🔻Notable Machine Learning Statistics in 2020. Market Share & Data Analysis


Many view machine learning as synonymous with artificial intelligence. In reality, machine learning is but a subset of AI, making the latter perform tasks faster and more intelligently by providing it with learning capabilities. These benefits make machine learning a key component of AI, a fact that will be affirmed by the latest machine learning statistics.

📌 Via: @cedeeplearning

link: https://financesonline.com/machine-learning-statistics/

#statistics
#data_analysis
#market
#machinelearning

🔻AI MAY KILL THESE 5 JOBS BY 2030, SAY EXPERTS🔻

1. Bookkeeping Clerks
2. Location-Based Jobs
3. Market Research Analyst
4. Retail Workers
5. Software Developers

📌 Via: @cedeeplearning

link: https://analyticsindiamag.com/ai-may-kill-these-5-jobs-by-2030-say-experts/

#AI
#job
#machinelearning
#datascience

🔹Google AI statistics show that the company’s deep learning prediction algorithm correctly diagnoses suspected tumors 89% of the time by analyzing medical heatmaps.

For comparison’s sake, a team of expert pathologists gave a correct diagnosis only 73% of the time. AI machine learning VS human statistics consistently show that medical AI is getting better and better at recognizing diseases that human doctors can’t detect.

📌 Via: @cedeeplearning

credit: google AI

#google_ai
#deeplearning
#healthcare

🔹DeepMind: The Podcast🔹
Curious about AI and want to learn more? Download the first season of our podcast with Hannah Fry.

https://deepmind.com/blog?filters=%7B%22category%22:%5B%22Podcasts%22%5D%7D

#deepmind
#deeplearning
#machinelearning
#AI

🔻Using #WaveNet technology to reunite #speech-impaired users with their original voices

This post details a recent project we undertook with #Google and #ALS campaigner Tim Shaw, as part of Google’s Euphonia project. We demonstrate an early proof of concept of how #text-to-speech technologies can synthesize a high-quality, natural sounding voice using minimal recorded speech data.

📌 Via: @cedeeplearning

link:https://deepmind.com/blog/article/Using-WaveNet-technology-to-reunite-speech-impaired-users-with-their-original-voices

#deepearning #deepmind
#machinelearning

🔹AlphaFold: Improved #protein structure #prediction using potentials from #deep_learning

https://deepmind.com/research/publications/AlphaFold-Improved-protein-structure-prediction-using-potentials-from-deep-learning
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Via: Cutting-edge Deep Learning
Credit: deepmind.com

#deepmind
#machinelearning
#neuralnetworks

🔹Proteins are complex molecules that are essential to life, and each has its own unique 3D shape.

Today we’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, #DeepMind has brought together experts from the fields of structural biology, physics, and #machine_learning to apply #cutting-edge techniques to #predict the 3D structure of a #protein based solely on its #genetic sequence.

📌Via: @cedeeplearning

link: https://deepmind.com/blog/article/alphafold-casp13

🔹Improved Techniques for Training GANs

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels.

📌Via: @cedeeplearning

link: https://arxiv.org/abs/1606.03498

#GANS
#generative_model
#deeplearning
#research
#machinelearning

🔻DeepMind's Losses and the Future of #Artificial_Intelligence

DeepMind, likely the world’s largest research-focused artificial intelligence operation, is losing a lot of money fast, more than $1 billion in the past three years. #DeepMind also has more than $1 billion in debt due in the next 12 months.
Does this mean that AI is falling apart?

📌Via: @cedeeplearning

link: https://www.wired.com/story/deepminds-losses-future-artificial-intelligence/

#deeplearning
#machinelearning
#AI

🔹Deep Learning #Algorithms Identify Structures in Living Cells

For cell biologists, fluorescence micro­scopy is an invaluable tool. Fusing dyes to antibodies or inserting genes coding for fluorescent proteins into the #DNA of living cells can help scientists pick out the location of #organelles, #cytoskeletal elements, and other subcellular #structures from otherwise #impenetrable microscopy images. But this technique has its #drawbacks.

📌Via: @cedeeplearning

link: https://www.the-scientist.com/notebook/deep-learning-algorithms-identify-structures-in-living-cells-65778

#deeplearning
#neuralnetworks
#machinelearning

🔹AI Networks Generate Super-Resolution from Basic Microscopy

A new study uses deep learning to improve the resolution of biological images, but elicits skepticism about its ability to enhance snapshots of sample types that it has never seen before.

📌Via: @cedeeplearning

link: https://www.the-scientist.com/news-opinion/ai-networks-generate-super-resolution-from-basic-microscopy-65219

#deeplerning
#neuralnetworks
#machinelearning

🔹Neural networks facilitate optimization in the search for new materials

Sorting through millions of possibilities, a search for battery materials delivered results in five weeks instead of 50 years. When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once.

📌Via: @cedeeplearning

link: http://news.mit.edu/2020/neural-networks-optimize-materials-search-0326

#MIT
#deeplearning
#neuralnetworks
#imagedetection

🔹Deep learning for mechanical property evaluation

New technique allows for more precise measurements of #deformation characteristics using nanoindentation tools.
A #standard method for testing some of the #mechanical properties of #materials is to poke them with a sharp point. This “indentation technique” can provide detailed measurements of how the material responds to the point’s force, as a function of its #penetration depth.

📌Via: @cedeeplearning

link: http://news.mit.edu/2020/deep-learning-mechanical-property-metallic-0316

#neuralnetworks
#deeplearning
#machinelearning

🔹Understanding Generative Adversarial Networks (GANs)

Yann LeCun described it as “the most interesting idea in the last 10 years in #Machine_Learning”. Of course, such a compliment coming from such a prominent researcher in the #deep_learning area is always a great advertisement for the subject we are talking about! And, indeed, #Generative Adversarial #Networks (#GANs for short) have had a huge success since they were introduced in 2014 by Ian J. #Goodfellow and co-authors in the article Generative Adversarial Nets.

📌Via: @cedeeplearning

link: https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29

🔹Structured learning and GANs in TF, another viral face-swapper, optimizer benchmarks, and more...

This week in #deep_learning we bring you a GAN library for TensorFlow 2.0, another viral #face-swapping app, an #AI Mahjong player from Microsoft, and surprising results showing random architecture search beating neural architecture search. You may also enjoy an interview with Yann LeCun on the AI Podcast, a primer on #MLIR from Google, a few-shot face-#swapping #GAN, benchmarks for recent optimizers, a structured learning #framework for #TensorFlow, and more!

📌Via: @cedeeplearning

link: https://www.deeplearningweekly.com/issues/deep-learning-weekly-issue-124.html

🔻When not to use deep learning

Despite #DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining #models and #features to general public is required.
So when not to use #deep_learning?

1. #Low-budget or #low-commitment problems

2. Interpreting and communicating model parameters/feature importance to a general audience

3. Establishing causal mechanisms

4. Learning from “#unstructured” features

📌Via: @cedeeplearning

link: https://www.kdnuggets.com/2017/07/when-not-use-deep-learning.html/2