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) …
As the popularity of artificial intelligence waxes and wanes, it feels like we are at a peak. Hardly a day goes by without an organization announcing "a pivot toward AI" or an aspiration to "become AI-driven." Banks and fintechs are using facial recognition to support know-your-customer guidelines; marketing companies are deploying unsupervised learning to capture new consumer insights; and retailers are experimenting with AI-fueled sentiment analysis, natural language processing, and gamification. A close examination of the activities undertaken by these organizations reveals that AI is mainly being used for tactical rather than strategic purposes -- in fact, finding a cohesive long-term AI strategic vision is rare. Even in well-funded companies, AI capabilities are mostly siloed or unevenly distributed.
Some thought leaders, such as Elon Musk and the late Stephen Hawking, have repeatedly warned about the potential danger of artificial intelligence and expressed fear that AI may annihilate humans someday. Such fear has not been shared by the vast majority of computer scientists and data scientists, who consider the hyped drama of "man vs. machine" a distraction that is grounded in an intriguing but misguided fiction. Meanwhile, a true AI crisis is upon us now, and is having a huge impact on the business world. As much as enterprises are eager to embrace AI to innovate products, transform business, reduce costs, and improve competitive advantages, they find it very difficult to productionize AI and realize its full benefits, due to the time, budget, and skills required. As a result, the rate of AI adoption has significantly lagged the level of interest, particularly for small- and medium-sized enterprises, which are more resource-constrained.
Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future. A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification.
Researchers at the University of Notre Dame are developing a new technique using artificial intelligence (AI) that would improve CT screening to more quickly identify patients with the coronavirus. The new technique will reduce the burden on the radiologists tasked with screening each image. Testing challenges have led to an influx of patients hospitalized with COVID-19 requiring CT scans which have revealed visual signs of the disease, including ground glass opacities, a condition that consists of abnormal lesions, presenting as a haziness on images of the lungs. "Most patients with coronavirus show signs of COVID-related pneumonia on a chest CT but with the large number of suspected cases, radiologists are working overtime to screen them all," said Yiyu Shi, associate professor in the Department of Computer Science and Engineering at Notre Dame and the lead researcher on the project. "We have shown that we can use deep learning -- a field of AI -- to identify those signs, drastically speeding up the screening process and reducing the burden on radiologists."
Artificial intelligence and machine learning bring exponential changes to the way physical security processes input from video cameras and sensors. Data is the fuel that feeds AI, and cameras provide massive amounts of video for review. AI's deep learning algorithms automatically detect differences between human and vehicle movements as opposed to animals, blowing leaves or reflections of light. One result is a tremendous reduction in false alarms and potentially related fines. We view AI as an added layer of security, helping, not replacing, humans to do a better job of securing people and assets.
Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.
Atomwise, which is using artificial intelligence for small molecule drug discovery, received a cash infusion of $123 million in an oversubscribed Series B financing. San Francisco-based Atomwise touts being the creator of the first convolutional neural networks, or visual imagery, using AI technology for drug discovery, a market estimated to reach $40 billion in value by 2027, according to Fior Markets research. To date, Atomwise has provided AI technology to more than 750 academic research collaborations addressing over 600 disease targets, Abraham Heifets, co-founder and CEO told Crunchbase News. B Capital Group and Sanabil Investments led the investment that also included existing investors DCVC, BV, Tencent, Y Combinator, Dolby Ventures, AME Cloud Ventures, as well as two undisclosed insurance companies. This brings the total amount of capital raised, since Atomwise's inception in 2012, to almost $175 million.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.
The UK Court of Appeal has unanimously reached a decision against a face-recognition system used by South Wales Police. The judgment, which called the use of automated face recognition (AFR) "unlawful", could have ramifications for the widespread use of such technology across the UK. But there is disagreement about exactly what the consequences will be. Ed Bridges, who initially launched a case after police cameras digitally analysed his face in the street, had appealed, with the support of personal rights campaign group Liberty, against the use of face recognition by police. The police force claimed in court that the technology was similar to the use of closed-circuit television (CCTV) cameras in cities.
This tutorial shows you how to perform supervised classification (e.g., Classification and Regression Trees [CART]) in Earth Engine. The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.