Category Archives: Artificial Intelligence
The Pentagon’s AI Shop Takes A Venture Capital Approach to Funding Tech – Defense One
The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams.
The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams. By PatrickTucker
Military leaders who long to copy the way Silicon Valley funds projects should know: the Valley isnt the hit machine people think it is, says Nand Mulchandani, chief technical officer of the Pentagons Joint Artificial Intelligence Center. The key is to follow the right venture capitalmodel.
Mulchandani, a veteran of several successful startups, aims to ensure JAICs investments in AI software and tools actually work out. So he is bringing a very specific venture-capital approach to thePentagon.
Heres the plan: when a DoD agency or military branch asks JAIC for help with some mission or activity, the Center will assign a mission team of, essentially, customer representatives to figure out what agency data might be relevant to theproblem.
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Next, the JAIC will assign a product manager not DoDs customary program manager, but a role imported from the techindustry.
He or she handles the actual building of the product, not the administrative logistics of running a program. The product manager will gather customer needs, make those into product features, work with the program manager, ask, What does the product do? How is it priced? Mulchandani told Defense One in a phone conversation onThursday.
The mission team and product manager will take a small part of the agencys data to the software vendors or programs that they hire to solve the problem. These vendors will need to prove their solution works before scaling up to take on all availabledata.
Were going to have a Series A, a seed amount of money. You [the vendor] get a half a million bucks to curate the data, which tends to be the problem. Do the problem x in a very tiny way, taking sample data, seeing if an algorithm applies to it, and then scale it, Mulchandani saidon Wednesday at an event hosted by the Intelligence and National Security Alliance, orINSA.
In the venture capital industry, you take a large project, identify core risk factors, like team risk, customer risk, etc. you fund enough to take care of these risks and see if you can overcome the risks through a prototype or simulation, before you try to scale, he addedlater.
The customer must also plan to turn the product into a program of record or give it some other life outside of theJAIC.
Thats very different from the way the Defense Department pays for tech today, he said. The unit of currency in the DoD seems to be Well, this was a great idea; lets stick a couple million bucks on it, see what happens. Were not doing that way anymore he said onWednesday.
The JAIC is working with the General Services Administration Centers of Excellence to create product manager roles in DoD and to figure out how to scale small solutions up. Recently, some members of the JAIC and the Centers of Excellence participated in a series of human-centered design workshops to determine essential roles and responsibilities for managing data assets, across areas that the JAIC will be developing products, like cybersecurity, healthcare, predictive maintenance, and business automation, according to thestatement.
Mulchandani urges the Pentagon not to make a fetish of Silicon Valley. Without the right business and funding processes, many venture startups fail just as badly as poorly thought out government projects. You just dont hear aboutthem.
When you end up in a situation where theres too much capital chasing too few good ideas that are real, you end up in a situation where you are funding a lot of junk. What ends up happening [in Silicon Valley] is many of those companies just fail, he said Wednesday. The problem in DOD is similar. How do you apply discipline up front, on a venture model, to fund the good stuff as opposed to funding a lot of junk and then seeing two or three products that becomesuccessful?
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The Pentagon's AI Shop Takes A Venture Capital Approach to Funding Tech - Defense One
New research on adoption of Artificial intelligence within IoT ecosystem – ELE Times
element14, the Development Distributor, has published new research on the Internet of Things (IoT) which confirms strong adoption of Artificial Intelligence (AI) within IoT devices, alongside new insights on key markets, enablers and concerns for design engineers working in IoT.
AIoT is the major emerging trend from the survey, demonstrating the beginning of the process to build a true IoT ecosystem. Research showed that almost half (49%) of respondents already use AI in their IoT applications, with Machine Learning (ML) the most used technology (28%) followed by cloud-based AI (19%). This adoption of AI within IoT design is coupled with a growing confidence to take the lead on IoT development and an increasing number of respondents seeing themselves as innovators. However, it is still evident that some engineers (51%) are hesitant to adopt AI due to being new to the technology or because they require specialized expertise in how to implement AI in IoT applications.
Other results from element14s second Global IoT Survey show that security continues to be the biggest concern designers consider in IoT implementation. Although 40% cited security as their biggest concern in 2018 and this has reduced to 35% in 2019, it is still ranked significantly higher than connectivity and interoperability due to the type of data collected from things (machines) and humans, which can be very sensitive and personal. Businesses initiating new IoT projects treat IoT security as a top priority by implementing hardware and software security to protect for any kind of potential threat. Ownership of collected data is another important aspect of security, with 70% of respondents preferring to own the data collected by an edge device as opposed to it being owned by the IoT solution provider.
The survey also shows that although many engineers (46%) still prefer to design a complete edge-to-cloud and security solution themselves, openness to integrate production ready solutions, such as SmartEdge Agile, SmartEdge IIoT Gateway, which offer a complete end-to-end IoT Solution, has increased. 12% more respondents confirmed that they would consider third party devices in 2019 than 2018, particularly if in-house expertise is limited or time to market is critical.
A key trend from last years survey results has continued in 2019 and survey results suggest that the growing range of hardware available to support IoT development continues to present new opportunities. More respondents than ever are seeing innovation coming from start-ups (33%, up from 26%), who benefit from the wide availability of modular solutions and single board
computers available on the market. The number of respondents adopting off-the-shelf hardware has also increased to 54% from 50% in 2018.
Cliff Ortmeyer, Global Head of Technical Marketing for Farnell and element14 says: Opportunities within the Internet of Things and AI continue to grow, fueled by access to an increasing number of hardware and software solutions which enable developers to bring products to market more quickly than ever before, and without the need for specialized expertise. This is opening up IoT to new entrants, and giving more developers the opportunity to innovate to improve lives. element14 provides access to an extensive range of development tools for IoT and AI which provide off-the shelf solutions to common challenges.
Despite the swift integration of smart devices such as Amazons Alexa and Google Home into daily life, evidencing a widespread adoption of IoT in the consumer space, in 2019 we saw a slight shift in focus away from home automation with the number of respondents who considered it to be the most impactful application in IoT in the next 5 years reducing from 27% to 22%. Industrial automation and smart cities both gained, at 22% and 16% respectively, underpinned by a growing understanding of the value that IoT data can bring to operations (rising from 44% in 2018 to 50% in 2019). This trend is witnessed in industry where more manufacturing facilities are converting to full or semi-automation in robotic manufacturing and increasing investment in predictive maintenance to reduce production down times.
The survey was conducted between September and December 2019 with 2,015 respondents participating from 67 countries in Europe, North America and APAC. Responses were predominantly from engineers working on IoT solutions (59%), as well as buyers of components related to IoT solutions, Hobbyists and Makers.
element14 provides a broad range of products and support materials to assist developers designing IoT solutions and integrating Artificial Intelligence. Products are available from leading manufacturers such as Raspberry Pi, Arduino and Beagleboard. element14s IoT hub and AI pages also provide access to the latest products for development and insights and white papers to support the design journey. Readers can view an infographic covering the full results of the element14 Global IoT Survey at Farnell in EMEA, Newark in North America and element14 in APAC.
For more information, visit http://www.element14.com
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New research on adoption of Artificial intelligence within IoT ecosystem - ELE Times
HKMA’s paper on Artificial Intelligence in the banking industry – Lexology
Last year, the HKMA commissioned a study into the application of Artificial Intelligence technology (AI) in the Hong Kong banking industry. The report, published on 23 December 2019, summarises insights from academics and industry experts on AI. One key finding was that almost 90% of the surveyed retail banks had adopted or planned to adopt AI applications. 95% of banks which had adopted AI expressed their intention to use AI to shape their corporate strategy, mainly prompted by the need to improve customer experience, stay cost effective and better manage risk.
To help the banking industry understand the risk and potential of AI, the report covered the latest development trends, potential use cases, status of AI development in banking, challenges and considerations in designing and deploying the technology, as well as the market outlook.
This report is the first in a series of AI-related publications produced by the HKMA. The full report can be accessed here.
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HKMA's paper on Artificial Intelligence in the banking industry - Lexology
Is Artificial Intelligence (AI) A Threat To Humans? – Forbes
Are artificial intelligence (AI) and superintelligent machines the best or worst thing that could ever happen to humankind? This has been a question in existence since the 1940s when computer scientist Alan Turing wondered and began to believe that there would be a time when machines could have an unlimited impact on humanity through a process that mimicked evolution.
Is Artificial Intelligence (AI) A Threat To Humans?
When Oxford University Professor Nick Bostroms New York Times best-seller, Superintelligence: Paths, Dangers, Strategies was first published in 2014, it struck a nerve at the heart of this debate with its focus on all the things that could go wrong. However, in my recent conversation with Bostrom, he also acknowledged theres an enormous upside to artificial intelligence technology.
You can see the full video of our conversation here:
Since the writing of Bostrom's book in 2014, progress has been very rapid in artificial intelligence and machine and deep learning. Artificial intelligence is in the public discourse, and most governments have some sort of strategy or road map to address AI. In his book, he talked about AI being a little bit like children playing with a bomb that could go off at any time.
Bostrom explained, "There's a mismatch between our level of maturity in terms of our wisdom, our ability to cooperate as a species on the one hand and on the other hand our instrumental ability to use technology to make big changes in the world. It seems like we've grown stronger faster than we've grown wiser."
There are all kinds of exciting AI tools and applications that are beginning to affect the economy in many ways. These shouldnt be overshadowed by the overhype on the hypothetical future point where you get AIs with the same general learning and planning abilities that humans have as well as superintelligent machines.These are two different contexts that require attention.
Today, the more imminent threat isn't from a superintelligence, but the usefulyet potentially dangerousapplications AI is used for presently.
How is AI dangerous?
If we focus on whats possible today with AI, here are some of the potential negative impacts of artificial intelligence that we should consider and plan for:
Change the jobs humans do/job automation: AI will change the workplace and the jobs that humans do. Some jobs will be lost to AI technology, so humans will need to embrace the change and find new activities that will provide them the social and mental benefits their job provided.
Political, legal, and social ramifications: As Bostrom advises, rather than avoid pursuing AI innovation, "Our focus should be on putting ourselves in the best possible position so that when all the pieces fall into place, we've done our homework. We've developed scalable AI control methods, we've thought hard about the ethics and the governments, etc. And then proceed further and then hopefully have an extremely good outcome from that." If our governments and business institutions don't spend time now formulating rules, regulations, and responsibilities, there could be significant negative ramifications as AI continues to mature.
AI-enabled terrorism: Artificial intelligence will change the way conflicts are fought from autonomous drones, robotic swarms, and remote and nanorobot attacks. In addition to being concerned with a nuclear arms race, we'll need to monitor the global autonomous weapons race.
Social manipulation and AI bias: So far, AI is still at risk for being biased by the humans that build it. If there is bias in the data sets the AI is trained from, that bias will affect AI action. In the wrong hands, AI can be used, as it was in the 2016 U.S. presidential election, for social manipulation and to amplify misinformation.
AI surveillance: AIs face recognition capabilities give us conveniences such as being able to unlock phones and gain access to a building without keys, but it also launched what many civil liberties groups believe is alarming surveillance of the public. In China and other countries, the police and government are invading public privacy by using face recognition technology. Bostrom explains that AI's ability to monitor the global information systems from surveillance data, cameras, and mining social network communication has great potential for good and for bad.
Deepfakes: AI technology makes it very easy to create "fake" videos of real people. These can be used without an individual's permission to spread fake news, create porn in a person's likeness who actually isn't acting in it, and more to not only damage an individual's reputation but livelihood. The technology is getting so good the possibility for people to be duped by it is high.
As Nick Bostrom explained, The biggest threat is the longer-term problem introducing something radical thats super intelligent and failing to align it with human values and intentions. This is a big technical problem. Wed succeed at solving the capability problem before we succeed at solving the safety and alignment problem.
Today, Nick describes himself as a frightful optimist that is very excited about what AI can do if we get it right. He said, The near-term effects are just overwhelmingly positive. The longer-term effect is more of an open question and is very hard to predict. If we do our homework and the more we get our act together as a world and a species in whatever time we have available, the better we are prepared for this, the better the odds for a favorable outcome. In that case, it could be extremely favorable.
For more on AI and other technology trends, see Bernard Marrs new book Tech Trends in Practice: The 25 Technologies That Are Driving The 4Th Industrial Revolution, which is available to pre-order now.
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Is Artificial Intelligence (AI) A Threat To Humans? - Forbes
Airlines take no chances with our safety. And neither should artificial intelligence – The Conversation AU
Youd thinking flying in a plane would be more dangerous than driving a car. In reality its much safer, partly because the aviation industry is heavily regulated.
Airlines must stick to strict standards for safety, testing, training, policies and procedures, auditing and oversight. And when things do go wrong, we investigate and attempt to rectify the issue to improve safety in the future.
Its not just airlines, either. Other industries where things can go very badly wrong, such as pharmaceuticals and medical devices, are also heavily regulated.
Artificial intelligence is a relatively new industry, but its growing fast and has great capacity to do harm. Like aviation and pharmaceuticals, it needs to be regulated.
A wide range of technologies and applications that fit under the rubric of artificial intelligence have begun to play a significant role in our lives and social institutions. But they can be used in ways that are harmful, which we are already starting to see.
In the robodebt affair, for example, the Australian government welfare agency Centrelink used data-matching and automated decision-making to issue (often incorrect) debt notices to welfare recipients. Whats more, the burden of proof was reversed: individuals were required to prove they did not owe the claimed debt.
The New South Wales government has also started using AI to spot drivers with mobile phones. This involves expanded public surveillance via mobile phone detection cameras that use AI to automatically detect a rectangular object in the drivers hands and classify it as a phone.
Read more: Caught red-handed: automatic cameras will spot mobile-using motorists, but at what cost?
Facial recognition is another AI application under intense scrutiny around the world. This is due to its potential to undermine human rights: it can be used for widespread surveillance and suppression of public protest, and programmed bias can lead to inaccuracy and racial discrimination. Some have even called for a moratorium or outright ban because it is so dangerous.
In several countries, including Australia, AI is being used to predict how likely a person is to commit a crime. Such predictive methods have been shown to impact Indigenous youth disproportionately and lead to oppressive policing practices.
AI that assists train drivers is also coming into use, and in future we can expect to see self-driving cars and other autonomous vehicles on our roads. Lives will depend on this software.
Once weve decided that AI needs to be regulated, there is still the question of how to do it. Authorities in the European Union have recently made a set of proposals for how to regulate AI.
The first step, they argue, is to assess the risks AI poses in different sectors such as transport, healthcare, and government applications such as migration, criminal justice and social security. They also look at AI applications that pose a risk of death or injury, or have an impact on human rights such as the rights to privacy, equality, liberty and security, freedom of movement and assembly, social security and standard of living, and the presumption of innocence.
The greater the risk an AI application was deemed to pose, the more regulation it would face. The regulations would cover everything from the data used to train the AI and how records are kept, to how transparent the creators and operators of the system must be, testing for robustness and accuracy, and requirements for human oversight. This would include certification and assurances that the use of AI systems is safe, and does not lead to discriminatory or dangerous outcomes.
While the EUs approach has strong points, even apparently low-risk AI applications can do real harm. For example, recommendation algorithms in search engines are discriminatory too. The EU proposal has also been criticised for seeking to regulate facial recognition technology rather than banning it outright.
The EU has led the world on data protection regulation. If the same happens with AI, these proposals are likely to serve as a model for other countries and apply to anyone doing business with the EU or even EU citizens.
In Australia there are some applicable laws and regulations, but there are numerous gaps, and they are not always enforced. The situation is made more difficult by the lack of human rights protections at the federal level.
One prominent attempt at drawing up some rules for AI came last year from Data61, the data and digital arm of CSIRO. They developed an AI ethics framework built around eight ethical principles for AI.
These ethical principles arent entirely irrelevant (number two is do no harm, for example), but they are unenforceable and therefore largely meaningless. Ethics frameworks like this one for AI have been criticised as ethics washing, and a ploy for industry to avoid hard law and regulation.
Read more: How big tech designs its own rules of ethics to avoid scrutiny and accountability
Another attempt is the Human Rights and Technology project of the Australian Human Rights Commission. It aims to protect and promote human rights in the face of new technology.
We are likely to see some changes following the Australian Competition and Consumer Commissions recent inquiry into digital platforms. And a long overdue review of the Privacy Act 1988 (Cth) is slated for later this year.
These initiatives will hopefully strengthen Australian protections in the digital age, but there is still much work to be done. Stronger human rights protections would be an important step in this direction, to provide a foundation for regulation.
Before AI is adopted even more widely, we need to understand its impacts and put protections in place. To realise the potential benefits of AI, we must ensure that it is governed appropriately. Otherwise, we risk paying a heavy price as individuals and as a society.
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Airlines take no chances with our safety. And neither should artificial intelligence - The Conversation AU
Artificial intelligence to sterilized robots are helping authorities to deal with coronavirus – indica News
indica News Bureau-
With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.
Artificial Intelligence (AI) can be used to identify disease outbreaks as well as forecast their nature of spread. Canadian startup BlueDot used AI and machine learning to detect the coronavirus outbreak even before the Chinese authorities. Its AI algorithm analyzed multiple sources such as news reports, social media platforms and government documents to predict the outbreak, Wired reported.
Autonomous sterilization robots are helping hospitals to contain the infections in quarantined wards by easily moving into a quarantined zone to sterilize virus without human intervention. Chinese medical robot developer TMiRob deployed 10 disinfection robots across major hospitals in Wuhan to contain the spread of COVID-19, reported Vox.
The first reports of the 2019 novel coronavirus (2019-nCoV) from Wuhan, China were made Dec. 31, 2019, and it quickly spread to become a global emergency. The number of deaths due to COVID-19 in China has already exceeded the SARS outbreak in 2003. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities.
John Brownstein, chief innovation officer at Harvard Medical School and an expert on mining social media information for health trends, is part of an international team using machine learning to comb through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs the virus is taking hold in countries outside of China.
The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.
A company called BlueDot used a similar approachminus the social media sourcesto spot the coronavirus in late December before Chinese authorities acknowledged the emergency.
We are moving to surveillance efforts in the US, Brownstein says. It is critical to determine where the virus may surface if the authorities are to allocate resources and block its spread effectively. Were trying to understand whats happening in the population at large, he says.
AI developer Infervision launched a coronavirus artificial intelligence solution in China this past month that is tailored for front-line use to help clinicians detect and monitor the disease more effectively. The outbreak has put significant pressure on imaging departments, which are now reading over a thousand cases a day. Patients and clinicians typically have to wait a few hours to get the CT results, but Infervision AI is improving the CT diagnosis speed for each case. The surging number of patients needing diagnosis and the strict laboratory requirements for the use of the rRT-PCR detection kit, to confirm the 2019-nCoV diagnosis, pose big challenges to regional and rural hospitals. Infervisions tools are helping sites with limited medical resources to immediately screen out suspected Coronavirus-infected patients for further diagnosis and treatment.
Drones are gaining popularity as the fastest and safest means to transport supplies during disease outbreaks. Singapores AI startup Network has launched the first urban air transportation channel to deliver medical supplies between Xinchang County Peoples Hospital and the countys disease control center, both located in Zhejiang, one of the most severe coronavirus hit provinces.
Near Seattle, for instance, a robot helped doctors treat an American man diagnosed with the novel coronavirus. The robot, which carried a stethoscope, helped the patient communicate with medical staff while limiting their own exposure to the illness.
Meanwhile, Chinese hospitals are now shipping in robots from the Danish company UVD Robots that can disinfect patient rooms, according to a statement. UVD Robots says that its roving robotic pods work by emitting ultraviolet light throughout an area, killing viruses and bacteria, including the coronavirus. (The robots are remotely controlled by a device operated by a health worker.)
Self-driving vehicles are even delivering supplies to medical workers in Wuhan. As CNN noted, the Chinese e-commerce company JD.com has been moving packages short distances to a hospital.
Flying robots, also known as drones, are also in the mix. Shenzhen MicroMultiCopter said in a statement earlier this month that it is deploying drones to patrol public places, spray disinfectant, and conduct thermal imaging. Chinese officials have used drones to track whether people are traveling outside without wearing face masks or violating other quarantine rules. More on this surveillance trend in a second.
Infervisions Coronavirus AI solution has been in use at the center of the epidemic outbreak at Tongji Hospital in Wuhan (Tongji Medical College of Huazhong University of Science & Technology), along with sites in other cities such as the Third Peoples Hospital of Shenzhen in Shenzhen City. Infervisions Coronavirus AI solution is accelerating pneumonia diagnosis and epidemic monitoring efforts.
The outbreak has put significant pressure on imaging departments, which are now reading over a thousand cases a day. Patients and clinicians typically have to wait a few hours to get the CT results, but Infervision AI is improving the CT diagnosis speed for each case, and each minute saved is critical to decreasing the chance of cross-contamination at the hospital. The surging number of patients needing diagnosis and the strict laboratory requirements for the use of the rRT-PCR detection kit, to confirm the 2019-nCoV diagnosis, pose big challenges to regional and rural hospitals. Infervisions tools are helping sites with limited medical resources to immediately screen out suspected Coronavirus-infected patients for further diagnosis and treatment.
While physicians are working day and night, Infervision AI is helping manage the process efficiently; assisting with pneumonia marking, abnormal and severe case analysis, patient triage, medical resources coordination, prior case comparisons and treatment assessments.
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Artificial intelligence to sterilized robots are helping authorities to deal with coronavirus - indica News
Integration of Data and Artificial Intelligence – Analytics Insight
Artificial intelligence is getting progressively widespread, influencing all aspects of society even Sonic drive-ins are intending to implement artificial intelligence to give better customer support. Obviously, every time another development shows up in the domain of AI, fears emerge with respect to its potential to supplant human jobs. While this is a truth of adapting to a more tech-driven society, these apprehensions will in general disregard the collaborative and job-creating characteristics that AI will have later on.
For as far back as barely any years weve seen big data and machine learning take even more a foothold in organizations, with many accepting the era of artificial intelligence (AI) is here. What is clear is that with advances being made on a practically regular schedule now, organizations need to plan for an immeasurably unique future. However, so as to take advantage, a few organizations may need to make a dramatic adjustment by the way they work.
Right now, for some organizations, AI and big data are seen such that constrains the potential they bring to the table. They are often observed as something that can help cut operational expenses, instead of as a crucial methodology for creating increases in profitability, output and improved assurance over the corporate direction. All together for AI and big data to be fruitful, organizations must consolidate them with business ability and insight making it something the C-suite cant overlook.
The rapid progression of data platforms and their abilities has seen analytical models progressively being utilized to display complex business scenarios for planning, operations, investment and innovation. Organizations keep on moving to data-driven decision making at all levels in the enterprise as data streams, processing and resulting insights become omnipresent. Given the availability of these technological capabilities, the critical question is the manner by which to make progress with these toolsets.
Before, moderately scarce skills were required to perform statistical analysis. Todays data ecosystems and platforms, however, can without much of a stretch encourage connection with sources, wrangling the information and afterwards structure, store and process with the elasticity of resources. Being on-demand in the cloud, these capacities encourage experimentation and ad hoc utilize that can deliver quick outcomes if you know the abilities, dangers and have the individuals with the knowledge and experience to utilize them.
While AI may not be granted decision-making capabilities for pivotal business assignments, its capacity to give solid, error-free data is as of now prompting imperative insights that totally change business operations.
Artificial intelligences automation abilities imply it is progressively being utilized to streamline unremarkable tasks and give laborers more opportunity for high-level activities. This can make organizations progressively effective by bringing down operating expenses and improving profitability. At the end of the day, as AI keeps on advancing, it will assist us with improving our own jobs.
However, the greatest potential for AI originates from machine learning.
As AI gains from new data inputs, it turns out to be progressively ground-breaking and better ready to help with increasingly complex tasks and algorithms, further growing opportunities for collaboration and increased efficiency. Machine learning is helping AI applications better comprehend a more extensive scope of guidelines, and even the context wherein a request is made.
This will prompt considerably faster and increasingly effective outcomes, and assisting with conquering normal issues we see today, for example, automated customer service systems being not able to explain solve complaints or requests. Indeed, even as these systems grow more developed, but, there will, in any case, be numerous instances where human interaction is expected to accomplish the ideal goals.
The pace of technological change is faltering and will just continue to gather pace, making new science, new systems, new organizations and new products. The ability to recognize and afterwards fuse the best solution for business and at the right time to expand advantage is a significant challenge. No place is this more the case than in the AI and big data area, where several start-ups are contending to be the next business pioneers.
Organizations must guarantee they have a well-structured architecture framework that empowers CIOs to respond with the flexibility required to join the new and replace the old. Along these lines, should something be seen not as working or a superior solution is found, the leaders can choose to evacuate or supplant it with something that may be a superior fit.
As AI applications become progressively intricate and more ingrained in everyday life, there will likewise be an increased requirement for people who can clarify the discoveries and decisions produced by a machine.
Supervision of AI applications will likewise be important to ensure that undesirable results, for example, discrimination and even bigotry are recognized and dispensed with to prevent harm. Regardless of how smart AI becomes, it will keep on requiring human guidance to discover new solutions and better satisfy its intended function.
Despite the fact that AI offers boundless opportunities for innovation and improvement, it wont have the option to achieve its full potential on its own. A community future will see programmers, engineers and everyday consumers and workers all the more completely integrating AI into their daily lives.
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Integration of Data and Artificial Intelligence - Analytics Insight
Maple Gold Receives $400,000 in Tax Refunds and Plans Additional Artificial Intelligence Study to Enhance Predictive Modelling at Douay – Yahoo…
Montreal, Quebec--(Newsfile Corp. - March 2, 2020) - Maple Gold Mines Ltd.(TSXV: MGM) (OTCQB: MGMLF) (FSE: M3G)("Maple Gold" or the "Company") is pleased to announce that the Company has received a Notice of Assessment from Revenu Quebec ("RQ") for $399,966 in tax refunds pertaining to qualified 2018 exploration expenditures. The Company has received the first installment of $325,644 from RQ, and expects to receive the balance in the coming weeks.
Maple Gold is also planning to complete additional Artificial Intelligence ("AI") work to update and expand on a previous AI study that was completed at the Douay Project back in 2008. Maple Gold has made significant progress with its in-house modelling efforts over the course of the last 18 months, delivering a new 3D model that incorporates significantly more data than would have been available a decade ago along with updated geological and structural interpretations.
The 2008 study was completed by Diagnos Inc., and covered an area of about 50km, extending about 7km to the NW beyond what is now known as the Resource Area, but without covering the 531 and Main Zones. The dataset used included 67,836 samples from 505 drill-holes, as well as geophysical, lithological, geochemical and alteration data, with a total of 21 variables considered. Maple Gold imported one of the resulting prospectivity maps into the Company's GIS compilation in order to compare the higher-potential areas defined in this work with those outlined with traditional data layering in GIS. The results of this exercise were fairly compelling, with the historical AI work supporting the potential for additional zones of mineralisation within and beyond what is now the Nika Zone and even stronger indications from the predictive modeling for higher grade gold mineralisation beyond the current conceptual pits, most notably to the NW of the NW Zone, and also outlining a large essentially undrilled target south of the Porphyry Zone (see hatched area in Figure 1 below).
Maple Gold's VP, Exploration, Fred Speidel, commented: "We view these numerical approaches to target definition as another valuable layer of information that, if adequately supported by key underlying datasets and input from geologists, has the potential to further optimize drill targeting. The previous study did not cover the 531 or Main zones or their extensions, so we are keen to update and expand this work there and incorporate another tool into our targeting efforts."
This updated AI exercise will allow us to apply more powerful algorithms than previously, to expand the area of coverage to include 531 and Main zones that are of particular interest to us given recent drilling and geophysical results there, and also to include some additional concepts recently developed by the current team. Furthermore, the South Porphyry target, which overlaps with the 531 SW Target defined earlier, only has two drill holes on its northern edge, where significant syenite was intersected with several (narrow) gold intercepts. We plan to complete an IP survey over this area in order to determine the possible distribution of sulfides, prior to initial drill testing.
Figure 1:2008 PM5 AI prospectivity map, with 2019 RPA conceptual pits, historical and planned 2020 drill collars superimposed, Note three large AI targets, with Porphyry South in particular having very little drilling.
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Qualified Person
The scientific and technical data contained in this press release was reviewed and prepared under the supervision of Fred Speidel, M. Sc, P. Geo., Vice-President Exploration, of Maple Gold. Mr. Speidel is a Qualified Person under National Instrument 43-101 Standards of Disclosure for Mineral Projects. Mr. Speidel has verified the data related to the exploration information disclosed in this news release through his direct participation in the work. Click the following link to review the Company's QA-QC standards and protocols: http://maplegoldmines.com/index.php/en/projects/qa-qc-qp-statement.
About Maple Gold
Maple Gold is an advanced gold exploration and development company focused on defining a district-scale gold project in one of the world's premier mining jurisdictions. The Company's ~355 km Douay Gold Project is located along the Casa Berardi Deformation Zone (55 km of strike) within the prolific Abitibi Greenstone Belt in northern Quebec, Canada. The Project benefits from excellent infrastructure and has an established gold resource that remains open in multiple directions. For more information please visit http://www.maplegoldmines.com.
ON BEHALF OF MAPLE GOLD MINES LTD.
"Matthew Hornor"B. Matthew Hornor, President & CEO
For Further Information Please Contact:
Mr. Joness LangVP, Corporate DevelopmentCell: 778.686.6836Email: jlang@maplegoldmines.com
NEITHER THE TSX VENTURE EXCHANGE NOR ITS REGULATION SERVICES PROVIDER (AS THAT TERM IS DEFINED IN THE POLICIES OF THE TSX VENTURE EXCHANGE) ACCEPTS RESPONSIBILITY FOR THE ADEQUACY OR ACCURACY OF THIS PRESS RELEASE.
Forward-Looking Statements:
This news release contains "forward-looking information" and "forward-looking statements" (collectively referred to as "forward-looking statements") within the meaning of applicable Canadian securities legislation in Canada, including statements about the prospective mineral potential of the Porphyry Zone, the potential for significant mineralisation from other drilling in the referenced drill program and the completion of the drill program. Forward-looking statements are based on assumptions, uncertainties and management's best estimate of future events. Actual events or results could differ materially from the Company's expectations and projections. Investors are cautioned that forward-looking statements involve risks and uncertainties. Accordingly, readers should not place undue reliance on forward-looking statements. Forward-looking statements include, but are not limited to, statements regarding timing and completion of the private placement. When used herein, words such as "anticipate", "will", "intend" and similar expressions are intended to identify forward-looking statements.
Forward-looking statements are based on certain estimates, expectations, analysis and opinions that management believed reasonable at the time they were made or in certain cases, on third party expert opinions. Such forward-looking statements involve known and unknown risks, and uncertainties and other factors that may cause our actual events, results, performance or achievements to be materially different from any future events, results, performance, or achievements expressed or implied by such forward-looking statements. For a more detailed discussion of such risks and other factors that could cause actual results to differ materially from those expressed or implied by such forward-looking statements, refer to Maple Gold Mines Ltd.'s filings with Canadian securities regulators available on http://www.sedar.com or the Company's website at http://www.maplegoldmines.com. The Company does not intend, and expressly disclaims any intention or obligation to, update or revise any forward-looking statements whether as a result of new information, future events or otherwise, except as required by law.
To view the source version of this press release, please visit https://www.newsfilecorp.com/release/52993
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Maple Gold Receives $400,000 in Tax Refunds and Plans Additional Artificial Intelligence Study to Enhance Predictive Modelling at Douay - Yahoo...
OneConnect Uses Artificial Intelligence to Accelerate Digital Transformation at Financial Institutions in Response to COVID-19 Outbreak – Yahoo…
SHENZHEN, China, March 2, 2020 /PRNewswire/ -- OneConnect Financial Technology Co., Ltd. ("OneConnect" or the "Company") (NYSE: OCFT), a world-leading technology service platform for financial institutions, is supporting digital transformation at financial institutions that are grappling with operational challenges revealed by the novel coronavirus (COVID-19) disruption.
The COVID-19 outbreak has highlighted the need for financial institutions to upgrade their range of online products and trading tools to serve their corporate and retail customers who are turning to online services without leaving home. OneConnect provides comprehensive end-to-end solutions for financial institutions by integrating extensive financial servicewith market-leading technology. It enables customers' digital transformations to increase revenue, manage risks, improve efficiencyand reduce costs.
OneConnect's artificial intelligence product solution offers three series of products and services: intelligent deposits, online retail loans and loans for small and medium-sized enterprises (SMEs).These products help banksbetter serve corporate and individual customers, allowing customers to enjoy full online business services without leaving home during the spread of COVID-19.
Intelligent deposit solution improves liquidity
Banks are unable to continue their usual offline marketing and services due to the outbreak, resulting in extremely low levels of retail deposits. OneConnect offers its intelligent deposit solution, which integrates industry platform resources to help banks provide retail customers with high-yield current and time deposit products without leaving home. The solution enables banks to retain the flow of deposits and improve liquidity.
Online financial supermarket gives SMEs access to emergency loans
To help SMEs deal with financing difficulties related to the COVID-19 outbreak, OneConnect connects cash-strapped merchants with financial institutions through its digital financial supermarket. With access to various online loan products, including loans for the e-commerce and food service sectors to meet their urgent needs, merchants can handle the entire process online, from loan application to disbursal of funds. Enterprises can access emergency loans without the need for in-person contact.
OneConnect's AI operation solution supports online services
For some financial institutions, switching from offline to online services may overload their current digital capability. OneConnect's intelligent operation solution offers three services-- financial cloud, smart voice and smart claims -- to enable financial institutions to safely expand their online services.
The financial cloud products offered by OneConnect help financial institutions quickly expand their resources in the short term and cope with online traffic surges that must be handled efficiently by employees working remotely.
OneConnect's smart voice service supports staff working remotely who need to service large numbers of customers with urgent issues during this coronavirus disruption. Manned by smart outgoing robots, which can handle calls, and AI agents, which support human agents, the smart voice service can reduce workload for staff, save manpower and provide 24/7 service.
Smart claim offers quick processing of traffic accident insurance claims
To minimize the face-to-face contact usually required to report traffic accidents, OneConnect's integrated video solution for quick processing and claims-handling of traffic accidents is a safer alternative. The solution allows the parties concernedto report the traffic accident by video while enabling the traffic police to obtain evidence remotely, determine the responsibility online, and issue the electronic version of their findings.
To date, the video-based traffic accident processing platform has handled over 330,000 accidents, with the average processing time being reduced from 40 to five minutes. The number of incidents that can be handled by law enforcement per day has increased from 20 per police officer to 300, and the traffic jams caused by minor accidents has been reduced from 1 hour to 15 minutes.
SOURCE OneConnect
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OneConnect Uses Artificial Intelligence to Accelerate Digital Transformation at Financial Institutions in Response to COVID-19 Outbreak - Yahoo...
Issue of the day: The Ethics of Artificial Intelligence – HeraldScotland
FOR better or for worse time will tell, but the rise of Artificial Intelligence (AI) is already beginning to transform society. Now, the Pope has teamed up with tech giants to ensure its ethical development is prioritised.
What has the Pope said?Pope Francis has raised concerns about the speed of the spread of AI, noting its development is one of the most important changes affecting todays world and is at the very heart of the epochal change we are experiencing.
Who is he working with?Microsoft and IBM at the moment. Leaders from both firms have met with senior Vatican officials and agreed to collaborate on human-centred ways of designing AI.
And respect is key?At a conference in Rome, Microsoft president, Brad Smith, and IMB Executive Vice President, John Kelly, issued a joint document with the Pontiff calling for the regulation of intrusive technologies, such as facial recognition, saying AI should respect dignity, privacy and human rights and operate transparently.
Their comments cameas a woman in London - who allegedly assaulted an emergency services worker - became the first person in the UK to be arrested using facial recognition technology.
There are concerns over this?The Scotland Yard van-mounted cameras have begun being trialled in London, ahead of a city-wide roll-out. They record faces and send an alert to an officers phone if someone look like a suspect on a wanted list. However, human rights groups have been protesting this, saying the cameras could lead to innocent members of the public being stopped, searched and even arrested.
So regulation is the main aim?The joint document makes a specific reference to any potential abuse of facial recognition, saying: New forms of regulation must be encouraged to promote transparency and compliance with ethical principles, especially for advanced technologies that have a higher risk of impacting human rights, such as facial recognition.
Algorithms?Pope Francis also called for the ethical development of algorithms - known as algor-ethics, pointing out that they allow a select few to know everything about us, while we know nothing about them, saying that this dulls critical thought.
This edges into Dark AI territory?Dark AI - where malevolent applications of AI could include anything from algorithms that manipulate trade to tech giants using it to manipulate the economy to their own benefit - is seemingly what the new approach is aiming to halt.
Will other firms sign up to the document?It is not immediately clear how the principles of the document will be put into place or who else will come on board, but examples were given of what they are working toward. These include IBM wanting a doctor to be in the loop when its AI technology makes healthcare recommendations.
Ultimately, there is hope?The Pope offered light at the end of the tunnel. He said that although algorithms can mean knowledge and wealth accumulate in a few hands with grave risks for democratic societies, these dangers must not detract from the immense potential that new technologies offer.
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Issue of the day: The Ethics of Artificial Intelligence - HeraldScotland