In October 2016, two leading P2P platforms in the U.S. -- Lending Club and Prosper -- announced a new increase in interest rates for lower-grade loans. The decision was made in order to sustain investor demand, as the model platforms are operating under challenges faced during the last months due to compliance issues with Lending Club and the general turbulence of the P2P lending industry. However, keeping the investor demand stable is not the only reason for recent changes -- as Lending Club announced, delinquencies are growing, especially when it comes to high-risk loans. October 2016 was not the first time P2P platforms changed their interest rates that year. Initially, the decision was caused by the Federal Reserve to raise interest rates in response to signs of a stable and strengthening economy in December 2015, following a period of low rates designed to promote quicker economic growth in the U.S. and worldwide after the financial crisis.

The topic of automation of jobs is a source of much debate in the business world. People are worried about how many jobs will be lost to the advancement of AI, however, a recent study made PwC also added that this new technology would create new professions in the process. Yet, if the United States' Treasury Secretary, Steve Mnuchin, is to be believed the US doesn't think that its a matter worth worry about at the moment and believes that it's "50 to 100 more years away." While talking to Axios' Mike Allen on Friday, the Treasury Secretary was asked to address to concerns of people like Mark Cuban and Elon Musk regarding the potential loss of jobs to automation. His response confirmed that it isn't something this current administration is very concerned about. "We had an Axios event the other day with Mark Cuban who was very focused on artificial intelligence and how that was going to affect the workforce," Allen divulged.

Once heavily invested in the AI and machine learning systems that helped run the NASA Space Shuttle, Splice Machine CEO Monte Zweben is trying to overhaul the structure that lies at its very core. Despite 2017 being described as the breakthrough year for machine learning and AI, the process by which computers learn complex skills and functions without human intervention is nothing new. One person who can attest to that is Monte Zweben, CEO of Splice Machine, who in a previous life was the deputy branch chief of NASA Ames Research Center's artificial intelligence (AI) hub. Spending seven years there during the 1980s and 1990s, Zweben and the rest of his team were using machine learning, not only to discover cosmic phenomena through radio telescopes, but also to maintain and plan the famous space shuttle missions. This, Zweben said in conversation with Siliconrepublic.com,

AI is helping cybersecurity but researchers are warning about over-promising. Cybersecurity pros shouldn't rely on artificial intelligence and machine learning just yet, according to a new report. The report from security firm Carbon Black, which surveyed 410 cybersecurity researchers and 74 percent said that AI-driven security solutions are flawed, citing "high false-positive rates", while 70 percent claimed attackers can bypass machine learning techniques. The respondents did not write off AI or machine learning as unhelpful but rather said that they just aren't there yet and cannot be solely relied on to make big decisions when it comes to security. AI and machine learning should be used "primarily to assist and augment human decision making," said the report.

Drones could someday have a sort of invisible license plate that allows local authorities to determine who the unmanned aerial system (UAS) belongs too. Pitched by Chinese drone manufacturer DJI, the concept for an electronic identification system for small drones is just one of many ideas as the Federal Aviation Administration looks into potential ways of identifying drone users. DJI suggests drones should use the radio equipment already on board most systems to transmit a unique registration number. That number would identify the drone owner to law enforcement in the event of a complaint or flight through a restricted area. Areas with restricted drone flight, such as airports, could use radio equipment to read that number and report the ID number to the authorities.

It prints out the president's tweets, sets the paper alight and then dumps the ashes into a tray. See how it destroyed Trump's recent post bemoaning the "failing" New York Times here:

The United States has repeatedly attacked AQAP, the Yemeni arm of the militant group, in strikes this month from aircraft and unmanned drones in what U.S officials says is a campaign to degrade the group's ability to coordinate militant attacks abroad.

Idaho has become the second U.S. state to pass legislation to permit unmanned, ground-based delivery robots to rove around on sidewalks across the state. Earlier this month, Virginia made robotics history as the first state to pass a law specifically addressing the use of autonomous terrestrial delivery robots. The new Idaho law, which was signed by the governor today, goes into effect July 1. The legislation was championed by state Republican lawmakers, Jason Monks in the House and Bert Brackett in the Senate. Monks worked with Starship Technologies, an Estonia-based robot delivery company, on the legislation, which sailed through both state houses to pass in less than a month.

Donald Trump's tweets seem to provide some catharsis for the man himself, allowing him to work out some anger at Hillary Clinton, leakers inside his own administration, and FAKE NEWS. But each one is also an opportunity for his opponents to work out their own frustrations, quoting and replying to his messages with their own sick burns. It's difficult, though, given how often Trump tweets, to think of witty replies to each of his wild statements. That's where new Twitter account @burnedyourtweet comes in. The account replies to each Trump tweet with a short video showing a robot printing the tweet out and grabbing it with a claw, before setting it on fire and dumping it in an ashtray.

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences. This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds. The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed.