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) …
Companies rely on real-world data to train artificial-intelligence models that can identify anomalies, make predictions and generate insights. To detect credit-card fraud, for example, researchers train AI models to look for specific patterns of known suspicious behavior, gleaned from troves of data. But unique, or rare, types of fraud are difficult to detect when there isn't enough data to support the algorithm's training. To get around that, companies are learning to fake it, building so-called synthetic data sets designed to augment training data. At American Express Co., machine-learning and data scientists have been experimenting with synthetic data for nearly two years in hopes of improving the company's AI-based fraud-detection models, said Dmitry Efimov, head of the company's Machine Learning Center of Excellence. The credit-card company uses an advanced form of AI to generate fake fraud patterns aimed at bolstering the real training data.
Robot Enabled Nike Warehouse fulfills? Nike at their Chiba (Japan) warehouse started using Automated Mobile Robots(?? It is a robot that can understand and move through its environment without being overseen directly by an operator. Rather than being restricted to fixed routes, an AMR can navigate dynamically using a map, allowing it to plan its own paths and travel quickly and efficiently. Let's get back to Nike Use case. Geek, a startup based in Beijing, is considered the Amazon Kiva ( now Amazon Robotics) of China.
Researchers have developed an artificial intelligence monitoring system to keep track of whether people are social distancing in public spaces, and alerting local authorities if they are not. Griffith University researchers developed the system to monitor the movement of crowds of people in real-time and then applied it to look for instances of people not maintaining social distancing. An AI system which can detected social distancing breaches in real time has been praised by its creators, but raised worries with privacy advocates.Credit:Paul Jeffers Lead researcher Professor Dian Tjondronegoro, an expert in the integration of AI and business innovation, said they moved to allay any privacy concerns around the system by ensuring that no data was stored by it at any time. "We knew we couldn't keep everything on the server because it would be very slow in processing and there are also privacy concerns," he said.
The leaders of the Senate Homeland Security and Government Affairs Committee are looking to get the federal workforce – particularly program managers and acquisition specialists – on board with artificial intelligence. A new bill, the Artificial Intelligence Training for the Acquisition Workforce Act, would set up a training program for federal workers to learn more about AI technology, from its scientific underpinnings to risks associated with its use. "Federal employees must be aware of the ethical implications, risks, and benefits associated with AI," Sen. Gary Peters (D-Mich.), the chair of the committee and a co-sponsor of the bill, said in a statement. "This important legislation will help protect our national security, help us remain competitive in the long run, and make sure AI technology is used properly." The bill tasks the Office of Management and Budget with establishing training to help the federal workforce understand the science behind AI, how AI can benefit government programs as well as the risks associated with AI, including privacy violations and inherent bias in algorithms that power AI programs.
The US military continues to experiment with artificial intelligence technologies. According to network sources, the US Northern Command has completed a series of tests as part of the Global Information Dominance Experiment (GIDE) by building an information system based on AI algorithms and cloud computing, which is capable of predicting the development of events for several days in advance. The source notes that the use of this technology will lead to major changes in strategy during military and government operations. GIDE's machine learning system is able to track the slightest changes in data, indicating the possibility of any problems, in real time. For example, if satellite images record the preparation of a submarine from a port of a hostile country, then the AI algorithm will note this fact, assuming that the boat is likely to leave the berth.
Artificial intelligence has made its place in all our lives, from correcting our bad grammar, personalizing our music on apps, to automating work in several industries. AI holds a massive potential to transform the future of work. But to understand this disruptive technology, the general public needs to have a working knowledge of the capabilities. To start slow and avoid the feeling of being overwhelming, here are 10 books that will help you grasp the concept. This book is beginner-friendly and gives a less technical overview of several AI topics.
Editor's note: This episode is part of our podcast series on emerging problems in data science and machine learning, hosted by Jeremie Harris. Apart from hosting the podcast, Jeremie helps run a data science mentorship startup called SharpestMinds. AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat -- and even wholesale replacement of -- human beings by artificial superintelligences that have their own sense of agency, and their own goals. Divya Siddarth disagrees with this framing. Instead, she argues, this perspective leads us to focus on applications of AI that are neither as profitable as they could be, nor safe enough to prevent us from potentially catastrophic consequences of dangerous AI systems in the long run.
Scania, the Swedish manufacturer of heavy lorries, trucks and buses, is testing L4 level self-driving trucks on the E4 motorway between Södertälje and Jönköping, in collaboration with San Diego-based company TuSimple. Participating truck provides actual commercial services to The Scania Transport Laboratory, loading materials required for production operations. The truck is controlled by the TuSimple's unmanned driving system, with a safety officer and test engineer onboard for monitoring. Scania has been testing self-driving trucks for mining transportation in Australia since 2017. TuSimple has also partnered with companies like Volkswagen and Navistar to test commercial vehicles.
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The Society for Imaging Informatics in Medicine (SIIM) 2021 annual meeting was a four-day virtual venue for the second year in a row due to the global pandemic. AI was probably the conference's main topic, as it has been for a few years now. But this year's meeting was hardly an AI meeting, as SIIM 2021 also had content related to AI implementation, enterprise imaging, workflow, career development, and much more. As a result, we saw less hype and more reality as our industry becomes more mature year after year. As the field has grown in size and complexity, the ability of systems from different vendors to communicate their data seamlessly becomes paramount for integration.