If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
GUANGZHOU, China,--(BUSINESS WIRE)--XPeng Inc. ("XPeng" or the "Company", NYSE: XPEV), a leading Chinese smart electric vehicle ("Smart EV") company, today announced the launch of a long-distance navigation-assisted autonomous driving expedition from March 19 to 26, 2021, covering a total distance of 3,675 km across six provinces in China. The performance of XPeng's newly released autonomous driving assistance function - Navigation Guided Pilot (NGP) - will be fully tested in 3,145 km of highway driving, starting from Guangzhou, all the way north to Beijing. The Xpeng P7 Premium version, equipped with XPILOT 3.0 which supports the NGP function, will be driven by members of the media and third parties during the 7-day drive expedition, the longest driving challenge to date for an autonomous driving assistance function in mass-produced models in China. The total distance of 3,675 km consists of about 3,145 km of highways, where the key functionalities and reliability of the NGP, including automatic highway ramp entering and exiting, automatic switching of highways and optimization of lane choices, automatic lane changing, overtaking and speed limit adjustment, will be fully tested by the press and third parties. The frequency of human driver intervention, and the success rate for the functions listed above, are among the key indicators to be tested in these sophisticated driving scenarios on China's highways. Over the course of 7 days, the fleet of P7s will drive across 10 cities in China in six provinces, starting from Guangzhou, to Shantou, Quanzhou, Wenzhou, Hangzhou, Shanghai, Nanjing, Qingdao, Jinan, finishing in Beijing.
Companies are looking to grab any technology-driven advantage they can as they adapt to new ways of working, managing employees, and serving customers. They are making bigger moves toward the cloud, e-commerce, digital supply chains, artificial intelligence (AI) and machine learning (ML), data analytics, and other areas that can deliver efficiency and innovation. At the same time, enterprises are trying to manage risk -- and the same digital initiatives that create new opportunities can also lead to risks such as security breaches, regulatory compliance failures, and other setbacks. The result is an ongoing conflict between the need to innovate and the need to mitigate risk. "There is always going to be some amount of tension relating to managing risk and engaging in digital transformation work," says Ryan Smith, CIO at healthcare provider Intermountain Healthcare.
Corey Jaskolski is the founder and CEO of Synthetaic, the leading synthetic data company for impossible AI. It's a common misconception that most businesses are drowning in data and that AI is spoiled for choice when it comes to training data. The truth is that despite the big data boom, most businesses still lack the quantity of high-quality data they need, and it's holding back the development of the highest-value AI applications. Current AI research is yielding phenomenal results, but the AI beating the world's best players at Go or outperforming human lip readers are the exception, not the rule. AI keeps getting better by training on increasingly massive models, some of which have a billion tunable parameters.
A key component to make sure that we develop responsible AI is diversity. This is because an AI application reflects and even amplifies the biases of its developers. As I discussed in my previous post, a diverse team will see things from many different points of view and help to reflect many different perspectives in the data. What can we do besides making sure our teams are diverse? Without claiming to have the complete answer, I would like to share some thoughts.
Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.
In the last online technical talk, Adam Bry and Hayk Martiros from Skydio explained how their company tackles real-world issues when it comes to drone flying. Skydio is the leading US drone company and the world leader in autonomous flight. Our drones are used for everything from capturing amazing video, to inspecting bridges, to tracking progress on construction sites. At the core of our products is a vision-based autonomy system with seven years of development at Skydio, drawing on decades of academic research. This system pushes the state of the art in deep learning, geometric computer vision, motion planning, and control with a particular focus on real-world robustness.
As part of the workshop programme at NeurIPS2020, Climate Change AI (CCAI) held an all-day session on "Tackling climate change with machine learning". You can watch the talks from this side event in full in a recording provided by CCAI. In this workshop, the speakers, from both industry and academia, discuss how artificial intelligence and remote sensing can be used to monitor global carbon impact. They also consider trust and accountability issues relating to governments, companies, and international projects. You can find out more about this event, and the main workshop, here.
Considering various factors such as the research areas, research focus, courses offered, duration of the program, location of the university, honors, awards, and job prospects, we came up with the best universities to help you in your choosing process. This article is most suited for individuals who'd like to pursue a master's degree with a focus on machine learning and need some guidance on their decision making. Feel free to jump to the end if you are only looking for the university names. Note: The universities mentioned below are in no particular order.
This resource is continuously updated. If you know any other suitable and open datasets, please let us know by emailing us at firstname.lastname@example.org or by dropping a comment below. Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they are hosted, whether it's a publisher's site, a digital library, or an author's web page. It's a phenomenal dataset finder, and it contains over 25 million datasets. Kaggle: Kaggle provides a vast container of datasets, sufficient for the enthusiast to the expert.