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‘The Walking Dead’: Samantha Morton on adapting to the coronavirus – Insider – INSIDER

Posted on March 19, 2020 by Danzig

Samantha Morton's character, Alpha, has lived according to the motto, "We are the end of the world," on AMC's apocalyptic zombie series, "The Walking Dead."

Currently, that motto may hit a little too close to home as people practice social distancing amid the coronavirus pandemic.

But Morton is much more positive about the state of our world, despite her character's nihilism.

"I don't feel we're at the end of the world at all," Morton told Insider when asked about any parallel between her character's outlook on life and reality.

Alpha led a cult telling a group of survivors they were the end of the world. Jace Downs/AMC

"My feelings are the world is constantly changing and we have to adapt and change with it," she continued. "If, as a society, we need to learn new habits and new behaviors to prosper whether it's to do with the environment or to do with love or respecting other cultures we just have to adapt and survive. I don't think it's the end of the world at all."

Morton's character was killed off "TWD" Sunday. In a nod to the comics, Negan infiltrated the Whisperers, gained their trust, and when the timing was right, took her out. Morton told Insider she knew exactly how she would be killed off since joining the series as the leader of the Whisperers on season nine.

Now, with Alpha out of the picture, it's looking less like the Whisperers will be able to bring their "end of the world" agenda to life.

"The Walking Dead" airs Sundays at 9 p.m. on AMC. You can follow along with our "Walking Dead" coverage here.

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'The Walking Dead': Samantha Morton on adapting to the coronavirus - Insider - INSIDER

Coronavirus is a chance to reset our relationship with our phones – The Guardian

Posted on March 19, 2020 by Danzig

We are in for a long haul. We, who have become accustomed to expecting things now, are going to have to wait. It could be months before our world returns to normal, if it ever does. Or if it even should. We are experiencing something unprecedented: a pandemic in the digital age.

Yet this is a unique opportunity which we should not pass up. In this moment of pause, we have the chance to reset our relationship to tech. For the last decade, tech has been running us. Now is our chance to reset that relationship.

It wasnt so long ago that we all started carrying smartphones. We gave them to our kids. These devices seemed like such cool things to have they gave us everything now: information, transportation, entertainment, food, even sex. We never liked to think about how they were changing our behavior. Making us more aggressive with each other. More judgmental, narcissistic, impatient, impulsive. More likely to treat each other as objects to consume and discard or ghost.

We surrendered our power to companies who used these devices to modify our behavior, with algorithms designed to do just that. We agreed without agreeing to become programmed something we still dont like to contemplate because the convenience of everything we were getting so quickly felt so good.

That is, until it didnt feel good any more. Until we began to feel more anxious and depressed. The companies making these devices promised that they would bring our world closer together. Yet we felt less connected, not more.

When medical professionals began to insist on social distancing as a way to curb the spread of coronavirus, people reacted with alarm. Its scary to think that we cant connect with each other. We need each other. We have evolved to need each other not only to feel good, but to survive.

Humans are difficult and complicated and messy; it was easier to have our primary relationships with our phones

And yet, if were honest about it, we began this process of social distancing years ago. About the same time we started carrying around these phones, we found ourselves having fewer in-person conversations; we visited each other less; we had fewer parties and dinner parties; we stopped going on real dates. It seemed easier to just not deal with each other. Humans are difficult and complicated and messy; it was easier to have our primary relationships with our phones. All of this served tech companies quite well. Every click, scroll, swipe provided them with more data, which translated into more money in their profit columns.

But now were in a moment when we need each other more than ever. We will need each other to provide information, comfort, solace, distraction, entertainment, jokes. We will need each other to listen. We will need to support each other, like family members do, or should; we will need to see ourselves once again as all belonging to the same family of humankind.

And we can use these devices to do just that. We can revert our relationship with tech to the utopian vision of the early days of the internet, when it was seen as something that was going to help us grow and evolve and learn new and better ways of communicating.

But in order to do that, we have to modify our own behavior behavior that has been perversely modified by the companies seeking our data, over these last few years. We cannot troll. We cannot be the snarky one with that smartass comment that gets attention at the expense of someones feelings. We cannot neglect or ignore those to whom we bear responsibility. We cannot spread negativity. We cannot spread nihilism and death, by which I mean the death of social connection.

In order to do this, we will have to use social media very consciously. We must think before we post. We must use this unprecedentedly powerful medium with the same sense of consciousness Kafka once wrote about, in a passage that seems more relevant than ever in the time of the coronavirus: We human beings ought to stand before one another as reverently, as reflectively, as lovingly, as we would before the entrance to hell.

We can start by simply asking each other: How are you?

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Coronavirus is a chance to reset our relationship with our phones - The Guardian

Artificial Intelligence is Becoming the Future of Investment Platforms – EnterpriseTalk

Posted on March 19, 2020 by Danzig

How can AI help in investment decisions? And if there are challenges, how does your platform help to resolve those challenges?

As to why investors in general need AI, there are enormous amounts of data out there, and there is an ongoing battle over that available data. The industry as a whole now produces all kinds of data-based financial reporting and statements, and investors and industry players alike can buy really well-structured data as a result. AI has the ability to study massive amounts of this data and identify patterns.

Let us assume we identified a stock pattern today, and we want to figure out what to do next: buy or sell. AI can find somewhat similar patterns that existed in history and then analyze what happened right after. Knowing what happened after the pattern in the past may suggest what may happen in the future from today.

How Bots Are Altering the Future of Enterprise

We can identify patterns for stocks, Forex, ETFs, mutual funds, and even currencies. With that said, some patterns will not work for certain stocks; that is why people need a complete picture, including discovery, testing, and a presentation of results.

What if there is a challenge and if they are having a problem identifying the patterns? How does then AI support this kind of investor?

Challenges can also be patterns. Let us assume there is a significant drop in the market today; AI can go back through historical data and find similar significant drops in the market to come to pattern-based conclusions, such as which particular stocks continue to go down and which stocks tend to quickly bounce back. And in that regard, AI helps to solve the challenges in conjunction with human involvement, where humans can take these signals and use them for making better trading decisions.

That perspective raises the question: can AI effectively trade or manage a portfolio without any human involvement? So far, there is only one recorded example, a hedge fund claiming no human involvement. In all other cases, at this moment, humans have some kind of involvement. Today, the best minds in the finance industry are working on solutions that can help interpret challenges or anomalies in the market, including significant drops or significant jumps. Beyond AI, many companies use robots to work on these solutions, too. They look at the expense ratio and come up with the best-case scenario we are talking about the fully automated robots which can solve the challenges that arise.

Are there any security challenges in data processing of this type?

Data security challenges are the same whether AI is involved or not you have to be secure either way. With that said, you do need to protect against the black swans when something unexpected happens, and the AI can react and perform a problematic money maneuver.

Voice-based AI Assistant Certainly the Future of Workplace

Think about the verification challenges when people put driverless cars on autopilot, and the driverless car sees something unexpected. There is a chance it will crash, like Tesla demonstrated recently when the human fully relied on autopilot. When it comes to AI and investing a lot of money could be on the line.

So you see AI as a future of investment platforms? How is your platform leveraging AI differently?

Ans: Yes, absolutely. It is an enormous amount of power, and no human being can compete with the speed and volume of this power when applied to trade.

Here is the main difference with AI in our approach: to make it convenient for our users, we test a lot of strategies in advance, and that means that a typical investor gets access to a secure cloud. In our secure, local cloud, we run a lot of pre-calculations over different strategies. We run tens of thousands of different strategies simultaneously. We dont know what is going to happen with these tens of thousands of strategies, but we know that if the user on our site wants to use one of them, then it is going to be pre-calculated. That way, the person has more immediate access to our data and analysis. And that is our main feature that a person can use our AI on request.

Rebirth of Industries in the Era of Intelligent Automation

Is Your Company Using Artificial Intelligence To Transform An Industry? Nominations For The Forbes 2020 AI 50 List Are Now Open – Forbes

Posted on March 19, 2020 by Danzig

Is AI core to growing your business?

Artificial intelligence technology is powering big changes across all industries, but its tough to separate out the companies with truly transformative applications from marketing hype. Thats why Forbes is compiling a list of promising startups that are emerging as leaders in this space.

Is AI at the heart of what your company does, not just a driver for an auxiliary business or way to improve an existing product? We want to hear from you.

Nominations are now open for the second annual Forbes AI list, which seeks to highlight private companies that are applying artificial intelligence to solve problems in innovative ways.

Forbes, in partnership with Sequoia Capital and Meritech Capital, will evaluate hundreds of companies based on metrics including revenue, growth and valuation, with a panel of experts weighing in on how innovative and mission-critical each companys use of AI is (versus buzzwords thrown onto a slide-deck).

We welcome any U.S.-based private company to apply by filling out this form by Friday, April 10. The number of nominations wont influence our selection, so stick to just one per company, please.

We look forward to hearing from you!

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Is Your Company Using Artificial Intelligence To Transform An Industry? Nominations For The Forbes 2020 AI 50 List Are Now Open - Forbes

Artificial intelligence myths: Reality check – Livemint

Posted on March 19, 2020 by Danzig

Very few subjects in science and technology have caused much excitement right now as artificial intelligence as some of the worlds brightest minds have said that its potential to revolutionise all aspects of our lives.

AI makes it practical for machines to understand from experience, act human-like jobs, and adapt to the latest inputs. The concept works by amalgamating enormous data with quick, smart algorithms, and iterative processing, enabling the software to decipher by analysing patterns in the data in an automatic way.

There is science and well thought algorithm behind all the artificial solutions, where you need to set up proper expectations and clarification to avoid any rumours and myths around the outputs.

While the notion of AI is turning into a massive component of business and consumer transformations, its execution is generally stagnated because of some misconceptions associated with it.

Myth 1: AI will deliver magical resultsimmediately

The path to AI success is hard and takes time, and not just because of the technology. You also need a strategic framework and an iterative approach to avoid delivering a random set of disconnected AI solutions. The temptation is to go for moonshots to deliver the magic, but such projects often fail to live up to expectations because you dont have the basics homework done.

AI is not a magic, it requires rigour, logical thinking and long term strategy with a patience to do multiple iteration to get to the result.

Myth 2: AI Will Replace Human Jobs

Most of the times, management look at AI solutions to replace human and reduce the operational cost, creating a sense of fear among the employees.

So, if you think that AI solutions might strip human from their jobs, then you are undeniably wrong.

Reality is, AI and human need each other. AI is at its most valuable when it augments peoples capabilities. It can remove the duplicate work, freeing people up for more strategic activities. That has the added benefit of making people more motivated, productive, and loyal. Enterprise AI also relies on people to feed it the right data and work with it the right way. Often, AI doesnt provide conclusive answers to issues, but rather highly informed recommendations that an actual human can weigh to make the final decision.

Myth 3: AI Implementation Needs Huge Investment

Artificial developments resolutions appear to be tremendously scientific and complicated. This inclination recommends that just a modern tech organisation, including Google, Amazon, or Apple, with an extended team of experts and billion-dollar budgets can pay for implementing AI. In reality, there are a lot of smart tools existing for an enormous variety of organisation, which can be utilised to implement AI in their business procedures.

Myth 4: AI Algorithms are Competent to Process Any Data

Most of you must believe that ML algorithms are one of the most crucial elements in the entire system. An algorithm might appear to be robust and linked with the human brain, which can make intellect of any untidy data.

It is not possible, for algorithms, to make decisions without human intervention as they dont have magic power. It requires a specific piece of data to get impeccable results.

Myth 5: AI will Conquer Humanity

Machines are powerless to imagine similar to people and will barely be taught to do so. In fact, computers are going to have an optimistic impact on the world by supporting people in a lot of fields, building innovative business models, communities, and skills. Its certainly true that the advent of AI and automation has the potential to seriously disrupt labour and in many situations it is already doing just that. However, seeing this as a straightforward transfer of labour from humans to machines is a vast over-simplification. In fact, a lot of AI focus has been on reducing the drudgery" of day-to-day aspects of the work. AI gives an opportunity to upgrade your skills and move up in your career ladder at the same time.

About the Author: A technology and product leader, Rahul Kumar is Group Chief Product Officer with HT Media Group. An alumni of BIT Mesra, who later on honed his technology management skills from IIT Delhi, has been leveraging AI, ML and IOT to solve business and consumer problems across technology led startups and conglomerate.

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Artificial intelligence myths: Reality check - Livemint

On the Role of Artificial Intelligence in Genomics to Enhance Precisio | PGPM – Dove Medical Press

Posted on March 19, 2020 by Danzig

scar lvarez-Machancoses,1,2 Enrique J DeAndrs Galiana,1 Ana Cernea,1 J Fernndez de la Via,1 Juan Luis Fernndez-Martnez2

1Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo 33007, Spain; 2DeepBiosInsights, NETGEV (Maof Tech), Dimona 8610902, Israel

Correspondence: Juan Luis Fernndez-MartnezGroup of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico Garca Lorca, 18, Oviedo 33007, SpainEmail jlfm@uniovi.es

Abstract: The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

Keywords: artificial intelligence, big data, genomics, precision medicine, drug design

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License.By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

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On the Role of Artificial Intelligence in Genomics to Enhance Precisio | PGPM - Dove Medical Press

Canon Medical’s 3T MR System Receives FDA Clearance for Artificial Intelligence-Based Image Reconstruction Technology – BioSpace

Posted on March 19, 2020 by Danzig

TUSTIN, Calif.--(BUSINESS WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T MR system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medicals CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction.

AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. Capabilities include:

AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medicals leadership and commitment to innovation in diagnostic imaging, said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. With the expansion of this unique DLR method across modalities and into MR, were elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before.

About Canon Medical Systems USA, Inc.

Canon Medical Systems USA, Inc., headquartered in Tustin, Calif., markets, sells, distributes and services radiology and cardiovascular systems, including CT, MR, ultrasound, X-ray and interventional X-ray equipment. For more information, visit Canon Medical Systems website at https://us.medical.canon.

About Canon Medical Systems Corporation

Canon Medical offers a full range of diagnostic medical imaging solutions including CT, X-Ray, Ultrasound, Vascular and MR, as well as a full suite of Healthcare IT solutions, across the globe. In line with our continued Made for Life philosophy, patients are at the heart of everything we do. Our mission is to provide medical professionals with solutions that support their efforts in contributing to the health and wellbeing of patients worldwide. Our goal is to deliver optimum health opportunities for patients through uncompromised performance, comfort and safety features.

At Canon Medical, we work hand in hand with our partners - our medical, academic and research community. We build relationships based on transparency, trust and respect. Together as one, we strive to create industry-leading solutions that deliver an enriched quality of life. For more information, visit the Canon Medical website: https://global.medical.canon.

* AiCE MR is applicable to neuro and knee imaging

View source version on businesswire.com: https://www.businesswire.com/news/home/20200318005103/en/

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Canon Medical's 3T MR System Receives FDA Clearance for Artificial Intelligence-Based Image Reconstruction Technology - BioSpace

The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence – The National Interest

Posted on March 19, 2020 by Danzig

(Washington, D.C.) The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot tanks to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human.

For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks, Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md.

The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines. Drawing upon advanced algorithms, computer technology can organize, and disseminate otherwise dis-aggregated pools of data in seconds -- or even milliseconds. AI-empowered sensors can bounce incoming images, video or data off a seemingly limitless existing database to assess comparisons, differences and perform near real-time analytics.

At the speed of the most advanced computer processing, various AI systems can simultaneously organize and share information, perform analyses and solve certain problems otherwise impossible for human address within any kind of comparable timeframe. At the same time, there are many key attributes, faculties and problem solving abilities unique to human cognition. The optimal approach is, according to Perelman, to simultaneously leverage the best of both.

We will use the power of human intelligence and the speed of AI to get novel interactions, Perelman added.

This blending, or synthesis of attributes between mind and machine is expected to evolve quickly in coming years, increasingly giving warzone commanders combat-sensitive information much faster and more efficiently. For instance, a forward operating robotic wingman vehicle could identify a target that might otherwise escape detection, and instantly analyze the data in relation to terrain, navigational details, previous missions in the area or a database of known threats.

You have an AI system that is not better than a human but different than a human. It might be faster and it might be more efficient at processing certain kinds of data. It will deal with threats in concert with human teammates that are completely different than the way we do things today, Perelman said.

With these goals in mind, the ARL is now working on mock up interfaces intended to go into the services emerging family of Next Generation Combat Vehicles. Smaller robots such as IED-clearing PackBots have been in existence for more than a decade; many of them have integrated software packages enabling various levels of semi-autonomy, able to increasingly perform a range of tasks without needing human intervention. Current ARL efforts now venture way beyond these advances to engineer much greater levels of autonomy and also engineer larger robots themselves such as those the size of tanks.

Army Research Lab Mock Up of Next-Gen Combat Vehicle AI-Enabled System

Bringing this kind of manned-unmanned teaming to fruition introduces new strategic and tactical nuances to combat, enabling war commanders a wider and more immediate sphere of options.

Commanders will be able to view a target through vehicle sensor packages, or if there is an aided target recognition technology or some kind of AI to spot targets, they might see battlespace target icons pop up on the map indicating the location of that target, Perelman said.

AI-oriented autonomous platforms can greatly shorten sensor-to-shooter time and enable war commanders to quickly respond to, and attack, fast emerging moving targets or incoming enemy fire.

Everything that a soldier does today. Shooting, moving, communicating.. Will be different in the future because you do not just have human to human teammates, you have humans working with AI-teammates, Perelman said.

Enabling robots to understand and properly analyze humans is yet another challenging element of this complex equation. When you have two humans, they know when the other is cold and tired, but when you bring in an AI system you dont necessarily have that shared understanding, Perelman said.

Various kinds of advanced autonomy, naturally, already exists, such as self-guiding aerial drones and the Navys emerging ghost fleet of coordinated unmanned surface vessels operating in tandem. Most kinds of air and sea autonomous vehicles confront fewer operational challenges when compared to ground autonomy. Ground warfare is of course known to incorporate many fast-changing variables, terrain and maneuvering enemy forces - at times to a greater degree than air and sea conditions - fostering a need for even more advanced algorithms in some cases. Nevertheless, the concepts and developmental trajectory between air, land and ground autonomy have distinct similarities; they are engineered to operate as part of a coordinated group of platforms able to share sensor information, gather targeting data and forward-position weapons -- all while remaining networked with human decision makers.

You can take risks you would never do with a manned platform. A robotic system with weapons does not need to account for crew protection, Perelman said.

Interestingly, the Army Research Lab current efforts with human-machine interface are reinforced by an interesting 2015 essay in the International Journal of Advanced Research in Artificial Intelligence, which points to networking, command and control and an ability to integrate with existing technologies as key to drone-human warfare.

They (drones) should effectively interact with manned components of the systems and operate within existing command and control infrastructures, to be integral parts of the system, in Military Robotics: Latest Trends and Spatial Grasp Solutions, by Peter Simon Sapaty - Institute of Mathematical Machines and Systems, National Academy of Sciences.

Increased use of networked drone warfare not only lowers risks to soldiers but also brings the decided advantage of being able to operate in more of a dis-aggregated, or less condensed formation, with each drone and soldier system operating as a node in a larger integrated network. Dispersed forces can not only enable longer-range connectivity and improved attack options but also reduce force vulnerability to enemy fire by virtue of being less aggregated.

Despite the diversity of sizes, shapes, and orientations, they (drones and humans) should all be capable of operating in distributed, often large, physical spaces, thus falling into the category of distributed systems, Sapaty writes in the essay.

Also of great significance, Army thinkers explain, is that greater integration of drone attack assets can streamline a mission, thereby lessening the amount of soldiers needed for certain high-risk operations.

When you are calling in artillery or air support, there is a minimum distance from where you are able to do that as a human being. You dont have the same restrictions with robotic systems, so it allows you to take certain risks, Perelman.

A paper in an Army University Press publication explains how drones can expand the battlefield. By utilizing drone systems for combatfewer warfighters are needed for a given mission, and the efficacy of each warfighter is greater. Next, advocates credit autonomous weapons systems with expanding the battlefield, allowing combat to reach into areas that were previously inaccessible, the essay states. (Amitai Etzioni, Phd, Oren Etzioni, Phd)

This article by Kris Osborn originally appeared in WarriorMaven in 2020.

Kris Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

Image: Reuters

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The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence - The National Interest

Battery Researchers Look to Artificial Intelligence to Slash Recharging Times – Greentech Media News

Posted on March 19, 2020 by Danzig

The battery sector is turning to artificial intelligence for clues on how to improve recharging rates without increasing the degradation of lithium-ion batteries.

Last month, a team from Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute published findings from battery testing aimed at cutting electric-vehicle charging times down to 10 minutes. The research, published in Nature, revealed how artificial intelligence could speed up the testing process required for novel charging techniques.

The researchers wrote a program that predicted how batteries would respond to different charging approaches and was able to cut the testing process from almost two years to 16 days, Stanford reported. The technique was used to evaluate 224 possible high-cycle-life charging processes in just over two weeks, the researchers said.

The research effort has been in progress for at least three years. In 2017, the Toyota Research Institute committed $35 million to artificial intelligence battery research, initially focusing on new materials.

Last year, the research partners claimed artificial intelligence could help predict the useful life of lithium-ion batteries to within 9 percent of the actual life cycle of the products.

The standard way to test new battery designs is to charge and discharge the cells until they die,co-lead author Peter Attia, now of Tesla but then a Stanford doctoral candidate in materials science and engineering, said in a press release at the time.

Since batteries have a long lifetime, this process can take many months and even years. Its an expensive bottleneck in battery research.

Independentof these efforts, a Canadian firm called GBatteries is using artificial intelligence in a bid to cut lithium-ion battery charging times down to five minutes. The company has succeeded in recharging an electric scooter battery in less than 10 minutes.

The main challenge with extremely fast charging is that it heats up and degrades the battery, GBatteries co-founder and Chief Commercial Officer Tim Sherstyuk told GTM.

The rates that can be achieved with todays fast-charging technology, which are slow by gas-station filling standards, are already problematic for batteries, he said.

Most fast-charging initiatives focus on novel chemistries that wont degrade easily, Sherstyuk said. GBatteries, meanwhile, uses artificial intelligence to monitor the state of the battery as it is charging.

Once the impedance of the battery reaches a critical level, the GBatteries algorithm pauses charging long enough to avoid irreversible damage. This allows charging to proceed in a series of high-intensity pulses at a rate much faster than is possible with traditional methods.

The GBatteries technology works for small batteries and has been demonstrated on power tools, cutting charging times from between 30 to 60 minutes down to 11. But scaling it up to cope with an electric vehicle battery pack is going to take a while, said Sherstyuk.

Even if artificial intelligence can help crack the means to charge electric vehicles as quickly as you now fill your tank with gas, it will take a while for the auto industry to incorporate the technology into the mainstream. The time horizon is years, not months.

Nevertheless, there is plenty of industry interest in tackling the problem.

Charging time is usually the fourth concern that people raise when considering to go electric or not, after upfront cost, range of the vehicle and where [to] charge, said Aaron Fishbone, director of communications at GreenWay, which operates a fast-charging network across Eastern Europe.

So, while not a top-tier issue, its still one raised by many people.

GBatteries pulse charging will require a lot more testing before it might be considered appropriate for the 50+ kilowattpower ratings required for electric vehicles, Fishbone said. In the meantime, high-power recharging is already reducing the time it takes to charge a battery.

Although there are not yet many cars that can take them, a 150-kilowatt charger can add 100 kilometers (62 miles) of range to an electric vehicle within a little over seven minutes, Fishbone said.

Nonetheless, anything which can speed up charging time without degrading battery life is a welcome development and can lead to other innovations which push the whole industry."

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Battery Researchers Look to Artificial Intelligence to Slash Recharging Times - Greentech Media News

Artificial intelligence: The new power dynamic of today – Daily Sabah

Posted on March 19, 2020 by Danzig

A new industrial revolution is taking place now and AI (AI) is transforming countries economically. The answer to the question of who is ahead and who is behind is determined by the new economic model based on this AI. Dozens of countries, from China to the U.S., from Finland to Kenya, are making significant investments in the area. It should be noted that by 2030, AI studies will generate a gross domestic product (GDP) greater than the current size of the Chinese economy ($15 trillion). From this new economy, China will generate nearly $7 trillion, the U.S. $3.7 trillion, Northern Europe $1.8 trillion, Africa-Oceania $1.2 trillion, the rest of Asia $0.9 trillion and Latin America $0.5 trillion. So, what will we do as a country? What kind of road map will we follow? How will we move forward in the digital economy revolution?

From driverless subways to flying taxis, from AI doctors to political consultants, from Smart TV announcers and robot muezzins to robot soldiers to autonomous attack planes, how ready are we for the new era?

Digital change and Turkey

AI creates change in society and adds new powers to people's power by enabling groundbreaking developments in areas such as healthcare, agriculture, education and transport. As AI technology continues to grow, we will work to ensure the ethical, pervasive and transparent dissemination of AI around the world, enabling everyone to take advantage of this technology, Microsoft President Brad Smith says.

Technological developments take hold of the entire world today, where digital transformation takes place fast. With new technologies; the processes of transformation and adaptation are taking place in economics, politics, healthcare and many other fields. In this context, studies on AI, 5G, Industry 4.0, big data and the "internet of things" (IoT) largely occupy the agenda. In particular, it is necessary to elaborate on AI studies here.

The studies of AI, which have undergone many ups and downs from the 1950s to the present, have entered a revolutionary process as of the 2010s with the use of machine learning and artificial neural networks.

Especially the fact that technologically and economically developed countries like the U.S., China and Germany have taken interest in AI studies both at the public and private levels and that they are competing with each other, has created a competitive environment across the globe.

The necessity of putting studies in a system within a specific plan has pushed countries to determine strategies and policies. The importance of the situation becomes evident considering that 35 countries have set a national AI strategy and international structures such as the U.N. and the EU joining the process as of January 2020.

Before going into practice in the context of Turkey and AI studies, the following should be noted: We are late in this race, but we can make up for it. Resources are limited, but progress can be made. Reasonable targets should be set. Target sectors should be determined.

The following suggestions should be noted in the area of practice. Research and development (R&D) funds should be created. Higher quality coding education should be offered in primary and secondary levels.

Besides, the field is not composed of engineering, so experts should be trained to interact around the world. Cooperation should be made with developed countries in this area.

It should be turned into a state policy. AI research centers should be established. Specialist import is required. The industrial incentive is required (on a sectoral basis).

Other critical suggestions

When the strategy documents released by other countries and the work they have performed are examined, we can list what needs to be done for Turkey as follows.

The impact of universities in the process should be boosted. AI workshops should be held urgently under the leadership of the academy. Encouraging the meetings to be held in the social sciences rather than in engineering is essential for ensuring that society can keep up with the age of AI and digital transformation. The results of the workshop should be presented to the Digital Transformation Office of the Presidency of the Republic of Turkey and should be taken as a basis in the strategy-building process.

A new academic title can be created to promote academic studies, boost international interest and ensure reverse brain drain. (E.g., the Alexander von Humboldt Professorship created by Germany in the context of AI strategy)

Science and social science departments based on AI-oriented studies should be established in universities and a skilled workforce should be trained in the fields of production, economics, management, law, philosophy and sociology.

The strategic plan should direct what kind of work will be done in what areas and clearly point out the opportunities. After studies are done in the determined fields, the sector can identify the advantageous positions internationally and carry out processes accordingly in different fields like military, healthcare, finance, education, environmental management, biotechnology, and industrial production, etc.

For society to adapt to the age of AI and digital transformation, an instructive website should be prepared and released to the public in visual and digital publications through public service ads.

Economically and technologically advanced countries such as the U.S., Germany, France and Canada attach importance to start-up companies in their strategies due to their advantageous positions. On the contrary, to use Turkeys economic resources in an effective, fast and solution-oriented way, instead of supporting start-up companies; companies that are already strong in the sector should be supported, employment incentives should be provided to ensure the employment of trained personnel in these companies.

To reduce the costs of start-up companies during the founding phase, cash incentives should be provided only for the supply of fixtures.

AI Made in Turkey trademark registration should be created. Manufactured products should be launched worldwide.

Workshops, meetings and consultations in public, private and academic fields should be increased and cooperation agreements should be made to ensure cooperation with leading countries in AI studies.

The use of AI-based programs in public institutions should be encouraged and necessary infrastructure transformation should be carried out.

All of the techno-cities owned by universities based in Istanbul should be collected in Istanbul Technopark. For a formation like Silicon Valley, a city other than Istanbul should be determined and the necessary material and financial infrastructure should be established.

To prevent the transfer of resources to inefficient work, the institutions and organizations that are provided with incentives should be supervised regularly.

Today, we are on the eve of a new era of geographical explorations; what we do will determine our future. If we believe, if we work hard, why not?

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Artificial intelligence: The new power dynamic of today - Daily Sabah

Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic – Forbes

Posted on March 19, 2020 by Danzig

Since the first report of coronavirus (COVID-19) in Wuhan, China, it has spread to at least 100 other countries. As China initiated its response to the virus, it leaned on its strong technology sector and specifically artificial intelligence (AI), data science, and technology to track and fight the pandemic while tech leaders, including Alibaba, Baidu, Huawei and more accelerated their company's healthcare initiatives. As a result, tech startups are integrally involved with clinicians, academics, and government entities around the world to activate technology as the virus continues to spread to many other countries. Here are 10 ways artificial intelligence, data science, and technology are being used to manage and fight COVID-19.

Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic

1. AI to identify, track and forecast outbreaks

The better we can track the virus, the better we can fight it. By analyzing news reports, social media platforms, and government documents, AI can learn to detect an outbreak. Tracking infectious disease risks by using AI is exactly the service Canadian startup BlueDot provides. In fact, the BlueDots AI warned of the threat several days before the Centers for Disease Control and Prevention or the World Health Organization issued their public warnings.

2. AI to help diagnose the virus

Artificial intelligence company Infervision launched a coronavirus AI solution that helps front-line healthcare workers detect and monitor the disease efficiently. Imaging departments in healthcare facilities are being taxed with the increased workload created by the virus. This solution improves CT diagnosis speed. Chinese e-commerce giant Alibaba also built an AI-powered diagnosis system they claim is 96% accurate at diagnosing the virus in seconds.

3. Process healthcare claims

Its not only the clinical operations of healthcare systems that are being taxed but also the business and administrative divisions as they deal with the surge of patients. A blockchain platform offered by Ant Financial helps speed up claims processing and reduces the amount of face-to-face interaction between patients and hospital staff.

4. Drones deliver medical supplies

One of the safest and fastest ways to get medical supplies where they need to go during a disease outbreak is with drone delivery. Terra Drone is using its unmanned aerial vehicles to transport medical samples and quarantine material with minimal risk between Xinchang Countys disease control centre and the Peoples Hospital. Drones also are used to patrol public spaces, track non-compliance to quarantine mandates, and for thermal imaging.

5. Robots sterilize, deliver food and supplies and perform other tasks

Robots arent susceptible to the virus, so they are being deployed to complete many tasks such as cleaning and sterilizing and delivering food and medicine to reduce the amount of human-to-human contact. UVD robots from Blue Ocean Robotics use ultraviolet light to autonomously kill bacteria and viruses. In China, Pudu Technology deployed its robots that are typically used in the catering industry to more than 40 hospitals around the country.

6. Develop drugs

Googles DeepMind division used its latest AI algorithms and its computing power to understand the proteins that might make up the virus, and published the findings to help others develop treatments. BenevolentAI uses AI systems to build drugs that can fight the worlds toughest diseases and is now helping support the efforts to treat coronavirus, the first time the company focused its product on infectious diseases. Within weeks of the outbreak, it used its predictive capabilities to propose existing drugs that might be useful.

7. Advanced fabrics offer protection

Companies such as Israeli startup Sonovia hope to arm healthcare systems and others with face masks made from their anti-pathogen, anti-bacterial fabric that relies on metal-oxide nanoparticles.

8. AI to identify non-compliance or infected individuals

While certainly a controversial use of technology and AI, Chinas sophisticated surveillance system used facial recognition technology and temperature detection software from SenseTime to identify people who might have a fever and be more likely to have the virus. Similar technology powers "smart helmets" used by officials in Sichuan province to identify people with fevers. The Chinese government has also developed a monitoring system called Health Code that uses big data to identify and assesses the risk of each individual based on their travel history, how much time they have spent in virus hotspots, and potential exposure to people carrying the virus. Citizens are assigned a color code (red, yellow, or green), which they can access via the popular apps WeChat or Alipay to indicate if they should be quarantined or allowed in public.

9. Chatbots to share information

Tencent operates WeChat, and people can access free online health consultation services through it. Chatbots have also been essential communication tools for service providers in the travel and tourism industry to keep travelers updated on the latest travel procedures and disruptions.

10.Supercomputers working on a coronavirus vaccine

The cloud computing resources and supercomputers of several major tech companies such as Tencent, DiDi, and Huawei are being used by researchers to fast-track the development of a cure or vaccine for the virus. The speed these systems can run calculations and model solutions is much faster than standard computer processing.

In a global pandemic such as COVID-19, technology, artificial intelligence, and data science have become critical to helping societies effectively deal with the outbreak.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

Artificial intelligence recruited to find clues about Covid-19 – The Star Online

Posted on March 19, 2020 by Danzig

WASHINGTON: US health and technology specialists on March 16 said they had launched a new collaborative venture to assemble a dataset of tens of thousands of scientific papers and literature on the coronavirus, which would then be analysed by artificial intelligence programs to find patterns and answer questions raised by the World Health Organisation about the pandemic.

The dataset includes 29,000 articles, including 13,000 full-text pieces of medical literature, which will be made available on a special website allowing data scientists and artificial intelligence programmers to propose tools and software code that can unearth insights from the articles, White House officials and experts told reporters in a conference call.

The venture came together after the White House Office of Science and Technology Policy issued a call to tech companies and research groups to figure out how artificial intelligence tools could be used to sift through thousands of research articles being published worldwide on the pandemic, said Lynn Parker, deputy chief technology officer at the White House office.

With data scientists and machine language experts mining the literature compilation known as Covid-19 Open Research Dataset, experts and White House officials expect to get help developing vaccines, forming new guidelines on how long social distancing should be maintained and other insights, Michael Kratsios, the US chief technology officer said.

The venture includes the National Library of Medicine, which is part of the National Institutes of Health, Microsoft, Allen Institute of AI, Georgetown University's Center for Security and Emerging Technology, the Chan Zuckerberg Initiative (named for Mark Zuckerberg, Facebook's founder, and his wife Priscilla Chan), and Kaggle, which is a unit of Google.

The Allen Institute's Semantics Scholar website will host the database of scientific articles and add to the collection over time, while Kaggle's platform, which provides access to about 4 million artificial intelligence researchers, will receive suggestions from the experts on tools and codes to use to mine the database, experts from both organisations said.

Scientists have been working and publishing their findings on various strains of coronavirus over the years, including other variants such as SARS, MERS, and the latest, Covid-19. The application of artificial intelligence tools to look for commonalities and differences among the thousands of such published articles will help the scientists spot things they may have missed, Eric Horvitz, Microsoft's chief scientific officer said.

"It's difficult for people to manually go through more than 20,000 articles and synthesise their findings," Anthony Goldbloom, co-founder and CEO of Kaggle said. "Recent advances in technology can be helpful here. We're putting machine readable versions of these articles in front of our community of more than 4 million data scientists. Our hope is that AI can be used to help find answers to a key set of questions about Covid-19."

"Sharing vital information across scientific and medical communities is key to accelerating our ability to respond to the coronavirus pandemic," said Cori Bargmann, head of science at the Chan Zuckerberg Initiative. "The new Covid-19 Open Research Dataset will help researchers worldwide to access important information faster."

Publishers of scientific journals and literature have agreed to make their full articles available to researchers so that machine learning algorithms can look for key insights from them, the experts said. As scientists around the world continue to publish new research, journal publishers have agreed to provide those articles in electronic form ahead of their printed versions, they said. CQ-Roll Call/Tribune News Service

Artificial intelligence recruited to find clues about Covid-19 - The Star Online

Rethinking Financial Services with Artificial Intelligence Tools – The Financial Brand

Posted on March 19, 2020 by Danzig

Subscribe to The Financial Brand via email for FREE!

Applying artificial intelligence to everything were comfortable doing in banking is much easier than changing how we do things which would make the greatest use of AI.

Few in financial services would argue that the future belongs to those institutions that harness data-driven machine intelligence to do more, better and faster. The insights and efficiencies needed to compete and thrive will come from AI-driven service personalization and optimization.

But AI should do more than speed up a financial assembly line. As Ernst & Young stated in a report: AI-driven financial health systems will become personal financial operating systems. Consumer finance will unbundle products and rebundle personalized and holistic value propositions based on life events.

While that is a worthy goal, the retail banking industry will not come any closer to achieving that if it continues the way it is thinking about and implementing AI.

I call the current mindset for applying AI to financial services the Product Gun. Its the familiar banking model of manufacturing a product, targeting a market segment for distribution, ensuring everything complies, and then shooting it to potential consumers. Its worked well for many years, but its had its day.

Hopes of providing consumers with personal financial operating systems and solutions tailored to life events wont happen merely by blending AI with the same old thing. In fact, applying complexity and leverage to well-understood financial products and processes may produce unintended consequences.

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But rethinking from the ground up can be rare. Models and the basic process behind them often dont change because business typically likes to save energy. Take the Boeing 737. The jets design dates back to 1964. The first one flew in 1967. The latest iteration still flies today. This makes a perfect example of leveraging an old business model to sustain profits as the saying goes, if it aint broke

Because banking is a regulated industry that deals with heaps of money and risk, a control structure has evolved to organize competencies and lines of business. Risk and profit are put in little boxes for success. Boxes like manufacture, target, and comply all have executives, KPIs, spreadsheets and politics. On the whole, it has worked well.

The problem is, innovative tools like AI get shoved into the same old boxes. Instead of using this technology to reimagine traditional processes, we use AI to build a supercharged 737.

This has some benefits to financial institutions business lines. This could include improving the consumer credit process, reducing compliance exceptions or automating support desks. Each of these, and similar applications of AI, could benefit the industry and those it serves.

However, AI can produce missteps, such as unwittingly biased outcomes. At best staying trapped inside old processes with new AI insides will do no harm, but its still not going to bring us closer to a vision for personalized, holistic financial advice.

I want to propose something radical: Financial institutions should be optimizing for their clients needs. This sounds extremely simple but the commitment required, and the roadmap to making this reality is serious, expensive and difficult when it can take a long time to deliver on expectations.

Jeff Bezos said: Put the customer first. Invent. And be patient. It took Amazon 20 years to be profitable, and during that time Bezos kept investing to optimize his understanding of and delivery for his customers. Amazons impressive margins came about relatively recently, and only after a long battle.

The alternative to the traditional Product Gun attitude is something I call Mother Mind. This goes beyond simply shooting products at people. It gathers intelligence about what and who people are and what they need. It understands deeply what customers are going through in their lives, then it guides them with strategies that are actually going to be useful in the context of their lives. Used in this way, AI can keep guiding an institution in ways to better serve people and businesses.

The Crawl, Walk, Fly Approach to CX Like the Big Brands

Leading consumer brands provide a better customer experience by using data and insights to drive relevancy and personalization and integrate the experience with other channels.

Thursday, April 2nd at 3 pm EST

Change is hard for financial institutions, but it can happen. Here are three achievable pivots that can help put a bank or credit union on the path to success.

Gather: Move from system-centric data to human-centric data. Even under the progressive framework of Europes PSD2 open banking framework, financial institutions still store and access data in a system-centric way: transactions, products, accounts and balances. Data organized this way makes it very difficult to understand much about peoples individual circumstances.

Today data comes in the language of systems and ledgers. To do anything radically different requires shifting to the language of human lives. This means building interfaces to data that will allow financial institutions to ask questions about peoples behavior and needs. What are their financial personalities? What events in their lives offer the chance to be of assistance?

Understand: Move from products to journeys. The word customer-centric means nothing if products continue to be bankings foundation. How do we distribute the product for less? How do we recommend products to customers at the right time? such thinking is inverse, today.

Consumers needs change as their lives and circumstances change. At any point and time they have problems that need solutions and questions that need answers. Whether or not these journeys are successful is going to start meaning a lot more. Customer love or hate is going to be a profitability issue in a world where switching providers is easy. Focusing on understanding people will result in institutions working in a completely different way the measure wont be on sales but on problems resolved.

Guide: Move from selling to advising. By virtue of living in a product-centric world, financial institutions have become sales-driven. But when the barrier to entry to manufacturing and distributing products keeps lowering, traditional institutions increasingly find themselves fighting fintechs and others for turf they used to think they owned. Shiny objects may grab attention and move a sale once, but when thats over, if an institution hasnt built a meaningful relationship, people will leave.

People want to be understood, and they want to be cared for. In the context of financial services, this means people want advice. Advice is not about buying a product. Its about working towards goals, planning for transitions and hopefully creating an overarching, happy story of personal wealth.

Putting energy into human-centric data and focusing on understanding makes the aspiration of providing personalized holistic advice more possible.

The personal financial operating system wont happen overnight, but institutions can move towards it. Personal financial management offerings that keep people aware of their situation, tools that help them plan for retirement, and hybrid advice platforms that enable collaboration are all steps in the right direction.

Original post:

Rethinking Financial Services with Artificial Intelligence Tools - The Financial Brand

IIT-M to reskill women in artificial intelligence – Campus Varta

Posted on March 19, 2020 by Danzig

The Indian Institute of Technology-Madras is offering 150 hours of training to reskill women who have taken a break from their career.

The certification course includes artificial intelligence, machine learning, cyber security data science and big data.

The Career Back 2 Women is an initiative through the Institutes Digital Skills Academy. Candidates can choose the level of training.

The institute has tied up with the Forensic Intelligence Surveillance and Security Technologies to offer the programme.

IIT-M director Bhaskar Ramamurthi said, In the IT field, the technology changes are so rapid that they [women who take a break] are unable to get back to their careers as their skills are probably outdated. Despite this, their industry experience and knowledge about IT are immense and can be useful to many IT companies if they can fit into current requirements immediately. IIT-Madras is happy to pioneer this programme to help them get back to work and retrieve their careers.

Women who complete the advance module in select tracks would also receive assistance in job placement.

Digital Skills Academy, IIT-Madras, also plans to offer more courses at various levels for students and working professionals in association with NASSCOM and in partnership with training companies incubated at IIT Madras Research Park and industry partners.

K. Mangala Sunder, Head, Digital Skills Academy, said, IIT-M works with NASSCOM IT-ITeS Sector Skill Council to ensure that right industry partners are involved in training. Faculty from premier institutions provide fundamental knowledge to all learners.

According to C. Mohan Ram, Chief Mission Integrator and Innovator, FISST, all participants will take a 20-hour programme after which they can choose their area of specialisation. There are four tracks offered initially. Each track has basic and advanced modules.

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IIT-M to reskill women in artificial intelligence - Campus Varta

An Unexpected Ally in the War With Bacteria – The Atlantic

Posted on March 19, 2020 by Danzig

Using computers and machine learning to make sense of mountains of biomedical data is nothing new. But the team at the Massachusetts Institute of Technology, led by James Collins, who studies applications of systems biology to antibiotic resistance, and Regina Barzilay, an artificial-intelligence researcher, achieved success by developing a neural network that avoids scientists potentially limiting preconceptions about what to look for. Instead, the computer develops its own expertise.

Read: Antibiotic resistance is everyones problem

With this discovery platform, which has been made freely available, youre going to identify molecules that dont look like antibiotics youre used to seeing, Collins said. It really shows how you can use the emerging technology of deep learning in an innovative manner to discover new chemistries.

Ever since Alexander Fleming derived the first antibiotic from fungus, nature has been the font for our antibacterial drugs. But isolating, screening and synthesizing thousands of natural compounds for laboratory tests is extremely expensive and time-consuming.

To narrow the search, researchers have sought to understand how bacteria live and multiply, and then pursued compounds that attack those processes (such as by damaging bacterias cell walls, blocking their reproduction, or inhibiting their protein production). You start with the mechanisms, and then you reverse engineer the molecule, Barzilay said.

Even with the introduction of computer-assisted, high-throughput screening methods in the 1980s, however, progress in antibiotic development was virtually nonexistent in the decades that followed. Screening occasionally turned up drug candidates that were toxic to bacteria, but they were too similar to existing antibiotics to be effective against resistant bacteria. Pharmaceutical companies have since largely abandoned antibiotic development, despite the need, in favor of more lucrative drugs for chronic conditions.

Read: How antibiotic resistance could make common surgeries more dangerous

The new work by Barzilay, Collins, and their colleagues, however, takes a radically fresh, almost paradoxical approach to drug discovery: It ignores how the medicine works. Its an approach that can succeed only with the support of extremely powerful computing.

Behind the new antibiotic finding is a deep neural network, in which the nodes and connections of its learning architecture are inspired by the interconnected neurons in the brain. Neural networks, which are adept at recognizing patterns, are deployed across various industries and consumer technologies for uses such as image and speech recognition. Conventional computer programs might screen a library of molecules to find certain defined chemical structures, but neural networks can be trained to learn for themselves which structural signatures might be usefuland then find them.

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An Unexpected Ally in the War With Bacteria - The Atlantic

San Diego-Based Company takes Digital Marketing to the next Level by Launching the First Artificial Intelligence Marketing Agency in the United States…

Posted on March 19, 2020 by Danzig

SAN DIEGO, March 18, 2020 (GLOBE NEWSWIRE) -- smartboost, an AI Digital Agency, announced today its new company name and business model. The founders of CNG Marketing and SIO Digital have merged their companies together to create smartboost, the first ever AI marketing agency.

In 2014, smartboost founders Giovanni Letellier and Clement Connor created CNG Digital Marketing, a digital marketing agency that was focused on building small businesses. CNG utilized advanced technology to reach and exceed clients goals. The company quickly grew from two founders to over 15 employees in its first year.

In 2016, CNG created its sister company, SiO Digital, that focused more on medium to large businesses, SiO Digital, was also an AI-powered and data-driven marketing agency. Giovanni transferred his responsibilities as CEO of CNG to Clement and took the role of Chief Strategist, so he could dedicate more time to SIOs growth and future projects.

After a successful six years in partnership with CNG and SIO and while servicing over 100 clients and growing, Giovanni and Clement wanted to merge the two companies to become smartboost.

"Our new name goes much deeper than just a new website and brand colors. It represents the merging of one of the first AI-powered Marketing Agencies with a best-in-class creative digital agency. The future starts now, said Giovanni Letellier, Founder and CEO of smartboost.

smartboost is proud to be the first AI-powered marketing agency alongside an innovative digital creative agency. smartboost is at the forefront of digital marketing and has a proven track record of building businesses through data-driven digital analytics. When business owners are working with smartboost marketers, designers, developers, and engineers, theyre all doing the same job: driving results through data.

smartboost will continue to grow as a collective and is dedicated to staying ahead of marketing trends through advanced AI-technology. This is an exciting time for many types of businesses looking to take advantage of the digital age. By working with smartboost, business owners will be working with the most innovative and technologically advanced agency in the United States.

About smartboost

smartboost is comprised of a highly-skilled team of creative marketers, scientists, and mathematicians all experts in our fields. With a proven track record of data-driven results by the notion of excellence, we see people and Artificial Intelligence working in symbiosis to help businesses survive and grow. smartboost is focused on impact and transparency and our technology is in a constant state of transformation.

PR Contact Kathleen Gonzales kathleen@elevated-pr.com

Compliance For A Digital World: BSA/AML The New ABC’s: Artificial Intelligence, Blockchain And How Each Complements The Other – JD Supra

Posted on March 19, 2020 by Danzig

Updated: May 25, 2018:

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The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? – Foundry-Planet.com

Posted on March 19, 2020 by Danzig

Kasper and Frans, thank you for joining us today. To kick off, can you tell us briefly why using Artificial Intelligence (AI) in a green sand foundry is a good idea?Kasper: DISA has been helping foundries collect, visualise and analyse their data with our Monitizer suite for a few years now. Adding AI capabilities to do more with this data is a logical next step and its a big one. Monitizer | PRESCRIBE which is what our AI product is called harnesses the power of AI to optimise the whole foundry process, significantly reducing scrap while increasing capacity and production predictability.

Frans: Theres a lot of hype around AI so at DataProphet, we like to quote real results to show whats possible. Over the last two years, the average AI-driven defect reduction across all of our manufacturing customers is 40%. With some, its 80% or 100%. Few foundries take full advantage of Industry 4.0 techniques so the potential for them is enormous.

Our Expert Execution System (EES), enabled by AI, has helped a foundry in South Africa cut defect rates in grey iron engine block castings by 50% in the first month. For the first time ever, they achieved zero internal defects on all shipped castings over three months and now save over $100k every month.

How does AI help deliver these kinds of results?Kasper: The key word here is automation. Many green sand foundries already collect and analyse process data but its usually limited to single sub-processes like moulding or pouring. The data for each process stays separate and basic manual analysis is done using spreadsheets or simple statistics.With an entire foundry line, optimisation can involve hundreds or even thousands of variables across all the different process stages. Making sense of that complexity manually is just impossible. AI automates this analysis, using the cloud to access vast computing capacity. Thats the only way to handle the complex, large sets of data that will give us new insight that will in turn make a genuine difference to a foundrys performance.

So what does an AI solution like Monitizer | PRESCRIBE actually do?Frans: It starts by analysing historic production and quality data to learn from past mistakes and corrections, to find what works and what doesnt. It considers how the parameters within and across all the different processes are related, how each one influences the other and what the ultimate combined effect on quality is.From that analysis, Monitizer | PRESCRIBE finds the optimal process parameters and tolerances for a particular casting and process. Knowing the best recipe, it can prescribe hence the name the best actions to take to improve quality.

Kasper: A good example is where, even though all your process parameters are within tolerance, you still might see bad quality castings. Often, this is because one metric is slightly high, another is slightly low and so on. Its a specific combination of values that produces the defect, not a single extreme one. Because the AI has learnt how parameters like grain size, moisture content, pouring speed or inoculation rate influence each other, it can pick the right settings for minimum defects.

So thats like a much more effective version of todays offline analysis. How does AI help you apply those learnings during real production?Kasper: Monitizer | PRESCRIBE applies what it has learnt to live data keeping an eye on what your foundry is doing right now, in real time. That gives you dynamic process control, reacting instantly as conditions change, like ambient air temperature or sand moisture content, and telling operators on the line the optimal settings or actions to take in time to prevent defects occurring. It keeps on learning too, constantly optimising the production process towards zero scrap and improving other metrics like productivity and resource use.Frans: Data-driven, real-time optimisation is sophisticated second-order control. By constantly monitoring machine and process data, then telling you which adjustments to make and again monitoring their effect, our AI tool gradually gets every part of your process running in harmony. You achieve a stable operating regime with the best quality and minimum quality variance. A good analogy is with an autonomous car which can automatically keep you in the middle of a motorway lane.By constantly computing the optimum process parameters, our AI keeps your process in the middle of the lane.

Its clear that automation and data analytics have enormous potential but many foundries have yet to adopt the basics here. So is it really possible for any green sand foundry to make use of AI?Kasper: We see digital as a four-step journey where you start with data collection and visualisation, then move at your own speed towards analytics, AI and automatic process control. Of course, we can help customers do all of that very quickly if they want to.Our NoriGate is the only hardware involved for data collection and everything else is a cloud service which we can deploy in any foundry or with existing data collection infrastructure. That makes it very quick and resource-efficient to deploy. You wont need any new IT hardware, data scientists or any extra staff.

We can digitise every step in the green sand process, take data from paper records or pull it from Excel, and give you a single trustworthy, time-stamped database ready for investigation. At each step, you can achieve significant benefits.The point is that, no matter if you are just starting out or are digitally advanced, there are things we can do that help you take the next step very rapidly indeed.

So you dont have to be a rocket scientist to make use of AI?Frans: AIs inner workings can be complicated to understand but together we have developed it into a packaged service that works for foundries. Its not hard to implement it and its not capital-intensive. As Kasper says, everything you need to collect, store and report on the data is already available from DISA and well proven.Some foundries think they are too old school for digital, but AI projects can be realised when theres no strong data environment or even if they havent really previously captured data at all. Our partnership with DISA enables very rapid digital progress in any type of foundry.

Does your partnership between an industrial AI company and a foundry equipment expert make your solution different to the other AI products we see emerging?Kasper: A lot of vendors say they have an AI system, but a pure IT company may never have seen a foundry from the inside before. We bring a combination of deep foundry experience and DataProphets award-winning expertise in manufacturing data science with more than 35 engineers, statisticians and computer scientists dedicated to developing AI solutions. This collaboration makes our service uniquely practical and effective. Its already tried and tested in a green sand foundry environment and were finding that fact is very attractive for customers. For example, we are currently installing the full Monitizer suite including MonitizerPRESCRIBE at a large European foundry group.

From DataProphets point of view, how does DISAs experience in green sand foundries help an AI project succeed?Frans: When you implement an AI solution in manufacturing, its vital to capture domain knowledge completely and correctly. As the leading OEM supplier, DISA know green sand intimately and are very much the experts in the foundry environment. They know what to do and which questions to ask right at the start. That means value from a running system arrives in weeks, not months or years.

DISAs customers also trust them to keep their promises and they understand that MonitizerPRESCRIBE will be delivered and managed through them. If DISA puts its name to it, customers know it will be an effective, high quality product and that will be supported in five years time and in ten or twenty years too.

Is this AI solution just for DISA customers?Kasper: The entire Monitizer suite, including NoriGate and MonitizerPRESCRIBE, is machine-agnostic, so its not limited to DISA machines or even to the green sand process. Monitizer is a Norican-wide solution, so every foundry can benefit from it, whether its pouring iron or die-casting aluminium.

Frans, with your experience, how do you think foundries compare to other manufacturers in their application of digital tools?Frans: Some other manufacturing environments are now quite sophisticated in their use of software and data, which is not often the case for foundries. With IoT infrastructure and Expert Execution Systems like MonitizerPRESCRIBE, there is a real opportunity for foundries to leapfrog the older IoT systems and access the very latest technology without having to make an enormous investment.

Are there any common misconceptions about AI you hear from your foundry customers?Frans: They can be worried that their data might be used in another customers AI which never happens. MonitizerPRESCRIBE can ingest and interpret all a customers foundry data and that certainly doesnt include data from other customers.

Monitizer | PRESCRIBE is designed with full tenant sandboxing: every clients datastore, database, and model is uniquely encrypted, and every component is isolated from every other component in the system. It is not possible to mix data or models between clients and the data is safeguarded with every possible measure.

Kasper: Some people think AI needs another in-house IT system thats big, complex and very expensive. But Monitizer | PRESCRIBE is an online service, it simply gives you a tool to optimise quality and productivity. Also, when we talk to foundry staff, some fear an AI system will come in and take over their job. But this isnt about taking jobs. The information AI gives will help them make better decisions and improve their own performance. It will make them look good.

Are there any other AI-related advantages for foundry owners and their workforces?Kasper: Theres a generational change going on in our industry. Engineers with 30 or 40 years experience are retiring and our customers are worried that their knowledge of how to keep their own unique processes running correctly will be lost. But their knowledge is encoded within historical process data. Monitizer | PRESCRIBE can access that and put it to work. With more automation, the foundry also becomes a cleaner, more attractive place to work. You can spend most of the time in an office-like control room, which will be more appealing to todays potential recruits.

Frans: By learning from human intelligence, expressed in millions of decisions made over the years, the AI becomes the central knowledgebase for the foundry. Then it can support less experienced engineers and operators in their decision making. A lot of value for manufacturing customers lies in selecting and extracting those good decisions so theyre never lost.

If AI helps foundries move from offline analysis to continuous guidance, what comes next?Frans: The end goal is a foundry that runs its own processes automatically similar to what the autonomous vehicle industry is aiming to achieve with cars. Staff will gradually move from continuously analysing processes and adjusting machines to focus on tasks theyre better suited for like innovation, creation and ideation. The plant of the future will re-configure itself for the optimal delivery of new customer orders, adjusting its configuration, production schedule, energy consumption and staff roles to give maximum efficiency.

Kasper: The system will adjust settings automatically, for example, when sand properties change, and you need more additives, or if the humidity changes and the sand dries out faster so you need to add more moisture. All these variations are corrected manually today and, even with Monitizer PRESCRIBEs real-time advice, usually still will be, but the system will handle it all automatically in future.

How close is this fully autonomous future?Frans: Were not there yet, but it will definitely happen for some foundries in the next few years. Most foundries are starting to collect data and analyse it, so they are being assisted by data today. Our system goes from there to guiding them with specific real-time recommendations. The self-driving foundry is the next stop on the journey.

Kasper: Were already helping customers fully automate parts of their DISA line, like moulding and pouring, or sand mixing and moulding, though complete automation of the whole line is a little way ahead at the moment. But I think it will arrive a lot sooner than completely autonomous cars.

Many thanks to both Kasper and Frans for a fascinating explanation of how they are working together to bring AI to foundries.

DISAs AI solution Monitizer | PRESCRIBE is currently live with selected pilot customers and will be available in the coming months. More information can be found here. [https://www.disagroup.com/en-gb/foundry-products/digital-solutions/monitizer/monitizer-prescribe]

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The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? - Foundry-Planet.com

Insights into the North America Artificial Intelligence in Fashion Market to 2027 – Drivers, Restraints, Opportunities and Trends -…

Posted on March 19, 2020 by Danzig

DUBLIN--(BUSINESS WIRE)--The "North America Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" report has been added to ResearchAndMarkets.com's offering.

The North America artificial intelligence in fashion market accounted for US$ 128.7 Mn in 2018 and is expected to grow at a CAGR of 37.9% over the forecast period 2019-2027, to account for US$ 2254.2 Mn in 2027.

The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years. In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further. The North American countries are developing various policies and outlining best practices to implement artificial intelligence for promoting innovation in various industry sectors.

Further, the political agendas for North American countries are aligned with the development of Machine Learning (ML) and Artificial Intelligence (AI). Artificial intelligence technologies such as self-adapting machine learning, deep learning or Natural language processing are expected to transform the way businesses work. Governments of various North American countries are working on drafting robust and comprehensive set of regulations and policies for a holistic development of artificial intelligence in this region.

Reasons to Buy

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology

3.1 Coverage

3.2 Secondary Research

3.3 Primary Research

4. Artificial Intelligence in Fashion Market Landscape

4.1 Market Overview

4.2 PEST Analysis - North America

4.3 Ecosystem Analysis

4.4 Expert Opinions

5. Artificial Intelligence in Fashion Market - Key Market Dynamics

5.1 Key Market Drivers

5.1.1 Availability of a huge amount of data originating from different data sources

5.1.2 Increase in adoption of artificial intelligence in fashion industry to enhance operational efficiency and improve customer experiences

5.2 Key Market Restraints

5.2.1 Concerns related to data privacy and security

5.3 Key Market Opportunities

5.3.1 Huge investments in developing NLP enabled solutions are anticipated to flourish the market growth

5.4 Future Trend

5.4.1 Use of AI for predicting fashion trends

5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Fashion Market - North America Market Analysis

6.1 Overview

6.2 North America Artificial Intelligence in Fashion Market Forecast and Analysis

7. North America Artificial Intelligence in Fashion Market - By Offerings

7.1 Overview

7.2 North America Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027

7.3 Solutions

7.4 Services

8. North America Artificial Intelligence in Fashion Market - By Deployment

8.1 Overview

8.2 North America Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027

8.3 On-premise

8.4 Cloud

9. North America Artificial intelligence in fashion Market - By Application

9.1 Overview

9.2 North America Artificial intelligence in fashion Market Breakdown, By Application, 2018 & 2027

9.3 Product Recommendation

9.4 Virtual Assistant

9.5 Product Search and Discovery

9.6 Creative Designing and Trend Forecasting

9.7 Customer Relationship Management (CRM)

9.8 Others

10. North America Artificial intelligence in fashion Market Analysis - By End User Industry

10.1 Overview

10.2 North America Artificial intelligence in fashion Market Breakdown, By End User Industry, 2018 & 2027

10.3 Apparel

10.4 Accessories

10.5 Cosmetics

10.6 Others

11. North America Artificial Intelligence in Fashion Market - Country Analysis

11.1 Overview

11.1.1 North America Artificial Intelligence in Fashion Market Breakdown, by Key Countries

11.1.1.1 US Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.2 Canada Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.3 Mexico Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

12. Artificial Intelligence in Fashion Market - Industry Landscape

12.1 Overview

12.2 Market Initiative

12.3 New Development

12.4 Top Five Company Ranking

13. Company Profiles

13.1 Adobe Inc.

13.2 Alphabet Inc. (Google)

13.3 Amazon.com, Inc.

13.4 Catchoom

13.5 Facebook Inc.

13.6 Huawei Technologies Co., Ltd.

13.7 IBM Corporation

13.8 Microsoft Corporation

13.9 Oracle Corporation

13.10 SAP SE

For more information about this report visit https://www.researchandmarkets.com/r/mgbpsb

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Insights into the North America Artificial Intelligence in Fashion Market to 2027 - Drivers, Restraints, Opportunities and Trends -...

Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time – Centrum…

Posted on March 19, 2020 by Danzig

CWI researchers, together with the department of radiation oncology of Amsterdam UMC, have developed software based on Artificial Intelligence (AI) that quickly proposes multiple radiation treatment plans for each patient. The software functions as a type of route planner for the doctor: it presents multiple plans based on the data of the patient that represent trade-offs between giving sufficient radiation dose to the tumor with as little damage possible to the surrounding organs. This not only helps the doctors to make plans faster, it will also improve plan quality. Amsterdam UMC has treated the first patient with a plan proposed by the new AI on March 17, 2020. The innovative technique will be used for the treatment of prostate cancer with internal radiation.

Radiation demands delicate maneuvering

Radiation is one of the most important treatments for cancer. In the case of prostate cancer, brachytherapy -a form of internal radiation with the use of catheters- is a very suitable treatment. Multiple catheters are inserted into a patient, through which a radio-active source is being led. After insertion of the catheters, a treatment plan is made by the doctors. In the case of brachytherapy this is being done while the patient is waiting with the catheters that have been inserted earlier. An uncomfortable situation, that preferably lasts as short as possible. Usually it takes the doctors quite some time to design a treatment plan for a specific patient. It takes the new software only a few minutes to come up with a whole range of treatment plans, however. Each plan states for how long the radioactive source should be focused on a specific area, in order to deliver a specific amount of radiation dose in that spot. The focus is on establishing a treatment that gives the desired amount of radiation dose to the tumor, while sparing as much of the surrounding healthy tissue as possible.

In the research and development stages, a blind test was performed with a team of radiation oncologists, showing both plans previously made and used in the clinic and new AI-made plans for the same patients. The radiation oncologists were very much convinced by the abilities of the new AI technique: they preferred an AI-based plan in 98% of the cases.

Peter Bosman, senior researcher at CWIs Life Sciences and Health group and project leader: Our form of AI delivers a spectrum of plans very fast, that represents the trade-offs between delivering sufficient radiation dose with as little damage to surrounding tissue as possible. This gives immediate insight into what is feasible for a specific patient. This relieves doctors from undertaking a complex approach to configuring a treatment plan using existing software that requires intensive human-computer interaction.

Amsterdam UMC has performed research in the area of computer-aided support for making radiation treatment plans for years already, says Arjen Bel, head of the clinical physics of the department of radiation oncology. The challenge is to make these plans quickly, as well as of high quality.

Bradley Pieters, radiation oncologist at Amsterdam UMC: The improved radiation treatment plans can lead to better results for patients with prostate cancer. The extra time allows us to deliver tailor-made plans for the patient. Besides that, we can now use our medical knowledge optimally, as well as extra knowledge about the patient that the computer does not have.

Unique collaboration

The development of the new software was made possible by a close research collaboration between CWIs Life Sciences and Health group, the department radiation oncology of Amsterdam UMC-location AMC, and Elekta, a company that delivers radiation equipment and software to hospitals. The team decided to develop software for this problem with a form of AI (evolutionary algorithms) at its core. These algorithms are very suitable to effectively and efficiently search for good solutions to complex problems, especially when there are multiple conflicting goals to be achieved.

The team especially focused on a form of evolutionary algorithms that arise from Bosmans long-running research line. These algorithms display intelligent search behavior. They have the ability to analyze a certain problem and subsequently teach themselves how they can come up with better solutions for that problem. The research team made special adjustments to search for configurations of treatment plans in the case of brachytherapy for prostate cancer as good as possible. They did that by letting the algorithm use knowledge about the build-up of radiation dose from the inserted catheters. Ultimately, much better results could be booked with this algorithm than with other algorithms.

Broadening the scope

One great advantage of the developed AI-software is that it can be expanded to other types of cancer relatively easily. Calculating a spectrum of possible treatment plans, considering the trade-off between a good chance of a successful treatment and possible side effects, is something that is needed in the making of radiation treatment plans for multiple forms of cancer. A follow-up project has already been planned. With funding of the Dutch Cancer Society (KWF Kankerbestrijding), CWI, Amsterdam UMC, and Elekta will expand this research to the area of internal radiation for cervical cancer, led by new project partner Leiden University Medical Center (LUMC).

Tanja Alderliesten, senior researcher at LUMC (previously Amsterdam UMC) and project leader: This time we will even execute a national validation study in order to create national impact. Business partner Elekta is working on the worldwide distribution of the software, to make sure the rest of the medical world can also profit from this innovation.

YouTube video about the project (in Dutch)

Interview with Peter Bosman in Bits&Chips (in Dutch)

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Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time - Centrum...

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