If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
People have myths because that is one kind of response to the unknown. If you take away their myths, you may leave them with nothing. That's why a very well-intentioned, thoughtful effort of scholars over at the Mozilla dot org foundation to debunk nonsense about artificial intelligence is bound to fail. The new website, AI Myths, purports to debunk pernicious lies and mischaracterizations about artificial intelligence, such as the notion that AI has agency, or that "superintelligence is coming soon." What the very astute authors have failed to confront is that people have no idea what AI is.
MONTREAL – A research project located at the intersection between genomics, artificial intelligence and medicinal chemistry will attempt to accelerate the identification of new molecules that could prove useful in the fight against the coronavirus. The project brings together Génome Québec, the Institute for Research in Immunology and Cancer (IRIC) of the University of Montreal, the University of Montreal, Mila – Quebec Institute of Artificial Intelligence and McMaster University. "When you start from scratch with traditional methods, developing a new drug can take ten or fifteen years," said Professor Michael Tyers of IRIC / University of Montreal. We hope that this approach will allow us to go considerably faster. Each discipline will go there with a contribution of its own.
IN MARCH Starsky Robotics, a self-driving lorry firm based in San Francisco, closed down. Stefan Seltz-Axmacher, its founder, gave several reasons for its failure. Investors' interest was already cooling, owing to a run of poorly performing tech-sector IPOs and a recession in the trucking business. His firm's focus on safety, he wrote, did not go down well with impatient funders, who preferred to see a steady stream of whizzy new features. But the biggest problem was that the technology was simply not up to the job.
Self-supervised learning could lead to the creation of AI that's more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online. Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it'll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data's parts, a step believed to be critical to achieving human-level intelligence. "Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It's basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way," said LeCun.
While probing is a common technique for identifying knowledge in the representations of pretrained models, it is unclear whether this technique can explain the downstream success of models like BERT which are trained end-to-end during finetuning. To address this question, we compare probing with a different measure of transferability: the decrease in finetuning performance of a partially-reinitialized model. This technique reveals that in BERT, layers with high probing accuracy on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. In addition, dataset size impacts layer transferability: the less finetuning data one has, the more important the middle and later layers of BERT become. Furthermore, BERT does not simply find a better initializer for individual layers; instead, interactions between layers matter and reordering BERT's layers prior to finetuning significantly harms evaluation metrics. These results provide a way of understanding the transferability of parameters in pretrained language models, revealing the fluidity and complexity of transfer learning in these models.
Visual question answering (VQA) is a task that requires AI systems to display multi-modal understanding. A system must be able to reason over the question being asked as well as the image itself to determine reasonable answers to the questions posed. In many cases, simply reasoning over the image itself and the question is not enough to achieve good performance. As an aid of the task, other than region based visual information and natural language questions, external textual knowledge extracted from images can also be used to generate correct answers for questions. Considering these, we propose a deep neural network model that uses an attention mechanism which utilizes image features, the natural language question asked and semantic knowledge extracted from the image to produce open-ended answers for the given questions. The combination of image features and contextual information about the image bolster a model to more accurately respond to questions and potentially do so with less required training data. We evaluate our proposed architecture on a VQA task against a strong baseline and show that our method achieves excellent results on this task.
Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components. This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three "godfathers of deep learning," have all spoken about the limits of neural networks. The question is, what is the path forward?
COVID-19 officially became a global pandemic on Wednesday. As public health officials and governments respond; businesses brace for losses; and events like trade shows, SXSW, and Google's I/O shutter around the world, the disease is also impacting scientific conferences. Ironically, a coronavirus conference got canceled this week, and on Tuesday the International Conference on Learning Representations (ICLR), one of the fastest-growing machine learning conferences in the world, shared that it will now be a virtual event held entirely online. Papers will be presented in prerecorded five-minute videos with a slide deck, while researchers invited to make longer presentations can submit 15-minute videos. In a post about the change to an all-digital conference, organizers called the cancellation of an in-person event an "… opportunity to innovate on how to host an effective remote conference."
This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components. This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three "godfathers of deep learning," have all spoken about the limits of neural networks.