The new decade of 2020 or the next stage of "digital evolution" welcomes the world with a promise of hyper intuitive cognitive capabilities and emotionally intelligent interfaces that will rebuild businesses in numerous unpredictable ways. As the tech community (for invested implementation) prepares itself for the new age of disruptive changes to arrive at it's matured stage, it becomes wise and necessary to have a look at these digital transformation trends. Conversational Artificial Intelligence- Siri and Google Assistant are always at swords for their accuracy in answers, but still they both lack in understanding the right intent. Applied conversational AI, fixes this disconnects as it understands the relevance and personalization within humans for successful computer interaction. Conversational AI has an automated speech recognition program that understands natural language and forms a response that exhibits a customized dialogue.
In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.
When the 1970s and 1980s were colored by banking crises, regulators from around the world banded together to set international standards on how to manage financial risk. Those standards, now known as the Basel standards, define a common framework and taxonomy on how risk should be measured and managed. This led to the rise of professional financial risk managers, which was my first job. The largest professional risk associations, GARP and PRMIA, now have over 250,000 certified members combined, and there are many more professional risk managers out there who haven't gone through those particular certifications. We are now beset by data breaches and data privacy scandals, and regulators around the world have responded with data regulations.
Machine learning (ML) is the current paradigm for modeling statistical phenomena by harnessing algorithms that exploit computer intelligence. It is common place to build ML models that predict housing prices, aggregate users by their potential marketing interests, and use image recognition techniques to identify brain tumors. However, up until now these models have required scrupulous trial and error in order to optimize model performance on unseen data. The advent of automated machine learning (AutoML) aims to curb the resources required (time and expertise) by offering well-designed pipelines that handle data preprocessing, feature selection, and model creation and evaluation. While AutoML may initially only appeal to enterprises that want to harness the power of ML without consuming precious budgets and hiring skilled data practitioners, it also contains very strong promise to become an invaluable tool for the experienced data scientist.
Phantom AI has secured raised $22 Million in Series A funding round. Celeres Investments led the latest funding round. Other investors participated in the round includes Ford Motor Company and KT (Korea's largest telco). The company intends to use the latest funds to speed up product development and scale its operations in Europe and Asia regions. Executive Opinion Co-founder and CEO of Phantom AI, Hyunggi Cho, "We founded Phantom AI to fundamentally change the economics of ADAS by developing modern software-based solutions that are high performing, cost effective, and infinitely flexible and customizable. To the automakers frustrated with the lack of options in computer vision technologies--Phantom AI is here to help. We are thrilled to bring our AI-based perception technology, including computer vision, sensor fusion and control capabilities to market, and to have the support of our new investors to help us accelerate production globally."
In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning models across the organization. At the center of this scaling up effort is ModelOp, the company that builds solutions to scale the processes that take models from the data science lab into production. Even before their recent $6 million Series A funding led by Valley Capital Partners with participation from Silicon Valley Data Capital, they are already the leader providing ModelOps solutions to Fortune 1000 companies. ModelOps is a capability that focuses on getting models into 24/7 production.
PyTorch: Deep Learning and Artificial Intelligence new udemy course Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems. What you'll learn Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs) Predict Stock Returns Time Series Forecasting How to build a Deep Reinforcement Learning Stock Trading Bot GANs (Generative Adversarial Networks) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Natural Language Processing (NLP) with Deep Learning Demonstrate Moore's Law using Code Transfer Learning to create state-of-the-art image classifiers Description Welcome to PyTorch: Deep Learning and Artificial Intelligence! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?
Crypto-ML offers cryptocurrency trading signals that are generated by a sophisticated machine learning platform. This system has evolved over the years, culminating in Release 5 which uses Deep Neural Networks to deliver predictions to the trading engine. In an effort to provide continued transparency and insight, this post will provide a peek into how the Crypto-ML works behind the scenes. To deliver a trade signal, here's what happens on a continuous basis (from left to right above): That is the system in a nutshell. The second step in our diagram shows the data going through a Deep Neural Network to generate a price prediction.
And while that was happening, the financial sector was also taking note. Among the many boons of AI tech for finance is the practice called algorithmic trading: the idea that an advanced AI may be able to assist the investors by predicting the market dynamics with enough precision to make consistent profit. And while many advanced machine learning models developed for this purpose stay outside the reach of the general public, others are eager to make AI-driven trading available to a broader audience. One of the leaders in this sphere is the Israel-based company with an ambitious name I Know First. With its powerful cloud-based AI capable of predicting the price dynamics for more than 10,000 financial instruments, including stock ideas, ETFs, world indices, commodities and currencies, it offers its forecasts to private and institutional investors alike.
Hyunsoo Kim, a 29-year-old entrepreneur in South Korea, is on a mission to democratize artificial intelligence to enable more companies, both large and small, to utilize the emerging technology. So it's only fitting that Kim, cofounder of Superb AI, has been selected as the featured honoree for the Enterprise Technology category of this year's Forbes 30 Under 30 Asia list, leading a pack of several fellow honorees who founded startups based on AI. Since launching Superb AI in April 2018 with four cofounders, Kim has grown his startup to $2 million in revenues last year and 21 employees, fueled by increasing demand for AI. Profits are still in the future, but Superb AI also managed last year to join Y Combinator, a prominent Silicon Valley startup accelerator. So far, it has raised $2 million in funding from Y Combinator, Duke University and VC firms in Silicon Valley, Seoul and Dubai, giving it a valuation of $12 million as of March 2019.