🔹Statistics Vs. Machine Learning

As an organization’s information infrastructure matures, the most appropriate next step is to begin adding advanced analytics. We use the specific term advanced analytics with purpose in this context for two few reasons:

🔻It assumes migration from historical analytics into current and future based analytics
🔻It encompasses statistical analysis as well as machine learning

📌 Via: @cedeeplearning

link: https://www.rocketsource.co/blog/machine-learning-models/

#statistics
#machinelearning
#modeling

🔹Successfully Deploying Machine Learning Models
There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all — it’s a lifecycle. Why? It’s an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion.

📌 Via: @cedeeplearning

link: https://www.rocketsource.co/blog/machine-learning-models/

#end_to_end
#deployment
#machine_learning

🔻 Massively Scaling Reinforcement Learning with SEED RL

Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as Go and Dota 2. Models, or agents, learn by exploring an environment, such as a game, while optimizing for specified goals. However, current RL techniques require increasingly large amounts of training to successfully learn even simple games, which makes iterating research and product ideas computationally expensive and time consuming.

📌 Via: @cedeeplearning

link: https://ai.googleblog.com/

#reinforcement
#RL
#deep_learning
#architecture
#training

🔻 Open Images V6 — Now Featuring Localized Narratives

Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.

📌 Via: @cedeeplearning

link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10

#image_detection
#machinelearning
#deeplearning

🔹How Conversational AI creates new business cases

The era of conversational artificial intelligence is rapidly changing the business of both traditional websites and mobile applications. What are, then, the benefits of “conversational AI” that new business systems can offer? Well, to begin with: it seems that voice and dialogue interfaces are finally ripe to compete against traditional ones.

📌 Via: @cedeeplearning

link: https://chatbotsmagazine.com/how-conversational-ai-create-new-business-cases-aed0740903c0

#AI
#business_case
#chatbot
#machine_learning

🔻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
———————————————————

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