The scientists didn't have high expectations when they asked their AI model to complete the poem. "He saw a carrot and had to grab it," they prompted the model. "His hunger was like a starving rabbit," it replied. The rhyming couplet wasn't going to win any poetry awards. But when the scientists at AI company Anthropic inspected the records of the model's neural network, they were surprised by what they found.

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GDL is a subfield of deep learning (Goodfellow et al., Reference Goodfellow, Bengio and Courville2016) with a focus on generation of new data. Following the definition provided by Foster (Reference Foster2019), a generative model describes how a dataset is generated (in terms of a probabilistic model); by sampling from this model, we are able to generate new data. Nowadays, machine-generated artworks have entered the market (Vernier et al., Reference Vernier, Caselles-Dupré and Fautrel2020), they are fully accessible online,Footnote 1 and they have the focus of major investments.Footnote 2 Ethical debates have, fortunately, found a place in the conversation (for an interesting summary of machine learning researches related to fairness, see Chouldechova and Roth (Reference Chouldechova and Roth2020)) because of biases and discrimination they may cause (as happened with AI Portrait Ars [O'Leary, Reference O'Leary2019], leading to some very remarkable attempts to overcome them, as in Xu et al. (Reference Xu, Yuan, Zhang and Wu2018) or Yu et al. (Reference Yu, Li, Zhou, Malik, Davis and Fritz2020)). In this context, it is possible to identify at least three problems: the use of protected works, which have to be stored in memory until the end of the training process (even if not for more time, in order to verify and reproduce the experiment); the use of protected works as training set, processed by deep learning techniques through the extraction of information and the creation of a model upon them; and the ownership of intellectual property (IP) rights (if a rightholder would exist) over the generated works. Although these arguments have already been extensively studied (e.g., Sobel (Reference Sobel2017) examines use as training set and Deltorn and Macrez (Reference Deltorn and Macrez2018) discuss authorship), this paper aims at analyzing all the problems jointly, creating a general overview useful for both the sides of the argument (developers and policymakers); aims at focusing only on GDL, which (as we will see) has its own peculiarities, and not on artificial intelligence (AI) in general (which contains too many different subfields that cannot be generalized as a whole); and is written by GDL researchers, which may help provide a new and practical perspective to the topic.

Deep Longevity, which specialises in the development and the application of next-generation AI for aging and longevity research, has announced the publication of an article in Nature Aging titled Artificial Intelligence in Longevity Medicine, written by Alex Zhavoronkov, Evelyne Yehudit Bischof and Kai-Fu Lee. Longevity.Technology: Longevity and AI are deeply enmeshed; from accelerating innovation and technology transfer, to developing personalised health therapies, the presence of AI is a key factor in extending lifespan and healthspan and ensuring maximum wellness. Next-generation AI could not only improve longevity investigative strategies and research, but push them in entirely new directions – vive la révolution! Hong Kong-based Deep Longevity was spun out of Insilico Medicine and quickly acquired by Regent Pacific. It develops explainable AI systems to track the rate of aging at the molecular, cellular, tissue, organ, system, physiological and psychological levels, as well as developing systems for the emerging field of longevity medicine. Creators of deep aging clocks that leverage data from multiple biomarkers, Deep Longevity, through a research partnership with Human Longevity, Inc, provides various aging clocks to physicians and researchers.

Artificial intelligence already is making strides in the development of new drugs, and now the pesticide industry wants in on the action. Switzerland's Syngenta has teamed up with Insilico Medicine to use its deep-learning tools to produce sustainable weedkillers. As well as taking on some of the early-stage work traditionally conducted in a lab, AI could design molecules used in crop-protection tools that are more sustainable and environmentally friendly, the companies said last week. AI is among new methods emerging as environmental and health concerns spur a quest for sustainable alternatives to traditional pesticides used by farmers. Demand also is being supported by regulatory pressures and lawsuits, most notably Bayer's $11 billion settlement deal over claims its long-used glyphosate herbicide causes cancer.

In July 2017, I sat in on a closed-door meeting coordinated by the State Department and the National Academies of Science, Engineering, and Medicine. In the room were research scientists, government officials, and policy wonks with PhDs in the hard sciences. Our task that day was to talk about the future of Crispr-Cas9. Back then, the public wasn't yet aware of this powerful genetic editing tool, but today you probably know it as the set of "molecular scissors" that use biological processes to cut and paste genetic information. Crispr might be new, but the key points of our conversation were hardly original.

The story of machine learning starts in 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts introduced a mathematical model of a neural network. The field gathered steam in 1956 at a summer conference on the campus of Dartmouth College. There, 10 researchers came together for six weeks to lay the ground for a new field that involved neural networks, automata theory and symbolic reasoning. The distinguished group, many of whom would go on to make seminal contributions to this new field, gave it the name artificial intelligence to distinguish it from cybernetics, a competing area of research focused on control systems. In some ways these two fields are now starting to converge with the growth of IoT, but that is a topic for another day.

Machine learning has continued to evolve since its beginnings some seven decades ago. Learn how deep learning has catalyzed a new phase in the evolution of machine learning. Machine learning had a rich history long before deep learning reached fever pitch. Researchers and vendors were using machine learning algorithms to develop a variety of models for improving statistics, recognizing speech, predicting risk and other applications. While many of the machine learning algorithms developed over the decades are still in use today, deep learning -- a form of machine learning based on multilayered neural networks -- catalyzed a renewed interest in AI and inspired the development of better tools, processes and infrastructure for all types of machine learning.

It's not easy to break into a new field, especially one as complex and multi-faceted as data science. What a data scientist used to do, used to need to understand, and the types of companies that need to hire data scientists are in a state of rapid evolution. Why in the world would there be only one path to follow? Honestly, if you're trying to break into data science, I can't think of a better time to get started! If you can take on a graduate degree, go for it!

When researchers at University of Washington pulled together a clip of a faked speech by President Obama using video segments of the President's earlier speeches run through artificial intelligence, we watched with a queasy feeling. The combination wasn't perfect – we could still see some seams and stitches showing – but it was good enough to paint a vision of the future. Soon we would not be able to trust our own eyes and ears. Now the researchers at University of Washington (who clearly seem intent on ruining our society) have developed the next level of AI visual wizardry – fake people good enough to fool real people. As reported recently in Wired Magazine, the professors embarked on a Turing beauty contest, generating thousands of virtual faces that look like they are alive today, but aren't.