From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Ala., and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work "unsupervised," Dr. Clapham said, to see how well it could spot differences between known bears from 935 photographs. First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features. The system identified bears at an accuracy rate of 84 percent, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve.

We have seen the advent of state-of-the-art (SOTA) deep learning models for computer vision ever since we started getting bigger and better compute (GPUs and TPUs), more data (ImageNet etc.) and easy to use open-source software and tools (TensorFlow and PyTorch). Every year (and now every few months!) we see the next SOTA deep learning model dethrone the previous model in terms of Top-k accuracy for benchmark datasets. The following figure depicts some of the latest SOTA deep learning vision models (and doesn't depict some like Google's BigTransfer!). However most of these SOTA deep learning models are brought down to their knees when it tries to make predictions on a specific class of images, called as adversarial images. The whole idea of an adversarial example can be a natural example or a synthetic example.

Artificial intelligence has become such a big part of our lives, you'd be forgiven for losing count of the algorithms you interact with. But the AI powering your weather forecast, Instagram filter, or favorite Spotify playlist is a far cry from the hyper-intelligent thinking machines industry pioneers have been musing about for decades. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn only one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data--including the many bad decisions people have made, like marginalizing people of color and women. Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence--machines that can multitask, think, and reason for themselves. Beyond the answer to how we might develop technologies capable of common sense or self-improvement lies yet another question: who really benefits from the replication of human intelligence in an artificial mind? "Most of the value that's being generated by AI today is returning back to the billion dollar companies that already have a fantastical amount of resources at their disposal," says Karen Hao, MIT Technology Review's senior AI reporter and the writer of The Algorithm. "And we haven't really figured out how to convert that value or distribute that value to other people."

AutoML tools have been gaining traction for the last couple of years, both due to technological advancements and their potential to be leveraged by'Citizen Data Scientists'. Citizen Data Science, is an interesting (often controversial) aspect of Data Science (DS) that aims to automate the design of Machine Learning (ML)/Deep Learning (DL) models, making it more accessible to people without the specialized skills of a Data Scientist. In this article, we will try to understand AutoML, its promise, what is possible today?, where AutoML fails (today)?, is it meant only for Citizen Data Scientists, or does it hold some value for skilled Data Scientists as well? Let us start with a very high-level primer on Machine Learning (ML). Most of today's ML models are supervised and applied on a prediction/classification task.

New, sophisticated AI models such as OpenAI's GPT-3 are making headlines for their ability to mimic human-like language. Does this mean humans will be replaced with computers? Despite the hype, these algorithms still have major flaws. Not to mention, ethical concerns such as bias in AI still are far from a solution. For these reasons, humans still need to be in the loop in most practical AI applications, especially in nuanced areas such as language.

Dr. Eli David is a leading AI expert specializing in deep learning and evolutionary computation. He is the Co-Founder of DeepCube. Over the last several years, deep learning -- a subset of machine learning in which artificial neural networks imitate the inner workings of the human brain to process data, create patterns and inform decision-making -- has been responsible for significant advancements in the field of artificial intelligence. Building on what is possible with the human brain, deep learning is now capable of unsupervised learning from data that is unstructured or unlabeled. This data, often referred to as big data, can be drawn from various sources such as social media, internet history and e-commerce platforms, among others.

For this week's ML practitioner's series, Analytics India Magazine got in touch with Hiroki Yamamoto (tereka), a Kaggle Master. Hiroki is currently working as a data scientist and is ranked in the top 100 of the world's largest platforms for data science competitions– Kaggle. In this interview, Hiroki shares his experience of competing on Kaggle and how it has helped in growing as a data scientist. Hiroki: I got a master's degree in information technology back in 2015. During my graduation, I have worked on image processing research using deep learning -- for example, autoencoders.

If you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! In this post, we will dive into the details of TensorFlow Tensors. Tensors are TensorFlow's multi-dimensional arrays with uniform type.

We bring you a selection of some interesting and popular tweets about AI from October. Ok I wrote another one for the nerds. Incredible paper out of @Caltech from @AnimaAnandkumar, @kazizzad, Andrew Stuart & students in which they introduced a new deep-learning technique for finding solutions to differential equations at record speeds. In a healthcare test that went horribly wrong, GPT-3 told a mock patient to kill themself. GPT-3 isn't just lacking context, it's lacking *meaning* and *communicative intent* (not to mention accountability for what it says).

In Computer Vision, specifically, Image processing has become more efficient with the use of deep learning algorithms. To show the performance of these neural networks some basic preprocessed datasets were built, namely the MNIST and its variants such as KMNIST, QKMNIST, EMNIST, binarized MNIST and 3D MNIST. Ever since these datasets were built, it has been popular amongst beginners and researchers. In today's article, we'll be talking about the very basic and primarily the most curated datasets used for deep learning in computer vision. MNIST(Modified National Institute of Standards and Technology) database contains handwritten digits.