A courageous boy from New Jersey received a letter of support from NASA after he applied for the role of planetary protection officer. Jack Davis, 9, applied for NASA's new position that was listed this week in hopes of finding help to protect the Earth against aliens. In official terms, the position involves the assistance with the'avoidance of organic-constituent and biological contamination in human and robotic space exploration'. Jack Davis, 9, applied for NASA's planetary protection officer new position that was listed this week in hopes of finding help to protect the Earth against aliens It adds that'space flight missions may intentionally or unintentionally carry Earth organisms and organic constituents to the planets or other solar system bodies'. Jack was one of the first applicants to apply, writing an adorable letter explaining why he was qualified for the job. 'My name is Jack Davis and I would like to apply for the planetary protection officer job,' he said in the letter.

Japanese technology company SoftBank Group Corp. has reported a 98 percent drop in its April-June profit on losses stemming from investments in the Chinese e-commerce company Alibaba. SoftBank said Monday its quarterly net profit was 5.5 billion yen ($50 million), down from 254 billion yen the previous year. Quarterly sales added 3 percent to 2.19 trillion yen ($20 billion). The Tokyo-based company's operating profit, which highlights core operations, logged a 50 percent increase year-on-year as its U.S. mobile carrier Sprint boosted profitability. Softbank, which sells the Pepper robot, did not give an annual forecast, which is not unusual for the company.

Researchers developed a "cognitive-computational model of human norms in a representation that can be coded into machines," according to DARPA's website. Further, a smart algorithm has been developed that "allows machines to learn norms in unfamiliar situations drawing on human data," the weapons developing agency said. If smart robots are ever to become "trustworthy collaborators with human partners," they will need to obey basic intuitive norms about human interaction. According to the Pentagon's account, they've completed a significant -- if mostly confidential -- breakthrough in philosophy: training robots to recognize value judgments like good and bad, right and wrong. These human faculties are critical for until-now strictly human tasks like planning, setting goals and organizing, and will be incorporated, at least on a basic level, into the robots of the future.

University of Washington researchers at the UW Graphics and Image Laboratory have developed new algorithms that turn audio clips into a realistic, lip-synced video, starting with an existing video of that person speaking on a different topic. As detailed in a paper to be presented Aug. 2 at SIGGRAPH 2017, the team successfully generated a highly realistic video of former president Barack Obama talking about terrorism, fatherhood, job creation and other topics, using audio clips of those speeches and existing weekly video addresses in which he originally spoke on a different topic decades ago. Realistic audio-to-video conversion has practical applications like improving video conferencing for meetings (streaming audio over the internet takes up far less bandwidth than video, reducing video glitches), or holding a conversation with a historical figure in virtual reality, said Ira Kemelmacher-Shlizerman, an assistant professor at the UW's Paul G. Allen School of Computer Science & Engineering. This beats previous audio-to-video conversion processes, which have involved filming multiple people in a studio saying the same sentences over and over to try to capture how a particular sound correlates to different mouth shapes, which is expensive, tedious and time-consuming. The new machine learning tool may also help overcome the "uncanny valley" problem, which has dogged efforts to create realistic video from audio.

Nervous about the thought of your connected car falling victim to hacks, especially when self-driving cars hit the streets in earnest? So is the British government -- it just issued tougher guidelines for the security of networked vehicles. It wants security to be part of the design process across every partner involved, even at the board of directors' level, and for companies to keep cars updated throughout their lifespans. UK officials also call for a "defence-in-depth" strategy that minimizes vulnerabilities (such as by walling off systems to limit the damage of a hack), and very limited use of personal data that gives you control over what the car transmits. The government has also reiterated its hopes to build a "new framework" for self-driving car insurance, making it clear who pays when an autonomous vehicle crashes.

It's just something that I think, anything that represents--that is a risk to the public deserves at least insight from the government because one of the mandates of the government is the public well-being. And that insight is different from oversight, so at least the government should have insight to understand what's going on, and then decide what rules are appropriate to ensure public safety.That is what I'm advocating for. I'm not advocating for that we stop the development of AI, or any of the sort of straw man, hyperbole things that have been written. I do think there are great benefits to AI. We just need to make sure that they're indeed benefits, and we don't do something really dumb.

What is supervised machine learning and how does it relate to unsupervised machine learning? In this post you will discover supervised learning, unsupervised learning and semis-supervised learning. Supervised and Unsupervised Machine Learning Algorithms Photo by US Department of Education, some rights reserved. The majority of practical machine learning uses supervised learning. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

The number of jobs that machine learning could render redundant over the coming decades is a growing cause for concern. According to research by PwC, 38% of US jobs will be automated by 2030, while other parts of the world fare little better. In Germany, it is 35%, and in the UK, 30%. However, it may be inevitable that jobs will be lost, but as with all periods of great technological advance, new jobs will also be created. Many of these will in fact be focused on developing and supervising machine learning algorithms, helping businesses to integrate and implement the technology and bring in efficiencies hitherto unimaginable.

The government said it was concerned that smart vehicles, which allow drivers to do things such as access maps and travel information, could be targeted by hackers to access personal data, steal cars that use keyless entry systems, or take control of technology for malicious reasons.

Artificial intelligence is all over the news, with tech titans arguing over whether it will be a force for good or bad. An equally important question is whether AI will stratify society even more, and create a world of haves and have nots. AI is already impacting multiple industries and will take over many blue collar and white collar jobs in the years to come. The speed and severity with which this happens are what creates the biggest challenges for the US and countries around the world. Add to this the geopolitical implications, recently outlined in an important op ed by Kai Fu Lee, and even weak AI can be seen as a scary thing.