Skip to main content

How is AI Saving the Future

how_is_AI_Saving_the_Future_cerelabs_12_05_2016
Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer.

It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns.

Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible:

  • Cancer Diagnostics: Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives.

  • Low Cost Renewable Energy: Artificial-intelligence is helping wind power forecasts of unprecedented accuracy that are making it possible for Colorado to use far more renewable energy, at lower cost.

  • Global Conservation: National Science Foundation (NSF) funded researchers are using Artificial Intelligence to solve poaching and illegal logging. They have created an AI-driven application called Protection Assistant for Wildlife Security (PAWS) which has led to significant improvements when tested in Uganda and Malaysia in 2014, thus protecting forests and wildlife.

  • Precision Based Medicine: AI is turning out to be a powerful tool for precision-based medicine, where treatments are tailored made. Custom diagnostics and treatments seems possible because of the recent advancements in AI.

We at Cere Labs are continuously thinking how we can develop applications based on AI that can help humanity, especially in healthcare. We will keep you updated of the progress we make in this area. AI is not just about robots winning a game of chess or  a game of GO.

References:

  1. Chen, C. L. et al. Deep Learning in Label-free Cell Classification. Sci. Rep. 6, 21471; doi: 10.1038/srep21471 (2016).

Comments

  1. Thanks for Sharing a useful content about Artificial Intelligence
    Please visit our website At SFJ Business Solutions we to shared some blogs about AI
    AI online course

    ReplyDelete
  2. Learn about the various Error functions, which are also called Cost functions or Loss functions. Also, understand about the entropy and its use in measuring error. Understand the various optimization techniques, drawbacks and ways to overcome the same. This you will learn alongside various terms in implementing neural networks.ai courses

    ReplyDelete
  3. Through this post, I know that your good knowledge in playing with all the pieces was very helpful. I notify that this is the first place where I find issues I've been searching for. You have a clever yet attractive way of writing. 360DigiTMG AI Course in Malaysia
    AI Course
    AI Courses

    ReplyDelete
  4. I think I have never watched such online diaries ever that has absolute things with all nuances which I need. So thoughtfully update this ever for us.
    360DigiTMG machine learning course malaysia

    ReplyDelete
  5. Great to become visiting your weblog once more, it has been a very long time for me. Pleasantly this article that i've been sat tight for such a long time. I will require this post to add up to my task in the school, and it has identical theme along with your review. Much appreciated, great offer.
    data scientist training hyderabad

    ReplyDelete
  6. stunning, incredible, I was thinking about how to fix skin inflammation normally.I've bookmark your site and furthermore include rss. keep us refreshed.

    AI Course

    ReplyDelete
  7. thanks for the information seeks such more blogs with complete knowledge.
    data analytics course

    ReplyDelete

Post a Comment

Popular posts from this blog

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. ( http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf )  What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.  A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. T

Understanding Generative Adverserial Networks - Part 1

This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs. Generative networks have been in use for quite a while now. And so have discriminative networks. But only in 2014 did someone get the brilliant idea of using them together. These are the generative adversarial networks. This kind of deep learning model was invented by Ian Goodfellow . When we work with data already labelled, it’s called supervised learning. It’s much easier compared to unsupervised learning, which has no predefined labels, making the task more vague.  "Generative Adversarial Networks is the most interesting idea in the last ten years in Machine Learning." - Yann LeCun In this post, we’ll discuss what GANs are and how they work, at a higher , more abstract level. Since 2014, many variations of the traditional GAN have co