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) …
Big data refers to incredibly broad collections of structured and unstructured data that can not be managed using conventional methods. Big data research can make sense of data by uncovering trends and patterns. Machine learning can accelerate this process by decision-making algorithms. It can categorize incoming data, identify trends, and convert data into insights that are useful for business operations. Machine learning algorithms are useful for gathering, analyzing, and incorporating data for large organizations.
Machine learning can ascertain a lot about you -- including some of your most sensitive information. For instance, it can predict your sexual orientation, whether you're pregnant, whether you'll quit your job, and whether you're likely to die soon. Researchers can predict race based on Facebook likes, and officials in China use facial recognition to identify and track the Uighurs, a minority ethnic group. Now, do the machines actually "know" these things about you, or are they only making informed guesses? And, if they're making an inference about you, just the same as any human you know might do, is there really anything wrong with them being so astute?
The nasal test for Covid-19 requires a nurse to insert a 6-inch long swab deep into your nasal passages. Now, imagine that your nurse is a robot. A few months ago, a nasal swab robot was developed by Brain Navi, a Taiwanese startup. The company's intent was to minimize the spread of infection by reducing staff-patient contact. So, here we have a robot autonomously navigating the probe down into your throat, and carefully avoiding channels that lead up to the eyes.
It is no brainer that the e-commerce market has transformed the Indian market like never before. Thanks to factors like rising smartphone penetration, the launch of 4G network, and increasing consumer wealth, analytics-driven customer engagement, and digital payment, the e-commerce sector is on an upward trajectory. It is projected that this industry will surpass the US to become the second-largest E-commerce market in the world by 2034. According to the PWC survey, with Internet penetration expected to almost double to 60% by 2022, the nation is arguably the world's most promising Internet economy, with a rapidly increasing'netizen' population. Further owing to improving data affordability, consumption growth, and newer financial products, the e-commerce market is set to grow, be it across e-tail, travel, consumer services or online financial services.
An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade.The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012. Related: Latest photos from NASA's Mars Reconnaissance Orbiter"AI can't do the kind of skilled analysis a scientist can," Kiri Wagstaff, JPL computer scientist, said in the statement.
Although the COVID era continues to have an outsized impact on small businesses, frontline lending experts say SMBs have recently begun to catch a break when it comes to getting loans through FinTechs and other non-bank lenders. Ken So, founder and CEO of Flowcast, told PYMNTS that today's $3 trillion worldwide SMB "credit gap" is narrowing because the criteria to secure loans are changing, and increasingly, embracing alternative data sources."For "In contrast, the big banks have gone in the other direction."He said this credit gap has only emerged in the past month or so, but its timing couldn't be better given the dichotomy he sees between big companies that are doing well and small, young companies (as well as certain industries) that are often left struggling with cash flow."Retail,
Description When people talk about artificial intelligence, they usually talk about machine learning. Most people have not heard about Agent-Based modeling AI . Agent-Based modeling is much simpler than machine learning. You basically just let agents interact in an environment and watch for any emergent behavior. You practically do not have to have any math background and you are able to create amazing things.
Dozens of AI experts signed an article in Nature saying that unlike research in other scientific fields, top AI studies are often not transparent and reproducible, and they're frequently published without details such as full code, models, and methodology. Those findings are then picked up in mainstream media headlines worldwide. They point to a study also published in Nature this past January, where Google Health reported an AI system that could screen for breast cancer faster and better than radiologists. The study apparently lacked details like methodology and code. "On paper and in theory, the study is beautiful. But if we can't learn from it, then it has little to no scientific value," says lead author Benjamin Haibe-Kains, senior scientist at Princess Margaret Cancer Centre.
Machine learning has been making plenty of headlines in the past few years. Rightfully so, even though headlines tend to oversell. Advances in computing power, algorithmic complexity, data handling capacities, and models of learning mean that machine learning/AI is increasingly being used in many fields. In previous posts, I have written about machine learning/AI in general science and art, but also more specifically in (warning, link fest) historical research, genetic enhancement, mental health, aging research (including the development of'aging clocks'), video game ecology, Hollywood, astrobiology, epidemiology, stock markets, and the job market. Plenty of AI to go around, it seems.