“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
― Eliezer Yudkowsky
Well, we second that. Just when you are catching up with the extraordinary capabilities of AI, something else is getting in action, ready to leave you surprised. In the previous article, we came up with some interesting areas where AI is outpacing humans. And, here we are again, extending our list in the second part of the blog. Let’s know about some more interesting areas where AI beats humans.
AI players are better than humans
A computer program under the name of AlphaGo is the first one to defeat a professional Go player, a World champion. It beat the 3-times European Champion Fan Hui with a statistical significance of 5-0. It then went on to defeat the legendary player Lee Sedol who happens to own 18 world titles to his names. Although the rules are simple, the complexity of Go makes it more multifarious than chess. AlphaGo uses deep neural networks and tree search to master the game of Go.
Poker has been an exemplary game of imperfect information, and a time-honoured challenge problem in artificial intelligence. DeepStack, an algorithm for imperfect information settings combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. DeepStack has defeated professional poker players with quite a margin in heads-up no-limit Texas hold’em.
Detecting Diabetic Retinopathy
A specific type of neural network optimized for image classification called a deep convolutional neural network was trained to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. The algorithm was validated in January and February 2016 using 2 different datasets. The deep learning algorithm was recognized for having high sensitivity and specificity for detecting referable diabetic retinopathy. However, it requires further research before applying into the clinical setting.
Real-Time Adaptive Image Compression
This is a machine learning approach for image compression that outperforms all the existing codecs while running in real-time. The algorithm produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of generic images across all quality levels. This design is deployable and lightweight. It can code or decode Kodak dataset in around 10ms per image on GPU. You can download the full pdf here.
Better Data scientist than Humans?
The job of a data scientist is to extract and interpret meaning from the data by the means of statistics and machine learning algorithms. But it turns out, this job is taken up by AI now. AI has outsmarted Human Data Scientists at writing algorithms for text classification. The Neural Architecture Search Neural Network generated a new cell called the NASCell that outperforms all the previous human generated ones, so much that is already available in Tensorflow.
AI predicts Earthquake
Predicting earthquake has proved to be a wild goose chase so far. Researchers around the world have spent years to find something reliable to predict earthquake including foreshocks, electromagnetic disturbances, changes in groundwater chemistry. But nothing seemed to work. A team of researchers, headed by Chris Marone and Johnson, from several institutes has collaborated to seek the help of AI for that matter. They are feeding machine’s raw data—massive sets of measurements taken continuously before, during and after lab-simulated earthquake events. They then allow the algorithm to shift through the data to look for patterns that reliably signal when an artificial quake will happen. In addition to lab simulations, the team has also begun doing the same type of machine-learning analysis using raw seismic data from real temblors. The work is still in progress and if this method succeeds, experts would be able to predict earthquakes months or year ahead of its time.
Looking inside a machine’s brain
A Bristol-based startup, Graphcore has created a series of ‘AI brain scans’, using its development chip and software, to produce Petri dish-style images that reveal what happens as the machine learning processes run. Put in simple words, this is basically seeing what machines see as they learn new skills. Machine Learning systems go through two phases- construction and execution. During the construction phase, graphs showing the computations needed are created. While in the execution phase, the machine uses the computations highlighted in the graph to run through its training processes. In Graphcore’s images, the movement of these passes and the connections between them have been assigned different colors. Read the complete technology behind it here.
AI in Neuroanatomy
AI has surpassed humans in making detailed 3D reconstructions of brain microstructures. In a recent report, a Google team and its collaborators were able to solve the problem of recreating 3D neurites in microscopy images of the brain.
Google’s new algorithm (an RN-augmented network) is able to take an unstructured input – like an image and implicitly reason about the relations of objects contained within it. For instance, an RN network is given a set of objects in an image and is trained to figure out the relation between the objects- say, if the sphere in the image is bigger than the cube. All the relations are added to produce a final outcome for all the pairs of shape in the setting. The ability for deep neural networks to perform complicated relational reasoning with unstructured data has been documented in these two papers- A simple neural network module for relational reasoning and Visual Interaction Networks.
The technical advances in AI are evolving fast and so are the fields it has been deployed into. Human effort has been reduced drastically as the machines evolve. AI has outsmarted humans in a significant number of fields and it won’t be bizarre to think that in the near future, most of the human jobs would be taken over by the machines.