Artificial Intelligence is here to stay. The development of AI is speeding up on a daily basis. Only recently, Google's DeepMind created the AI AlphaStar that secured a decisive victory against two grandmaster players of the game of StarCraft II. In a series of test matches they played, the algorithm won 5-0. This victory is a decisive moment for artificial intelligence, as the game of StarCraft II is fundamentally more difficult than the other games where Deepmind's algorithm already claimed victory.

Companies and investors will find valuable AI/ML software across all three layers. At Insight, we initially focused on layers two and three. We invested in startups creating robust ML systems that addressed specific problems, either vertically (like credit underwriting company Zest AI) or horizontally (like cybersecurity company SentinelOne). We thought that economic moats were hardest to build at layer one; in part as a result of robust open source ecosystems and because large public cloud vendors deliver many of these tools at low prices.

When humanity contemplates sending assets to other planets, what should be our goal? The choice is between taking pride in what nature manufactured over 4.5 billion years on Earth through unsupervised evolution and natural selection, or aspiring to a more intelligent form of supervised evolution elsewhere. The first choice -- AI -- is apt to an industrial duplication line, for which the proof of concept for the assembly line was already demonstrated on Earth and we can duplicate it in an Earth-like environment. We are emotionally attracted to the second choice, because we are attached to ourselves and our natural path for maintaining the longevity of our genetic-making through biological reproduction. Prioritizing the natural processes of the second choice is misguided for two reasons.

Machine learning has found wide success across a wide variety of fields. In order to understand different ML algorithms, it becomes important to understand the different data types and how they are preprocessed before training models on them. To understand the different data types found in machine learning, read this blog.

When software providers talk about the technologies they say "democratize" AI, they also talk a lot about "guardrails." That's because the rapidly evolving world of AI tools is still more like a republic governed by the machine-learning elite. Although no-code and low-code AI tools promise to give everyone a chance to build business analytics models or simple applications that use AI to complete tedious tasks, the amateurs whom no-code AI companies refer to as "citizen data scientists" are often required to play with the bumper rails up. That's because toolmakers and management are worried about the risks inherent in allowing just anyone to create sophisticated AI systems. "As you go into low-code and actually more the no-code environment, then there are guardrails as to what you can and can't do," said Ed Abbo, president and chief technology officer at C3 AI, which provides software designed to help people with zero coding experience build machine learning models.

Job description Artificial intelligence, machine learning and digital twins have the potential to transform cardiology. A cardiac digital twin is the computational replica of the cardiac system of a specific patient. The digital twin provides an unprecedented ability to both depict an integrated and comprehensive diagnostic picture, and to predict the prognosis under a range of therapeutic strategies. We are seeking to appoint a data scientist/engineer to develop and apply the technology that allows the creation of cardiac digital twins at scale. The successful candidate will develop and apply state of the art machine learning and data assimilation methods to automatically analyse longitudinal patient data that will be encoded in a digital twin of the patient's heart.

Research into the therapeutic potential of psychedelic drugs was pioneered by psychiatrists way back in the 1950s, but the emergence of advanced technologies in pharma appears to have breathed new life into the field. As interest in the psychedelics market gains stream, a number of drug companies are now employing artificial intelligence (AI) methods in their search for new psychedelic compounds to treat a range of mental and physical conditions. One in four people in the UK will experience some kind of mental health problem every year, and figures are almost identical in the US. Despite this, treatments for psychological conditions are relatively limited – and for many patients, the drugs that are available come with side effects that negatively impact their quality of life. Psychedelics are hallucinogenic drugs that alter a person's perception and mood and affect their thought processes.

As data scientists, we often work with high-dimensional data with more than 3 features, or dimensions, of interest. In supervised machine learning, we may use this data for training and classification for example and may reduce the dimensions to speed up the training. In unsupervised learning, we use this type of data for visualization and clustering. In single-cell RNA sequencing (scRNA-seq), for example, we accumulate measurements of tens of thousands of genes per cell for upwards of a million cells. That's a lot of data that provides a window into the cell's identity, state, and other properties.

Artificial intelligence (AI) promises to be one of the most transformative modern technologies we've ever seen, capable of quickly completing complex tasks that once required thousands of hours of human input. Machine learning is a subset of AI that uses data to improve processes, and also make predictions. It's already being applied to dozens of industries globally, streamlining everything from racing to internet search results. According to one industry report, the machine learning market was worth $1.6 billion in 2017 and is set to explode to $20.8 billion by 2023. That's a compound annual growth rate of 53%, and the following two companies are leading the charge.

Learn to use TensorFlow 2.0 for Deep Learning Leverage the Keras API to quickly build models that run on Tensorflow 2 Perform Image Classification with Convolutional Neural Networks Use Deep Learning for medical imaging Forecast Time Series data with Recurrent Neural Networks Use Generative Adversarial Networks (GANs) to generate images Use deep learning for style transfer Generate text with RNNs and Natural Language Processing Serve Tensorflow Models through an API Use GPUs for accelerated deep learning Learn to use TensorFlow 2.0 for Deep Learning This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.