Machine Learning Applications in the world around you
As we all trying to figure out how and where we might use Machine Learning (ML) in our software businesses we all can find beyond the more general cognitive services (like speech to text, image recognition, keyword extraction, etc.) that I’ve already talked about and that – by themselves – are incredibly valuable and offer a near-immediate payoff.
According to TechCrunch there has already been over $10 billion in Venture Capital to 1500 AI/ML start-ups in 70 countries, which is predicted to rise to more than four times that in 2017!
Even so there are more than 40 applications described here, in addition to the Cognitive Services as stand-alone ML tools.
Cognitive Services
These services are available for integration into apps and workflows now. From IBM Watson alone we have:
• Speech to text
• Text to speech
• Natural Language Processing – sentiment, keywords, entities, high level concepts etc.
• Natural Language Classifier – which understands the in depth of text and returns a corresponding classification.
• Conversation/Chabot’s
• Dialog – script branching conversations between a user and an application
• Language Translation
• Personality Insights which is based on how people want to match them to other individuals, products, or the opportunities and tailor with their experience.
• Rank and Retrieve the most relevant information from a collection of documents.
• Linguistic analysis is used by Tone Analysers for detecting emotions, social tendencies and writing style, for understanding the emotional context of conversations and communications.
• Visual recognition: understand the content of images to tag the image, find human faces, approximate age and gender and find similar images in a collection. Trainable to teach it custom concepts for specific applications. The Take has launched a site for consumers to buy that thing they saw in that movie
That’s just IBM Watson. Similar services are available from Google and Microsoft Crotona.
Medical
· Predicting Emergency Room Wait Times
Health tech companies and healthcare organizations are using ML to predict wait times for patients in emergency department waiting rooms. The models uses factors like staffing levels, data of the patients, charts of emergency department, and also the layout of the emergency room itself to predict wait times.
· Predicting Psychopaths
The Online Privacy Foundation sponsored a competition to see if it’s possible to predict whether someone is a psychopath based on his twitter usage and apparently, you kind of can.
· Identifying Heart Failure
The researchers of IBM have found a way to extract heart failure diagnosis criteria from free-text physician notes. They developed a ML algorithm that combs through physicians free-form text notes (in the electronic health records) and “reads it.” A computer can now simulate a cardiologist reading through another physician’s notes and figuring out whether a patient has heart failure.
· Prediction of Strokes and Seizures
Singapore-based start-up Healing launched an app called Just Shake as it enables the user for sending an emergency alert to all the emergency contacts and/or caregivers simply by shaking the phone with one hand. These types of program uses with the help of machine learning algorithm to distinguish between actual everyday jostling and emergency shakes. The Just Shake It app, Healint is working on a model that analyses patients’ cell phone accelerometer data for helping the identify warning signs for all the chronic neurological conditions.
· Diagnosing Cancer
Google’s Deep Learning AI has been applied to cancer diagnosis, and results were better than expected “out of the box” but after “tweaking” it has delivered stunning performance. Clinician’s accuracy is about 48% but by the end Google’s ML was scoring 89% accurate diagnosis.
· Prediction of Hospital Readmissions
For the machine learning model, Additive Analytics, also works that identifies which patients are at high risk of readmission. Hospitals also predict emergency room admissions before anything happen.
· Identify Skin Lesions
Stanford researchers have trained one of Google’s deep neural networks to recognize skin lesions in photographs. The neural network, by the end was competitive with dermatologists when it came to diagnosing cancers using images. As still not perfect, it’s an impressive result.
· Managing Diabetes Patients
Health management solution provider used a machine learning platform to gain a better understanding of diabetic patients who are at risk for avoidable hospitalization or emergency room use. It gives trainings to the platform on a database of approximately 8 million patients.
Text and Language
· Machine Translation
Machine Translation is a task where given words or the phrase or sentence in one language, that automatically translates it into another language.
Automatic machine translation has been around for a long time, but deep learning is achieving top results in two specific areas:
1. Automatic Translation of Text.
2. Automatic Translation of Images.
3. How Google Translate squeezes deep learning onto a phone
- · Handwriting Generation
This is a task where given a large number of handwriting examples, generate new handwriting for a given word or phrase.-Interactive Handwriting Generation Demo
• Text Generation
Text generation is an interesting task, where a corpus of text is learned and also from this model new texts are also generated, by the method of word-by-word or character-by-character. The model is capable of learning how to spell, punctuate, form sentences and even capture the style of the text in the corpus.
1. The Unreasonable Effectiveness of Recurrent Neural Networks
2. Auto-Generating Click bait With the help of Recurrent Neural Networks
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