With the ever-increasing amount of data, the central challenge in multimodal learning involves limitations of labelled samples. For the task of classification, techniques such as meta-learning, zero-shot learning, and few-shot learning showcase the ability to learn information about novel classes based on prior knowledge. Recent techniques try to learn a cross-modal mapping between the semantic space and the image space. However, they tend to ignore the local and global semantic knowledge. To overcome this problem, we propose a Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent space of image features and the semantic space. In our approach we concatenate multimodal data to a single embedding before passing it to the VAE for learning the latent space. We propose the use of a multi-modal loss during the reconstruction of the feature embedding through the decoder. Our approach is capable to correlating modalities and exploit the local and global semantic knowledge for novel sample predictions. Our experimental results using a MLP classifier on four benchmark datasets show that our proposed model outperforms the current state-of-the-art approaches for generalized zero-shot learning.

Artificial Intelligence is being used to treat and tackle the most deadly parasites, diseases that are developing all over the world, and declared by the tech company, DeepMind AI. There is a London-based Alphabet lab owned that is going to work with the Drugs for Neglected Diseases Initiative (DNDI) to treat Leishmaniasis and Chagas disease. Scientists have spent so many years in laboratories for mapping protein structures. But last year, DeepMind's AlphaFold program and somehow was able to achieve the same accuracy in some days. As of now one of biology's biggest mysteries is largely solved by AI, a Protein structure that is key to heart muscle that is defective.

The project was born in July 2020 as a quest to replicate OpenAI GPT-family models. A group of researchers and engineers decided to give OpenAI a "run for their money" and so the project began. Their ultimate goal is to replicate GPT-3-175B to "break OpenAI-Microsoft monopoly" on transformer-based language models. Since the transformer was invented in 2017, we've seen increased effort in creating powerful language models. GPT-3 is the one that became a superstar, but all over the world companies and institutions are competing to find an edge that allows them to take a breath at a hegemonic position.

In November 2020, Alphabet-owned AI firm DeepMind announced that it had cracked one of biology's trickiest problems. For years the company had been working on an AI called AlphaFold that could predict the structure of proteins – a challenge that could prove pivotal for developing drugs and vaccines, and understanding diseases. When the results of the biennial protein-predicting challenge CASP were announced at the end of 2020, it was immediately clear that AlphaFold had swept the floor with the competition. John Moult, a computational biologist at the University of Maryland who co-founded the CASP competition, was both astonished and excited at AlphaFold's potential. "It was the first time a serious scientific problem had been solved by AI," he says.

"We've been excited by the potential for this technology to help fill in some of the gaps in our understanding of biology and accelerate scientific research to enable new, effective treatments for diseases," DeepMind AI-for-science head Pushmeet Kohli said.

Computer scientists are questioning whether DeepMind, the Alphabet-owned U.K. firm that's widely regarded as one of the world's premier AI labs, will ever be able to make machines with the kind of "general" intelligence seen in humans and animals. In its quest for artificial general intelligence, which is sometimes called human-level AI, DeepMind is focusing a chunk of its efforts on an approach called "reinforcement learning." This involves programming an AI to take certain actions in order to maximize its chance of earning a reward in a certain situation. In other words, the algorithm "learns" to complete a task by seeking out these preprogrammed rewards. The technique has been successfully used to train AI models how to play (and excel at) games like Go and chess.

Artificial intelligence experts DeepMind is perhaps one of the most advanced and renowned in the world. They are behind the most advanced artificial intelligence systems such as the famous Alpha Zero and Alpha Go. Recently however they made news when they submitted a paper to the peer-reviewed Artificial Intelligence journal. In it they propose that'reinforcement learning' will be enough to make machines attain general artificial intelligence as seen in humans and animals. This is commonly known as artificial general intelligence (AGI).

We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i.e. Depression Symptoms Detection (DSD) from text. We start with a comprehensive synthesis of different components of our ZSL modeling and analysis of our ground truth samples and Depression symptom clues curation process with the help of a practicing clinician. We next analyze the accuracy of various state-of-the-art ZSL models and their potential enhancements for our task. Further, we sketch a framework for the use of ZSL for hierarchical text-based explanation mechanism, which we call, Syntax Tree-Guided Semantic Explanation (STEP). Finally, we summarize experiments from which we conclude that we can use ZSL models and achieve reasonable accuracy and explainability, measured by a proposed Explainability Index (EI). This work is, to our knowledge, the first work to exhaustively explore the efficacy of ZSL models for DSD task, both in terms of accuracy and explainability.

How often have you heard "The Machine Learning Application worked well in the lab, but it failed in the field. It is not the fault of the Machine Learning Model! This blog is not yet another blog article (YABA) on DataOps, DevOps, MLOps, or CloudOps. I do not mean to imply xOps is not essential. For example, MLOps is both strategic and tactical. It promises to transform the "ad-hoc" delivery of Machine Learning applications into software engineering best practices. We know the symptoms: Most machine-learning models trained in the lab perform poorly on real-world data [1, 2, 3, 4]. Machine Learning created profits in the year 2020 and will continue to increase profits in the future. However, many problems hold back the progress and success of Machine Learning application rollout to production. I focus on what it is the most significant problem or cause: the quality and quantity of input data in Machine Learning models [1,4]. We realized the quantity of high-quality data was the bottleneck in predictive accuracy when we started showing near, or above, human-level performance in structured data, imagery, game playing, and natural language tasks. How many times do we look at the Machine Learning application lifecycle's conceptualization to realize a Machine Learning model is not at the beginning (Figure 2)? We can research and improve the tools of the Machine Learning application lifecycle. But that only lowers the cost of deployment. Arguably, the Machine Learning model's choice is not a critical part of deploying a Machine Learning application. We have a "good enough" process or pipeline to choose and change the Machine Learning model, given a training input dataset. However, when achieving State-of-the-Art (SOTA) results, the input data seems to have the most significant impact on the output predictive data (Figure 2). We seem to know the cause: input data that was garbage results in garbage output predictive data. New data input to a trained Machine Learning model determines the accuracy of the output. We divide Machine Learning input data into four arbitrary categories, defined by the Machine Learning application output accuracy. GPT-3 is an example [6]. GPT-3 trained with an enormous amount of data [6]. GPT-3 is frozen in time as a transformer that you access through an API. Concept Drift is a change in what to predict. For example, the definition of "what is a spammer." We do not cover Concept Drift here. I do not think of it as a problem but rather as a change in the solution's scope. An example of Case 2: Data Drift, is that Case 1: "It works!, is a temporal phenomenon.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.