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Since the initial breakthrough in Computer Vision achieved by A. Krizhevsky et al. (2012) and their AlexNet Network, we have definitely come a long way. Computer Vision has since been making its way into day-to-day human lives without even us knowing about it. The one thing Deep Learning Algorithms need is data, and with the progress in portable camera technology in our mobile devices, we have it. A lot more, and a lot better. With great data, comes great responsibility. Data Scientists and Vision Engineers have been using data to create value in the form of awesome Vision applications.
Computer Vision has found applications in very diverse and challenging fields and these algorithms have been able to assist, and in some cases, outperform human beings. Be it Medical Diagnosis (Biology), Production Automation (Industry), Recommender Systems (Marketing), or everyday activities like driving or even shopping, Vision Systems are everywhere around us. In this blog, I am going to discuss some applications of computer vision, and how companies are implementing scalable vision systems to solve problems and generate value for their customers.
A timeline of some seminal computer vision papers against milestone events in computer vision product applicationsSelf Driving Vehicles
Tesla uses 8 cameras on the vehicle to feed their models, and the models do pretty much everything that can be done using video data, to guide the vehicle. The granular sub-applications that Tesla Autopilot needs to function are:
Detection and Recognition of Objects (Road Signs, Crosswalks, Traffic Lights, Curbs, Road Markings, Moving Objects, Static Objects) (Object Detection)
Following the Car Ahead (Object Tracking)
Differentiating between Lanes/ Lanes and Sidewalk / Switching Lanes (Semantic Segmentation)
Identifying Specific Objects (Instance Segmentation)
Responding to events (Action Recognition)
Smart Summon (Road Edge Detection)
Evidently, this is an extremely multitasked setting, where there is a need to know a lot about the scene at once. That is why the tech stack is designed in such a way that there are multiple outputs for a given input sequence of images. The way it is implemented is that for a set of similar tasks, there is a shared backbone, with a set of tasks, at the end, all of which give a specific output.
Some tasks require features from specific camera feeds to make a prediction, so each camera has its own HydraNet trained for camera-specific tasks. But there are more complicated tasks like steering the wheel, depth estimation, or estimating road layout, which might need information from multiple cameras, and therefore, features from multiple HydraNets at the same time to make a prediction. Many of these complicated tasks can be recurrent, adding another layer of complexity to the network.
Summing it up, Tesla’s Network consists of 8 HydraNets (for 8 cameras), each responsible for specific tasks. In addition to that, the features from these HydraNets go into another run of processing which requires camera interactions with each other, and spread over time, to derive meaningful insights and is responsible for more complex tasks.
According to Tesla, there are nearly a hundred such tasks. This modular approach has many benefits for Tesla’s specific use case:
It allows the network to be specifically trained for specific tasks. The network is subsampled for that specific task and is then trained for it.
It drastically reduces the overall number of trainable parameters, thus amortizing the process.
It allows certain tasks to be run in shadow mode while the overall system performs as usual.
It allows for quicker improvements to the overall network, as updates can be installed in parts rather than overall.
What Tesla has done well, and many other efforts at autonomous driving failed to achieve is data generation. By giving more and more products in the hands of consumers, Tesla now has a large source of quality data. They are able to capture disagreements between the Human and the Autopilot by deploying models in live mode as well as shadow mode. In this way, they have been able to improve their models by inference capabilities on real-world data, capturing disagreements and mistakes made by both, the Human and the Autopilot. As long as they receive well-labeled data, their models keep on improving with minimal effort.Medical Imaging
Arterys is one of such leading players, reducing subjectivity and variability in medical diagnosis. They have used Computer Vision to reduce the downtime to image blood flow in the heart, which took hours initially, to minutes. This allowed cardiologists to not only visualize, but also quantify blood flow in the heart and cardiovascular vessels, thus improving the medical assessment from an educated guess to directed treatment. It allowed cardiologists as well as AI to diagnose heart diseases and defects within minutes of MRI.
But why did it take hours for scans to generate flows in the first place? Let’s break this down.
Multiple in vivo scans are done to capture 3D Volume cycles over various cardiac phases and breathing cycles.
Iterative Reconstruction Methods on MRI data to evaluate flow increases reconstruction times automatically.
Along with 4D flow generation, object detection algorithms (Fast R-CNN(2023), R-FCN(2023)) in Arterys’ tech stack help to identify unidentifiable abnormalities and contours in the heart, lungs, brain, and chest scans. It automatically indexes and measures the size of the lesions in 2D and 3D space. Image Classification Networks help to identify pathologies like fracture, dislocation, and pulmonary opacity. Arterys trained its CardioAI network, which can process CT and MRI scans, on NVIDIA TITAN X GPUs running locally and on Tesla GPU accelerators running in Google Cloud Platform. Both were supported by the Keras and TensorFlow deep learning libraries. Inference occurs on Tesla GPUs running in the Amazon cloud.
Though these insights are very important for the medical professional, their availability to the medical professional can cause bias in the medical professional’s assessment of the case. Arterys mitigates this problem by flagging certain cases for attention but not specifying the exact location of the abnormality in the scan. These can be accessed once the specialist has made an unbiased assessment of the case.
Cloud-based deployment of its stack has allowed Arterys to provide reconstructions as well as invaluable visual and quantifiable analysis to its customers on a zero-footprint web-based portal in real-time. Computer Vision’s biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. The high-value applications are in rural and medically underdeveloped areas where physicians or specialists are hard to come by.Visual Search
Visual Search is a search based on images rather than text. It heavily depends on computer vision algorithms to detect features that are difficult to put into words or need cumbersome filters. Many online marketplaces, as well as search tools, have been quick to adopt this technology and consumer feedback for the same has been strongly positive. Forbes has forecasted that early adopters of visual search are projected to increase their digital revenue by 30%. Let us talk about a few early adopters and even late adopters to the visual search technology and how they have gone about implementing it.
Pinterest’s Visual Lens added a unique value to their customers’ experience wherein they could search for something difficult to put in words. Essentially, Pinterest is a giant Bipartite Graph. On one side are objects, which are pinned by the users, and on the other side are boards, where mutually coherent objects are present. An edge represents pinning an object (with its link on the internet) on a board that contains similar objects. This structure is the basis of Pinterest’s data and this is how Lens can provide high-quality references of the object in similar as well as richly varied contexts. As an example, if Lens detects Apple as an object, it can recommend Apple Pie Recipe and Apple farming techniques to the user, which belong to very separate domains. To implement this functionality, Pinterest has separated Lens’ architecture into two separate components.
In the first component, a query understanding layer has been implemented. Here, certain visual features are generated like lighting conditions and image quality. Basic object detection and colour features are also implemented as a part of the query understanding layer. Image Classification algorithms are also used to generate annotations and categories for the queried images.
In the second component, results from many models are blended to generate a continuous feed of results relevant to the queried image. Visual search is one of these models which returns visually similar results where the object and its context are strongly maintained. Another one would be an object search model which gives results that have the given object in the results. The third model uses the generated categories and annotations from the query understanding layer to do a textual image search to get the results. The blender does a very good job of dynamically changing the blending ratios as the user scrolls through the search results. Confidence thresholds are also implemented such that low confidence results from the query understanding layer are skipped while generating the final search results. Evidently, the basic technology supporting Pinterest Lens is object detection. It supports Visual Search, Object Search, and Image Search. Let’s understand in detail how object detection is done at Pinterest.
In step 1, the input image is fed into an already trained CNN, which identifies regions that might contain objects, and converts the image into a feature map. Once the feature map is generated, in step 2, A Region proposal network is used to extract sub-mappings of various sizes and aspect ratios from the original feature map. These sub-mappings are fed into a binary softmax classifier which predicts whether a given sub-region contains an object. If a promising object is found, it is indexed into a list of possible objects, along with the region bounded sub-mappings, which are then classified into object categories by an object detection network.
This is how Lens has been able to use Computer Vision and Pinterest’s bipartite graph structure to generate highly relevant and diverse results for visual search.Gaming
Eye-tracking is a technology that makes it possible for a computer system to know where a person is looking. An eye-tracking system can detect and quantify the presence, attention, and focus of the user. Eye-tracking systems were primarily developed for gaming analysis to quantify the performance of top gamers; but since then, these systems have found utility in various devices like consumer and business computers.
Tobii is the world leader in eye-tracking tech and the applications they support have moved from gaming to gesture control and VR. Data acquisition by Tobii is done via a custom-designed sensor which is pre-set on the device where eye-tracking information is needed. The system consists of projectors and customised image sensors as well as custom pre-processing with embedded algorithms. Projectors are used to create an infrared light-map on the eyeballs. The camera takes high-resolution images of the eyeballs to capture the movement pattern. Computer Vision algorithms are then used to map the movement of eyeballs from the images onto a point on the screen, thus generating the final gaze point. The stream of temporal data thus obtained is used to determine the attention and focus of the subject.
The human and economic cost of workplace injuries around the world is a staggering $250 billion per year. With AI-enabled intelligent hazard detection systems, workplaces prone to a high level of onsite injuries are realizing the decrease in number as well as the severity of injuries. Imagine a resource that works 24/7 without fatigue and keeps a watchful eye on whether safety regulations are being followed in the workplace!
Intenseye is an AI-powered employee health and safety software platform that helps the world’s largest enterprises to scale employee health and safety across their facility footprints. With real-time 24/7 monitoring of safety procedures, they can detect unsafe practices in the workplace, flag them and generate employee level safety scores along with live safety-norm violation notifications. Assistance is also provided in operationalising the response procedures, thus helping the employer in being compliant with the safety norms. Along with normal compliance procedures, they have also developed Covid-19 compliance features which help in tracking whether covid appropriate norms like masking and social distancing are being followed in the workplace.
The product is implemented on two levels. The basic driver for the product is Computer Vision. A big challenge for the org was to implement real-time predictions from live video streams. This is inherently a slow process and requires parallelisation and GPU computation to achieve due to the nature of the data pipeline. The vision systems employed range from anomaly detection to object as well as activity detection. Finally, the predictions generated are aggregated to rapidly create analysis, scores, and alerts on the suite available with the EHS professionals in the workplace who can ensure compliance from the workers.
Intenseye has developed general-purpose suites for workplaces, like PPE Detection, Area Controls, Vehicle Controls, Housekeeping, Behavioural Safety, and Pandemic Control measures. With their AI-based inspection system, Intenseye has been able to add a lot of value to the businesses they support. Along with saved lives, there have been decreased costs in damages, a boost in productivity, improved morale, and gain in goodwill for their clients.Retail Stores
In-aisle innovation is shifting how we perceive the future of retail, opening the possibilities of what can be done to shape customer experiences. Computer vision is posed to tackle many retail store pain points and can potentially transform both customer and employee experiences.
Amazon opened the doors on its first AmazonGo store in Seattle in January 2023 after a year of testing its Just Walk Out technology on its employees at its headquarters. This concept creates an active shopping session, links the shopping activity to the amazon app, and allows the customer to have a truly hassle-free experience. It eliminates the need to group with other buyers at checkout points to make the purchase, thus creating a unique value proposition in the current pandemic stricken world.
How does Amazon do it? The process which is primarily driven by Computer Vision can be divided into a few parts:
Data Acquisition: Along with an array of application-specific sensors (pressure, weight, RFID), the majority of data is visual data extracted from several cameras. Visual data ranges from images to videos. Other data is also available to the algorithm, like amazon user history, customer metadata, etc.
Data Processing: Computer Vision algorithms are used to perform a wide array of tasks that capture the customer’s activity and add events to the current shopping session in real-time. These tasks include activity detection (eg. article picked up by customer), object detection (number of articles present in cart), image classification (eg. customer has a given product in the cart). Along with tracking customer activity, visual data is used to assist the staff in other store-specific operations like inventory management (object detection), store layout optimisation(customer heat maps), etc.
Charging the customer: As soon as the customer has made their purchase and moves to the store’s transition area, a virtual bill is generated on the items present in the virtual cart for the customer’s current shopping session. Once the system detects that the customer has left the store, their Amazon account is charged with that purchase.
AmazonGo is a truly revolutionary use of computer vision and AI. It solves a very inherent problem for the in-store retail customers, assisting staff, improving overall productivity, and generating useful insights from data in the process. Though, it still needs to make economic sense in the post-pandemic world. There are privacy concerns that also need to be addressed before this form of retail shopping is adopted by the larger world.Industrial Quality Control
The ability of Computer Vision to distinguish between different characteristics of products makes it a useful tool for object classification and quality evaluation. Vision applications can sort and grade materials by different features, such as size, shape, colour, and texture so that the losses incurred during harvesting, production, and marketing can be minimized.
The involvement of humans introduces a lot of subjectivity, fatigue, delay, and irregularity in the quality control process for a modern-day manufacturing line/sorting line. Machines are able to sort unacceptable items better than humans 24/7 and with high consistency. The only requirement is a robust computer vision system. Vision systems are being implemented for the same on a large scale to push products in the refurbished market or improve on manufacturing shortcomings.
Let’s solve the problem of detecting cracks in smartphone displays. The system needs a few important things to function as intended.
Data: Quality data is imperative for the success of any machine learning system. Here, we require a camera system that captures the screen at various angles in different lighting conditions.
Labels: We might want to eliminate human shortcomings in the process, but quality labels generated by humans under normal working conditions are crucial for the success of a vision system. For a robust, large-scale process, it is best to employ multiple professional labellers and let them agree on different labelling results, thus eliminating subjectivity from the process.
Modelling: Models must be designed and deployed keeping in mind the throughput required for a given sorting line. Simple classification/object detection models are enough for detecting cracks. The main focus should be on prediction time which will be different for different sorting lines.
Inference and Monitoring: Models can be initially deployed in shadow mode where their performance is evaluated against human workers on live data. They can be deployed if the performance is acceptable, otherwise, another modelling iteration can be adopted. Data Drift must be monitored manually / automatically along with model performance when data drift is high. Retraining should be done when results are not acceptable.
Many manufacturing lines have implemented automated control systems for screen quality, be it televisions, phones, tablets, or other smart devices. Companies like Intel are also providing services to develop such systems which provide great value to many businesses.
Another quality control application using vision systems has been launched by food technology specialist, OAL, who have developed a vision system, April Eye, for automatic date code verification. The CV-based system achieves full automation of the date-verification process, removing the need for a human operator. They have reduced the risk of product recalls and emergency product withdrawals (EPWs) which are majorly caused by human error on packaging lines. The product is aimed at solving mistakes that cost food manufacturers £60-80 million a year. The system has managed to increase throughput substantially to 300 correct packs a minute. The precision of the system is also highly acceptable. A neural network trained on acceptable and unacceptable product date codes is used to generate predictions on live date codes in real-time. The system ensures that no incorrect labels can be released into the supply chain, thus protecting consumers, margins, and brands.
Computer Vision adds value to quality control processes in a number of ways. It adds automation to the process, thus making it productive, efficient, precise, and cost-effective.
With a deeper understanding of how scalable vision-based systems are implemented at leading product-based organisations, you are now perfectly equipped to disrupt yet another industry using computer vision. May the accuracy be with you.References
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Here is how computer vision impacts marketing
Digital systems can recognize and make sense of the information within images using computer vision, analogous to how humans view and interpret the world around them using their eyes and minds. On social media, you’ve probably already dealt with many aspects of computer vision. In a broader sense, computer vision algorithms may deconstruct and transform visual content into metadata, which can subsequently be stored, categorized, and analyzed in the same way as any other dataset.
How is Computer Vision Used in Marketing?Smarter Online Merchandising
Generally, eCommerce merchandising has been all about tagging. Each product includes several tags, allowing customers to filter for specific characteristics while also allowing recommendation engines to uncover similar products.
If an online shop wants to highlight a particularly significant product, they can override the algorithms.
Nevertheless, AI-based software like Sentient Aware now enables visual product discovery, which eliminates the requirement for most metadata and surfaces related products solely on their visual affinity. This means that instead of utilizing a traditional filtering method, customers can pick a product they like and be presented with visually related products.More Effective Retargeting
Retailers may be confused if a buyer has already purchased a specific product offline, thus dynamic creativity that shows a range of seemingly comparable products may have more effectiveness.Real-World Product Discovery
Pinterest has only recently released a feature called Lens, which works similarly to Shazam but is focused on visual content. This allows customers to point their camera phone at a thing and then run a Pinterest search to find that product or related content.
This may be used to locate a supplier for a certain piece of furniture or a recipe for an unusual unknown veggie.
Since 2023, the social media platform has had a degree of visual search functionality that allows users to select a portion of an image and search for relevant things, but Lens has pushed this even further by allowing brands to highlight products found within photos.Image-Aware Social Listening
Brands are primarily looking for mentions of their products and services on the internet. However, writing is only a small percentage of what people post on social media; photos and videos are perhaps just as significant.
There are already firms (such as Ditto or gum) that provide social listening services that can detect the use of company logos, assisting community managers in identifying positive and negative feedback.Frictionless Store Experiences
In December 2023, Amazon Go made the news. Users enter the store by scanning a barcode on their Amazon Go application at a gate.
The user is then tracked around the store using computer vision technology (probably in conjunction with some type of phone monitoring, though Amazon has not provided further details), and sensors on the shelf detect when the consumer chooses a product.
You just walk out the door once you’ve got everything you need, with the Go app tracking what you’ve brought with you. Amazon’s Seattle concept shop appears to have cleared the way for machine vision to play a part in alleviating the pain of checkout.Retail Analytics
Density is a start-up that uses a small piece of gear that can monitor movement through doorways to discreetly track people’s movements as they walk around work environments.
This data can be used for a variety of purposes, like measuring how busy a shop is or how lengthy a line / wait time is.
Computerized footfall counters have existed for some time, but developments in computer vision have made people tracking smart enough to be employed in merchandising optimization.Emotional Analytics
MediaCom stated in January 2023 that it would use Realeyes’ facial identification and analytics technologies in content testing and media relations.
Emotional intelligence is “quicker and cheaper” than typical internet surveys or focus groups, according to Realeyes Founder Mikhel Jaatma, and obtains direct replies rather than relying on subjective or assumed opinions.Image Search
As computer vision develops, it will be possible to use it to execute automated image labeling. This could potentially eliminate the need for manual and inconsistent labeling, allowing for faster and more accurate picture organization on a broad scale.
When used to video, the amount of data available will ultimately be mind-boggling, and how we acquire and preserve imagery may shift dramatically.
But it is still a way off, many people are already familiar with the capability of picture search in Google Photos, which has been trained to identify thousands of objects, as well as reverse image searches in Google’s search results or in a stock photo library.Augmented Reality Conclusion
Someone maybe snooping around on your computer, and that is definitely a problem. In many cases, the person who is accessing your computer is likely one who is known such as a family member or friend. In other situations, a colleague at work might have gained access if you had left your laptop unattended for a period of time.How to find out if someone was snooping around on your computer?
The question is, how can we find out if this has happened for sure. The first step is knowing where to begin, and that is something we plan to discuss in this article.
Bear in mind that a trace of almost all actions taken on your computer is stored, which means, there are ways to tell if someone has been messing around without your consent. Now, nothing here will determine who the culprit is, but this should give an idea:
Check for newly installed apps
Check your web browser history
Check Quick access
Take a look at Windows 10 Logon Events
Turn on logon auditing on Windows 10 Pro
One of the best safe computing habits to cultivate is to lock the screen of the computer with a password when you are not at it. It hardly takes a moment. You just have to press WinKey+L to lock the computer. This prevents others from snooping into your computers when you are not around.1] Check for newly installed apps
From here, you should see the latest apps that were installed recently. If none were done by you, then chances are a third-party might have been playing around with your computer.
Read: How to avoid being watched through your own Computer.2] Check your web browser history
In several cases, a person who uses your computer without consent might decide to use the web browser or whatever reasons. With this in mind, we suggest checking your web browser history just in case the culprit did not delete evidence of their transgressions.
If you have multiple web browsers, then check the history of each and not just the one you use on a regular basis.
Most browsers support pressing Ctrl+H keys to open the browsing history panel.
Read: Find out if your online account has been hacked and email & password details leaked.3] Check Quick access
For those who had no idea, well, let us make it clear that Windows 10 makes it possible for folks to check the recent user activity.
For example, you can open Microsoft Word to check if any files have been modified. The same goes for Excel, PowerPoint, or any other tools that fall under Microsoft Word.
Additionally, press the Windows key + E to open the File Explorer. From the top of the menu, look for Quick Access, and select it.
Right away, you should see a list of recently added or modified files. Check if anyone of them were modified by you in order to find out if another has accessed the device.
Read: How do I know if my Computer has been Hacked?4] Take a look at Windows 10 Logon Events
You should also look for 4672 with the Task Category name, Special Logon. We suggest looking for “4634”, which suggests someone turned off your computer; 4624 means Logon, 4634 means Logoff.
Read: Safe Computing Tips, Practices and Habits for PC users.5] Turn on logon auditing on Windows 10 Pro
Here’s the thing, this feature is automatically up and running in Windows 10 Home, but when it comes down to the Pro version, you may have to manually enable it.
Let us know if you have any tips.
The sudden ethereum price has put its market valuation at US$2.7 trillion
The first and foremost altcoin,Ethereum Price Prediction
According to a previous report submitted by a panel of 42 cryptocurrency experts in October, etherum price was anticipated to breach the US$4,500 mark by the end of 2023 and reach US$10,000 by 2023. However, things have changed upside down now. Even previously some enthusiasts have boosted ethereum’s value to reach the US$10k mark before the end of 2023. However, not many were very positive about the anticipation and thought it was overvalued. But the recent price rally and ethereum’s capability to part away from bitcoin and perform well has brought value to its stance.Anticipations and Expectations on Ether
According to some analysts, bitcoin and ethereum were predicted to double their value before the end of the year. While an analyst behind Plan B handle on Twitter has said that bitcoin will reach US$98,000 this month, it also indicated an upcoming ethereum price rally. However, ether has proved that the cryptocurrency can grow on its own without bitcoin’s help. Ethereum is the first altcoin that emerged out of bitcoin’s existence. Usually, ether follows bitcoin’s trend and keeps up with it. Whether it is a price surge or plummet, ethereum will follow the same path as Bitcoin does. But the recent price rally has indicated otherwise. Without a bitcoin price rally, ethereum has experienced a value surge. According to Goldman Sachs, ethereum network could well jump 80% to US$8,000 in the next two months if it keeps tracking inflations expectations. However, they also warned that central banks won’t let inflation rise sharply. JPMorgan has also said that they have seen signs of inflation in the cryptocurrency market. It has driven many investors to hold on to bitcoin and others rather than a gold investment. On the other hand, billionaire investor Mark Cuban has said that ethereum has the most upside as an investment model. He added that because according to him, ethereum blockchain, smart contracts, or collection of code has changed a lot in the cryptocurrency market.Why Ethereum 2.0 is a Big Success?
Many anticipated that Ethereum 2.0 could be a big success and might outperform bitcoin soon. They are not wrong. Ethereum saw the difference between the number of tokens issued and destroyed turn negative in the last seven days on aggregate for the first time. This is a tactic that bitcoin has used to trade its digital token since its inception. Bitcoin started its supply with a cap of 21 million. Once this number is reached, we can’t mine anymore bitcoins. The scarcity of the digital token has kept its value in the virtual currency ecosystem. Now, ethereum has boarded the same train with its update to 2.0. Ether is gaining value from a process called burning, where coins are taken out of circulation. Whenever an ether transaction is made, a small quantity of the coin is burned. As more transactions take place, it puts more ether at the cost of burning, which could eventually drive its price. On the other hand, Etherem ETF is also approaching the government approval stage. Although Bitcoin ETF is already on the radar, US regulators are more likely to approve ethereum ETF before giving a green signal to bitcoin.So, Is it the Right Time to Invest?
The first and foremost altcoin, ethereum , has skyrocketed to an all-time high with the coin hitting a value of US$4,470 yesterday. At the time of writing, the cryptocurrency’s value has surged even more and was being traded at US$4,556.70 with a 5% 24-hour growth. The sudden growth has put ether ’s market valuation at US$2.7 trillion, giving a stronghold to the second most adopted digital token in the cryptocurrency market. But what triggered the ethereum price when bitcoin is still maintaining a moderate value for a week straight? It is the metaverse and NFT announcements. Ethereum is expected to expand its service range to the metaverse. Metaverse is a digital space where you can work, play, or even create a community in the digital environment just like the physical one. On the other hand, ethereum has also come forward to tell that its technology is being used to sell the digital craze, non-fungible tokens (NFTs). In a nutshell, ethereum price has gained over 1,000% in the past year. Owing to the increasing adoption of disruptive methods, ethereum price is predicted to skyrocket to further highs in the coming days. Some enthusiasts even suggest that ether will breach its US$10,000 resistance by this year-end. In this article, we explore the price predictions of ether and talk about the right time to invest in a profitable cryptocurrency.According to a previous report submitted by a panel of 42 cryptocurrency experts in October, etherum price was anticipated to breach the US$4,500 mark by the end of 2023 and reach US$10,000 by 2023. However, things have changed upside down now. Even previously some enthusiasts have boosted ethereum’s value to reach the US$10k mark before the end of 2023. However, not many were very positive about the anticipation and thought it was overvalued. But the recent price rally and ethereum’s capability to part away from bitcoin and perform well has brought value to its stance.According to some analysts, bitcoin and ethereum were predicted to double their value before the end of the year. While an analyst behind Plan B handle on Twitter has said that bitcoin will reach US$98,000 this month, it also indicated an upcoming ethereum price rally. However, ether has proved that the cryptocurrency can grow on its own without bitcoin’s help. Ethereum is the first altcoin that emerged out of bitcoin’s existence. Usually, ether follows bitcoin’s trend and keeps up with it. Whether it is a price surge or plummet, ethereum will follow the same path as Bitcoin does. But the recent price rally has indicated otherwise. Without a bitcoin price rally, ethereum has experienced a value surge. According to Goldman Sachs, ethereum network could well jump 80% to US$8,000 in the next two months if it keeps tracking inflations expectations. However, they also warned that central banks won’t let inflation rise sharply. JPMorgan has also said that they have seen signs of inflation in the cryptocurrency market. It has driven many investors to hold on to bitcoin and others rather than a gold investment. On the other hand, billionaire investor Mark Cuban has said that ethereum has the most upside as an investment model. He added that because according to him, ethereum blockchain, smart contracts, or collection of code has changed a lot in the cryptocurrency chúng tôi anticipated that Ethereum 2.0 could be a big success and might outperform bitcoin soon. They are not wrong. Ethereum saw the difference between the number of tokens issued and destroyed turn negative in the last seven days on aggregate for the first time. This is a tactic that bitcoin has used to trade its digital token since its inception. Bitcoin started its supply with a cap of 21 million. Once this number is reached, we can’t mine anymore bitcoins. The scarcity of the digital token has kept its value in the virtual currency ecosystem. Now, ethereum has boarded the same train with its update to 2.0. Ether is gaining value from a process called burning, where coins are taken out of circulation. Whenever an ether transaction is made, a small quantity of the coin is burned. As more transactions take place, it puts more ether at the cost of burning, which could eventually drive its price. On the other hand, Etherem ETF is also approaching the government approval stage. Although Bitcoin ETF is already on the radar, US regulators are more likely to approve ethereum ETF before giving a green signal to bitcoin.There is no time as the right time when it comes to cryptocurrency investment. Even investing in the dip is not a wise method. So if you are planning to try your hand on ehtereum, you can do it right away. But make sure you invest an amount that can be handled in case of value decrease.
How to Combine Multiple Browser Bookmarklets
2. Go to the Bookmarklet Combiner website. You will see the following interface:
Copy the entire code shown in the “Location” field of the boomarklet.
Soumen is the founder/author for Ampercent, a tech blog that writes on computer tricks, free online tools & software guides.
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If the IPO Fairy suddenly appeared at the foot of my bed and promised to grant me control of any company in technology, I think I would pick Facebook.
Sure, I know Facebook is bleeding cash, and could easily slip into loser mode like MySpace did. But with the right moves, Facebook could become the most important company on the Internet — more important even than you-know-whoogle.
How? By becoming indispensible to everybody as the ultimate mobile social networking service.
Here’s what Facebook should do:
Facebook’s iPhone app, as well as other Facebook cell phone apps, should feature a button that uses Bluetooth to scan the room for other people who have also activated their Facebook button. Once you and the other person have tapped your respective buttons, you’ll now be “Friends” on Facebook.
This single feature would replace business cards for business people, and the standard processes for casual connections among younger people.
It would leverage the existing user base to practically “force” non users to sign up. Imagine a business meeting or nightclub where everyone is connecting, and you’re sitting there like a schmuck muttering stuff like, “er, I don’t really use Facebook…”
Once everyone got into the habit of connecting with their cell phones, all data on friends, family and colleagues would be in Facebook, not Outlook, Gmail or dedicated contact software.
Facebook should then enable users to add any and all contacts, or to import them from other applications. That would make Facebook the preferred contact application.
The new iPhone app that shipped last month makes it super easy to tap the “Friends” icon, and get to what is essentially an address book. (The address book is one tab called “Info” and the other two tabs are: “Wall” and “Photos.”)
First, the contact data is maintained by the owner of that data, not you, so it’s always up to date. Second, it comes with “Wall” data, so you can easily see what people are up to before you call or e-mail.
Everybody has a love-hate relationship with e-mail. We love it because it’s so useful and universal. But we hate it because of spam.
Facebook is in a position to offer a superior alternative to e-mail, because people can only send messages to you if you’ve pre-approved them (by friending them).
Unfortunately, Facebook’s “Inbox” feature is slow and cumbersome to use. It should work more like e-mail and less like some kind of dumb message board. It should also let you send messages outbound over e-mail, and people should be able to send you messages from the outside only if they’re replying to your Facebook-originating message.
In other words, it would work exactly like e-mail, but people or companies that are not on your Facebook friends list would not be able to initiate messages to you.
Good-bye spam! Hello forcing everyone to use Facebook!
Nokia announced today that some of its smartphones will be able to use a Nokia-developed application to push location data to Facebook as part of a status update.
First of all, this is just scratching the surface of how location data can enhance Facebook. Second, Facebook should be building this, not partners.
Facebook should be able to tell you when friends are nearby. This should be user controllable, so you can choose to be alerted to all friends, just some friends or no friends.
The idea is that when you get within, say, a half-mile of someone you care about, your phone bleeps, and it says, “Joe Schmo is just around the corner!” Facebook should then offer options to chat, call, meet up or ignore.
Give users the ability to auto-reject cause, group and other invitations. It’s just so much spam to most of us, and makes us long for an alternative to Facebook.
By letting people who don’t want to get this junk to turn it off, Facebook would suddenly become wonderful to use, rather than annoying.
Sure, there are a gazillion tweaks Facebook could make to improve the service for users. But to become massively powerful, Facebook should own the future of mobile social networking, improve messaging and get rid of the junk that makes Facebook annoying.
Unfortunately, I don’t think Facebook will aggressively pursue any of this. Based on past performance, I think Facebook will squander the opportunity of the decade. They’ll muddle along as a popular social network, and let Google, Microsoft and others make off with the future of mobile social networking.
Too bad. Facebook, you coulda been a contender.
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