A daylong conference will cover a wide-range of topics related to computational data analysis, from how languages spread to ways of improving the value of crowdsourcing. The Data Science Workshop on Computational Social Science takes place Oct. 20. It's the first of what Dragomir Radev, the A. Bartlett Giamatti Professor of Computer Science, expects will be a regular event. "We decided we should try to cover different areas of data science," said Radev, one of the event's organizers. "We're starting with computational social science first and then switch to other areas in which data science and computer science have made an impact, for example, digital humanities, medicine, finance, etc." Radev said the event is something that likely would not have happened 10 or even five years ago.
Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson's adage applies well to AI adoption: The future is already here, it's just not evenly distributed. The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities.
We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behavior different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring... [Show full abstract]
In this special guest feature, Ran Sarig, Co-founder and CEO of Datorama, discusses the importance of applying machine learning to data integration or'cleansing' processes with speed and at a scale in order to keep up with the ever increasing number of data sources. And why Big Data needn't be a big mess anymore. Ran has 14 years of management, product, engineering and leadership experience. He co-founded Datorama in 2012 and is its chief executive officer. Prior to this, he worked for MediaMind as its VP of R&D where he managed a group of 130 engineers and product managers.
Everyone is talking about artificial intelligence these days actually if you look at searches and mention of artificial intelligence online you can see a clear exponential trend AI is not new it actually started over 65 years ago but today we finally have a cost efficient way to store transfer and compute a massive amount of data in a way that was never possible before. In 2017 I have been wondering the growth of AI industry and I have to say to you that I'm quite amazed by the attention AI is receiving recently. At some point I realized that all the tech newsletters I'm subscribed whenever I receive an email, almost every time there are news about AI. Developers are talking about AI. Engineers are talking about AI.
Today, BBC's R&D team announced a five-year initiative to use machine learning to work out what audiences want to watch. To accomplish this, the team is partnering up with data scientists and experts from UK universities as well as media and tech companies based in Europe and internationally. The Data Science Research partnership intends to create "a more personal BBC" that can entertain in new ways. Researchers will analyze user data and apply algorithms to get marketing and media insights about audiences' preferences. The details are vague for now, but the team says it plans to use machine learning on its own digital and traditional broadcasting content to gain new insights.
As an extension to its Data Lake Management Platform, Zaloni has introduced a machine-learning data matching engine, which leverages the data lake to create "golden" records and enable enriched data views for multiple use cases across business sectors. Zaloni's data matching engine provides a new approach for creating an integrated, consistent view of data that is updated, efficiently maintained and can drive customer-facing applications. It addresses a gap in the marketplace for a simplified, much less expensive and faster-to-implement solution for data mastering. Many master data records solutions are complex, inflexible, expensive and underperform for the cost," said Ben Sharma, Zaloni's CEO. "Zaloni's data matching engine, which is offered as an extension to Zaloni's Data Lake Management Platform, enables a practical, unique solution at a great value that will interest any organization that has a Customer or Product 360 initiative.
Several major corporations are already using quantum computers for machine learning and artificial intelligence. The quantum computers that are already in use have more than 100 million times the computing power of any classical computer. Artificial intelligence that's powered by classic computers is often only capable of finding one solution to a problem at a time. However, quantum computers can come up with multiple solutions to a problem simultaneously. This is because quarks act as the computer's bits (qubits).
Big Data was a big deal just a few years ago. Farmers worried about who had access to their data; farm groups worried about who owned a farmer's data; and the agribusiness sector was trying to figure out how to make money with data. Year after year, farmers were able to collect more and more data, but the practical benefits of all this data remained somewhat limited. Yield data was the first to be deeply analyzed and still remains one of the main reasons growers subscribe to data analysis services. Crop inputs were next as growers learned how to determine which inputs were needed, which performed well, and which actually were worth the investment.
It's not necessary to understand the inner workings of a Machine Learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you. I've written two blog posts on evaluation--the broccoli of Machine Learning. Both types are important not only to data scientists but also to managers and executives, who must evaluate project proposals and results. To managers I would say: It's not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem.