Virtual Identity

•  Paradigm for the Connected World • 

Author: VIE

  • What is differential privacy in machine learning (preview)?

    What is differential privacy in machine learning (preview)?

    As the amount of data that an organization collects and uses for analyses increases, so do concerns of privacy and security. Analyses require data. Typically, the more data used to train machine learning models, the more accurate they are. When personal information is used for these analyses, it’s especially important that the data remains private throughout its use.

    How differential privacy works

    Differential privacy is a set of systems and practices that help keep the data of individuals safe and private. In machine learning solutions, differential privacy may be required for regulatory compliance.

    Differential privacy machine learning process.

    In traditional scenarios, raw data is stored in files and databases. When users analyze data, they typically use the raw data. This is a concern because it might infringe on an individual’s privacy. Differential privacy tries to deal with this problem by adding “noise” or randomness to the data so that users can’t identify any individual data points. At the least, such a system provides plausible deniability. Therefore, the privacy of individuals is preserved with limited impact on the accuracy of the data.

    In differentially private systems, data is shared through requests called queries. When a user submits a query for data, operations known as privacy mechanisms add noise to the requested data. Privacy mechanisms return an approximation of the data instead of the raw data. This privacy-preserving result appears in a report. Reports consist of two parts, the actual data computed and a description of how the data was created.

    Differential privacy metrics

    Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

    Epsilon values are non-negative. Values below 1 provide full plausible deniability. Anything above 1 comes with a higher risk of exposure of the actual data. As you implement machine learning solutions with differential privacy, you want to data with epsilon values between 0 and 1.

    Another value directly correlated to epsilon is delta. Delta is a measure of the probability that a report isn’t fully private. The higher the delta, the higher the epsilon. Because these values are correlated, epsilon is used more often.

    Limit queries with a privacy budget

    To ensure privacy in systems where multiple queries are allowed, differential privacy defines a rate limit. This limit is known as a privacy budget. Privacy budgets prevent data from being recreated through multiple queries. Privacy budgets are allocated an epsilon amount, typically between 1 and 3 to limit the risk of reidentification. As reports are generated, privacy budgets keep track of the epsilon value of individual reports as well as the aggregate for all reports. After a privacy budget is spent or depleted, users can no longer access data.

    Reliability of data

    Although the preservation of privacy should be the goal, there’s a tradeoff when it comes to usability and reliability of the data. In data analytics, accuracy can be thought of as a measure of uncertainty introduced by sampling errors. This uncertainty tends to fall within certain bounds. Accuracy from a differential privacy perspective instead measures the reliability of the data, which is affected by the uncertainty introduced by the privacy mechanisms. In short, a higher level of noise or privacy translates to data that has a lower epsilon, accuracy, and reliability.

    Open-source differential privacy libraries

    SmartNoise is an open-source project that contains components for building machine learning solutions with differential privacy. SmartNoise is made up of the following top-level components:

    • SmartNoise Core library
    • SmartNoise SDK library

    SmartNoise Core

    The core library includes the following privacy mechanisms for implementing a differentially private system:

    Component Description
    Analysis A graph description of arbitrary computations.
    Validator A Rust library that contains a set of tools for checking and deriving the necessary conditions for an analysis to be differentially private.
    Runtime The medium to execute the analysis. The reference runtime is written in Rust but runtimes can be written using any computation framework such as SQL and Spark depending on your data needs.
    Bindings Language bindings and helper libraries to build analyses. Currently SmartNoise provides Python bindings.

    SmartNoise SDK

    The system library provides the following tools and services for working with tabular and relational data:

    Component Description
    Data Access

    Library that intercepts and processes SQL queries and produces reports. This library is implemented in Python and supports the following ODBC and DBAPI data sources:

    • PostgreSQL
    • SQL Server
    • Spark
    • Preston
    • Pandas
    Service Execution service that provides a REST endpoint to serve requests or queries against shared data sources. The service is designed to allow composition of differential privacy modules that operate on requests containing different delta and epsilon values, also known as heterogeneous requests. This reference implementation accounts for additional impact from queries on correlated data.
    Evaluator

    Stochastic evaluator that checks for privacy violations, accuracy, and bias. The evaluator supports the following tests:

    • Privacy Test – Determines whether a report adheres to the conditions of differential privacy.
    • Accuracy Test – Measures whether the reliability of reports falls within the upper and lower bounds given a 95% confidence level.
    • Utility Test – Determines whether the confidence bounds of a report are close enough to the data while still maximizing privacy.
    • Bias Test – Measures the distribution of reports for repeated queries to ensure they aren’t unbalanced

    Next steps

    Learn more about differential privacy in machine learning:

  • Responsible AI – Privacy and Security Requirements

    Responsible AI – Privacy and Security Requirements

    Training data and prediction requests can both contain sensitive information about people / business which has to be protected. How do you safeguard the privacy of the individuals? What steps are taken to ensure that individuals have control of their data? There are regulations in countries to ensure privacy and security.

     In Europe you have the GDPR (General Data Protection Regulations) and in California there is CCPA (California Consumer Privacy Act,). Fundamentally, both give an individual control over its Data and requires that companies should protect the Data being used in the model. When Data processing is based on consent, then am individual has the right to revoke the consent at any time.

     Defending ML Models against attacks – Ensuring privacy of consumer data:

     I have discussed about very briefly about the tools for adversarial training – CleverHans and FoolBox Python libraries here: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis  . Let us now look at more stringent means of protecting a ML model against attacks. It is important to protect the ML model against attacks, thus, ensuring the privacy and security of data. An ML model may be attacked in different ways – some literature classifies the attacks into: “Information Harms” and “Behavioural Harms”. Information Harm occurs when the information is allowed to leak from the model. There are different forms of Information Harms: Membership Inference, Model Inversion and Model Extraction. In Membership Inference, the attacker can determine if some information is part of the training data or not. In Model Inversion, the attacker can extract all the training data from the model and Model Extraction, the attacker is able to extract the entire model!

     Behavioural Harm occurs when the attacker can change the behaviour of the ML model itself – example: by inserting malicious data. In this post – I have given an example of an autonomous vehicle in this article: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis

    Cryptography | Differential privacy to protect data

    You should consider privacy enhancing technologies like Secure Multi Party Computation ,(SMPC) and Fully Homomorphic Encryption (FHE). SMPC involves multiple systems to train or serve the model whilst the actual data is kept secure

    In FHE the data is encrypted. Prediction requests involve encrypted data and training of the model is also carried out on encrypted data. This results in heavy computational cost because the data is never decrypted except by the user. Users will send encrypted prediction requests and will receive back an encrypted result. The goal is that using cryptography you can protect the consumers data.

    Differential Privacy in Machine Learning

    Differential privacy involves protection of the data by adding noise to the data so that the attackers cannot identify the real content. SmartNoise is an open-source project that contains components for building machine learning solutions with differential privacy. SmartNoise is made of following top level components:

    ✔️Smart Noise Core Library

    ✔️Smart Noise SDK Library

    This is a good read to understand about Differential Privacy: https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

     Private Aggregation of Teacher Ensembles (PATE)

    This follows the Knowledge Distillation concept that I discussed here: Post 1- Knowledge Distillation, Post – 2 Knowldge Distillation. PATE begins by dividing the data into “k” partitions with no overlaps. It then trains k models on that data and then aggregates the results on an aggregate teacher model. During the aggregation for the aggregate teacher, you will add noise to the data and the output.

    For deployment, you will use the student model. To train the student model you take unlabelled public data and feed it to the teacher model and the result is labelled data with which the student model is trained. For deployment, you use only the student model.

    The process is illustrated in the figure below:

    No alt text provided for this image

    PATE (Private Aggregation of Teacher Ensembles)

    Source

    Credits:

  • Employee monitoring software became the new normal during COVID-19. It seems workers are stuck with it

    Employee monitoring software became the new normal during COVID-19. It seems workers are stuck with it

    Many employers say they’ll keep the surveillance software switched on — even for office workers.

    In early 2020, as offices emptied and employees set up laptops on kitchen tables to work from home, the way managers kept tabs on white-collar workers underwent an abrupt change as well.

    Bosses used to counting the number of empty desks, or gauging the volume of keyboard clatter, now had to rely on video calls and tiny green “active” icons in workplace chat programs.

    In response, many employers splashed out on sophisticated kinds of spyware to claw back some oversight.

    “Employee monitoring software” became the new normal, logging keystrokes and mouse movement, capturing screenshots, tracking location, and even activating webcams and microphones.

    At the same time, workers were dreaming up creative new ways to evade the software’s all-seeing eye.

    Now, as workers return to the office, demand for employee tracking “bossware” remains high, its makers say.

    Surveys of employers in white-collar industries show that even returned office workers will be subject to these new tools.

    What was introduced in the crisis of the pandemic, as a short-term remedy for lockdowns and working from home (WFH), has quietly become the “new normal” for many Australian workplaces.

    A game of cat-and-mouse jiggler

    For many workers, the surveillance software came out of nowhere.

    The abrupt appearance of spyware in many workplaces can be seen in the sudden popularity of covert devices designed to evade this surveillance.

    Before the pandemic, “mouse jigglers” were niche gadgets used by police and security agencies to keep seized computers from logging out and requiring a password to access.

    Mouse jigglers for sale on eBay
    An array of mouse jigglers for sale on eBay.(Supplied: eBay)

    Plugged into a laptop’s USB port, the jiggler randomly moves the mouse cursor, faking activity when there’s no-one there.

    When the pandemic hit, sales boomed among WFH employees.

    In the last two years, James Franklin, a young Melbourne software engineer, has mailed 5,000 jigglers to customers all over the country — mostly to employees of “large enterprises”, he says.

    Often, he’s had to upgrade the devices to evade an employers’ latest methods of detecting and blocking them.

    It’s been a game of cat-and-mouse jiggler.

    “Unbelievable demand is the best way to describe it,” he said.

    And mouse jigglers aren’t the only trick for evading the software.

    In July last year, a Californian mum’s video about a WFH hack went viral on TikTok.

    Leah told how her computer set her status to “away” whenever she stopped moving her cursor for more than a few seconds, so she had placed a small vibrating device under the mouse.

    “It’s called a mouse mover … so you can go to the bathroom, free from paranoia.”

    Others picked up the story and shared their tips, from free downloads of mouse-mimicking software to YouTube videos that are intended to play on a phone screen, with an optical mouse resting on top. The movement of the lines in the video makes the cursor move.

    “A lot of people have reached out on TikTok,” Leah told the ABC.

    “There were a lot of people going, ‘Oh, my gosh, I can’t believe I haven’t heard of this before, send me the link.’”

    Tracking software sales are up — and staying up

    On the other side of the world, in New York, EfficientLab makes and sells an employee surveillance software called Controlio that’s widely used in Australia.

    It has “hundreds” of Australian clients, said sales manager Moath Galeb.

    “At the beginning of the pandemic, there was already a lot of companies looking into monitoring software, but it wasn’t such an important feature,” he said.

    “But the pandemic forced many people to work remotely and the companies started to look into employee monitoring software more seriously.”

    An online dashboard showing active time and productivity score for a worker
    Managers can track employees’ productivity scores on a realtime dashboard.(Supplied: Controlio)

    In Australia, as in other countries, the number of Controlio clients has increased “two or three times” with the pandemic.

    This increase was to be expected — but what surprised even Mr Galeb was that demand has remained strong in recent months.

    “They’re getting these insights into how people get their work done,” he said.

    The most popular features for employers, he said, track employee “active time” to generate a “productivity score”.

    Managers view these statistics through an online dashboard.

    Advocates say this is a way of looking after employees, rather than spying on them.

    Bosses can see who is “working too many hours”, Mr Galeb said.

    “Depending on the data, or the insights that you receive, you get to build this picture of who is doing more and doing less.”

    Nothing new for blue-collar workers

    But those being monitored are likely to see things a little differently. 

    Ultimately, how the software is used depends on what power bosses have over their workers.

    For the increasing number of people in insecure, casualised work, these tools appear less than benign.

    In an August 2020 submission to a NSW senate committee investigating the impact of technological change on the future of work, the United Workers Union featured the story of a call centre worker who had been working remotely during the pandemic. 

    One day, the employer informed the man that monitoring software had detected his apparent absence for a 45-minute period two weeks earlier.

    The submission reads:

    Unable to remember exactly what he was doing that particular day, the matter was escalated to senior management who demanded to know exactly where he physically was during this time. This 45-minute break in surveillance caused considerable grief and anxiety for the company. A perceived productivity loss of $27 (the worker’s hourly rate) resulted in several meetings involving members of upper management, formal letters of correspondence, and a written warning delivered to the worker.

    There were many stories like this one, said Lauren Kelly, who wrote the submission.

    “The software is sold as a tool of productivity and efficiency, but really it’s about surveillance and control,” she said.

    “I find it very unlikely it would result in management asking somebody to slow down and do less work.”

    Ms Kelly, who is now a PhD candidate at RMIT with a focus on workplace technologies including surveillance, says tools for tracking an employee’s location and activity are nothing new — what has changed in the past two years is the types of workplaces where they are used.

    Before the pandemic, it was more for blue-collar workers. Now, it’s for white-collar workers too.

    “Once it’s in, it’s in. It doesn’t often get uninstalled,” she said.

    “The tracking software becomes a ubiquitous part of the infrastructure of management.”

    The ‘quid pro quo’ of WFH?

    More than half of Australian small-to-medium-sized businesses used software to monitor the activity and productivity of employees working remotely, according to a Capterra survey in November 2020.

    That’s about on par with the United States.

    “There’s a tendency in Australia to view these workplace trends as really bad in other places like the United States and China,” Ms Kelly said.

    “But actually, those trends are already here.”

    A screenshot of a dashboard showing a graph with different emotions
    The latest software claims to monitor employee emotions like happiness and sadness.(Supplied: StaffCircle)

    In fact, a 2021 survey suggested Australian employers had embraced location-tracking software more warmly than those of any other country.

    Every two years, the international law firm Herbert Smith Freehills surveys thousands of its large corporate clients around the world for an ongoing series of reports on the future of work.

    In 2021, it found 90 per cent of employers in Australia monitor the location of employees when they work remotely, significantly more than the global average of less than 80 per cent.

    Many introduced these tools having found that during lockdown, some employees had relocated interstate or even overseas without asking permission or informing their manager, said Natalie Gaspar, an employment lawyer and partner at Herbert Smith Freehills.

    “I had clients of mine saying that they didn’t realise that their employees were working in India or Pakistan,” she said.

    “And that’s relevant because there [are] different laws that apply in those different jurisdictions about workers compensation laws, safety laws, all those sorts of things.”

    She said that, anecdotally, many of her “large corporate” clients planned to keep the employee monitoring software tools — even for office workers.

    “I think that’s here to stay in large parts.”

    And she said employees, in general, accepted this elevated level of surveillance as “the cost of flexibility”.

    “It’s the quid pro quo for working from home,” she said.

    Is it legal?

    The short answer is yes, but there are complications.

    There’s no consistent set of laws operating across jurisdictions in Australia that regulate surveillance of the workplace.

    In New South Wales and the ACT, an employer can only install monitoring software on a computer they supply for the purposes of work.

    With some exceptions, they must also advise employees they’re installing the software and explain what is being monitored 14 days prior to the software being installed or activated.

    In NSW, the ACT and Victoria, it’s an offence to install an optical or listening device in workplace toilets, bathroom or change rooms.

    South Australia, Tasmania, Western Australia, the Northern Territory and Queensland do not currently have specific workplace surveillance laws in place.

    Smile, you’re at your laptop

    Location tracking software may be the cost of WFH, but what about tools that check whether you’re smiling into the phone, or monitor the pace and tone of your voice for depression and fatigue?

    These are some of the features being rolled out in the latest generation of monitoring software.

    Zoom, for instance, recently introduced a tool that provides sales meeting hosts with a post-meeting transcription and “sentiment analysis”.

    A screenshot of a sales video with analytics and sentiment analysis
    Zoom IQ for Sales offers a breakdown of how the meeting went.(Supplied: Zoom)

    Software already on the market trawls email and Slack messages to detect levels of emotion like happiness, anger, disgust, fear or sadness.

    The Herbert Smith Freehills 2021 survey found 82 per cent of respondents planned to introduce digital tools to measure employee wellbeing.

    A bit under half said they already had processes in place to detect and address wellbeing issues, and these were assisted by technology such as sentiment analysis software.

    Often, these technologies are tested in call centres before they’re rolled out to other industries, Ms Kelly said.

    “Affect monitoring is very controversial and the technology is flawed.

    “Some researchers would argue it’s simply not possible for AI or any software to truly ‘know’ what a person is feeling.

    “Regardless, there’s a market for it and some employers are buying into it.”

    The movement of the second hand of an analogue wristwatch moves an optical mouse cursor a tiny amount.(Supplied: Reddit)

    Back in Melbourne, Mr Franklin remains hopeful that plucky inventors can thwart the spread of bossware.

    When companies switched to logging keyboard inputs, someone invented a random keyboard input device.

    When managers went a step further and monitored what was happening on employees’ screens, a tool appeared that cycled through a prepared list of webpages at regular intervals.

    “The sky’s the limit when it comes to defeating these systems,” he said.

    And sometimes the best solutions are low tech.

    Recently, an employer found a way to block a worker’s mouse jiggler, so he simply taped his mouse to the office fan.

    “And it dragged the mouse back and forth.

    “Then he went out to lunch.”

     

  • A one-up on motion capture

    A one-up on motion capture

    A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.
     

    MIT researchers used the RISP method to predict the action sequence, joint stiffness, or movement of an articulated hand, like this one, from a target image or video.

    From “Star Wars” to “Happy Feet,” many beloved films contain scenes that were made possible by motion capture technology, which records movement of objects or people through video. Further, applications for this tracking, which involve complicated interactions between physics, geometry, and perception, extend beyond Hollywood to the military, sports training, medical fields, and computer vision and robotics, allowing engineers to understand and simulate action happening within real-world environments.

    As this can be a complex and costly process — often requiring markers placed on objects or people and recording the action sequence — researchers are working to shift the burden to neural networks, which could acquire this data from a simple video and reproduce it in a model. Work in physics simulations and rendering shows promise to make this more widely used, since it can characterize realistic, continuous, dynamic motion from images and transform back and forth between a 2D render and 3D scene in the world. However, to do so, current techniques require precise knowledge of the environmental conditions where the action is taking place, and the choice of renderer, both of which are often unavailable.

    Now, a team of researchers from MIT and IBM has developed a trained neural network pipeline that avoids this issue, with the ability to infer the state of the environment and the actions happening, the physical characteristics of the object or person of interest (system), and its control parameters. When tested, the technique can outperform other methods in simulations of four physical systems of rigid and deformable bodies, which illustrate different types of dynamics and interactions, under various environmental conditions. Further, the methodology allows for imitation learning — predicting and reproducing the trajectory of a real-world, flying quadrotor from a video.

    “The high-level research problem this paper deals with is how to reconstruct a digital twin from a video of a dynamic system,” says Tao Du PhD ’21, a postdoc in the Department of Electrical Engineering and Computer Science (EECS), a member of Computer Science and Artificial Intelligence Laboratory (CSAIL), and a member of the research team. In order to do this, Du says, “we need to ignore the rendering variances from the video clips and try to grasp of the core information about the dynamic system or the dynamic motion.”

    Du’s co-authors include lead author Pingchuan Ma, a graduate student in EECS and a member of CSAIL; Josh Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; Wojciech Matusik, professor of electrical engineering and computer science and CSAIL member; and MIT-IBM Watson AI Lab principal research staff member Chuang Gan. This work was presented this week the International Conference on Learning Representations.

    While capturing videos of characters, robots, or dynamic systems to infer dynamic movement makes this information more accessible, it also brings a new challenge. “The images or videos [and how they are rendered] depend largely on the on the lighting conditions, on the background info, on the texture information, on the material information of your environment, and these are not necessarily measurable in a real-world scenario,” says Du. Without this rendering configuration information or knowledge of which renderer is used, it’s presently difficult to glean dynamic information and predict behavior of the subject of the video. Even if the renderer is known, current neural network approaches still require large sets of training data. However, with their new approach, this can become a moot point. “If you take a video of a leopard running in the morning and in the evening, of course, you’ll get visually different video clips because the lighting conditions are quite different. But what you really care about is the dynamic motion: the joint angles of the leopard — not if they look light or dark,” Du says.

    In order to take rendering domains and image differences out of the issue, the team developed a pipeline system containing a neural network, dubbed “rendering invariant state-prediction (RISP)” network. RISP transforms differences in images (pixels) to differences in states of the system — i.e., the environment of action — making their method generalizable and agnostic to rendering configurations. RISP is trained using random rendering parameters and states, which are fed into a differentiable renderer, a type of renderer that measures the sensitivity of pixels with respect to rendering configurations, e.g., lighting or material colors. This generates a set of varied images and video from known ground-truth parameters, which will later allow RISP to reverse that process, predicting the environment state from the input video. The team additionally minimized RISP’s rendering gradients, so that its predictions were less sensitive to changes in rendering configurations, allowing it to learn to forget about visual appearances and focus on learning dynamical states. This is made possible by a differentiable renderer.

    The method then uses two similar pipelines, run in parallel. One is for the source domain, with known variables. Here, system parameters and actions are entered into a differentiable simulation. The generated simulation’s states are combined with different rendering configurations into a differentiable renderer to generate images, which are fed into RISP. RISP then outputs predictions about the environmental states. At the same time, a similar target domain pipeline is run with unknown variables. RISP in this pipeline is fed these output images, generating a predicted state. When the predicted states from the source and target domains are compared, a new loss is produced; this difference is used to adjust and optimize some of the parameters in the source domain pipeline. This process can then be iterated on, further reducing the loss between the pipelines.

    To determine the success of their method, the team tested it in four simulated systems: a quadrotor (a flying rigid body that doesn’t have any physical contact), a cube (a rigid body that interacts with its environment, like a die), an articulated hand, and a rod (deformable body that can move like a snake). The tasks included estimating the state of a system from an image, identifying the system parameters and action control signals from a video, and discovering the control signals from a target image that direct the system to the desired state. Additionally, they created baselines and an oracle, comparing the novel RISP process in these systems to similar methods that, for example, lack the rendering gradient loss, don’t train a neural network with any loss, or lack the RISP neural network altogether. The team also looked at how the gradient loss impacted the state prediction model’s performance over time. Finally, the researchers deployed their RISP system to infer the motion of a real-world quadrotor, which has complex dynamics, from video. They compared the performance to other techniques that lacked a loss function and used pixel differences, or one that included manual tuning of a renderer’s configuration.

    In nearly all of the experiments, the RISP procedure outperformed similar or the state-of-the-art methods available, imitating or reproducing the desired parameters or motion, and proving to be a data-efficient and generalizable competitor to current motion capture approaches.

    For this work, the researchers made two important assumptions: that information about the camera is known, such as its position and settings, as well as the geometry and physics governing the object or person that is being tracked. Future work is planned to address this.

    “I think the biggest problem we’re solving here is to reconstruct the information in one domain to another, without very expensive equipment,” says Ma. Such an approach should be “useful for [applications such as the] metaverse, which aims to reconstruct the physical world in a virtual environment,” adds Gan. “It is basically an everyday, available solution, that’s neat and simple, to cross domain reconstruction or the inverse dynamics problem,” says Ma.

    This research was supported, in part, by the MIT-IBM Watson AI Lab, Nexplore, DARPA Machine Common Sense program, Office of Naval Research (ONR), ONR MURI, and Mitsubishi Electric.

    Lauren Hinkel | MIT-IBM Watson AI Lab

    Source

  • How Fast Is Technology Advancing in 2022?

    How Fast Is Technology Advancing in 2022?

    Almost everything we do today has an atom of technology in it. From our smart devices to advanced computers, and sophisticated engineering equipment – technology is all around us.

    But this question remains – How fast is technology advancing?

    Statistics that illustrate how fast technology is growing over the years have shown breakthrough technologies from all aspects of life. Experts predict that there is more to come. 

    We compiled some of the most groundbreaking stats to enlighten you more on how far technology is progressing. In addition to this, we will shed more light on some of the upcoming trends, sure to leave you stunned!

    Fascinating Technology Growth Statistics

    The following are some eye-opening stats handpicked from the most reliable sources:

    • Globally, there are about 1.35 million tech startups around the world.
    • The number of smart devices collecting, analyzing, and sharing data should hit 50 billion by 2030.
    • The Internet adoption rate sits at 59% in 2021.
    • The computing and processing capacity of computers hits double figures every 18 months.
    • The world has produced 90% of its Big Data in the past two years.
    • Every second, 127 new devices are connected to the internet.
    • In Q1 of 2021, 4.66 billion people are using the internet.

    Sounds amazing right? That’s just the tip of the iceberg – we have more in store for you. Read on to find out!

    General Technology Growth Statistics

    The following are some generalized statistics about how the growth of technology is influencing every sector.

    1. The internet penetration rate in the world is at 59% as of January 2021.

    (Source: Data Reportal)

    In the last few decades, there has been a growing telecommunications implementation. This has led to an ongoing internet usage rise. 

    According to technology adoption statistics, the rate stands at almost 60% as of January 2021. Compared to Q1 of 2020, the rate has gone up by 7%. 

    2. $183.18 billion – that is how much the web hosting services marketplace is expected to have generated by 2026.

    (Source: Fortune Business Insight)

    In 2017, the global web hosting market had a value of $32.12 billion, and in 2018 that figure rose to $60.90 billion. By Maintaining a Compound Annual Growth Rate (CAGR) of 15.1%, experts predict that the web hosting industry will be worth more than $100 billion in a few years as a result of global tech market growth. That’s why there’s such a fierce battle between the best hosting providers on the market. 

    3. There are 4.88 billion phone users in the world as of January 2021.

    (Source: Bank My Cell)

    According to technology growth statistics, 62% of the world’s population owns a mobile phone. Compared to 2020, the number of phone owners has gone up by 0.1 billion. 

    That includes both smart and feature phones. 

    Breaking down the number even further:

    Smartphone owners are the majority here, amounting to 3.8 billion. On the other hand, feature phone owners are 1.08 billion. 

    4. By 2025, there will be 75 billion connected devices in the world. 

    (Source: Statista, MTA)

    In 2025, the number of Internet of Things (IoT) will be thrice that of 2019. Think slow cookers, wearable technology like smartwatches, smart meters, smartphones, etc. 

    The technology has become so popular that industry experts predict that every consumer will own about 15 IoT devices by 2030!

    5. Google got 2.5 trillion searches in 2020.

    (Source: Backlinko)

    As you may know, Google continues to dominate the search engine space. 

    When it comes to how fast technology is growing statistics, Google got more than two trillion searches in 2020 alone. 

    Let’s have a closer look at the numbers:

    There were 81,000 searches every second in 2020. That translates to about seven billion probes per month.

    6. The need to reach new customers is the primary factor pioneering technological growth in the last few years (46%).

    (Source: Finance Online)

    Technology adoption statistics reveal that factors such as selling new business lines (38%), overall improvement of business operations (41%), improving sales and marketing (35%), improving standard internal processes (33%), are the main drivers for tech growth.

    7. By 2040, 95% of purchases will be online.

    (Source: Nasdaq)

    Buying over the internet is so convenient because you can get whatever you need from the comfort of your home regardless of time or location. According to technology growth stats, ecommerce will have grown so much that buyers will conduct almost all of their purchases online in the next two decades.

    8. There are 3.96 billion social media users in the globe as of 2021.

    (Source: Backlinko)

    Social media allows people to connect regardless of their geographical location and at negligible costs if you have to buy data bundles. 

    Lucky for you if you’re using your office or public wifi.

    You’ll only have to part with $0.

    According to technology statistics, for 2021, there are almost four billion social media users globally. That’s almost twice the number in 2015.

    AI and Machine Learning Statistics

    Technology stats and facts show that AI remains one of the most sought-after technological advancements pioneering technological growth around the world. Read on to find out some amazing stats on how AI and machine learning are impacting society.

    9. Google Translate algorithm has increased its efficiency from 55% to 85% following the implementation of machine learning into its translation services.

    (Source: Finance Online)

    Google Translate is a service developed by Google to help customers translate text and websites to any desired language. Before the introduction of AI, it would typically take more time to translate a series of words in a foreign language, as the process is done one text at a time. However, with the application of deep learning (a sub-function of AI), the Google Translate service is now able to interpret a whole sentence or website at once.

    10. The global machine learning market is expected to reach $20.83 billion in 2024. 

    (Source: Finance Online, Forbes)

    Tech growth stats indicate that machine learning is currently one of the most popular and most successful sub-functions of AI.

    It should come as no surprise that the market is growing in value. Worth around $1.58B in 2017, it is expected to reach $20.83B in 2024, growing at a CAGR of 44.06%.

    11. The Compound Annual Growth Rate (CAGR) for AI will be 42.2% by 2027.

    (Source: Grand View Research)

    Stats on how fast technology is advancing reveal that the artificial intelligence market was worth $10.1 billion in 2018. In 2019, that value increased to $39.9.

    As you can see, there has been positive growth over the years, which is likely to continue.

    Giant tech firms have been pouring big bucks into research and development, the reason why technology keeps advancing every day. Examples of big names investing heavily in this sector include Facebook, Amazon, Microsoft, Google, and Apple. 

    Industry players predict a CAGR of 42% between the period 2020 to 2027.  

    12. AI will replace around 85 million jobs in the US by 2025.

    (Source: Forbes)

    Does automation benefit the ordinary citizen?

    You be the judge.

    The pandemic led to massive job losses, leaving one in every four adults in serious financial difficulty. They had issues footing their bills.

    That led to 33% of Americans using their life savings to cater for their expenses. Others had to borrow loans and now have huge debts.

    And it looks like the labor market hasn’t seen anything yet. 

    AI statistics show that its adoption will lead to job losses to the tune of 85 million by the end of 2025.

    However, it’s not all bad:

    Experts predict that there will be 95 million job openings because of artificial intelligence. By 2025, humans and machines will strike a balance of 50-50 when it comes to working.

    13. Worldwide, only 37% of organizations have incorporated AI into their business.

    (Source: Gartner)

    Although the figure may not be high enough, it is still a significant rise from what we had in 2015 (about 270% increase).

    14. Artificial General Intelligence (AGI) has a 50% chance of rising to 90% by 2075.

    (Source: Zdnet) 

    AGI mimics human intellect. Think cooking or styling hair with precision.

    Experts predict that there are high chances that in most work environments in 2075, nine out of 10 companies will use AGI technology.

    15. IT hiring was 7% lower than usual in Q3 of 2020. 

    (Source: Dice)

    IT job posting between August and September 2020 was virtually nonexistent. However, experts forecasted that it was only a small hitch that would go away in the coming months. 

    Technology adoption stats show that 68% of large organizations created more positions than they had in the second quarter of 2020. Therefore, it appears that smaller firms were struggling and didn’t have hiring budgets.

    16. The fully and semi-automatic car market will be worth $26 billion by 2030.

    (Source: Electronic Design)

    Experts estimate that the number of connected cars in Europe, China, and the US will be about 470 million by 2025. Technology statistics show that the vehicles will generate data worth $750 billion.

    While that sounds impressive, we should think about the security aspect. The information that the technology will derive could land in hackers’ hands instead of genuine parties like manufacturers or vendors. 

    So:

    It will be paramount for developers to come up with top-of-the-range security programs to keep cybercriminals at bay.

    17. 71% of executives believe that artificial intelligence and machine learning are game-changers for businesses.

    (Source: AMC Laboratories)

    The world is beginning to wake up to the fact that robotics and automation powered by AI could be the future of work. However, some are more prepared than others. Those that fail to prepare may be left behind when the changes start to take effect.

    18. 16% of companies in Europe believe that AI would help them counter the adverse effects of COVID-19 on labor.

    (Source: IDC)

    Emerging technologies and automation will be at the forefront of cushioning businesses from the effects of the pandemic. Nearly 20% of organizations say that AI will be the only solution to the current shortage of workers.

    19. AI is the most significant portion of the data strategy of any business, according to 61% of marketers.

    (Source: Finance Online)

    Data strategy is a set of informed decisions taken from a position of insight (after careful study of available data) on how best to move a business forward. It is the job of AI to study the set of available data, and help to draw insights as to existing flaws and what needs to improve. 

    20. The AI market will be worth over $15 trillion by 2030.

    (Source: PWC)

    AI technology is progressing, and the industry is growing pretty fast. Businesses and individuals alike love its efficiency. Logically, demand will continue to rise in the coming years. By 2030, its value will be $15+ trillion. More than ¾ of emerging technologies already planned to own foundations as early as 2021. 

    Big Data Statistics

    Data that has become so large and complicated for the traditional computer system to make sense of is referred to as Big Data. However, Big Data impact statistics have shown that it can become a goldmine to whoever understands its capacity. Check out the statistics that follow to discover the impact of Big Data on technology and internet growth.

    21. Organizations that are data-driven are 23x more likely to acquire new leads than those without a data-driven strategy.

    (Source: McKinsey)

    Big Data can be a source of insight for those that care to put in the work, understand patterns in its data, and relate them to their various businesses. Facebook is an exemplary example of a company that is effectively utilizing both Big Data and AI to understand its audience better.

    22. 91.6% of Fortune 1000 companies are investing more in Big Data and AI.

    (Source: ZD Net)

    Big Data is like the new gold for businesses. Coupled with AI, a good deal of information can be extracted from both structured and unstructured data. The Fortune 1000 companies know this. The most successful entrepreneurs also know this. For this reason, technology growth statistics tell us that these companies always have a specified budget put in place for data analytics.

    23. Two-thirds of organizations that have utilized Big Data effectively have reportedly seen a decrease in operational expenses.

    (Source: Datamation)

    Big Data impact statistics reveal that for businesses that can do away with the junk of unuseful data, Big Data can provide direct and specific information about what works for such businesses and what does not. That way, these businesses can avoid the trouble of wasting time, effort, and resources on strategies that don’t give results. Instead, it enables them to focus all of those energy and resources on what works.

    24. We generate 2.5 quintillion bytes of data daily.

    (Source: Forbes)

    According to tech growth statistics, we now produce data in trillions and quintillions daily. This number has been on the rise over the last few years, meaning that we should expect to produce more in the next 2-3 years.

    25. Analytics and big data will bring in an income of $274 billion by 2024.

    (Source: Statista)

    Revenue from big data and analytics has been on an upward rise over the last few years. By Q4 of 2021, data center Internet Protocol (IP) traffic reached 19.5 zettabytes. Business Intelligence (BI) analytics will be worth $14.5 billion in 2022.

    26. By increasing their effectiveness at utilizing Big Data, Fortune 1,000 companies can increase their net income by up to $65 million.

    (Source: Forbes)

    According to Big Data impact statistics, the ability to extract, understand, and utilize Big Data has a direct impact on both sales and revenue. With Big Data, businesses can better understand their customers, thereby channeling their efforts towards what works and increasing conversion rates.

    27. 71% of companies find it difficult to protect and manage unstructured data.

    (Source: Forbes)

    As enticing as the idea of Big Data analytics may seem, it still requires a lot of technical and specialized kind of skillset to make sense of the large chunk of available data. Thankfully, the best data visualization software can transform huge amounts of raw data into easy-to-digest visuals. These can provide decision-makers with valuable insights quickly and easily. 

    28. 83% of organizations worldwide are currently investing in various Big Data projects.

    (Source: Forbes)

    Given how rapidly technology is growing, and the millions of data being generated daily, top company executives are beginning to realize the usefulness of Big Data. Some even argue that failure to invest in Big Data for any business is like walking your way towards bankruptcy.

    Mobile Technology Statistics

    Internet traffic growth statistics tell that over the last decade, mobile usage has been on the rise, even surpassing desktop web traffic for the very first time in late 2015. Almost anything can now be achieved on mobile. Check out some of these mobile tech statistics to discover how vital technological advancements on mobile have become.

    29. Over half the traffic comes from mobile phones as of Q1 Of 2021. 

    (Source: Oberlo)

    If you’re wondering how fast technology is growing – statistics for 2022 show phone traffic has increased by 49.47% since 2011. 

    Back then, 93% of visitors came from the desktop, while mobile brought in a meager 6%. 

    However, the two went head to head in 2016, when the difference was about 1%, i.e., 48.25% for mobile and 46.93% via computers. As of 2021, cell phone browsing had surpassed that of other internet-connected devices.

    30. 91% of internet users in 2020 were mobile phone owners. 

    (Source: Statista)

    According to internet traffic growth statistics, more than half the world population was actively browsing the internet as of 2020. That translates to around four billion people. It is indeed a global village with billions of people who might have otherwise never met connecting.

    31. People check their mobile phones about 150 times daily.

    (Source: Business Services Week)

    Call it an addiction!

    The fact remains that mobile phones have become a massive part of how most of us function daily. We check our phones almost all the time for messages, notifications, time, etc.

    32. Total mobile connections in 2021 amount to 10.24 billion.

    (Source: Bank My Cell)

    So, how’s that, yet ownership is only about four billion?

    Isn’t the world population 7.84 billion?

    Well, there are people with dual SIM cards. Then there are those with more than one device, not forgetting integrated devices like security systems or cars. 

    33. 80% of smartphone users make use of their phones during physical shopping.

    (Source: Business Services Week)

    This could be either to read up reviews of a particular product that they are about to purchase or to locate an alternative store where they can compare products and prices. Either way, this goes to show the impact of technology on how we live our daily lives. Smart business owners who understand this fact can begin to make adjustments towards mobile to boost their traffic and improve conversion rates.

    34. 95.1% of the Facebook audience access the platform through their mobile phone.

    (Source: Business Services Week)

    Platforms like Google and LinkedIn have already implemented a mobile-first standard for their websites, and the reason for such a move is not far-fetched. Internet access growth statistics reveal that mobile drives the majority of the traffic on Google and other social media platforms.

    35. Google Play and the Apple App Store have a combined 4.4 million mobile apps for download in 2020.

    (Source: Statista)

    90% of the mobile apps on Google Play and the Apple App Store are free to download. Notwithstanding, more businesses are beginning to understand the power of mobile apps and the amount of time consumers spend on various apps daily. Technology adoption stats show that mobile apps can help develop a brand image and improve customer loyalty when done right.

    36. About 56% of parents who have kids aged between 8 and 12 years have purchased mobile phones for them.

    (Source: NCL Net)

    Statistics about how fast technology is growing show that kids are growing up in a technologically advanced society. Back in the ’80s and ’90s, who would have ever thought that a 12-year-old would own a mobile phone, let alone an 8-year-old. However, that is the reality of today’s economy. 

    That’s why solutions like parental control software are becoming more and more popular.

    37. 98% of Generation Z have a mobile phone.

    (Source: Global Web Index)

    Technology advancement rate statistics go further to reveal that Generation X has a mobile penetration of 92%, Generation Z with 52%, and Baby Boomers with 42%.

    38. Mobile advertising will reach $247 billion by 2022.

    (Source: Statista)

    Technology adoption stats show that advertising through mobile will reach almost $300 billion by the end of 2022. That will be a $244 billion increase from 2011 figures. 

    39. There were 490 million new social media users in 2020.

    Source: (Data Reportal)

    Social media is getting more and more engaging by the day. The number of new users almost hit the five hundred million mark in 2020. 

    40. Increasing usage of mobile banking technologies could generate up to 95 million jobs.

    (Source: Leftronics)

    A McKinsey Global Institute study found that over 80% of adults in developing countries owned a mobile device. However, only 55% had a bank account.

    Mass adoption of mobile banking technologies has the potential to empower people financially. For one, technology adoption stats show that it can generate up to 95 million jobs and even increase GDP by a whopping $3.7 trillion by 2025.

    Internet of Things (IoT) Statistics

    Over the last few years, the concept of IoT has become a vital role player across various industries. More and more businesses now look to integrate its many benefits into their network infrastructures. The following are some of the most up-to-date statistics on the growth of IoT.

    41. Every second, 127 new devices are connected to the internet.

    (Source: McKinsey)

    With the availability of affordable computer chips (sensors) through nanotechnology and the ubiquity of wireless networks, almost anything can now be made a part of the IoT according to statistics about how fast technology is advancing. 

    42. There are 4.66 billion internet users as of the first quarter of 2021.

    (Source: Data Reportal)

    Just how fast is technology advancing in 2021?

    The first few months of 2021 show that 4.7 billion people are using the internet. That’s almost ¾ of people in the world, looking at it from a global perspective!

    43. North America had the highest internet penetration rate globally in 2020. 

    (Source: Internet World Stats)

    In December 2020, North America’s internet access was the highest globally, at almost 90%. Europe was second with 87%, while Latin America took the third position with 72%.

    Although Africa had the lowest internet penetration rates globally, it has made some significant advancements in the last few years. Its progression in the area was pretty fast that same year.

    Let’s look at the numbers:

    According to internet growth stats, Africa had the highest rate at 13,941%. The Middle East followed with 5,528% and finally Latin America with 2,545%.

    44. Cellular IoT connections could reach 3.5 billion by 2023.

    (Source: Forbes)

    Cellular IoT connection is a feature that allows sensors to be able to transfer information directly to a computer or your mobile device within a region or specified distance. Health wearables that transfer the information about the state of health of a patient to a doctor or hospital is an excellent example of cellular IoT.

    45. 75.44 billion IoT devices could be in existence by 2025.

    (Source: Statista)

    IoT statistics reveal that there were over 25 billion IoT devices around the globe at the end of 2019. Statista predicts that there could be well over 50 billion by 2023.

    46. 70% of all automobiles will be connected to the internet through the Internet of Things by 2023.

    (Source: Statista)

    Technology statistics and findings show that the automobile industry is one of the few places where innovations in the IoT have seen significant improvements in the past few years. Aside from developing self-driving cars, research is being made to add lots of other features to the automobile industry through the Internet of Things. Soon, we could have vehicles that detect bad driving, accidents, and possibly imminent collision. In addition, cars that detect flaws in design while sending a report back to the manufacturer could also be a norm in years to come. 

    47. The IoT could generate up to $11 trillion in economic value per year by 2025.

    (Source: McKinsey)

    Statistics that illustrate how fast technology is growing show that the global usefulness and availability of the Internet of Things is increasing at a breakneck pace. The IoT can save costs, increase productivity, create employment, and bring in billions and trillions in economic value in the process.

    48. Around 44% of businesses use IoT to reduce costs.

    (Source: Leftronics)

    IoT statistics show that more companies are pursuing smarter systems due to the technological growth in that sector.

    As of 2021, about 44% of businesses use IoT devices to reduce costs. 37% of them use it to enhance operational processes, and 30% use them to grow revenue.

    Interestingly, major tech websites are beginning to follow these technology trends and have started implementing similar systems.

    Global Tech Market Growth Statistics

    Technology touches our whole lives and has generated trillions of dollars in revenue and market size in the process. Discussed below are some incredible milestones to help you better understand the impact of technology on businesses around the world.

    49. Worldwide spending on IT will amount to $3 trillion by 2021. 

    (Source: Statista)

    Predictions show that consumers will spend upwards of $3 trillion by 2021. 33% of this budget will go to hardware, while the rest will be for apps and related software. It will be a positive growth from 2020 data which showed a slow down due to Corona when most businesses aimed at cutting costs.  

    50. By 2025, the wearable AI market is going to be worth $180 billion.

    (Source: Semrush)

    AI statistics reveal that as of 2018, the wearable AI market was already worth $35 billion. Growing at a CAGR of 30%, that figure is expected to surpass the $100 million mark by 2025.

    51. Big Data could attain a market size of $77 billion by 2023.

    (Source: Statista)

    Big Data volume statistics have shown that I cannot overemphasize its importance. This follows as hundreds of organizations around the world are already investing directly and indirectly into its befitting features. Insights from Big Data analytics can pioneer a small startup into becoming a multinational organization within the shortest possible time.

    52. Income from AI hardware will be worth $234.6 billion in 2025.

    (Source: Statista)

    Products in this category include storage devices, network products for Graphics Processing Units (GPU), and Central Processing Units (CPU). Forecasts show that in 2025, their market value will have surpassed that of 2018’s by around $22 billion. 

    53. Successful companies like Netflix have been able to save up to $1 billion monthly following the adoption of a machine learning algorithm.

    (Source: Finance Online)

    Netflix’s AI algorithm can accurately recommend which movies will get the attention of each user based on their interaction with the website. That way, user engagement is significantly increased, and the cancellation rate reduced, thus increasing the potential of having a user around for a more extended period. Without a doubt, Netflix’s machine learning algorithm is one of the essential elements that make it one of the best streaming services out there.

    54. Up to $657.31 billion would have been invested into the IoT by 2025.

    (Source: Analitics Insight)

    As of the end of 2019, the IoT market was already worth 193.60 billion. It could grow even further with a CAGR of 21% yearly if the technology growth rate is anything to go by.

    Internet Growth Statistics

    Initially designed only to interconnect government-owned research laboratories, the internet has expanded at an exponential rate over the last three decades

    55. Internet users around the world spend an average of 6 hours, 42 minutes online daily.

    (Source: Digital Information World)

    The most recent data presented by statistics that illustrate how fast technology is growing places the average time spent online at above six hours. Countries like the Philippines and Brazil have the highest amount of time spent online daily, with 10:02 hours and 9:20 hours, respectively. The US falls a little short of the global average, clocking in at 6:31 hours daily internet time. Others like Japan and France spend the least amount of time online daily with 3:45 hours and 4:38 hours, respectively.

    56. The median social media usage between 2019 and 2020 was 143 minutes daily.

    (Source: Statista)

    How fast technology is growing statistics show that two hours and 23 minutes is the amount of time that social media users spent on their favorite networking sites in 2020. When it comes to the country whose citizens spend the highest amount of time on the sites, the Philippines came first with about 3 hours. 

    57. Over 4.54 billion people are active internet users out of the 7.76 billion people in the world.

    (Source: Statista)

    According to statistics that illustrate internet growth, the internet is growing at a pace of 11 new users per second – that is about 1 million unique users daily. Between the fourth quarter of 2018 and that of 2019, 366 million new users were added to the total number of internet users, bringing the final figure for 2019 to 4.39 billion users. However, between the end of 2019 and the first quarter of 2020, that figure has risen to more than 4.54 billion.

    58. There were 1.83 billion websites in January 2021.

    (Source: Web Hosting Rating)

    Websites began getting popular in 2012. That year alone, businesses and individuals alike launched about one billion websites. Growth of the internet statistics indicates an upward trend, and the number has increased by approximately 800 million as of 2021. 

    As of 2020, there were 20 million domain registrations. That was close to a 5% increment from the last quarter of 2019. 

    59. 63% of 2021 internet surfers prefer Chrome.

    (Source: Oberlo)

    As of 2021, six out of every 10 people visiting the internet do so via Chrome. Safari, the second most popular browser, doesn’t even come close. It only has about 19% of regular users. Mozilla Firefox and Samsung Internet tie at number three, with 3.61%. 

    60. The global ecommerce market is set to hit $6.54 trillion by 2022.

    (Source: Forbes)

    As of the end of 2019, the ecommerce market already had $4.2 trillion in sales. That number is expected to grow even further given that ecommerce is becoming the most preferred form of buying and selling around the globe.

    61. More than 92% of internet users now consume video content online monthly.

    (Source: Data Reportal)

    Online video platforms like YouTube get massive traffic on a per-second basis every day. According to statistics, up to 500 hours of video is uploaded to YouTube every minute. Also, the platform has up to 1.9 billion users.

    62. The number one YouTube channel had 51.36 billion views.

    (Source: Statista) 

    Like Nastya’s – Anastasia Radzinskaya, has slightly over 51 billion lifetime views in January 2021. Technology facts show that users find videos more entertaining and memorable. So keep that in mind, marketers!

    When it comes to future technological trends that will rule the world shortly, a few inventions come to mind. They include Blockchain, cloud computing, AR/VR, robotics, and automation. The stats that follow will expose you to some of these future technology trends.

    63. There will be 8.4 billion voice assistants by 2024.

    (Source: Statista)

    In 2020, there were about 4 million virtual assistants. That number will double by 2024 and will be close to 8.5 billion units. The world’s human population is 7 billion, so let’s hope it’s not the dawn of the Matrix.

    64. By 2025, 500 million Virtual Reality headsets would have been sold.

    (Source: Forbes)

    An increasing smartphone adoption rate, the automobile industry, military and law enforcement training, the gaming industry, and growing technology awareness are some of the significant factors influencing the increasing need for VR headsets. 

    65. 94% of the internet workload will be processed on the cloud by the end of 2021.

    (Source: Network World)

    Since its introduction to the mainstream market, the cloud computing trend has shown massive year-over-year growth. Experts believe that it will soon cause traditional data centers to go obsolete. As of 2018, the cloud was already housing 45% of the internet workload, and that number will rise even further in a few years.

    66. The Blockchain technology industry’s revenue is predicted to hit $20 billion by 2024.

    (Source: GlobeNewswire)

    Experts predict that as time goes on, blockchain will find usefulness across multiple industries due to its secure and sophisticated network. Currently, there are ongoing successful experiments to combine Blockchain and Big Data to ensure uniformity and accuracy of results, especially in the insurance sector, and many more will follow in the years to come.

    How Is Technology Affecting Our Lives?

    There is no doubt about how vital technology has become to how we live our lives each day on earth. The technology process has made life both more comfortable and efficient for the average human. The following stats will expound more on how technology is influencing our lives in general:

    67. Technology has made communication easier.

    (Source: Thrive Global)

    The younger generations won’t remember the days when there were no mobile phones. Today, anyone can pick up the phone and place a phone call to loved ones, irrespective of their location in the world. Plus, the coming of the internet and social media has made staying connected even cheaper.

    68. Technology has improved advertising.

    (Source: Thrive Global)

    Billboards are becoming outdated, and door-to-door advertising is said to have gone extinct. With the internet, businesses can now reach their targeted audience with ease and still obtain better conversion rates than the old system of advertising.

    69. Learning is now more efficient and more comfortable to carry out with technology.

    (Source: Thrive Global)

    In the past, you had to scourge the library for books on specific subjects that you intend to study. Today, there are billions of videos, podcasts, audio, and text over the internet on almost anything you wish to study, making education more accessible.

    Wrap Up

    Technology has sure come a long way! There are billions of inventions yet to be discovered by the upcoming generations, and many more after them.

    So, if you’ve ever wondered how fast is technology growing, statistics answer – lightning fast. And it is showing no signs of slowing down.

    Can you imagine what life would be without technology?

    Jacquelyn Bulao
    May 02, 2022

    Source

  • What is blockchain ?

    What is blockchain ?

    Even if you only have a passing interest in cryptocurrencies, there are a couple of terms that you’re still likely to have come across.
    The first is ‘Bitcoin’. This is the oldest and best-known of the many hundreds of cryptocurrencies that now exist.
    With a market capitalisation of £690 billion (28 March 2022), it’s also the largest in terms of the value of digital ‘coins’ in circulation. You can find out more here about Bitcoin and its largest rivals.
    The second term is ‘blockchain’. This is an important component at the heart of nearly all cryptocurrencies.
    But what actually is blockchain? Here’s what you need to know.


    What is blockchain?

    It’s a form of technology – specifically, the database technology that underpins nearly all cryptocurrencies. Think of it as a database distributed across millions of computers via a worldwide network. In this context, these computers are often referred to as ‘nodes’.

    By distributing identical copies of a database across an entire network, blockchain makes it hard to hack or cheat the system. And while cryptocurrency is currently the most popular use for blockchain technology, there is potential for it to serve a wide range of applications.

    At its heart, blockchain is a distributed digital ledger that stores data of any kind. For example, a blockchain can record information about cryptocurrency transactions, or the ownership of Non-Fungible Tokens (NFTs), a form of digital asset that represent real-world objects, such as unique works of art.

    Blockchain has also been used as a digital ledger to verify and track the provenance, characteristics and history of diamonds.

    Any conventional database can store the sort of information outlined in the examples above. But the blockchain is unique in that it’s decentralised. Rather than being maintained in one place, numerous identical copies of a blockchain database are held on multiple computers spread around a network.

    How blockchain works

    The digital ledger referred to earlier is described as a ‘chain’ composed of individual ‘blocks’ of data. As fresh data gets added to the network, a new block is created and is linked into the chain.

    To remain identical, all the nodes (computers) are required to update their version of the blockchain ledger. The way new blocks are created underpins why blockchain is regarded as highly secure.

    That’s because a majority of nodes must verify and confirm the legitimacy of the new data before a new block can be added to the ledger. For cryptocurrency, this might involve ensuring that new transactions in a block were not fraudulent, or that coins had not been spent more than once.

    This is different from a standalone database or spreadsheet, where changes to a single version can be made without qualification.

    Once consensus has been reached, the block is added to the chain with the underlying transactions recorded in the distributed ledger. Blocks are securely linked together, forming a secure digital chain from the beginning of the ledger to the last addition.

    As a reward for their efforts in validating changes to the shared data, nodes are usually given new amounts of the blockchain’s native currency.

    Two types of blockchain

    Blockchains exist in both private and public formats. Anyone can take part in a public blockchain, which means they can read, write or audit the data on the blockchain in question. Because no single entity controls the nodes, it’s difficult to change the transactions logged within a public blockchain.

    In contrast, a private blockchain is controlled by an organisation or group. The group decides who gets invited on to the system and it also has the authority to alter the blockchain. A private blockchain is more akin to an in-house data storage system that’s spread over multiple nodes to enhance security.

    Blockchain and its uses

    Blockchain technology has been put to use for a variety of purposes, from providing financial services to administering voting systems.

    Cryptocurrency

    Blockchain was created in 2008 as the technology behind Bitcoin, the first cryptocurrency. The brains behind Bitcoin’s creation was the anonymous Satoshi Nakamato, either an individual or group of people, who initially published a paper on the cryptocurrency as well as designing it.

    Nowadays, blockchain technology is most commonly associated with cryptocurrencies, such as Bitcoin and Ethereum. When people buy, exchange or spend cryptocurrencies the transactions are registered on a blockchain.

    Banking

    In addition to cryptocurrencies, blockchain is also being used to process transactions in traditional currencies, such as pounds, dollars and euros. This can be faster than traditional methods that involve sending money through a bank, or other financial institution, because the transactions can be verified faster and processed outside normal business hours.

    Asset transfers

    Blockchain can also be used to record and transfer the ownership of different assets. This is popular with digital assets such as NFTs.

    Blockchain could also potentially be used to transact the ownership of real-life assets, such as the deeds to property.

    With a property transaction, for example, both seller and buyer could use the blockchain to satisfy their respective obligations in the sales process – from verifying ownership rights, to completing and recording the sale. All this could be done without the need to manually submit paperwork to update land registration records.

    Smart contracts

    A different blockchain innovation concerns self-executing legal contracts, also referred to as ‘smart contracts’.

    Digital contracts such as these would kick into action automatically when certain conditions are all satisfied. For example, a payment might be released instantly once a buyer and seller have met all specified parameters relating to a deal.

    Supply chain monitoring

    When they’re not being hampered by the pandemic, supply chains typically involve large amounts of information, especially when goods are being manufactured and then transported around the world. Traditional data storage methods can find it difficult tracing the source of problems. For example, in the case of a vendor responsible for poor quality goods.

    Storing supply data information on blockchain can make it easier to monitor which goods have come from certain sources.

    For example, IBM’s Food Trust is a collaborative network of growers, processors, distributors, manufacturers and retailers that uses blockchain technology to record food provenance, transaction data and processing details.

    Voting

    Blockchain could also potentially be used to prevent fraud in voting by allowing people to submit votes that couldn’t be tampered with.

    Advantages of blockchain

    Improves the accuracy of transactions

    Blockchain transactions are verified by multiple nodes which helps to reduce mistakes. If one node contains an error in the database, the others ought to see that it’s different and pick up on the mistake.

    This contrasts starkly with the way a traditional database works, where an error made by an individual is more likely to slip through the net. In addition, every asset is individually identified and tracked on the blockchain ledger. This eliminates the chance of assets being accounted for twice.

    Makes intermediaries redundant

    Two parties using blockchain can confirm and complete a transaction without needing a third party to facilitate the process. This can save time and reduce costs. For example, by not requiring an institution like a bank to act as an intermediary within a sales process.

    Security

    Theoretically, a decentralised network like blockchain should make it almost impossible for someone to make fraudulent transactions.

    To forge a transaction, every node would need to be hacked and every ledger altered. While this isn’t necessarily impossible, many cryptocurrency blockchain systems use ‘proof-of-stake’ or ‘proof-of-work’ transaction verification methods that make it difficult – and not in a participant’s best interests – to add fraudulent transactions.

    Efficient transfers

    Blockchain works 24/7 enabling people to make financial transactions and asset transfers more efficiently and without the need for a third-party, such as a bank or other overseeing organisation (such as a government department), to ratify the process.

    Disadvantages of blockchain

    Time limits on transactions

    Given that blockchain depends on a larger network to approve transactions, there’s a limit to how quickly it can move. What’s more, increasing numbers of transactions create network speed issues. Scalability is therefore a challenge until this situation improves.

    Energy costs

    With all the nodes working to verify transactions, the process takes up significantly more energy than a solo database or spreadsheet – a not insignificant consideration during these times of sky-high electricity prices.

    Risk of asset loss

    Some digital assets are secured using a cryptographic key, such as cryptocurrency held within a blockchain wallet. In any decentralised system, keys need to be guarded carefully. That’s because if the owner of a digital asset lost his/her key, there is no way it could be recovered, with the asset potentially being lost forever.

    Illegal activity

    Blockchain’s decentralisation adds a layer of privacy and confidentiality which makes it appealing to criminals. It’s trickier to track illicit transactions on blockchain than through banks, where accounts are tied to a name.

    Can I invest in blockchain?

    Not, as such, no. This is because it’s just a system for storing and procession transactions. You can, however, invest in assets and companies that make use of this technology.

    The easiest way is buying cryptocurrencies like Bitcoin and Ethereum and other tokens that run on blockchain. Read more here about how to buy cryptocurrencies.

    Note, however, that investing in cryptocurrencies is not for everyone. The UK’s financial watchdog, the Financial Conduct Authority, reminds would-be traders that cryptoassets are unregulated and high-risk adding that people are “ very unlikely to have any protection if things go wrong, so people should be prepared to lose all their money if they choose to invest in them”.

    Another option is to invest in companies that make use of blockchain. In 2019, for example, Banco Santander announced it had issued what it claimed to be the first $20 million “end-to-end blockchain bond”.

    Looking to the future

    To some, blockchain remains a niche technology, akin to the early days of the internet. Others believe blockchain technology is making significant progress in development and adoption – with no signs of slowing down.

    For example, according to Deloitte’s 2021 Global Blockchain survey, blockchain’s “applicability to myriad industries and applications is now a foregone conclusion, spurring the rapid evolution of digital assets”.

    In recent years, some governments – including the UK – have experimented with blockchain technology in a variety of ways including land registration, welfare benefits, healthcare, procurement, food supply chains and identity management.

    A combination of these developments, including blockchain’s potential scalability across numerous sectors, means there’s every possibility the technology will soon transform the ways we transact and interact with each other.

    Andrew Michael

    Source

Virtual Identity