A debate on the future of artificial intelligence (AI) in Europe drew a full house at the European Parliament - with MEPs and Commission leaders keen to find the best way to regulate and make the most of AI, while protecting us against its worst aspects, too. "We want to discuss this because you have to be cautious. Some artificial intelligence is simple, low risk, no risk, but some artificial intelligence may be life or death for you," says Margrethe Vestager, the Executive Vice-President of the European Commission. "So if it is very risky we have to be cautious, and all the rest of it, just go, go, go." And AI is already go, go, going fast, revolutionising areas like voice recognition and translation.

Lamb-succory (arnoseris minima), davall's sedge (carex davalliana) and red helleborine (cephalanthera rubra) are plants, native to the United Kingdom, that are endangered or already extinct1. The disappearances of these species might seem inconsequential in the grand scheme of things, but they're part of a global trend: A decrease in plant (and animal) biodiversity. Biodiversity is a critical component of the survival of any ecosystem. The variety of traits found in each plant (like a resistance to a certain type of insect, or prone to wilting) are critical to resilience of all species against shocks and stresses -- whether it be the arrival of invasive species, a natural disaster event or even climate change. Luckily, the growing availability of data storage and increasingly sophisticated machine learning techniques might be able to help.

We see the AI space poised for an acceleration in adoption, driven by more sophisticated AI models being put in production, specialized hardware that increases AI's capacity to provide quicker results based on larger datasets, simplified tools that democratize access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere. Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks, tasks that defy what traditional procedural logic and programming can handle--for example: image recognition, summarization, labeling, complex monitoring, and response. Get a free trial today and find answers on the fly, or master something new and useful. In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products--and a majority of companies are starting to adopt machine learning.

Want to learn more about how you can use machine learning for education within your organization? Attend ODSC East 2020 and learn from those who have made it happen. AI and Machine Learning are doing wonders in school education already. In their recent report, the Wall Street Journal covered the current effects of AI and Machine Learning on education in China. China's achievements in implementing AI and Machine Learning in education are incredible: Teachers, who were interviewed by the WSJ, unanimously support these innovations, saying that the implementation of AI in school education makes students more diligent and improves their academic performance.

You can probably walk up to any football fan right now and get their opinion on which team will win Sunday's Super Bowl LIV. But if you're looking for a really educated guess on the game's outcome, you'll want to ask Stanford computer scientist Louis Rosenberg, the founder of Unanimous A.I., a startup that combines the opinions of a lot of humans with artificial intelligence to make remarkably accurate predictions. In nature, many species exhibit something called swarm intelligence, meaning that they make smarter decisions as groups than as individuals -- in other words, a flock of birds or a school of fish is smarter than a single bird or fish. The idea behind Unanimous A.I. is to let well-informed humans create their own swarm intelligence. As a group, they can then answer questions, reach decisions, or make predictions with a greater accuracy than any one knowledgable person alone.

The University of Bristol (UoB) has partnered with Bristol Zoological Society (BZS) to develop a trailblazing approach to wildlife conservation, harnessing the power of machine learning and drone technology to transform wildlife conservation around the world. Backed by the Cabot Institute for the Environment, BZS and EPSRC's CASCADE grant, a team of researchers travelled to Cameroon in December last year to test a number of drones, sensor technologies and deployment techniques to monitor the critically endangered Kordofan giraffe populations in Bénoué National Park. "There has been significant and drastic decline recently of larger mammals in the park and it is vital that accurate measurements of populations can be established to guide our conservation actions," said Dr Gráinne McCabe, head of field conservation and science at BZS. "Bénoué National Park is very difficult to patrol on foot and large parts are virtually inaccessible, presenting a huge challenge for wildlife monitoring. What's more, the giraffe are very well camouflaged and often found in small, transient groups," said Dr Caspian Johnson, conservation science lecturer at BZS. Striving to uncover the best method for airborne wildlife monitoring, BZS reached out to Dr Matt Watson from the UoB's School of Earth Sciences, and Dr Tom Richardson from the University's Aerospace Department, as well as a member of the Bristol Robotics Laboratory (BRL). The team forged successful collaborations using drones to monitor and measure volcanic emissions to create a system for wildlife monitoring.

For many, music elicits an emotional response affecting how we feel, think, and act. With our current understanding, the exact way in which our brain and body responds to music remains an area of focus for researchers. In a new study, a group of psychologists and computer scientists at the University of Southern California worked in conjunction to establish how and why our brain reacts to music. Researchers recruited a set of participants, examining their heart rate, sweat gland activity, brain activity, and emotional responses to a few unfamiliar musical tracks. In the findings, it was noted that the Heschls' gyrus and the superior temporal gyrus, an area of the brain located in the auditory complex, was significantly influenced by the musical tracks.

LOUISVILLE – Even after Kentucky High School Athletic Association Commissioner Julian Tackett sent out an email notifying school officials that esports teams may not participate in the video game "Fortnite," there was nothing to be done among schools here. That's because "Fortnite," an online video game developed by Epic Games and released in 2017, was never included among the games played by Kentucky students in high school competitions. "Fortnite" is a third-person shooter game that doesn't include any blood, injuries or dead bodies, but nevertheless was given a Teen rating for violence by the Entertainment Software Rating Board. Epic Games and PlayVS, a software company that provides a platform for competitive esports, last week announced last Wednesday a partnership to introduce a competitive league for "Fortnite" across high schools and colleges. "There is no place for shooter games in our schools," Tackett said, adding that the KHSAA and the National Federation of State High School Associations had no knowledge that "Fortnite" was being added as part of the competition platform and are "strongly against it."

It wasn't so long ago that business analytics operated on a months-long cycle. For most of the twentieth century, the main interaction between a company and its data was a regular review of its most easily quantifiable measures, in the form of annual or quarterly financial assessments. Today, interacting with data this infrequently would be unimaginable in even a small business. As data availability and transfer speeds have grown at exponential rates, the time lag between intake and analysis of data has shortened to the point that, today, real-time data analytics is often part of an organisation's standard operating procedure. There are few industries which have not been lifted up by this rising tide of data.

The first step in any machine learning project is typically to clean your data by removing unnecessary data points, inconsistencies and other issues that could prevent accurate analytics results. Data cleansing can comprise up to 80% of the effort in your project, which may seem intimidating (and it certainly is if you attempt to do it by hand), but it can be automated. In this post, we'll walk through how to clean a dataset using Pandas, a Python open source data analysis library included in ActiveState's Python. All the code in this post can be found in my Github repository. If you already have Python installed, you can skip this step.