If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This may not be the best time to be thinking 15 years into the future, I know. For many associations, the rest of 2020 is stressful enough, and 2021 seems plenty forbidding too. But any association wise enough to have a strategic planning process knows that it has to look for potential headwinds. And a study released last week by the software company Citrix suggests that automation will have a substantial impact on leadership--calling to question what a leader might be good for, if AI can make decisions nearly as well as a human can. Citrix's report, Work 2035 [PDF], is based on the responses of 500 executives and 1,000 employees at large and mid-size companies in the United States and Europe, with a focus on artificial intelligence and productivity. In general, an always-on work mentality, combined with better analytics, have led people to wonder what role the C-suite ought to play.
Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.
Tijana Radivojevic (left) and Hector Garcia Martin working on mechanical and statistical modeling, data visualizations, and metabolic maps at the Agile BioFoundry last year. If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine – both products that are "grown" in the lab – then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically.
Oracle open-sources Tribuo to fill the gap for enterprise applications focused on machine learning in Java. Committed to deploying machine learning models to large-scale production systems, Oracle has released Tribuo under an Apache 2.0 license. What does Tribuo provide under machine learning? Tools for building and deploying classificationTools for clustering and regression models Unified interface for many popular third-party machine learning librariesA full suite of evaluations for each of the supported prediction tasksData loading pipelines, text processing pipelines, and feature level transformations for operating on dataIn addition to its implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. Apart from the features mentioned above, Tribuo Model knows when you've given it features it has never seen before, which is particularly useful when working with natural language processing.
Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an automated machine learning platform to empower business users with predictive analytics. The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them. Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed. Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate.
BEIJING -- Gaoxian Automation Technology Development, which operates commercial cleaning robot brand Gaussian Robotics, has raised 150 million yuan ($22 million) in its Series B financing round.Founded in 2013, Gaoxian Automation has developed a broad array of cleaning robots for business use and become the first in the industry to offer robotic cleaning solutions using simultaneous localization and mapping, or SLAM, technology.When they operate in an environment where they cannot rely on GPS, such as indoors, robots can use SLAM to build their own maps as they work. The technology allows them to know their position by aligning sensor data they collect with that already collected to create a map for navigation.The company's army of robots can carry out various cleaning and sanitizing tasks, including wiping, sweeping, scrubbing, floor washing and disinfection.It currently offers six series of products. The Ebot Scrubber 75 series are scrubbing robots good at removing grease both indoors and outdoors, while the Ebot Scrubber 50 series are fully automated scrubbing machines for indoor tasks and are capable of medical-grade disinfection. The Ebot Polistar 60 series are indoor polishing robots for stone maintenance, while the Ecobot Sweep Vacuum Mini is an autonomous sweeper for multi-story buildings.
Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
Loads of research came out this week! But FYI, we couldn't fit every story in this newsletter for space-saving reasons, so if you want complete coverage, follow our twitter, and as always, if you enjoy the read, please give it a and share with your enemies. And….yesterday, another update was made to the Super Duper NLP Repo and the Big Bad NLP Database: we added 10 datasets and 5 new notebooks. Highlights include the DialogRE dataset which may be the first human-annotated dialogue-based relation extraction dataset. Legend has it there's a bitcoin wallet worth $690 million that hackers have been attempting to crack for the past 2 years according to cybersecurity expert Alon Gal.
Before Covid-19 financial institutions saw a 10:1 ratio of bot-based malicious to legitimate login attempts, according to Aite Group's Fraud & AML practice. Malicious login attempts are setting new records every month. Between 2018 and 2019, there was an 84% increase in the number of breached data reports, reaching 15.1B accounts last year. Fraud operations funded by organized crime run much like legitimate businesses, complete with ongoing recruiting campaigns for AI, bot and machine learning expertise and office locations focused on developing breach strategies. As of June 2020, login credentials for online banking averaged about $35 on the dark web while payment card details averaged between $12 and $20 apiece, according to analysis again by Help Net Security.