Many think we'll see human-level artificial intelligence in the next 10 years. Industry continues to boast smarter tech like personalized assistants or self-driving cars. And in computer science, new and powerful tools embolden researchers to assert that we are nearing the goal in the quest for human-level artificial intelligence. Despite the hype, despite progress, we are far from machines that think like you and me. Last year Google unveiled Duplex -- a Pixel smartphone assistant which can call and make reservations for you.

Earthquake Alert! 6.7 temblor, epicenter 3.8 miles west of Ventura, California--impact will be in eleven minutes--evacuate, evacuate!" While you run to the hall closet to grab your earthquake kit, you shout out: "Alexa, where is my emergency evac location?" Walk north to Wilshire, then take a left on Warner," she responds. As you and your neighbors pour into the building stairwell, you hear audio from a phone: "Google Earth Q estimates substantial potential for structural damage in the West San Fernando Valley and Coastal West Los Angeles to pre-2006 code dwellings and buildings. Most of West LA will experience total loss of power for anywhere from six to twenty-four hours in duration."

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for instance, enable highly accurate facial recognition, see through the pixelation in photos, and even--as Facebook's Cambridge Analytica scandal showed--use public social media data to predict more sensitive traits like someone's political orientation. Those same machine-learning applications, however, also suffer from a strange sort of blind spot that humans don't--an inherent bug that can make an image classifier mistake a rifle for a helicopter, or make an autonomous vehicle blow through a stop sign. Those misclassifications, known as adversarial examples, have long been seen as a nagging weakness in machine-learning models. Just a few small tweaks to an image or a few additions of decoy data to a database can fool a system into coming to entirely wrong conclusions.

When Mount Vesuvius erupted in AD79 it destroyed the towns of Pompeii and Herculaneum, their inhabitants and their prized possessions – among them a fine library of scrolls that were carbonised by the searing heat of ash and gas. But scientists say there may still be hope that the fragile documents can once more be read thanks to an innovative approach involving high-energy x-rays and artificial intelligence. "Although you can see on every flake of papyrus that there is writing, to open it up would require that papyrus to be really limber and flexible – and it is not any more," said Prof Brent Seales, chair of computer science at the University of Kentucky, who is leading the research. The two unopened scrolls that will be probed belong to the Institut de France in Paris and are part of an astonishing collection of about 1,800 scrolls that was first discovered in 1752 during excavations of Herculaneum. Together they make up the only known intact library from antiquity, with the majority of the collection now preserved in a museum in Naples.

That's especially so in financial services where most banks and credit unions offer commodity-like products and say their brand stands for "service" or "value" or "convenience." If everyone else is saying the same thing, it doesn't matter if you're the best. The search for that elusive attribute to set it apart led Citizens Bank to roll out a new "brand platform," as the Rhode Island-based regional bank puts it. Others might call it a new brand promise. Either way it was a sharp change from the pedestrian message they had before.

As our ability to sequence genomes has skyrocketed, allowing us to churn out A's, C's, G's, and T's at breakneck speed, our capacity to decipher the sequences has not kept up. This issue was discussed at a recent meeting organized by Advances in Genome Biology and Technology. "We have well exceeded our ability as humans to deal with data," said Eric Topol, MD, founder and director of the Scripps Research Translational Institute, professor, molecular medicine, and executive vice president of Scripps Research. "We need help from machines." To have high-performance medicine, he asserted, "we need high-performance computing."

Trevor Paglen explores the unseen networks of power that monitor and control us, documenting secret US government bases, offshore prisons and surveillance drones. In the run up to his show at Milan's Fondazione Prada (until 24 February 2020), Paglen collaborated with the artificial intelligence researcher Kate Crawford to launch ImageNet Roulette, an online interactive project which revealed the often racist or misogynistic ways in which ImageNet--one of the largest online databases that is widely used to train machines how to read pictures--classifies images of people. At London's Barbican, Paglen is again examining ImageNet's classifications, starting from everyday objects like apples and moving towards more abstract concepts to arrive at the category of "anomaly". We spoke to him about surveillance, AI and how we can begin to imagine a different future. The Art Newspaper: In 2015, I joined you on a scuba-diving expedition off the coast of Florida to see the fibre-optic cables that carry internet communications between continents.

Think it's hard keeping up with your Twitter feed? Imagine keeping track of all of Twitter. "Every tweet is comprised of over 100 data points," says Matt Singer, a senior staff hardware engineer responsible for server architecture at Twitter. Data from every retweet, "unfollow", link-click and other actions feeds analytic and deep learning systems serving operational, advertising. How does an organization handle such hyper-scale demands?

Artificial Intelligence (AI) is the hot topic of the moment in technology, and the driving force behind most of the big technological breakthroughs of recent years. In fact, with all of the breathless hype we hear about it today, it's easy to forget that AI isn't anything all that new. Throughout the last century, it has moved out of the domain of science fiction and into the real world. The theory and the fundamental computer science which makes it possible has been around for decades. Since the dawn of computing in the early 20th century, scientists and engineers have understood that the eventual aim is to build machines capable of thinking and learning in the way that the human brain – the most sophisticated decision-making system in the known universe – does.

This post originally published on Medium. It is republished here with permission. I've been around computing since my older brother got a Commodore 64 for Christmas in 1983. I took my first "business machines" class in high school in 1991, attended my first computer science class in 1994 (learning Pascal), and moved to Silicon Valley in 1997 after Cisco converted my internship into a permanent position. I worked in Cisco's IT department for several years before moving to their engineering group, where I designed networking protocols. I went to grad school at MIT in 2004, where I met the founders of several companies in Y Combinator's first couple of batches and worked on Hubspot before it was Hubspot. After writing several books for O'Reilly and attending the first O'Reilly Web 2.0 and MIT Sloan Sports Analytics conferences, I started a "Web 2.0 for Sports" company called StatSheet.com in 2007, which, in 2010, pivoted into the first Natural Language Generation (NLG) company called Automated Insights. I recently stepped back at Automated Insights to become a Ph.D. student at UNC studying artificial intelligence.