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) …
Researchers at the University of California, San Francisco have recently created an AI system that can produce text by analyzing a person's brain activity, essentially translating their thoughts into text. The AI takes neural signals from a user and decodes them, and it can decipher up to 250 words in real-time based on a set of between 30 to 50 sentences. As reported by the Independent, the AI model was trained on neural signals collected from four women. The participants in the experiment had electrodes implanted in their brains to monitor for the occurrence of epileptic seizures. The participants were instructed to read sentences aloud, and their neural signals were fed to the AI model.
Scientists have developed an artificial intelligence system that can translate a person's thoughts into text by analysing their brain activity. Researchers at the University of California developed the AI to decipher up to 250 words in real-time from a set of between 30 and 50 sentences. The algorithm was trained using the neural signals of four women with electrodes implanted in their brains, which were already in place to monitor epileptic seizures. The volunteers repeatedly read sentences aloud while the researchers fed the brain data to the AI to unpick patterns that could be associated with individual words. The average word error rate across a repeated set was as low as 3%.
Perception-Action loops are at the core of most our daily life activities. Subconsciously, our brains use sensory inputs to trigger specific motor actions in real time and this becomes a continuous activity that in all sorts of activities from playing sports to watching TV. In the context of artificial intelligence(AI), perception-action loops are the cornerstone of autonomous systems such as self-driving vehicles. While disciplines such as imitation learning or reinforcement learning have certainly made progress in this area, the current generation of autonomous systems are still nowhere near human skill in making those decisions directly from visual data. Recently, AI researchers from Microsoft published a paper proposing a transfer learning method to learn perception-action policies from in a simulated environment and apply the knowledge to fly an autonomous drone.
Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published in Proceedings of the National Academy of Sciences.
If you can't explain it simply, you don't understand it well enough. Disclaimer: This article draws and expands upon material from (1) Christoph Molnar's excellent book on Interpretable Machine Learning which I definitely recommend to the curious reader, (2) a deep learning visualization workshop from Harvard ComputeFest 2020, as well as (3) material from CS282R at Harvard University taught by Ike Lage and Hima Lakkaraju, who are both prominent researchers in the field of interpretability and explainability. This article is meant to condense and summarize the field of interpretable machine learning to the average data scientist and to stimulate interest in the subject. Machine learning systems are becoming increasingly employed in complex high-stakes settings such as medicine (e.g. Despite this increased utilization, there is still a lack of sufficient techniques available to be able to explain and interpret the decisions of these deep learning algorithms.
Artificial intelligence has become an intricate part of our everyday lives. We encounter it consciously and subconsciously -- at the grocery store, when we call customer service, and even in our homes and cars. With an increasing reliance on a technology designed to constantly collect our data – one that is programmed to be "smarter" than the human brain – are we leaving ourselves open to significant issues such as data breaches or information misuse in the future? How can we mitigate the potential challenges posed by artificial intelligence in healthcare and other industries? The emergence of artificial intelligence in healthcare has brought about countless opportunities for improved patient care outcomes, machine learning-assisted care, and deep learning technological advancements.
Yann Lecun, in his talk, introduced the "cake analogy" to illustrate the importance of self-supervised learning. Though the analogy is debated(ref: Deep Learning for Robotics(Slide 96), Pieter Abbeel), we have seen the impact of self-supervised learning in the Natural Language Processing field where recent developments (Word2Vec, Glove, ELMO, BERT) have embraced self-supervision and achieved state of the art results. "If intelligence is a cake, the bulk of the cake is self-supervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL)." Curious to know how self-supervised learning has been applied in the computer vision field, I read up on existing literature on self-supervised learning applied to computer vision through a recent survey paper by Jing et. This post is my attempt to provide an intuitive visual summary of the patterns of problem formulation in self-supervised learning.
If you are following technology news, you have likely already read about how AI programs trained with reinforcement learning beat human players in board games like Go and chess, as well as video games. As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it's the right approach for a given problem. If we simplify the concept, at its foundation, reinforcement learning is a type of machine learning that has the potential to solve tough decision-making problems. Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment.
The digitisation of society means we are amassing data at an unprecedented rate. Healthcare is no exception, with IBM estimating approximately one million gigabytes accruing over an average person's lifetime and the overall volume of global healthcare data doubling every few years.1 To make sense of these big data, clinicians are increasingly collaborating with computer scientists and other allied disciplines to make use of artificial intelligence (AI) techniques that can help detect signal from noise.2 A recent forecast has placed the value of the healthcare AI market as growing from $2bn (£1.5bn; €1.8bn) in 2018 to $36bn by 2025, with a 50% compound annual growth rate.3 Deep learning is a subset of AI which is formally defined as "computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction."4 In practice, the main distinguishing feature between convolutional neural networks (CNNs) in deep learning and traditional machine learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition; they do not require domain expertise to structure the data and design feature extractors.5
The relation between syntax (how words are structured in a sentence) and semantics (how words contribute to the meaning of a sentence) is a long-standing open question in linguistics. It happens, however, to have practical consequences for NLP. In this blog post, I review recent work on disentangling the syntactic and the semantic information when training sentence autoencoders. These models are variational autoencoders with two latent variables and auxiliary loss functions specific for semantic and for syntactic representations. For instance, they may require the syntactic representation of a sentence to be predictive of word order and the semantic representation to be predictive of an (unordered) set of words in the sentence.