Category Archives: Artificial Intelligence
VA Looking to Expand Usage of Artificial Intelligence Data – GovernmentCIO Media
The agency is looking at how to best apply curated data sets to new use cases.
The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.
In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.
One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.
Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.
Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.
Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."
Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.
When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.
Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.
The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.
How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."
Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.
Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.
A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded
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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media
Global Artificial Intelligence in Healthcare Market – Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing &…
The Global Artificial Intelligence in Healthcare Market is expected to grow from USD 2,178. 37 Million in 2018 to USD 10,578. 45 Million by the end of 2025 at a Compound Annual Growth Rate (CAGR) of 25.
New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Healthcare Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871979/?utm_source=GNW 32%.
The positioning of the Global Artificial Intelligence in Healthcare Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).
The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Healthcare Market including are Google, IBM, Intel, Microsoft, NVIDIA, Amazon Web Services, General Electric Company, Medtronic, Micron Technology, and Siemens Healthineers.
On the basis of Offering, the Global Artificial Intelligence in Healthcare Market is studied across Hardware, Services, and Software.
On the basis of Technology, the Global Artificial Intelligence in Healthcare Market is studied across Computer Vision, Context-Aware Computing, Machine Learning, Natural Language Processing, and Querying Method.
On the basis of Application, the Global Artificial Intelligence in Healthcare Market is studied across Clinical Trial Participant Identifier, Cybersecurity, Drug Discovery, Emergency Room & Robot-Assisted Surgery, Fraud Detection, Healthcare Assistance Robots, Inpatient Care & Hospital Management, Lifestyle Management & Monitoring, Medical Imaging & Diagnostics, Patient Data and Risk Analysis, Precision Medicine, Research, Virtual Assistant, and Wearables.
On the basis of End User, the Global Artificial Intelligence in Healthcare Market is studied across Healthcare Payers, Hospitals and Providers, Patients, and Pharmaceutical and Biotechnology Companies.
For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.
In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.
Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.
The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Healthcare Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Healthcare Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Healthcare Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Healthcare Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Healthcare Market
The report answers questions such as:1. What is the market size of Artificial Intelligence in Healthcare market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Healthcare Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Healthcare Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Healthcare Market?5. What are the opportunities in the Global Artificial Intelligence in Healthcare Market?6. What are the modes of entering the Global Artificial Intelligence in Healthcare Market?Read the full report: https://www.reportlinker.com/p05871979/?utm_source=GNW
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The future through Artificial Intelligence – The Star Online
ARTIFICIAL Intelligence (AI) is the wave of the future. This area of computer science emphasising the creation of intelligent machines that work and react like humans is heavily influencing and taking over the way we get on with daily life.
Artificial Intelligence is revolutionising industries and improving the way business is conducted.
More importantly, it is revolutionising industries and improving the way business is done, being already widely used in applications including automation, data analytics and natural language processing.
On a bigger spectrum, from self-driving cars to voice-initiated mobile phones and computer-controlled robots, the presence of AI is seen and felt almost everywhere.
As more industries shift towards embracing the science of incorporating human intelligence in machines so the latter can function, think and work like humans, the demand for human capital with the relevant skill and expertise correspondingly increases.
As such, the question is, how do engineering students ride this wave and make the most of it?
AI has a high learning curve but the rewards of a career in AI far outweigh the investment of time and energy.
Unlike most conventional careers, AI is still in its infancy stage although several modern nations have fully embraced the Fourth Industrial Revolution.
Taking this into account, UCSI University has taken the initiative to develop the Bachelor of Computer Engineering (Artificial Intelligence) programme.
The nations best private university for two years in a row, according to the two recent QS World University Rankings exercises, proactively defines its own AI curriculum to offer educational content that can help increase the supply of AI engineers with job-ready graduates and real world experiences.
The AI programme at UCSI consists of a number of specialisations and several overlapping disciplines, including mathematical and statistical methods, computer sciences and other AI core subjects to provide a conceptual framework in providing solutions for real-world engineering problems.
The first two years covers core theoretical knowledge such as mathematics and statistics, algorithm design and computer programming, as well as electrical and electronics.
Students will progress to the AI subfields by selecting the specialisation elective tracks covering emerging areas such as machine learning, decision-making and robotics, perception and language and human-AI interaction, among others.
We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system, says Ang.
UCSI Faculty of Engineering, Technology and Built Environment dean Asst Prof Ts Dr Ang Chun Kit pointed out that AI was unavoidably the way forward.
We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system.
This programme was developed with a vision to provide the foundation for future growth in producing more complex and high-value products for industry sectors in Malaysia, he said.
Leading the faculty in which 46 of its members have PhDs, Ang emphasised the university focuses on research attachment abroad and has established partnerships with key industry players.
The faculty also stands out in terms of receiving grants to advance high impact projects.
Students from the faculty are also annually selected for researches at world-renowned universities such as Imperial College London and Tsinghua University.
The faculty also strives to give students field experience through internships at various top companies.
An example would be Harry Hoon Jian Wen, an Electrical and Electronic Engineering student. He was selected to go to the University of Queensland for a research attachment while also successfully completing his internship at Schneider Electric.
For further details, visit http://online.ucsiuniversity.edu.my/ or email info.sec@ucsiuniversity.edu.my
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The future through Artificial Intelligence - The Star Online
The Global Artificial Intelligence in Retail Market is expected to grow from USD 1,956.36 Million in 2018 to USD 5,512.37 Million by the end of 2025…
The positioning of the Global Artificial Intelligence in Retail Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).
New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Retail Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871981/?utm_source=GNW
The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Retail Market including are Amazon Web Services, Microsoft, Nvidia, Oracle, SAP, Google, Intel, Salesforce, Sentient Technologies, and ViSenze.
On the basis of Type, the Global Artificial Intelligence in Retail Market is studied across Offline Retail and Online Retail.
On the basis of Technology, the Global Artificial Intelligence in Retail Market is studied across Machine Learning and Deep Learning and Natural Language Processing.
On the basis of Service, the Global Artificial Intelligence in Retail Market is studied across Managed Services and Professional Services.
On the basis of Solution, the Global Artificial Intelligence in Retail Market is studied across Customer Relationship Management, Payment Services Management, Price Optimization, Product Recommendation and Planning, Supply Chain Management and Demand Planning, Virtual Assistant, and Visual Search.
On the basis of Deployment Mode, the Global Artificial Intelligence in Retail Market is studied across On-Cloud and On-Premises.
On the basis of Application, the Global Artificial Intelligence in Retail Market is studied across In-Store Visual Monitoring and Surveillance, Location-Based Marketing, Market Forecasting, Predictive Merchandising, and Programmatic Advertising.
For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.
In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.
Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.
The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Retail Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Retail Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Retail Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Retail Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Retail Market
The report answers questions such as:1. What is the market size of Artificial Intelligence in Retail market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Retail Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Retail Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Retail Market?5. What are the opportunities in the Global Artificial Intelligence in Retail Market?6. What are the modes of entering the Global Artificial Intelligence in Retail Market?Read the full report: https://www.reportlinker.com/p05871981/?utm_source=GNW
About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.
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The Global Artificial Intelligence in Retail Market is expected to grow from USD 1,956.36 Million in 2018 to USD 5,512.37 Million by the end of 2025...
Why transparency is key to promoting trust in artificial intelligence – IT PRO
Artificial intelligence (AI) is inescapable. In our daily lives we probably encounter it and its best friend machine learning much more frequently than we think. Did you buy something online yesterday, use face login on your smartphone, check your Facebook, look for something on Google, or use Google Maps? AI was right there.
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When AI is helping us find the most efficient route home, were often quite happy to let it do its job. But this technology already does so much more, from helping to decide whether to grant us bank loans and diagnose our illnesses, to presenting targeted advertising.
As AI gets more and more embedded in our lives and helps make decisions that are increasingly significant to us, were rightly concerned about transparency. When big new stories like the Cambridge Analytica scandal or ongoing discussion around inherent biases in facial recognition hit the headlines, we are concerned about bias (intentional or otherwise), and our trust in AI takes a hit.
Explainable AI gives us a route to greater trust in AI. It is designed to help us learn more about how AI works in any given situation. So, instead of the AI just giving us an answer to a question, it shows us how it got to the answer. The alternative is the so-called black box situation where an AI uses an unspecified range of information and algorithms to get to an answer, but doesnt make any of this transparent.
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In theory, explainable AI gives us confidence in the conclusions an AI system draws. Dr Terence Tse, Associate Professor of Finance at ESCP Business School, gives the following example: Imagine you want to obtain a loan and the approval is purely determined by an algorithm. Your loan gets rejected. If the algorithm in question is a black box its an issue for all parties. The bank cannot say why this is happening, and you don't know what to do in order to obtain the loan. Having explainable AI will help.
Explainable AI is a vital aspect of understanding an AIs competence in coming up with any particular set of outputs. Mark Stefik, Research Fellow and Lead of Explainable AI at PARC, a Xerox company, tells IT Pro: Typically, when people interact with AIs and the systems do the right thing, then people overestimate the AIs competence. They assume that the machines think like people, which they do not. They assume that machines have common sense, which they do not.
In fact, AI does not think like humans do at all. We use think in relation to AI to describe a way of working that in reality is different to that of our own brains. AI uses algorithms and machine learning to help it draw conclusions from data it is given, or from insights it generates. In showing how an AI has reached its decision, explainable AI can help uncover biases and in doing so not only provide individuals with redress, as in the banking example above, but also help refine the AI system itself.
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Oleg Rogynskyy, Founder and CEO of People.ai says: A lack of explainability on how the machine learning model thinks can result in biases. If there is a bias hidden in the data set a machine learning model is trained on, it will consider the bias a ground truth.
Explainability techniques can be used to detect and then remove biases and ensure a level of trust between the machines and the user.
As AI takes an increasingly important role in our everyday lives, we are getting more and more concerned about whether we can trust it. As Stefik puts it: The need for explainable AI increases if we want to use the systems in critical situations, where there are real consequences for good and bad decisions. People want to know when they can trust the systems before they rely on them.
The IT Pro Podcast: Looking forward to 2020
With 2019 behind us, we predict what trends the IT industry can expect over the next year
The industry recognises this need. In a recent IBM survey of 4,500 IT decision makers, 83% of respondents said being able to explain how AI arrived at a decision was universally important. That number rose to 92% among those already deploying AI, as opposed to 75% of those considering a deployment.
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Rogynskyy is unequivocal in his message, saying: Explainable AI must be prevalent everywhere. Tse was similarly forthright, adding: If we want to gain public trust in the deployment of AI, we have to make explainable AI a priority.
Stefik, however, has reservations, particularly when it comes to how we define terms like trust and explainable, which he argues are nuanced and complex concepts. Nevertheless, he hasnt written explainable AI off completely, saying: It is not ready as a complete (or well-defined) approach to making trustworthy systems, but it will be part of the solution.
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Why transparency is key to promoting trust in artificial intelligence - IT PRO
Coronavirus: Spain to use artificial intelligence to automate testing – ComputerWeekly.com
The Spanish government is planning to test 80,000 people a day for coronavirus with the roll-out of robot testers.
Technology will be used to speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far. According to Bloomberg, Spanish authorities now plan to increase daily testing from about 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing.
Speaking at a conference on Saturday 21 March, Raquel Yotti, head of Madrids health institute, said: A plan to automate tests through robots has already been designed and Spain has committed to buying four robots that will allow us to execute 80,000 tests per day.
Because of the ease that coronavirus spreads from person to person, testing has been identified as one of the best ways to control the disease. But testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners exposure to the virus.
No further details have been given about how the robots will work, but AI is increasingly being designed to work in the healthcare industry by automating some of the work of medical staff, giving them more time to treat patients.
The technology has proved successful in medical trials, including identifying cancer in breast scans.
A research paper from Google Health, published inNaturemagazine, has reported that machine learning, based on Googles TensorFlow algorithm, can be used to reduce false positives in breast cancer scans. A false positive is when a mammogram scan is incorrectly identified as cancerous, and a false negative is when it is wrongly diagnosed as not being cancerous.
In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representativedataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset, while the UK dataset showed a 1.2% reduction in false positives.
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Coronavirus: Spain to use artificial intelligence to automate testing - ComputerWeekly.com
Is artificial intelligence the answer to disease prevention? – The Burn-In
COVID-19 has taken the world by storm. Lockdowns, quarantines, and shutdowns have created an unpredictable scenario that looks almost apocalyptic. But with the right kind of precautionary measures, knowledge, and resources, we can fight this too. This global crisis is a glaring reminder of the gap between what our current healthcare systems can handle and what they should be equipped to handle.
To bridge this gap and in order to provide the needed care to those affected, Artificial Intelligence (AI) might just be our best bet.
Epidemiology tracks the source of an outbreak and analyzes which sections of the population face the highest risk. With AI, it can become easier to find the pattern of the course of the outbreak and then to predict possibly affected people.
Consider Blue Dot, an Artificial Intelligence agency from Canada that predicted the coronavirus outbreak days before it happened. This AI works by using data from around the world in different languages to comprehensively analyze trends in the disease patterns. This allows it to predict public outbreaks and track infectious diseases before they spread too much.
Using data of population sections, vulnerabilities, and previous diseases, AI can predict the possible turn of events with a pandemic such as a coronavirus. For example, we now know that COVID-19 affects people with respiratory diseases and elderly people more. With this knowledge, AI can use data analysis and predict that areas with larger populations of elderly people or countries with a high number of people with respiratory problems, will be most affected by COVID-19. Military veterans who have been exposed to asbestos become especially susceptible to something like coronavirus because of their compromised respiratory systems. This kind of information can become crucial in controlling COVID-19 from becoming fatal globally.
Currently, there are also cases of hackers stealing information with coronavirus map-tracker malware. Centralized AI performing this activity could have stopped this malware from reaching people. In this moment of widespread anxiety, it is important that we ensure we are reading the correct information and sharing information with safe sources.
In countries such as China and Italy, COVID-19 could only be controlled once its presence became known. Detecting disease before its too late might be one of the most important contributions AI can make to medical science.
An article on GCN by Steve Bennett, former director of the National Biosurveillance Integration Center within the Department of Homeland Security, talks about the potential of AI in terms of coronavirus. He writes that there are pilot approaches that use machine learning to mine social media data for indications of unusual flu symptoms. AI can also be used to examine near-real-time emergency medical services and ambulance data, using ML (machine learning) to look for anomalies in the medical notes as patients were admitted to hospitals. In these instances, AI was able to detect the disease much faster than physical tests saving it from spreading and also ensuring that patients get the treatment in time.
In terms of outbreaks such as COVID-19, early detection is key to both saving lives as well as keeping economies stable. As early as 2009, researchers were using data streams available via internet activity to monitor for listeria outbreaks. Studies like this can be used as roadmaps for AI outbreak detection research.
AI can also be of use in determining which treatments are the most effective for COVID-19. For example, if a treatment helps a patient recover faster in China, then AI can use that information to model and then apply the same treatment in Italy. In turn, AI can also quickly analyze other such cases and reach a possible method of treatment faster than humans alone.
Unfortunately, there is still no reliable vaccine for coronavirus leaving mankind vulnerable to it. It is especially difficult to find preventative and curative alternatives in todays post-antibiotic area. As stated by experts at Sani Professional, superbugs and new diseases are emerging that have greater resistance to common cleaners and chemicals we rely on to sanitize, disinfect, and clean up spaces and tools every day. That being said, there is a lot of research being done on possible cures in the form of antibody research. Since it is still too early to know a specific time when the vaccine or an alternate immediate treatment will be available, the use of AI might help to speed up the process, and possibly highlight other avenues for curative research. Heavy hitters like IBM and Amazon are offering up their supercomputers to help with the research.
Amidst the chaos and the flood of information, it is important that we put our safety first. Getting correct information from trusted sources is the first step towards this. Use updates from National Health Services, the WHO and more to keep yourself abreast of the current situation. It is especially important to comply with any imposed travel restrictions, and take precautions in case youre planning to travel. Unless it is absolutely necessary, it is best to stay at home and wait for this pandemic to pass. Regularly washing your hands for 20 seconds (with soap) and social distancing, are key to protecting yourself and those around you from this disease.
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Is artificial intelligence the answer to disease prevention? - The Burn-In
Bridging the gaps: joining human and artificial intelligence | Technology – Business Chief Canada
Technology is evolving at a rapid pace, transforming every business sector.
The security industry is no different, as emerging technologies are leveraged to enhance operations.
Much has been made of artificial intelligence (AI) and its potential, with companies of all kinds scrambling to implement it. Whilst the hype may presently outweigh the current benefits, AI in the security sector can be truly beneficial.
The buzz surrounding facial recognition, in particular, has dominated the public perception of AI in the security space. However, there are many applications of this tool which are already delivering benefits to businesses. Deep Learning (DL) is a subcategory of AI, which can empower surveillance technology to achieve unparalleled levels of accuracy. This, in turn, can make security professionals lives easier as they can focus on more pressing tasks, with full reassurance that DL is working in the background, improving protection and efficiency.
Deep Learning precision
In the past, surveillance applications that used video analytics to generate alerts often struggled to differentiate between a human intruder and other objects or wildlife, creating time-consuming false alarms.
However, DL can help overcome this hurdle by enabling users to pre-calibrate the system to detect real threats and ignore false ones. In the context of video analytics, the learning aspect of DL refers to the way that a developer can train an algorithm to only pick up on specific objects and features, much in the same way that a human would visually disseminate a scene and distinguish between objects.
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In a security application, the algorithm can be trained to recognise a person or a vehicle that could pose a threat. This level of sophistication in security tools means that the issue of false alerts is mitigated, and monitoring staff can focus their efforts on less tedious tasks, increasing their productivity and attention span, improving overall performance.
Ultimately, improved alert accuracy leads to a more secure perimeter. By detecting suspicious events in real-time, the technology enables staff to address incidents as they occur, reducing the need to analyse video footage in the wake of a security breach, when very little can be done.
Combining human and artificial intelligence
Its true that AI and automation stand to revolutionise every sector. However, this is not to say that they are always a viable replacement for human intelligence.
AI and DL really excel in the automation of manual tasks and making improvements to operations, but the value of human input cannot be underestimated.
The DL component of security analytics is invaluable for overworked and understaffed monitoring teams it can filter through hundreds of potential alerts and block those that arent useful. Staff are then left with only a handful of unusual situations to evaluate, which they are responsible for resolving. This is where human intelligence is still light years ahead of AI. The most successful businesses across the board are the ones who are able to combine the latest technologies with human intuition.
By Kevin Waterhouse, Managing Director at VCA Technology
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Bridging the gaps: joining human and artificial intelligence | Technology - Business Chief Canada
To fight the coronavirus spread, give artificial intelligence a chance – Livemint
The classic hockey stick curveits what investors and entrepreneurs desire but what medics despise. In the past week, Italy has seen that kind of curve in its coronavirus case numbers, leaving people and systems overwhelmed. German chancellor Angela Merkel has described coronavirus as Germanys greatest challenge since World War II.
This pandemic is the biggest black swan" event we have witnessed in our lives so far. A black swan event is characterized by a very low probability but extremely high impact. The last one was 9/11 in the US, which some still saw coming. But Covid-19 has taken us all by surprise.
Cases and deaths have had a geometric rise, which defeats understanding, because our minds tend to think in terms of linear progression. Were not programmed to fathom something that multiplies. India hasnt yet seen the ugly tipping point, and I hope we dont. This piece is not about hope against hope, but an earnest call for widespread adoption of artificial intelligence (AI) to counter such unpredictable events.
The initial, and by far most successful, application of AI is on the warfront. Thanks to the deployment of drones, unmanned craft, intelligent machines, humanoid robots and the like, the US has managed to drastically cut its casualties in Afghanistan and Iraq compared to the Vietnam and the Gulf wars. AI has not only lowered collateral damage but also radically increased the accuracy of assault.
But AIs applications can be far greater and more useful in humanitarian and disaster relief, conservation, disease control and waste management, among others. Machines have been shown to outperform humans in terms of labour, memory, intelligence and, in some cases even creativity.
At a time when citizens have been advised to practise social distancing, and we are fearfully confined to our homes, who will run the essentials? Someone will have to weather the storm, or perhaps something? We already have so much power offered by the brute force of machines that its up to us to tame it in meaningful ways, and Covid-19 could offer a precise opportunity.
At the time of writing this piece, Summit, the worlds most powerful supercomputer, housed at the US Department of Energys Oak Ridge National Laboratory, had identified 77 drug compounds that might stop coronavirus from infecting cells, a significant step in vaccine development. We are getting to know more about the spread of disease, hotspots and mortality rates on an almost real-time basis, thanks to affordable computing and communication networks. Can we up the ante further by relinquishing more control to machines?
Winston Churchill famously said, Never let a good crisis go to waste", and I think we have a great opportunity at hand. We can make machines take on the more hazardous tasks, while we watch and survive from the sidelines. This is the time for tech startups to leverage the power of general purpose technologies and conceive radical new solutions to address pandemics.
Private Kit: Safe Paths is an app developed by researchers at the Massachusetts Institute of Technology and Harvard. With help from Facebook and Uber, it lets you know if you have crossed paths with someone who is infected while protecting privacy. Its a first step, and like most technologies, it will improve with adoption. OneBreath, a Palo Alto-based medtech startup, has been working on an affordable, reliable ventilator for over a decade now, and should be ready to meet Covid-19.
As geography becomes history, we have become one large family. Our more robust, fast-learning cousins, the machines, must be deployed on the frontlines faster. We are truly at the inflection point towards singularity, and its a choice between speed and accuracy. A useful ethos for the times could be from Mark Twain who reminded us, Continuous improvement is better than delayed perfection."
Pavan Soni is the founder of Inflexion Point, an innovation and strategy consultancy.
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To fight the coronavirus spread, give artificial intelligence a chance - Livemint
What Is Artificial Intelligence (AI)? – IoT For All
Artificial Intelligence is a topic that has been getting a lot of attention, mostly because of the rapid improvement that this field has seen since the turn of the 21st century. Amazing innovations are laying the foundation for ongoing breakthrough achievements. In this article, Im going to focus on three specific topics:
In the 1950s, AI pioneers Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, was it performed by a human, would have required that human to apply intelligence to accomplish the task.
This is a fairly broad description. Nowadays, all tasks associated with human intelligence are described as AI when performed by a computer. This includes planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation and, to a lesser extent, social intelligence and creativity.
Artificial intelligence is defined as the branch of science and technology that [is] concerned with the study of software and hardware to provide machines the ability to learn insights from data and [the] environment and the ability to adapt in changing situation[s] with high precision, accuracy and speed.Amit Ray, Compassionate Artificial Superintelligence AI 5.0AI with Blockchain, BMI, Drone, IOT and Biometric Technologies
Now that we know what AI actually means, lets find out what its used for!
While surfing the web, have you ever wondered how most ads are related to your interests? Thats a representation of AI, more specifically, machine learning. However, AI is more commonly associated with robots, such as the ability of a robot to think on its own and the potential for computer consciousness. While these would be astounding achievements, they involve highly complex algorithms which we still cant produce today.
Machine learning is a big part of AI, and it might be the key reason for this fields meteoric rise. Its based on the principle of trial and error; every time we try to solve a problem, like a maze, were going to fail at least once. However, failing is a good thing in machine learning, because it enables the program to learn new information. That information is stored as data, and each time an AI goes down a specific path, it will reference the data from prior trials to see which one will work best this time.
To expand on the above example, Im going to teach you one of the first AI algorithms (often used to solve mazes), the A* algorithm.
To understand this algorithm, lets visualize our maze as a chess board with inaccessible regions (like a maze) that well call nodes.
This is a fun example of AI in action, since flying cars would be reliant on AI to function properly. In the future, scientists believe were going to have autonomous cars that transport people to their desired destinations. This involves cars having some sort of artificial intelligence, more specifically, machine learning,because they need to always find the best possible course to the destination, not crash into buildings and respect other vehicles. A very basic implementation of this, although extremely ineffective and slow, could be the A* algorithm, where buildings represent inaccessible nodes. However, some good alternatives exist that we didnt review in detail due to their high levels of complexity:
This article was written to provide a fun introduction to AI and to show its potential for future technologies. More than ever, its crucial to know the principles of artificial intelligence since it will be so important in the future. We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions of what this technology can achieve.
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What Is Artificial Intelligence (AI)? - IoT For All