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.

Pandas is a fast, powerful, and easy-to-use open-source data analysis and manipulation tool built on top of the Python programming language. It is widely accepted among the Python community and is used in many other packages, frameworks, and modules. Pandas is an extremely flexible framework and has a wide range of use-cases for preparing the data for machine learning and deep learning models. Pandas is available as a standard python library at PyPI, which can be easily installed using either pip or conda depending on the python environment. If you work with tabular data, such as data in spreadsheets or databases, pandas is the right tool for you.

Businesses today are leveraging ever-increasing volumes of data for competitive advantage. That means employing emerging technologies in data science, artificial intelligence, machine learning and even deep learning to prepare and organize big data and develop the machine learning algorithms and predictive models that support business intelligence applications used by analysts and information workers. Here's a look at 10 data science and machine learning startup companies with leading-edge products in the data science and machine learning arena that solution providers should be aware of.

"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.

If you're reading this article, you probably know about Deep Learning Transformer models like BERT. They're revolutionizing the way we do Natural Language Processing (NLP). In case you don't know, we wrote about the history and impact of BERT and the Transformer architecture in a previous post. These models perform very well. And why does BERT perform so well in comparison to other Transformer models? Some might say that there's nothing special about BERT.

PyTorch online course has been designed for those students who can learn the concepts at a fast pace. We will provide in-depth knowledge with the help of different PyTorch examples. We will also provide PyTorch tutorial in which you will learn different concepts like how to install PyTorch. You will also learn the process of configuring PyTorch. First the instructors will tell you about what is PyTorch and then they will gradually move towards basic and then to advanced topics.

You can see a complete working example in our Colab Notebook, and you can play with the trained models on HuggingFace. Since being first developed and released in the Attention Is All You Need paper Transformers have completely redefined the field of Natural Language Processing (NLP) setting the state-of-the-art on numerous tasks such as question answering, language generation, and named-entity recognition. Here we won't go into too much detail about what a Transformer is, but rather how to apply and train them to help achieve some task at hand. The main things to keep in mind conceptually about Transformers are that they are really good at dealing with sequential data (text, speech, etc.), they act as an encoder-decoder framework where data is mapped to some representational space by the encoder before then being mapped to the output by way of the decoder, and they scale incredibly well to parallel processing hardware (GPUs). Transformers in the field of Natural Language Processing have been trained on massive amounts of text data which allow them to understand both the syntax and semantics of a language very well.

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.

IMAGE: Deep Learning detects virus infected cells and predicts acute, severe infections. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system. The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus.

SoftBank Ventures Asia has participated in a $27 million Series A funding round in VoyagerX, a South Korea-based artificial intelligence (AI) software developer, according to an announcement. The early-stage venture capital arm of the SoftBank Group joined this round along with other investors including Altos Ventures and Yellowdog with each investing $9 million. VoyagerX will use the funds to develop more user-centric AI services and increase the depth of its talent pool, with the aim to hire 100 people by 2022. Founded in 2017 by Sedong Nam, VoyagerX's teams build AI solutions and tools that leverage deep learning capabilities – an offshoot of machine learning that imitates the human brain when creating patterns and processing data for decision-making. "With the rapid development of AI technology and the increased demand for digital solutions, we are seeing huge opportunities in the market. Artificial intelligence falls firmly in that category and VoyagerX, with their top-tier talent, creative innovation, and rapid time-to-market, have proven that they have the potential to be market leaders in this field," said JP Lee, CEO of SoftBank Ventures Asia.