After months of testing, Citizen, the crime and neighborhood watch app, is releasing Protect, a subscription-based feature that lets users contact virtual agents for help if they feel they're in danger. According to Citizen, the feature can connect users with a Protect agent either through video, audio, or text available around the clock. The company said audio and text-only communication allows users to discreetly call for help "in difficult situations" where they might not be able to or are scared to be seen calling 911. Protect began beta testing earlier this year as the feature has been available to 100,000 users, Citizen said. The new feature comes as Citizen currently has more than 8 million users who have sent out more than billion alerts in major U.S. cities including New York, Los Angeles, Chicago, Atlanta, Houston and the San Francisco Bay Area.
Buckfast honey bees fly near a beehive in Illinois, U.S. Photographer: Daniel Acker/Bloomberg Beewise, a agtech startup, has created the first fully autonomous beehive called Beehome that comes complete with a beekeeping robot that acts as both medic and guardian to complement the natural intelligence of bees. Beehome utilizes artificial intelligence, (AI) machine learning and precision robotics to rescue and protect the hives bees. The modular commercial AI-powered robotic apiary also has 24/7 monitoring and smart technology that increases pollination capacity and honey production. Saar Safra, CEO of Beewise says that Beehome is poised to protect the global food supply chain, stem the impacts of climate change and increase sustainability. "If the bees are protected, entire ecosystems are too."
Sense8 was an eight-hour Netflix Original series created by Lana and Andy Wachowski, and J. Michael Straczynski. The science fiction series starred eight characters worldwide, connected by a bond that can be felt through every sense. Sense8 follows the inhabitants of Chicago, who are all connected by more than just two or three senses; they are experiencing everything that their counterparts are seeing, sensing, hearing, and feeling. The series is a love story between two characters, and as they become more connected to their sense counterparts, they begin to feel their partners' pain. They also carry the responsibility of protecting their loved ones that are constantly in danger and fighting for freedom from some sort of outside threat.
There's creative AI and then there's the hard-working AI – the artificial intelligence that is able to replace humans in routine work, saving up costs and allowing the employees to take charge of more complex tasks. That is the AI McDonald's is already testing in drive-thrus in the U.S. and looking to implement on a larger scale soon. A few years ago, McDonald's started to test the technology in the hope that it might be ready one day to take over at drive-thru locations. They had help from Apprente, the startup that gave them the building blocks of the technology, enabling them to build their own voice assistant. Now, the AI system is in place at 10 drive-thrus in Chicago.
New Delhi [India], July 21 (ANI / PNN): According to the World Economic Forum, 133 million new jobs will be created in the field of artificial intelligence (AI) by 2022. Job demand and growth is projected in three key areas: data analysts and data scientists, AImachine learning specialists (including AI software engineers), and big data specialists. At the peak of decision-intelligence companies, use software that embeds AI within organizations across sales, marketing, planning, and supply chains to transform decision-making. The company has grown rapidly in the last 12 months, expanding its teams in Jaipur (India) and the United Kingdom, as well as opening new offices in the United States and Pune (India). As a result, Peak is creating 150 new jobs worldwide this year, including roles in data science and AI software engineering.
Proteins are the minions of life, working alone or together to build, manage, fuel, protect, and eventually destroy cells. To function, these long chains of amino acids twist and fold and intertwine into complex shapes that can be slow, even impossible, to decipher. Scientists have dreamed of simply predicting a protein's shape from its amino acid sequence—an ability that would open a world of insights into the workings of life. “This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. Now, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues have made AI-based structure prediction more powerful and accessible. Their method, described online in Science this week, works on not just simple proteins, but also complexes of proteins, and its creators have made their computer code freely available. Since the method was posted online last month, the team has used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker's and Baek's program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition ( Science , 4 December 2020, p. ). Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind's program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. DeepMind has yet to publish its method and computer code for others to take advantage of. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind's CEO, tweeted that AlphaFold2's details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” DeepMind's 30-minute presentation at CASP was enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI's ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns. When given a new protein to model, RoseTTAFold proceeds along multiple “tracks.” One compares the protein's amino acid sequence with all similar sequences in protein databases. Another predicts pairwise interactions between amino acids within the protein, and a third compiles the putative 3D structure. The program bounces among the tracks to refine the model, using the output of each one to update the others. DeepMind's approach, although still under wraps, involves just two tracks, Baek and others believe. Gira Bhabha, a cell and structural biologist at New York University School of Medicine, says both methods work well. “Both the DeepMind and Baker lab advances are phenomenal and will change how we can use protein structure predictions to advance biology,” she says. A DeepMind spokesperson wrote in an email, “It's great to see examples such as this where the protein folding community is building on AlphaFold to work towards our shared goal of increasing our understanding of structural biology.” But AlphaFold2 solved the structures of only single proteins, whereas RoseTTAFold has also predicted complexes, such as the structure of the immune molecule interleukin-12 latched onto its receptor. Many biological functions depend on protein-protein interactions, says Torsten Schwede, a computational structural biologist at the University of Basel. “The ability to handle protein-protein complexes directly from sequence information makes it extremely attractive for many questions in biomedical research.” Baker concedes that, in general, AlphaFold2's structures are more accurate. But Savvides says the Baker lab's approach better captures “the essence and particularities of protein structure,” such as identifying strings of atoms sticking out of the sides of the protein—features key to interactions between proteins. Agard adds that Baker's and Baek's approach is faster and requires less computing power than DeepMind's, which relied on Google's massive servers. However, the DeepMind spokesperson wrote that its latest algorithm is more than 16 times as fast as the one it used at CASP in 2020. As a result, she wrote, “It's not clear to us that the system being described is an advance in speed.” Beginning on 1 June, Baker and Baek began to challenge their method by asking researchers to send in their most baffling protein sequences. Fifty-six head scratchers arrived in the first month, all of which have now predicted structures. Agard's group sent in an amino acid sequence with no known similar proteins. Within hours, his group got a protein model back “that probably saved us a year of work,” Agard says. Now, he and his team know where to mutate the protein to test ideas about how it functions. Because Baek's and Baker's group has released its computer code on the web, others can improve on it; the code has been downloaded 250 times since 1 July. “Many researchers will build their own structure prediction methods upon Baker's work,” says Jinbo Xu, a computational structural biologist at the Toyota Technological Institute at Chicago. Moult agrees: “When there's a breakthrough like this, 2 years later, everyone is doing it as well if not better than before.” : http://www.sciencemag.org/content/370/6521/1144
Parker is in her first season with the WNBA's Chicago Sky after 13 with the Los Angeles Sparks. She is one of the most decorated basketball players in history, dating back to her days starring at the University of Tennessee, where she earned National Player of the Year accolades and Final Four Most Valuable Player honors while leading the Lady Vols to back-to-back NCAA championships in 2007 and 2008. The first overall pick of 2008 WNBA Draft, Parker is a WNBA champion and a two-time MVP of the league, as well as a five-time all-star and six-time member of the all-WNBA First Team.
Businesses that ensure protection from financial losses and mitigate risk are as old as human civilization. The Code of Hammurabi, King of Babylon, written in 1750-1755 BC, specified the first provision of what we now know as marine insurance. In the wake of the Great Fire of London in 1666, Sir Christopher Wren included the provision of an "insurance office" in his new plan for the city of London. Today, the global insurance market is estimated to be a $ 5,550,310 million industry consisting of major commercial life insurance, non-life insurance, health insurance and health insurance companies. Against this backdrop, a new wave of "InsurTech" solution companies is exploring transformation of the insurance business through the introduction of big data, machine learning and AI capabilities.
When gravitational waves were first detected in 2015 by the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), they sent a ripple through the scientific community, as they confirmed another of Einstein's theories and marked the birth of gravitational wave astronomy. As LIGO and its international partners continue to upgrade their detectors' sensitivity to gravitational waves, they will be able to probe a larger volume of the universe--making the detection of gravitational wave sources a daily occurrence rather than weekly or monthly. Scientists hope this will launch a new era of precision astronomy, because combining information from multiple kinds of signals from space is a much more powerful way to study the universe. But realizing this goal will require a radical re-thinking of existing methods used to search for and find gravitational waves. Recently, Argonne National Laboratory computational scientist Eliu Huerta, along with collaborators from the University of Chicago, the University of Illinois at Urbana-Champaign, NVIDIA and IBM, developed a new artificial intelligence framework that allows for accelerated, scalable and reproducible detection of gravitational waves.
This article was originally published on our sister site, Freethink. As if drive-through ordering wasn't frustrating enough already, now we might have a Siri-like AI to contend with. McDonald's just rolled out a voice recognition system at 10 drive-throughs in Chicago, expanding from the solitary test store they launched a few years ago. But when will it come to your neighborhood Golden Arches? "There is a big leap between going from 10 restaurants in Chicago to 14,000 restaurants across the U.S. with an infinite number of promo permutations, menu permutations, dialect permutations, weather -- I mean, on and on and on and on," admitted McDonald's CEO Chris Kempczinski, reports Nation's Restaurant News.