Lanet Team

What is CrowdSourcing?

What is crowdsourcing?

Crowdsourcing involves seeking knowledge, goods, or services from a large body of people. These people submit their ideas in response to online requests made either through social media, smartphone apps, or dedicated crowdsourcing platforms.

A great example of crowdsourcing is online reviews. If you’ve ever reviewed a restaurant, gym, or bar on Google, congratulations! You’re a productive crowdsourcing contributor.

Different types of crowdsourcing

Crowdsourcing can be used to find solutions to all kinds of tasks. This includes small things like a band asking its fans which cities they should play on their next tour, to ambitious projects like genetic researchers asking for help in sequencing the human genome.

The breadth and diversity of social media also offer huge potential for crowdsourcing. It can also take the form of ideas competitions such as Ideas for Action, a forum for students and young professionals to submit solutions to global innovation challenges.

Hackathons are another popular form of crowdsourcing, often hosted by tech companies to find inventive solutions to challenging problems.

A few crowdsourcing basics

While the concept of crowdsourcing may be simple, finding great ideas and solutions isn’t as easy as just asking customers and fans for their best ideas.

After all, nobody wants to give away their valuable knowledge and expertise for free. There has to be a clear incentive in place, such as a financial incentive (either a cash prize or a share of eventual sales) or professional recognition.

To be effective, a crowdsourcing project also needs:

A well-defined scope – respondents need to know exactly what is required in terms of a suggested idea or solution
Comprehensive background information – respondents may need access to technical data before they can make a submission
A clear preferred format for submissions
A defined deadline for submissions

You also need to decide whether you want to target a large group of people (e.g. through big public social networks like facebook or twitter) or a specific group with highly-developed skills and experience (e.g. through specific platforms with targeted people).

A specialized crowdsourcing platform can help to structure this process and guide interactions between the organization or business seeking input, and those looking to provide it.

What are the advantages of crowdsourcing?

Everyone knows two minds are better than one when it comes to solving problems. Even better than two minds? 10,000 minds.

By turning a question over to a wide talent pool, companies can gain access to amazing suggestions for a new product or service, or for a new solution to a challenging problem.

Not only does this help with problem-solving, but it also allows groups to feel connected to companies and organizations. Building this community of contributors can have huge benefits in terms of marketing, brand visibility, and customer loyalty.

Crowdsourcing offers a lot of other advantages, too:

Lower costs: While winning ideas should definitely be rewarded, offering these rewards is usually a lot cheaper than formally hiring people to solve problems.

Greater speed: Harnessing a wider pool of people can speed up the problem-solving process, especially when completing a large number of small tasks in real-time.

More diversity: Some companies (especially smaller companies) may not have a lot of internal diversity. By crowdsourcing ideas, they can benefit from others with different backgrounds, values, and life experiences.

Marketing and media coverage: Crowdsourcing can be an excellent and cost-effective source of marketing and media coverage.

Even with all of these advantages, you need to decide if crowdsourcing is right for your project.

Is crowdsourcing the right move for you?

Not every project is well-suited for crowdsourcing.

If you’re dealing with a sensitive problem, or a project involving a lot of valuable intellectual property, crowdsourcing may be one step too far. Although these problems may still benefit from collective intelligence, they may not be appropriate to turn over to a crowd.

While crowdsourcing is a powerful way to unlock innovation, it involves a lack of control – especially when involving online communities. Unless you oversee these projects closely, they can threaten brand reputation.

Also, bear in mind that once you turn a question or problem over to a crowd, you may be stuck with a less than ideal solution.

What is Machine Learning?

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.

Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether an email is spam.

The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple.

Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.

Data, and lots of it, is the key to making machine learning possible.

Main Three components of machine learning:

Data: There are two main ways to get the data — manual and automatic.

Manually collected data contains far fewer errors but takes more time to collect — that makes it more expensive in general.

Automatic approach is cheaper — you’re gathering everything you can find and hope for the best.

Features: Also known as parameters or variables. These are the factors for a machine to look at.

Algorithms: Most obvious part. Any problem can be solved differently. The method you choose affects the precision, performance, and size of the final model. There is one important nuance though: if the data is crappy, even the best algorithm won’t help. Sometimes it’s referred to as “garbage in – garbage out”. So don’t pay too much attention to the percentage of accuracy, try to acquire more data first.

How it works:

In a way, Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to “learn” to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained.

For instance, the programmer can tell the system that a particular object is a human being (=”human”) and another object is not a human being (=”no human”). The software receives continuous feedback from the programmer. These feedback signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly distinguish between “humans” and “non-humans” in the end.

Some machine learning methods
Machine learning algorithms are often categorized as supervised or unsupervised.

Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Advantages of Machine Learning:

Machine Learning undoubtedly helps people to work more creatively and efficiently. Basically, you too can delegate quite complex or monotonous work to the computer through Machine Learning – starting with scanning, saving and filing paper documents such as invoices up to organizing and editing images.

In addition to these rather simple tasks, self-learning machines can also perform complex tasks. These include, for example, the recognition of error patterns. This is a major advantage, especially in areas such as the manufacturing industry: the industry relies on continuous and error-free production. While even experts often cannot be sure where and by which correlation a production error in a plant fleet arises, Machine Learning offers the possibility to identify the error early – this saves downtimes and money.

Self-learning programs are now also used in the medical field. In the future, after “consuming” huge amounts of data (medical publications, studies, etc.), apps will be able to warn in case his doctor wants to prescribe a drug that he cannot tolerate. This “knowledge” also means that the app can propose alternative options which for example also take into account the genetic requirements of the respective patient.

How IoT Is Changing The World

The Internet of Things (IoT) is a phrase that is becoming more and more common. It can be used to refer to everything from smartphones to smart houses. In a nutshell, it is the interconnectivity between all these different smart gadgets flooding the market and the Internet.

IoT devices will have a significant impact on many aspects of our lives including how we live, drive, and farm animals and crops.

Let’s have a look at how IoT is changing the world.

Health industry

When it comes to IoT changing the world, one of the main points of IoT are the sensors. Devices that will be responsible for collecting data from outside and communicating them over the Internet.

In the health sector, these sensors, placed directly on patients’ bodies (or on healthy people being monitored), will collect useful information – cardiovascular activity, glucose levels or electro-dermal activity, for example – and will send it directly to health professionals and/or software responsible for analysing a patient’s well-being.

This example serves as an introduction to the concept of wearables: devices that will be found on our body with multiple purposes and, of course, will also establish communications through the Internet.

Smart Cities

As more and more people move into cities worldwide, there will be a higher demand for water and energy, and an increased potential for disease outbreak, pollution, traffic congestion, and crime. At the same time, most of the developed world needs to deal with an aging demographic. By installing sensors in every building and road, the government can construct a 3D virtual representation of the city to perform more in-depth analysis of the everyday challenges that the city faces.

For example, in Singapore, 3D geospatial data gives the government a detailed view of every location so that city planners can study incidents of flooding, energy consumption, traffic congestion, and disease outbreaks. Analysis of flooding episodes can help the government prepare emergency evacuation routes and waste systems. Examining energy use can help the government plan for the installment of solar panels on roofs and water retention features. Analysis of traffic flows can help the government plan for new transportation routes. 3D data can even help public health officials identify disease clusters by measuring the population density in areas where people have contracted the disease.

Overall, IoT-based smart cities use data and technology to create a more efficient and sustainable infrastructure to manage the resources, traffic flow, population behavior, develop the local economy, and improve the quality of life of residents.

Animal Farming

The use of IoT will enable farmers to reduce animal mortality as well as increase productivity. Farmers can install sensors on facilities that house animals to monitor the interior humidity and temperature as well as potential fire hazards. By having animals wear portable sensors, installing sensors on feeding and water troughs, or using drones to conduct aerial surveillance of the animals, farmers can monitor the daily calorie intake and the level of activity of each animal, and identify and isolate the sick animals early to prevent the spread of diseases. By monitoring the body temperature of the animals, farmers can increase animal fertility by identifying the optimal time to breed and give birth. Wearable sensors can also help farmers figure out the best time and frequency to milk cows.

Smart Agriculture

The farming of crops will be severely affected by two factors. First, climate change is increasing the frequency and severity of droughts, allowing harmful insects to thrive. Second, the retirement of the baby boomer generation of farmers and an inadequate number of replacement workers will leave farms short-handed. By installing sensors around the farm and collecting data on temperature, air and soil humidity, water level, or by flying drones over the fields to collect data, farmers can reduce waste in water and fertilizer by identifying the best time to irrigate, fertilize or harvest. Big Data can also provide localized weather forecasts that are based on past weather patterns, which will help farmers make better decisions.

Transport industry

In a few years, the vehicles will become a great communications device.

One of the most visible consequences will, of course, be as far as autonomous vehicles are concerned. These, equipped with all types of sensors, will communicate with each other and with different elements on the road to achieve a smoother and safer traffic.

But not only that. IoT will involve many more devices. The vehicle will be able to identify its occupants (e.g. through its mobile phone), thus anticipating their needs. In the same way, it will react to any event that affects its operation; it is true, this is already done by many vehicles today, but what they do not do is to arrange and go autonomously to an appointment in a workshop or service station if they detect a breakdown or the need to refuel more which can be changed in the coming time.

Smart homes

It’s the first area we think about when we imagine IoT. Indeed, IoT will also enter our homes through all kinds of devices that will seek to make our lives a little more comfortable. From intelligent refrigerators that will warn you when you are going to run out of milk (for example) and make the purchase autonomously, to alert the police if they notice unauthorized access, through some existing devices, such as intelligent speakers that help manage different elements of the house.

Conclusions

The Internet of Things holds great promise but also brings significant concerns. First, every connected device will be collecting a considerable amount of data. As data storage is becoming cheaper, indexing the data effectively for analytics may be a greater need. Second, the data needs to be stored securely, to prevent hacking attacks on company servers and identity thefts of the individual. Block chain is an upcoming technology that would provide an added layer of security. However, since data analytics already demands a large amount of computational power, the layering of blockchain on top of Big Data may place additional demands on the infrastructure and support. All told, IoT will continue to thrive because of the many benefits it will bring to the various segments of business, only to improve with time.

Benefits & Drawbacks of Technologies

Technology is the sum of all of the knowledge that we have to process actions, create tools, and use materials throughout each day. It is a term which has multiple meanings based on the subject matter being discussed. You are using technology to read this content right now. We use technology to plant a garden. Technology helps us to sleep at night, receive medical care, and cook food.

These processes offer us a way to simplify our lives or add value to it in some way. We can take the scientific approach to use technology to solve problems and accomplish tasks. It can help us to communicate with others, travel to new destinations, or pursue our goals and dreams.

Almost every task we do in our lives each day is an example of technology at work in some way. Whether you are trying to scale a business, develop manufacturing processes, or relaxing at home while watching a movie, then you benefit from the ingenuity of the human spirit. The application of technology typically results in products or services, but it can also be used for malicious purposes because every coin has two sides.

Benefits:-

1. Technology gives us access to more information.

The Internet might be the most significant social village that humanity has created in history. It is an informational resource that allows us to experience different perspectives, ideas, and cultures from all over the world. You can receive real-time updates of news stories, play games, or chat with someone who is on the other side of the planet. It is a straightforward experience that lets us access a wealth of knowledge.

Thanks to technology, you can access this information in the comfort of your home. You could drink some coffee as you read the news. There are even ways to stream what you get from the Internet to other devices to maximize your consumption.

2. You can save time by using technology.

If you are old enough to remember a time before smartphones and GPS, there was one primary way to navigate through a new city: paper maps. Thanks to the development of technology, you can take a trip anywhere and know exactly where to go with line-by-line directions. There are times when a GPS system might have you drive into a lake if you’re not paying close enough attention to your surroundings, but this upgrade to traveling offers additional benefits to consider too.

3. Technology gives us more mobility options.

Can you imagine what life was like when the best technology to help with your travel experience was a good pair of shoes? Forget about bicycles and cars when thinking about mobility and technology. In less than a single day, you can hop onto an airplane and travel to almost any point in the world. You can travel thousands of miles across the ocean with a high safety level to visit your favorite countries.

Although there are political challenges to travel that you must consider, the reality of modern technology is that we can travel further and faster than ever before in human history right now.

4. It inspires us to become innovators.

Digitization has come to many industries because of the emphasis on technology. From computer design to farming, we are in the middle of another global revolution. We can use new techniques on croplands to increase yields without spreading pesticides.

We can build new computers that let us access data faster than ever before. We can incorporate an Internet connection into a vehicle so that we can communicate with others whenever there is a need to chat. Technology lets us embrace the inner innovator.

5. We have developed better learning methods because of technology.

Software, gadgets, and even pencils are all technology options that make it easier to learn new skills. It is possible to integrate numerous tools into the modern classroom to facilitate the learning process. Even a calculator is a tool that wasn’t available not that long ago. We have more information available to us with what we consider to be simple tech items than previous generations got to experience during most of their lives.

Then there is the element of fun that technology provides us. When learning can be turned into a video game or an interactive presentation, then we retain more of the information provided to us at that moment. Listening to a teacher talk about a specific subject can create a retainment level of 5% to 10%. Interactive options increase our retainment level to almost 90%.

Drawbacks:-
1. There are data security concerns to manage.

As our access to technology grows, so does our willingness to incorporate these tools into the aspects of daily life. Vast amounts of data are collected from all of us every day. From the cookies that get logged onto our devices when visited a website to our credit card information when making a purchase, much of our private lives are stored somewhere on a HDD, SSD, or server. If there is a single breach of this data, then anyone could find out our details, like where we live, where we work, and who are closest relatives happen to be.

What is scary is the fact that this information is accessible online without a data breach as well. It might be creepy, but it is not illegal for some sites to give up information from public records if someone is looking for you. You can contact these services to remove your information, but more of them just keep springing up.

2. Technology can be addictive.

Technology can give you a rush that is similar to what drugs or alcohol can provide when you take them for the first time. Video games, streaming movies, television shows, and many more information elements that we can access today can cause people to make decisions to embrace their tools at the detriment of their lives. People are destroying personal relationships because they would prefer to stay connected.

3. Extreme dependability.

With each passing day, we are becoming more and more dependent on technology for almost every task without even realizing it. A common example of this is using a cab service. Another example is using GPS to know the directions of any desired location. A lot of people today do not bother learning or memorizing directions because they depend on Google Maps for it. In a lot of ways, that’s not a good path we’re going down!

4. Technology creates a social disconnect.

People are socializing with each other through digital means instead of face-to-face opportunities. Even though there is a certain satisfaction that comes from these interactions, digital-only relationships can also create intense feelings of isolation, loneliness, and disconnect. Taking away our ability to be physically social (even if that doesn’t mean intimacy) can be problematic for an individual’s mental health. The prevalence of mental illnesses and disorders may be increasing because we are using technology more than we are having contact with one another.

5. Technology can manipulate information to suit personal needs.

Deepfake videos are becoming more prevalent today thanks to technologies that make it seem like video content is real when it is not. Photographs and audio recordings are easy enough to edit as well, which means the data we collect is easy to manipulate. If we are unwilling to verify the authenticity of the information we access, then it is very easy to spread false information to others, creating an alternative set of facts.

Trending Technologies of 2020

The new year is always a great time for reflection on the past year, but also a time to look forward to the next year and to imagine what’s to come.

For programmers, this is a good time to think about new skills you want to learn or interesting projects you want to take part in.

The programming and developer communities are emerging at a rate faster than ever before. Various new programming languages are coming up that are suited for different categories of developers (beginners, intermediate, and experts) as well as for different use cases (web application, mobile applications, game development, distributed system, etc).

Let us take a look at best Programming Languages which are trending in 2020 and important to learn for a job and for future prospects:

1. Python

Python is a high-level programming language that is used as a ‘glue’ language to connect large existing software components. Also, its an object-oriented programming language that offer a vast collection of useful libraries and extension for developers and programmers. YouTube, Dropbox, Google, etc are all built-in Python.

Python is often described as simple and easy to learn, with a readable syntax that decreases the cost and time of program maintenance.

2. Go programming language

Go, also known as Golang, is a programming language built by Google. Go provides excellent support for multithreading and so, it is being used by a lot of companies that rely heavily on distributed systems. Go is widely used in startups in Silicon Valley. However, it is yet to be adopted by Indian companies/startups. Those who wish to join a Valley-based startup specializing in core systems should master Golang.

3. R

R programming language is one of the most commonly used programming languages for Data Analysis and Machine Learning. R provides an excellent framework and built-in libraries to develop powerful Machine Learning algorithms.

R is also used for general statistical computing as well as graphics. R has been well adopted by enterprises. Those who wish to join “Analytics” team of a large organization should definitely learn R.

4. MATLAB

MATLAB is a statistical analysis tool that is used in various industries for Data Analysis. MATLAB is used widely in the Computer Vision and Image processing industry as well. MATLAB enables engineers and domain experts to develop their own data analytics applications.

Easy-to-use functions and interactive apps help you design accurate predictive models quickly. You can then deploy them to both enterprise and embedded systems. There’s only one algorithm to develop and maintain – where you run it is up to you.

5. DART

Dart, the language used to write Flutter apps, has enjoyed an accompanying spike in usage on GitHub. Dart is not only used for mobile app development but is a programming language. It is used to build just about anything on the web, servers, desktop and of course, mobile applications.

Dart, when used in web applications, is transpiled to JavaScript so it runs on all web browsers. The Dart installation comes with a VM as well to run the .dart files from a command-line interface. The Dart files used in Flutter apps are compiled and packaged into a binary file (.apk or .ipa) and uploaded to app stores.

The age of Artificial Intelligence: Are we ready?

Information Technology goes as far to the time humans have lived. Communication has been a key part of human life. Humans first started communicating during the premechanical age through picture drawings usually carved on a rock. As today, we live in the Information age, technology has evolved just as humans. One such is Artificial Intelligence.

While we are on the topic, let us first understand what is Artificial Intelligence? Well, the textbook definition is the simulation of human intelligence processes by machines. In simple language, a computer works on its own, just like the movie Robot when Rajnikant turns into a villain. We hear many talks about AI being the fourth Industrial revolution. Well, I think it is true. Artificial Intelligence have changed our lives like never before. We live in a world where everything in on the tip of our fingers. In near future Machine learning and Artificial Intelligence will no doubt rule the world.

Let us talk about our transformation from Information age to the AI age: When computers were first invented, it was immobile, but today we have Laptops and Mobile phones with fast internet connectivity. We can connect to friends and families across the world with one easy step. Automatic cars, Machinery to handle manpower of 100 people, Robotics, Advanced Military weapons are already in the market, what more advanced facilities we can ask for. All these because of one invention computer chip. Technology along with Humans have evolved to the extent where now Machine has overtaken the human world.

In the long run, the question that will remain, will be, what if the quest for AI overpowers all the human tasks at once. Inventing such intelligent machinery will help us improve the economy, poverty, diseases and war. But the main concern is that AI will become more intelligent than Humans, if we are not aligned to our goals. The potential of AI cannot be measured, Humans are considered to be the smartest living being controlling the planet but if we’re no longer the smartest who will be in control.