Artificial intelligence researchers don’t like it when you ask them to name the top AI labs in the world, possibly because it’s so hard to answer.
U.S. Big Tech — Google, Facebook, Amazon, Apple and Microsoft — have all set up dedicated AI labs over the last decade.
There’s also DeepMind, which is owned by Google parent company Alphabet, and OpenAI, which counts Elon Musk as a founding investor.
… “Reputationally, there is a good argument to say DeepMind, OpenAI, and FAIR (Facebook AI research]) are the top three,” according to Mark Riedl, associate professor at the Georgia Tech School of Interactive Computing. Read More
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Big Tech In Edge Computing
Forget coding, you can now solve your AI problems with Excel
Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn’t touched. …But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch. And if you want to create machine learning systems that integrate and scale, you’ll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.
… To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks. Read More
What Happens When AI Has An Overactive Imagination?
Tech giants are giving China a Vital Edge in Espionage
U.S. officials say private Chinese firms have been enlisted to process stolen data for their country’s spy agencies.
In 2017, as U.S. President Donald Trump began his trade war with China, another battle raged behind the scenes. The simmering, decade long conflict over data between Chinese and U.S. intelligence agencies was heating up, driven both by the ambitions of an increasingly confident Beijing and by the conviction of key players in the new administration in Washington that China was presenting an economic, political, and national security challenge on a scale the United States had not faced for decades—if ever.
Beijing was giving China hawks in the United States plenty of ammunition. Read More
Alphabet’s Loon hands the reins of its internet air balloons to self-learning AI
Alphabet’s Loon, the team responsible for beaming internet down to Earth from stratospheric helium balloons, has achieved a new milestone: its navigation system is no longer run by human-designed software.
Instead, the company’s internet balloons are steered around the globe by an artificial intelligence — in particular, a set of algorithms both written and executed by a deep reinforcement learning-based flight control system that is more efficient and adept than the older, human-made one. The system is now managing Loon’s fleet of balloons over Kenya, where Loon launched its first commercial internet service in July after testing its fleet in a series of disaster relief initiatives and other test environments for much of the last decade. Read More
AutoX becomes China’s first to remove safety drivers from robotaxis
Residents of Shenzhen will see truly driverless cars on the road starting Thursday. AutoX, a four-year-old startup backed by Alibaba, MediaTek and Shanghai Motors, is deploying a fleet of 25 unmanned vehicles in downtown Shenzhen, marking the first time any autonomous driving car in China tests on public roads without safety drivers or remote operators.
The cars, meant as robotaxis, are not yet open to the public, an AutoX spokesperson told TechCrunch. Read More
Use real-time anomaly detection reference patterns to combat fraud
Businesses of every size and shape have a need to better understand their customers, their systems, and the impact of external factors on their business. How rapidly businesses mitigate risks and capitalize on opportunities can set apart successful businesses from businesses that can’t keep up. Anomaly detection—or in broader terms, outlier detection—allows businesses to identify and take action on changing user needs, detect and mitigate malignant actors and behaviors, and take preventive actions to reduce costly repairs.
The speed at which businesses identify anomalies can have a big impact on response times, and in turn, associated costs.
… At Google Cloud, our customer success teams have been working with an increasing number of customers to help them implement streaming anomaly detection. In working with such organizations to help them build anomaly detection systems, we realized that providing these reference patterns can significantly reduce the time to solution for those and future customers. Read More
ReBeL: A general game-playing AI bot that excels at poker and more
Combining reinforcement learning with search (RL+Search) has been tremendously successful for perfect-information games. But prior RL+Search algorithms break down in imperfect-information games. We introduce ReBeL, an algorithm that for the first time enables sound RL+Search in imperfect-information games like poker.
ReBeL achieves superhuman performance in heads-up no-limit Texas Hold’em while using far less domain knowledge than any prior poker bot and extends to other imperfect-information games as well, such as Liar’s Dice, for which we’ve open-sourced our implementation.
ReBeL is a major step toward creating ever more general AI algorithms. Read More
Autonomous balloons take flight with artificial intelligence
An artificially intelligent controller can station a stratospheric balloon for weeks at a time without full knowledge of surrounding winds, opening up the prospect of unsupervised environmental monitoring.
The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system’s current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks — for example, when playing arcade and turn-based board games1. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.2 describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known. Read More
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