Next February 28th and 29th, the 2nd edition of this Hackathon on Artificial Intelligence & Machine Learning is organized with a new partner, SESAMm. The event will be held the university Paris Dauphine and will gather students from leading French universities. Teams will tackle a specific challenge (to be revealed in real time) on the theme of applying artificial intelligence and machine learning approaches to an asset management problem. For 24 hours, participants will be able to test their coding skills in artificial intelligence and machine learning. The winner will receive a 2,000€ cash prize.

In this episode of the Data Exchange I speak Dafna Shahaf, Associate Professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. She also runs the hyadata lab, a research group that consistently produces unique and interesting projects at the intersection of computer science, data, and the social sciences. Our goal in this podcast is to build a community of people interested in Data, Machine Learning and AI. If you have suggestions for us on what to recommend (books, conferences, links), and guests to book, please visit TheDataExchange.media

A researcher from Queen's University Belfast has developed an algorithm that could help address the issue of artificial intelligence (AI) bias. Although AI has many applications, it also brings the risk of bias. As AI is trained using large volumes of data, if this data contains human biases, AI algorithms will make connections based on this. For example, if shown images of doctors that are predominantly male, AI will learn that doctors are less likely to be female. This creates a significant issue when the technology is used in recruitment, insurance or policing, as there is a danger of it reinforcing existing bias rather than help eliminate it.

Knock is on a mission to make trading in your house as simple and certain as trading in your car. Started by founding team members of Trulia.com We share the same top-tier investors as iconic brands like Netflix, Tivo, Match, HomeAway and Houzz. We're You will get to work with some of the most interesting data sets and have the freedom and responsibility to help shape our core products from initial data exploration to production models. You will design and implement the foundation of our predictive models and work closely with other team members to test the accuracy in local markets.

The ECMWF Summer of Weather Code (ESoWC), which has begun its 2020 edition, brings together teams to solve technical challenges in analysis and forecasting for weather, climate, and atmospheric applications. The Copernicus Climate Change Service (C3S) and Copernicus Atmosphere Monitoring Service (CAMS), which are implemented by ECMWF on behalf of the European Commission, are participating again this year by providing mentors and being featured in some of the challenges teams are asked to work on. The collaborative online programme has two phases: first, an application period in which teams form and write proposals for challenges; these challenges are classified into three streams: weather-related software and applications, machine learning and artificial intelligence, and cloud-based weather and climate innovations. After the proposal deadline, 22 April, up to 15 of these proposals will be selected to be carried forward, supported by ECMWF and the Copernicus Programme. Resources are provided, which vary by challenge, but include stipends of £5000 and/or cloud credits from Amazon Web Services or WEkEO, the Copernicus Data and Information Access Service.

Version 1 is delighted to announce that we are now a proud member of CeADAR, Ireland's National Centre for Applied Analytics & AI. CeADAR is a market-focused technology centre that drives the accelerated development and deployment of data analytics and AI technology and innovation. "We see this as a key strategic membership, across Version 1 and in particular for our Innovation Labs, where we are focusing on developing solutions for our customers using Disruptive Technology using Artificial Intelligence (AI) and Robotic Process Automation (RPA). Becoming a CeADAR member makes perfect sense for us, helping us to execute on our vision, and showing how technology can make a real difference to our customers' businesses. We look forward to working with Edward and the team at CeADAR, bringing the depth of their experience and knowledge to our customers."

With all these new artificial intelligence use cases comes the daunting question of whether machines will force humans into obsolescence. The jury is still out: Some experts vehemently deny that artificial intelligence will automate so many jobs that millions of people find themselves unemployed, while other experts see it as a pressing problem. "The structure of the workforce is changing, but I don't think artificial intelligence is essentially replacing jobs," Rahnama said. "It allows us to really create a knowledge-based economy and leverage that to create better automation for a better form of life. It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it's probably algorithms replacing white-collar jobs such as business analysts, hedge fund managers and lawyers."

In this tutorial we'll make a Machine Learning Pipeline which inputs Business News and generates predictions for Apple Stock Price re-training through time. We'll also measure how profitable it is in real life. It has the daily high, low, open and close prices and the percentual change during that day. Note: To get a token you need have to create an account on openblender.io Now let's look at the data: Let's plot the price and Change: Now, what we want is to detect if the price is going to increase or decrease on the next day so we can buy or short.

We have all seen the rapid rise of new technologies that are disrupting the way we live our everyday lives. From our mobile devices, e-commerce retailing, sharing economy companies and a plethora of automated functionalities – innovation has changed our lives beyond recognition. It is through this relentless drive of technological innovation that has revolutionised the automotive sector. Despite the ongoing controversies around diesel and petrol cars, and the gradual implementation of electric vehicles, one of the more sci-fi adaptations that will be arriving on our roads in the near future is autonomous vehicles. But when will this happen?

Most organizations use a variety of data collection and analysis methods to understand their stakeholders, whether through customer satisfaction surveys, product reviews, or employee pulse surveys. These ways of gaining insights fall into two broad categories: quantitative and qualitative data collection and analysis. Quantitative responses can be tallied and analyzed quickly, efficiently, and accurately with the help of mathematical logic formulas, algorithms, and, now, machines. Analyzing qualitative data is typically trickier, and it largely remains the province of human analysts, given that it requires a high degree of contextual understanding and social intelligence. For qualitative analysis insights to be considered valid--and taken seriously--the old-school method generally involves two or more people separately analyzing (for example, coding or categorizing) the data collected.