"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Artificial Intelligence or AI has already reached citizen scale making its mark in everyday applications such as healthcare, e-commerce, automobiles, financial services, defense etcetera. In layman terms, it is yet another zone of computer science that concentrates on the creation of intelligent machines that do the work of humans and indeed act like them. Prameya DS holds the best artificial intelligence training in Hyderabad with a complete collection of advanced cognitive science, various level of mathematical concepts such as probability, calculus, algorithms, statistics, and multiple programming languages and coding. Since Artificial Intelligence is to perform the logical tasks without the presence of a human, a lucrative Artificial Intelligence Course in Hyderabad must be needed to learn the nitty-gritty details and Prameya DS produced it for you. Our program starts with the essential languages - Python and R followed by a detailed survey of Mathematical and Statistical concepts.
I get asked many times "How can I do a good Exploratory Data Analysis (EDA) so that I get the necessary information for feature engineering and building machine learning model?" In this and the next post, I hope to get the question answered. I will NOT claim my process is the best but I hope as more people come into the field, they can use my process as a basis for better EDA and build better models. There are two main benefits of doing EDA and these benefits will reap benefits through the model building process. I will discuss EDA in two posts, non-visual (mainly through simple calculations) and visual.
Machine learning researchers have produced a system that can recreate lifelike motion from just a single frame of a person's face, opening up the possibility of animating not just photos but also paintings. It's not perfect, but when it works, it is -- like much AI work these days -- eerie and fascinating. The model is documented in a paper published by Samsung AI Center, which you can read here on Arxiv. It's a new method of applying facial landmarks on a source face -- any talking head will do -- to the facial data of a target face, making the target face do what the source face does. This in itself isn't new -- it's part of the whole synthetic imagery issue confronting the AI world right now (we had an interesting discussion about this recently at our Robotics AI event in Berkeley).
Aidan Wen is well on his way toward a career in artificial intelligence. The high school junior already has two semesters of machine-learning courses under his belt. Last summer he competed for a $12,000 prize sponsored by the Radiological Society of North America for the best ML model for spotting signs of pneumonia in lung X-rays. This year, he has entered another competition seeking a system for early detection of earthquakes using audio files. Next, he wants to try his hand at a project using natural language processing.
With the help of latest technology, this Spanish startup is working towards making logistics smarter. Scroll through their website and your attention is instantly drawn towards these lines: "You're in good company. Thanks to SmartMonkey, big corporations improve their logistic operations up to 30%. Below these lines are a reiteration of "up to 30% efficiency" and a list of companies that have benefitted from this Spanish startup. Formed in 2015 to make logistics smarter by using Machine Learning and Artificial Intelligence, the startup, in its own words, is "thriving to help companies optimize their distribution routes while learning from their clients' behaviors". "Our clever logistics products improve the companies' distribution operations, reduce the costs besides the operational risks by capturing the drivers' knowledge and transforming it into a new logistics data asset that helps the system learn and operate autonomously," explains SmartMonkey CEO Xavier Ruiz. The company's list of "satisfied" clients includes AGBAR and Heineken. But are clients the only source of revenue for them? "We have two main sources of funding: business angels and venture capital.
There's still much more that we haven't covered yet, such as how to actually train a CNN. Part 2 of this CNN series will do a deep-dive on training a CNN, including deriving gradients and implementing backprop. Subscribe to my newsletter if you want to get an email when Part 2 comes out (soon)! If you're eager to see a trained CNN in action: this example Keras CNN trained on MNIST achieves 99.25% accuracy.
Researchers from Samsung's AI Centre located in Moscow have created a new system that can transform still facial images into video sequences of the human face making speech expressions. According to the study, the system creates realistic virtual talking heads through applying the facial landmarks of a target face onto a source face -- for example, a still photo -- to allow the target face to control how the source face moves. "Such ability has practical applications for telepresence, including videoconferencing and multi-player games, as well as [the] special effects industry," Samsung said. While the existence of "deepfake" technology isn't something new, Samsung's new system does not use 3D modelling and only requires one photograph to create a face model. If the system is able to use 32 images to create a model, the system will be able to "achieve [a] perfect realism and personalisation score," Samsung said.
In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.
Deep learning is increasingly capable of assessing the emotion of human faces, looking across an image to estimate how happy or sad the people in it appear to be. What if this could be applied to television news, estimating the average emotion of all of the human faces seen on the news over the course of a week? While AI-based facial sentiment assessment is still very much an active area of research, an experiment using Google's cloud AI to analyze a week's worth of television news coverage from the Internet Archive's Television News Archive demonstrates that even within the limitations of today's tools, there is a lot of visual sentiment in television news. To better understand the facial emotion of television, CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news, were analyzed using Google's Vision AI image understanding API with all of its features enabled, including facial detection. Facial detection is very different from facial recognition.