Up to Speed on AI and Deep Learning: July 12 to July 24

Announcements

• A.I. has a bias problem and that can be a big challenge in cybersecurity
  (CNBC)
  Firewalls and antiviruses are viewed as tools of antiquity as the digital threat constantly evolves, and hackers are now using more advanced technologies, such as AI, to launch complex attacks against businesses.
• The Keystone keyboard powers your typing or gaming with built-in A.I.
  (Digital Trends)
  Input Club launched a Kickstarter project for a mechanical keyboard that features a built-in artificial intelligence that adapts its response to typists’ behavior. The new offering, known as the Keystone, is compatible with Windows, MacOS, and Linux devices, and uses the A.I. in its firmware to learn from consumers’ unique typing habits and adjust the responsiveness of each key to improve the accuracy of the keyboard overall.
• The Fashion Industry Is Getting More Intelligent With AI
  (Forbes)
  Despite the established nature of the fashion industry, AI is fundamentally transforming the industry from the way that fashion companies manufacture their products to the way they are marketed and sold. AI technologies are transforming the fashion industry in every element of its value chain such as designing, manufacturing, logistics, marketing and sales.

Research and Tutorials

• What does it mean to understand a neural network?
  (arXiv)
  We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain the knowledge about many object types across visual scenes. Such networks are thus dramatically easier to understand in terms of the code that makes them than the resulting properties, such as tuning or connections. In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties. The analogy suggests that neuroscience would benefit from a focus on learning and development.
• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
  (arXiv)
  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, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks.
• A new tool for data scientists and biologists and more
  (EurekAlert!)
  A new computational tool is able to quickly identify the hidden affiliations and interrelationships among groups/items/persons with greater accuracy than existing tools. Such a computational tool could be leveraged by various groups: political strategists trying to find voters’ overlapping values or shared attributes; or biologists who want to predict the potential of a drug’s side effects or interactions –without running years’ worth of live experiments.

AI and ML in Society

• AI warns motorcycle riders before they turn too abruptly
  (VentureBeat)
  Turns taken too quickly or sharply are responsible for nearly a fifth of all motorcycle accidents, according to estimates, which also suggest misjudged curves are to blame for 15% of rider fatalities. Fortunately, researchers at ETH Zurich and KU Leuven have recently proposed a solution in a paper on the preprint server Arxiv.org (“Learning a Curve Guardian for Motorcycles“). Their road curvature warning system leverages computer vision and mapping data to predict lane position and motorcycle roll angle, in addition to the road geometry of future paths.
• This AI magically removes moving objects from videos
  (The Next Web)
  There’s an AI-powered software that effortlessly removes moving objects from videos. All you need to do to wipe an object from footage is draw a box around it, and the software takes care of the rest for you.
• Future fighter jets will use AI-enabled weapons and sensors
  (Fox News)
  The Navy program, called Next-Generation Air Dominance is currently analyzing air frames, targeting systems, AI-enabled sensors, new weapons and engine technologies to engineer a new 6th-Generation fighter to fly alongside the F-35 and ultimately replace the F/A-18.
• I’m a data scientist who is skeptical about data
  (QUARTZ)
  “What does the data say?” Data doesn’t say anything. Humans say things. They say what they notice or look for in data—data that only exists in the first place because humans chose to collect it, and they collected it using human-made tools.

Join us in two weeks for the next edition of Up to Speed on AI and Deep Learning!

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