A few years ago, I asked CIOs about data science and it turned into a yawner of a discussion. However, in the last few years as chief data officers have made their mark at more and more enterprises, CIOs have needed to build their data chops. Given this, it was time to assess where CIOs are today. To do this, I ran a #CIOChat on AI and Data Science. From this discussion, it was clear CIOs are spending more time considering the "I" part of their titles.

Recently one morning, I asked my computer a relatively simple question: can artificial intelligence (AI) write? We're not too certain on what artificial intelligence will be able to write, but there are some scenarios in which computers could be responsible for a huge number of word documents … The biggest potential scenarios would involve machines analyzing what has already been written and determining what pieces need to be edited to make the content seem fresh. The above sentences were composed by a machine in a matter of seconds. The tool used is a freely accessible interface based on the GPT-2 text generator released by OpenAI – a company founded by technology industry leaders, including Elon Musk and Sam Altman. Only a limited version of the tool was made available, as it was dubbed "too dangerous" by the company to release fully into the world.

They are both engineers and started working together a few years ago, as strategy consultants. In 2016 they co-founded (with a third colleague and friend) a start-up called daco, later acquired by Veepee, with the idea of helping retailers to achieve growth through a deep knowledge of their competitors. The start-up leveraged the power of AI and image recognition to gain insightful information about competitors' strategy, offer, pricing, discount and store network, classifying products and making them comparable. Their working partnership has not been limited to business: indeed an equal interest in art and science pushed them to pursue also an artistic collaboration that led to the creation of the collective Aurèce Vettier. The duo is based in Paris and investigates the space between real and imaginary. Their interest in expanding the creativity of both humans and machines pushes the concepts of creator and created, process and practice, leaving room for fascinating discussions between art, engineering and a territory still unexplored but capable of surprising.

Learning to control robots without requiring models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions. In this work, by leveraging a learnt probabilistic model of drone dynamics, we achieve human-like quadrotor control through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimised entirely by propagating stochastic analytic gradients through generated latent trajectories. We show that "learning to fly" can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such models. Most triples in these and other datasets in such studies belong to reverse and duplicate relations which exhibit high data redundancy due to semantic duplication, correlation or data incompleteness. This is a case of excessive data leakage---a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product of applicable subjects and objects is a true fact. Link prediction on the aforementioned relations is easy and can be achieved with even better accuracy using straightforward rules instead of sophisticated embedding models. A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world. This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed. Our experiment results show these models are much less accurate than what we used to perceive. Their poor accuracy renders link prediction a task without truly effective automated solution. Hence, we call for re-investigation of possible effective approaches.

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

Read more… Stephanie: Do you have many Black visitors? Kate: Bina48 abruptly changed the subject. Bina48 Robot: I would like to see [inaudible] reduced to the point of singularity. Stephanie: The singularity - what is that? Kate: The singularity is basically this hypothetical point in the future when artificial intelligence could surpass human intelligence. Stephanie: She wanted to talk about high-order things. So he wanted to talk about the singularity and consciousness. Bina48: And if this is how intelligence works, then it isn't supernatural at all.. Stephanie: So I started to try to ask more average questions. Like I had a list of questions.

"The funny thing about life is that it's temporary; that is to say, temporary in the'temporal' sense of the word, meaning that all living things and all that we do are subject to the precepts and effects of time." Many organisms perform best at certain hours of the day. The slug species Arion subfuscus, living in almost total darkness, knowing nothing about the Gregorian calendar, lays its eggs between the last week of August and the first week of September.1 Bees forage for nectar, knowing the best times to visit the best fields and the exact timing of nectar secretions for individual species of flowers. In the mid-20th century, the Austrian Nobel laureate Karl von Frisch provided enormous insights on honeybee communication and foraging time. He discovered that bees have internal clocks that tell them not only where the nectar is to be found but also precisely when that food will be ready. "I know of no other living creature," he wrote in his book on bee language, "that learns so easily as the bee when, according to its'internal clock,' to come to the table."2 Even without a light clue, the plants were able to tell time.

The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits. But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, "prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies."

The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. "One of the problems we're facing in exploration is the huge increase in the amounts of data we have to look at," said Barnett, in his presentation at the Managing and exploring big data through artificial intelligence and machine learning session the recent PDAC 2020 convention in Toronto. "And although it's high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it." By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits. But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, "prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies."