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The future through Artificial Intelligence – The Star Online

March 28, 2020 | Author: admin

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…

March 28, 2020 | Author: admin

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

March 28, 2020 | Author: admin

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

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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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Is artificial intelligence the answer to disease prevention? – The Burn-In

March 28, 2020 | Author: admin

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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Coronavirus: Spain to use artificial intelligence to automate testing – ComputerWeekly.com

March 28, 2020 | Author: admin

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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Bridging the gaps: joining human and artificial intelligence | Technology – Business Chief Canada

March 28, 2020 | Author: admin

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.

To fight the coronavirus spread, give artificial intelligence a chance – Livemint

March 28, 2020 | Author: admin

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

Microsoft to end investments in facial recognition firms after AnyVision controversy – The Verge

March 27, 2020 | Author: admin

Microsoft says it will no longer invest in third-party facial recognition companies following a controversy around its funding of Israeli startup AnyVision, which critics and human rights activists say powered a surveillance program in the West Bank following an NBC News report about the companys relationship with the Israeli government.

Microsoft now says an independent investigation led by former US Attorney General Eric Holder and his team at international law firm Covington & Burling found that AnyVisions technology has not previously and does not currently power a mass surveillance program in the West Bank that has been alleged in media reports. Had it done so, Microsoft says it would have constituted a breach of the finance portfolios pledge on ethical facial recognition use.

Regardless, Microsoft says it is divesting from AnyVision and will no longer make minority investments in any facial recognition firms. For Microsoft, the audit process reinforced the challenges of being a minority investor in a company that sells sensitive technology, since such investments do not generally allow for the level of oversight or control that Microsoft exercises over the use of its own technology, reads an announcement on the website of the companys M12 venture arm.

By making a global change to its investment policies to end minority investments in companies that sell facial recognition technology, Microsofts focus has shifted to commercial relationships that afford Microsoft greater oversight and control over the use of sensitive technologies, the announcement goes on to say.

While Microsoft is stepping away from funding facial recognition firms, it does still have a facial recognition technology of its own through its Azure cloud computing platform. The Face API, as its called, allows any developer to embed facial recognition into your apps for a seamless and highly secured user experience. However, the companys chief legal officer, Brad Smith, said last year that Microsoft would never sell facial recognition for surveillance purposes, and Smith has gone on the record saying its denied law enforcement access to the technology over concerns it would contribute to civil and human rights abuses.

Its unclear if Microsofts new investment stance means it can still acquire facial recognition firms or whether it is making any adjustments to its own use of internal facial recognition software as a result of the change in direction. Microsoft was not immediately available for comment.

Facial recognition, specifically the variety of the technology powered by advanced machine learning and other artificial intelligence tools, has come under a spotlight in recent years. At the same time, concern grows among politicians and activists that it could be used by law enforcement and governments to surveil citizens without their consent and in ways that violate privacy and human rights laws.

In January, Facebook was hit with a $550 million fine as part of a settlement for a class action lawsuit over its use of facial recognition without clear opt-in provisions for users of its social networking products. Tech leaders like Google CEO Sundar Pichai, who helped oversee the formation of the companys AI ethics principles in 2018, has said a temporary ban on the technology might be warranted in response to the European Unions ongoing efforts to more aggressively regulate it.

One notable provider, Clearview AI, has found itself at the center of the growing controversy around the tech, as its database of billions of photos scraped largely from social media sites is already in use by thousands of private companies and law enforcement agencies. As a result of the Clearview story, more attention is now being paid to lesser-known facial recognition firms, and especially whether they have deals with local law enforcement groups or under-the-radar relationships with big tech firms.

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Microsoft to end investments in facial recognition firms after AnyVision controversy - The Verge

Bringing the cloud to Africa – ITWeb

March 27, 2020 | Author: admin

Stephane Duproz, CEO, Africa Data Centres.

The way we use technology has shifted rapidly over the past few years. From having to keep a local copy of all data on your device, on your PC or on a local server to ensure access to critical information, consumers and companies have rapidly embraced the benefits that the cloud offers.

A select group of large public cloud providers have led the push by creating the computing platforms that have transformed the way we look at technology. These cloud providers include some of the worlds largest companies as well as other, more focused technology companies. They have invested billions of dollars in deploying infrastructure, including some of the largest data centres ever built, across the globe, but there are still some regions where they rely on partners to create the infrastructure they need.

Stephane Duproz, the CEO of Africa Data Centres, explains that while Africa may still be lagging when it comes to the kind of data centre infrastructure that these cloud providers need, this is rapidly changing.

The way computing has evolved has made it essential for cloud providers to get their infrastructure as close to the end-user as possible. Today consumers, in terms of both organisations and individuals, need access to their data and applications no matter where in the world they are. And user experience is everything in todays world, he says.

The connectivity boom

Africa has seen connectivity explode as fibre networks have been built out to crisscross the continent, and organisations and consumers have gained access to high-speed Internet connections. With multiple, independent networks available, the ability to build carrier-neutral data centres across the continent has emerged.

A carrier-neutral data centre is a critical component for any organisation looking to host mission-critical applications. Being carrier-neutral means that you have access to multiple, independent networks at a single location. The client then has access to redundant networks, ensuring that even when one network goes down, their users will always be able to access their data. Not only that, but with the growth in more sophisticated networking technologies, such as software-defined networks, traffic can be automatically routed across the best link without the need for manual intervention, he says.

A data centre that is serviced by only a single connectivity provider simply presents too great a risk for global organisations.

Maintaining high standards

For large cloud providers, the ability to access a specific standard of a data centre is key to their decision to expand their footprint into new geographies.

Cloud providers expect that they will have the same standard of facilities wherever they are in the world, and this includes not only the physical infrastructure but also support services, power supply and network connectivity, says Duproz.

He adds that being able to work with the same data centre provider across multiple countries allows them to leverage established relationships to smooth the expansion of their footprint, using standard terms and conditions and service level agreements.

There needs to be a level of trust between the data centre provider and the cloud operator. In Africa, this is not always the case. The African data centre business isnt very old, but some operators are able to deliver at global standards.

He explains that the industry is starting to see the emergence of specific hubs based on access to skills and network resources. Cities like Kigali and Nairobi are emerging as ICT hubs alongside more established locations, like SA.

Key to the success of the African data centre is the ability to tap into local skills as well as international knowledge that has been built up over the past decades; skills that were gained as organisations moved from building their infrastructure to utilising capacity in centralised data centres. Bringing these together ensures that the local skill base expands but also that they gain necessary insights from experienced operators.

As society becomes increasingly dependent on technology, the demand for data centres across the continent is only going to increase. With every additional data centre that comes online, the benefits of the hyperconnected society will become more evident. These will include economic growth and a more educated society, driven by better communications.

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Bringing the cloud to Africa - ITWeb

Intel + Cornell Pioneering Work in the Science of Smell – insideBIGDATA

March 27, 2020 | Author: admin

Nature Machine Intelligence published a joint paper from researchers at Intel Labs and Cornell University demonstrating the ability of Intels neuromorphic test chip, Loihi, to learn and recognize 10 hazardous chemicals, even in the presence of significant noise and occlusion. The work demonstrates how neuromorphic computing could be used to detect smells that are precursors to explosives, narcotics and more.

Loihi learned each new odor from a single example without disrupting the previously learned smells, requiring up to 3000x fewer training samples per class compared to a deep learning solution and demonstrating superior recognition accuracy. The research shows how the self-learning, low-power, and brain-like properties of neuromorphic chips combined with algorithms derived from neuroscience could be the answer to creating electronic nose systems that recognize odors under real-world conditions more effectively than conventional solutions.

We are developing neural algorithms on Loihi that mimic what happens in your brain when you smell something, said Nabil Imam, senior research scientist in Intels Neuromorphic Computing Lab. This work is a prime example of contemporary research at the crossroads of neuroscience and artificial intelligence and demonstrates Loihis potential to provide important sensing capabilities that could benefit various industries.

Intel Labs is driving computer-science research that contributes to a third generation of AI. Key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing, which createsalgorithmic approaches to dealing with the uncertainty, ambiguity, and contradiction in the natural world.

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Intel + Cornell Pioneering Work in the Science of Smell - insideBIGDATA

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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…

Why transparency is key to promoting trust in artificial intelligence – IT PRO

Is artificial intelligence the answer to disease prevention? – The Burn-In

Coronavirus: Spain to use artificial intelligence to automate testing – ComputerWeekly.com

Bridging the gaps: joining human and artificial intelligence | Technology – Business Chief Canada

To fight the coronavirus spread, give artificial intelligence a chance – Livemint

Microsoft to end investments in facial recognition firms after AnyVision controversy – The Verge

Bringing the cloud to Africa – ITWeb

Intel + Cornell Pioneering Work in the Science of Smell – insideBIGDATA

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