The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world. However, there are also increasing pressures on politicians to combat the threat posed by Climate Change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other? What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)? A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.
The graph represents a network of 1,495 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 16 September 2020 at 12:31 UTC. The requested start date was Wednesday, 16 September 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 4-day, 7-hour, 25-minute period from Friday, 11 September 2020 at 16:35 UTC to Wednesday, 16 September 2020 at 00:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Verta, an AI/ModelOps company whose founder created the open source ModelDB catalog for versioning models, has launched with a $10 million Series A led by Intel Capital. The Verta system tackles what is becoming an increasingly familiar problem: not only enabling ML models to get operationalized, but to track their performance and drift over time. Verta is hardly the only tool in the market to do so, but the founder claims that it tracks additional parameters not always caught by model lifecycle management systems. While Verta shares some capabilities with the variety of data science platforms that have grown fairly abundant, its focus is more on the operational challenges of deploying models and keeping them on track. As noted, it starts with model versioning, ModelDB was created by Verta founder Manasi Vartak, a software engineering veteran of Facebook, Google, Microsoft, and Twitter, as part of her doctoral work at MIT. It versions four aspects of models, encompassing code, data sources, hyperparameters, and the compute environment on which the model was designed to run.
Understanding extreme asset price changes involves combining price history, news, events and social media data, much of which is only available in the form of unstructured text. By applying machine learning technologies to a real-time data pipeline, Refinitiv Labs has developed a prototype to help traders identify and respond to extreme price moves at pace. For more data-driven insights in your Inbox, subscribe to the Refinitiv Perspectives weekly newsletter. Data is abundant, not only in volume, but also in the number of sources it is derived from, the frequency at which it is updated, and the variety of formats it may take. Time spent sorting through that data, however, can keep businesses from generating actionable information at pace.
Anything'for the first time' will certainly have plenty of predictions made and discussions on how it would be taken over in the future. Earlier when the computers got invented or when the idea of'Machine Learning' was born it was all the same through predictions and foresight. However, reality overtook all the fear and concern as the technology got even smarter than anticipated. Machine Learning in daily life is getting successfully applied in all aspects right from speech recognition apps in smartphones to YouTube recommendations and more. We literally teach machines on what we like and wish through our smart devices on a daily basis.
It is a career field that stems from multiple disciplines. Data is the necessity of industries and therefore, Data Science has a large number of applications. In this article, we will discuss some of the important data science applications and see how it is shaping the industries of the world today. Data Science has dominated almost all the industries of the world today. There is no industry in the world today that does not use data.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Technology is now evolving at such a rapid pace that annual predictions of trends can seem out-of-date before they even go live as a published blog post or article. As technology evolves, it enables even faster change and progress, causing an acceleration of the rate of change, until eventually, it will become exponential. Technology-based careers don't change at the same speed, but they do evolve, and the savvy IT professional recognizes that his or her role will not stay the same. And an IT worker of the 21st century will constantly be learning (out of necessity if not desire). What does this mean for you?
Description -- This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.