Drones in and of themselves are not necessarily an IoT technology, but IoT connectivity takes them further. The introduction of connected sensors to these devices has expanded their functionality, leading to rising adoption in the commercial sector. Shipments of IoT-connected drones surpassed 351,000 in 2019, and experts predict they'll pass one million by 2023.

As IoT drone adoption continues to rise, new use cases emerge. Thanks to the mobile IoT, drone technology is disrupting multiple industries.

Remote Monitoring and Security

By far, the largest use case for IoT-connected drones is remote monitoring, especially in the construction industry. Construction monitoring accounted for more than 141,000 drone shipments, almost half of the total across all industries. It‘s easy to understand why, too, given construction's high risks and expenses.

Construction work is equally dangerous and time-sensitive, so worksite monitoring is crucial. With flight times as high as 90 minutes, IoT drones let stakeholders inspect or monitor projects without being physically present. As a result, these technologies help construction companies ensure safety and productivity while minimizing travel, saving money and promoting efficiency.

IoT drones' success in the construction industry will likely spur other sectors to employ them as well. Companies could use drones to patrol buildings, supplementing their security teams. Autonomous, connected security drones have already emerged in the consumer sector, so their use in commercial settings is imminent.

Drones for Scientific Research

IoT connectivity has also made drones more fit for research applications. Effective research relies on efficient data collection, and the IoT's primary benefit is its ability to gather and send data remotely. Combining this connectivity with remotely or even self-guided vehicles lets researchers investigate remote or hazardous settings safely.

Collecting measurements at sea, for example, can cost hundreds of millions of dollars between fuel and crew expenses. Rough conditions like high waves and strong currents can also make in-person research expeditions dangerous. Some researchers have started using wind-powered oceanic drones to gather data at sea instead, saving money and staying safe.

Drones can also make it easier for scientists to observe animals in the wild. These devices are far smaller and easier to camouflage than people, so they're less likely to scare skittish species away. IoT connectivity lets researchers control them and gather data from further away, too, helping them access remote areas.

Efficient, Transparent Deliveries

Outside of drone technology, one of the most familiar instances of mobile IoT is supply chain tracking. While telematics sensors themselves may not be mobile, they move with shipments, providing needed visibility into logistics operations. Drones can take this a step further through expanding options for last-mile deliveries.

Drones can take packages to their destination to conserve truck space and shorten drivers' routes. IoT connectivity could enable customers to track the location of the drone carrying their delivery in real-time, leading to higher customer satisfaction. As online ordering soars and logistics companies grow increasingly busy, these benefits are hard to ignore.

At least three companies, including Amazon and UPS, have already received FAA approval for operating delivery drones. As more companies embrace these technologies, e-commerce deliveries as a whole will become faster and more transparent. Further IoT implementation in these devices could enable predictive maintenance schedules, making these drone investments even more cost-effective.

The IoT Is Reaching New Heights

IoT technologies are making drones more versatile and valuable than ever before. Consequently, as more companies employ these devices, the IoT itself is growing, seeing use in a broader range of applications. IoT drone technology is already disrupting multiple industries, and there are plenty of opportunities still ahead.

The post Mobile IoT Developing Drone Technology appeared first on Datafloq.

A therapy tool

Exposure therapy

Since the early days of virtual reality, psychotherapists recognized the potential of the technology for exposure therapy, in which patients are exposed to things they fear in safe and controlled settings. Exposure therapy targets behaviors (most often, avoidance) that people show in response to situations or memories that they find frightening or anxiety-provoking. The goal of the therapy is to diminish the avoidance behavior by actively confronting the things that a person fears to learn that anxiety and fear will lessen on their own. It is important, however, that a person confronts a situation that closely maps onto the fear, which is not always possible. For example, a veteran who developed PTSD as a result of combat exposure would not be able to confront a combat situation again.

And here comes virtual reality: in virtual reality exposure therapy (VRET), a person is immersed in a computer-generated virtual environment, either by using a head-mounted display device or entering into a computer-automated room where images are present all around. This environment can be programmed to help the person directly confront feared situations or locations that may not be safe to encounter in real life. Such virtual environments provide visual, auditory, tactile, vibratory, vestibular, and olfactory stimuli to patients. During the session, the therapist closely tracks the patient's state of arousal by monitoring physiological indicators of stress, including heart rate and respiration.

To date, VRET for PTSD has primarily been examined in Vietnam War combat veterans and to address specific phobias, like claustrophobia, fear of driving, acrophobia (or a fear of heights), fear of flying, arachnophobia (fear of spiders), and social anxiety.

Substance use disorders treatment

Similarly to overcome phobias, virtual reality might be able to help people with substance use disorders. Virtual experiences prove to trigger cravings in people being treated for dependence on tobacco, alcohol or illicit drugs. Such cravings can be used in treatment for addiction, since they give people a chance to practice what they'll do in high-risk situations. In essence, the mechanism is the same as in PTSD treatment: with the help of highly controlled and adjustable virtual setting, people are confronted with the most challenging situations and try to overcome their usual cravings in a safer environment.

A research tool

Virtual reality, apart from being a promising practical tool, can also assist in studying and measuring human behavior and cognition. The central goal of any psychological research is to understand real life human behaviour. Yet, to have scientifically proven results, psychologists have to examine behaviour under highly controlled conditions, ideally with only a few manipulations at a time to allow rigorous scientific inferences. Hence, researchers face a dilemma between experimental control and ecological validity. Virtual reality seems to offer a way out that would help researchers explore human nature in the control surroundings.

Going beyond practice, virtual reality tools are used in research assessing and preventing combat-related PTSD. For example, STRIVE (Stress resilience in virtual environments) system is used to simulate combat situations. Its primary goal is to guide the combatant through a virtual experience while coaching him or her in relaxation and emotion self-regulation. However, it also provides a useful tool for predicting the risk of developing PTSD or other disorders in new recruits prior to actual combat exposure. Recruits who display high resilience and thus presumably at relatively lower risk of developing PTSD might be more suitable for direct combat roles and vice versa.

Virtual reality is widely used in studies of spatial cognition and motor control, as well as in the research of human social interaction, where a person interacts with another (real or virtual) person. With virtual characters, it is possible to create infinitely many combinations of social variables and test them against each other in studies of social perception and mimicry, trust and rapport and prejudice in social interaction.

New virtual reality systems also represent a person's body or body parts within the virtual scene, which is used to study how changes to one's avatar might affect perception. Similar research using virtual reality could help scientists better understand body image disorders.

At a more general level, virtual reality helps explore basic questions about human perception and cognition. For example, in controlled settings, it is possible to remove all visual aspects of the environment that can be used to navigate, such as street signs or distal mountains, to explore what that means for spatial cognition.

One of such studies explored the phenomenon of vastness, the feeling that the environment around you extends almost forever, making you feel insignificant by comparison. Stefanucci and colleagues showed they could evoke a sensation of vastness using virtual mountain landscapes, suggesting that virtual reality could be helpful for understanding this complex phenomenon.

Benefits of VR therapy

Comparing to the traditional approach, VR-assisted therapy has a number of benefits to patients and therapists:

More controlled: as a patient makes progress confronting his or her fears, virtual scenes can be adjusted to make the feared stimulus more provocative, so that they stay at the level the patient is ready to confront.

More practical: in the real world, exposing people to their fears can be inconvenient or too expensive, like the case of fear to fly where psychotherapists can't always accompany their clients on airplanes. In this case, facing virtual threats is much easier.

Similar physiological response: though it may sound counterintuitive, VR experiences trigger the same physiological reactions that the real world. As an important part of managing anxiety is learning to control the body's fear response people can experience a racing heart and rapid breathing during their encounter with a virtual danger and learn to control their bodies.

How to go virtual

Virtual reality systems for psychotherapeutic use are taking off with the first tools available on the market:

• Virtually Better phobias, substance use disorders, skills development, PTSD, stress/pain management
• Psious phobias, PTSD, pain management, ADHD, stress management
• Limbix exposure therapy for anxiety-based disorders, relaxation, mindfulness, skills training, relapse prevention, pain management
• The Virtual Reality Medical Center fear of flying, pain management, PTSD

However, it is important to understand that using virtual reality in exposure therapy requires special training for sessions not to go out of control leaving the patients facing their fears.

Virtual reality is a relatively new tool in psychotherapy and it is still bumpy and underexplored. It may lead us to a better understanding of real life human behaviour and add to our well-being.

The post Virtual Reality in Psychology: Therapy and Research appeared first on Datafloq.

These learning episodes follow predictable adapt, learn and innovate cycles were considered actions based on knowledge are fed back into further discovery. The organisational journey of knowledge discovery changes the business view of the world and a learning organisation should leverage this wisdom to better inform the decisions, products and service offerings made in their interactions with customers.

Adapt

The growth in data digitisation and storage, utilised by machine learning and artificial intelligence increases the capacity of organisations and individuals to grow and create new knowledge about themselves and other parties. This investment in information storage reflects the view of the organisation as a rational learning machine that engineers transition through technology. This rational learning allows the business to grow and change as an organic entity would, as a result of the social action this learning brings about. Learning brings organisations and individuals towards an understanding of the true nature of the action in the problem domain.

The search for reality is, of course, not an objective measure. As social animals embedded in an environment we only have partial control, and our worldview may be replaced in future by more accurate models. In the interim, knowledge is used to construct the understanding, meaning and, therefore, the actions which we undertake. In which case, reality becomes merely a series of experiments with the truth, a best guess approximation of the way things are in the world.

Learn

The search for knowledge begins with questioning the view of reality we already hold. To ask the best questions you need to have and understanding of how to create new knowledge. There are many routes to knowledge from questions, but the longest established and most generalizable is the application of following the scientific method to prove or disprove hypotheses about the nature of reality by using observation and statistical inference to ascertain where the true' reality lies. Discovering new knowledge involves querying the status quo, validating the new viewpoint and introducing change.

The methods used to investigate questions of reality must adapt to, explain or discount the distractions of many influences and cross-field concerns. Some phenomena may have impacts in different areas with the same common root cause. Seeking the root cause by observing the problem manifestations and causes indicates where the action is needed.

Change is necessary where our deeper understanding of why' we carry out actions does not tally with our understanding of how the underlying mechanisms through which reality makes itself known works. Understanding how the world works challenges attitudes, which in turn affect our intentions and motivations, and this is reflected in behaviour.

Innovate

In management research, the theory-practice gap can be uncovered to give insight into how the vision of reality converges or diverges from the opinions of others. These others can be shareholders, auditors, banks, and customers. They are the external Subject Matter Experts that matter.

Combining the results of rational analysis with the moral compass of social awareness promotes the wellbeing of both customers and the companies. Embedding a change and growth mindset allows growing the size of the pie to increase the portions of it that go to the organisations, the customers that support it and the community that sustains it.

Summary

Implementing changes and insights gained through technology use poses challenges. If our view of reality is not fixed, then valid challenges to that reality and the way things are interpreted may be resisted or blocked, with the result that the cultural changes and adaptation that should flow from increased knowledge will fail to deliver benefit.

Responsibility for, and acceptance of, the changes proposed as a result of knowledge discovery depends upon the reputation of the communicating party who relies on social norms and values in assuring others of their intentions.

This is why shouldering the responsibility of managing stakeholders in organisations, not shirking the duties of compliance, and earning the trust of customers through consistent and ethical corporate behaviour benefits positive organisational change implementation and service innovation.

The post Learning, Innovation and Business Change appeared first on Datafloq.

A secure and efficient way of conducting exams is the need of the hour and using Blockchain could be the perfect solution to overcome the drawbacks in the current system.

How bad is the situation?

Before we delve into how using Blockchain can help, let me address the predicament in question.

Case in point: On April 1, 2018, the economics paper for the class 12 CBSE exam was leaked in Delhi & Haryana just 90 minutes before the actual exam (leaked at 8:15 am when the test was supposed to start at 9:45 am).

The result?

There was a re-exam conducted for everyone 25 days later. Many students & parents filed petitions saying a re-exam jeopardizes their career but the supreme court dismissed all petitions saying the final decision lay with the CBSE board.

While we may think this is a rare scenario and an extreme action, it is not so uncommon. Quite recently, an A-level mathematics paper in the UK was also alleged to be leaked online. These are the only instances that are reported. Many aren't even caught or reported.

What are the core problems with such an examination system? Is there a solution to this?

Paper leaks, manipulation of exam marks, human biases and errors are only a subset of the problems. The root of the problem lies in the way administrative rights are given and revoked; exam content shared and distributed.

Solution?

A smart and automated way of configuring contracts such that access rights are shared and revoked at appropriate times i.e. a secure distributed way of storing & sharing all data pertaining to the exam

How can Blockchain solve the issue?

I have broken down centrally conducted exams into 3 main stages. The problems that can arise in each of these stages can be effectively addressed by using blockchain as a solution.

Creation

The examination questions are created by a handful of people (3-4) and they are validated and finalized by a set of moderators. Moderators try to ensure that all sets of question papers are comparable and are more or less of the same difficulty level.

Naturally, the associated problems are:

• Secure storage of questions. Possibility of leaks.
• Misuse of power/ access
• Identity of paper creators likely to be revealed

How can blockchain help?

1. Once the question paper is uploaded by the creator – an encrypted hash of the same is stored.
2. Access to relevant people is assigned or revoked basis their time and role. For example, the access for creators is immediately removed as soon as the paper is uploaded and thereby, moderators are given immediate access once the paper is uploaded for review.
3. Blockchain eases the process of keeping the creators' identity hidden.

Distribution

In this stage, the most apparent challenges are:

• Secure storage of exam papers until exam day
• Securely sharing exam papers with regional centers and actual exam centers

How can blockchain help?

• Regional centers receive access to exam papers only the night before the exam to print the actual paper. These papers are then collected physically on the day of the exam, as is the process now.
• If the entire exam is digitized, regional centers could be eliminated totally. Students are given appropriate access only on the exam day and consequently, access is revoked immediately after the exam is over

Evaluation

Centralized evaluation is susceptible to score manipulation by students, external reachers or internal people with administrative access.

The associated challenges are:

• Since all the data is stored centrally and managed by a handful of database administrators, it is quite vulnerable to threats or bribery.
• Breaches or manipulations are possible during the re-evaluation phase.

How can blockchain help?

Case 1: Paper & Pen Exam

1. Evaluators are given access to upload marks for individual candidates on the blockchain network. This access is only given during a specified time period configured at the very beginning into the smart contract.
2. Once the marks are uploaded into the network, access to the evaluators is revoked. The smart contract automatically gives view access to students on result day.
3. Re-evaluation is done in a similar manner. However, the evaluator has to first request edit access into the network during the re-evaluation window. Access to upload revised marks is then granted for only a specific duration.

Case 2: Digital Exam

1. In the case of digital exams, all answers are stored into the blockchain network as soon as the exam is over, in an encrypted form.
2. Evaluation can work a little differently. Evaluators are allotted individual questions & answers randomly for evaluation rather than an entire exam paper. After the evaluation window is over, separate answers evaluated are combined and the final result for the paper is calculated with no human intervention.
3. Viewing results remains the same as in case 1.
4. Re-evaluation works in a similar manner as evaluation does in this case.

Closing thoughts

With an increasing number of students appearing for such exams conducted by universities or educational institutes, there is a dire need for a more reliable approach of managing and conducting these exams. A university or board has to rise to the occasion and take the responsibility of providing adequate security to the examination process.

Blockchain presents itself as an excellent solution across many facets of evaluation, from that of students- which we looked at extensively in the above article, to that of products, services and even professionals like designers and developers.

The post How Blockchain Could Solve Problems Related to Central Exams appeared first on Datafloq.

The 5th annual Flink Forward conference'will take place on October 7-9, 2019,'
in Berlin.‘This year's Flink Forward'is moving to a new venue in the heart of Berlin'bringing over 600 software engineers,'Flink core committers,'
data scientists, and DevOps data professionals‘under the spatial roof of bcc Berlin Congress Center.

Experience Apache Flink in action, with multiple'full-day training sessions on October 7 and more than 70 talks across five tracks on October 8 & 9. Some of the session highlights include among others:'

• First-time speakers'such as'Goldman Sachs, Emirates NBD, Pinterest, Stripe, Workday, Neo4j, OVH or Airbus sharing'their Apache Flink use cases, experiments and benchmarks.

• Technical deep-dives into topics like Flink's new batch architecture, unified runtime or refurbished Table & SQL APIs, self-service data analytics platforms and event-driven application development, among others.

• A dedicated research track with talks by KTH Stockholm, ETH Zurich and TU Berlin on stateful serverless functions, state management optimization and graph processing.

• A new Community track to facilitate key discussions in the open source and Apache Flink community such as how to maintain healthy growth and contributions to open source software.'
  

This year, we added two'new training sessions to the schedule, including:

• Apache Flink Developer Training – an introductory session tailored for Java and Scala developers covering the core concepts of streaming dataflows, event time, and key-partitioned state.
• Apache Flink Operations Training – a course for developers and operations staff responsible for deploying and maintaining Flink clusters.

See the full conference program‘and get excited about Flink Forward Europe 2019 coming to Berlin, October 7-9, 2019

Get your pass‘and meet'the Apache Flink community at Flink Forward Europe 2019!

“Apache“, “Apache Flink”, and their logos are registered trademarks or trademarks of The Apache Software Foundation in the U.S. and/or other countries

The post Flink Forward Europe 2019 appeared first on Datafloq.

R&D should be an essential part of bringing innovation to any company via IoT projects. And though we can all agree how important R&D is, it requires a great deal of experience, senior experts, and specific toolsets ‘resources that not every company can say they have handy.

However, there is a risk when deriving the strategic decisions that the executive directors consider to be technological towards the R & D departments. Many times, oblivious to the reality of the markets, those responsible for R & D with the invaluable aid of the subsidies of the different Administrations, they launch to develop products and technologies for problems that do not exist, just for the fact of obtaining recognition or to continue living without pressures of the Top Management. I am enemy of granted subsidies granted most of the time by unqualified Administration organisms that do not understand that need to prevail the utility, the business model, the business case and the commercialization over the innovation that R & D said to be developed.

Now, if we ask the sellers of IoT technology, products and services, they may not be so happy with the idea of having to talk with the R & D areas instead of with other areas of the company more likely to buy. Most time, R &D departments decide to do it themselves. Vendors know, that with great probability, they will not close deals due to lack of budget of the R &D or the low visibility of this area by the rest of the departments of the company.

The Importance of R&D for the Internet of Things

Innovation in IoT is a major competitive differentiator. See below some advice to have a decisive advantage over competitors:

IoT-focused companies need to invest in R&D to keep up with the rapidly changing and expanding market. It is important that an organization's R&D iteration turn times are quick; otherwise the company is not going to be able to keep pace with the expected IoT market growth. However, it's not enough to simply speed up R&D ‘innovative IoT firms, both start-ups and established companies, must also make sure their R&D processes are extremely reliable.

• You can't solve R&D speed issues just by increasing the budget.
• Executives must maintain robust and steady communication with R&D regarding the department's priorities over a particular time frame and how progress will be measured.
• Guidelines are invaluable: The more structured and streamlined R&D procedures are, the better IoT companies will be able to move from conception to delivery.
• Design innovative IoT products but accelerate time to market.
• Internal collaboration: R&D team should share real-time data across internal departments to spur intelligent product design
• External collaboration: Connect with customers and partners to ensure success
• Differentiation: Drive overall business value with IoT.

Outsource or not Outsource R & D for your IoT project

Just like any other technology, IoT products and solutions require thorough research and development, and it better be done by professionals. Despite the noise generated by analysts and companies around the IoT, the reality is that there have not been many IoT projects and therefore it is not easy to find good professionals with proven experience in IoT to hire.

When I think of Outsourcing IoT projects, Eastern European and Indian companies immediately come to my mind. No doubt because the R & D talent seems to be cheaper there. Spain could also be a country to outsource IoT, but at the moment I do not see it.

The benefits of Outsourcing R&D for IoT Projects:

• Expertise and an Eye for Innovation
• Bring an IoT Project to Market Faster
• Optimize Your Costs
• Control and Manage Risks

I am not sure about the quality of most of these companies or the experience of their teams in the development of IoT products or the implementation of IoT projects, but there is no doubt that there are benefits to Outsource R & D for some IoT Projects. You should select any of these companies after a careful evaluation.

Recommendation: Do not stop your IoT projects if you do not have the skills and professionals in house. Luckily, some companies offer outsourcing R&D for IoT projects.

Note: Remember, I can help you to identify and qualify the most suitable Outsource R&D for your IoT project.

Spain is not different in R & D for IoT

I have not believed in R & D in Spain for years. There are exceptions without a doubt, but it seems evident that the prosperity and welfare of Spain are not due to our R & D. Fortunately we have sun and beach and a lot of brick to put in houses that are not sold because of high prices and low wages.

With the entry into the EU, I thought that we had great markets open to us. I was also optimistic that we would have great opportunities in the Latin American market, thanks to the fact that our research and development capacity would have been consolidated effectively in our companies and universities because it would be profitable and worldwide recognized.

But it has not been that way. The technology developed in Spain and more specifically that relating to the IoT has little chance of being commercialized in France, Germany and not to mention in the UK. If we add the development gap of the countries of South America and that our local market is averse to technological risk, it is difficult to flourish R & D in IoT or Industry 4.0 here in our lovely Spain.

That does not mean that we do not have public R & D budgets for these areas. What happens is that the same thing that happened during the last 30 years has happened. The incentives and aids are few and for the most part, used to finance large companies with little return to society. There is no rigorous control of the aid granted and, above all, there is no plan to encourage the local and global marketing of the products developed with the talent of our scientists and researchers.

I have stopped believing and trusting in our successive Governments for the change in R & D, but some exceptions are worthwhile to follow and work with them. For this reason, I continue to help them demonstrate that SPAIN CAN BE DIFFERENT .

Key Takeaway

After years of unfulfilled expectations, companies are sceptical of the potential growth of the IoT market or the benefits in their business. R&D department can be a cure to boost IoT initiatives but also a poison to kill IoT initiatives.

IoT may have started in R&D, but their benefits don't have to end there. To drive overall business value, it's important to share IoT data both internally and externally. Facilitating open collaboration, discovering new ways to innovate products, and accelerating time to market, you can differentiate R&D and your business.

As fast turn-around times and reliability become a focal part of companies‘ R&D processes, these companies will be well-positioned to thrive within the IoT market.

Thanks for your Likes and Comments

The post Is R&D a Cure or a Poison for the Sceptical IoT Market? appeared first on Datafloq.

They also have to do this in a situation where they are often being pressurised by an overly keen CEO, or more likely they're struggling to get board-level approval for AI. In fact, Corinium's recent research of c-suite IT leaders in the US found that the top barrier for AI adoption for respondents was getting board approval or buy-in (54%).

This may well be because of the perceived risk associated with taking on innovative technology but perhaps the problem is deeper than this it may even be the IT leader's own fault. Why? Well, in most projects, when technology is involved, it is usually because there is a need for a solution, and it makes sense for certain technology service or product to be implemented. According to our research, the smallest proportion (10%) said they would only investigate the use of AI in cases where the business is already seeking a solution for a specific use case. That suggests that technology leaders are looking for AI products before thinking about the solution they need it for, or they have thought a product could benefit the organisation in a way which they perhaps hadn't thought about before. That doesn't mean that this method is flawed many innovative ideas come through chance rather than exploration. However, it does mean it may be harder to get buy-in for these ideas because of the perceived risk associated with AI solutions of this kind.

Other issues that may be holding back businesses from implementing AI could be a lack of resources or having archaic technology that would be hard to integrate into new AI products.

The Unknowns

Perhaps the biggest stumbling block for AI is actually how much is not known about it. For example, reading industry papers may make us think that data science is the most in-demand skill, but our C-level respondents actually picked out data analytics as the biggest requirement to exploit AI, while they also didn't realise how much the HR department could benefit from AI, as it was the bottom-ranked function, despite there already being capabilities like candidate screening in this space.

This unknown' part of AI is also impacting buy-in, and not just from CEOs and boards, but from employees. Employees may be scared of taking up AI, but that is almost entirely due to scaremongering and perhaps even from what their own definition of AI is. Often, people think of AI as robots taking over the world, because of fiction books, TV and films, but the reality is that AI just enhances or automates mundane tasks, meaning employees can benefit in other ways.

The positive sign is though, that despite these holdbacks, IT leaders clearly believe there's a future for AI in business. We're still at the early stages of the technology really coming to the fore.

To find out exactly what IT leaders including CIOs, CTOs and CDOs make of AI, download the full report here: https://bit.ly/2ZllXNI

The post The Reality of AI: Overcoming the Hype to Reach Substance appeared first on Datafloq.

As I discussed earlier, the publication process of scientific papers is flawed. Commercial publishers such as Elsevier, Springer-Nature, Wolters-Kluwer, Taylor-Francis or Sage control the world of academic publishing. Together they share the $32 billion market. Because they do not have to pay for the product they sell, their product margin is higher than Google or Facebook. For decades, researchers have complained about this process, because as soon as academics want to publish their articles, which were paid for by tax money, they are required to give away their copyright to the academic publishers.

The problems with the publication of academic papers have been around for decades. They have been very well documented in a recent 55-minute documentary. Paywall: The Business of Scholarship shows the massive problems we face. It is a must-see for anyone with interest in research and the problems the industry faces.

Paywall: The Business of Scholarship (Full Movie) CC BY 4.0 from Paywall The Movie

Imagjn a Solution to Enable Open Access for All

Science drives innovation. Unfortunately, as the documentary clearly shows, scientific knowledge is locked behind closed doors. The current system requires academics to publish in high impact journals. This is inefficient knowledge sharing. It is slow, bureaucratic and requires academics to give away their copyright. Above all, it is very expensive. The University of California pays $11 million per year in such fees. Often, this is public money to obtain access to research which was paid by the university in the first place. Money that could have been spent on research.

Imagjn open knowledge, where everybody has access to all scientific papers without artificial barriers such as paywalls. To do that, we have to change the rules in how we judge scientific impact. We should no longer focus on where someone publishes. Instead, we should focus on what someone publishes.

Therefore, we want to move from a Journal Impact factor to an individual impact factor, controlled and owned by academics. The individual impact factor will combine more variables than just writing papers. It will also incorporate the quality of peer reviews and other academic contributions. This formula will be open source to ensure full transparency and give back control over their career to the academics.

We develop a platform that simplifies writing, citing, reviewing and publishing scientific papers, making knowledge freely available to anyone. We are working together with universities and funders to change the rules of the game.

Imagjn Open Knowledge

With Imagjn we have solutions for the three major problems in academic publishing. Once our platform is live, we will offer academics a streamlined process to publish and share their knowledge with the world. The four main benefits of Imagjn will be:

1. An improved and accelerated publication and blind peer review process, from 6-12 months to 2-6 weeks, resulting in more published papers for universities and academics;
2. A platform to easily collaborate on academic papers through an integrated cloud-based citation/editing package;
3. Migrating from a Journal Impact Factor to an Individual Impact Factor (IIF), where control and ownership lie with the academics and universities and not with the publishers. An IIF makes valorisation easier for universities;
4. Research that has been done with public money must be publicly available. Imagjn makes academic research publicly available and universities will eventually save millions per year on commercial academic licenses and article processing costs (APCs).

Bringing the Scientific Method to the 21^st Century

We live in exponential times, and it is key that the internet evolves to the new decentralised paradigm, where data is controlled and owned by the user, collaboration is key and artificial intelligence governs most of the online systems. For too long, the scientific method, which is the backbone of all the innovation in the modern world, has not evolved and remains tightly controlled by an oligopoly of scientific publishers. It is time for change.

Advances in technologies such as Big Data, Blockchain and AI have now made it possible to change the scientific method and bring it into the 21st century, giving back control and ownership to academics and universities, speeding up the process of writing, reviewing and publishing scientific knowledge and sharing that with the global public.

Imagjn aims to be the backbone of the improved version of the scientific method, offering academics, universities, papers and non-academics an immutable, verifiable and traceable Individual Impact Factor that they control, enabling them to effortlessly collaborate with each other across time and space, in a decentralised, secure and transparent way. This will foster innovation. Help us to achieve this vision by supporting Imagjn.

The post Why Academic Research Should Be Freely Available to All appeared first on Datafloq.

That's an expected response because of how rapidly growing and influential this branch of computer science has become. It is part of our lives in ways unimaginable, and most of us carry on with our lives, unaware of how much we rely on it.

AI Is the Steam Engine of Our Time and Age

Applications of artificial intelligence are in every field imaginable. That is because of the need for automation, which goes back to the core philosophy of how machines work: to help us do tasks with ease.

In this day and age, we're surrounded by automated devices that don't even require people running them, and while the idea of computer-controlled services is a frightening one for some, it's already part of our daily lives, without us realizing.

Facebook and Google

Facebook, which everyone uses on an hourly basis, uses deep AI learning mechanisms for catering its content for the user. Google's AI manipulates search results depending upon the user's region, their habits and their history, to give them the best results. This is all made possible by artificial intelligence and deep learning algorithms.

Tesla

Tech companies are spending millions in the development of AIs to maximize their sales and for technological advancements. Tesla, being one of the biggest companies in the automobile and technology industry, has been developing advanced AIs for automated driving, in which the vehicle, based on various external and internal factors, navigates without any involvement of the driver.

NVIDIA

NVIDIA, which is primarily a graphics card manufacturer, has been working very closely with AI as well in various subfields, from restoring images to automated vehicles that learn various aspects of human drivers in real time by observing how we work.

McKinsey Global Institute has commented on the significance of this field by stating that AIs could increase annual gross domestic product growth by about 1.2% through at least 2030, which equates to around $13 trillion of new economic activity. This figure is massive enough to justify the comparison with the steam engine that revolutionized the industrial market.

AI Is Not Just for Research

The uses of AI for industrial growth aren't only limited to research, as modern companies have employed the use of financially competent AIs that can provide feedback when it comes to hiring individuals, predicting growth, expecting failures, and understanding the behavior of the stock market and strategizing according to it. Analysts have predicted that around 70% of companies will adopt some form of artificial intelligence by 2030.

Many countries are racing to invest heavily in this field, and on the top are the United States and China. China even has a five-year plan that runs through 2020, and their goal is to become a leader in the field of technology. If China does become successful in becoming the leader in AI, they'll likely capture 20% more benefits in economic growth.

The Rise of AI and Its Impact on Employment

The number one threat anyone working in the industry has when questioned about the rapid growth in AI is how it affects employment for people because of machines taking over jobs that can be done exponentially faster than humans.

The answer is quite tricky, and it's disheartening, to say the least, but the outcome of automation isn't always beneficial for the working class. According to a study, it is predicted that around 2030, around 400 and 800 million jobs will be displaced, and almost 375 million people will have to switch their careers entirely, to facilitate the growth of AI and its automation.

While this might be a frightening reality, with every technological rise, more jobs are always created, and history has been proof of that. When the industrial revolution happened, the agricultural sector suffered, but the job requirement generally became far more expansive. Even though AI is expected to create jobs with advanced requirements, the prospects and benefits will definitely create far more lucrative living conditions for consumers and employees alike.

We still have time to see how the AI revolution in the job sector will rise and what sort of jobs it will create. For sure, it will take away the mundane jobs that most computers can be done more efficiently.

The need for AI and Automation

At its core, AIs are designed and implemented to facilitate much of what we do, and the past few years have been proof of the capabilities of this field. The need for AI isn't solely based on automation, however, but to predict and solve many problems relating to our social, economic, and medical lives.

Deep machine learning and neural networks are on the rise and aim to replicate the behavior of the brain. This gives us a new way of deducing things, through the use of largely connected data. Machine learning especially deals with a system based on previous data to make very accurate assumptions and predictions. This has helped us conserve time and money by focusing on tasks with a higher success rate.

The medical sector has seen various advancements including prediction of a disease's presence and chance of occurrences. This is all possible by the deductive ability and the power of computers to process data at such an exponential rate, that we humans can't even conceptualize. Cancer can be predicted by AI-powered machines based on the symptoms of patients, further strengthening the fight against these hideous diseases. This has resulted in pharmaceutical companies to heavily invest in this growing field as well.

Aside from obvious usages, AIs have also been used in more creative fields like arts and music. Free websites allow content creators to use AI-generated music for their content. Various AI scripts are used to restore vintage images and even create art pieces through deep learning.

In this day and age, when our smartphones are capable of calling people and able to carry out entire conversations, it's clear that our reliance on automation and AIs is only going to grow. The market has recognized this and is heavily investing in it to create more jobs, expand exponentially, and earn a profit.

The post Why AI Is the Steam Engine for This Time and Age appeared first on Datafloq.

Discovery is done through observation and what researchers discover is written down, which we call data. This data can reveal structure (describing an object numerically) or behaviour (written descriptions about an organism or object). These observations are not the end-goal.

To understand what is going on, researchers need to analyse the data and generate generalisations based on observations. These generalisations help to make predictions, which are then tested through carefully controlled experiments to see if they stand firm. The objective is to prove or disprove the original hypothesis and with that answer the original question. The results are shared through scientific papers, which are presented to the academic world for thorough scrutiny.

The Scientific Method

The scientific process has to be systematic, which means that other researchers should be able to repeat the test to verify the results. It is therefore important that the data remains available after the initial experiment. Once a finding has been presented, other researchers not only aim to verify the results but also aim to falsify the statements made. Falsification means the act of trying to falsify a statement, hypothesis or theory. The more falsifications fail, the stronger the original statement, hypothesis or theory. This can be summarised as the scientific method: ask questions, make hypotheses and collect observations to test those hypotheses.

The scientific method is based on observations and tests, and it has led to a collaborative endeavour resulting in ever incremental knowledge. As such, a crucial component of the scientific method is to share the observations and tests with the rest of the world through scientific papers. Often, our knowledge can be advanced the most when someone presents a radical new idea, which is subsequently published and verified, resulting in a new branch of knowledge that can bring new opportunities.

The scientific method is one of mankind's greatest inventions as it helps to advance our knowledge of the world. However, there is still ample room for improving the scientific method as there are multiple problems to it, which are especially related to the sharing of statements, hypotheses and theories, i.e. the scientific publishing. Therefore, if we want to improve our understanding of the world, we first have to improve the scientific method.

Problems Associated with the Scientific Method

There are many problems with the scientific method, and this is nothing new. For decades, researchers have complained about the method. As such the scientific method is ripe for disruption. Although the scientific method has proven itself over the centuries, the publication of scientific papers is flawed.

The $32 billion market is controlled by just a few large scientific publishers who demand all copyrights to be transferred to them, require expensive fees for access, while returning none of it to the original authors (resulting in a 36% profit margin for them, which is significantly higher than Google). In addition, the process is slow, not transparent and no longer encourages radical new ideas that could advance our knowledge. In general, there are three problem areas, each with multiple problems.

1. Publishing: A Slow and Not Transparent Method

Publishing an academic paper requires a lot of patience. It can easily take 6-9 months before an article is published. However, generally a lot longer as often a paper is rejected the first or second time. Since each process from submitting to receiving a first decision takes around four months, the time-to-publication is often longer than a year.

During this time, the paper is sent for peer review, which is an important aspect of the scientific method. However, in its current model, it does not work properly. The first phase of the process consists of an editor making the first shift of all the papers sent to a journal. It helps a lot if you know the editor and cite the editor in your paper. If not, your chances to get through the first round have reduced significantly.

If your paper discusses a radical idea, your chances to get published are even lower. As mentioned, often our knowledge of the world advances the most when someone comes up with a new radical idea. Unfortunately, the academic publishing world does not like radical ideas. To explain the problem, let' see what Google's AI guru Geoffrey Hinton has to say:

One big challenge the community faces is that if you want to get a paper published in machine learning now it's got to have a table in it, with all these different data sets across the top, and all these different methods along the side and your method has to look like the best one. If it doesn't look like that, it's hard to get published. I don't think that's encouraging people to think about radically new ideas.

Now if you send in a paper that has a radically new idea, there's no chance in hell it will get accepted, because it's going to get some junior reviewer who doesn't understand it. Or it's going to get a senior reviewer who's trying to review too many papers and doesn't understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that's really bad.

Once selected for peer review, the editor has to select from a limited pool of available academics. Often the peer reviewers do not have a full understanding of the subject and are not the right people to determine whether or not a paper should be published. As a result, it can take two or three iterations for a good paper to be finally accepted by a journal.

Once that happens, the problems get worse. Publications in journals is a prerequisite for academics if they want to grow their reputation and get a job promotion. However, whenever you submit to a journal, you have to give away your copyright to the scientific publisher. This is strange, you work on a paper for a long time, sometimes years, and the moment you wish to publish it you have to give away your copyright. This is outright theft. Here is an example from a journal:

Please confirm that, if your article is accepted for publication, you are willing to transfer all rights to your submission to [Journal A]. (You will be allowed to republish your article in your own work at no additional cost.)

Due to the slow, not transparent, bureaucratic and unfair publishing process, it becomes increasingly difficult for (new) scientists to get their knowledge out to the world. However, once they finally manage to publish a paper in a respectable journal, the problems don't end.

2. Accessibility: publicly-funded knowledge not publicly available

As a scientist, of course, you want your paper to be read by as many people as possible. Only due to new knowledge which is publicly available can we truly advance our world. However, the moment a scientific paper has been accepted by a journal, it disappears behind an expensive paywall. Journals charge on average $30 per article for access to the paper, none of which is paid to the authors of the paper. In addition, universities have to purchase expensive subscriptions to obtain access to the papers required for their academics (staff and students). On average, universities pay several million euros per year to obtain access to these papers. Money that is often paid for by a government.

Fortunately, there is a movement towards Open Access. Open Access papers are published by journals but are immediately available to the public. This seems to be a good thing. However, it comes at a cost for universities: It can easily cost ” 1000-” 2000 per article, to be paid for by universities, to make an article open access. This means that universities pay for research and also have to pay to make that research publicly available. There is a trend towards more open access papers, which means that publishing becomes very expensive for universities. If there would be a system that would make peer-reviewed papers publicly available, it could save universities a lot of, public, money.

3. Funding: unfair distribution of (public) money

Academic publishers take a lot of the public funds provided to universities. In general, governments provide funding to universities in one way or another, often through research grants (apart from private universities). With these funds, universities can pay academics to do research, and they purchase subscriptions to journals. As a result, a large chunk of publicly funded money ends up in the hands of commercial organisations who do not share this with the public. Publicly funded research predominantly benefits commercial organisations. Any research paid for by public money should be publicly available for free or at cost price.

The Time for Change is Now

It may be clear, the scientific method has its problems and challenges, and there are plenty of problems with the existing academic model. For centuries, it is scientific research that has made possible all the advancements we have made. However, often those who benefit from this research are not the ones who paid for it. It is time for change.

Technological advancements in areas such as big data analytics, Blockchain and AI have made it possible to come up with new, radical, solutions to today's problems. The speed at which these technologies are developed enables us to look at existing problems from a different angle and propose radical solutions. Across the globe we see new startups combining these technologies in innovative ways to build solutions for a better world. The configuration of new technologies, as well as the intent of the platform, determine how it can contribute to improving today's world.

Therefore, I am proud to announce that we are working on a solution. Imagjn aims to apply these technological advancements to change the academic publishing process. We believe that publicly-funded knowledge should be publicly available, and academics should retain the full copyright of their work. Imagjn will offer the academic world a new approach. We aim to do so, by building a new solution and to improve the scientific method.

The post To Save Innovation, We Need to Change Science appeared first on Datafloq.