Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of Artificial Intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and Causality of the models. Pearl also went on record to say that Deep learning was stuck with curve fitting and called it "sacrilege". From the point of view of the mathematical hierarchy, Pearl said that no matter how well the data is manipulated, it's still a curve-fitting exercise. This a very controversial accusation coming from Pearl, who was awarded the ACM Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

Today, Neural Networks have made the headlines in many fields, such as image classification of cancer tissues, text generation, or even credit scoring. Nevertheless, one big issue that is rarely tackled with these models, is the uncertainty of the prediction. When we humans learn, one of our greatest strengths is knowing our weaknesses, and not acting when there is too much uncertainty. However, the same is not true for most machine learning models, where decisions are taken without taking into account the uncertainty. For instance, if you train a classifier on cats and dogs, it will only be able to output cat or dog.

At OSN New York 2019, Darius Moshfeghi, MD, discusses the progress being made around the world in research into artificial intelligence, deep learning and big data in the areas of diabetic and pediatric eye disease, among other areas of medicine.

Convolutional Neural Networks (CNN) are frequently preferred in computer vision applications because of their successful results on object recognition and classification tasks. CNNs are composed of many neurons stacked together. Computing convolutions across neurons require a lot of computation, so pooling processes are often used to reduce the size of network layers. Convolutional approaches make it possible to learn many complex features of our data with simple computations. By performing many matrix multiplications and summations on our input, we can arrive at an answer to our question.

Artificial Intelligence Discovers Tool Use in Hide-and-Seek Games Programmers at OpenAI, an artificial intelligence research company, recently taught a gaggle of intelligent artificial agents -- bots -- to play hide-and-seek. Not because they cared who won: The goal was to observe how competition between hiders and seekers would drive the bots to find and use digital tools. The idea is familiar to anyone who's ever played the game in real life; it's a kind of scaled-down arms race. When your opponent adopts a strategy that works, you have to abandon what you were doing before and find a new, better plan. It's the rule that governs games from chess to StarCraft II; it's also an adaptation that seems likely to confer an evolutionary advantage. So it went with hide-and-seek.

Can you interpret a deep neural network? Building a complex and dense machine learning model has the potential of reaching our desired accuracy, but does it make sense? Can you open up the black-box model and explain how it arrived at the final result? These are critical questions we need to answer as data scientists. A wide variety of businesses are relying on machine learning to drive their strategy and spruce up their bottomline. Building a model that we can explain to our clients and stakeholders is key.

China's history of censoring the internet and using propaganda to give its citizens a false sense of reality is well documented. And now, Chinese regulators have announced new rules that will allow them to further dictate what citizens perceive as reality. From January 1, 2020, publishing and distributing what the regulators deem to be "fake news" created with artificial intelligence (AI), deep learning, or virtual reality (VR) will be banned. The Cyberspace Administration of China (CAC) added that content produced with AI, deep learning, or VR will need to be clearly labeled when these new rules come into effect. Failure to label such content under the new rules could be a criminal offense.

The unification of low-level perception and high-level reasoning is a long-standing problem in artificial intelligence, which has the potential to not only bring the areas of logic and learning closer together but also demonstrate how abstract concepts might emerge from sensory data. Precisely because deep learning methods dominate perception-based learning, including vision, speech, and linguistic grammar, there is fast-growing literature on how to integrate symbolic reasoning and deep learning. Broadly, efforts seem to fall into three camps: those focused on defining a logic whose formulas capture deep learning, ones that integrate symbolic constraints in deep learning, and others that allow neural computations and symbolic reasoning to co-exist separately, to enjoy the strengths of both worlds. In this paper, we identify another dimension to this inquiry: what do the hidden layers really capture, and how can we reason about that logically? In particular, we consider autoencoders that are widely used for dimensionality reduction and inject a symbolic generative framework onto the feature layer.

Engineers at Waymo, owned by Alphabet, teamed up with researchers at DeepMind, another Alphabet division dedicated to AI, to find a more efficient process to train and fine-tune the company's self-driving algorithms.

Engineers at Waymo, owned by Alphabet, teamed up with researchers at DeepMind, another Alphabet division dedicated to AI, to find a more efficient process to train and fine-tune the company's self-driving algorithms.