1. The Cost of Self-Driving Cars Will Be the Biggest Barrier to Their Adoption
By shifting responsibility for driving from humans to machines, this technology minimizes opportunities for behavioral errors blamed in most road crashes. These individuals are more likely to die on the road partly because they own older vehicles that lack advanced safety features and have lower crash-test ratings. This setup, analogous to modern day taxis, distributes operating costs over a large number of consumers making mobility services more affordable. Read More
2. A Smart Stethoscope Puts AI in Medics’ Ears
You wake up one morning to discover that your child is ill: His forehead feels hot to the touch, and his rapid breathing has a wheezing sound. Through the windows, open in the heat of the day, come the sounds of people talking, the thrum of a generator, and the roar of a moped on the main road. These acute lower respiratory infections kill nearly 1 million children each year worldwide, causing more deaths than HIV and malaria combined. Read More
3. A Must-Read NLP Tutorial on Neural Machine Translation – The Technique Powering Google Translate
There are so many little nuances that we get lost in the sea of words.
In this article, we will walk through the steps of building a German-to-English language translation model using Keras. The system had a pretty small vocabulary of only 250 words and it could translate only 49 hand-picked Russian sentences to English. Read More
4. Deep learning hope and hype: ’s Will Knight
Both the progress and the hype around cutting-edge machine learning techniques were on vivid display at the December 2018 NeurIPS Conference in Montreal, Quebec, says Will Knight, MIT Technology Review’s senior editor for artificial intelligence. One big question hanging over the meeting, he says, was how to detect and reverse the sexism, racism, and other forms of bias that seep into machine-learning algorithms that train themselves using real-world data.
Will: One of the most interesting things I think was that there was a huge focus on diversity and bias. Read More
5. This robot can probably beat you at Jenga—thanks to its understanding of the world
Despite dazzling advances in AI, robots are still horribly ham-fisted.
Increasingly, researchers and companies are turning to machine learning to make them more adaptive and dexterous.
That is what inspired a new robot, developed by Nima Fazeli and his colleagues at MIT, that has been given a fundamental understanding of the real world’s physics—and a usable sense of touch. Read More
6. Online Pandora’s Boxes and Bandits
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7. Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge
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8. Machine Learning Integration Options
by Paul DeBeasi | | Submit a Comment
Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development.
IoT is one of the most disruptive forces organizations must contend with today.
As the number of IoT endpoints proliferate, the need for organizations to understand how to design systems that integrate machine learning inference with IoT will grow rapidly. Read More
9. Efficient Adversarial Robustness Evaluation of AI Models with Limited Access
Recent studies have identified the lack of robustness in current AI models against adversarial examples—intentionally manipulated prediction-evasive data inputs that are similar to normal data but will cause well-trained AI models to misbehave.
Notably, adversarial examples are often generated in the “white-box” setting, where the AI model is entirely transparent to an adversary. In the practical scenario, when deploying a self-trained AI model as a service, such as an online image classification API, one may falsely believe it is robust to adversarial examples due to limited access and knowledge about the underlying AI model (aka the “black-box” setting). Read More
10. Opinion | A.I. Could Worsen Health Disparities
Artificial intelligence is beginning to meet (and sometimes exceed) assessments by doctors in various clinical situations. can now diagnose skin cancer like dermatologists, seizures like neurologists, and diabetic retinopathy like ophthalmologists.
It’s enough to make doctors like myself wonder why we spent a decade in medical training learning the art of diagnosis and treatment. Read More
11. Data Notes: How to Teach an AI to Dance
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12. Atari master: New AI smashes Google DeepMind in video game challenge
A new breed of algorithms has mastered Atari video games 10 times faster than state-of-the-art AI, with a breakthrough approach to problem solving.
In the new method developed at RMIT University in Melbourne, Australia, computers set up to autonomously play Montezuma’s Revenge learnt from mistakes and identified sub-goals 10 times faster than Google DeepMind to finish the game.
Associate Professor Fabio Zambetta from RMIT University unveils the new approach this Friday at the 33rd AAAI Conference on Artificial Intelligence in the United States. Read More
13. Calculators didn’t replace mathematicians, and AI won’t replace humans
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