Until pretty recently, computers were hopeless at producing sentences that actually made sense. But the field of natural-language processing (NLP) has taken huge strides, and machines can now generate convincing passages with the push of a button. These advances have been driven by deep-learning techniques, which pick out statistical patterns in word usage and argument structure from vast troves of text. But a new paper from the Allen Institute of Artificial Intelligence calls attention to something still missing: machines don't really understand what they're writing (or reading). This is a fundamental challenge in the grand pursuit of generalizable AI--but beyond academia, it's relevant for consumers, too.
What is artificial intelligence and why can it be dangerous? As learning algorithms can process data sets with precision and speed beyond human capacity, artificial intelligence (AI) applications have become increasingly common in finance, healthcare, education, the legal system and beyond. However, reliance on AI also carries risks, especially where decisions are made without human oversight. Machine learning relies on pattern-recognition within datasets. Problems arise when the available data reflects societal bias.
For video game fans, the concept of artificial intelligence (AI) is just as familiar as extra lives, respawns, and end bosses. Gamers have spent decades going up against computer-controlled opponents, whether a Pong paddle trying to prevent them from scoring a point or Bowser trying to stop Mario from rescuing Princess Peach. But recent developments in AI are pushing the gaming field even further, as researchers develop algorithms that can help fans make exciting new titles on their own. The history of AI and that of gaming are inexorably intertwined. Early AI researchers saw games like chess as markers of intelligence, and thus perfect testing grounds for their work.
It can also reflect human flaws and inconsistencies, including 180 known types of bias. Biased AI is everywhere, and like humans, it can discriminate against gender, race, age, disability and ideology. AI bias has enormous potential to negatively affect women, minorities, the disabled, the elderly and other groups. Computer vision has more issues with false-positive facial identification for women and people of color, according to research by MIT and Stanford University. A recent ACLU experiment discovered that nearly 17 percent of professional athlete photos were falsely matched to mugshots in an arrest database.
AI is the method by which self-driving cars perceive joggers, cyclists, traffic lights, road signs, trees, shrubs, and more, and it informs the way in which they choose to behave when encountered with those signals. The vehicles in Waymo's fleet aren't an exception to the rule -- they tap AI to make real-time driving decisions, in part by matching obstacles spotted by their onboard sensors to the billions of objects in the Alphabet company's database. Large data sets are invaluable in the autonomous driving domain because they enable the underpinning AI to self-improve. But it's been historically tough for engineers to surface samples within those sets without investing time and manual effort. That's why Waymo developed what it calls Content Search, which draws on tech similar to that which powers Google Photos and Google Image Search to let data scientists quickly locate almost any object in Waymo's driving history and logs.
With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere. The new virus emerged in Wuhan, China, in December, triggering a global health emergency. It remains uncertain how deadly or contagious the virus is, and how widely it might have already spread. Infections and deaths continue to rise. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities there Friday.
Plenty of people have a pet project that they are drawn to or consider themselves particularly good at. As the leader of the data science department at Trupanion in Seattle, David Jaw's projects are actually around pets. Jaw, GeekWire's latest Geek of the Week, uses artificial intelligence and machine learning to help automate medical insurance claims for pets, streamlining the process and removing the worry about what's covered and what's not. Born and raised in a suburb near Toronto, Jaw's family moved to Albuquerque, N.M., when he was 13 years old. He stayed there through college, where he studied mechanical engineering, pursuing a childhood dream of designing airplanes and spaceships.
A new drug created with artificial intelligence (AI) to treat patients with obsessive-compulsive disorder (OCD) will be entering human clinical trials this March – a first for AI. It has cut the drug development time from four and a half years to just 12 months, accelerating the time it typically takes to develop drugs for clinical trials. Exscientia, an Oxford-based AI start-up, collaborated with the Japanese pharmaceutical firm Sumitomo Dainippon Pharma to develop the OCD drug. It has been difficult to invent new drugs with AI that are safe and effective for humans to use. But, it has been successful with machine learning algorithms to look through data and identify which patients can benefit the most from existing medicines.
The lack of deep learning skills is hampering the performance of British businesses, according to new research from operational AI firm Peltarion. Its survey of firms across the UK and Nordic regions found 83 percent of AI decision-makers believe the deep learning skills shortage is affecting their business's ability to compete in the market. Almost half (49 percent) said the shortage is delaying projects, while 44 percent see the shortage as posing a major barrier to further investment in deep learning. The talent shortage is a cause for serious concern among businesses, who see deep learning (a sub-field of artificial intelligence) as an avenue to optimising processes and creating more intelligent data-driven products. As it stands, 71 percent of businesses are actively recruiting in an effort to remedy the skills gap.
Advancements in robotics are continually taking place in the fields of space exploration, health care, public safety, entertainment, defense, and more. These machines--some fully autonomous, some requiring human input--extend our grasp, enhance our capabilities, and travel as our surrogates to places too dangerous or difficult for us to go. Gathered here are recent images of robotic technology, including a machine built to draw portraits, battle robots, a dance performance, an autonomous mobile vending machine, an art installation, an agri-bot, a robotic priest, a Mars rover, a grocery-store bot, and much more.