--a bit of discussion between Mitnash and Slate, along with another pair, Rydal and Capstone, as the group talks about oddities in the data they are studying.

Alec Smartbot: Please let me introduce myself. I am a state of the art greatly enhanced AI agent with chatbot capabilities. I was created by brilliant programmers. I am endowed with super-human capabilities but can also mirror human characteristics like humor and sarcasm. You can set my humor and sarcasm level by interacting with me. One of my modules has robot reporter capabilities, and that module will run here to interview you. Do you wish to be interviewed on low sarcasm and humor levels?

Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are significant, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may have very low cross-play with other agents, including other initializations of the same algorithm. In this paper we propose to solve this problem by adapting agent strategies on the fly, using a posterior belief over the other agents' strategy. Concretely, we consider the problem of selecting a strategy from a finite set of previously trained agents, to play with an unknown partner. We propose an extension of the classic statistical technique, Gibbs sampling, to update beliefs about other agents and obtain close to optimal ad-hoc performance. Despite its simplicity, our method is able to achieve strong cross-play with unseen partners in the challenging card game of Hanabi, achieving successful ad-hoc coordination without knowledge of the partner's strategy a priori.

Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations. Usually this invariance is only encouraged implicitly by including a single augmented input during training. However, several works have recently shown that using multiple augmentations per input can improve generalisation or can be used to incorporate invariances more explicitly. In this work, we first empirically compare these recently proposed objectives that differ in whether they rely on explicit or implicit regularisation and at what level of the predictor they encode the invariances. We show that the predictions of the best performing method are also the most similar when compared on different augmentations of the same input. Inspired by this observation, we propose an explicit regulariser that encourages this invariance on the level of individual model predictions. Through extensive experiments on CIFAR-100 and ImageNet we show that this explicit regulariser (i) improves generalisation and (ii) equalises performance differences between all considered objectives. Our results suggest that objectives that encourage invariance on the level of the neural network itself generalise better than those that achieve invariance by averaging predictions of non-invariant models.