How Artificial Intelligence Is Outpacing Humans
How Artificial Intelligence Is Outpacing Humans
Is artificial intelligence out-performing humans? Without a doubt, the answer is yes. This article sums up where and how.
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“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” ― Eliezer Yudkowsky
I'd second the statement above. Artificial intelligence has been pushing the boundaries of human imagination. Machines today are capable of doing a lot of things that we could not imagine them doing 20 years back. Artificial intelligence has changed the way we look at learning and inventing. From drug discovery to sports analysis to protecting the oceans, AI has marked its presence everywhere. But is artificial intelligence out-performing humans? Without a doubt, the answer is yes. In this article, I will try to sum up where and how.
AI Impersonating Celebrities
A Canadian Artificial Intelligence Company, LyreBird, uses realistic voice audio by listening to audio for a minute. By analyzing the voice of Trump, Hilary Clinton, Barack Obama, and others, the system was able to reproduce their voices with incredible accuracy. It’s Trump impression that beat Alec Baldwin's!
LyreBird takes the sample of any voice to analyze its waveforms and cues. It then picks up the deviations from the platonic ideal of an English voice and instructs its voice synthesis component to make the exact same adjustments to its audio waveforms as those sound curves are generated. This process not only delivers an impeccable accent/general sound but also takes care of minor quirks and jerks.
In the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), Google came in first place with a convolutional neural network approach that resulted in just a 6.6 percent error rate — almost half the previous year’s rate of 11.7 percent. The program correctly identified 74.9 percent of the sketches it analyzed, while the humans participating in the study only correctly identified objects in sketches 73.1 percent of the time.
This started an arms race between different research groups of the world with deeper neural network architectures coming up and changing the state-of-the-art design. Residual networks, dense networks, and (very recently) DIRAC networks have kept coming up with deeper architectures to increase machines’ accuracy of visual recognition.
These convolutional neural networks have enabled the machines to even write suitable captions to images. There are still some situations in which machines stagger but the continuous advancement is making it look promising.
Google’s AI for Detecting Cancer
Source: Deccan Chronicle
Alphabet, Google’s parent company, has been working in the direction of diversifying its research work so as to have a broader impact on human life. In a white paper, Detecting Cancer Metastases on Gigapixel Pathology Images, Google disclosed its research on the diagnosis of breast cancer using its deep learning AI.
For testing the system, Google’s experts used a data set of images courtesy of the Radboud University Medical Center. After customizing and training the model to examine the image at different magnifications, it exceeded the performance of human doctors. Google’s algorithm produced improved prediction heat maps and its localization score reached 89%, higher than humans’ 73%. Also, the time it took to complete the diagnosis was far less than that of humans.
AI Is an Expert at Lip Reading
Lip reading has been considered a human art. However, the recent state-of-the-art deep learning technologies have outperformed even the best of the lip readers. One such model is LipNet.
LipNet is the first end-to-end sentence-level deep lipreading model that simultaneously learns spatiotemporal visual features and a sequence model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level, overlapped speaker split tasks, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy.
Your Very Own Language Translator
Following in the success of neural machine translation systems, researchers at Google thought of “zero-shot translation.” The idea was if the machine was taught to translate English to Korean and vice versa and also English to Japanese and vice versa… could it translate Korean to Japanese, without resorting to English as a bridge between them? The answer is yes! The “translations” are quite reasonable for the two languages with no explicit linking whatsoever.
There’s one more side to this achievement. If the computer is able to establish connections between concepts and words with no previous connection, has the machine formed a concept of shared meaning for those words? Simply put, is it possible that the computer has developed its own internal language? Based on the relation of various sentences with each other in the memory space of the neural network, Google’s language experts and AI researchers believe so.
A visualization of the translation system’s memory.
At Arxiv, you can read a paper describing research work on efficient multi-language translations. This paper also scratches the surface of the above-mentioned “interlingua” issue, although a lot of research is required to reach to any conclusion for the mystery. Until then, we can live with the idea of such a possibility.
Speech Transcription AI Performs Better Than Human Professionals
A paper from Microsoft claims to have achieved a better transcription level than that of humans. To test how their algorithm stacked up against humans, Microsoft hired a third-party service to tackle a piece of audio for which they had a confirmed 100% accurate transcription. The professionals worked in two stages: one person types up the audio, and then a second person listens to the audio and corrects any errors in the transcript. Based on the correct transcript for the standardized tests, the professionals had 5.9% and 11.3% error rates. After getting trained for 2,000 hours of human speech, Microsoft’s system tackled the audio while and managed to score 5.9% and 11.1% error rates which are minute but significant.
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 three-time 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.
Source: Tech Times
Poker has been an exemplary game of imperfect information, and a time-honored 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
Source: News Medical
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 two 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, two 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 DF here.
A 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 as it turns out, this job is taken 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.
Machines Playing Doctors
A team of researchers at Stanford University led by Andrew Ng, has shown that a machine learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than a human expert. This approach could revolutionize everyday medical treatment by diagnosing heartbeat irregularities that could get fatal. In Andrew's words:
"I've been encouraged by how quickly people are accepting the idea that deep learning can diagnose with an accuracy superior to doctors in select verticals."
With the advancements at this pace, AI algorithms will metamorphose healthcare.
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. 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.
Source: Education Quizzes
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, whether 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.
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 wouldn’t be bizarre to think that in the near future, most human jobs will have been taking over by machines.
Published at DZone with permission of Gargi Sharma . See the original article here.
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