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Category: robotics/AI – Page 2282

Nice job Harley-Davidson when can I have my discount for my new wheels?

Harley-Davidson Says Artificial Intelligence Drives 40% of New York Sales Lookalike modeling is a key component of lead generation, and for motorcycle brand Harley-Davidson, the tactic now goes hand in hand with artificial intelligence (AI). In March 2016, the company began working with machine learning technology provider Adgorithms to grow its ecommerce reach and hasn’t looked back since. Asaf Jacobi, president of Harley-Davidson’s New York City division, spoke with eMarketer’s Maria Minsker about the brand’s experience with AI and discussed the results he has seen so far.

EMarketer: What are some of the business challenges that drove you to try artificial intelligence?

Asaf Jacobi: One of the biggest challenges of having a business in New York City is that it’s a very competitive environment. To get the response rate brands want, they have to reach as many people as possible. That’s where artificial intelligence comes in. I started reading about how artificial intelligence boosts online marketing reach, and contacted Adgorithms. We started using their platform, Albert, for our ecommerce ads in March.

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Fortifying cybersecurity is on everyone’s mind after the massive DDoS attack from last week. However, it’s not an easy task as the number of hackers evolves the same as security. What if your machine can learn how to protect itself from prying eyes? Researchers from Google Brain, Google’s deep Learning project, has shown that neural networks can learn to create their own form of encryption.

According to a research paper, Martín Abadi and David Andersen assigned Google’s AI to work out how to use a simple encryption technique. Using machine learning, those machines could easily create their own form of encrypted message, though they didn’t learn specific cryptographic algorithms. Albeit, compared to the current human-designed system, that was pretty basic, but an interesting step for neural networks.

To find out whether artificial intelligence could learn to encrypt on its own or not, the Google Brain team built an encryption game with its three different entities: Alice, Bob and Eve, powered by deep learning neural networks. Alice’s task was to send an encrypted message to Bob, Bob’s task was to decode that message, and Eve’s job was to figure out how to eavesdrop and decode the message Alice sent herself.

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As shown in this in vivo two-photon image, neuronal transplants (blue) connect with host neurons (yellow) in the adult mouse brain in a highly specific manner, rebuilding neural networks lost upon injury. (credit: Sofia Grade/LMU/Helmholtz Zentrum München)

Embryonic neural stem cells transplanted into damaged areas of the visual cortex of adult mice were able to differentiate into pyramidal cells — forming normal synaptic connections, responding to visual stimuli, and integrating into neural networks — researchers at LMU Munich, the Max Planck Institute for Neurobiology in Martinsried and the Helmholtz Zentrum München have demonstrated.

The adult human brain has very little ability to compensate for nerve-cell loss, so biomedical researchers and clinicians are exploring the possibility of using transplanted nerve cells to replace neurons that have been irreparably damaged as a result of trauma or disease, leading to a lifelong neurological deficit.

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In Brief:

  • Watson recommended treatment plans that matched suggestions from oncologists in 99 percent of the cases it analyzed and offered options doctors missed in 30 percent of them.
  • AI could be revolutionary for healthcare as it can process many more research papers and case files than any human doctor could manage.

Artificial intelligence (AI) is about more than just the promise of a robot butler — it can actually save lives. AI’s contribution to the healthcare industry and in medical research could be hugely significant. IBM sees that and wants Watson, its AI technology, at the forefront of this development.

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In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts.

But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it’s sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque.

At the Association for Computational Linguistics’ Conference on Empirical Methods in Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions.

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