The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference. It was motivated by the observation that while many students are keen to learn about machine learning, and an increasing number of researchers want to apply machine learning methods to their research problems, only few machine learning courses are taught at universities. Machine learning summer schools present topics which are at the core of modern Machine Learning, from fundamentals to state-of-the-art practice. The speakers are leading experts in their field who talk with enthusiasm about their subjects. After having numerous discussions among the organizers, and gathering thoughts from our speakers and potential students, we concluded that holding the event this year in a virtual form is more beneficial than postponing it to next year, as there is no guarantee that holding it physically would be possible next summer. Therefore, the MLSS 2020 will be a virtual event between 28 June to 10 July 2020.

Ubuntu 20.04 LTS is the best Ubuntu yet. It's been over a month since it got released, and it has excellent reception among both desktop and server users. Many organisations are already starting using the latest Ubuntu. Others might be using previous versions of Ubuntu which are still supported under LTS or ESM - such as 19.10, 18.04 LTS, 16.04 LTS or even 14.04 LTS. If you use the previous version of Ubuntu you are probably wondering if you should migrate, when is the right time, and what factors should you take into account when planning a migration.

Number two is Machine Learning by Stanford.

Today, global innovation company Hitachi has announced its next-generation digital transformation solutions will run on Microsoft. The two companies have signed a strategic agreement to advance AI, Robotics, and IoT capabilities across logistics and manufacturing industries based in South Asia and Japan. The digital solutions would also be made available to the North American market. Each industry is unique in the way it adopts digital tools to transform its core operations. Logistics, manufacturing and supply industries are the most potent markets for digitalization.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Consider the animal in the following image. If you recognize it, a quick series of neuron activations in your brain will link its image to its name and other information you know about it (habitat, size, diet, lifespan, etc…). But if like me, you've never seen this animal before, your mind is now racing through your repertoire of animal species, comparing tails, ears, paws, noses, snouts, and everything else to determine which bucket this odd creature belongs to. Your biological neural network is reprocessing your past experience to deal with a novel situation. Our brains, honed through millions of years of evolution, are very efficient processing machines, sorting out the ton of information we receive through our sensory inputs, associating known items with their respective categories. That picture, by the way, is an Indian civet, an endangered species that has nothing to do with cats, dogs, and rodents.

A few months ago, NASA unveiled its next-generation space suit that will be worn by astronauts when they return to the moon in 2024 as part of the agency's plan to establish a permanent human presence on the lunar surface. The Extravehicular Mobility Unit--or xEMU--is NASA's first major upgrade to its space suit in nearly 40 years and is designed to make life easier for astronauts who will spend a lot of time kicking up moon dust. It will allow them to bend and stretch in ways they couldn't before, easily don and doff the suit, swap out components for a better fit, and go months without making a repair. Instead, they're hidden away in the xEMU's portable life-support system, the astro backpack that turns the space suit from a bulky piece of fabric into a personal spacecraft. It handles the space suit's power, communications, oxygen supply, and temperature regulation so that astronauts can focus on important tasks like building launch pads out of pee concrete.

Artificial intelligence and its subsets have been revolutionizing the business and marketing landscape for quite some time. Still, there's a new frontier that this advanced technology is yet to conquer completely – content marketing. Certain AI algorithms have become terrifyingly good at generating plausible stories, to the extent that Open AI initially decided not to release its GPT-2 publicly out of fear that it could be potentially misused. However, although these articles written by a machine are coherent and engaging thanks to massive amounts of data fed to these algorithms, lack of genuine critical thinking and creativity renders AI inferior in comparison with human writers. But, while this means that you can't rely on an AI tool to come up and generate entire blog posts, your content marketing can tremendously benefit from implementing this technology.

Three days ago, in a letter to members of the United States Congress, IBM announced that it was abandoning the development of general-purpose facial recognition technologies because of their potential for mass surveillance, human rights violations and racial discrimination. In his letter, IBM CEO Arvind Krishna called for a reconsideration of the sale of this kind of technology to law enforcement, a gesture with which the company, which after all was announcing the abandonment of a technology in which it is not a leader and that has little impact on its bottom line, managed to put pressure on the companies that do have contracts with those law enforcement agencies, notably Amazon and Microsoft. The next day, Timnit Gebru, one of the leaders of Google's artificial intelligence team, said in an interview with the New York Times that the use of facial recognition technologies by law enforcement or security forces should be banned for the moment, and that he did not know how the issue would evolve in the future. One day later, on Wednesday 10, Amazon announced a one-year moratorium on the police's use of its facial recognition technology, the controversial Rekognition, so as to continue improving it and, above all, to give the government time to reach a reasonable consensus and establish stricter regulations for its ethical use. The company will continue to facilitate the use of this technology by institutions that use it for other purposes, such as preventing human trafficking or reuniting missing children with their families, but will temporarily stop offering it to the police and law enforcement agencies, one of its main customers.

As schoolchildren and students of all ages will widely confirm after the Covid-19 (Coronavirus) pandemic with the imposition of home-schooling for many, it's harder to learn in a vacuum. It's not impossible, but it's generally agreed that we humans learn better in groups through mutual discovery, intercommunication on problem-solving and through the general process and pursuit of team-based challenges and goals. This, after all, is why we have schools. Could the same need for interconnected cross-fertilization also help computers to'learn' as they build their data-powered Artificial Intelligence (AI) knowledge bases and software-driven analytics engines? More examples of open AI are surfacing all the time.

This course is intended to be an initiation to learn #BigData and #MachineLearning & #AI with #Python programming for absolute beginners that have no background in programming. In this course, we will step by step, using the example of real data, we will go through the main processes related to the topic "Big data and machine learning". Since the material turned out to be voluminous, I divided the course into five parts. We will examine in detail the basic types, terms and algorithms of machine learning. We go through the basic concepts of machine learning that beginners need.