8th Global Summit on Artificial Intelligence and Neural Networks

Today’s world smartphones are using facial recognition for access control while animated movies use it to bring realistic movement and expression to life. Surveillance cameras uses face recognition software to identify citizens. And we’ve used in apps for auto tagger that classifies us, our friends, and our family. It can be used in many different fields of applications, but not all facial recognition libraries are equal in accuracy and performance and most state-of-the-art systems are proprietary black boxes. OpenFace is a deep learning facial recognition model, it’s widely adopted because it offers high levels of accuracy similar to facial recognition models found in private state-of-the-art systems, OpenFace uses Torch, a scientific computing framework to do training offline, meaning it’s only done once by OpenFace and the user doesn’t have to get their hands dirty training hundreds of thousands of images themselves. Then the images are kept into a neural net for feature extract

Neural Networks and Deep Learning have grown widely over the last few years. By using neural network architecture, softwares of AI can go through and check millions of images to find the right tone to fit any image. This method could be used to colorize still frames of white and black movies, surveillance footage or any number of images. Because neural networks can derive data from any number of resources with access to millions of sounds and videos, it can make predictive judgments. Neural network architecture can now synthesize audio to fill in the blank spots of a silent video. Neural network architecture can perform translations of text without preprocessing the sequence so that the algorithm can learn word relationships. The network then processes these relationships through its image mapping technology to create a contextual solution to a translation issue. By getting access to a wide variety of images and learning the context of each one, neural network architecture can 

Intel’s first “neuromorphic” chip Loihi, designed to mimic the way a human brain learns and understands is now “fully functional”. In the fast developing world of AI chips Loihi is Intel’s effort, an area where Nvidia and start-ups like Graphcore are also attempting to stake a claim. In order to increase the efficiency of the system, the idea is that the processes involved in AI will be more complex and require more computing power and some of that can be moved to the chip. As of other AI systems it “learns” over time and gets smarter. But it doesn’t require masses of training data to learn a process. Initial applications are most likely to be in robots and self-driving cars. The chip itself is conceived as modeled on the human brain or at least how we know it to work with pulses and spikes based around synapses, with different learning functions were taken by different parts of the chip. The 49-qubit chip has improved thermal performance and reduced radio frequenc

Artificial Intelligence software from IBM has employed a new method, in which the atoms are considered as letters and molecules as words. Then this method is used to translate the language to predict outcomes of organic chemical reactions, which could speed the development of new drugs. In recent past years, scientists have been trying to teach computers how chemistry works so that computers can help to predict the results of organic chemical reactions. However, organic chemicals can be extraordinarily complex, and simulations of their behavior can prove time-consuming and inaccurate. IBM analysts took the sort of AI Program ordinarily used to translate languages and applied it towards organic chemistry. But instead of translating English into Chinese or German, they had the same artificial intelligence technology to look at hundreds of thousands or millions of chemical reactions and had it learn the basic structure of the 'language' of organic chemistry, and the