Last June OpenAI released the most powerful language model ever created, which became the topic of much discussion among developers, researchers, and entrepreneurs. Its capabilities of zero- and one-shot learning blew people's minds, with many GPT-3 powered applications going viral on twitter every second day. This API is being released in an era when polarization and bias have never been as intense, with technology that is powerful, scalable, and potentially dangerous -- imagine a fake news generator or a social media bullying bot powered by the human-like GPT-3. Understanding the harmful potential of its API technology, OpenAI has taken a unique Go To Market approach, strictly limiting access to a small number of vetted developers. By doing so, it became one of the first companies to voluntarily forfeit short-term profits in favor of being socially-responsible.

Michael Shuffet didn't waste any keystrokes when responding to a message about the automated email writer he's building. He tapped out "Yes 45m" and clicked a button marked "Generate email." Shuffet checked it over and clicked Send. Compose is one of several automated writing tools built on striking new text-generation technology known as GPT-3, revealed in June by OpenAI, an artificial intelligence research institute. GPT-3 went viral this summer after people marveled at how it could fluently crank out memes, code, self-help blog posts, and Hemingway-style Harry Potter fanfic.

Last month, the deep learning powered online tool Toonify Yourself! Designed "for fun and amusement using deep learning and Generative Adversarial Networks," the system was developed by a pair of independent researchers, Justin N. M. Pinkney and Doron Adler, and let anyone change selfies or portraits into impressive animation-style images. Demand for the high-performance homemade model caused the site to crash, but it quickly returned thanks to support from user donations. In a paper submitted to the NeurIPS 2020 Machine Learning for Creativity and Design workshop, Pinkney and Adler present their research, which enables image generation in novel domains and with a degree of creative control on the output. The team's resolution dependant GAN interpolation method combines high resolution layers of an FFHQ model with low resolution layers from a model transferred to animated character faces to enable the combination of realistic facial textures with the structural characteristics of a cartoon. Generative Adversarial Networks (GANs) are the current SOTA approach for many image synthesis and translation tasks.

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The collaboration between data scientists from the Wyss Institute's Predictive BioAnalytics Initiative and synthetic biologists in Wyss Core Faculty member Jim Collins' lab at MIT was created to apply the computational power of machine learning, neural networks, and other algorithmic architectures to complex problems in biology that have so far defied resolution. As a proving ground for their approach, the two teams focused on a specific class of engineered RNA molecules: toehold switches, which are folded into a hairpin-like shape in their "off" state. When a complementary RNA strand binds to a "trigger" sequence trailing from one end of the hairpin, the toehold switch unfolds into its "on" state and exposes sequences that were previously hidden within the hairpin, allowing ribosomes to bind to and translate a downstream gene into protein molecules. This precise control over the expression of genes in response to the presence of a given molecule makes toehold switches very powerful components for sensing substances in the environment, detecting disease, and other purposes.

A few years ago, scientists learned something remarkable about mallard ducklings. If one of the first things the ducklings see after birth is two objects that are similar, the ducklings will later follow new pairs of objects that are similar, too. Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same colour, even if they are blue, over two spheres that are each a different colour. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world's Go champion Lee Sedol in 2016.

This article is an end-to-end example of training, testing and saving a machine learning model for image classification using the TensorFlow python package. TensorFlow is a machine learning (primarily deep learning) package developed and open-sourced by Google; when it was originally released TensorFlow was a relatively low-level package for experienced users, however in the last few years and especially since the release of TensorFlow 2.0 it is now aimed at a wider range of users. A few years ago I ran a PoC with one of our developers that looked at running TensorFlow models offline on one of our mobile applications. Whilst we found that it was possible we also encountered a few challenges that made the solution quite fiddly. Roll forward to 2020 and TensorFlow has improved a lot; the latest version has greater integration with the Keras APIs, it's being extended to cover more of the data processing pipeline and has also branched out to support for new languages, with the TensorFlow.js

Examples of correct and incorrect visual identifications of the erythema migrans (EM) rash commonly seen in patients with Lyme disease. The images in the top right quadrant actually are EM (true positives). The upper right photos are false negatives, the lower left are false positives and the lower right were correctly ruled out as EM (true negatives). A new AI/deep learning technique from Johns Hopkins Medicine and the Johns Hopkins Applied Research Laboratory greatly increases the chances of correctly identifying EM in photographs. Johns Hopkins Medicine and Johns Hopkins Applied Research Laboratory (APL) researchers have shown that cell phone images of rashes taken by patients can be evaluated using artificial intelligence (AI) and deep learning (DL) technologies to more accurately detect and identify the erythema migrans (EM) skin redness associated with acute Lyme disease.

Weight and bias are the adjustable parameters of a neural network, and during the training phase, they are changed using the gradient descent algorithm to minimize the cost function of the network. However, they must be initialized before one can start training the network, and this initialization step has an important effect on the network training. In this article, I will first explain the importance of the wight initialization and then discuss the different methods that can be used for this purpose. Currently Medium supports superscripts only for numbers, and it has no support for subscripts. So to write the name of the variables, I use this notation: Every character after is a superscript character and every character after _ (and before if its present) is a subscript character. Before we discuss the weight initialization methods, we briefly review the equations that govern the feedforward neural networks. For a detailed discussion of these equations, you can refer to reference [1].

Data Scientist Pasi Helenius is always up for engaging others in discussions on how the use of Artificial Intelligence and Advanced Analytics improves business, whether it is optimizing Supply Chain, improving Manufacturing efficiency or improving the Customer Journey. Artificial Intelligence, Machine Learning, Forecasting and Predictive Analytics are his forte. Whether it's speaking'nerdy' or explaining advanced analytics in terms that are understandable to others, Pasi Helenius has experience to do just that. Some of his recent presentations include an introduction to business forecasting using a hybrid approach that combines deep learning with an econometric approach. Such knowledge is useful for a wide range of people - from data scientists, business analysts and applied AI experts to managers, gambling experts and modelers – and beneficial for forecasting the most difficult business problems.