It's hard to believe, but a year in which the unprecedented seemed to happen every day is just weeks from being over. In AI circles, the end of the calendar year means the rollout of annual reports aimed at defining progress, impact, and areas for improvement. The AI Index is due out in the coming weeks, as is CB Insights' assessment of global AI startup activity, but two reports -- both called The State of AI -- have already been released. Last week, McKinsey released its global survey on the state of AI, a report now in its third year. Interviews with executives and a survey of business respondents found a potential widening of the gap between businesses that apply AI and those that do not.
ServiceNow Inc. is beefing up its artificial intelligence development capabilities with the acquisition today of a company called Element AI Inc. that's widely known as one of the pioneers in the field. Montreal-based Element AI launched back in 2016 as a professional services firm focused on helping traditional enterprises implement machine learning. The startup garnered significant industry attention from the outset thanks in part to its high-profile co-founder, the well-known deep learning researcher Yoshua Bengio, who won the Turing Award in 2018 for his contributions to the field. Element AI has gradually expanded its focus since its launch by creating a fund to support fellow machine learning companies and introducing ready-made AI tools. The company's offerings include Knowledge Scout, a search engine for manufacturers that speeds up the diagnosis and repair of production line issues by giving technicians relevant information about previous incidents with similar characteristics.
As the world is anticipating the end of the COVID-19 pandemic, energy consumption in industry and services is likely to grow. In the longer term, the developing world will increase its energy utilization, leading to growth of global primary energy demand by of 0.4% - 0.6% per year, or a 25% increase by 2050. According to scenarios calculated by energy giant Total SE, massive electrification of transportation will lead to decarbonization, and will require a rapid growth in renewables as a source of electricity. This energy transformation will see an explosion of growth in Artificial Intelligence (AI) utilization in the sector – up 50% between 2020 and 2024 – to allow smart, 21st century grids to become the gold standard, gradually replacing the "dumb" grids laid down in the late 19th – early 20th century in Europe, North America, Japan, China and beyond. The grid is a meta-system of generation facilities, be it nuclear, gas, coal, solar, wind, and hydro, connected by high voltage wire networks to transformers, and then to sub-stations and individual buildings, households, and apartments.
Last month, Microsoft released the first major version of .NET for Apache Spark, an open-source package that brings .NET development to the Apache Spark platform. The new release allows .NET developers to write Apache Spark applications using .NET user-defined functions, Spark SQL, and additional libraries such as Microsoft Hyperspace and ML.NET. Apache Spark is an open-source, general-purpose analytics engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning, and graph processing. Initially developed by the AMPLab team at UC Berkeley, it can be used in conjunction with different data repositories, including the Hadoop Distributed File System, NoSQL databases, and relational data stores. Since all data is processed in-memory (RAM), Spark can be 100x faster than Hadoop for large-scale data processing.
In mathematics, there is this age-old question of whether new math is discovered or invented. It makes sense to ask the same sort of question about modern drug discovery. When using artificial intelligence to identify drug candidates, are these new drug candidates being developed, or simply exposed through a process of narrowing down the possibilities using mathematics and science? Are these new drug candidates discovered or designed? A flurry of progress in the race to identify a COVID-19 vaccine has produced new automated techniques for drug discovery using artificial intelligence.
There is nothing ordinary about the year 2020, and in this highly charged political year, everything gets more attention than might have been deserved in previous years. A few weeks ago, Emily Murphy, Administrator of the General Services Administration (GSA) started making waves in the news. For many who may not have known her before, she has been leading the GSA for a few years helping bring innovative programs and initiatives to the agency. Among many things that Emily Murphy is responsible for, one of the most consequential every four years is the ascertainment of a Presidential election winner so that a transition of power can proceed. That particular aspect of the GSA got a lot more news and publicity over the past few weeks than perhaps it ever has in recent history.
TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers. According to the Hacker News Hiring Trends, May 2020, TensorFlow jobs are in great demand. Here are five reasons behind TensorFlow's popularity: TensorFlow is the only framework available for running machine learning models from the cloud to the tiniest microcontroller device. Models trained with TensorFlow can be optimized for CPU and GPU.
This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 22 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits! Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.
We are looking for a Technical Data Analyst and Program Manager to build out our extended data collection and performance analysis activities. Your job will be to gather and analyze large amounts of raw information from both internal and external sources such as Salesforce, AWS, StackOverflow, Couchbase, GitHub, Google Analytics or custom APIs. You will establish routine reporting and analysis derived from that data, evaluating the trends of our KPI's such that we remain informed as we evolve our objectives. We will rely on you to extract valuable business insights from this work as well as lead cross-functional projects and discussions as program manager for teams that are influenced by this information. In this role, you should be highly analytical with a background in analysis, math and statistics.