Do I need to attend the live lectures? All lectures are recorded and shared with the class, so you can catch up whenever your schedule allows. Will there be homework or quizzes?

NOTE: The contents of this page reflect NVIDIA's results from MLPerf 0.5 in December 2018.

AI computing needs high levels of data processing and conventional AI systems function by transmitting data to a cloud server to be processed. Insights about the data and the decisions to be taken by the system are then transmitted back to connected devices. This approach works fine but for the rapidly increasing number of IoT devices, this is not ideal. There are issues both with the processing power, cloud connectivity and battery capacities in the mobile devices. While connected devices are not ideal to support large data crunching, sometimes they are designed for purposes that need insights in real-time, such as in self-driving cars or in anomaly detection systems.

Last week, all of us who live in the UK, and all who visit us, discovered that our faces were being scanned secretly by private companies and have been for some time. We don't know what these companies are doing with our faces or how long they've been doing it because they refused to share this with the Financial Times, which reported on Monday that facial recognition technology is being used in King's Cross and may be deployed in Canary Wharf, two areas that cover more than 160 acres of London. We are just as ignorant about what has been happening to our faces when they're scanned by the property developers, shopping centres, museums, conference centres and casinos that have also been secretly using facial recognition technology on us, according to the civil liberties group Big Brother Watch. But we can take a good guess. They may be matching us against police watchlists, maintaining their own watchlists or sharing their watchlists with the police, other companies and other governments.

Robots continue to be used in a variety of ways for useful and creative purposes. In this podcast, hosts Kathleen Walch and Ron Schmelzer interview Adam Cantor and Erika Angle from Robots in Service of the Environment (RSE). They share how underwater robots are helping control the invasive lion fish population, some of the technical challenges they encountered developing an underwater robot, and how they were able to transfer technology and knowledge from industry (particularly iRobot) to help make this robot a reality. This podcast was recorded live in person at the Amazon re:Mars event. For those of our listeners that were not able to attend this event was June 3-7, 2019.

Should Elon Musk's robot-surgeon start inserting electrodes into human brains to connect humans and computers via a high-bandwidth brain-machine? What exactly are the implications for medical insurance? Should a self-driving flying taxi crash and kill civilians? These are the questions our CEO, Lizé Lambrechts, is asking. The insurance industry is developing new ways to assess and underwrite risk as artificial intelligence (AI) and automation advance.

Their lifeless eyes peer from building facades, lampposts and streetlight poles. They never sleep, never even blink. And now, enabled by advances in computing power and artificial intelligence, surveillance cameras can do more than just watch. They can recognize, and they can remember. The district attorney for Pennsylvania's second-most-populous county has assembled a network of advanced surveillance cameras in and around Pittsburgh and has enlisted colleagues in four surrounding counties to extend its reach into their jurisdictions.

Organizations today are focused on identifying avenues to introduce AI into daily tasks and deliverables. While the common perception is that it creates a sense of insecurity among employees, contrary to this belief, employees are in fact more receptive and ready to deploy AI into their work, a study by Dale Carnegie reveals. During a roundtable discussion on "Preparing people for the Human Machine Partnerships of the future," conducted by Dale Carnegie in New Delhi, experts explored ways in which industry leaders can incorporate AI technology into their HR Tech, performance feedback systems, upskilling initiatives, etc. The panel discussion was led by Dale Carnegie representatives including Pallavi Jha, MD & Chairperson, Dale Carnegie of India; Mark Marone, Director - Research & Thought Leadership, Dale Carnegie and Associates; Juliette Dennett, Managing Director, Dale Carnegie Northern England; and Jordan Wang, Managing Director New South Wales, Dale Carnegie Australia. The survey that saw participation from 3,846 respondents across 13 countries, aimed to assess the readiness of the global workforce to accept AI in their work, feedback systems, skilling needs, etc., highlighted that 42 percent of the organizations globally are already using AI in one form or the other.

Experienced machine learning experts will know about the challenge's complexity and rightfully question the results' validity. At the same time, submissions like this Notebook illustrate how the Titanic competition's leaderboard can be forged effortlessly; A top-performing model can be created by collecting and including the publicly accessible list of survivors. Clearly, such overfit models only work for one very specific use case and are virtually useless for predicting outcomes in any other situation (not to mention the ethics of cheating). So, how can we make sure we have trained or are provided with a model that we can actually use in production? How can machine learning systems be deployed without likely ensuing disaster?