This article is adapted from GreenBiz's weekly newsletter, VERGE Weekly, running Wednesdays. Subscribe here.

As global losses rack up from climate change-exacerbated natural disasters from voracious wildfires to ferocious hurricanes communities are scrambling to prepare (and to hedge their losses).

While information technologies such as machine learning and predictive analytics may not be able to prevent these catastrophes outright, they could help communities be better prepared to handle the aftermath. Thats the spirit behind a unique collaboration between Chicago-based technology services company Exigent and the Schulich School of Business at York University in Toronto, one that aims to create a more cost-effective and efficient marketplace for disaster relief and emergency response services.

The idea is to help state and provincial governments collectively build a more centralized inventory of relief supplies and other humanitarian items based on the data from a particular wildfire or hurricane season.

Rather than buying supplies locally based on the predictions something many small towns in fire-prone areas can ill-afford a community would buy "options" for these services in the marketplace being developed through this partnership. If the town ultimately doesn't need the items, it could "trade" them to another region that does have a need, either in the same state or another location. In effect, towns across a state or region or even country could arrange for protection, without having to make that investment outright.

"Why are we not packing those crates in March, because they are going to go somewhere?" asked Exigent CEO David Holme, referring to the current system.

The most obvious reason is that its expensive: Relief suppliers won't invest in making items unless they have certainty of orders. The intention of the Exigent-Schulich project is to move from a system that is 100 percent reactive, and consequently very slow, to one that is at least 50 percent predictive that can deliver help far more quickly, he said.

To do this, Exigent is working with AI students at Schulich to use information about a communitys demographics, geology and topography, and existing infrastructure to predict what areas affect could need: how many first-aid kits to treat local citizens or how many cement bags to rebuild structures or how many temporary housing units for residents and relief workers. All sorts of data is being consulted, from census information to historical weather data to forward-looking models for wind direction, temperature and humidity, noted Murat Kristal, program director for the Schulich masters program that is involved in the project.

Governments and decision makers are acting in a reactive way right now.

The initial focus of the joint Exigent-Schulich work is on gathering data related to wildfires in Canada and the United States. The prevalence of Californias fires captures many headlines: the insurance losses from the Camp, Hill and Woolsey fires in November 2017 have topped $12 billion. Although it gets far less attention, Texas is also highly prone to wildfires and 80 percent of them are within two miles of a community. To the north, Canadian provinces such as Alberta and Ontario are also at risk: There are an average of 6,000 fires in Canada annually.

Exigent estimates that by deploying supplies to affected regions more quickly, the platform its developing a pilot version is due in June might cut recovery costs by 20 percent and drive down premiums in at-risk regions. "The municipalities and insurers can collaboratively benefit," Holme said. "The more Ive studied the idea, the more useful it seems."

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It's artificial intelligence to the rescue (and response and recovery) - GreenBiz

Artificial Intelligence, Machine Learning and Cloud Predictions by Jerry Kurata and Barry Luijregts, Pluralsight. In this article, they share their predictions for the ways that AI, ML and the cloud will be used differently in 2020 and beyond.

This decade has seen a seismic shift in the role of technology, at work and at home. Just ten years ago, technology was a specialist discipline in the workplace, governed by experts. At home things were relatively limited and tech was more in the background. Today technology is at the centre of how everyone works, lives, learns and plays. This prominence is shifting the way we think about, use, interact with and the expectations we have for technology, and we wanted to share some reflections and predictions for the year ahead.

AI Jerry Kurata

Increased User Expectations

As users experience assistants like Alexa and Siri, and cars that drive themselves, the expectations of what applications can do has greatly increased. And these expectations will continue to grow in 2020 and beyond. Users expect a stores website or app to be able to identify a picture of an item and guide them to where the item and accessories for the item are in the store. And these expectations extend to consumers of the information such as a restaurant owner.

This owner should rightfully expect the website built for them to help with their business by keeping their site fresh. The site should drive business to the restaurant by determining the sentiment of reviews, and automatically display the most positive recent reviews to the restaurants front page.

AI/ML will go small scale

We can expect to see more AI/ML on smaller platforms from phones to IoT devices. The hardware needed to run AI/ML solutions is shrinking in size and power requirements, making it possible to bring the power and intelligence of AI/ML to smaller and smaller devices. This is allowing the creation of new classes of intelligent applications and devices that can be deployed everywhere, including:

AI/ML will expand the cloud

In the race for the cloud market, the major providers (Amazon AWS, Microsoft Azure, Google Cloud) are doubling down on their AI/ML offerings. Prices are decreasing, and the number and power of services available in the cloud are ever increasing. In addition, the number of low cost or free cloud-based facilities and compute engines for AI/ML developers and researchers are increasing.

This removes much of the hardware barriers that prevented developers in smaller companies or locales with limited infrastructure from building advanced ML models and AI applications.

AI/ML will become easier to use

As AI/ML is getting more powerful, it is becoming easier to use. Pre-trained models that perform tasks such as language translation, sentiment classification, object detection, and others are becoming readily available. And with minimal coding, these can be incorporated into applications and retrained to solve specific problems. This allows creating a translator from English to Swahili quickly by utilizing the power of a pre-trained translation model and passing it sets of equivalent phrases in the two languages.

There will be greater need for AI/ML education

To keep up with these trends, education in AI and ML is critical. And the need for education includes people developing AI/ML applications, and also C-Suite execs, product managers, and other management personnel. All must understand what AI and ML technologies can do, and where its limits exist. But of course, the level of AI/ML knowledge required is even greater for people involved with creating products.

Regardless of whether they are a web developer, database specialist, or infrastructure analyst, they need to know how to incorporate AI and ML into the products and services they create.

Cloud Barry Luijbregts

Cloud investment will increase

In 2019, more companies than ever adopted cloud computing and increased their investment in the cloud. In 2020, this trend will likely continue. More companies will see the benefits of the cloud and realize that they could never get the same security, performance and availability gains themselves. This new adoption, together with increased economies of scale, will lower prices for cloud storage and services even further.

Cloud will provide easier to use services

Additionally, 2020 will be the year where the major cloud providers will offer more and easier-to-use AI services. These will provide drag-and-drop modelling features and more, out-of-the-box, pre-trained data models to make adoption and usage of AI available for the average developers.

Cloud will tackle more specific problems

On top of that, in 2020, the major cloud vendors will likely start providing solutions that tackle specific problems, like areas of climate change and self-driving vehicles. These new solutions can be implemented without much technical expertise and will have a major impact in problem areas.

Looking further ahead

As we enter a new decade, we are on the cusp of another revolution, as we take our relationship with technology to the next level. Companies will continue to devote ever larger budgets to deploying the latest developments, as AI, machine learning and the cloud become integral to the successful running of any business, no matter the sector.

There have been murmurings that this increase in investment will have an impact on jobs. However, if the right technology is rolled out in the right way, it will only ever complement the human skillset, as opposed to replacing it. We have a crucial role to play in the overall process and our relationship with technology must always remain as intended; a partnership.

Jerry Kurata and Barry Luijregts are expert authors at Pluralsight and teach courses on topics including Artificial Intelligence (AI) and machine learning (ML), big data, computer science and the cloud. In recent years, both have seen first-hand the development of these technologies, the different tools that organisations are investing in and the changing ways they are used.

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8 Artificial Intelligence, Machine Learning and Cloud Predictions To Watch in 2020 - Irish Tech News

Editors Note:All opinion section content reflects the views of the individual author only and does not represent a stance taken by The Collegian or its editorial board. Letters to the Editor reflect the view of a member of the campus community and are submitted to the publication for approval.

To the Editor,

I am writing this essay to bring awareness and recognition to a fast-approaching topic in the field of military technology weaponized artificial intelligence.

Weaponized AI is any military technology that operates off a computer system that makes its own decisions. Simply put, anything that automatically decides a course of action against an enemy without human control would fall under this definition.

Weaponized AI is a perfect example of a sci-fi idea that has found its way into the real world and is not yet completely understood. This said, weaponized AI places global security at risk and must be recognized by institutions like Colorado State University before it becomes widely deployed on the battlefield.

Nations are constantly racing to employ the next best weapon as it is developed. AI is no exception. Currently, AI is responsible for the one of the largest technology competitions since that of nuclear weapons during the Cold War. At the top of this competition is China and the United States.

With little to no international restrictions on the deployment of AI weaponry, a modern arms race will continue to develop, creating tensions between world powers as fear of the opposing team reaching the perfect AI weapon arises.

The other inherent danger is the gap that is being created between advanced world powers and countries who are incapable of developing such technology. The tendency for global conflict to occur between these nations increases, as powers that wield weaponized AI have a distinct edge over countries that do not employ AI. This allows room for misuse of this power given the lack of international regulations on using this tech.

What we have is a blurring of moral boundaries as we come closer to allowing this technology to determine who is a true threat.

Going further, my studies have shown that this technology poses considerable risk to international human rights laws. In its current state, weaponized AI is found to be unreliable in doing what it is intended to do. As an example, Project Maven, a current AI used by the United States, only identifies military threats using complex algorithms.

While this seems harmless, the direction in which the world is taking this technology is not. What would happen if this technologys unreliability costs innocent lives due to a targeting error that AIs, like Project Maven, are prone to making? Likewise, who would take responsibility for the actions of a machine?

What we have is a blurring of moral boundaries as we come closer to allowing this technology to determine who is a true threat. These kinds of errors cannot be tolerated by the rules of modern warfare.

A final obstacle surrounding AI is the United Nations inability to come to a consensus on its use. Researcher Eugenio Garcia with the United Nations stated, Advanced military powers remain circumspect (guarded) about introducing severe restrictions on the use of these technologies.

Although people easily recognize the dangers that AI poses to national security, countries are not willing to restrict the development. Furthermore, with minimal current legislation on the unreliability of the technology, weaponized AI will move further than what we can control.

While I make these claims, one must recognize that the technology does offer the benefit of removing soldiers from the battlefield. However, nations around the world are not monitoring this rising issue.

Colorado State University, being a tier one research facility that has investment in military technology, will be the institution that does step up to the plate and recognize catastrophe before it happens. These threats to global security may not be present now, but if we do not advocate for international legislation, these dangers will become reality.

Sincerely,

Thomas Marshall

Third-year mechanical engineering student at CSU

Working under Azer Yalin as an undergraduate research assistant exploring Air Force technology

The Collegians opinion desk can be reached atletters@collegian.com. To submit a letter to the editor, pleasefollow the guidelines at collegian.com.

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LTTE: It's important to know of weaponized artificial intelligence - Rocky Mountain Collegian

In the run-up to Ludwig van Beethoven's 250th birthday, a team of musicologists and programmers is using artificial intelligence to complete the composer's unfinished tenth symphony.

The piece was started by Beethoven alongside his famous ninth, which includes the well-known Ode To Joy.

But by the time the German composer died in 1827, there were only a few notes and drafts of the composition.

The experiment risks failing to do justice to the beloved German composer. Tthe team said the first few months yielded results that sounded mechanical and repetitive.

But now the project leader, Matthias Roeder, from the Herbert von Karajan Institute, insists the AI's latest compositions are more promising.

"An AI system learns an unbelievable amount of notes in an extremely short time," said Roeder. "And the first results are a bit like with people, you say 'hmm, maybe it's not so great'. But it keeps going and, at some point, the system really surprises you. And that happened the first time a few weeks ago. We're pleased that it's making such big strides."

The group is in the process of training an algorithm that will produce a completed symphony. They're doing this by playing snippets of Beethoven's work and leaving the computer to improvise the rest of it. Afterwards, they correct the improvisation so it fits with the composer's style.

Similar projects have been undertaken before. Schubert's eighth symphony was finished using AI developed by Huawei. It received mixed reviews.

The final result of the project will be performed by a full orchestra on 28 April next year in Bonn as part of a series of celebrations of Beethoven's work.

The year of celebrations begins on December 16th with the opening of his home in Bonn as a museum after renovation.

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Artificial intelligence is writing the end of Beethoven's unfinished symphony - Euronews

Anyone whos worked in technology knows that certainbuzzwords rip through the industry every few years, sending executives into afever. Artificial intelligence, Big Data, Hadoop, and Web 2.0 (please,lets do our best to forget that last one) are just a few of the more notable.But which ones will translate into actual opportunities and jobs for all thetechnologists out there?

If the hype doesnt match the actual industry impact, thenmany thousands of workers will have pursued a particular technology ordiscipline for nothing. But if the hype is justified, then folks can buildsatisfying careers (and make a lot of money). The stakes couldnt be higher.

As we head into 2020, one thing is pretty clear: Artificial intelligence (A.I.) seems like one of those much-hyped terms that might actually translate into a really robust sub-industry. For example, LinkedIns 2020 Emerging Jobs Report (PDF) puts Artificial Intelligence Specialist as its number-one emerging job, with 74 percent annual growth over the past four years.

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That outpaced robotics engineer (40 percent annual growthduring the same four-year period), datascientist (37 percent annual growth), full stack engineer (35 percentannual growth), and site reliability engineer (34 percent growth). (In order toarrive at its conclusions, LinkedIn crunched data from all of its publicprofiles over the past five years.)

Sounds pretty solid, right? Even so, the A.I. industry comeswith a relatively high bar to entry, which could restrict the pipeline oftalent for the next few years. Employers want A.I. experts skilled in machinelearning, deep learning, Python,natural language processing, and platforms suchas TensorFlow. Those are skills that take quite some time to learn, to putit mildly, and demand a pretty strong background in programming andmathematics.

Theres also the issue of company buy-in. Executives lovebuzzwords, but they often balk at the cost of spinning up the relatedtechnology. At this years The WallStreet Journals Future of Everything Festival, Arvind Krishna, IBMs seniorvice president of cloud and cognitive software, suggested that projects tend todie once companies realize theyll need to spend a lot of time prepping thenecessary datasets: And so you run out of patience along the way, because youspend your first year just collecting and cleansing the data.

Plus, existing A.I. initiatives have amixed track record so far. Ubers attempt to build a self-driving car platformhas hit some snags, toput it mildly; IBMs much-hyped Watson platform has failedto meet some hospitals expectations for successful healthcare dataanalysis; and some analysts and pundits think that even well-monetized projectssuch as Googles DeepMind haventeither scaled or commercialized.

Nonetheless, the future seems prettybright for artificial intelligence and machine-learning initiatives. Even ifsome high-profile projects crash and burn, its clear from the data thatcompanies are rapidly hiring various types of employees with A.I. skillsclusters. According to Burning Glass, which analyzes millions of job postingsfrom across the U.S., jobs that involve A.I. are projected to grow 40.1 percentover the next decade; the median salary for these positions is $105,007 (forthose with a PhD, it drifts up to $112,300).

Positions associated with A.I. skills clusters include:

If you work in any of these roles, A.I. and machine learningtools and techniques will likely become a part of your workflow over the nextseveral years. That means its important to learn as much as possible about A.I.Fortunately, there are a lot of resources online that can help you out,including a Google crashcourse,completewith 25 lessons and 40+ exercises, thats a good introduction to machinelearning concepts. HackerNoonalso offersan interesting breakdown of machine learning andartificialintelligence.

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Artificial Intelligence Job Demand Could Live Up to Hype - Dice Insights

PARIS--(BUSINESS WIRE)--Iktos, a company specialized in Artificial Intelligence for novel drug design and Almirall, S.A. (ALM), a leading skin-health focused global pharmaceutical company, today announced a collaboration agreement in Artificial Intelligence (AI), where Iktos generative modelling technology will be used to design novel optimized compounds, to speed up the identification of promising drug candidates for undisclosed Almirall drug discovery program(s).

Iktos AI technology, based on deep generative models, helps bring speed and efficiency to the drug discovery process, by automatically designing virtual novel molecules that have all desirable characteristics of a novel drug candidate. This tackles one of the key challenges in drug design: rapid and iterative identification of molecules which simultaneously validate multiple bioactive attributes and drug-like criteria for clinical testing.

This partnership is an example of how we intend to explore the enormous possibilities offered by technology to find new molecules and to speed up clinical development, said Dr. Bhushan Hardas, Executive Vice President R&D, Chief Scientific Officer of Almirall. The health sector lags behind others in the digital world. Almirall wants to be at the forefront of innovation to develop holistic and transversal approaches. Artificial Intelligence will provide Almirall a unique opportunity to combine our proficiency with the preciseness and celerity to truly make a difference in patients' lives.

We are thrilled to initiate a new research collaboration with Almirall commented Yann Gaston-Math, President and CEO of Iktos. This new collaboration is further testimony to the leadership position that Iktos has developed in the field of AI for de novo drug design, in little more than two years of existence. We are eager to demonstrate to our collaborators the power of Iktos technology to accelerate their research, and to get the opportunity to further improve by confronting our approach to a new use case, consistently with our strategy to prove our value in real-life projects.

Iktos has recently announced several collaborations with biopharmaceutical companies where Iktos AI technology is used to accelerate the design of promising compounds, and has published, at the EFMC 2018 meeting, an experimental validation of the technology in a real-life drug discovery project. Iktos generative modelling SaaS software, Makya, is now available on the market, and Iktos intends to release its retrosynthesis SaaS platform Spaya as a beta version, before the end of 2019.

About Iktos

Incorporated in October 2016, Iktos is a French start-up company specialized in the development of artificial intelligence solutions applied to chemical research, more specifically medicinal chemistry and new drug design. Iktos is developing a proprietary and innovative solution based on deep learning generative models, which enables, using existing data, to design molecules that are optimized in silico to meet all the success criteria of a small molecule discovery project. The use of Iktos technology enables major productivity gains in upstream pharmaceutical R&D. Iktos offers its technology both as professional services and as a SaaS software platform, Makya.

About Almirall

Almirall is a leading skin-health focused global pharmaceutical company that partners with healthcare professionals, applying Science to provide medical solutions to patients and future generations. Our efforts are focused on fighting against skin health diseases and helping people feel and look their best. We support healthcare professionals by continuous improvement, bringing our innovative solutions where they are needed.

The company, founded almost 75 years ago with headquarters in Barcelona, is listed on the Spanish Stock Exchange (ticker: ALM). Almirall has been key in value creation to society according to its commitment with to major shareholders and through its decision to help others, to understand their challenges and to use Science to provide solutions for real life. Total revenues in 2018 were 811 million euros. More than 1,800 employees are devoted to Science.

For more information, please visit almirall.com

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Iktos and Almirall Announce Research Collaboration in Artificial Intelligence for New Drug Design - Business Wire