Here are the largest device recalls in the past 20 years. This handout photo taken and released by Gwangju Bukbu Police Station on Sept. 13, 2016, shows a blown-up Samsung Galaxy Note7 smartphone in Gwangju, 270 kms south of Seoul. The owner claims he received burn injuries on a finger while attempting to extinguish his burning phone after he was jolted from his bed by the sound of an explosion early Sept. 13. SAN FRANCISCO -- Troubles with Samsung's Galaxy Note 7 phones put the South Korean company at the head a long line of companies that have faced major tech recalls. Some go back to the beginnings of the personal computer era, with many linked to overheating and fire danger from malfunctioning lithium ion batteries.

Today on In Case You Missed It: The European Space Agency and Russia are joining up to launch their ExoMars project next week, which will look for traces of extraterrestrial life on the red planet. Meanwhile, a newer kind of wind energy harvester is being installed in Paris, where leaves on a fake tree can capture energy in breezy conditions. The Red Bull video showing skydivers swinging in between two hot air balloons should get heart rates going, and the Disney robot is today's dose of squee, because even machines can look adorable to us. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

Elon Musk named his electric-car company after the engineering genius Nikola Tesla, but the sweeping nature of his vision to replace fossil fuels is reminiscent of Thomas Edison, Tesla's arch-rival. After creating the incandescent bulb, the home electric meter, and one of the first alkaline batteries, Edison spent much of his personal fortune building factories to produce them--all in the service of a grand plan to electrify society using his direct-current transmission technology. Eighty years before Musk was born, Edison was urging U.S. cities to set up networks of charging stations so those newfangled horseless carriages could run on electricity rather than gasoline. For Musk's fans and investors, the comparison should not be entirely comforting. In the course of a few short years, the Wizard of Menlo Park was unceremoniously forced out of the electricity game. After he stubbornly refused to embrace the transmission technology that became the foundation of the U.S. grid and focused increasingly on developing inventions such as the phonograph and the motion picture, his board of directors merged his Edison General Lighting with a rival to create today's General Electric--leaving the 46-year-old Edison with no management role.

Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate. I. INTRODUCTION X-ray free-electron lasers (XFELs) 1-3 are emerging as one of the most versatile tools in x-ray research, becoming widely used by the scientific community, as well as industry, in many fields including physics, chemistry, biology, and material science. Their brightness, coherence, tun-ability, and ability to generate pairs of few-fs multicolor pulses for pump-probe experiments 4-7 make them ideal sources to perform diffract-before-destroy imaging 8, resonant x-ray spectroscopy 9, and a range of time resolved measurements of picosecond to few-femtosecond dynamics in molecules and atoms 10-16 . A drawback to XFELs is their current poor stability. XFELs are driven by single-pass electron linear accelerators (LINAC) typically several hundred meters in length.

When Facebook, Amazon, IBM, Microsoft and Google team up and form a partnership for the development of a rapidly advancing technology, it's time to start paying attention. You've heard of it, surely. You may know it as Apple's Siri or IBM's Watson. You may know it as Tesla's autopilot. Maybe your mind goes straight to Westworld or Ex Machina.

In the beginning, there was Statistics, and for a time, it was good: it allowed Fisher to measure farm productivity. Then came Computer Science, and for a time, it was good: combined with Stats, it allowed the allies to save lives and fuel in WW2. Then came the IT barbarians, with their tool-centric religions, fads and next-coolest-framework cult. Analytics went downhill from there. Nowadays, we can even see BigData ads for IT tools in airplane mags!

Like many of the students around them, robots at Carnegie Mellon University are constantly learning -- learning how to think, how to move, and how to be more like humans. "It's sort of this wonderland of innovation," says 60 Minutes producer Nichole Marks in the video above. "Everywhere you go, every corner of the campus, there are robots -- robots in the hallways, robots picking things up, robots talking to you." Marks and correspondent Charlie Rose visited the Carnegie Mellon campus in Pittsburgh while reporting their two-part story on artificial intelligence, or A.I., for this week's episode of 60 Minutes. What they found in the old steel town was a glimpse into the future, says Rose.

It is hypothesized by some thinkers that benign looking AI objectives may result in powerful AI drives that may pose an existential risk to human society. We analyze this scenario and find the underlying assumptions to be unlikely. We examine the alternative scenario of what happens when universal goals that are not human-centric are used for designing AI agents. We follow a design approach that tries to exclude malevolent motivations from AI agents, however, we see that objectives that seem benevolent may pose significant risk. We consider the following meta-rules: preserve and pervade life and culture, maximize the number of free minds, maximize intelligence, maximize wisdom, maximize energy production, behave like human, seek pleasure, accelerate evolution, survive, maximize control, and maximize capital. We also discuss various solution approaches for benevolent behavior including selfless goals, hybrid designs, Darwinism, universal constraints, semi-autonomy, and generalization of robot laws. A "prime directive" for AI may help in formulating an encompassing constraint for avoiding malicious behavior. We hypothesize that social instincts for autonomous robots may be effective such as attachment learning. We mention multiple beneficial scenarios for an advanced semi-autonomous AGI agent in the near future including space exploration, automation of industries, state functions, and cities. We conclude that a beneficial AI agent with intelligence beyond human-level is possible and has many practical use cases.

Tom Fawcett is Principal Data Scientist at Silicon Valley Data Science. Co-author of the popular book Data Science for Business, Tom has over 20 years of experience applying machine learning and data mining in practical applications. He is a veteran of companies such as Verizon and HP Labs, and an editor of the Machine Learning Journal. A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn't mean that one causes the other. This is a lesson worth learning.

Imagine if computers could read and interpret documents. Humans could focus their efforts on understanding what analysis results mean to make better decisions. Our interest, at Dynamic Risk, is to improve the safety and reliability of energy pipeline networks by taking full advantage of the vast amounts of data locked in cumbersome formats, handwritten documents, drawings, photographs, and in paper archives. We want to fundamentally change how we ask questions and receive answers. Today, we ask questions based on the data we have available in structured databases.