Up to Speed on AI and Deep Learning: July 25 to August 7

Michael Osakwe
August 8, 2019
Up to Speed on AI and Deep Learning: July 25 to August 7Up to Speed on AI and Deep Learning: July 25 to August 7
Michael Osakwe
August 8, 2019
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  • Google’s DeepMind is training Waymo’s self-driving cars like StarCraft II bots
    (Digital Trends)
    DeepMind is teaming up with Waymo, a fellow unit of Google parent Alphabet, to train self-driving cars, using the same method that was created to teach artificial intelligence bots how to play StarCraft II. Waymo’s self-driving vehicles utilize neural networks to carry out tasks such as detecting objects on the road, predicting how other cars will behave, and planning its next moves. Training the neural networks has required “weeks of fine-tuning and experimentation, as well as enormous amounts of computational power.
  • No coding required: Companies make it easier than ever for scientists to use artificial intelligence
    AI used to be the specialized domain of data scientists and computer programmers. But companies such as Wolfram Research, which makes Mathematica, are trying to democratize the field, so scientists without AI skills can harness the technology for recognizing patterns in big data.
  • China has started a grand experiment in AI education. It could reshape how the world learns.
    (MIT Technology Review)
    Experts agree AI will be important in 21st-century education—but how? While academics have puzzled over best practices, China hasn’t waited around. In the last few years, the country’s investment in AI-enabled teaching and learning has exploded. Tech giants, startups, and education incumbents have all jumped in. Tens of millions of students now use some form of AI to learn.
    Chase is getting more creative with its marketing language—by tapping machines to write it. The bank announced Tuesday it has signed a five-year deal with Persado, a New York-based company that applies artificial intelligence to marketing creative.
  • DeepMind touts predictive healthcare AI ‘breakthrough’ trained on heavily skewed data
    (Tech Crunch)
    DeepMind, the Google-owned U.K. AI research firm, has published a research letter in the journal Nature in which it discusses the performance of a deep learning model for continuously predicting the future likelihood of a patient developing a life-threatening condition called acute kidney injury (AKI).
    Data scientists have changed almost every industry. In medicine, their algorithms help predict patient side effects. In sports, their models and metrics have redefined “athletic potential.” Data science has even tackled traffic, with route-optimizing models that capture typical rush hours and weekend lulls.
  • Facebook empowers OpenStreetMap community with AI-enhanced tools
    (Tech Crunch)
    If we’re going to map the world, we’re not going to do it with ever-greater volumes of elbow grease. There’s just too much work to do. AI and computer vision are helpful assistants in this task, however, as a Facebook effort has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries.
  • Machine Learning Helps Discover New Polymers Which Can Be Used For 5G Connectivity
    (Analytics India Magazine)
    Over the last few years, the chances of creating new conducting polymers with the help of machine learning have caught the attention of many researchers in the field of chemistry. Now, a team of researchers has discovered a new kind of polymer which contains high thermal conductivity and can be beneficial to the 5G mobile communication technologies.

Research and Tutorials

  • Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
    Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today’s research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models. In this work, the authors report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, they considered 18 algorithms that were presented at top-level research conferences in the last years.
  • ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
    Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness, and discourse relations. In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, the authors propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning.
  • RoBERTa: A Robustly Optimized BERT Pretraining Approach
    Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as the authors will show, hyperparameter choices have significant impact on the final results. They present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. They find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it.

AI and ML in Society

  • World’s First AI-Powered Bar Uses Facial Recognition To Serve Customers In Proper Order
    (Tech Times)
    Some people are really just the type who cut in line, and bars are not exempted from encounters with such people. However, customers of a special bar in London can say goodbye to the problem of queue-cutting thanks to the AI-powered facial recognition system that determines which customer should be served next.
  • AI and Bionic Eyes Are Helping to Contain Raging Wildfires
    In a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. Only these eyes aren’t human. They don’t blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA.
    (The Tech Edvocate)
    Mental health problems like anxiety and depression can interfere with a student’s studies and hinder performance. Depression is associated with poor academic performance and dropping out of school. Traditionally clinicians have interviewed patients, asking questions about mood, lifestyle, and previous mental problems to identify whether a patient is depressed or not. That method might be something of the past. Machine learning might step in to diagnose depression in patients.
  • Understanding Explainable AI
    As artificial intelligence becomes an increasing part of our daily lives and we are finding that the need to trust these AI based systems with all manner of decision making and predictions is paramount. The sorts of decisions and predictions being made by AI-enabled systems is becoming much more profound, and in many cases, critical to life, death, and personal wellness.

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