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Tesla's updated Full Self-Driving beta needs fewer human interventions

Engadget

Tesla's Full Self-Driving beta is already making some significant (and arguably needed) strides forward. Electrek says the automaker has rolled out an update that, according to Elon Musk, should reduce the need for human intervention by about a third. He didn't elaborate on what led to the improvements besides more real-world use, but that's still a huge leap for an initial update. You can expect more Full Self-Driving updates every five to ten days, Musk added. He further acknowledged that the system would never be perfect, but hoped the likelihood of an error would eventually dip "far lower" than what you'd expect from a human.



Canada crawling toward AI regulatory regime, but experts say reform is urgent

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Public trust in artificial intelligence becomes increasingly crucial as machine-learning companies move from the conceptual to the commercial stage, she …





From cloud to device: The future of AI and machine learning on the Edge

#artificialintelligence

For a more comprehensive survey, read our full paper on this topic. We are surrounded by smart devices: from mobile phones and watches to glasses, jewelry, and even clothes. But while these devices are small and powerful, they are merely the tip of a computing iceberg that starts at your fingertips and ends in giant data and compute centers across the world. Data is transmitted from devices to the cloud where it is used to train models that are then transmitted back to be deployed back on the device. Unless used for learning simple concepts like wake words or recognizing your face to unlock your phone, machine learning is computationally expensive and data has no choice but to travel these thousands of miles before it can be turned into useful information.


Statistical Tests in Machine Learning

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When it comes to statistics in machine learning, a common approach to accept or reject a null hypothesis is to check for the p-values and give a result without really having an idea of what goes on in the background. Without getting into any kind of fancy jargons or mathematical technicalities, this article attempts to sum up the intuition behind statistics using some real life examples especially for people from a non-statistics background. Why do we need hypothesis testing? But what if suddenly, Dunkin' happens to shut down because Krispe Kreme claims the weight of their donuts is less than what Dunkin' claims. How do we choose sides?


Explaining Machine Learning Classifiers with LIME

#artificialintelligence

Machine learning algorithms can produce impressive results in classification, prediction, anomaly detection, and many other hard problems. Understanding what the results are based on is often complicated, since many algorithms are black boxes with little visibility into their inner working. Explainable AI is a term referring to techniques for providing human-understandable explanations of ML algorithm outputs. Explainable AI is interesting for many reasons, including being able to reason about the algorithms used, the data we have to train them, and to understand better how to test the system using such algorithms. LIME, or Local Interpretable Model-Agnostic Explanations is one technique that seems to have gotten attention lately in this area. The idea of LIME is to give it a single datapoint, and the ML algorithm to use, and it will try to build understandable explanation for the output of the ML algorithm for that specific datapoint. Such as "because this person was found to be sneezing and coughing (datapoint features), there is a high probability they have a flu (ML output)".


The upskilling imperative

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COVID-19 and efforts to contain it have had a profound impact on businesses and workers worldwide. As Canada and other countries take their first steps towards recovery, it's clear that the way we live and work will change significantly--and even permanently--in the new normal. The COVID-19 experience has dramatically accelerated companies' digital transformation. Organizations increasingly see data-driven decision making as crucial to their survival today and their success tomorrow, and they are driving forward with investments in AI, analytics, automation, and digitization to secure their future in a changing world. AI, digitization, and automation will open the door to tremendous opportunities for innovation and growth--and create new challenges and complexities for employers.