Article | July 30, 2020
With the release of MariaDB Platform X5, we’ve added new tooling and features that offload development complexity so developers can focus on creating innovation solutions. MariaDB Platform X5 includes MariaDB Enterprise Server 10.5, pluggable engines (such as InnoDB, ColumnStore, MyRocks, Spider and now Xpand), connectors, and the MaxScale database proxy. And because there’s so much that’s included with the MariaDB Platform X5 release I figured it might be best to start with the high points before drilling down into more details in the future.
Article | August 13, 2020
The coronavirus outbreak in China has grown to a pandemic and is affecting the global health & social and economic dynamics. An ever increasing velocity and scale of analysis — in terms of both processing and access is required to succeed in the face of unimaginable shifts of market; health and social paradigms. The COVID-19 pandemic is accompanied by an Infodemic. With the global Novel Coronavirus pandemic filling headlines, TV news space and social media it can seem as if we are drowning in information and data about the virus. With so much data being pushed at us and shared it can be hard for the general public to know what is correct, what is useful and (unfortunately) what is dangerous. In general, levels of trust in scientists are quite high albeit with differences across countries and regions. A 2019 survey conducted across 140 countries showed that, globally, 72% of the respondents trusted scientists at “high” or “medium” levels. However, the proportion expressing “high” or “medium” levels of trust in science ranged from about 90% in Northern and Western Europe to 68% in South America and 48% in Central Africa (Rabesandratana, 2020).
In times of crisis, like the ongoing spread of COVID-19, both scientific & non-scientific data should be a trusted source for information, analysis and decision making. While global sharing and collaboration of research data has reached unprecedented levels, challenges remain. Trust in at least some of the data is relatively low, and outstanding issues include the lack of specific standards, co-ordination and interoperability, as well as data quality and interpretation. To strengthen the contribution of open science to the COVID-19 response, policy makers need to ensure adequate data governance models, interoperable standards, sustainable data sharing agreements involving public sector, private sector and civil society, incentives for researchers, sustainable infrastructures, human and institutional capabilities and mechanisms for access to data across borders.
The COVID19 data is cited critical for vaccine discovery; planning and forecasting for healthcare set up; emergency systems set up and expected to contribute to policy objectives like higher transparency and accountability, more informed policy debates, better public services, greater citizen engagement, and new business development. This is precisely why the need to have “open data” access to COVID-19 information is critical for humanity to succeed. In global emergencies like the coronavirus (COVID-19) pandemic, open science policies can remove obstacles to the free flow of research data and ideas, and thus accelerate the pace of research critical to combating the disease. UNESCO have set up open access to few data is leading a major role in this direction. Thankfully though, scientists around the world working on COVID-19 are able to work together, share data and findings and hopefully make a difference to the containment, treatment and eventually vaccines for COVID-19.
Science and technology are essential to humanity’s collective response to the COVID-19 pandemic. Yet the extent to which policymaking is shaped by scientific evidence and by technological possibilities varies across governments and societies, and can often be limited. At the same time, collaborations across science and technology communities have grown in response to the current crisis, holding promise for enhanced cooperation in the future as well.
A prominent example of this is the Coalition for Epidemic Preparedness Innovations (CEPI), launched in 2017 as a partnership between public, private, philanthropic and civil society organizations to accelerate the development of epidemic vaccines. Its ongoing work has cut the expected development time for a COVID-19 vaccine to 12–18 months, and its grants are providing quick funding for some promising early candidates. It is estimated that an investment of USD 2 billion will be needed, with resources being made available from a variety of sources (Yamey, et al., 2020).
The Open COVID Pledge was launched in April 2020 by an international coalition of scientists, lawyers, and technology companies, and calls on authors to make all intellectual property (IP) under their control available, free of charge, and without encumbrances to help end the COVID-19 pandemic, and reduce the impact of the disease. Some notable signatories include Intel, Facebook, Amazon, IBM, Sandia National Laboratories, Hewlett Packard, Microsoft, Uber, Open Knowledge Foundation, the Massachusetts Institute of Technology, and AT&T. The signatories will offer a specific non-exclusive royalty-free Open COVID license to use IP for the purpose of diagnosing, preventing and treating COVID-19.
Also illustrating the power of open science, online platforms are increasingly facilitating collaborative work of COVID-19 researchers around the world. A few examples include:
1. Research on treatments and vaccines is supported by Elixir, REACTing, CEPI and others.
2. WHO funded research and data organization.
3. London School of Hygiene and Tropical Medicine releases a dataset about the environments that have led to significant clusters of COVID-19 cases,containing more than 250 records with date, location, if the event was indoors or outdoors, and how many individuals became infected. (7/24/20)
4. The European Union Science Hub publishes a report on the concept of data-driven Mobility Functional Areas (MFAs). They demonstrate how mobile data calculated at a European regional scale can be useful for informing policies related to COVID-19 and future outbreaks. (7/16/20)
While clinical, epidemiological and laboratory data about COVID-19 is widely available, including genomic sequencing of the pathogen, a number of challenges remain:
1. All data is not sufficiently findable, accessible, interoperable and reusable (FAIR), or not yet FAIR data.
2. Sources of data tend to be dispersed, even though many pooling initiatives are under way, curation needs to be operated “on the fly”.
3. In addition, many issues arise around the interpretation of data – this can be illustrated by the widely followed epidemiological statistics. Typically, the statistics concern “confirmed cases”, “deaths” and “recoveries”. Each of these items seem to be treated differently in different countries, and are sometimes subject to methodological changes within the same country.
4. Specific standards for COVID-19 data therefore need to be established, and this is one of the priorities of the UK COVID-19 Strategy. A working group within Research Data Alliance has been set up to propose such standards at an international level.
Given the achievements and challenges of open science in the current crisis, lessons from prior experience & from SARS and MARS outbreaks globally can be drawn to assist the design of open science initiatives to address the COVID-19 crisis. The following actions can help to further strengthen open science in support of responses to the COVID-19 crisis:
1. Providing regulatory frameworks that would enable interoperability within the networks of large electronic health records providers, patient mediated exchanges, and peer-to-peer direct exchanges. Data standards need to ensure that data is findable, accessible, interoperable and reusable, including general data standards, as well as specific standards for the pandemic.
2. Working together by public actors, private actors, and civil society to develop and/or clarify a governance framework for the trusted reuse of privately-held research data toward the public interest. This framework should include governance principles, open data policies, trusted data reuse agreements, transparency requirements and safeguards, and accountability mechanisms, including ethical councils, that clearly define duties of care for data accessed in emergency contexts.
3. Securing adequate infrastructure (including data and software repositories, computational infrastructure, and digital collaboration platforms) to allow for recurrent occurrences of emergency situations. This includes a global network of certified trustworthy and interlinked repositories with compatible standards to guarantee the long-term preservation of FAIR COVID-19 data, as well as the preparedness for any future emergencies.
4. Ensuring that adequate human capital and institutional capabilities are in place to manage, create, curate and reuse research data – both in individual institutions and in institutions that act as data aggregators, whose role is real-time curation of data from different sources.
In increasingly knowledge-based societies and economies, data are a key resource. Enhanced access to publicly funded data enables research and innovation, and has far-reaching effects on resource efficiency, productivity and competitiveness, creating benefits for society at large. Yet these benefits must also be balanced against associated risks to privacy, intellectual property, national security and the public interest.
Entities such as UNESCO are helping the open science movement to progress towards establishing norms and standards that will facilitate greater, and more timely, access to scientific research across the world. Independent scientific assessments that inform the work of many United Nations bodies are indicating areas needing urgent action, and international cooperation can help with national capacities to implement them. At the same time, actively engaging with different stakeholders in countries around the dissemination of the findings of such assessments can help in building public trust in science.
Article | December 23, 2020
Imagination, creativity, and ambition are what brought us here to this modern high-tech age that we live in today. These characteristics are the reason behind mankind’s innovative creations that started from inventing the wheel in the stone ages, to the development of the first car in 1885.
“Every generation needs a new revolution”
For science, the sky’s the limit when it comes to new creations and inventions that serve mankind in their daily life tasks, or just to entertain. Whether it’s a smartphone that keeps you connected with people, a cleaning robot that keeps your floor shiny and spotless, or even a cup holder that just holds your drink inside your car. As time passes, we notice how technology and science prosper to bring new tools and gadgets to the world to facilitate human tasks. It’s all thanks to humans’ ability to imagine and create.
Now, we have reached a point where Artificial Intelligence has been introduced and is helping us do tasks much easier and faster. This remarkable technology has the ability to do what humans can in a more efficient way. It doesn’t have emotions like us but it sure can think and act like it. Therefore, as you think about it, is it possible for them to reach that point of creating, as humans have?
What Is AI Technology?
Artificial intelligent technology (also known as AI) is the process in which robots and machines, created by man, are able to solve problems, complete motor-related tasks, and think like humans by just learning from the experiences they approach. By using the method of deep learning, machine learning, and natural language processing, robots can process all the data we give them in an algorithmic manner. By doing so, they determine certain patterns and features to produce the ability to act and think like humans.
Although for some scientists and researchers, the definition of AI technology can go beyond the description I just provided you. Moreover, it’s known as robots that think like humans. You can notice all the amazing inventions originated by using AI like self-parking cars, voice-activated lights, and much more. Although does it stop there? Can more come from AI technology rather than just providing help around the house? Or when giving directions on a built-in GPS?
What I want to get at is…
Can AI Technology Create and Write Music?
It has been known across history and for generations that creating an artistic masterpiece requires passion, dedication, and emotions to bring forth a spectacular piece of art.
Great artistic minds like Da Vinci, were able to paint what to be considered today as one of the iconic paintings that symbolize art, known as the Mona Lisa painting. Another poetic artistic mind like Bach was able to create extraordinary and moving pieces of music that set new standards for musicians back in the 18th century. It is because of humans’ ability to imagine and their willingness to express their emotions through art, that they were able to originate outstanding masterpieces.
Although making music, for instance, does require that sense of passion and emotion to generate it. It still relies on basic rules, patterns, and fundamentals that are very important to know, and acquire, to make or write music from scratch. You see, any piece of music is made up of musical notes, certain chords, and a rhythmic bassline. These musical elements create what we call a melody in a song or just a full song. You can’t apply any chord to any bassline without referring to the rules of music first, because making music is all about assembling all of those key factors in a musical pattern. While following the rules at the same time. Of course, some musicians bypass those rules and still are able to create great songs. Even so, that’s just a different story from what I’m trying to explain.
Emotions in Music
When listening to any type of song, you can instantly tell if it’s a sad, happy, motivating, or horrifying song just from the lyrics. The instrumental melody in the background plays a huge factor in determining the mood of the song. Some chords are considered to provide a happy and uplifting feeling when played. Other musical notes and patterns generate that sense of sadness. Music Theory has assigned certain feelings to certain chords. So, when writing a happy or sad song you can easily pick which chord to use for the certain feeling you want to express in your musical piece. Some instruments used in songs are also related to specific feelings as well. That’s how the genre of music is determined most of the time.
Since making and writing music is mostly about knowing the rules and choosing which chord to go with which note. Then it’s possible to create a song relying only on the fundamentals of Music Theory. Also, if we consider these chords and notes as patterns and data, can’t it be implemented and processed by AI technology?
AI Technology Seen Today
A lot of modern age gadgets have been introduced to the market that uses AI technology to serve our wants and needs. Even music tech. Musical gadgets have been invented like automatic tuners that can tune your guitar hands-free, metronome watches, portable guitars, and many more. What’s truly fascinating is the implementation of this technology into next-level music inventions.
You might not know this but AI technology has been implemented on most of the music platforms you use today. Platforms like Pandora, Spotify, Amazon Music, and others use mathematical algorithms in their operating system that enable them to predict and suggest songs that might seem appealing to you. It’s all according to the thumbs-up feedback you provide for each song you listen to. It’s also associated with the constant clicks and searches you conduct for certain types of artists and songs.
It’s simply remarkable how these music platforms are able to provide the user with song suggestions and playlists that suit their music taste. This is all possible through AI technology’s ability to process the patterns in the music being played, the tempo, and the instruments used. With that data processed, it can predict the next song that will meet the user’s desire. Since AI can identify the type of music being played, then it’s safe to say that there’s a possibility it can write music as well. Actually, it already has.
Music Platforms That Create Music
Throughout history, many great musical artists have used technology and machines to make writing music an easier task. While for some it was just a source of inspiration. AI technology had been used by musicians for a long time to assist them in originating their masterpieces. Alan Turing, the godfather of computer science, built a machine in 1951 that generated three simple melodies. David Bowie used the lyric randomizer in the ’90s for inspiration. In addition, a music theory professor was able to create a computer program that was able to write new music in the style of Bach. These few examples make it clear that AI was able to assist artists in their music careers.
After a few thorough tests and analyses, AI has become a part of the songwriting process. The research has led to the development of songwriting platforms like Watson Beat, Amper, and Google Magenta NSynth Super. These platforms use the essence of AI in deep learning by processing the data given. They search for patterns in the styles, chords, and other musical elements between songs to produce new material in the end.
Songwriting platforms, like Amper, allow anyone without the musical knowledge or experience to create a full song instantly. The process is quite easy. All you have to do is pick a genre, mood, and tempo while Amper takes care of the rest. This program has been used to create music for podcasts, commercials, and videos for companies. Still, it hasn’t been able to produce a hit song that will reach the top spot in the music billboards. My guess is because it lacks passion and emotion when creating a song.
Will AI Technology Take Over the Music Industry?
While a lot believe that someday AI Technology is going to backfire on us and take over our jobs. Some say that if they do take over everything then more jobs will emerge from it in the process. It’s a conflicting discussion.
What has been made clear though, is that today’s modern age technology has been able to create and write music just like humans can. It’s faster and efficient. Even though it might seem that AI will take over the music industry. It still doesn’t have that emotional side of making music. Furthermore, it’s been proven that even music writing platforms, like Amper, don’t have the ability to create a hit number one song. Or one that will catch the attention of millions. Although they can create music for marketing, promotional, and commercial purposes easily.
In conclusion, Artificial Intelligence is truly a mind-blowing invention. Having it helps us with our everyday life tasks and daily routines. Nevertheless, making it write music is also remarkable and sets high standards for technology nowadays. We don’t know what technology has installed for us but it’s no doubt making the world a better place.
Article | May 20, 2020