Deep learning is the current darling of AI. Used by behemoths such as Microsoft, Google and Amazon, it leverages artificial neural networks that "learn" through exposure to immense amounts of data. By immense we mean internet-scale amounts -- or billions of documents at a minimum. If your project draws upon publicly available data, deep learning can be a valuable tool. The same is true if budget isn't an issue.
AbbVie is a research-based biopharmaceutical company that serves more than 30 million patients in 175 countries. With its global scale, AbbVie partnered with Intel to optimize processes for its more than 47,000 employees. This whitepaper highlights two use cases that are important to AbbVie's research. The first is Abbelfish Machine Translation, AbbVie's language translation service based on the Transformer NLP model, that leverages second-generation Intel Xeon Scalable processors and the Intel Optimization for TensorFlow with Intel oneAPI Deep Neural Network Library (oneDNN). AbbVie was able to achieve a 1.9x improvement in throughput for Abbelfish language translation using Intel Optimization for TensorFlow 1.15 with oneAPI Deep Neural Network Library when compared to TensorFlow 1.15 without oneDNN.1
Uber is spinning off Postmates' autonomous delivery division into a separate startup called Serve Robotics. The company inherited the unit when it acquired Postmates last year for $2.65 billion. According to Bloomberg, Uber will invest approximately $50 million in a Series A financing round that will make the company a minority stakeholder in Serve Robotics. The startup will operate independently of its former parent. However, it will maintain a close relationship with the company through a partnership that will see its sidewalk robots deliver groceries and other essentials to Uber customers.
Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable possibilities to explore using Image Classification. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification tasks. Image Classification tasks follow a standard flow – where you pass an image to a deep learning model and it outcomes the class or the label of the object present. While learning Computer Vision, most often a project that would be equivalent to your first hello world project, will most likely be an image classifier. You attempt to solve something like the digit recognition on MNIST Digits dataset or maybe the Cats and Dog Classification problem.
As we enter the new year, several promising technologies are poised to lead the way by improving how businesses and consumers use and experience the digital world. Here are some of the most important technologies and the practical solutions they will provide in the year ahead. The fifth generation of the mobile internet is going to bring the kind of speed most people associate with Wi-Fi to uploading and downloading data from remote locations. This will lead to sharp improvements in the way applications can be written, deployed and interacted with by mobile users. This also includes the development of data-intensive applications and the Internet of Things (IOT) -- physical objects with sensors that connect to and share data with the internet, autonomous vehicles and similar projects.
Reliving the past might not be possible, but bringing the past to life has been converted to reality by the Deep Nostalgia feature of MyHeritage's genealogy service website. This feature uses AI to let you upload photographs comprising either one or multiple people. The pictures are then animated and converted into a short video clip. The people in the pictures move their heads, blink their eyes and change their expressions minimally, giving the perfect illusion of reality. It looks as though the video was captured while the people were prepping for the portrait.
The importance of considering distributive justice in climate policy motivates research in AI-based decision support to search for balanced alternatives across multiple sectors, regions, and generations and counteract existing asymmetries in policy design. This PhD position is one of the four PhD positions in the Hippo Lab (Hyper-heuristics for interpretable public policy analysis), which is part of the TU Delft Artificial Intelligence initiative to channel expertise in AI foundations to tackle societal and scientific challenges. With its excellent education and research at the intersection of technology, society and policy, the Faculty of TPM contributes to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber) security. Stay updated on last news about Artificial Intelligence. Check your inbox or spam folder to confirm your subscription.
As per report of, "Recent results from a large survey of machine learning researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024) all the way to working as a surgeon (by 2053). Researchers also believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years." Nearly every aspect of our lives is being affected by artificial intelligence machines in order to boost profitability and enhance our human capabilities. After playing a significant role in defining the area devoted to the creation of intelligent machines, John McCarthy, an American computer scientist pioneer and inventor, was called the "Father of Artificial Intelligence." In his 1955 proposal for the 1956 Dartmouth Conference, the first artificial intelligence conference, the cognitive scientist coined the term.
Machine learning is a type of artificial intelligence, but it's not the style and kind of A.I. While artificial intelligence is a measure of a computer's intellectual ability, machine learning is a type of artificial intelligence used to build intellectual ability in computers. Today, IBM offers a service called IBM Watson Machine Learning that allows third parties to use their technology to build, train, and test predictive software like the kind used by the Watson supercomputer, which needed the ability to independently'understand' and'respond' to human writing and speech. Watson the supercomputer is artificial intelligence, while Watson's ability to'understand' language and respond using it is machine learning, much the same kind a digital assistant like Alexa uses to be able to talk to you. Artificial intelligence as we most often see it in the movies is much more advanced than IBM's Watson, but machine learning will be an essential component of higher-level A.I., like proper robots and androids, just as it's an important component of Watson.
Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.